commit 56c1c3cc1f6ae5cfd2e3dbd06fee8651927c8174 Author: Giuseppe Nucifora Date: Tue Dec 10 23:29:54 2024 +0100 - move models to separate repository diff --git a/.dvc/.gitignore b/.dvc/.gitignore new file mode 100644 index 0000000..528f30c --- /dev/null +++ b/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/.dvc/config b/.dvc/config new file mode 100644 index 0000000..ae5faaa --- /dev/null +++ b/.dvc/config @@ -0,0 +1,6 @@ +[core] + autostage = true + remote = storage +['remote "storage"'] + url = s3://olive-oil-dataset + region = eu-west-1 diff --git a/.dvcignore b/.dvcignore new file mode 100644 index 0000000..af6e304 --- /dev/null +++ b/.dvcignore @@ -0,0 +1,3 @@ +!*.h5 +!*.keras +!*.parquet \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b1eb170 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +/sources +.idea +*.parquet +*.h5 +*.keras \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..31d964b --- /dev/null +++ b/README.md @@ -0,0 +1,49 @@ +# Olive Oil Transformer Model + +This repository contains transformer-based models for various predictions, including olive oil production forecasting. Here's a guide to the key components of the project: + +## Project Structure + +### Model Notebooks Location +The model notebooks are located in the `/models` directory, organized by different prediction tasks: + +- **Olive Oil Model**: `/models/olive_oli/olive_oil-v2.ipynb` + - Contains the implementation of the transformer model for olive oil production forecasting + - Includes model training, evaluation, and visualization components + +- **Solar Energy Model**: `/models/solarenergy/solarenergy_model_v1.ipynb` + - Transformer model for solar energy prediction + +- **Solar Radiation Model**: `/models/solarradiation/solarradiation_model.ipynb` + - Implementation for solar radiation forecasting + +- **UV Index Model**: `/models/uv_index/uv_index_model.ipynb` + - Model for UV index prediction + +### Synthetic Data Generation +The script for generating synthetic training data is located at: ```/olive_oil_train_dataset/create_train_dataset.py``` + +This script is responsible for creating synthetic data used in training the olive oil production model. + +### Utility Functions +Common utility functions and helper methods are stored in: ```/utils/helpers.py``` + +## Model Artifacts +Each model directory contains its associated artifacts, including: +- Trained model weights +- Scalers for data normalization +- Training logs +- Model architecture visualizations +- Performance analysis plots + +For example, the olive oil model directory contains: +- Model weights in the `weights` subdirectory +- Scalers for static and temporal features +- Training logs in the `logs` subdirectory +- Model architecture and performance visualization plots + +## Getting Started +To work with the models: +1. Start with the respective notebook in the `/models` directory +2. For olive oil prediction, first generate synthetic data using the script in `/olive_oil_train_dataset` +3. Utilize the utility functions from `/utils/helpers.py` as needed \ No newline at end of file diff --git a/models/olive_oli/.ipynb_checkpoints/olive_oil-512-60_30-checkpoint.ipynb b/models/olive_oli/.ipynb_checkpoints/olive_oil-512-60_30-checkpoint.ipynb new file mode 100644 index 0000000..3302a5b --- /dev/null +++ b/models/olive_oli/.ipynb_checkpoints/olive_oil-512-60_30-checkpoint.ipynb @@ -0,0 +1,4266 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n", + "Fetched 384 kB in 1s (519 kB/s) \n", + "Reading package lists... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 07:35:27.011449: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-12-07 07:35:27.011494: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-12-07 07:35:27.011539: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-12-07 07:35:27.020703: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keras version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "CUDA available: True\n", + "GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n", + "1 Physical GPUs, 1 Logical GPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 07:35:29.539283: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9725 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import keras\n", + "\n", + "print(f\"Keras version: {keras.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n", + "print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n", + "\n", + "# GPU configuration\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Limita la crescita della memoria GPU\n", + "gpus = tf.config.experimental.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " # Imposta la crescita di memoria dinamica\n", + " for gpu in gpus:\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + " \n", + " # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)\n", + " # tf.config.experimental.set_virtual_device_configuration(\n", + " # gpus[0],\n", + " # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)] # 4GB\n", + " # )\n", + " \n", + " logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n", + " print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n", + " except RuntimeError as e:\n", + " print(e)\n", + " \n", + "# Imposta le opzioni di logging\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Riduce i messaggi di log\n", + " \n", + "# Configura la modalità mista di precisione\n", + "tf.keras.mixed_precision.set_global_policy('float32')\n", + "\n", + "# Imposta il seed per la riproducibilità\n", + "##tf.random.set_seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c0155cde4740b0a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tensorflow_addons as tfa\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "import re\n", + "from typing import List\n", + "\n", + "random_state_value = None\n", + "execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "base_project_dir = './'\n", + "data_dir = '../../sources/'\n", + "models_project_dir = base_project_dir\n", + "\n", + "os.makedirs(base_project_dir, exist_ok=True)\n", + "os.makedirs(models_project_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1347fb59-50cc-4aa8-b805-ca9403037af5", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_column_name(name: str) -> str:\n", + " \"\"\"\n", + " Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.\n", + "\n", + " Parameters\n", + " ----------\n", + " name : str\n", + " Nome della colonna da pulire\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " Nome della colonna pulito\n", + " \"\"\"\n", + " # Rimuove caratteri speciali\n", + " name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n", + " # Converte in snake_case\n", + " name = name.lower().replace(' ', '_')\n", + "\n", + " # Abbreviazioni comuni\n", + " abbreviations = {\n", + " 'production': 'prod',\n", + " 'percentage': 'pct',\n", + " 'hectare': 'ha',\n", + " 'tonnes': 't',\n", + " 'litres': 'l',\n", + " 'minimum': 'min',\n", + " 'maximum': 'max',\n", + " 'average': 'avg'\n", + " }\n", + "\n", + " for full, abbr in abbreviations.items():\n", + " name = name.replace(full, abbr)\n", + "\n", + " return name\n", + "\n", + "\n", + "def clean_column_names(df: pd.DataFrame) -> List[str]:\n", + " \"\"\"\n", + " Pulisce tutti i nomi delle colonne in un DataFrame.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : pd.DataFrame\n", + " DataFrame con le colonne da pulire\n", + "\n", + " Returns\n", + " -------\n", + " list\n", + " Lista dei nuovi nomi delle colonne puliti\n", + " \"\"\"\n", + " new_columns = []\n", + "\n", + " for col in df.columns:\n", + " # Usa regex per separare le varietà\n", + " varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n", + " if varieties:\n", + " new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n", + " else:\n", + " new_columns.append(col)\n", + "\n", + " return new_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4da1f1bb67343e3e", + "metadata": {}, + "outputs": [], + "source": [ + "def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n", + " filename = filename.replace(' ', '_').lower()\n", + " filepath = os.path.join(output_dir, f\"{filename}.png\")\n", + " plt.savefig(filepath, bbox_inches='tight', dpi=300)\n", + " print(f\"Plot salvato come: {filepath}\")\n", + "\n", + "\n", + "def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + "\n", + " # Trova tutte le colonne delle tecniche\n", + " tech_columns = [col for col in df.columns if col.endswith('_tech')]\n", + "\n", + " # Applica il mapping a tutte le colonne delle tecniche\n", + " for col in tech_columns:\n", + " df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n", + "\n", + " return df\n", + "\n", + "\n", + "def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + " reverse_mapping = {v: k for k, v in technique_mapping.items()}\n", + " reverse_mapping[0] = ''\n", + "\n", + " return reverse_mapping.get(technique_value, '')\n", + "\n", + "\n", + "def prepare_comparison_data(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + "\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + " comparison_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n", + "\n", + " if not variety_data.empty:\n", + " avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " comparison_data.append({\n", + " 'Variety': variety,\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(comparison_data)\n", + "\n", + "\n", + "def plot_variety_comparison(comparison_data, metric):\n", + " plt.figure(figsize=(12, 6))\n", + " bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n", + " plt.title(f'Comparison of {metric} across Olive Varieties')\n", + " plt.xlabel('Variety')\n", + " plt.ylabel(metric)\n", + " plt.xticks(rotation=45, ha='right')\n", + "\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width() / 2., height,\n", + " f'{height:.2f}',\n", + " ha='center', va='bottom')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Oil Efficiency (L/kg)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Oil Efficiency (L oil / kg olives)')\n", + " plt.tight_layout()\n", + " save_plot(plt, 'efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Water Efficiency (L oil/m³ water)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Water Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Water Efficiency (L oil / m³ water)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_need_vs_oil_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n", + " comparison_data['Avg Oil Production (L/ha)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Production vs Water Need by Variety')\n", + " plt.xlabel('Average Water Need (m³/ha)')\n", + " plt.ylabel('Average Oil Production (L/ha)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_need_vs_oil_production')\n", + " plt.close()\n", + "\n", + "\n", + "def analyze_by_technique(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " technique_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0]\n", + "\n", + " if not variety_data.empty:\n", + " for tech in variety_data[tech_col].unique():\n", + " tech_data = variety_data[variety_data[tech_col] == tech]\n", + "\n", + " avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n", + "\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " technique_data.append({\n", + " 'Variety': variety,\n", + " 'Technique': tech,\n", + " 'Technique String': decode_single_technique(tech),\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(technique_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9aa4bf176c4affb9", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_real_error(model, test_data, test_targets, scaler_y):\n", + " # Fare predizioni\n", + " predictions = model.predict(test_data)\n", + "\n", + " # Denormalizzare predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + "\n", + " # Calcolare errore percentuale per ogni target\n", + " percentage_errors = []\n", + " absolute_errors = []\n", + "\n", + " for i in range(predictions_real.shape[1]):\n", + " mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n", + " mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n", + " percentage_errors.append(mape)\n", + " absolute_errors.append(mae)\n", + "\n", + " # Stampa risultati per ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " print(\"\\nErrori per target:\")\n", + " print(\"-\" * 50)\n", + " for i, target in enumerate(target_names):\n", + " print(f\"{target}:\")\n", + " print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n", + " print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n", + " print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n", + " print(\"-\" * 50)\n", + "\n", + " return percentage_errors, absolute_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3ba2b96ba678389", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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A6Onpibt370rlixcvVvqf6NwvQQMHDpTKcnJyRMuWLYWOjo548uSJVP7q1SuFeDIzM0W1atXE999/r1AOQGhoaIgrV64o7dv7X16MjY0VvtS+LzMzU1hYWIhq1aqJ9PR0qXz37t0CgBg/frzSvkycOFGhD1dX149+8Xz8+LHQ0dERzZs3V0jazZs3TwAQK1askMpyv5S9e2w+ZPPmzQKAuHnzphBCiJSUFKGrq6uQhMn9bC5duqSwbZUqVRSO78CBA4VMJpOSfEK8/cJlamr60aRU7nn17heZvDg7OwtTU1PpvSpJqYCAAKGtrS2SkpKksoyMDFGyZEnh5+cnlfn7+wtra2vx9OlThf58fHyEsbGx0jn2PltbW9G8eXPx5MkT8eTJE3Hx4kXh4+OjcA7nJjGMjIzE48ePFbZ3cXERFhYW4tmzZ1LZxYsXhYaGhvSFVQghvL29ha6ursLvz9WrV4WmpuYnJ6W6d+8uNDQ0xLlz55Ta5iZMNm3alGeiRgjlpNSsWbMEAIUEbmZmpqhbt64wMDAQKSkpCjGWKlVK4fPZsWOHACB27dqlNNa7li1blue5mbuPub+/w4cPFxoaGmLlypUKbWbMmCEAiO3bt0tl6enpwtHRMc997datm8J+qhN/XufPunXrBABx4sQJpbo+ffoIPT29D+5/fv1OnjxZyGQyhXOkYcOGwtDQUKFMCKGQEMv9fercubNCG1Wv+9u2bRMA8jyPcg0ePFgYGRmJN2/efHTf3uXt7S0AfDD5s3XrVgFAzJkzRyr7WFIqOTlZyOVyMXz4cIW+pk2bpnAM79y5IzQ1NcXvv/+u0O7SpUtCS0tLqfx9b968EdbW1qJu3boK5YsWLRIAxIEDB4QQb5Pv717nhXh7nsnlcoV/P3ITTw4ODkrnwPtJqZycHFGxYkXh4eGh8Hm/evVK2Nvbi2bNmkllwcHB+V6v37+2Tpo0Sejr64sbN24otBs9erTQ1NQU9+7dE0J8+mdORFTQePseEdF/UEpKCgCovLD13r17AQDDhg1TKM9d3Pn9taeqVKmCunXrSu9r164NAPj+++9Rrlw5pfLbt28rjfnuY91zb7/LzMzEoUOHpHI9PT3p5+fPnyM5ORkNGjRQutUOABo1aoQqVap8ZE/frst05swZPHjwIM/6yMhIPH78GP369VNYv6Nly5ZwdHTMcx2u99cVatCgQZ77/K5Dhw4hMzMTQ4YMUVhktnfv3jAyMvpX632FhYWhZs2a0qLohoaGaNmypcItfO3atYOWlhY2bNgglV2+fBlXr15VeFrf/v37UbduXbi4uEhlpqamKq2TkvtUqo+dh4aGhtI5q6pOnTohKysLW7dulcoOHjyIFy9eSPELIbBlyxa0bt0aQgg8ffpUenl4eCA5OTnPc+l9Bw8ehLm5OczNzVGjRg1s2rQJ3bp1w9SpUxXatW/fHubm5tL7hw8fIjo6Gj169ICpqalU7uzsjGbNmkm/d9nZ2Thw4AC8vb0Vfn+cnJzg4eGh1nHJlZOTg+3bt6N169ZKtyoB+KS1k/bu3QsrKyt07txZKtPW1sagQYOQmpqK48ePK7Tv1KmTwjphDRo0AJD39eBdube+5bfGmBACAwYMwOzZs7FmzRqltdL279+P0qVLK9wOpquri969eyv1lZOTg/379+d5654q8b97jXr9+jWePn2KOnXqAECe55aJiQnS09Px6tWrPPctr37T0tLw9OlT1KtXD0II6ZboJ0+e4MSJE/Dz81M4b4C8P9/3r1OqXvdz17LbvXs3srKy8oy3ZMmSSEtLQ3h4+Af3632qXCNy69S5RuTeHrdx40aF28I3bNiAOnXqSMdr69atyMnJQceOHRWuD1ZWVqhYsSKOHj36wXE0NTXh4+ODiIgIhVuZ165dC0tLSzRt2hTA23Wbcq/z2dnZePbsGQwMDFC5cuU8zxNfX1+FcyAv0dHRuHnzJrp06YJnz55JsaelpaFp06Y4ceKE0u12qti0aRMaNGgAExMThWPi7u6O7OxsnDhxAsCnf+ZERAWNSSkiov8gIyMjAPjgo6rfdffuXWhoaCg92c3KygolS5ZUehrV+1+AjI2NAQBly5bNs/z58+cK5RoaGkrrfVSqVAkAFP7Hfvfu3ahTpw50dXVhamoKc3NzLFy4EMnJyUr7YG9v/7HdBPB2ra3Lly+jbNmy+PbbbxEYGKjwJTN3XytXrqy0raOjo9Kx0NXVVUhEAG+/eL6/z+/LbxwdHR04ODh88hPAXrx4gb1796JRo0aIi4uTXvXr10dkZCRu3LgBADAzM0PTpk2xceNGadsNGzZAS0sL7dq1U4gzryf+qfIUwNwvkx87Dz/lyYA1atSAo6OjQlJtw4YNMDMzkxaRfvLkCV68eIElS5ZISaXcV8+ePQEgz7Vg3le7dm2Eh4fj0KFDOH36NJ4+fYrVq1crfWl8/xz80Lnk5OQkfYF88uQJ0tPTUbFiRaV2eW2riidPniAlJQXVqlX7pO3zcvfuXVSsWFHpSV1OTk5S/bvev07kJng+9ruR691kwrtWr16N+fPnY+7cuQoJsnfjLF++vFJiJq9z9ty5c3jy5EmeSSlV4k9KSsLgwYNhaWkJPT09mJubS+dBXtep3H36WFLw3r17UjIzd626Ro0aKfSbe91S9TPO6/xU5brfqFEjtG/fHkFBQTAzM0Pbtm0RGhqqsO5Uv379UKlSJXh5eaFMmTLw8/OT1vX7EFWuEaomt9/XqVMn/PPPP4iIiAAA3Lp1C1FRUQpJ95s3b0IIgYoVKypdI65du6bS9SE3Qb927VoAwP3793Hy5En4+PhIi6/n5OQgJCQEFStWhFwuh5mZGczNzRETE/PJ/57dvHkTwNsE1vuxL1u2DBkZGXn2rUq/+/fvV+ozd4233GPyqZ85EVFB49P3iIj+g4yMjGBjY4PLly+rtZ2qsyfye8pRfuX5fbn8kJMnT6JNmzZo2LAhFixYAGtra2hrayM0NFT6n/93feyvyrk6duyIBg0aYNu2bTh48CCCg4MxdepUbN26FV5eXmrHWdRPfPqYTZs2ISMjAzNmzMCMGTOU6sPCwqQFzn18fNCzZ09ER0fDxcUFGzduRNOmTWFmZlYgsVSoUAFaWlqIiYnJt01GRgZiY2PznM3zMZ06dcLvv/+Op0+fwtDQEDt37kTnzp2hpfX2f09yZwn8+OOP+T550NnZ+aPjmJmZqbTotqrn4L+R3++oqgvrF6VPvR6UKlUKwNvkT5kyZZTq69evj+joaMybNw8dO3ZUmIWmrr1798LOzi7PWZaqxN+xY0ecPn0aI0aMgIuLCwwMDJCTkwNPT888Z6k8f/4cJUqU+OC5kp2djWbNmiEpKQmjRo2Co6Mj9PX1kZCQgB49enzS7Bcg//PzY9d9mUyGzZs34++//8auXbtw4MAB+Pn5YcaMGfj7779hYGAACwsLREdH48CBA9i3bx/27duH0NBQdO/ePc8HEuRycnLC9u3bERMTg4YNG+bZJvf6ocpM2He1bt0aJUqUwMaNG1GvXj1s3LgRGhoa0uL+wNtrhEwmw759+/L8vA0MDD46jpubGxwdHbFu3TqMGTMG69atgxBCYTbpH3/8gV9//RV+fn6YNGkSTE1NoaGhgSFDhuT5eapyLcndLjg4WGEmq7rx59Vvs2bNMHLkyDzrc/+A9KmfORFRQWNSiojoP6pVq1ZYsmQJIiIiFG61y4utrS1ycnJw8+ZNadYD8PYpbS9evICtrW2BxpaTk4Pbt29L/3MLQJrBY2dnBwDYsmULdHV1ceDAAYVHVoeGhv7r8a2trdGvXz/069cPjx8/xjfffIPff/8dXl5e0r7GxsYqPbY9Nja2wI7Fu+O8O2ssMzMT8fHxKj957H1hYWGoVq2a9MS+dy1evBhr166VklLe3t746aefpNlGN27cQEBAgFKccXFxSn3lVfY+fX19NGnSBEeOHMHdu3fzPHYbN25ERkYGWrVqpdL+vatTp04ICgrCli1bYGlpiZSUFPj4+Ej15ubmMDQ0RHZ29icfz3/j3c/4fdevX4eZmRn09fWhq6sLPT09aebDu97fNne2zrtPaASUZymZm5vDyMjoo4lpdW7js7W1RUxMDHJychRmS12/fl2qLwiOjo4AgPj4eFSvXl2pvkKFCpg2bRoaN24MT09PHD58WGEWja2tLa5evQohhML+5XXO7tmzBy1atPikOJ8/f47Dhw8jKCgI48ePl8rz+hxzxcfHK1xj83Lp0iXcuHEDq1atQvfu3aXy92+Tyr1uqPvHh1zqXvfr1KmDOnXq4Pfff8fatWvRtWtXrF+/Hr169QLwdpZn69at0bp1a+Tk5KBfv35YvHgxfv3113xnVrZq1QqTJ0/G6tWr80xKZWdnY+3atTAxMUH9+vXV2j99fX20atUKmzZtwsyZM7FhwwY0aNAANjY2Upvy5ctDCAF7e3uFf4/U1bVrV/z666+IiYnB2rVrUbFiRdSqVUuq37x5M5o0aYLly5crbPfixYtP/iNA+fLlAbz9I9THrm/q/J6XL18eqampKl0zP+UzJyIqaLx9j4joP2rkyJHQ19dHr1698OjRI6X6W7duSY9uzv1SNmvWLIU2M2fOBIA8b235t+bNmyf9LITAvHnzoK2tLa3BoampCZlMpjAD5M6dO9i+ffsnj5mdna10O4OFhQVsbGykW1Fq1qwJCwsLLFq0SOH2lH379uHatWsFdizc3d2ho6ODOXPmKMy8WL58OZKTkz9pnH/++QcnTpxAx44d8cMPPyi9evbsibi4OJw5cwbA2zVBPDw8sHHjRqxfvx46Ojrw9vZW6NPDwwMRERGIjo6WypKSkhTWp/qQcePGQQiBHj16KD3SPT4+HiNHjoS1tTV++ukntffXyckJ1atXx4YNG7BhwwZYW1srfLHV1NRE+/btsWXLljy/uD958kTtMdVhbW0NFxcXrFq1SiGJdPnyZRw8eFD6vdPU1ISHhwe2b9+Oe/fuSe2uXbuGAwcOKPRpZGQEMzMzaV2XXAsWLFB4r6GhAW9vb+zatQuRkZFKseWec/r6+gCUk1x5adGiBRITExVumXzz5g3mzp0LAwMD6fayf8vNzQ06Ojp5xp3L2dkZe/fuxbVr19C6dWuFc8vDwwMJCQnYuXOnVPb69WssXbpUoY9Hjx7h/Pnzn/w7nTuz5v2ZX+9fR991/vx51KtXT+1+hRDS9TqXubk5GjZsiBUrViicN3nFlBdVr/vPnz9X6i93Zk7uNTJ3HbBcGhoa0izEd6+j76tXrx7c3d0RGhqK3bt3K9WPHTsWN27cwMiRIz9pJmKnTp3w4MEDLFu2DBcvXlS4dQ94u7aepqYmgoKClPZRCKG0X/nJnRU1fvx4REdHK625p6mpqdT/pk2bkJCQoO4uSdzc3FC+fHlMnz4dqampSvXvXt/U+T3v2LEjIiIilK49udu/efMGwKd/5kREBY0zpYiI/qPKly+PtWvXolOnTnByckL37t1RrVo1ZGZm4vTp09i0aRN69OgB4O36PL6+vliyZAlevHiBRo0a4ezZs1i1ahW8vb3RpEmTAo1NV1cX+/fvh6+vL2rXro19+/Zhz549GDNmjLQ+U8uWLTFz5kx4enqiS5cuePz4MebPn48KFSp88HawD3n58iXKlCmDH374ATVq1ICBgQEOHTqEc+fOSbe6aWtrY+rUqejZsycaNWqEzp0749GjR5g9ezbs7OwwdOjQAjkG5ubmCAgIQFBQEDw9PdGmTRvExsZiwYIFqFWrFn788Ue1+1y7di2EEAoLPL+rRYsW0NLSQlhYmLQIfadOnfDjjz9iwYIF8PDwkBY1zjVy5EisWbMGzZo1w8CBA6Gvr49ly5ahXLlySEpK+uhf4Bs2bIjp06dj2LBhcHZ2Ro8ePWBtbY3r169j6dKlyMnJwd69e/Nd1PpjOnXqhPHjx0NXVxf+/v5K6x1NmTIFR48eRe3atdG7d29UqVIFSUlJOH/+PA4dOoSkpKRPGldVwcHB8PLyQt26deHv74/09HTMnTsXxsbGCAwMlNoFBQVh//79aNCgAfr16ycle6pWrap0vvfq1QtTpkxBr169ULNmTZw4cUKaafiuP/74AwcPHkSjRo3Qp08fODk54eHDh9i0aRNOnTqFkiVLwsXFBZqampg6dSqSk5Mhl8vx/fffw8LCQqm/Pn36YPHixejRoweioqJgZ2eHzZs346+//sKsWbPUXvMnP7q6umjevDkOHTqEiRMn5tuuTp062LFjB1q0aIEffvgB27dvh7a2Nn766SfMmzcPnTt3xuDBg2FtbY2wsDDpwQW55+zevXuhq6v7ydc3IyMjNGzYENOmTUNWVhZKly6NgwcPIj4+Ps/2UVFRSEpKQtu2bT/Yr6OjI8qXL49ffvkFCQkJMDIywpYtW/Jci2vOnDn47rvv8M0336BPnz6wt7fHnTt3sGfPHoVEcl5Uve6vWrUKCxYswP/+9z+UL18eL1++xNKlS2FkZCQltnr16oWkpCR8//33KFOmDO7evYu5c+fCxcXlozPDVq9ejaZNm6Jt27bo0qULGjRogIyMDGzduhXHjh1Dp06dMGLEiA/2kZ8WLVrA0NAQv/zyi5Skflf58uXx22+/ISAgAHfu3IG3tzcMDQ0RHx+Pbdu2oU+fPvjll18+Oo69vT3q1auHHTt2AIBSUqpVq1aYOHEievbsiXr16uHSpUsICwtTWltRHRoaGli2bBm8vLxQtWpV9OzZE6VLl0ZCQgKOHj0KIyMj7Nq1C8DbBBbwNsnn4+MDbW1ttG7dWkpWvWvEiBHYuXMnWrVqhR49esDNzQ1paWm4dOkSNm/ejDt37sDMzOxffeZERAWqiJ7yR0REn+jGjRuid+/ews7OTujo6AhDQ0NRv359MXfuXPH69WupXVZWlggKChL29vZCW1tblC1bVgQEBCi0EeLt46NbtmypNA7eeVR7rtxHqwcHB0tlvr6+Ql9fX9y6dUs0b95clChRQlhaWooJEyYoPTJ7+fLlomLFikIulwtHR0cRGhoqPd78Y2O/W5f76PCMjAwxYsQIUaNGDWFoaCj09fVFjRo1xIIFC5S227Bhg3B1dRVyuVyYmpqKrl27ivv37yu0yd2X9+UVY37mzZsnHB0dhba2trC0tBQ///yz0uPRc/t78uTJB/uqXr26KFeu3AfbNG7cWFhYWIisrCwhhBApKSlCT09PABBr1qzJc5sLFy6IBg0aCLlcLsqUKSMmT54s5syZIwCIxMRElfbzxIkTom3btsLMzExoa2uLcuXKid69e4s7d+4otc3r+L3/2PJcN2/eFAAEAHHq1Kk8x3706JHo37+/KFu2rNDW1hZWVlaiadOmYsmSJR+NO7/z/V15nefvOnTokKhfv77Q09MTRkZGonXr1uLq1atK7Y4fPy7c3NyEjo6OcHBwEIsWLcrzWLx69Ur4+/sLY2NjYWhoKDp27CgeP36scK7nunv3rujevbswNzcXcrlcODg4iP79+4uMjAypzdKlS4WDg4PQ1NRUeOR8o0aNRKNGjRT6e/TokejZs6cwMzMTOjo6onr16iI0NFTl45FXjHnZunWrkMlk0uPn393+/d/1HTt2CC0tLdGpUyfpGnL79m3RsmVLoaenJ8zNzcXw4cPFli1bBADx999/CyGE+OGHH0SLFi2UxlYn/vv374v//e9/omTJksLY2Fh06NBBPHjwIM/9HDVqlChXrpzIycn56P5fvXpVuLu7CwMDA2FmZiZ69+4tLl68KAAoHe/Lly9LMejq6orKlSuLX3/9Var/0PVDlev++fPnRefOnUW5cuWEXC4XFhYWolWrViIyMlJqs3nzZtG8eXNhYWEhdHR0RLly5cRPP/0kHj58+NF9FUKIly9fisDAQFG1alWhp6cn/Tu1cuXKPI/X+8c3NDRUABDx8fFKbbt27SoACHd393zH37Jli/juu++Evr6+0NfXF46OjqJ///4iNjZWpfiFEGL+/PkCgPj222+V6l6/fi2GDx8urK2thZ6enqhfv76IiIhQ+h07evSoACA2bdqk1EduXe7vZ64LFy6Idu3aiVKlSgm5XC5sbW1Fx44dxeHDhxXaTZo0SZQuXVpoaGgoHKu8rq0vX74UAQEBokKFCkJHR0eYmZmJevXqienTp4vMzEwhxL//zImICopMiE9YvZaIiL5aPXr0wObNm/O83YC+HEOGDMHixYuRmpr6n1vsnb582dnZqFKlCjp27IhJkyYVSJ+zZs3C0KFDcf/+fVhaWqJUqVKYPHky+vXrVyD9f0hGRgbs7OwwevRoDB48uNDHIyIi+lpwTSkiIqJi7v21oJ49e4Y///wT3333HRNSVCg0NTUxceJEzJ8//5MS2O+fs69fv8bixYtRsWJFlC5dGklJSRg6dCj+97//FVTIHxQaGgptbW307du3SMYjIiL6WnCmFBERqYUzpb48Li4uaNy4MZycnPDo0SMsX74cDx48wOHDh/N9jDvR5+Tl5YVy5crBxcUFycnJWLNmDa5cuYKwsDB06dLlc4dHREREBYQLnRMRERVzLVq0wObNm7FkyRLIZDJ88803WL58ORNS9J/l4eGBZcuWISwsTLoVcP369UpPXyMiIqIvG2dKERERERERERFRkeOaUkREREREREREVOSYlCIiIiIiIiIioiLHNaUKSE5ODh48eABDQ0PIZLLPHQ4RERERERER0WchhMDLly9hY2MDDY3850MxKVVAHjx4gLJly37uMIiIiIiIiIiI/hP++ecflClTJt96JqUKiKGhIYC3B9zIyOgzR0NERERERERE9HmkpKSgbNmyUq4kP0xKFZDcW/aMjIyYlCIiIiIiIiKir97HljfiQudERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURE9P+dOHECrVu3ho2NDWQyGbZv365Q/+jRI/To0QM2NjYoUaIEPD09cfPmTaV+IiIi8P3330NfXx9GRkZo2LAh0tPTldplZGTAxcUFMpkM0dHRCnUHDhxAnTp1YGhoCHNzc7Rv3x537tz56D7s2bMHtWvXhp6eHkxMTODt7S3VPXv2DJ6enrCxsYFcLkfZsmUxYMAApKSkqHJ4iIiIiIgKFJNSRERE/19aWhpq1KiB+fPnK9UJIeDt7Y3bt29jx44duHDhAmxtbeHu7o60tDSpXUREBDw9PdG8eXOcPXsW586dw4ABA6ChofxP7siRI2FjY6NUHh8fj7Zt2+L7779HdHQ0Dhw4gKdPn6Jdu3YfjH/Lli3o1q0bevbsiYsXL+Kvv/5Cly5dpHoNDQ20bdsWO3fuxI0bN7By5UocOnQIffv2VecwEREREREVDPEZ/fHHH6JmzZrCwMBAmJubi7Zt24rr168rtElPTxf9+vUTpqamQl9fX7Rr104kJiYqtLl7965o0aKF0NPTE+bm5uKXX34RWVlZCm2OHj0qXF1dhY6OjihfvrwIDQ1VimfevHnC1tZWyOVy8e2334ozZ86ovC/JyckCgEhOTlb9ABAR0X8WALFt2zbpfWxsrAAgLl++LJVlZ2cLc3NzsXTpUqmsdu3aYty4cR/tf+/evcLR0VFcuXJFABAXLlyQ6jZt2iS0tLREdna2VLZz504hk8lEZmZmnv1lZWWJ0qVLi2XLlqmxl0LMnj1blClTRq1tiIiIiIg+RNUcyWedKXX8+HH0798ff//9N8LDw5GVlYXmzZsr/MV56NCh2LVrFzZt2oTjx4/jwYMHCn8pzs7ORsuWLZGZmYnTp09j1apVWLlyJcaPHy+1iY+PR8uWLdGkSRNER0djyJAh6NWrFw4cOCC12bBhA4YNG4YJEybg/PnzqFGjBjw8PPD48eOiORhERPSflpGRAQDQ1dWVyjQ0NCCXy3Hq1CkAwOPHj3HmzBlYWFigXr16sLS0RKNGjaT6XI8ePULv3r3x559/okSJEkpjubm5QUNDA6GhocjOzkZycjL+/PNPuLu7Q1tbO8/4zp8/j4SEBGhoaMDV1RXW1tbw8vLC5cuX892nBw8eYOvWrWjUqJHax4OIiIiI6F8roiSZSh4/fiwAiOPHjwshhHjx4oXQ1tYWmzZtktpcu3ZNABARERFCiLd/adbQ0FCYPbVw4UJhZGQkMjIyhBBCjBw5UlStWlVhrE6dOgkPDw/p/bfffiv69+8vvc/OzhY2NjZi8uTJKsXOmVJERMUL3psplZmZKcqVKyc6dOggkpKSREZGhpgyZYoAIJo3by6EECIiIkIAEKampmLFihXi/PnzYsiQIUJHR0fcuHFDCCFETk6O8PT0FJMmTRJCCBEfH680U0oIIY4dOyYsLCyEpqamACDq1q0rnj9/nm+869atEwBEuXLlxObNm0VkZKTo3LmzKFWqlHj27JlCWx8fH6GnpycAiNatW4v09PR/f8CIiIiIiP6/L2Km1PuSk5MBAKampgCAqKgoZGVlwd3dXWrj6OiIcuXKISIiAsDbtTuqV68OS0tLqY2HhwdSUlJw5coVqc27feS2ye0jMzMTUVFRCm00NDTg7u4utXlfRkYGUlJSFF5ERFR8aWtrY+vWrbhx4wZMTU1RokQJHD16FF5eXtJ6UTk5OQCAn376CT179oSrqytCQkJQuXJlrFixAgAwd+5cvHz5EgEBAfmOlZiYiN69e8PX1xfnzp3D8ePHoaOjgx9++AFCiDy3yR177NixaN++Pdzc3BAaGgqZTIZNmzYptA0JCcH58+exY8cO3Lp1C8OGDfvXx4eIiIiISF1anzuAXDk5ORgyZAjq16+PatWqAXj7P+U6OjooWbKkQltLS0skJiZKbd5NSOXW59Z9qE1KSgrS09Px/PlzZGdn59nm+vXrecY7efJkBAUFfdrOEhHRF8nNzQ3R0dFITk5GZmYmzM3NUbt2bdSsWRMAYG1tDQCoUqWKwnZOTk64d+8eAODIkSOIiIiAXC5XaFOzZk107doVq1atwvz582FsbIxp06ZJ9WvWrEHZsmVx5swZ1KlTRym2vMaWy+VwcHCQxs5lZWUFKysrODo6wtTUFA0aNMCvv/4q9UFEREREVBT+MzOl+vfvj8uXL2P9+vWfOxSVBAQEIDk5WXr9888/nzskIiIqIsbGxjA3N8fNmzcRGRmJtm3bAgDs7OxgY2OD2NhYhfY3btyAra0tAGDOnDm4ePEioqOjER0djb179wJ4u7bh77//DgB49eqV0tP6NDU1AfzfjKj3ubm5QS6XK4ydlZWFO3fuSGPnJbe/3DWziIiIiIiKyn9iptSAAQOwe/dunDhxAmXKlJHKrayskJmZiRcvXijMlnr06BGsrKykNmfPnlXo79GjR1Jd7n9zy95tY2RkBD09PWhqakJTUzPPNrl9vE8ulyv9lZuIiL5sqampiIuLk97Hx8cjOjoapqamKFeuHDZt2gRzc3OUK1cOly5dwuDBg+Ht7Y3mzZsDAGQyGUaMGIEJEyagRo0acHFxwapVq3D9+nVs3rwZAFCuXDmFMQ0MDAAA5cuXl/4NbNmyJUJCQjBx4kR07twZL1++xJgxY2BrawtXV1cAwNmzZ9G9e3ccPnwYpUuXhpGREfr27YsJEyagbNmysLW1RXBwMACgQ4cOAIC9e/fi0aNHqFWrFgwMDHDlyhWMGDEC9evXh52dXeEdWCIiIiKiPHzWmVJCCAwYMADbtm3DkSNHYG9vr1Dv5uYGbW1tHD58WCqLjY3FvXv3ULduXQBA3bp1cenSJYWn5IWHh8PIyEi6haFu3boKfeS2ye1DR0cHbm5uCm1ycnJw+PBhqQ0RERV/kZGRcHV1lRI/w4YNg6urq/RE14cPH6Jbt25wdHTEoEGD0K1bN6xbt06hjyFDhiAgIABDhw5FjRo1cPjwYYSHh6N8+fIqx/H9999j7dq12L59O1xdXeHp6Qm5XI79+/dDT08PwNvZVLGxscjKypK2Cw4Oho+PD7p164ZatWrh7t27OHLkCExMTAAAenp6WLp0Kb777js4OTlh6NChaNOmDXbv3v2vjhsRERER0aeQifxWTC0C/fr1w9q1a7Fjxw5UrlxZKjc2Npb+p/vnn3/G3r17sXLlShgZGWHgwIEAgNOnTwMAsrOz4eLiAhsbG0ybNg2JiYno1q0bevXqhT/++APA2790V6tWDf3794efnx+OHDmCQYMGYc+ePfDw8ADw9rYJX19fLF68GN9++y1mzZqFjRs34vr160prTeUlJSUFxsbGSE5OhpGRUYEeJyIiIiIiIiKiL4WqOZLPmpSSyWR5loeGhqJHjx4AgNevX2P48OFYt24dMjIy4OHhgQULFijcVnf37l38/PPPOHbsGPT19eHr64spU6ZAS+v/7k48duwYhg4diqtXr6JMmTL49ddfpTFyzZs3D8HBwUhMTISLiwvmzJmD2rVrq7QvTEoREREREREREX0hSanihEkpIiIiIiIiIiLVcyT/iYXOiYiI8mM3es/nDoEKwZ0pLT93CERERET0mX3Whc6JiIiIiIiIiOjrxKQUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZH7rEmpEydOoHXr1rCxsYFMJsP27dsV6mUyWZ6v4OBgqY2dnZ1S/ZQpUxT6iYmJQYMGDaCrq4uyZcti2rRpSrFs2rQJjo6O0NXVRfXq1bF3795C2WciIiIiIiIiIvrMSam0tDTUqFED8+fPz7P+4cOHCq8VK1ZAJpOhffv2Cu0mTpyo0G7gwIFSXUpKCpo3bw5bW1tERUUhODgYgYGBWLJkidTm9OnT6Ny5M/z9/XHhwgV4e3vD29sbly9fLpwdJyIiIiIiIiL6yml9zsG9vLzg5eWVb72VlZXC+x07dqBJkyZwcHBQKDc0NFRqmyssLAyZmZlYsWIFdHR0ULVqVURHR2PmzJno06cPAGD27Nnw9PTEiBEjAACTJk1CeHg45s2bh0WLFv2bXSQiIiIiIiIiojx8MWtKPXr0CHv27IG/v79S3ZQpU1CqVCm4uroiODgYb968keoiIiLQsGFD6OjoSGUeHh6IjY3F8+fPpTbu7u4KfXp4eCAiIqKQ9oaIiIiIiIiI6Ov2WWdKqWPVqlUwNDREu3btFMoHDRqEb775Bqampjh9+jQCAgLw8OFDzJw5EwCQmJgIe3t7hW0sLS2lOhMTEyQmJkpl77ZJTEzMN56MjAxkZGRI71NSUv7V/hERERERERERfU2+mKTUihUr0LVrV+jq6iqUDxs2TPrZ2dkZOjo6+OmnnzB58mTI5fJCi2fy5MkICgoqtP6JiIiIiIiIiIqzL+L2vZMnTyI2Nha9evX6aNvatWvjzZs3uHPnDoC361I9evRIoU3u+9x1qPJrk986VQAQEBCA5ORk6fXPP/+os0tERERERERERF+1LyIptXz5cri5uaFGjRofbRsdHQ0NDQ1YWFgAAOrWrYsTJ04gKytLahMeHo7KlSvDxMREanP48GGFfsLDw1G3bt18x5HL5TAyMlJ4ERERERERERGRaj5rUio1NRXR0dGIjo4GAMTHxyM6Ohr37t2T2qSkpGDTpk15zpKKiIjArFmzcPHiRdy+fRthYWEYOnQofvzxRynh1KVLF+jo6MDf3x9XrlzBhg0bMHv2bIXb/gYPHoz9+/djxowZuH79OgIDAxEZGYkBAwYU7gEgIiIiIiIiIvpKfdY1pSIjI9GkSRPpfW6iyNfXFytXrgQArF+/HkIIdO7cWWl7uVyO9evXIzAwEBkZGbC3t8fQoUMVEk7GxsY4ePAg+vfvDzc3N5iZmWH8+PHo06eP1KZevXpYu3Ytxo0bhzFjxqBixYrYvn07qlWrVkh7TkRERERERET0dZMJIcTnDqI4SElJgbGxMZKTk3krHxFRAbIbvedzh0CF4M6Ulp87BCIiIiIqJKrmSL6INaWIiIiIiIiIiKh4YVKKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBEVCydOnEDr1q1hY2MDmUyG7du3K9T36NEDMplM4eXp6anQ5vz582jWrBlKliyJUqVKoU+fPkhNTZXqV65cqdRH7uvx48cAgIcPH6JLly6oVKkSNDQ0MGTIEJXiHzRoENzc3CCXy+Hi4pJnGyEEpk+fjkqVKkEul6N06dL4/fffVT5GRFR0/ivXpHf99ddf0NLSyvcak+v169fo0aMHqlevDi0tLXh7e+fZLiMjA2PHjoWtrS3kcjns7OywYsUKlY4PEREREcCkFBEVE2lpaahRowbmz5+fbxtPT088fPhQeq1bt06qe/DgAdzd3VGhQgWcOXMG+/fvx5UrV9CjRw+pTadOnRS2f/jwITw8PNCoUSNYWFgAePslzdzcHOPGjUONGjXU2gc/Pz906tQp3/rBgwdj2bJlmD59Oq5fv46dO3fi22+/VWsMIioa/5VrUq4XL16ge/fuaNq06Udjz87Ohp6eHgYNGgR3d/d823Xs2BGHDx/G8uXLERsbi3Xr1qFy5cof7Z+IiIgol9bnDoCIqCB4eXnBy8vrg23kcjmsrKzyrNu9eze0tbUxf/58aGi8zdcvWrQIzs7OiIuLQ4UKFaCnpwc9PT1pmydPnuDIkSNYvny5VGZnZ4fZs2cDgFozBubMmSP1GRMTo1R/7do1LFy4EJcvX5a+9Nnb26vcPxEVrf/KNSlX37590aVLF2hqairN2nqfvr4+Fi5cCODt7KoXL14otdm/fz+OHz+O27dvw9TUFMDb6x8RERGROjhTioi+GseOHYOFhQUqV66Mn3/+Gc+ePZPqMjIyoKOjI335AyB92Tt16lSe/a1evRolSpTADz/8ULiBA9i1axccHBywe/du2Nvbw87ODr169UJSUlKhj01EhaOorkmhoaG4ffs2JkyYUGCx79y5EzVr1sS0adNQunRpVKpUCb/88gvS09MLbAwiIiIq/piUIqKvgqenJ1avXo3Dhw9j6tSpOH78OLy8vJCdnQ0A+P7775GYmIjg4GBkZmbi+fPnGD16NIC360TlZfny5ejSpYvCTIXCcvv2bdy9exebNm3C6tWrsXLlSkRFRRVJQoyICl5RXZNu3ryJ0aNHY82aNdDSKrgJ8rdv38apU6dw+fJlbNu2DbNmzcLmzZvRr1+/AhuDiIiIij/evkdEXwUfHx/p5+rVq8PZ2Rnly5fHsWPH0LRpU1StWhWrVq3CsGHDEBAQAE1NTQwaNAiWlpYKMxVyRURE4Nq1a/jzzz+LJP6cnBxkZGRg9erVqFSpEoC3X0Dd3NwQGxvLdVyIvjBFcU3Kzs5Gly5dEBQUJF03CkpOTg5kMhnCwsJgbGwMAJg5cyZ++OEHLFiwoEiS9URERPTl40wpIvoqOTg4wMzMDHFxcVJZly5dkJiYiISEBDx79gyBgYF48uQJHBwclLZftmwZXFxc4ObmViTxWltbQ0tLS+GLpZOTEwDg3r17RRIDERWewrgmvXz5EpGRkRgwYAC0tLSgpaWFiRMn4uLFi9DS0sKRI0c+OV5ra2uULl1aSkgBb69JQgjcv3//k/slIiKirwuTUkT0Vbp//z6ePXsGa2trpTpLS0sYGBhgw4YN0NXVRbNmzRTqU1NTsXHjRvj7+xdVuKhfvz7evHmDW7duSWU3btwAANja2hZZHERUOArjmmRkZIRLly4hOjpaevXt2xeVK1dGdHQ0ateu/cnx1q9fHw8ePEBqaqpUduPGDWhoaKBMmTKf3C8RERF9XXj7HhEVC6mpqQozDOLj4xEdHQ1TU1OYmpoiKCgI7du3h5WVFW7duoWRI0eiQoUK8PDwkLaZN28e6tWrBwMDA4SHh2PEiBGYMmUKSpYsqTDWhg0b8ObNG/z44495xhIdHS3F9OTJE0RHR0NHRwdVqlQBAGzbtg0BAQG4fv26tE1cXBxSU1ORmJiI9PR0qY8qVapAR0cH7u7u+Oabb+Dn54dZs2YhJycH/fv3R7NmzQr8thwi+vf+C9ckDQ0NVKtWTaHMwsICurq6CuXz5s3Dtm3bcPjwYans6tWryMzMRFJSEl6+fCldk1xcXAC8ncU1adIk9OzZE0FBQXj69ClGjBgBPz8/3rpHREREKmNSioiKhcjISDRp0kR6P2zYMACAr68vFi5ciJiYGKxatQovXryAjY0NmjdvjkmTJkEul0vbnD17FhMmTEBqaiocHR2xePFidOvWTWms5cuXo127dkpfDHO5urpKP0dFRWHt2rWwtbXFnTt3AADJycmIjY1V2KZXr144fvy4Uh/x8fGws7ODhoYGdu3ahYEDB6Jhw4bQ19eHl5cXZsyYod6BIqIi8V+6Jn3M06dPFWZhAkCLFi1w9+5d6X3uNUkIAQBSomzgwIGoWbMmSpUqhY4dO+K33377pBiIiIjo6yQTuf93Qf9KSkoKjI2NkZycDCMjo88dDhFRsWE3es/nDoEKwZ0pLT93CERERERUSFTNkXBNKSIiIiIiIiIiKnJMShERERERERERUZHjmlJEVGh421Xxw1uu6EvGa1Lxw2sSERHRl40zpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERExdiJEyfQunVr2NjYQCaTYfv27fm27du3L2QyGWbNmqVUt2fPHtSuXRt6enowMTGBt7e3Qv2gQYPg5uYGuVwOFxcXpe0DAwMhk8mUXvr6+irtx7Nnz1CmTBnIZDK8ePFCKj916hTq16+PUqVKQU9PD46OjggJCVGpTyL6vD5rUupjF8cePXooXbA8PT0V2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3Vvg+0tERERERFTU0tLSUKNGDcyfP/+D7bZt24a///4bNjY2SnVbtmxBt27d0LNnT1y8eBF//fUXunTpotTOz88PnTp1yrP/X375BQ8fPlR4ValSBR06dFBpP/z9/eHs7KxUrq+vjwEDBuDEiRO4du0axo0bh3HjxmHJkiUq9UtEn4/W5xw89+Lo5+eHdu3a5dnG09MToaGh0nu5XK5Q37VrVzx8+BDh4eHIyspCz5490adPH6xduxYAkJKSgubNm8Pd3R2LFi3CpUuX4Ofnh5IlS6JPnz4AgNOnT6Nz586YPHkyWrVqhbVr18Lb2xvnz59HtWrVCmnviYiIiIiICp+Xlxe8vLw+2CYhIQEDBw7EgQMH0LJlS4W6N2/eYPDgwQgODoa/v79UXqVKFYV2c+bMAQA8efIEMTExSmMYGBjAwMBAen/x4kVcvXoVixYt+ug+LFy4EC9evMD48eOxb98+hTpXV1e4urpK7+3s7LB161acPHlS+s5HRP9N/yoplZGRoZQkUocqF0e5XA4rK6s8665du4b9+/fj3LlzqFmzJgBg7ty5aNGiBaZPnw4bGxuEhYUhMzMTK1asgI6ODqpWrYro6GjMnDlTukDNnj0bnp6eGDFiBABg0qRJCA8Px7x581S6QBIREREREX2pcnJy0K1bN4wYMQJVq1ZVqj9//jwSEhKgoaEBV1dXJCYmwsXFBcHBwf/qj/jLli1DpUqV0KBBgw+2u3r1KiZOnIgzZ87g9u3bH+33woULOH36NH777bdPjo2IioZat+/t27cPvr6+cHBwgLa2NkqUKAEjIyM0atQIv//+Ox48eFDgAR47dgwWFhaoXLkyfv75Zzx79kyqi4iIQMmSJaWEFAC4u7tDQ0MDZ86ckdo0bNgQOjo6UhsPDw/Exsbi+fPnUht3d3eFcT08PBAREZFvXBkZGUhJSVF4ERERERERfWmmTp0KLS0tDBo0KM/63ERQYGAgxo0bh927d8PExASNGzdGUlLSJ435+vVrhIWFKcy8yktGRgY6d+6M4OBglCtX7oNty5QpA7lcjpo1a6J///7o1avXJ8VGREVHpaTUtm3bUKlSJfj5+UFLSwujRo3C1q1bceDAASxbtgyNGjXCoUOH4ODggL59++LJkycFEpynpydWr16Nw4cPY+rUqTh+/Di8vLyQnZ0NAEhMTISFhYXCNlpaWjA1NUViYqLUxtLSUqFN7vuPtcmtz8vkyZNhbGwsvcqWLfvvdpaIiIiIiKiIRUVFYfbs2Vi5ciVkMlmebXJycgAAY8eORfv27eHm5obQ0FDIZDJs2rTpk8bdtm0bXr58CV9f3w+2CwgIgJOTE3788ceP9nny5ElERkZi0aJFmDVrFtatW/dJsRFR0VHp9r1p06YhJCQEXl5e0NBQzmN17NgRwNv7kOfOnYs1a9Zg6NCh/zo4Hx8f6efq1avD2dkZ5cuXx7Fjx9C0adN/3f+/ERAQgGHDhknvU1JSmJgiIiIiIqIvysmTJ/H48WOFWUjZ2dkYPnw4Zs2ahTt37sDa2hqA4hpScrkcDg4OuHfv3ieNu2zZMrRq1UppcsD7jhw5gkuXLmHz5s0AACEEAMDMzAxjx45FUFCQ1Nbe3h7A2++Ojx49QmBgIDp37vxJ8RFR0VApKfWh29jeVbp0aUyZMuVfBfQhDg4OMDMzQ1xcHJo2bQorKys8fvxYoc2bN2+QlJQkrUNlZWWFR48eKbTJff+xNvmtZQW8vQj/m/W0iIiIiIiIPrdu3brluZRJ7pP2AMDNzQ1yuRyxsbH47rvvAABZWVm4c+cObG1t1R4zPj4eR48exc6dOz/adsuWLUhPT5fenzt3Dn5+fjh58iTKly+f73Y5OTnIyMhQOzYiKlqf9el76rp//z6ePXsmZerr1q2LFy9eICoqCm5ubgDeZtJzcnJQu3Ztqc3YsWORlZUFbW1tAEB4eDgqV64MExMTqc3hw4cxZMgQaazw8HDUrVu3CPeOiIiIiIio4KWmpiIuLk56Hx8fj+joaJiamqJcuXIoVaqUQnttbW1YWVmhcuXKAAAjIyP07dsXEyZMQNmyZWFra4vg4GAAQIcOHaTt4uLikJqaisTERKSnpyM6OhrA2xlW767xu2LFClhbW+f50Ktt27YhICAA169fBwClxNPTp08BAE5OTihZsiQAYP78+ShXrhwcHR0BACdOnMD06dPzXSOLiP47Pikpdf/+fezcuRP37t1DZmamQt3MmTNV7udDF0dTU1MEBQWhffv2sLKywq1btzBy5EhUqFABHh4eAN5eiDw9PdG7d28sWrQIWVlZGDBgAHx8fGBjYwMA6NKlC4KCguDv749Ro0bh8uXLmD17NkJCQqRxBw8ejEaNGmHGjBlo2bIl1q9fj8jISCxZsuRTDg8REREREdF/RmRkJJo0aSK9z12GxNfXFytXrlSpj+DgYGhpaaFbt25IT09H7dq1ceTIEekP/QDQq1cvHD9+XHrv6uoK4O33PDs7OwBvZzCtXLkSPXr0gKamptI4ycnJiI2NVWv/cnJyEBAQgPj4eGhpaaF8+fKYOnUqfvrpJ7X6IaKiJxO5N+Wq6PDhw2jTpg0cHBxw/fp1VKtWDXfu3IEQAt988w2OHDmicl/Hjh1TuDjm8vX1xcKFC+Ht7Y0LFy7gxYsXsLGxQfPmzTFp0iSF+46TkpIwYMAA7Nq1CxoaGmjfvj3mzJkDAwMDqU1MTAz69++Pc+fOwczMDAMHDsSoUaMUxty0aRPGjRuHO3fuoGLFipg2bRpatGih8r6kpKTA2NgYycnJMDIyUnk7ouLMbvSezx0CFbA7U1oW+Zg8j4onnktUED7HeUREREQfp2qORO2ZUgEBAfjll18QFBQEQ0NDbNmyBRYWFujatSs8PT3V6qtx48b4UE7swIEDH+3D1NQUa9eu/WAbZ2dnnDx58oNtOnTooDD1lIiIiIiIiIiICo/yo/Q+4tq1a+jevTsAQEtLC+np6TAwMMDEiRMxderUAg+QiIiIiIiIiIiKH7VnSunr60vrSFlbW+PWrVuoWrUqgP9bdI6IiIiIiIjyx1uKiyfeVkykHrWTUnXq1MGpU6fg5OSEFi1aYPjw4bh06RK2bt2KOnXqFEaMRERERERERERUzKidlJo5cyZSU1MBAEFBQUhNTcWGDRtQsWJFtZ68R0REREREREREXy+1k1IODg7Sz/r6+li0aFGBBkRERERERERERMWf2kmpXJmZmXj8+DFycnIUysuVK/evgyIiIiIiIiIiouJN7aTUjRs34O/vj9OnTyuUCyEgk8mQnZ1dYMEREREREREREVHxpKHuBj179oSGhgZ2796NqKgonD9/HufPn8eFCxdw/vz5woiRirETJ06gdevWsLGxgUwmw/bt2xXqAwMD4ejoCH19fZiYmMDd3R1nzpxR6mfPnj2oXbs29PT0YGJiAm9vb4X6w4cPo169ejA0NISVlRVGjRqFN2/eKLQRQmD69OmoVKkS5HI5Spcujd9///2j+/Cxsc+dO4emTZuiZMmSMDExgYeHBy5evKjS8SEiIiIiIiIqrtSeKRUdHY2oqCg4OjoWRjz0lUlLS0ONGjXg5+eHdu3aKdVXqlQJ8+bNg4ODA9LT0xESEoLmzZsjLi4O5ubmAIAtW7agd+/e+OOPP/D999/jzZs3uHz5stTHxYsX0aJFC4wdOxarV69GQkIC+vbti+zsbEyfPl1qN3jwYBw8eBDTp09H9erVkZSUhKSkpA/G/7GxU1NT4enpiTZt2mDBggV48+YNJkyYAA8PD/zzzz/Q1tb+t4eQiIiIiIiI6IukdlKqSpUqePr0aWHEQl8hLy8veHl55VvfpUsXhfczZ87E8uXLERMTg6ZNm+LNmzcYPHgwgoOD4e/vL7WrUqWK9POGDRvg7OyM8ePHAwAqVKiAadOmoWPHjpgwYQIMDQ1x7do1LFy4EJcvX0blypUBAPb29h+MXZWxr1+/jqSkJEycOBFly5YFAEyYMAHOzs64e/cuKlSo8LFDRERERERERFQsqXT7XkpKivSaOnUqRo4ciWPHjuHZs2cKdSkpKYUdL33FMjMzsWTJEhgbG6NGjRoAgPPnzyMhIQEaGhpwdXWFtbU1vLy8FGYrZWRkQFdXV6EvPT09vH79GlFRUQCAXbt2wcHBAbt374a9vT3s7OzQq1evD86UUmXsypUro1SpUli+fDkyMzORnp6O5cuXw8nJCXZ2dgV4dIiIiIiIiIi+LColpXLXwjExMUGzZs3w999/o2nTprCwsJDKc9sQFbTdu3fDwMAAurq6CAkJQXh4OMzMzAAAt2/fBvB27alx48Zh9+7dMDExQePGjaWEkoeHB06fPo1169YhOzsbCQkJmDhxIgDg4cOHUj93797Fpk2bsHr1aqxcuRJRUVH44Ycf8o1LlbENDQ1x7NgxrFmzBnp6ejAwMMD+/fuxb98+aGl98sMviYiIiIiIiL54Kn0rPnr0aGHHQZSvJk2aIDo6Gk+fPsXSpUvRsWNHnDlzBhYWFsjJyQEAjB07Fu3btwcAhIaGokyZMti0aRN++uknNG/eHMHBwejbty+6desGuVyOX3/9FSdPnoSGxtu8bE5ODjIyMrB69WpUqlQJALB8+XK4ubkhNjZWuqXvXaqMnZ6eDn9/f9SvX19Kik2fPh0tW7bEuXPnoKenV+jHj4iIiIiIiOi/SKWkVKNGjbBixQq0bt1aWlyaqKjo6+ujQoUKqFChAurUqYOKFSti+fLlCAgIgLW1NQDFdZzkcjkcHBxw7949qWzYsGEYOnQoHj58CBMTE9y5cwcBAQFwcHAAAFhbW0NLS0tKSAGAk5MTAODevXt5JqVUGXvt2rW4c+cOIiIipATY2rVrYWJigh07dsDHx6dAjhERERERERHRl0al2/cAYM2aNShbtizq1auHqVOn4tq1a4UZF1G+cmc1AYCbmxvkcjliY2Ol+qysLNy5cwe2trYK28lkMtjY2EBPTw/r1q1D2bJl8c033wAA6tevjzdv3uDWrVtS+xs3bgCAUj+5VBn71atX0NDQgEwmk9rkvs+daUVERERERET0NVI5KXXkyBE8fPgQ/fr1Q1RUFGrXro2KFSti+PDhOHHiBL9g0ydJTU1FdHQ0oqOjAQDx8fGIjo7GvXv3kJaWhjFjxuDvv//G3bt3ERUVBT8/PyQkJKBDhw4AACMjI/Tt2xcTJkzAwYMHERsbi59//hkApDYAEBwcjEuXLuHKlSuYNGkSpkyZgjlz5kBTUxMA4O7ujm+++QZ+fn64cOECoqKi8NNPP6FZs2bS7KmzZ8/C0dERCQkJKo/drFkzPH/+HP3798e1a9dw5coV9OzZE1paWmjSpEnhH2AiIiIiIiKi/yiVk1IAYGJigh9//BEbN27E06dPMXfuXKSnp6Nr166wsLBA9+7dsXnzZqSlpRVWvFTMREZGwtXVFa6urgDe3mbn6uqK8ePHQ1NTE9evX0f79u1RqVIltG7dGs+ePcPJkydRtWpVqY/g4GD4+PigW7duqFWrFu7evYsjR44oLLy/b98+NGjQADVr1sSePXuwY8cOeHt7S/UaGhrYtWsXzMzM0LBhQ7Rs2RJOTk5Yv3691ObVq1eIjY1FVlaWymM7Ojpi165diImJQd26ddGgQQM8ePAA+/fvl27/IyIiIiIi+hKcOHECrVu3ho2NDWQyGbZv365QHxgYCEdHR+jr68PExATu7u44c+aMVH/nzh34+/vD3t4eenp6KF++PCZMmIDMzEypTWxsLJo0aQJLS0vo6urCwcEB48aNU/getnTpUjRo0EB68Jq7uzvOnj370fjnz58PJycn6OnpoXLlyli9erVSmxcvXqB///6wtraGXC5HpUqVsHfv3k84WqSKT378l46ODjw9PeHp6YkFCxYgMjISO3fuxKRJk3Dt2jX8+uuvBRknFVONGzeGECLf+q1bt360D21tbUyfPh3Tp0/Pt82RI0c+2o+NjQ22bNmSb31esaoydrNmzdCsWbOPjk9ERERERPRflpaWhho1asDPzw/t2rVTqq9UqRLmzZsHBwcHpKenIyQkBM2bN0dcXBzMzc1x/fp15OTkYPHixahQoQIuX76M3r17Iy0tTfpOpa2tje7du+Obb75ByZIlcfHiRfTu3Rs5OTn4448/AADHjh1D586dUa9ePejq6mLq1Klo3rw5rly5gtKlS+cZ+8KFCxEQEIClS5eiVq1aOHv2LHr37g0TExO0bt0aAJCZmYlmzZrBwsICmzdvRunSpXH37l2ULFmycA4oQSY+lBH4RFlZWdDW1i7obv/TUlJSYGxsjOTkZBgZGX3ucIj+E+xG7/ncIVABuzOlZZGPyfOoeOK5RAXhc5xHRAWF16Ti6Wu6LslkMmzbtk3hDpT35X5PPnToEJo2bZpnm+DgYCxcuBC3b9/Ot59hw4bh3LlzOHnyZJ712dnZMDExwbx589C9e/c829SrVw/169dHcHCwVDZ8+HCcOXMGp06dAgAsWrQIwcHBuH79+leX0yhoquZI1J4pNWzYsDzLZTIZdHV1UbFiRbRp0wampqbqdk1ERERERERExUBmZiaWLFkCY2Nj1KhRI992ycnJH8wfxMXFYf/+/XnOzMr16tUrZGVlfbCfjIwM6OrqKpTp6enh7Nmz0sSanTt3om7duujfvz927NgBc3NzdOnSBaNGjZLWI6aCpXZS6sKFCzh//jyys7NRuXJlAG+fUqapqQlHR0csWLAAw4YNw6lTp1ClSpUCD5gKH/9qU/x8TX+xISIiIiKiz2f37t3w8fHBq1evYG1tjfDwcJiZmeXZNi4uDnPnzs1zOZR69erh/PnzyMjIQJ8+fTBx4sR8xxw1ahRsbGzg7u6ebxsPDw8sW7YM3t7e+OabbxAVFYVly5YhKysLT58+hbW1NW7fvo0jR46ga9eu2Lt3L+Li4tCvXz9kZWVhwoQJ6h8M+ii1FjoHgLZt28Ld3R0PHjxAVFQUoqKicP/+fTRr1gydO3dGQkICGjZsiKFDhxZGvERERERERET0H9WkSRNER0fj9OnT8PT0RMeOHfH48WOldgkJCfD09ESHDh3Qu3dvpfoNGzbg/PnzWLt2Lfbs2ZPvOr5TpkzB+vXrsW3bNqWZUO/69ddf4eXlhTp16kBbWxtt27aFr68vgLcPvgKAnJwcWFhYYMmSJXBzc0OnTp0wduxYLFq06FMOBalA7aRUcHAwJk2apHBPoLGxMQIDAzFt2jSUKFEC48ePR1RUVIEGSkRERERERET/bfr6+qhQoQLq1KmD5cuXQ0tLC8uXL1do8+DBAzRp0gT16tXDkiVL8uynbNmyqFKlCjp37owpU6YgMDAQ2dnZCm2mT5+OKVOm4ODBg3B2dv5gXHp6elixYgVevXqFO3fu4N69e7Czs4OhoSHMzc0BANbW1qhUqZLCrXpOTk5ITExUeEIgFRy1k1LJycl5ZjmfPHmClJQUAEDJkiX5gRERERERERF95XJycpCRkSG9T0hIQOPGjeHm5obQ0FBpltLH+sjKykJOTo5UNm3aNEyaNAn79+9HzZo1VY5HW1sbZcqUgaamJtavX49WrVpJMdSvXx9xcXEK49y4cQPW1tbQ0dFReQxSndprSrVt2xZ+fn6YMWMGatWqBQA4d+4cfvnlF2nV/bNnz6JSpUoFGigRERERERERfT6pqamIi4uT3sfHxyM6OhqmpqYoVaoUfv/9d7Rp0wbW1tZ4+vQp5s+fj4SEBHTo0AHA/yWkbG1tMX36dDx58kTqy8rKCgAQFhYGbW1tVK9eHXK5HJGRkQgICECnTp2kJ+JNnToV48ePx9q1a2FnZ4fExEQAgIGBAQwMDAAAAQEBSEhIwOrVqwG8TS6dPXsWtWvXxvPnzzFz5kxcvnwZq1atkmL4+eefMW/ePAwePBgDBw7EzZs38ccff2DQoEGFeFS/bmonpRYvXoyhQ4fCx8cHb968eduJlhZ8fX0xc+ZMAICjoyOWLVtWsJESERERERER0WcTGRmJJk2aSO+HDRsGAPD19cWiRYtw/fp1rFq1Ck+fPkWpUqVQq1YtnDx5ElWrVgUAhIeHIy4uDnFxcShTpoxC30IIAG/zC1OnTsWNGzcghICtrS0GDBigsG71woULkZmZiR9++EGhjwkTJiAwMBAA8PDhQ9y7d0+qy87OxowZMxAbGwttbW00adIEp0+fhp2dndSmbNmyOHDgAIYOHQpnZ2eULl0agwcPxqhRo/79waM8yUTuJ6+m1NRU3L59GwDg4OAgZSO/VikpKTA2NkZycrLCeltfIj59r/j5XE/f47lU/HyOc4nnUfHEc4kKAp8uS18yXpOKJ16XiN5SNUei9ppSR48eBfB2WpyzszOcnZ2lhNT8+fM/MVwiIiIiIiIiIvqaqJ2UateuXZ5P1ps9ezYCAgIKJCgiIiIiIiIiIire1F5TKjg4GF5eXjhx4gQcHR0BADNmzMDEiROxZw+noBIREREREREVFd4KWvx8TbeBqp2U6tWrF5KSkuDu7o5Tp05hw4YN+OOPP7B3717Ur1+/MGIkIiIiIiIiIqJiRu2kFACMHDkSz549Q82aNZGdnY0DBw6gTp06BR0bEREREREREREVUyolpebMmaNUVrp0aZQoUQINGzbE2bNncfbsWQDAoEGDCjZCIiIiIiIiIiIqdlRKSoWEhORZrqmpib/++gt//fUXAEAmkzEpRUREREREREREH6VSUio+Pr6w4yAiIiIiIiIioq+IxucOgIiIiIiIiIiIvj4qJaWmTJmCV69eqdThmTNnsGcPH0lJRERERERERET5UykpdfXqVdja2qJfv37Yt28fnjx5ItW9efMGMTExWLBgAerVq4dOnTrB0NCw0AImIiIiIiIiIqIvn0prSq1evRoXL17EvHnz0KVLF6SkpEBTUxNyuVyaQeXq6opevXqhR48e0NXVLdSgiYiIiIiIiIjoy6ZSUgoAatSogaVLl2Lx4sWIiYnB3bt3kZ6eDjMzM7i4uMDMzKww4yQiIiIiIiIiomJE7YXONTQ04OLigrZt28LHxwfu7u6fnJA6ceIEWrduDRsbG8hkMmzfvl2qy8rKwqhRo1C9enXo6+vDxsYG3bt3x4MHDxT6sLOzg0wmU3hNmTJFoU1MTAwaNGgAXV1dlC1bFtOmTVOKZdOmTXB0dISuri6qV6+OvXv3ftI+ERERERERERHRx33Wp++lpaWhRo0amD9/vlLdq1evcP78efz66684f/48tm7ditjYWLRp00ap7cSJE/Hw4UPpNXDgQKkuJSUFzZs3h62tLaKiohAcHIzAwEAsWbJEanP69Gl07twZ/v7+uHDhAry9veHt7Y3Lly8Xzo4TEREREREREX3lVL59rzB4eXnBy8srzzpjY2OEh4crlM2bNw/ffvst7t27h3LlyknlhoaGsLKyyrOfsLAwZGZmYsWKFdDR0UHVqlURHR2NmTNnok+fPgCA2bNnw9PTEyNGjAAATJo0CeHh4Zg3bx4WLVpUELtKRERERERERETv+KwzpdSVnJwMmUyGkiVLKpRPmTIFpUqVgqurK4KDg/HmzRupLiIiAg0bNoSOjo5U5uHhgdjYWDx//lxq4+7urtCnh4cHIiIi8o0lIyMDKSkpCi8iIiIiIiIiIlLNZ50ppY7Xr19j1KhR6Ny5M4yMjKTyQYMG4ZtvvoGpqSlOnz6NgIAAPHz4EDNnzgQAJCYmwt7eXqEvS0tLqc7ExASJiYlS2bttEhMT841n8uTJCAoKKqjdIyIiIiIiIiL6qnwRSamsrCx07NgRQggsXLhQoW7YsGHSz87OztDR0cFPP/2EyZMnQy6XF1pMAQEBCmOnpKSgbNmyhTYeEREREREREVFxonZSKi0tDVOmTMHhw4fx+PFj5OTkKNTfvn27wIID/i8hdffuXRw5ckRhllReateujTdv3uDOnTuoXLkyrKys8OjRI4U2ue9z16HKr01+61QBgFwuL9SkFxERERERERFRcaZ2UqpXr144fvw4unXrBmtra8hkssKIC8D/JaRu3ryJo0ePolSpUh/dJjo6GhoaGrCwsAAA1K1bF2PHjkVWVha0tbUBAOHh4ahcuTJMTEykNocPH8aQIUOkfsLDw1G3bt2C3ykiIiIiIiIiIlI/KbVv3z7s2bMH9evX/9eDp6amIi4uTnofHx+P6OhomJqawtraGj/88APOnz+P3bt3Izs7W1rjydTUFDo6OoiIiMCZM2fQpEkTGBoaIiIiAkOHDsWPP/4oJZy6dOmCoKAg+Pv7Y9SoUbh8+TJmz56NkJAQadzBgwejUaNGmDFjBlq2bIn169cjMjISS5Ys+df7SEREREREREREytROSpmYmMDU1LRABo+MjESTJk2k97lrNPn6+iIwMBA7d+4EALi4uChsd/ToUTRu3BhyuRzr169HYGAgMjIyYG9vj6FDhyqs9WRsbIyDBw+if//+cHNzg5mZGcaPH48+ffpIberVq4e1a9di3LhxGDNmDCpWrIjt27ejWrVqBbKfRERERERERESkSO2k1KRJkzB+/HisWrUKJUqU+FeDN27cGEKIfOs/VAcA33zzDf7++++PjuPs7IyTJ09+sE2HDh3QoUOHj/ZFRERERERERET/ntpJqRkzZuDWrVuwtLSEnZ2dtE5TrvPnzxdYcEREREREREREVDypnZTy9vYuhDCIiIiIiIiIiOhroqHuBhMmTPjgi4iIiIiICsaJEyfQunVr2NjYQCaTYfv27Qr1W7duRfPmzVGqVCnIZDJER0fn25cQAl5eXnn2M2jQILi5uUEulyut5woAx44dQ9u2bWFtbQ19fX24uLggLCxMpX1YuXIlnJ2doaurCwsLC/Tv3z/PdnFxcTA0NETJkiVV6peIiL58aielckVFRWHNmjVYs2YNLly4UJAxERERERERgLS0NNSoUQPz58/Pt/67777D1KlTP9rXrFmzIJPJ8q338/NDp06d8qw7ffo0nJ2dsWXLFsTExKBnz57o3r07du/e/cExZ86cibFjx2L06NG4cuUKDh06BA8PD6V2WVlZ6Ny5Mxo0aPDR/SAiouJD7dv3Hj9+DB8fHxw7dkz6K8aLFy/QpEkTrF+/Hubm5gUdIxERERHRV8nLywteXl751nfr1g0AcOfOnQ/2Ex0djRkzZiAyMhLW1tZK9XPmzAEAPHnyBDExMUr1Y8aMUXg/ePBgHDx4EFu3bkWrVq3yHPP58+cYN24cdu3ahaZNm0rlzs7OSm3HjRsHR0dHNG3aFKdPn/7gvhARUfGh9kypgQMH4uXLl7hy5QqSkpKQlJSEy5cvIyUlBYMGDSqMGImIiIiI6BO9evUKXbp0wfz582FlZVVg/SYnJ8PU1DTf+vDwcOTk5CAhIQFOTk4oU6YMOnbsiH/++Ueh3ZEjR7Bp06Z8Z4MREVHxpXZSav/+/ViwYAGcnJyksipVqmD+/PnYt29fgQZHRERERET/ztChQ1GvXj20bdu2wPrcuHEjzp07h549e+bb5vbt28jJycEff/yBWbNmYfPmzUhKSkKzZs2QmZkJAHj27Bl69OiBlStXwsjIqMDiIyKiL4Pat+/l5ORAW1tbqVxbWxs5OTkFEhQREREREf17O3fuxJEjRwp0DdijR4+iZ8+eWLp0KapWrZpvu5ycHGRlZWHOnDlo3rw5AGDdunWwsrLC0aNH4eHhgd69e6NLly5o2LBhgcVHRERfDrVnSn3//fcYPHgwHjx4IJUlJCRg6NChCveKExERERHR53XkyBHcunULJUuWhJaWFrS03v5Nun379mjcuLHa/R0/fhytW7dGSEgIunfv/sG2uWtXValSRSozNzeHmZkZ7t27J8U3ffp0KTZ/f38kJydDS0sLK1asUDs+IiL6sqg9U2revHlo06YN7OzsULZsWQDAP//8g2rVqmHNmjUFHiAREREREX2a0aNHo1evXgpl1atXR0hICFq3bq1WX8eOHUOrVq0wdepU9OnT56Pt69evDwCIjY1FmTJlAABJSUl4+vQpbG1tAQARERHIzs6WttmxYwemTp2K06dPo3Tp0mrFR0REXx61k1Jly5bF+fPncejQIVy/fh0A4OTkBHd39wIPjoiIiIjoa5aamoq4uDjpfXx8PKKjo2Fqaopy5cohKSkJ9+7dk+5iiI2NBQBYWVkpvN5Xrlw52NvbS+/j4uKQmpqKxMREpKenIzo6GsDbWU46Ojo4evQoWrVqhcGDB6N9+/ZITEwEAOjo6EiLnW/btg0BAQHSd4RKlSqhbdu2GDx4MJYsWQIjIyMEBATA0dERTZo0AQCFdWoBIDIyEhoaGqhWrVpBHD4iIvqPUzspBQAymQzNmjVDs2bNCjoeIiIiIiL6/yIjI6UEDgAMGzYMAODr64uVK1di586dCouN+/j4AAAmTJiAwMBAlcfp1asXjh8/Lr13dXUF8DYJZmdnh1WrVuHVq1eYPHkyJk+eLLVr1KgRjh07BuDt0/hyk2K5Vq9ejaFDh6Jly5bQ0NBAo0aNsH///jzXqCUioq+PTAghPtZozpw56NOnD3R1dTFnzpwPth00aFCBBfclSUlJgbGxMZKTk7/4J4fYjd7zuUOgAnZnSsvPMi7PpeLnc5xLPI+KJ55LVBA+179vRAWB16Tiif++UUEoDv++qZojUWmmVEhICLp27QpdXV2EhITk204mk321SSkiIiIiIiIiIlKdSkmp+Pj4PH8mIiIiIiIiIiL6FGqvKTVx4kT88ssvKFGihEJ5eno6goODMX78+AILjoiIiIjov4a3yhQ/xeFWGSKiL5GGuhsEBQUhNTVVqfzVq1cICgoqkKCIiIiIiIiIiKh4UzspJYSATCZTKr948aL0OFgiIiIiIiIiIqIPUfn2PRMTE8hkMshkMlSqVEkhMZWdnY3U1FT07du3UIIkIiIiIiIiIqLiReWk1KxZsyCEgJ+fH4KCgmBsbCzV6ejowM7ODnXr1i2UIImIiIiIiIiIqHhROSnl6+sLALC3t0f9+vWhpaX2GulEREREREREREQAPmFNqbS0NBw+fFip/MCBA9i3b1+BBEVERERERERERMWb2kmp0aNHIzs7W6lcCIHRo0cXSFBERERERERERFS8qZ2UunnzJqpUqaJU7ujoiLi4uAIJioiIiIiIiIiIije1k1LGxsa4ffu2UnlcXBz09fULJCgiIiIiIiIiIire1E5KtW3bFkOGDMGtW7eksri4OAwfPhxt2rQp0OCIiIiIiIiIiKh4UjspNW3aNOjr68PR0RH29vawt7eHk5MTSpUqhenTpxdGjEREREREREREVMxoqbuBsbExTp8+jfDwcFy8eBF6enpwdnZGw4YNCyM+IiIiIiIiIiIqhtROSgGATCZD8+bN0bx584KOh4iIiIiIiIiIvgJqJ6UmTpz4wfrx48d/cjBERERERERERPR1UDsptW3bNoX3WVlZiI+Ph5aWFsqXL8+kFBERERERERERfZTaSakLFy4olaWkpKBHjx743//+VyBBERERERERERFR8ab20/fyYmRkhKCgIPz6668F0R0RERERERERERVzBZKUAoDk5GQkJycXVHdERERERERERFSMqX373pw5cxTeCyHw8OFD/Pnnn/Dy8iqwwIiIiIiIiIiIqPhSOykVEhKi8F5DQwPm5ubw9fVFQEBAgQVGRERERERERETFl9pJqfj4+MKIg4iIiIiIiIiIviIFtqYUERERERERERGRqlSaKdWuXTuVO9y6desnB0NERERERERERF8HlWZKGRsbSy8jIyMcPnwYkZGRUn1UVBQOHz4MY2NjtQY/ceIEWrduDRsbG8hkMmzfvl2hXgiB8ePHw9raGnp6enB3d8fNmzcV2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3avWvhARERERERERkepUSkqFhoZKL0tLS3Ts2BHx8fHYunUrtm7ditu3b8PHxwdmZmZqDZ6WloYaNWpg/vz5edZPmzYNc+bMwaJFi3DmzBno6+vDw8MDr1+/ltp07doVV65cQXh4OHbv3o0TJ06gT58+Un1KSgqaN28OW1tbREVFITg4GIGBgViyZInU5vTp0+jcuTP8/f1x4cIFeHt7w9vbG5cvX1Zrf4iIiIiIiIiISDVqL3S+YsUKnDp1CpqamlKZpqYmhg0bhnr16iE4OFjlvry8vODl5ZVnnRACs2bNwrhx49C2bVsAwOrVq2FpaYnt27fDx8cH165dw/79+3Hu3DnUrFkTADB37ly0aNEC06dPh42NDcLCwpCZmYkVK1ZAR0cHVatWRXR0NGbOnCklr2bPng1PT0+MGDECADBp0iSEh4dj3rx5WLRokbqHiIiIiIiIiIiIPkLthc7fvHmD69evK5Vfv34dOTk5BRIU8PYpf4mJiXB3d5fKjI2NUbt2bURERAAAIiIiULJkSSkhBQDu7u7Q0NDAmTNnpDYNGzaEjo6O1MbDwwOxsbF4/vy51ObdcXLb5I5DREREREREREQFS+2ZUj179oS/vz9u3bqFb7/9FgBw5swZTJkyBT179iywwBITEwEAlpaWCuWWlpZSXWJiIiwsLBTqtbS0YGpqqtDG3t5eqY/cOhMTEyQmJn5wnLxkZGQgIyNDep+SkqLO7hERERERERERfdXUTkpNnz4dVlZWmDFjBh4+fAgAsLa2xogRIzB8+PACD/C/avLkyQgKCvrcYRARERERERERfZHUvn1PQ0MDI0eOREJCAl68eIEXL14gISEBI0eOVFhn6t+ysrICADx69Eih/NGjR1KdlZUVHj9+rFD/5s0bJCUlKbTJq493x8ivTW59XgICApCcnCy9/vnnH3V3kYiIiIiIiIjoq6V2UirXkydPEBMTg5iYGDx9+rQgYwIA2Nvbw8rKCocPH5bKUlJScObMGdStWxcAULduXbx48QJRUVFSmyNHjiAnJwe1a9eW2pw4cQJZWVlSm/DwcFSuXBkmJiZSm3fHyW2TO05e5HI5jIyMFF5ERERERERERKQatZNSaWlp8PPzg7W1NRo2bIiGDRvC2toa/v7+ePXqlVp9paamIjo6GtHR0QDeLm4eHR2Ne/fuQSaTYciQIfjtt9+wc+dOXLp0Cd27d4eNjQ28vb0BAE5OTvD09ETv3r1x9uxZ/PXXXxgwYAB8fHxgY2MDAOjSpQt0dHTg7++PK1euYMOGDZg9ezaGDRsmxTF48GDs378fM2bMwPXr1xEYGIjIyEgMGDBA3cNDREREREREREQqUDspNWzYMBw/fhy7du2Sbt/bsWMHjh8/rvaaUpGRkXB1dYWrq6vUt6urK8aPHw8AGDlyJAYOHIg+ffqgVq1aSE1Nxf79+6Grqyv1ERYWBkdHRzRt2hQtWrTAd999hyVLlkj1xsbGOHjwIOLj4+Hm5obhw4dj/Pjx6NOnj9SmXr16WLt2LZYsWYIaNWpg8+bN2L59O6pVq6bu4SEiIiIiIiIiIhWovdD5li1bsHnzZjRu3Fgqa9GiBfT09NCxY0csXLhQ5b4aN24MIUS+9TKZDBMnTsTEiRPzbWNqaoq1a9d+cBxnZ2ecPHnyg206dOiADh06fDhgIiIiIiIiIiIqEGrPlHr16hUsLS2Vyi0sLNS+fY+IiIiIiIiIiL5Oaiel6tatiwkTJuD169dSWXp6OoKCgj64MDgREREREREREVEutW/fmzVrFjw9PVGmTBnUqFEDAHDx4kXo6uriwIEDBR4gEREREREREREVP2onpapXr46bN28iLCwM169fBwB07twZXbt2hZ6eXoEHSERERERERERExY9aSamsrCw4Ojpi9+7d6N27d2HFRERERERERERExZxaa0ppa2srrCVFRERERERERET0KdRe6Lx///6YOnUq3rx5UxjxEBERERERERHRV0DtNaXOnTuHw4cP4+DBg6hevTr09fUV6rdu3VpgwRERERERERERUfGkdlKqZMmSaN++fWHEQkREREREREREXwm1k1KhoaGFEQcREREREREREX1FVF5TKicnB1OnTkX9+vVRq1YtjB49Gunp6YUZGxERERERERERFVMqJ6V+//13jBkzBgYGBihdujRmz56N/v37F2ZsRERERERERERUTKmclFq9ejUWLFiAAwcOYPv27di1axfCwsKQk5NTmPEREREREREREVExpHJS6t69e2jRooX03t3dHTKZDA8ePCiUwIiIiIiIiIiIqPhSOSn15s0b6OrqKpRpa2sjKyurwIMiIiIiIiIiIqLiTeWn7wkh0KNHD8jlcqns9evX6Nu3L/T19aWyrVu3FmyERERERERERERU7KiclPL19VUq+/HHHws0GCIiIiIiIiIi+jqonJQKDQ0tzDiIiIiIiIiIiOgrovKaUkRERERERERERAWFSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5D4pKfXnn3+ifv36sLGxwd27dwEAs2bNwo4dOwo0OCIiIiIiIiIiKp7UTkotXLgQw4YNQ4sWLfDixQtkZ2cDAEqWLIlZs2YVdHxERERERERERFQMqZ2Umjt3LpYuXYqxY8dCU1NTKq9ZsyYuXbpUoMEREREREREREVHxpHZSKj4+Hq6urkrlcrkcaWlpBRIUEREREREREREVb2onpezt7REdHa1Uvn//fjg5ORVETEREREREREREVMxpqbvBsGHD0L9/f7x+/RpCCJw9exbr1q3D5MmTsWzZssKIkYiIiIiIiIiIihm1k1K9evWCnp4exo0bh1evXqFLly6wsbHB7Nmz4ePjUxgxEhERERERERFRMaN2UgoAunbtiq5du+LVq1dITU2FhYVFQcdFRERERERERETFmNprSv3222+Ij48HAJQoUYIJKSIiIiIiIiIiUpvaSalNmzahQoUKqFevHhYsWICnT58WRlxERERERERERFSMqZ2UunjxImJiYtC4cWNMnz4dNjY2aNmyJdauXYtXr14VRoxERERERERERFTMqJ2UAoCqVavijz/+wO3bt3H06FHY2dlhyJAhsLKyKuj4iIiIiIiIiIioGPqkpNS79PX1oaenBx0dHWRlZRVETEREREREREREVMx9UlIqPj4ev//+O6pWrYqaNWviwoULCAoKQmJiYkHHR0RERERERERExZCWuhvUqVMH586dg7OzM3r27InOnTujdOnShREbEREREREREREVU2o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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_olive_production_kg_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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r1qlevXrWv3/++Wf16tVLAQEBevPNN1WjRg1J0tGjR9WxY0ft379fK1euVPny5R1VMu4CGQHn+vXr9ffff+vy5ctq0KCBqlSpoqNHj+rNN9/UunXr1K5dO/Xv39/R5eIONWHCBB07dkxvvvmm9TSpxo0ba926dQoLC7M5BT01NVURERHau3evIiIi9PTTTyt//vwOrB53qqt/1JOufB5q0qSJSpcurc8++0zS/8KF1NRU/f3336pdu7bNMjKCKyDD1WHS7t27NXbsWPXu3VtBQUFatWqV3n33Xe3cuVNRUVGqUaOGjh49quPHj+vy5csKCgqyjjHFZ3NI/9tPbdy4Uf/8849cXV1VtmxZBQcHS7INppo3b84YwWBMKdjX1RnopUuXFBcXp5UrV+rAgQPWdh8fH3Xv3l29evXS/Pnz9fLLL0uSNZAyxvCmB0VHRys8PNzmyh2FCxeWk5OTli9fbv1wlZ6eLl9fX0VFRalChQqqXbu29u3b56CqcTewWCxatGiRmjVrpokTJ2r8+PGqWbOmZs6cKV9fXw0ePFh169bVN998oxEjRji6XNyhChQooGeeeUYuLi5KSUmRi4uLvvvuOzVv3lw7d+7Ul19+aR1vw9XVVfPmzVPx4sW1ePFipaSkOLh63InS09OtgdSePXt09uxZ5cuXT23bttWmTZsUExMj6X/jTB04cEBjxozRn3/+abMcAilk+OqrryT9b5v57LPPrGNH+fv7S7pyZdn+/furUqVK6tixozZt2iRfX1/VqFFDtWvXtobufDZHhoyxEx988EG9++67evbZZ9W9e3cNGzZM0pUxpsLCwvTss8/q66+/5uIwkAxgJ9u3bzcXLlwwxhgzYsQI8/fff5s9e/aYl156yRQqVMhMnz7dpn98fLwZNGiQefzxx016erojSsYd7ujRo8YYYw4ePGht27hxo6lWrZqpXbu2OX/+vDHGWLefw4cPmxYtWpidO3fav1jcNTZv3mxKlChhZs+ebc6ePWuSkpLMqFGjTL58+cysWbOMMcYcOHDAREREmLCwMHPy5EkHV4w72c8//2z69Oljtm3bZowx5uTJk6ZRo0amQYMG5ptvvjFpaWnWvikpKebQoUOOKhV3sKu3k2HDhpnHHnvMxMTEGGOMiYuLM40bNzbt2rUzy5YtM8YYs2/fPtOqVSvTsGFDc/nyZYfUjDvbBx98YOrXr2/S0tKs28i8efNMaGio8fT0NIcPH7bp/+uvv5o2bdqYYsWKmd27dzuiZNzBrt7PbN682Xh5eZkZM2aY5ORks337djN69GhTpkwZM3z4cGu/5s2bm4CAAHP27FkHVIw7CaEUbrv09HSzefNmY7FYzIcffmh69+5t8ufPb7Zu3WqMMWb37t3mpZdeMoGBgWbmzJk28546dcoaKBBMISs7duwwFovFvPfee9a2jRs3msDAQFO3bl1rEJqx/fDhHNe6dt8SExNj7r//fnP06FGbaSNGjDAFChSwhppHjx61BqPA1aFBamqq9f+zZs0yFSpUMK+88orZvn27McaYEydOmIYNG5oGDRqYZcuW2cwL3MiAAQNMyZIlzZdffmmOHz9ubV+5cqVp2bKlKV68uPH39zdVq1Y1wcHB1m2RbQzXio+Pt34m+v33363tS5YsMcHBwaZRo0Zm//79NvOsXLnSvP7663yWgtWUKVMy7V++/PJLU7VqVXPmzBlrW0JCghkxYoQJDg62/khjjOGHGBhjCKVgR+PHjzfu7u6mQIECJi4uzhjzvy+DO3bssAZTH3zwQaZ5CaRwI/369TP58+c3H330kbUtI5iqX7++9YgpIEPGB6irP0idOHHCpKenm2+++cY4OTlZfyVOSUkxxhhz5MgRExAQYBYvXmz/gnFX2Ldvnzl37pwxxpivvvrKjBw50hhjzOTJk02tWrXMiy++aBNMhYaGmqpVq5offvjBYTXj7vHTTz+ZMmXKmLVr1xpjjElOTjb79u0z0dHR5tixYyY1NdXExcWZqVOnmqVLl1qDg0uXLjmybNxhBgwYYH1fM8aY2NhYY7FYzOTJk61tixYtMg899JBp0qSJOXDgQJbLIZjCtm3bTL169TKdgbBixQrj4+Nj/vjjD5v2TZs2mfz585vly5fbs0zcBRhTCrddWlqaJCkgIECXLl1ScnKyNm/erKSkJOvYCJUqVVLv3r31yCOP6LXXXtPXX39ts4yrB/XEvc38/7hk69atU1RUlNLT0/X222/r9ddfV8+ePTV79mxJUs2aNRUVFaXdu3erZcuWjiwZdyAnJyft379fQ4YMkSQtXrxYjzzyiE6ePKkHH3xQDRo0UN++fXXs2DHrAJyurq5yd3dn3AxkKTk5WR07dlTdunU1f/58tW3b1nolxhdffFFdu3bVzz//rOnTp2vHjh0qXry4Fi1apDJlyqhy5coOrh53A4vFouLFi6tgwYLauHGjhgwZooceekjPP/+8mjRpom3btuk///mP+vTpo5YtW8rZ2VlpaWnss2C1Z88eTZ8+XQ899JB1HJ9y5cpp4MCBGjFihKZNmyZJateunV544QU5OTmpe/fuWY7FydhkqFixolasWKGKFStq7dq1Sk9Pl3RlfOCiRYvq888/V0JCgrW/v7+/AgMDrf0AK0enYsi7rj2U8/Lly+by5cvmzTffNE5OTmbKlCkmKSnJps+RI0fMxIkT+fUFWco4Ym7RokWmRIkSZsCAAWbz5s3W6cOHDzfOzs42R0xt3rzZ7Nq1y+614s6Wnp5uxo8fb2rWrGlatWpl8uXLZ+bNm2edPmfOHBMaGmpatWpldu3aZXbs2GGGDh1q/Pz8Mp3OAGQ4dOiQ8fPzM25ubub99983xlw5miXDpEmTTK1atcwrr7xiPYWd06qQlayGLvjjjz9MQECAadKkiSlUqJDp0aOHWbhwofnll19MYGCgWbJkiaPKxV1k/fr15r777jP169e3nt556NAhM3ToUFO4cGEzdepUa9/Fixeb6tWrmz59+jiqXNwFjh8/bqpUqWJq165tfU97//33TaFChcyrr75qfvrpJ3PkyBHTv39/4+vrazMWLGAMp+/hNrn6Q/Zvv/1mfvjhBxMbG2ttGzZsmHFycjIzZsywBlPdunWzfkg3hsOCkbU1a9YYDw8P8/7772d5SsLw4cNN/vz5Mw2cD2QlIiLCWCwW07x5c5v29PR0M3fuXNOkSRNjsVhMlSpVTEBAgFm/fr2DKsXd4OjRo8bDw8MUL17c/Oc//7Geynf1qTKTJ082ZcqUMf379zepqamcno5Mrv4Mdfz4cXP27FlreLB69Wrz3nvvmWXLllkHB7548aKpVauW+eqrrxxRLu5C69evN5UqVTL/+c9/rNvWwYMHrcHUtGnTrH1XrVrFZ3Lc0KVLl8w333xjgoKCbC6uMGvWLFOzZk1TrFgxU6VKFVOmTBmzYcMGB1eLO5HFmP8/Fwa4DQYMGKBvvvlGFy9eVMmSJZUvXz79/PPPslgsGjVqlEaPHq3OnTtr+/btOnr0qHbs2MFh5siSMUYWi0VjxoxRXFycli5dap2WlpZmcxh5v379NHfuXO3atUseHh6OKBd3uEuXLslisWjAgAE6ePCgjhw5ouDgYL311lsqUKCAtV96erp++eUXFSpUSL6+vvL19XVg1bgbHDx4UCkpKWrZsqUKFSqk2NhYFSxYUKmpqdZTQT/55BM1aNBA5cuXd3C1uNOkp6fLyenK6Brjx4/X0qVLlZqaKm9vb82fP19FixbV5cuXlS9fPqWkpOjs2bOKiIjQyZMntWbNGk6pQpYyPkNd/feff/6pJ598UiVKlNBPP/0kFxcXHTp0SB988IGmTp2qAQMGqH///tZ5rv2sBVwtJSVFq1atUr9+/VS0aFGtXr1azs7O2rVrl06dOqXz588rMDCQz1HIEqEUcs3VH6QkadKkSRo9erSWLVumevXqaezYsRo8eLCio6PVrFkzSdLUqVO1bt06ubi46P3335eLiwtverihV155RRs2bNCqVatstjfpyjhTderUkZOTk44fP64SJUo4qErcqa79YJ7RNmLECP3www/6z3/+YxNMHT58WL6+vpm2NUD63/Z0/Phx6/tX8eLFlZ6ers2bN6tjx47y8PBQTEyMChYsqHfffVfnz5+3jmUGXO3q/dPgwYP10UcfafTo0SpWrJgGDRokV1dXfffddypdurSSk5P19ttva9WqVbpw4YJ+/vlnPkMhS1d/Pk9PT1dqaqrc3d0lSX/++aeeeOIJlSxZ0hpMHT58WG+//ba2bNmi5cuXS2JsV/xPxn7qjz/+0B9//CGLxaL69eurRo0aNsGUp6enVq9ezecnZAtbCXLF8ePH5eTkZB3UPC0tTX/99ZfefPNNhYSEaOnSpRo7dqzef/99NWvWTGfPnpUk9e3bVx988IFmz54tFxcXXb58mQ9TsMrIzA8dOmT9f6lSpbRly5ZMg24mJydr/vz5+vbbbyWJQAqZZHyQiomJUd++fTVkyBCtWrVKFotF/fv3V/PmzbVu3ToNGDBAiYmJGj58uJ588kldvHjR0aXjDpSxPX377bdq2bKlQkNDVb9+fcXExMjJyUk1a9bUZ599pnPnzqly5crq1KmTXn/9dbVq1crRpeMOs3v3bkn/++K/fPlyfffdd1q8eLGeffZZubi46OjRozp16pQaN26sQ4cOyd3dXWFhYWrRooV+/fVXPkMhS1cHUhMnTtRTTz2lkJAQjR8/XuvXr1etWrW0ePFiHTt2TKGhobp06ZJKlSqlwYMHa/ny5YRRsJHxvvfll1+qVatWmjNnjqKiotSkSRPFxMTIzc1NDz30kCZMmKBz586pVq1aDGqO7LH7CYPIc0aMGGEKFChg9u7da4y5MhZCWlqaadSokXn//fdNdHS0KVSokJkxY4Yx5spYUe+8846ZP3++zXIYVwNXy9gevv76a1O9enUzZ84c67R69eqZ6tWrm+3bt5vz58+b5ORkM3DgQOPv72/27dvnoIpxN/jmm2+Mu7u7adq0qalTp47x9PQ0CxYsMMYYc+HCBTN69GhTo0YNU7ZsWePj42N+++03B1eMO9k333xjChUqZMaNG2d++eUX88wzz5iCBQvavL8lJCSY3r17m+eff978/fffDqwWd6LHH3/cDB061KZtxYoV5o033jDGGPP9998bLy8vM336dLN582bj5eVlgoKCzJ49e2zmYcwfXO3az9QDBgwwxYsXN4MHDzYvvPCCqVy5smnRooX54YcfjDHGbNiwwdx3332mQoUKNuN18tkc1/rpp59MiRIlzAcffGCMuTI+mcViMS4uLmbx4sXGmCvjKH799demfv36fC5HthBK4V/75ZdfTHh4uKlQoYL1Q9Lly5fNwIEDzYMPPmiKFCli3nvvPWv/+Ph48+ijj5opU6Y4qmTcJb799lvj7u5uJk+ebPNlbufOnaZRo0amePHipkaNGiY0NNSUKFGCwRNxQydOnDDTp0+3fpDau3evee2114zFYjGffPKJMebKldJ+++03ExUVlelLH3C1/fv3myZNmph33nnHGGPMgQMHTPny5U3VqlVNvnz5zMcff2wzYHXGYMLA1X799VfrQPjx8fHW9kOHDpmUlBTz8MMPm8GDBxtjjElKSjINGjQwTk5OplWrVsYYQgNcX8b+Z9OmTaZSpUo2FxyKjY01rVq1Mm3atDEHDx406enpJi4uznTo0IGAEzc0cuRIM2TIEGPMlcHxy5QpY55++mnTo0cPky9fPhMdHW2MuRJMZVzsA7gZQincss8//9z6/3Xr1pmmTZuacuXKmd27dxtjjPn9999NqVKlTO3atc2WLVtMWlqaOXz4sHnkkUdM/fr1edPDDZ07d840bdrUDBo06Lp9Zs+ebcaOHWumTp1q3e6ArGzevNkUKVLEVK1a1fqByZgrV7bKCKYyjpgCricjADh9+rRJTU01b775pjl16pQ5cuSICQwMNM8884xJTU017du3N56enuajjz4iNEC2TJkyxbRt29bmx5X9+/ebsmXLmuXLlxtjjDl16pTp0KGD+f33320CTyBD//79zcCBA23atmzZYry9vc3q1att2leuXGmKFi1q3b6uxmd0ZMh4D1u+fLnZsmWL2blzp/n111/N2bNnTf369U3Pnj2NMVeutm6xWIzFYjFLly51ZMm4CzGmFG7JDz/8oA4dOmj06NGSpLp16+rNN99UpUqV9PDDD2vXrl2qU6eOFi5cqCNHjqhLly6qVKmS2rdvr+PHj1uvyJAxBhVwreTkZO3cuVPVqlWTJJtz0s3/jy/VvXt39e/fX3369OEqVrghFxcXtWvXTrt379bp06clXdmOvLy81L9/fw0YMEBdunTR4sWLHVwp7mQWi0VRUVEKDg7WxYsX9fTTT8vT01NTp05V+fLlNXHiRLm4uMjf319OTk7q37+/kpKSHF027kDmmusM+fv767ffftO0adP0119/SZLKlCmjkiVLasCAAfriiy/Utm1bHT58WLVr17YZxxOQpMTERB09evT/2rvv+Brv/o/jr5MlIgkaI0pr1KgVmxZVjda6bbVnGxoxo4hRtXcVrUhTTYlNbSptzZhBEFE1isZepZSINJHz/f3hl3Mnpffd9i4neD//cy2fy+N6nHN5n+/382XLli1MnDjRtv3evXtYLBYuXLhg+zPAG2+8wYsvvsjevXsfuJZ6k0kqi8XC9u3badKkCTExMRQuXJiqVaty/PhxkpKS6NOnDwBZs2alVatWDBkyhMKFC9u5annSKJSSv6VmzZqEhIQwatQoRo8eDdwPpsaMGUPRokV56623OHHiBDVq1GDTpk0MHTqUbt26MWjQIHbv3q2GnPJfeXh44OXlxb59+wDSvYDv27ePsLAw27G/f7kX+b1ixYoxYMAAWrZsiZ+fH5s2bbI1cPXy8qJv374MGzaM4sWL27lSycguXLjA/Pnz6devH56ennh7ewNw7Ngx8ubNi6enJ3B/sY85c+bw448/kjVrVnuWLBlU6ufPnj17SEhIoEmTJoSFhbFx40amTp1KbGwsAJ9++imZMmVi9OjRZMqUic2bN+Pg4IDVatU7lKSTNWtWPvroIypXrkxERARjx44FwMfHh7fffht/f392796Nk5MTADdv3sQYw/PPP2/PsiWDO3PmDBEREQwdOpR27drZtt+4cYMDBw6QkJAAwIIFC7hx4waDBw/Wu5T8dXYdpyVPtN9++82EhIQYR0dHW0NOY+5P5atdu7YpUKCAOXnypDHmwZ4HGhYsaaU+H8nJySYhIcG2vW/fvqZMmTK2xompgoKCzKuvvmpu3LjxOMuUJ0Tq8xQXF2eOHz9uDh06ZNt37Ngx07lzZ5M9e3azcePGdMdrOoz8J/v27TNt27Y1derUMZcvX073PTZs2DCTJUsWM378eNOxY0eTPXt2c/z4cTtWKxlV2s+ZtWvXGh8fHzNlyhSTmJhojDFm3bp15sUXXzSdOnUyR44csR179uzZdN+VImml/TzauHGjadmypSlcuLCt550xxrRu3dq4uLiYoKAgM3LkSFO7dm1TunRpPU/yh44cOWKqVq1qChQoYD7//HNjzL+ftd9++820bNnSWCwWU7FiRePh4WEOHjxoz3LlCWYxRkMM5M8z/78UaKrExERmzZpFr169GDFiBB9++CEA0dHRfPjhh5w6dYqIiAiKFClir5Ilg0t9piIiIpg3bx6xsbH861//ol69elStWpWmTZvyyy+/ULZsWXx8fIiOjmbFihXs2LEDHx8fe5cvGUzq87R69Wo+/PBDbt++jZubG7Vr12bq1KnA/VEtEydOJCIigjlz5lC3bl07Vy1PglGjRjFnzhwSEhL48ccf8fDwIDk5GWdnZ+7cucOQIUOIjIwke/bsTJs2jbJly9q7ZMlgrFYrDg73JynMnz+fQ4cOMXPmTLJnz06/fv3o0qULrq6uRERE0L17d3x9fenevTsVK1Z86DVEfq9fv37Exsbi4ODAwYMHcXNzIyAggIEDBwIwbtw4Nm/eTFJSEgUKFODLL7/E2dmZlJQUjbyTh+rVqxfz5s2jVq1ahIeH4+HhYXvXun79OhEREfz666/UrVtX0/bk77NnIiZPlrS/7iUnJ6cb/TR9+nTj4ODwwIip8uXLm7fffvux1ilPjtRnaPXq1cbNzc18+OGHZt68eaZmzZqmYMGC5vTp0+bmzZtm6NChpmbNmqZ06dKmcePG6Ua+iPxeRESEcXd3NzNmzDA//vijmTFjhrFYLKZbt262Y44dO2aaN29uChYsaO7cuaNm1PJfJSUlmcmTJ5u8efOaDh06mJs3bxpj0o8E/uWXX8ydO3fsVaI8IYYOHWprhD937lxTvXp1U6ZMGTN16lRz9+5dY8z9z7FMmTKZMWPG2LlaeVIsXrzYZMuWzezdu9fcvXvXXLlyxXTq1MlUqFDBTJo0yXbcr7/+mm5klUZKSao/ehfq16+fKVGihBk9erT59ddfH3NV8izQSCn5U9L+Mjdt2jQOHjzIyZMnadasGU2aNKFQoUKEhITQu3dvhg8fbhsxdfToUYoVK6Zf9cQmIiKCfPny4ePjgzGGa9eu0aJFC5o0aUJgYCB3794lf/78tG/fnsmTJ6d7duLj43FxccHFxcWOdyAZ2bVr1+jSpQs1atTg/fff59KlS1StWpWiRYuyc+dOWrdubetHduLECdzd3cmTJ4+dq5aMxvz/r8BXrlyxjYR64YUXuHfvHh9//DErVqygcuXKjBs3Dg8PD+7du2fr0yLyR4wxXLhwgVq1avHhhx/Svn17AO7cuUPXrl2Jjo6mT58+thFTUVFRVK5cWSNY5E+ZNGkSixYtIjo62vZ5dP78eQICAti/fz8DBgygb9++6c4xv5sBIc+u1Gdh7969REVF4eLiQqFChahTpw4AgYGB7Nixg6ZNm9KrVy88PT01clP+MXqK5E9J/cAZNGgQY8eOpVKlSvj6+hIWFoa/vz8JCQn4+fkxffp0xowZQ1BQEADFixe3NeQUuXLlCj179mTatGkcPXoUi8WCm5sb8fHx1KtXj7i4OIoUKUKTJk2YMmUKDg4OfPfdd5w4cQIAd3d3BVKSjjHG1uj+xIkT5MiRgzfffJNGjRpx9epVateuTZ06dVizZg19+/Zl1qxZtv8IFilSRIGUPCD1xXzVqlXUq1ePKlWq8MYbbzBmzBicnJzo378/TZo0Yd++fQwdOpRbt24pkJI/lPa3X4vFQpYsWXBwcLA1B7537x5ZsmRh7ty5ODg4EBISwsyZM/ntt9949dVXtVKx/Fep79i5cuXCarXaVtmzWq3ky5ePIUOGkJCQwKeffkp4eHi6cxVISSqLxcLy5ct56623WLp0KSEhITRo0MD2f7pp06bx6quv8vXXXzNx4kRu376tQEr+MXqS5L9KfaHau3cva9asYe3atfTo0YPq1asTFxdHu3btcHNzI1OmTAQEBDB69GiioqLSvYjpQ0sAcufOzbJlyzh8+DBTpkzh8OHDODo6cvfuXSIjI6lduzb16tXjs88+A+D06dOEh4dz8uRJO1cuGc3t27eB+y9RFouFNWvW8Prrr3PkyBH8/f0pXLgwS5cuJXfu3IwcOZJMmTKRN29eKlSoQFRUlO2lXeT3LBYLGzdupHXr1nTu3JmRI0fSq1cvRo4ciZ+fH46OjvTv359GjRqxfv16xowZoxVA5aHSjkK5du0aAM7Oznh6erJx40YAnJycSElJwcnJiXLlyuHi4sKyZcvYsWOH7ToaKSVp/f6H3tR37EqVKhEXF8cnn3xCQkKCbXtycjKvvfYaffv2pWPHjo+9XnkynDhxgp49ezJhwgR27NjB1q1bCQ8PJzg4mEGDBgEwffp0SpYsSVRUFElJSXauWJ4qdpgyKE+A4cOHm7Vr16bbtmXLFlO8eHFjjDHLli0zHh4e5rPPPjPGGBMfH29WrVpl7ty5Y1JSUmxzktWnRR7mwIEDpnz58sbPz89cvHjRBAcHG4vFYurXr5/uuCFDhphSpUqZs2fP2qlSyYi6du1q3nnnHZOUlGSMMebMmTOmVatWJjQ0NN1xAQEBpnLlyrY/DxgwwIwfPz7dCo8iaaV+ZwUEBJi2bdum27dlyxbj4OBgJk6caIy5v/LQxx9/bOLi4h53mfIESNuHc+XKlaZWrVrmhx9+MMYYs2fPHuPm5mZ69uxp69FptVpNmzZtTEREhClXrpxp0aKFvUqXDCzte3VoaKh5//33zbBhw8yZM2eMMfffzx0dHY2/v79Zt26d+eGHH0zdunVNQECA7VytgC0Ps2vXLlOsWDFz/vz5dNvnzJljMmfObCIjI23bLl++/LjLk6ecxpvLAw4fPsyGDRvYuXMnrq6uvPnmm8D9X/y8vLxYtGgR3bp1Y+LEiXTr1g2A3bt3s3r1akqWLGlbecFonrr8gXLlyhEWFsa7777LsGHDaN26Nf369WPq1Kl89NFHAMTFxTF//ny2bdvGCy+8YOeKJaNYvHgxq1atYv369Tg7OxMTE0NISAgXLlzA19cX+HcPvCZNmjB79myaNm2Ki4sL3333HVFRUWTOnNnOdyEZTer3VUJCAlmyZCEuLo7s2bPb9iUnJ1OzZk1Gjx7NggUL6NSpE7lz5+b999+3c+WSEaXts7J582aWL1/OgQMHGDFiBCNHjqRy5crMnz+fdu3aERMTQ+7cubl48SK//PILCxcuZNeuXWzZskX9WiSdtM/DoEGDmDVrFmXKlOHq1avMmjWLjRs30rx5c9asWcOAAQNYt24djo6O5MiRgzVr1mCxWDDGaOSdPJSzszMnTpzgxIkT5M2b1/a96OvrS548ebh06ZLt2Ny5c9uxUnka6ZtOHlCqVCnGjh2Lq6srkyZN4rvvvgPgjTfe4Ndff6Vdu3aMGzeOgIAAABITE5kyZQq3b9+mUKFCtusokJL/pFy5csyaNYuYmBiWLl1K7dq1mTZtGnPmzGH58uXcvHmTXbt2aVl1SefcuXN4eXlRtmxZvv32Wzp16sT27dvZt28fcXFxwL+nMlStWpXZs2dz584dHBwc2LZtG8WLF7dn+ZIBpb54b9y4kWHDhnH27FkaN27Mli1b2LdvHxaLBWdnZwCyZ8+OxWLB09PTzlVLRpb6GfT+++/Tq1cvnnvuOV577TW2bdvG0KFDOXbsGE2bNiU2NpayZcvi6elJlSpVOHz4MHB/kZhChQppWqikk/pcXb16lYSEBL777js2bNjAwoUL8fHx4ZVXXuHYsWPUr1+f9evXs2nTJpYsWcLevXtxdnbm3r17ejcX4N+tWY4ePcr27duJi4ujfPnyNGzYkBkzZnDw4EHbs5IzZ06yZcum6XrySGn1PUknOTnZ9vK9ePFi5s2bR3x8PCNGjOCNN97g6NGjNG7cGC8vL/z9/bl37x5Llizh8uXLxMTE4OTkpF/25C85cOAA/v7+lC1bllGjRuHt7Y3FYiExMRFXV1d7lycZTHR0NB06dOD5559n69atrF+/nuTkZPr370+hQoUYNmwYFStWTHeO1WolOTmZTJky2alqyehWrFhB+/btGTx4MPXr18fV1ZXBgweTkpLCqFGjqFChAgD9+/dn//79rFmzBg8PDztXLRnZ1q1badWqFStXruTVV18FICwsjPDwcHLnzs2YMWMoXrw4KSkptpErV69e5aOPPiI8PJytW7dSokQJe96CZEDz588nICCAEiVKsGzZMttI8pMnT9KnTx+ioqKIioqiWLFi6c5L+5yJAKxatYoOHTrg7e3NuXPnCAsL4+7duyxatAhPT0/8/f0pUKAAc+bMYfbs2ezZs4cCBQrYu2x5Wtlp2qBkcMOHDzctW7Y0Pj4+xsHBwVSvXt1s3LjRGGPMiRMnTK1atUypUqVMtWrVTKdOnWy9XTRPXf6OAwcOmEqVKplWrVqZw4cPG2PUj0z+WPfu3Y3FYjFVqlSxbVu4cKGpWLGi6dChg9m/f79te9q+LiIPc/z4cVOwYEETEhKSbvuqVatMw4YNjZeXl6lfv76pU6eO8fT0NDExMfYpVJ4omzZtMl5eXrbvtFSffvqpyZQpk2nevLmtx5Qxxpw7d86MGzfOFC1aVM+Y/KHNmzebOnXqGHd3d1sfqdT3pZMnT5qGDRsai8Vizp07Z88yJQNLSUkx169fN9WqVTOff/65OXHihBk9erRxcnIyM2bMMF988YVp1aqVcXBwMC+//LIpXLiwOXDggL3LlqecRkrJA0JCQhg0aBBr166lcOHCREVFERwcjIODA0OHDrX1bfn5559xc3MjS5YswP1ljbUstvxd0dHRDBgwgEWLFpEnTx57lyMZ1N27d2nQoAGFChVi165d+Pj4sGjRIgAWLlzI1KlTKVWqFAEBAVSuXNnO1cqTYOPGjfTo0YP169eTP3/+dKN9jx07xv79+1m/fj358uWjQ4cOvPzyy3auWDIy8//TQXfv3k27du349NNP+de//mV7rlJSUvDx8cHNzY2SJUsyfvx48uTJg9Vq5dKlSzg5OalfiwA8dOaBMYZ9+/YREBDArVu32LlzJzlz5rQ9d8ePHycsLIzx48frnVzSSX1GEhMTMcYwZswY+vfvb+ufOHXqVIKCgpg8eTJt2rTh9u3bJCUl4eXlRa5cuexcvTzt9GklD9i7dy+NGjXi9ddfB+Dtt9/G3d2dfv36MWzYMBwcHKhZsyY5c+a0nWOM0Zef/E8qVarEt99+qyl78h9lzpyZtWvX4ubmxqxZs5g0aRJt27Zl4cKFtG3b1haeu7q6UqZMGU3Zk/8qPj6eu3fvptuWOtXl8uXLVKtWjXbt2tmpOsnofh8cpPZheeWVVyhatCh9+vThhRdewMfHB4DLly9TunRpihcvzty5czly5Ah58uTBwcGBvHnz2uUeJONJ+1ytXLmSixcvYrVaeeutt6hUqRIzZ86kd+/e1KxZky1btpArVy6MMRQrVsy2YIx+LJa0LBYLq1ev5rPPPuPcuXNYrVZatWplC6X69u2LxWIhKCiIq1evMmTIENvAA5FHTY1/5AHPPfcc169fT/eSXrduXTp27Mj+/fsJDAxkz5496c5R40T5JyiQkj/Dzc0NgJYtWzJw4EBiYmJo27YtAK1bt2bChAkEBQUpkJI/pUyZMly7do2ZM2cC95sJp/ZeWbVqFbNnz1aDV3motMHB0qVLGT58ONOnT2fr1q0AREREkDNnTho1asS4ceMIDw+nU6dOxMfHM3z4cIwxfPPNN/a8BcmgUp+roKAgevToQWRkJLNmzaJt27bMmjWL8uXLM2nSJLy8vHjzzTe5fPnyA+/iCqQkrX379tGxY0cKFixI5cqVOXXqFLNmzeLMmTO2YwIDAxk1ahQhISEkJibasVp51iiUkgf4+Piwa9cuNm7cmG7ll9y5c1O1alXefvttKlWqZMcKRUTA3d2dli1bEhQUxPfff0+DBg2A+6M7CxYsaOfq5ElRsGBBgoOD+eijjwgKCuLw4cMcPXqUgQMHMmfOHNq0aYOLi4u9y5QMxhiTLjgIDAxk//79rFy5kgEDBjBv3jwsFgtRUVG8+eabrFu3jvHjx+Pi4sLSpUsByJMnD0WLFrXnbUgGtmjRIhYtWsSaNWtYunQpvXv35ocffiBbtmzA/RVmJ0+eTFJSEv3797dvsZKhnTp1irVr1zJ48GA+++wzZs+ezSeffMLy5csJDQ1NF0wNHDiQn376CS8vLztWLM8aRejygM6dO7Nz507at29PaGgo5cuXx9vbmxUrVuDr68uQIUOwWCxaZU9E7C5Lliy0bNmSxMREwsPDuXDhgqbAyF/WuXNnPDw88Pf3Z9GiRbi6uuLo6MjmzZvVQ0oekPb9Z8aMGXz11VcsX76cV155hZCQEPr27cvw4cNJSEjA39+fsLAwbt68iTHGNlVm2LBhxMXFUatWLXveimRgJ0+epEaNGlSsWJGlS5cSGBjIJ598QrNmzYiPj+fq1atUrlyZZcuWUbx4cXuXKxnUrVu3aN26NadPn+a9996zbQ8ICMBqtTJ+/HgcHR3x8/Oz/aCXGnyKPC5qdC7ppH3R6tmzJ6tWrSIlJQUPDw8cHR35/vvvcXJysjXLExHJCBISEkhOTiZr1qz2LkWeYBcvXuTMmTNYLBYKFiyohtPygLTvP7du3WLIkCEUKFCA/v37s2bNGjp27EifPn04ceIE27ZtY8KECbRv3952/okTJxg2bBhbt25l3bp1lCtXzl63IhnIw37oHTRoEI6OjjRs2JC33nqLjz76iG7dumGMITw8nF9++YXevXvj7OwM/LsXnsjvxcTE0KpVK3LlykVoaCilSpWy7QsNDaVv374MHjyYIUOGaNqn2IVCKXlA2heuqKgorl27xp07d2jRogWOjo760hMREZFnzpYtW7h48SLt2rXD39+f7Nmz06dPH+7evUtKSgr169enR48eBAYGsnLlStq0aYOzszNz586ladOmACQmJrJhwwaKFy9O4cKF7XxHkhGkDaROnTpF5syZyZkzJ9HR0VSvXh2AJUuW0KJFCwDu3LlDs2bNKFWqFB9//LHd6pYny6FDh+jUqROVK1emd+/elCxZ0rbvyy+/pEaNGhQpUsSOFcqzTKGUPNQfTc1TICUiIiLPEmMM8fHxNG/enKSkJDw9Pdm6dSvbt2+3rao3f/58pk+fzvr168maNSvr16/n888/p169erzzzjt6d5KHSvtD8KBBg1i9ejU///wzJUuWtPWz6969O7NmzaJatWrcunWLAQMGcPXqVfbu3atRLfKXxMTE0KVLF8qXL0/fvn0pUaKEvUsSAdTo/JlhtVofuv2PMsnUQOr35+mlSkRERJ4lFosFDw8PFi9ezOXLl/n6668ZMmSILZACcHZ25uzZs2zfvp2EhASmT59OgQIF8PPzs40yF0nLarXaAqnFixczZ84cJkyYwMcff0yVKlUIDAwkOjqaSZMm4efnx6uvvkrHjh1JSkpiz549ODk56bmSv6RcuXKEhYVx6NAhRo8ezbFjx+xdkgigRufPhLSjng4fPkxCQgK5cuWiQIECWCyWPxz9lHZlmWPHjvHiiy/almIXEREReZY4ODjw0ksvkTt3bjZt2kS+fPlo164dACVKlKBGjRp07NiRbNmykSVLFlasWIHFYsEYox/15AGp79iRkZFs2rSJoKAgGjduDNzvV1agQAEGDRrEokWL+OGHHzh37hyenp6UKVMGBwcH7t27p5FS8peVK1eO4OBgBgwYoD6ckmFo+t5TLu2w4A8++IA1a9Zw9uxZqlSpQuXKlRkzZgzw4LS8tOdNnz6d8ePHs2vXLgoUKPDY70FEREQko7h8+TJ+fn7cvXsXPz8/WzB1/Phxjh07xu3bt2nTpg2Ojo4KDuQ/unz5MtWrV+fq1asMHDiQDz74wLbv+vXr+Pn58cILLzB9+vR052kFbPlfJSYm4urqau8yRABN33vqpQZLY8aMISwsjE8++YSTJ0+SN29egoOD6dmzJ0C6oeVpA6nPP/+cESNGMGXKFAVSIiIi8szz9vYmODgYNzc35syZw6xZs0hJSaF79+58//33tG/f3vZepUBK/hNvb29WrFhBrly5WLFiBTExMbZ9Xl5e5MiRg1OnTj1wngIp+V8pkJKMRJ9oT6m0A+COHDnCypUrWbBgAb6+vsTGxvLVV19Ru3Ztvv32WwIDA4H7wVRycnK6QCooKIiZM2fSunVre9yGiIiISIZTsGBBpk+fjoeHB5MnT6ZIkSJcvXqVoKAg2zGasid/ho+PDytWrCAlJYVp06Zx8OBBAG7fvs3Ro0fJly+ffQsUEXnENH3vKZR2pFNsbCw+Pj6EhYXRrFkzDh8+TOvWrRk9ejTvvvsuDRo0YPPmzTRr1oyFCxfarjFz5kyCgoL48ssvad68ub1uRURERCTDunTpEvv37+fKlSt06tQJJycnTdmTvyUmJob27dvzyy+/ULFiRVxcXIiLi2P37t24uLike78XEXmaKJR6yvx+adndu3ezePFicufOjcViISAgACcnJ6ZMmYKzszMDBgwgOjqaEiVKEBwcjIODA2vWrKFJkyYsW7aMZs2a2fmORERERJ4Mf7R4jMifcfjwYRo1akS+fPlo27Yt3bp1AyA5ORlnZ2c7Vyci8mho+t5TJjWQOnbsGFFRUYwdOxZvb2/b9ri4OM6fP4+zszMpKSmcOXOGDh06MGPGDNv89AYNGrBlyxYFUiIiIiJ/gQIp+V+UKlWKFStWkJSUxIEDBzh58iSAAikReapppNRTaPz48URGRuLq6sr8+fPx8PDAarUCMG3aNObNm0eePHm4desWN2/eJDY2FkdHR4wxasopIiIiImJHMTExdOvWjUKFCjF8+HBefvlle5ckIvLIaKTUU6h48eJs2LCBHTt2cPr0aeD+Kh0ODg60adOGjh07kjVrVkqWLElMTIxthRiLxaJASkRERETEjsqVK0dwcDCXLl0ia9as9i5HROSR0kipJ9wfNT3ctGkTtWvXpnPnzrYpfH9EDTlFRERERDKWxMREXF1d7V2GiMgjpZFSTzCr1WoLpK5evcrZs2dt+2rVqsWqVasIDw9nzJgxXLlyJd15qYwxCqRERERERDIYBVIi8ixQKPWEslqttsbko0aNol69elSqVIm6desSGRlJYmIiDRs2ZNWqVYSGhjJ27FguXboEYDsP0NKyIiIiIiIiImIXCqWeQMYYW7A0fPhwQkNDCQwMJCoqip9++omhQ4eydu3adMFUcHAwixYtsnPlIiIiIiIiIiL3ad7WE+To0aMUL17c9uedO3eyevVq5s+fj6+vL9u3b+fChQsYYxg6dCiOjo7Ur1+fBg0asH37dqpUqWLH6kVERERERERE/k0jpZ4QkydPtgVPFosFYwzZs2enZ8+e+Pr6smnTJpo1a8aMGTM4ceIEiYmJTJkyhSVLlpCUlES1atVwcnLi3r179r4VERERERERERGFUk+K0qVLU6NGDfr06WMLpooUKULDhg1JTk5m2rRpdO3alY4dO2KMoUiRIsTGxrJz505cXFxs11FTcxERERERERHJCBRKZXBffPEFAHXq1KF79+4ULlyYXr16sW3bNpydncmdOzdJSUlcu3YNLy8vW6+pF198kcjISEJDQ+1ZvoiIiIiIiIjIQ2nYTAa2ceNG/P39iY2NJTg4mNdffx1jDCEhIfTu3Zvp06fz2muv4eDggJOTE8uWLePWrVts376d69evU65cORwcHEhJScHR0dHetyMiIiIiIiIiYqORUhlYpUqVmDlzJsuWLaNHjx4A1KxZk+7du1O0aFF69epFZGQkmTNnZvny5bi5ubFz5048PDzYt28fDg4OWK1WBVIiIiIiIiIikuFYjDHG3kXIH7t9+zaLFy/mgw8+oEWLFsyYMQOAyMhIQkJC+PHHH5kyZQq+vr4kJiZijMHV1RWLxcK9e/fUQ0pEREREREREMiQlFhmQMQaLxQKAh4cHLVq0AGDIkCEAzJgxg5o1awIQEhLCgAEDGD9+PLVr1053DQVSIiIiIiIiIpJRKbXIYKxWq61ZudVq5d69e2TLlo1OnToBMHjwYODfwZTFYmH06NEsXLgwXSiVGmqJiIiIiIiIiGRECqUykLSB1Mcff0xsbCwHDhzA39+fN954g65duwLwwQcfYLFYbM3PPT09KVOmjD1LFxERERERERH5S9RTKgMaPHgwX375JcOGDSM+Pp6wsDBefvllFi9eTEpKCkuXLmXo0KHUqlWLBQsW2M5LG2qJiIiIiIiIiGRkSjAymL1797Jq1SrWrl1Lz549qV69OmfPnqVly5a4u7uTNWtWOnTowODBg7l58yZWq9V2rgIpEREREREREXlSKMXIYKxWK66urlSpUoWvvvqKevXq8emnn9KxY0fu3LlDREQEAO+99x5ff/01Dg4O6YIpEREREREREZEngUIpO3pYmBQfH09iYiKLFy/mvffeY8KECXTr1g2AXbt2sXDhQs6ePUvmzJmxWCwYYzRCSkRERERERESeOOopZSdp+z+FhoYC2MKnOnXqsGHDBqZPn06PHj0ASExM5O233yZz5swsWbJEQZSIiIiIiIiIPNG0+p6dpIZKAwYMYMmSJXTq1Inz58+TL18+xo0bx6+//srUqVPJmjUrN27cYO3atVy8eJGDBw/apuwpmBIRERERERGRJ5VGStnR/Pnzef/99/nmm2+oUKGCbbvVauXYsWOMGjWK2NhYcuXKRZEiRfjss89wdnbm3r17ODkpTxQRERERERGRJ5dCKTsaMmQIFy5cYM6cOaSkpODo6PhA4HTlyhW8vLxs2xRIiYiIiIiIiMjTQPO/7OjChQvExcUB4OjoiDEGJycnEhMT2bhxIwC5c+e2hVCp+0VEREREREREnnQKpR6Dh62yB1CuXDmuXLnCli1bSEpKwmKxAHDr1i1GjhzJN998k+741P0iIiIiIiIiIk86Td97xNI2JI+OjsZqteLo6EjFihX57bffqFatGgCDBw+mWrVqxMfHExgYyI0bN9i2bRuOjo72LF9ERERERERE5JFQKPUIGWNso5sGDhzIokWLsFgsXLlyhTZt2jBp0iQ8PDxo3LgxFy5c4OTJk5QoUQJnZ2d27NiBs7OzrdeUiIiIiIiIiMjTRKHUYxAcHMzIkSNZvXo1Xl5enDt3jg4dOlClShUWLFiAi4sLR44c4fjx4+TOnZvq1as/tOm5iIiIiIiIiMjTQqHUY9CpUycyZ85MaGiobfTUwYMHqVGjBr169WLs2LEPnKMRUiIiIiIiIiLyNFOj83/Y7zO+5ORkLly4QGJiom1/UlISZcuWZcSIESxdupQbN26QkpKS7jwFUiIiIiIiIiLyNFMo9Q+yWq22HlI//fQTV69exdnZmY4dO7Js2TI2bdqEg4MDzs7OAGTKlIkcOXKQJUsWhVAiIiIiIiIi8kxRKPUPSl1lb8iQITRq1IgSJUoQFBSEu7s77777Lj169ODbb7/FarXy66+/8vXXX5M3b15bSCUiIiIiIiIi8qxQF+1/gNVqtQVSS5cuZe7cuQQHB3Po0CG+/fZbzp49yyuvvELDhg1p0KABhQoVwtHRkUyZMhEdHY3FYkm3Up+IiIiIiIiIyNNOjc7/Qdu2bWP58uWUKVOGd999F4A1a9Ywffp0smfPTteuXcmVKxd79uzB3d2dVq1aaZU9EREREREREXkmKZT6h1y+fJnq1avz888/M3LkSAIDA2371q5dy7Rp0/D09GTw4MFUrlzZtk+r7ImIiIiIiIjIs0g9pf4h3t7erFixAm9vbyIiIvj+++9t+xo2bEi/fv04efIkK1euTHeeAikREREREREReRZppNQ/LDY2lnfeeYeKFSvSp08fSpYsadu3a9cuqlSpoiBKRERERERERJ55CqUegZiYGLp06UKFChUIDAykRIkS6fZryp6IiIiIiIiIPOsUSj0iMTEx+Pv7kz9/fiZNmkTBggXtXZKIiIiIiIiISIahnlKPSLly5QgODsbDw4P8+fPbuxwRERERERERkQxFI6UeMWMMFosFq9WKg4MyQBERERERERERUCj1WKQGUyIiIiIiIiIicp+G7jwGCqRERERERERERNJTKCUiIiIiIiIiIo+dQikREREREREREXnsFEqJiIiIiIiIiMhjp1BKREREREREREQeO4VSIiIiIiIiIiLy2CmUEhEREXnCWCwWVq1aZe8yRERERP4nCqVEREREHoGGDRtSt27dh+7bvn07FouFQ4cO/a1rX7p0iXr16v3p4zt37kyTJk3+1t8lIiIi8qgolBIRERF5BPz8/NiwYQPnz59/YN/s2bOpWLEiPj4+f+maSUlJAHh7e5MpU6Z/pE4RERERe1EoJSIiIvIINGjQgJw5cxIeHp5ue3x8PEuXLqVJkya0adOGvHnz4ubmRunSpVm0aFG6Y2vWrEnPnj0JDAwkR44c1KlTB3hw+t65c+do2bIl2bJl47nnnqNx48acPn0agBEjRjBnzhxWr16NxWLBYrEQGRmJr68vPXv2TPf3/fzzz7i4uLBp06Z//N9DRERE5PcUSomIiIg8Ak5OTnTs2JHw8HCMMbbtS5cuJSUlhfbt21OhQgXWrVvH4cOHee+99+jQoQN79+5Nd505c+bg4uLCzp07CQ0NfeDvSU5Opk6dOnh4eLB9+3Z27tyJu7s7devWJSkpif79+9OyZUvq1q3LpUuXuHTpElWrVqVLly4sXLiQ3377zXat+fPnkzdvXnx9fR/dP4yIiIjI/1MoJSIiIvKIvPvuu5w6dYqtW7fats2ePZvmzZuTP39++vfvT9myZSlUqBC9evWibt26fPXVV+muUaRIESZNmkSxYsUoVqzYA3/HkiVLsFqthIWFUbp0aYoXL87s2bM5e/YskZGRuLu7kzlzZjJlyoS3tzfe3t64uLjQrFkzAFavXm27Vnh4OJ07d8ZisTyifxERERGRf1MoJSIiIvKIvPzyy1StWpVZs2YBcPLkSbZv346fnx8pKSmMHj2a0qVL89xzz+Hu7s53333H2bNn012jQoUK//HviI2N5eTJk3h4eODu7o67uzvPPfcciYmJnDp16g/Pc3V1pUOHDrbaDhw4wOHDh+ncufP/dtMiIiIif5KTvQsQEREReZr5+fnRq1cvZsyYwezZs3nppZd4/fXXmThxIp988gnTpk2jdOnSZMmShcDAQFsz81RZsmT5j9ePj4+nQoUKLFiw4IF9OXPm/I/ndunShbJly3L+/Hlmz56Nr68v+fPn/+s3KSIiIvI3KJQSEREReYRatmxJnz59WLhwIXPnziUgIACLxcLOnTtp3Lgx7du3B8BqtfLjjz9SokSJv3T98uXLs2TJEnLlyoWnp+dDj3FxcSElJeWB7aVLl6ZixYp88cUXLFy4kODg4L9+gyIiIiJ/k6bviYiIiDxC7u7utGrVisGDB3Pp0iXb9LgiRYqwYcMGdu3axdGjR/H39+fKlSt/+frt2rUjR44cNG7cmO3btxMXF0dkZCS9e/fm/PnzABQoUIBDhw5x/Phxrl27RnJysu38Ll26MGHCBIwxNG3a9B+5ZxEREZE/Q6GUiIiIyCPm5+fHjRs3qFOnDs8//zwAQ4cOpXz58tSpU4eaNWvi7e1NkyZN/vK13dzc2LZtGy+++CLNmjWjePHi+Pn5kZiYaBs51bVrV4oVK0bFihXJmTMnO3futJ3fpk0bnJycaNOmDa6urv/I/YqIiIj8GRaTdo1iEREREXmmnD59mpdeeono6GjKly9v73JERETkGaJQSkREROQZlJyczPXr1+nfvz9xcXHpRk+JiIiIPA6aviciIiLyDNq5cyd58uQhOjqa0NBQe5cjIiIizyCNlBIRERERERERkcdOI6VEREREREREROSxUyglIiIiIiIiIiKPnUIpERERERERERF57BRKiYiIiIiIiIjIY6dQSkREREREREREHjuFUiIiIiIiIiIi8tgplBIRERERERERkcdOoZSIiIiIiIiIiDx2CqVEREREREREROSx+z/Ng4n3nxVd9gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-07_07-35_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-07_07-35_plots/water_need_vs_oil_production.png\n", + " Variety Technique Technique String \\\n", + "0 nocellara_delletna 3 tradizionale \n", + "1 nocellara_delletna 1 intensiva \n", + "2 nocellara_delletna 2 superintensiva \n", + "3 leccino 1 intensiva \n", + "4 leccino 2 superintensiva \n", + "5 leccino 3 tradizionale \n", + "6 frantoio 2 superintensiva \n", + "7 frantoio 3 tradizionale \n", + "8 frantoio 1 intensiva \n", + "9 coratina 1 intensiva \n", + "10 coratina 2 superintensiva \n", + "11 coratina 3 tradizionale \n", + "12 taggiasca 3 tradizionale \n", + "13 taggiasca 2 superintensiva \n", + "14 taggiasca 1 intensiva \n", + "15 pendolino 1 intensiva \n", + "16 pendolino 2 superintensiva \n", + "17 pendolino 3 tradizionale \n", + "18 moraiolo 2 superintensiva \n", + "19 moraiolo 1 intensiva \n", + "20 moraiolo 3 tradizionale \n", + "\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "0 9564.638687 2088.362004 \n", + "1 13699.079622 2991.183032 \n", + "2 17826.710664 3892.059753 \n", + "3 16432.379678 3229.053194 \n", + "4 20528.499013 4033.942398 \n", + "5 10937.982122 2149.449585 \n", + "6 24621.040119 6047.876212 \n", + "7 13740.739760 3375.103688 \n", + "8 20550.900635 5047.942655 \n", + "9 16429.706879 4215.265516 \n", + "10 19164.700743 4916.649709 \n", + "11 12318.510310 3160.037128 \n", + "12 6839.506230 1381.247995 \n", + "13 16433.741502 3319.210170 \n", + "14 10968.603159 2215.371493 \n", + "15 13705.431414 2468.678455 \n", + "16 19183.689269 3455.879324 \n", + "17 10960.549241 1974.357984 \n", + "18 17793.971752 3885.415851 \n", + "19 13144.222436 2870.020002 \n", + "20 8765.195655 1913.745255 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "0 32997.227891 0.218342 \n", + "1 33079.012125 0.218349 \n", + "2 33118.708645 0.218327 \n", + "3 25013.303736 0.196506 \n", + "4 24989.459147 0.196504 \n", + "5 24981.219100 0.196512 \n", + "6 28874.473543 0.245639 \n", + "7 29003.452741 0.245628 \n", + "8 28921.261327 0.245631 \n", + "9 38270.638622 0.256564 \n", + "10 38264.650562 0.256547 \n", + "11 38253.676395 0.256528 \n", + "12 26219.134374 0.201951 \n", + "13 26253.317778 0.201975 \n", + "14 26284.027794 0.201974 \n", + "15 26154.359691 0.180124 \n", + "16 26153.199618 0.180147 \n", + "17 26152.823801 0.180133 \n", + "18 32561.911109 0.218356 \n", + "19 32577.899255 0.218348 \n", + "20 32594.860153 0.218335 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "0 0.063289 \n", + "1 0.090425 \n", + "2 0.117518 \n", + "3 0.129093 \n", + "4 0.161426 \n", + "5 0.086043 \n", + "6 0.209454 \n", + "7 0.116369 \n", + "8 0.174541 \n", + "9 0.110144 \n", + "10 0.128491 \n", + "11 0.082607 \n", + "12 0.052681 \n", + "13 0.126430 \n", + "14 0.084286 \n", + "15 0.094389 \n", + "16 0.132140 \n", + "17 0.075493 \n", + "18 0.119324 \n", + "19 0.088097 \n", + "20 0.058713 \n", + "Comparison by Variety:\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "Variety \n", + "nocellara_delletna 13696.683690 2990.507461 \n", + "leccino 15971.162702 3138.439782 \n", + "frantoio 19648.631813 4826.360700 \n", + "coratina 15974.164423 4098.136472 \n", + "taggiasca 11412.636779 2305.011278 \n", + "pendolino 14617.432649 2633.129635 \n", + "moraiolo 13232.961913 2889.399172 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "Variety \n", + "nocellara_delletna 33064.983905 0.218338 \n", + "leccino 24994.676451 0.196507 \n", + "frantoio 28932.932409 0.245633 \n", + "coratina 38262.995517 0.256548 \n", + "taggiasca 26252.184893 0.201970 \n", + "pendolino 26153.461822 0.180136 \n", + "moraiolo 32578.228327 0.218349 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "Variety \n", + "nocellara_delletna 0.090443 \n", + "leccino 0.125564 \n", + "frantoio 0.166812 \n", + "coratina 0.107104 \n", + "taggiasca 0.087803 \n", + "pendolino 0.100680 \n", + "moraiolo 0.088691 \n", + "\n", + "Best Varieties by Water Efficiency:\n", + " Variety Avg Olive Production (kg/ha) \\\n", + "2 frantoio 19648.631813 \n", + "1 leccino 15971.162702 \n", + "3 coratina 15974.164423 \n", + "5 pendolino 14617.432649 \n", + "0 nocellara_delletna 13696.683690 \n", + "\n", + " Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "2 4826.360700 28932.932409 0.245633 \n", + "1 3138.439782 24994.676451 0.196507 \n", + "3 4098.136472 38262.995517 0.256548 \n", + "5 2633.129635 26153.461822 0.180136 \n", + "0 2990.507461 33064.983905 0.218338 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "2 0.166812 \n", + "1 0.125564 \n", + "3 0.107104 \n", + "5 0.100680 \n", + "0 0.090443 \n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "# Esecuzione dell'analisi\n", + "comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n", + "\n", + "# Genera i grafici\n", + "plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n", + "plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n", + "plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n", + "plot_efficiency_vs_production(comparison_data)\n", + "plot_water_efficiency_vs_production(comparison_data)\n", + "plot_water_need_vs_oil_production(comparison_data)\n", + "\n", + "# Analisi per tecnica\n", + "technique_data = analyze_by_technique(simulated_data, olive_varieties)\n", + "\n", + "print(technique_data)\n", + "\n", + "# Stampa un sommario statistico\n", + "print(\"Comparison by Variety:\")\n", + "print(comparison_data.set_index('Variety'))\n", + "print(\"\\nBest Varieties by Water Efficiency:\")\n", + "print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bbe87b415168368", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_transformer_data(df, olive_varieties_df):\n", + " # Crea una copia del DataFrame per evitare modifiche all'originale\n", + " df = df.copy()\n", + "\n", + " # Ordina per zona e anno\n", + " df = df.sort_values(['zone', 'year'])\n", + "\n", + " # Definisci le feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha'] # Feature statiche base\n", + " target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Ottieni le varietà pulite\n", + " all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " # Crea la struttura delle feature per ogni varietà\n", + " variety_features = [\n", + " 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n", + " 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n", + " 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n", + " ]\n", + "\n", + " # Prepara dizionari per le nuove colonne\n", + " new_columns = {}\n", + "\n", + " # Prepara le feature per ogni varietà\n", + " for variety in varieties:\n", + " # Feature esistenti\n", + " for feature in variety_features:\n", + " col_name = f\"{variety}_{feature}\"\n", + " if col_name in df.columns:\n", + " if feature != 'tech': # Non includere la colonna tech direttamente\n", + " static_features.append(col_name)\n", + "\n", + " # Feature binarie per le tecniche di coltivazione\n", + " for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n", + " col_name = f\"{variety}_{technique}\"\n", + " new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n", + " df[f\"{variety}_tech\"].str.lower() == technique\n", + " ).fillna(False)\n", + " static_features.append(col_name)\n", + "\n", + " # Aggiungi tutte le nuove colonne in una volta sola\n", + " new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n", + "\n", + " # Ordiniamo per zona e anno per mantenere la continuità temporale\n", + " df_sorted = new_df.sort_values(['zone', 'year'])\n", + "\n", + " # Definiamo la dimensione della finestra temporale\n", + " window_size = 41\n", + "\n", + " # Liste per raccogliere i dati\n", + " temporal_sequences = []\n", + " static_features_list = []\n", + " targets_list = []\n", + "\n", + " # Iteriamo per ogni zona\n", + " for zone in df_sorted['zone'].unique():\n", + " zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)\n", + "\n", + " if len(zone_data) >= window_size: # Verifichiamo che ci siano abbastanza dati\n", + " # Creiamo sequenze temporali scorrevoli\n", + " for i in range(len(zone_data) - window_size + 1):\n", + " # Sequenza temporale\n", + " temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values\n", + " # Verifichiamo che non ci siano valori NaN\n", + " if not np.isnan(temporal_window).any():\n", + " temporal_sequences.append(temporal_window)\n", + "\n", + " # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)\n", + " static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)\n", + "\n", + " # Target (prendiamo quelli dell'ultimo timestep della finestra)\n", + " targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)\n", + "\n", + " # Convertiamo in array numpy\n", + " X_temporal = np.array(temporal_sequences)\n", + " X_static = np.array(static_features_list)\n", + " y = np.array(targets_list)\n", + "\n", + " print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n", + "\n", + " # Split dei dati (usando indici casuali per una migliore distribuzione)\n", + " indices = np.random.permutation(len(X_temporal))\n", + "\n", + " #train_idx = int(len(indices) * 0.7) # 70% training\n", + " #val_idx = int(len(indices) * 0.85) # 15% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_idx = int(len(indices) * 0.65) # 65% training\n", + " val_idx = int(len(indices) * 0.85) # 20% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " #train_idx = int(len(indices) * 0.60) # 60% training\n", + " #val_idx = int(len(indices) * 0.85) # 25% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_indices = indices[:train_idx]\n", + " val_indices = indices[train_idx:val_idx]\n", + " test_indices = indices[val_idx:]\n", + "\n", + " # Split dei dati\n", + " X_temporal_train = X_temporal[train_indices]\n", + " X_temporal_val = X_temporal[val_indices]\n", + " X_temporal_test = X_temporal[test_indices]\n", + "\n", + " X_static_train = X_static[train_indices]\n", + " X_static_val = X_static[val_indices]\n", + " X_static_test = X_static[test_indices]\n", + "\n", + " y_train = y[train_indices]\n", + " y_val = y[val_indices]\n", + " y_test = y[test_indices]\n", + "\n", + " # Standardizzazione\n", + " scaler_temporal = StandardScaler()\n", + " scaler_static = StandardScaler()\n", + " scaler_y = StandardScaler()\n", + "\n", + " # Standardizzazione dei dati temporali\n", + " X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n", + " X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n", + " X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n", + "\n", + " # Standardizzazione dei dati statici\n", + " X_static_train = scaler_static.fit_transform(X_static_train)\n", + " X_static_val = scaler_static.transform(X_static_val)\n", + " X_static_test = scaler_static.transform(X_static_test)\n", + "\n", + " # Standardizzazione dei target\n", + " y_train = scaler_y.fit_transform(y_train)\n", + " y_val = scaler_y.transform(y_val)\n", + " y_test = scaler_y.transform(y_test)\n", + "\n", + " print(\"\\nShape dopo lo split e standardizzazione:\")\n", + " print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n", + " print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n", + " print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n", + "\n", + " # Prepara i dizionari di input\n", + " train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n", + " val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n", + " test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n", + "\n", + " joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n", + " joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n", + " joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n", + "\n", + " return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9c4d5f0f3fafdc2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset completo - Temporal: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)\n", + "\n", + "Shape dopo lo split e standardizzazione:\n", + "Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)\n", + "Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)\n", + "Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)\n", + "Temporal data shape: (2548000, 41, 3)\n", + "Static data shape: (2548000, 113)\n", + "Target shape: (2548000, 5)\n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "\n", + "(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n", + "\n", + "scaler_temporal, scaler_static, scaler_y = scalers\n", + "\n", + "print(\"Temporal data shape:\", train_data['temporal'].shape)\n", + "print(\"Static data shape:\", train_data['static'].shape)\n", + "print(\"Target shape:\", train_targets.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "604c952c7195f40c", + "metadata": {}, + "outputs": [], + "source": [ + "@keras.saving.register_keras_serializable()\n", + "class DataAugmentation(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n", + "\n", + " def __init__(self, noise_stddev=0.03, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.noise_stddev = noise_stddev\n", + "\n", + " def call(self, inputs, training=None):\n", + " if training:\n", + " return inputs + tf.random.normal(\n", + " shape=tf.shape(inputs),\n", + " mean=0.0,\n", + " stddev=self.noise_stddev\n", + " )\n", + " return inputs\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"noise_stddev\": self.noise_stddev})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class PositionalEncoding(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'encoding posizionale\"\"\"\n", + "\n", + " def __init__(self, d_model, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.d_model = d_model\n", + "\n", + " def build(self, input_shape):\n", + " _, seq_length, _ = input_shape\n", + "\n", + " # Crea la matrice di encoding posizionale\n", + " position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n", + " div_term = tf.exp(\n", + " tf.range(0, self.d_model, 2, dtype=tf.float32) *\n", + " (-tf.math.log(10000.0) / self.d_model)\n", + " )\n", + "\n", + " # Calcola sin e cos\n", + " pos_encoding = tf.zeros((1, seq_length, self.d_model))\n", + " pos_encoding_even = tf.sin(position * div_term)\n", + " pos_encoding_odd = tf.cos(position * div_term)\n", + "\n", + " # Assegna i valori alle posizioni pari e dispari\n", + " pos_encoding = tf.concat(\n", + " [tf.expand_dims(pos_encoding_even, -1),\n", + " tf.expand_dims(pos_encoding_odd, -1)],\n", + " axis=-1\n", + " )\n", + " pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n", + " pos_encoding = pos_encoding[:, :, :self.d_model]\n", + "\n", + " # Salva l'encoding come peso non trainabile\n", + " self.pos_encoding = self.add_weight(\n", + " shape=(1, seq_length, self.d_model),\n", + " initializer=tf.keras.initializers.Constant(pos_encoding),\n", + " trainable=False,\n", + " name='positional_encoding'\n", + " )\n", + "\n", + " super().build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " # Broadcast l'encoding posizionale sul batch\n", + " batch_size = tf.shape(inputs)[0]\n", + " pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n", + " return inputs + pos_encoding_tiled\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"d_model\": self.d_model})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n", + " warmup_lr = self.initial_learning_rate * warmup_pct\n", + " decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n", + " decayed_lr = self.initial_learning_rate * decay_factor\n", + " return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'initial_learning_rate': self.initial_learning_rate,\n", + " 'warmup_steps': self.warmup_steps,\n", + " 'decay_steps': self.decay_steps\n", + " }\n", + "\n", + "\n", + "def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n", + " d_model=128, num_heads=8, ff_dim=256,\n", + " num_transformer_blocks=4, mlp_units=None,\n", + " dropout=0.2):\n", + " \"\"\"\n", + " Crea un transformer per la predizione della produzione di olio d'oliva.\n", + " \"\"\"\n", + " # Input layers\n", + " if mlp_units is None:\n", + " mlp_units = [256, 128, 64]\n", + "\n", + " temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n", + " static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n", + "\n", + " # === TEMPORAL PATH ===\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n", + " x = DataAugmentation()(x)\n", + "\n", + " # Temporal projection\n", + " x = tf.keras.layers.Dense(\n", + " d_model // 2,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + " x = tf.keras.layers.Dense(\n", + " d_model,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Positional encoding\n", + " x = PositionalEncoding(d_model)(x)\n", + "\n", + " # Transformer blocks\n", + " skip_connection = x\n", + " for _ in range(num_transformer_blocks):\n", + " # Self-attention\n", + " attention_output = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // num_heads,\n", + " value_dim=d_model // num_heads\n", + " )(x, x)\n", + " attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n", + "\n", + " # Residual connection con pesi addestrabili\n", + " residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Feed-forward network\n", + " ffn = tf.keras.layers.Dense(ff_dim, activation=\"swish\")(x)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + " ffn = tf.keras.layers.Dense(d_model)(ffn)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + "\n", + " # Second residual connection\n", + " x = tfa.layers.StochasticDepth()([x, ffn])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Add final skip connection\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])\n", + "\n", + " # Temporal pooling\n", + " attention_pooled = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // 4\n", + " )(x, x)\n", + " attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)\n", + "\n", + " # Additional pooling operations\n", + " avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)\n", + " max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)\n", + "\n", + " # Combine pooling results\n", + " temporal_features = tf.keras.layers.Concatenate()(\n", + " [attention_pooled, avg_pooled, max_pooled]\n", + " )\n", + "\n", + " # === STATIC PATH ===\n", + " static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n", + " for units in [256, 128, 64]:\n", + " static_features = tf.keras.layers.Dense(\n", + " units,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(static_features)\n", + " static_features = tf.keras.layers.Dropout(dropout)(static_features)\n", + "\n", + " # === FEATURE FUSION ===\n", + " combined = tf.keras.layers.Concatenate()([temporal_features, static_features])\n", + "\n", + " # === MLP HEAD ===\n", + " x = combined\n", + " for units in mlp_units:\n", + " x = tf.keras.layers.BatchNormalization()(x)\n", + " x = tf.keras.layers.Dense(\n", + " units,\n", + " activation=\"swish\",\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + "\n", + " # Output layer\n", + " outputs = tf.keras.layers.Dense(\n", + " num_outputs,\n", + " activation='linear',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Create model\n", + " model = tf.keras.Model(\n", + " inputs={'temporal': temporal_input, 'static': static_input},\n", + " outputs=outputs,\n", + " name='OilTransformer'\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def create_transformer_callbacks(target_names, val_data, val_targets):\n", + " \"\"\"\n", + " Crea i callbacks per il training del modello.\n", + " \n", + " Parameters:\n", + " -----------\n", + " target_names : list\n", + " Lista dei nomi dei target per il monitoraggio specifico\n", + " val_data : dict\n", + " Dati di validazione\n", + " val_targets : array\n", + " Target di validazione\n", + " \n", + " Returns:\n", + " --------\n", + " list\n", + " Lista dei callbacks configurati\n", + " \"\"\"\n", + "\n", + " # Custom Metric per target specifici\n", + " class TargetSpecificMetric(tf.keras.callbacks.Callback):\n", + " def __init__(self, validation_data, target_names):\n", + " super().__init__()\n", + " self.validation_data = validation_data\n", + " self.target_names = target_names\n", + "\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " x_val, y_val = self.validation_data\n", + " y_pred = self.model.predict(x_val, verbose=0)\n", + "\n", + " for i, name in enumerate(self.target_names):\n", + " mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n", + " logs[f'val_{name}_mae'] = mae\n", + "\n", + "\n", + " callbacks = [\n", + " # Early Stopping\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=20,\n", + " restore_best_weights=True,\n", + " min_delta=0.0005,\n", + " mode='min'\n", + " ),\n", + "\n", + " # Model Checkpoint\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " ),\n", + "\n", + " # Metric per target specifici\n", + " TargetSpecificMetric(\n", + " validation_data=(val_data, val_targets),\n", + " target_names=target_names\n", + " ),\n", + "\n", + " # Reduce LR on Plateau\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.5,\n", + " patience=10,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + "\n", + " # TensorBoard logging\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./logs_{execute_name}',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " )\n", + " ]\n", + "\n", + " return callbacks\n", + "\n", + "\n", + "def compile_model(model, learning_rate=1e-3):\n", + " \"\"\"\n", + " Compila il modello con le impostazioni standard.\n", + " \"\"\"\n", + " lr_schedule = WarmUpLearningRateSchedule(\n", + " initial_learning_rate=learning_rate,\n", + " warmup_steps=500,\n", + " decay_steps=5000\n", + " )\n", + "\n", + " model.compile(\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.01\n", + " ),\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n", + " \"\"\"\n", + " Configura e prepara il transformer con dimensioni dinamiche basate sui dati.\n", + " \"\"\"\n", + " # Estrai le shape dai dati\n", + " temporal_shape = (train_data['temporal'].shape[1], train_data['temporal'].shape[2])\n", + " static_shape = (train_data['static'].shape[1],)\n", + " num_outputs = train_targets.shape[1]\n", + "\n", + " print(f\"Shape rilevate:\")\n", + " print(f\"- Temporal shape: {temporal_shape}\")\n", + " print(f\"- Static shape: {static_shape}\")\n", + " print(f\"- Numero di output: {num_outputs}\")\n", + "\n", + " # Target names basati sul numero di output\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Assicurati che il numero di target names corrisponda al numero di output\n", + " assert len(target_names) == num_outputs, \\\n", + " f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n", + "\n", + " # Crea il modello con le dimensioni rilevate\n", + " model = create_olive_oil_transformer(\n", + " temporal_shape=temporal_shape,\n", + " static_shape=static_shape,\n", + " num_outputs=num_outputs\n", + " )\n", + "\n", + " # Compila il modello\n", + " model = compile_model(model)\n", + "\n", + " # Crea i callbacks\n", + " callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n", + "\n", + " return model, callbacks, target_names\n", + "\n", + "\n", + "def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):\n", + " \"\"\"\n", + " Funzione principale per l'addestramento del transformer con ottimizzazioni.\n", + " \"\"\"\n", + " # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n", + " .cache()\\\n", + " .shuffle(buffer_size=1024)\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n", + " .cache()\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " # Setup del modello\n", + " strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n", + " \n", + " with strategy.scope():\n", + " model, callbacks, target_names = setup_transformer_training(\n", + " train_data, train_targets, val_data, val_targets\n", + " )\n", + "\n", + " # Mostra il summary del modello\n", + " model.summary()\n", + " \n", + " try:\n", + " keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not create model plot: {e}\")\n", + "\n", + " # Training con gestione degli errori\n", + " try:\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " workers=4,\n", + " use_multiprocessing=True\n", + " )\n", + " except tf.errors.ResourceExhaustedError:\n", + " print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n", + " # Riprova con batch size più piccolo\n", + " batch_size = batch_size // 2\n", + " train_dataset = train_dataset.unbatch().batch(batch_size)\n", + " val_dataset = val_dataset.unbatch().batch(batch_size)\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " # Salva il modello finale\n", + " try:\n", + " save_path = f'{execute_name}_{save_name}.keras'\n", + " model.save(save_path, save_format='keras')\n", + " \n", + " os.makedirs(f'{execute_name}/weights', exist_ok=True)\n", + " model.save_weights(f'{execute_name}/weights')\n", + " print(f\"\\nModello salvato in: {save_path}\")\n", + " except Exception as e:\n", + " print(f\"Warning: Could not save model: {e}\")\n", + "\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35490e902e494c4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape rilevate:\n", + "- Temporal shape: (41, 3)\n", + "- Static shape: (113,)\n", + "- Numero di output: 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 08:38:26.936272: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"OilTransformer\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " temporal (InputLayer) [(None, 41, 3)] 0 [] \n", + " \n", + " layer_normalization (Layer (None, 41, 3) 6 ['temporal[0][0]'] \n", + " Normalization) \n", + " \n", + " data_augmentation (DataAug (None, 41, 3) 0 ['layer_normalization[0][0]'] \n", + " mentation) \n", + " \n", + " dense (Dense) (None, 41, 64) 256 ['data_augmentation[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 41, 64) 0 ['dense[0][0]'] \n", + " \n", + " dense_1 (Dense) (None, 41, 128) 8320 ['dropout[0][0]'] \n", + " \n", + " positional_encoding (Posit (None, 41, 128) 5248 ['dense_1[0][0]'] \n", + " ionalEncoding) \n", + " \n", + " multi_head_attention (Mult (None, 41, 128) 66048 ['positional_encoding[0][0]', \n", + " iHeadAttention) 'positional_encoding[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 41, 128) 16512 ['positional_encoding[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 41, 128) 0 ['multi_head_attention[0][0]']\n", + " \n", + " tf.math.multiply (TFOpLamb (None, 41, 128) 0 ['dense_2[0][0]', \n", + " da) 'dropout_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 41, 128) 0 ['positional_encoding[0][0]', \n", + " icDepth) 'tf.math.multiply[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 41, 128) 256 ['stochastic_depth[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_3 (Dense) (None, 41, 256) 33024 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_2 (Dropout) (None, 41, 256) 0 ['dense_3[0][0]'] \n", + " \n", + " dense_4 (Dense) (None, 41, 128) 32896 ['dropout_2[0][0]'] \n", + " \n", + " dropout_3 (Dropout) (None, 41, 128) 0 ['dense_4[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 41, 128) 0 ['layer_normalization_1[0][0]'\n", + " sticDepth) , 'dropout_3[0][0]'] \n", + " \n", + " layer_normalization_2 (Lay (None, 41, 128) 256 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_1 (Mu (None, 41, 128) 66048 ['layer_normalization_2[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_2[0][0]\n", + " '] \n", + " \n", + " dense_5 (Dense) (None, 41, 128) 16512 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_4 (Dropout) (None, 41, 128) 0 ['multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_1 (TFOpLa (None, 41, 128) 0 ['dense_5[0][0]', \n", + " mbda) 'dropout_4[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 41, 128) 0 ['layer_normalization_2[0][0]'\n", + " sticDepth) , 'tf.math.multiply_1[0][0]'] \n", + " \n", + " layer_normalization_3 (Lay (None, 41, 128) 256 ['stochastic_depth_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_6 (Dense) (None, 41, 256) 33024 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dropout_5 (Dropout) (None, 41, 256) 0 ['dense_6[0][0]'] \n", + " \n", + " dense_7 (Dense) (None, 41, 128) 32896 ['dropout_5[0][0]'] \n", + " \n", + " dropout_6 (Dropout) (None, 41, 128) 0 ['dense_7[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 41, 128) 0 ['layer_normalization_3[0][0]'\n", + " sticDepth) , 'dropout_6[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 41, 128) 256 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_2 (Mu (None, 41, 128) 66048 ['layer_normalization_4[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_4[0][0]\n", + " '] \n", + " \n", + " dense_8 (Dense) (None, 41, 128) 16512 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dropout_7 (Dropout) (None, 41, 128) 0 ['multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_2 (TFOpLa (None, 41, 128) 0 ['dense_8[0][0]', \n", + " mbda) 'dropout_7[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 41, 128) 0 ['layer_normalization_4[0][0]'\n", + " sticDepth) , 'tf.math.multiply_2[0][0]'] \n", + " \n", + " layer_normalization_5 (Lay (None, 41, 128) 256 ['stochastic_depth_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_9 (Dense) (None, 41, 256) 33024 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dropout_8 (Dropout) (None, 41, 256) 0 ['dense_9[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 41, 128) 32896 ['dropout_8[0][0]'] \n", + " \n", + " dropout_9 (Dropout) (None, 41, 128) 0 ['dense_10[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 41, 128) 0 ['layer_normalization_5[0][0]'\n", + " sticDepth) , 'dropout_9[0][0]'] \n", + " \n", + " layer_normalization_6 (Lay (None, 41, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_3 (Mu (None, 41, 128) 66048 ['layer_normalization_6[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_6[0][0]\n", + " '] \n", + " \n", + " dense_11 (Dense) (None, 41, 128) 16512 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dropout_10 (Dropout) (None, 41, 128) 0 ['multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_3 (TFOpLa (None, 41, 128) 0 ['dense_11[0][0]', \n", + " mbda) 'dropout_10[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 41, 128) 0 ['layer_normalization_6[0][0]'\n", + " sticDepth) , 'tf.math.multiply_3[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 41, 128) 256 ['stochastic_depth_6[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_12 (Dense) (None, 41, 256) 33024 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " dropout_11 (Dropout) (None, 41, 256) 0 ['dense_12[0][0]'] \n", + " \n", + " dense_13 (Dense) (None, 41, 128) 32896 ['dropout_11[0][0]'] \n", + " \n", + " static (InputLayer) [(None, 113)] 0 [] \n", + " \n", + " dropout_12 (Dropout) (None, 41, 128) 0 ['dense_13[0][0]'] \n", + " \n", + " layer_normalization_9 (Lay (None, 113) 226 ['static[0][0]'] \n", + " erNormalization) \n", + " \n", + " stochastic_depth_7 (Stocha (None, 41, 128) 0 ['layer_normalization_7[0][0]'\n", + " sticDepth) , 'dropout_12[0][0]'] \n", + " \n", + " dense_14 (Dense) (None, 256) 29184 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_8 (Lay (None, 41, 128) 256 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_13 (Dropout) (None, 256) 0 ['dense_14[0][0]'] \n", + " \n", + " stochastic_depth_8 (Stocha (None, 41, 128) 0 ['layer_normalization_8[0][0]'\n", + " sticDepth) , 'positional_encoding[0][0]']\n", + " \n", + " dense_15 (Dense) (None, 128) 32896 ['dropout_13[0][0]'] \n", + " \n", + " multi_head_attention_4 (Mu (None, 41, 128) 131968 ['stochastic_depth_8[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_8[0][0]'] \n", + " \n", + " dropout_14 (Dropout) (None, 128) 0 ['dense_15[0][0]'] \n", + " \n", + " global_average_pooling1d ( (None, 128) 0 ['multi_head_attention_4[0][0]\n", + " GlobalAveragePooling1D) '] \n", + " \n", + " global_average_pooling1d_1 (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " (GlobalAveragePooling1D) \n", + " \n", + " global_max_pooling1d (Glob (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " alMaxPooling1D) \n", + " \n", + " dense_16 (Dense) (None, 64) 8256 ['dropout_14[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 384) 0 ['global_average_pooling1d[0][\n", + " 0]', \n", + " 'global_average_pooling1d_1[0\n", + " ][0]', \n", + " 'global_max_pooling1d[0][0]']\n", + " \n", + " dropout_15 (Dropout) (None, 64) 0 ['dense_16[0][0]'] \n", + " \n", + " concatenate_1 (Concatenate (None, 448) 0 ['concatenate[0][0]', \n", + " ) 'dropout_15[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 448) 1792 ['concatenate_1[0][0]'] \n", + " Normalization) \n", + " \n", + " dense_17 (Dense) (None, 256) 114944 ['batch_normalization[0][0]'] \n", + " \n", + " dropout_16 (Dropout) (None, 256) 0 ['dense_17[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 256) 1024 ['dropout_16[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_18 (Dense) (None, 128) 32896 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_17 (Dropout) (None, 128) 0 ['dense_18[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 128) 512 ['dropout_17[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_19 (Dense) (None, 64) 8256 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_18 (Dropout) (None, 64) 0 ['dense_19[0][0]'] \n", + " \n", + " dense_20 (Dense) (None, 5) 325 ['dropout_18[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 972077 (3.71 MB)\n", + "Trainable params: 965165 (3.68 MB)\n", + "Non-trainable params: 6912 (27.00 KB)\n", + "__________________________________________________________________________________________________\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 08:38:44.061185: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7ade632071d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-07 08:38:44.061220: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-07 08:38:44.066715: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-12-07 08:38:44.130163: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-07 08:38:44.261917: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 5/4977 [..............................] - ETA: 3:14 - loss: 0.7255 - mae: 1.1227 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0329s vs `on_train_batch_end` time: 0.0391s). Check your callbacks.\n", + "4977/4977 [==============================] - 329s 62ms/step - loss: 0.0547 - mae: 0.2020 - val_loss: 0.0149 - val_mae: 0.0886 - val_olive_prod_mae: 0.0985 - val_min_oil_prod_mae: 0.0962 - val_max_oil_prod_mae: 0.0947 - val_avg_oil_prod_mae: 0.0915 - val_total_water_need_mae: 0.0625 - lr: 1.0111e-04\n", + "Epoch 2/150\n", + "4977/4977 [==============================] - 297s 59ms/step - loss: 0.0254 - mae: 0.1444 - val_loss: 0.0135 - val_mae: 0.0853 - val_olive_prod_mae: 0.0954 - val_min_oil_prod_mae: 0.0936 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0893 - val_total_water_need_mae: 0.0550 - lr: 1.0219e-05\n", + "Epoch 3/150\n", + "4977/4977 [==============================] - 295s 59ms/step - loss: 0.0245 - mae: 0.1423 - val_loss: 0.0133 - val_mae: 0.0847 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0545 - lr: 1.0328e-06\n", + "Epoch 4/150\n", + "4977/4977 [==============================] - 302s 61ms/step - loss: 0.0244 - mae: 0.1421 - val_loss: 0.0133 - val_mae: 0.0849 - val_olive_prod_mae: 0.0942 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0554 - lr: 1.0438e-07\n", + "Epoch 5/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-07 08:59:07.216568: W tensorflow/tsl/framework/bfc_allocator.cc:485] Allocator (GPU_0_bfc) ran out of memory trying to allocate 26.27MiB (rounded to 27541504)requested by op OilTransformer/multi_head_attention_3/einsum/Einsum\n", + "If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \n", + "Current allocation summary follows.\n", + "Current allocation summary follows.\n", + "2024-12-07 08:59:07.216654: I tensorflow/tsl/framework/bfc_allocator.cc:1039] BFCAllocator dump for GPU_0_bfc\n", + "2024-12-07 08:59:07.216677: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (256): \tTotal Chunks: 197, Chunks in use: 196. 49.2KiB allocated for chunks. 49.0KiB in use in bin. 3.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216687: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (512): \tTotal Chunks: 171, Chunks in use: 166. 90.5KiB allocated for chunks. 87.5KiB in use in bin. 83.4KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216695: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1024): \tTotal Chunks: 63, Chunks in use: 59. 75.2KiB allocated for chunks. 70.0KiB in use in bin. 66.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216702: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2048): \tTotal Chunks: 6, Chunks in use: 3. 12.5KiB allocated for chunks. 6.0KiB in use in bin. 5.8KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216711: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4096): \tTotal Chunks: 1, Chunks in use: 0. 4.0KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-12-07 08:59:07.216720: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8192): \tTotal Chunks: 1, Chunks in use: 1. 10.0KiB allocated for chunks. 10.0KiB in use in bin. 10.0KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216727: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16384): \tTotal Chunks: 1, Chunks in use: 1. 20.5KiB allocated for chunks. 20.5KiB in use in bin. 20.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216735: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (32768): \tTotal Chunks: 16, Chunks in use: 16. 579.5KiB allocated for chunks. 579.5KiB in use in bin. 512.0KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216742: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (65536): \tTotal Chunks: 98, Chunks in use: 98. 6.74MiB allocated for chunks. 6.74MiB in use in bin. 6.54MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216749: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (131072): \tTotal Chunks: 66, Chunks in use: 66. 9.26MiB allocated for chunks. 9.26MiB in use in bin. 8.52MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216756: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (262144): \tTotal Chunks: 7, Chunks in use: 7. 2.51MiB allocated for chunks. 2.51MiB in use in bin. 2.47MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216763: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (524288): \tTotal Chunks: 3, Chunks in use: 3. 1.76MiB allocated for chunks. 1.76MiB in use in bin. 1.44MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216770: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1048576): \tTotal Chunks: 1, Chunks in use: 1. 1.28MiB allocated for chunks. 1.28MiB in use in bin. 1.28MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216777: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2097152): \tTotal Chunks: 6, Chunks in use: 5. 14.89MiB allocated for chunks. 12.81MiB in use in bin. 12.81MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216785: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4194304): \tTotal Chunks: 6, Chunks in use: 6. 33.15MiB allocated for chunks. 33.15MiB in use in bin. 28.19MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216792: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8388608): \tTotal Chunks: 42, Chunks in use: 42. 437.22MiB allocated for chunks. 437.22MiB in use in bin. 430.50MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216801: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16777216): \tTotal Chunks: 12, Chunks in use: 12. 263.74MiB allocated for chunks. 263.74MiB in use in bin. 252.20MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216810: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (33554432): \tTotal Chunks: 1, Chunks in use: 1. 34.48MiB allocated for chunks. 34.48MiB in use in bin. 26.27MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216819: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (67108864): \tTotal Chunks: 1, Chunks in use: 1. 97.20MiB allocated for chunks. 97.20MiB in use in bin. 97.20MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216826: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (134217728): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-12-07 08:59:07.216833: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (268435456): \tTotal Chunks: 12, Chunks in use: 12. 8.62GiB allocated for chunks. 8.62GiB in use in bin. 8.62GiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.216839: I tensorflow/tsl/framework/bfc_allocator.cc:1062] Bin for 26.27MiB was 16.00MiB, Chunk State: \n", + "2024-12-07 08:59:07.216845: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 1608187904\n", + "2024-12-07 08:59:07.216855: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdd6000000 of size 385728000 next 704\n", + "2024-12-07 08:59:07.216862: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdecfdbe00 of size 354368000 next 653\n", + "2024-12-07 08:59:07.216867: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe021cf800 of size 385728000 next 455\n", + "2024-12-07 08:59:07.216873: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe191ab600 of size 10747904 next 89\n", + "2024-12-07 08:59:07.216880: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe19beb600 of size 10747904 next 563\n", + "2024-12-07 08:59:07.216886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1a62b600 of size 16793600 next 544\n", + "2024-12-07 08:59:07.216892: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1b62f600 of size 27541504 next 40\n", + "2024-12-07 08:59:07.216897: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d073600 of size 83968 next 667\n", + "2024-12-07 08:59:07.216903: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d087e00 of size 10747904 next 615\n", + "2024-12-07 08:59:07.216909: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dac7e00 of size 83968 next 759\n", + "2024-12-07 08:59:07.216915: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dadc600 of size 5373952 next 103\n", + "2024-12-07 08:59:07.216921: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dffc600 of size 2686976 next 713\n", + "2024-12-07 08:59:07.216927: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e28c600 of size 83968 next 525\n", + "2024-12-07 08:59:07.216932: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2a0e00 of size 83968 next 705\n", + "2024-12-07 08:59:07.216938: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2b5600 of size 83968 next 471\n", + "2024-12-07 08:59:07.216944: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2c9e00 of size 83968 next 645\n", + "2024-12-07 08:59:07.216949: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2de600 of size 83968 next 458\n", + "2024-12-07 08:59:07.216955: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2f2e00 of size 83968 next 621\n", + "2024-12-07 08:59:07.216960: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7abe1e307600 of size 2183168 next 703\n", + "2024-12-07 08:59:07.216966: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e51c600 of size 10747904 next 715\n", + "2024-12-07 08:59:07.216972: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1ef5c600 of size 21495808 next 675\n", + "2024-12-07 08:59:07.216978: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe203dc600 of size 21495808 next 750\n", + "2024-12-07 08:59:07.216983: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2185c600 of size 10747904 next 588\n", + "2024-12-07 08:59:07.216989: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2229c600 of size 10747904 next 497\n", + "2024-12-07 08:59:07.216996: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe22cdc600 of size 10747904 next 722\n", + "2024-12-07 08:59:07.217001: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2371c600 of size 10747904 next 479\n", + "2024-12-07 08:59:07.217007: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2415c600 of size 10747904 next 121\n", + "2024-12-07 08:59:07.217012: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe24b9c600 of size 10747904 next 419\n", + "2024-12-07 08:59:07.217018: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe255dc600 of size 10747904 next 507\n", + "2024-12-07 08:59:07.217023: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2601c600 of size 2686976 next 721\n", + "2024-12-07 08:59:07.217029: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe262ac600 of size 8060928 next 599\n", + "2024-12-07 08:59:07.217034: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe26a5c600 of size 10747904 next 541\n", + "2024-12-07 08:59:07.217039: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2749c600 of size 16793600 next 669\n", + "2024-12-07 08:59:07.217045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe284a0600 of size 27541504 next 719\n", + "2024-12-07 08:59:07.217051: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe29ee4600 of size 10747904 next 517\n", + "2024-12-07 08:59:07.217056: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2a924600 of size 10747904 next 681\n", + "2024-12-07 08:59:07.217062: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2b364600 of size 10747904 next 432\n", + "2024-12-07 08:59:07.217067: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2bda4600 of size 10747904 next 95\n", + "2024-12-07 08:59:07.217072: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2c7e4600 of size 21495808 next 565\n", + "2024-12-07 08:59:07.217078: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2dc64600 of size 21495808 next 724\n", + "2024-12-07 08:59:07.217084: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2f0e4600 of size 10747904 next 66\n", + "2024-12-07 08:59:07.217089: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fb24600 of size 2686976 next 747\n", + "2024-12-07 08:59:07.217094: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fdb4600 of size 10747904 next 533\n", + "2024-12-07 08:59:07.217100: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe307f4600 of size 10747904 next 571\n", + "2024-12-07 08:59:07.217106: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31234600 of size 10747904 next 488\n", + "2024-12-07 08:59:07.217111: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31c74600 of size 10747904 next 710\n", + "2024-12-07 08:59:07.217116: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe326b4600 of size 10747904 next 552\n", + "2024-12-07 08:59:07.217122: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe330f4600 of size 10747904 next 46\n", + "2024-12-07 08:59:07.217127: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe33b34600 of size 36157952 next 18446744073709551615\n", + "2024-12-07 08:59:07.217133: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296\n", + "2024-12-07 08:59:07.217139: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad3f2000000 of size 2507232000 next 4\n", + "2024-12-07 08:59:07.217145: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad487715300 of size 101920000 next 5\n", + "2024-12-07 08:59:07.217151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848000 of size 256 next 6\n", + "2024-12-07 08:59:07.217157: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848100 of size 256 next 7\n", + "2024-12-07 08:59:07.217163: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848200 of size 256 next 8\n", + "2024-12-07 08:59:07.217168: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848300 of size 256 next 9\n", + "2024-12-07 08:59:07.217175: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848400 of size 256 next 10\n", + "2024-12-07 08:59:07.217181: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848500 of size 708736000 next 11\n", + "2024-12-07 08:59:07.217187: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4b7c2f900 of size 771456000 next 12\n", + "2024-12-07 08:59:07.217193: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e5be7500 of size 31360000 next 13\n", + "2024-12-07 08:59:07.217199: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cf900 of size 256 next 14\n", + "2024-12-07 08:59:07.217204: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cfa00 of size 256 next 15\n", + "2024-12-07 08:59:07.217210: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cfb00 of size 256 next 16\n", + "2024-12-07 08:59:07.217215: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cfc00 of size 256 next 17\n", + "2024-12-07 08:59:07.217221: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cfd00 of size 256 next 18\n", + "2024-12-07 08:59:07.217226: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cfe00 of size 256 next 19\n", + "2024-12-07 08:59:07.217232: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cff00 of size 256 next 21\n", + "2024-12-07 08:59:07.217238: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0000 of size 256 next 22\n", + "2024-12-07 08:59:07.217244: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0100 of size 256 next 20\n", + "2024-12-07 08:59:07.217249: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0200 of size 256 next 586\n", + "2024-12-07 08:59:07.217255: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0300 of size 256 next 27\n", + "2024-12-07 08:59:07.217260: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0400 of size 256 next 23\n", + "2024-12-07 08:59:07.217266: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0500 of size 256 next 26\n", + "2024-12-07 08:59:07.217272: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0600 of size 256 next 30\n", + "2024-12-07 08:59:07.217278: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0700 of size 256 next 24\n", + "2024-12-07 08:59:07.217283: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0800 of size 256 next 79\n", + "2024-12-07 08:59:07.217289: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0900 of size 256 next 592\n", + "2024-12-07 08:59:07.217296: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0a00 of size 256 next 25\n", + "2024-12-07 08:59:07.217301: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0b00 of size 256 next 31\n", + "2024-12-07 08:59:07.217307: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0c00 of size 256 next 32\n", + "2024-12-07 08:59:07.217314: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0d00 of size 256 next 57\n", + "2024-12-07 08:59:07.217320: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0e00 of size 256 next 24960\n", + "2024-12-07 08:59:07.217326: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0f00 of size 256 next 24951\n", + "2024-12-07 08:59:07.217332: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1000 of size 256 next 24929\n", + "2024-12-07 08:59:07.217338: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1100 of size 256 next 584\n", + "2024-12-07 08:59:07.217345: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1200 of size 256 next 41\n", + "2024-12-07 08:59:07.217351: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1300 of size 256 next 35\n", + "2024-12-07 08:59:07.217374: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1400 of size 256 next 36\n", + "2024-12-07 08:59:07.217380: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1500 of size 256 next 49\n", + "2024-12-07 08:59:07.217387: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1600 of size 256 next 45\n", + "2024-12-07 08:59:07.217393: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1700 of size 256 next 43\n", + "2024-12-07 08:59:07.217398: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1800 of size 256 next 44\n", + "2024-12-07 08:59:07.217406: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1900 of size 256 next 24946\n", + "2024-12-07 08:59:07.217413: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1a00 of size 256 next 604\n", + "2024-12-07 08:59:07.217419: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1b00 of size 256 next 435\n", + "2024-12-07 08:59:07.217425: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1c00 of size 256 next 564\n", + "2024-12-07 08:59:07.217432: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1d00 of size 256 next 523\n", + "2024-12-07 08:59:07.217437: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1e00 of size 256 next 48\n", + 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size 1280 next 506\n", + "2024-12-07 08:59:07.217497: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3800 of size 256 next 611\n", + "2024-12-07 08:59:07.217504: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3900 of size 256 next 172\n", + "2024-12-07 08:59:07.217511: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3a00 of size 256 next 677\n", + "2024-12-07 08:59:07.217518: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3b00 of size 256 next 613\n", + "2024-12-07 08:59:07.217524: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3c00 of size 256 next 489\n", + "2024-12-07 08:59:07.217531: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3d00 of size 256 next 24973\n", + "2024-12-07 08:59:07.217537: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3e00 of size 256 next 124\n", + "2024-12-07 08:59:07.217544: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3f00 of size 256 next 24979\n", + "2024-12-07 08:59:07.217551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4000 of size 256 next 453\n", + "2024-12-07 08:59:07.217557: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4100 of size 256 next 651\n", + "2024-12-07 08:59:07.217564: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4200 of size 256 next 658\n", + "2024-12-07 08:59:07.217570: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4300 of size 256 next 642\n", + "2024-12-07 08:59:07.217577: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4400 of size 768 next 135\n", + "2024-12-07 08:59:07.217584: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4700 of size 768 next 691\n", + "2024-12-07 08:59:07.217590: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4a00 of size 768 next 560\n", + "2024-12-07 08:59:07.217597: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4d00 of size 768 next 428\n", + "2024-12-07 08:59:07.217603: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5000 of size 512 next 64\n", + "2024-12-07 08:59:07.217610: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5200 of size 512 next 527\n", + "2024-12-07 08:59:07.217616: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5400 of size 256 next 143\n", + "2024-12-07 08:59:07.217623: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5500 of size 512 next 486\n", + "2024-12-07 08:59:07.217629: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5700 of size 512 next 501\n", + "2024-12-07 08:59:07.217635: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5900 of size 768 next 539\n", + "2024-12-07 08:59:07.217642: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5c00 of size 768 next 60\n", + "2024-12-07 08:59:07.217648: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5f00 of size 512 next 423\n", + "2024-12-07 08:59:07.217655: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6100 of size 256 next 173\n", + "2024-12-07 08:59:07.217662: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6200 of size 256 next 590\n", + "2024-12-07 08:59:07.217669: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6300 of size 512 next 98\n", + "2024-12-07 08:59:07.217676: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6500 of size 512 next 531\n", + "2024-12-07 08:59:07.217684: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6700 of size 768 next 663\n", + "2024-12-07 08:59:07.217690: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6a00 of size 768 next 548\n", + "2024-12-07 08:59:07.217697: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6d00 of size 512 next 131\n", + "2024-12-07 08:59:07.217704: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6f00 of size 512 next 482\n", + "2024-12-07 08:59:07.217711: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7100 of size 512 next 109\n", + "2024-12-07 08:59:07.217717: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7300 of size 256 next 115\n", + "2024-12-07 08:59:07.217724: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7400 of size 256 next 116\n", + "2024-12-07 08:59:07.217731: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7500 of size 256 next 741\n", + "2024-12-07 08:59:07.217739: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7600 of size 512 next 86\n", + "2024-12-07 08:59:07.217745: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79d7800 of size 768 next 491\n", + "2024-12-07 08:59:07.217751: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7b00 of size 256 next 502\n", + "2024-12-07 08:59:07.217758: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7c00 of size 256 next 24995\n", + "2024-12-07 08:59:07.217765: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7d00 of size 256 next 641\n", + "2024-12-07 08:59:07.217771: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7e00 of size 256 next 81\n", + "2024-12-07 08:59:07.217778: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7f00 of size 256 next 577\n", + "2024-12-07 08:59:07.217787: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8000 of size 256 next 123\n", + "2024-12-07 08:59:07.217795: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8100 of size 256 next 117\n", + "2024-12-07 08:59:07.217801: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8200 of size 256 next 118\n", + "2024-12-07 08:59:07.217807: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8300 of size 256 next 438\n", + "2024-12-07 08:59:07.217814: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8400 of size 256 next 505\n", + "2024-12-07 08:59:07.217820: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8500 of size 256 next 628\n", + "2024-12-07 08:59:07.217826: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8600 of size 256 next 127\n", + "2024-12-07 08:59:07.217833: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8700 of size 256 next 113\n", + "2024-12-07 08:59:07.217840: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8800 of size 256 next 126\n", + "2024-12-07 08:59:07.217846: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8900 of size 256 next 129\n", + "2024-12-07 08:59:07.217853: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8a00 of size 256 next 130\n", + "2024-12-07 08:59:07.217859: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8b00 of size 256 next 164\n", + "2024-12-07 08:59:07.217865: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8c00 of size 256 next 499\n", + "2024-12-07 08:59:07.217872: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8d00 of size 256 next 114\n", + "2024-12-07 08:59:07.217879: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8e00 of size 256 next 451\n", + "2024-12-07 08:59:07.217885: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8f00 of size 256 next 542\n", + "2024-12-07 08:59:07.217891: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9000 of size 256 next 593\n", + "2024-12-07 08:59:07.217898: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9100 of size 256 next 450\n", + "2024-12-07 08:59:07.217904: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9200 of size 256 next 480\n", + "2024-12-07 08:59:07.217911: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9300 of size 256 next 550\n", + "2024-12-07 08:59:07.217919: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9400 of size 512 next 102\n", + "2024-12-07 08:59:07.217926: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9600 of size 512 next 554\n", + "2024-12-07 08:59:07.217933: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9800 of size 1280 next 643\n", + "2024-12-07 08:59:07.217939: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9d00 of size 768 next 132\n", + "2024-12-07 08:59:07.217946: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79da000 of size 1792 next 139\n", + "2024-12-07 08:59:07.217952: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79da700 of size 1792 next 140\n", + "2024-12-07 08:59:07.217959: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dae00 of size 256 next 141\n", + "2024-12-07 08:59:07.217965: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79daf00 of size 256 next 142\n", + "2024-12-07 08:59:07.217971: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db000 of size 256 next 598\n", + "2024-12-07 08:59:07.217978: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db100 of size 256 next 463\n", + "2024-12-07 08:59:07.217985: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db200 of size 256 next 692\n", + "2024-12-07 08:59:07.217993: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db300 of size 256 next 467\n", + "2024-12-07 08:59:07.217999: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db400 of size 256 next 690\n", + "2024-12-07 08:59:07.218006: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db500 of size 256 next 466\n", + "2024-12-07 08:59:07.218012: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db600 of size 256 next 616\n", + "2024-12-07 08:59:07.218019: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db700 of size 256 next 24947\n", + "2024-12-07 08:59:07.218025: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db800 of size 256 next 559\n", + "2024-12-07 08:59:07.218032: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db900 of size 256 next 633\n", + "2024-12-07 08:59:07.218038: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dba00 of size 256 next 24980\n", + "2024-12-07 08:59:07.218045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dbb00 of size 256 next 144\n", + "2024-12-07 08:59:07.218052: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dbc00 of size 1024 next 148\n", + "2024-12-07 08:59:07.218058: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc000 of size 1024 next 149\n", + "2024-12-07 08:59:07.218065: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc400 of size 1280 next 676\n", + "2024-12-07 08:59:07.218072: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc900 of size 256 next 151\n", + "2024-12-07 08:59:07.218080: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dca00 of size 512 next 154\n", + "2024-12-07 08:59:07.218087: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dcc00 of size 512 next 155\n", + "2024-12-07 08:59:07.218093: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dce00 of size 256 next 156\n", + "2024-12-07 08:59:07.218100: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dcf00 of size 256 next 157\n", + "2024-12-07 08:59:07.218106: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd000 of size 256 next 158\n", + "2024-12-07 08:59:07.218113: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd100 of size 256 next 161\n", + "2024-12-07 08:59:07.218120: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd200 of size 256 next 162\n", + "2024-12-07 08:59:07.218126: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd300 of size 256 next 160\n", + "2024-12-07 08:59:07.218133: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd400 of size 256 next 169\n", + "2024-12-07 08:59:07.218139: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd500 of size 256 next 166\n", + "2024-12-07 08:59:07.218147: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd600 of size 256 next 167\n", + "2024-12-07 08:59:07.218154: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd700 of size 256 next 168\n", + "2024-12-07 08:59:07.218160: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd800 of size 256 next 163\n", + "2024-12-07 08:59:07.218167: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd900 of size 256 next 170\n", + "2024-12-07 08:59:07.218173: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dda00 of size 256 next 171\n", + "2024-12-07 08:59:07.218180: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79ddb00 of size 2048 next 165\n", + "2024-12-07 08:59:07.218186: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de300 of size 256 next 174\n", + "2024-12-07 08:59:07.218193: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de400 of size 256 next 175\n", + "2024-12-07 08:59:07.218200: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de500 of size 256 next 176\n", + "2024-12-07 08:59:07.218206: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de600 of size 768 next 177\n", + "2024-12-07 08:59:07.218212: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de900 of size 768 next 178\n", + "2024-12-07 08:59:07.218220: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dec00 of size 256 next 179\n", + "2024-12-07 08:59:07.218226: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79ded00 of size 256 next 180\n", + "2024-12-07 08:59:07.218233: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dee00 of size 512 next 181\n", + "2024-12-07 08:59:07.218239: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df000 of size 512 next 182\n", + "2024-12-07 08:59:07.218246: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df200 of size 512 next 185\n", + "2024-12-07 08:59:07.218252: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df400 of size 512 next 186\n", + "2024-12-07 08:59:07.218258: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df600 of size 512 next 188\n", + "2024-12-07 08:59:07.218265: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df800 of size 512 next 189\n", + "2024-12-07 08:59:07.218273: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfa00 of size 512 next 192\n", + "2024-12-07 08:59:07.218280: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfc00 of size 512 next 193\n", + "2024-12-07 08:59:07.218286: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfe00 of size 512 next 196\n", + "2024-12-07 08:59:07.218293: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0000 of size 512 next 197\n", + "2024-12-07 08:59:07.218300: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0200 of size 512 next 200\n", + "2024-12-07 08:59:07.218306: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0400 of size 512 next 201\n", + "2024-12-07 08:59:07.218313: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0600 of size 512 next 202\n", + "2024-12-07 08:59:07.218319: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0800 of size 768 next 29\n", + "2024-12-07 08:59:07.218326: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0b00 of size 512 next 78\n", + "2024-12-07 08:59:07.218332: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0d00 of size 512 next 558\n", + "2024-12-07 08:59:07.218339: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0f00 of size 512 next 657\n", + "2024-12-07 08:59:07.218346: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1100 of size 768 next 528\n", + "2024-12-07 08:59:07.218353: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1400 of size 512 next 33\n", + "2024-12-07 08:59:07.218369: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1600 of size 768 next 664\n", + "2024-12-07 08:59:07.218376: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1900 of size 256 next 515\n", + 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size 256 next 557\n", + "2024-12-07 08:59:07.218437: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2900 of size 256 next 521\n", + "2024-12-07 08:59:07.218443: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2a00 of size 256 next 659\n", + "2024-12-07 08:59:07.218450: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2b00 of size 256 next 24934\n", + "2024-12-07 08:59:07.218456: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2c00 of size 256 next 543\n", + "2024-12-07 08:59:07.218463: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2d00 of size 768 next 568\n", + "2024-12-07 08:59:07.218470: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e3000 of size 768 next 639\n", + "2024-12-07 08:59:07.218476: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3300 of size 512 next 53\n", + "2024-12-07 08:59:07.218483: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3500 of size 1024 next 606\n", + "2024-12-07 08:59:07.218489: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3900 of size 256 next 575\n", + "2024-12-07 08:59:07.218495: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3a00 of size 256 next 607\n", + "2024-12-07 08:59:07.218502: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3b00 of size 1280 next 420\n", + "2024-12-07 08:59:07.218508: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4000 of size 256 next 689\n", + "2024-12-07 08:59:07.218515: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4100 of size 2048 next 465\n", + "2024-12-07 08:59:07.218522: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4900 of size 256 next 712\n", + "2024-12-07 08:59:07.218529: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4a00 of size 256 next 468\n", + "2024-12-07 08:59:07.218535: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4b00 of size 256 next 742\n", + "2024-12-07 08:59:07.218542: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4c00 of size 256 next 578\n", + "2024-12-07 08:59:07.218549: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4d00 of size 512 next 39\n", + "2024-12-07 08:59:07.218556: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4f00 of size 512 next 635\n", + "2024-12-07 08:59:07.218564: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5100 of size 256 next 446\n", + "2024-12-07 08:59:07.218569: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e5200 of size 2048 next 540\n", + "2024-12-07 08:59:07.218576: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5a00 of size 768 next 34\n", + "2024-12-07 08:59:07.218583: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5d00 of size 256 next 623\n", + 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size 256 next 610\n", + "2024-12-07 08:59:07.218644: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e6f00 of size 512 next 646\n", + "2024-12-07 08:59:07.218651: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7100 of size 512 next 576\n", + "2024-12-07 08:59:07.218657: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e7300 of size 512 next 583\n", + "2024-12-07 08:59:07.218663: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7500 of size 256 next 487\n", + "2024-12-07 08:59:07.218670: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7600 of size 256 next 522\n", + "2024-12-07 08:59:07.218677: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7700 of size 256 next 459\n", + "2024-12-07 08:59:07.218683: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7800 of size 256 next 654\n", + "2024-12-07 08:59:07.218690: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7900 of size 768 next 596\n", + "2024-12-07 08:59:07.218697: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7c00 of size 512 next 648\n", + "2024-12-07 08:59:07.218703: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7e00 of size 512 next 625\n", + "2024-12-07 08:59:07.218710: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8000 of size 1280 next 735\n", + "2024-12-07 08:59:07.218716: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8500 of size 512 next 652\n", + "2024-12-07 08:59:07.218722: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8700 of size 512 next 457\n", + "2024-12-07 08:59:07.218729: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8900 of size 512 next 28\n", + "2024-12-07 08:59:07.218736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8b00 of size 20992 next 37\n", + "2024-12-07 08:59:07.218743: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79edd00 of size 32768 next 159\n", + "2024-12-07 08:59:07.218750: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79f5d00 of size 246784 next 619\n", + "2024-12-07 08:59:07.218756: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a32100 of size 164608 next 566\n", + "2024-12-07 08:59:07.218763: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5a400 of size 256 next 42\n", + "2024-12-07 08:59:07.218770: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5a500 of size 256 next 546\n", + "2024-12-07 08:59:07.218776: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5a600 of size 4096 next 644\n", + "2024-12-07 08:59:07.218782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5b600 of size 512 next 569\n", + "2024-12-07 08:59:07.218789: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5b800 of size 1536 next 421\n", + 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size 512 next 650\n", + "2024-12-07 08:59:07.218851: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5d600 of size 768 next 137\n", + "2024-12-07 08:59:07.218858: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5d900 of size 512 next 431\n", + "2024-12-07 08:59:07.218866: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5db00 of size 512 next 145\n", + "2024-12-07 08:59:07.218873: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5dd00 of size 512 next 481\n", + "2024-12-07 08:59:07.218881: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5df00 of size 512 next 464\n", + "2024-12-07 08:59:07.218888: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e100 of size 1536 next 442\n", + "2024-12-07 08:59:07.218895: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e700 of size 512 next 730\n", + "2024-12-07 08:59:07.218903: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e900 of size 256 next 518\n", + "2024-12-07 08:59:07.218910: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ea00 of size 512 next 110\n", + "2024-12-07 08:59:07.218918: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5ec00 of size 256 next 494\n", + "2024-12-07 08:59:07.218925: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ed00 of size 256 next 100\n", + "2024-12-07 08:59:07.218932: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ee00 of size 32768 next 693\n", + "2024-12-07 08:59:07.218940: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a66e00 of size 1024 next 696\n", + "2024-12-07 08:59:07.218946: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a67200 of size 512 next 470\n", + "2024-12-07 08:59:07.218953: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a67400 of size 59648 next 138\n", + "2024-12-07 08:59:07.218960: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a75d00 of size 32768 next 56\n", + "2024-12-07 08:59:07.218967: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a7dd00 of size 65536 next 629\n", + "2024-12-07 08:59:07.218974: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a8dd00 of size 1024 next 666\n", + "2024-12-07 08:59:07.218981: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a8e100 of size 130560 next 493\n", + "2024-12-07 08:59:07.218988: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7aadf00 of size 65536 next 80\n", + "2024-12-07 08:59:07.218995: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7abdf00 of size 131072 next 38\n", + "2024-12-07 08:59:07.219002: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7addf00 of size 512 next 545\n", + "2024-12-07 08:59:07.219009: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ade100 of size 65536 next 697\n", + "2024-12-07 08:59:07.219017: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7aee100 of size 65536 next 637\n", + "2024-12-07 08:59:07.219024: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7afe100 of size 115712 next 58\n", + "2024-12-07 08:59:07.219031: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b1a500 of size 512 next 108\n", + "2024-12-07 08:59:07.219038: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b1a700 of size 131072 next 439\n", + "2024-12-07 08:59:07.219045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b3a700 of size 131072 next 74\n", + "2024-12-07 08:59:07.219052: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b5a700 of size 1024 next 601\n", + "2024-12-07 08:59:07.219059: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b5ab00 of size 65536 next 424\n", + "2024-12-07 08:59:07.219066: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b6ab00 of size 512 next 609\n", + "2024-12-07 08:59:07.219072: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b6ad00 of size 131072 next 685\n", + "2024-12-07 08:59:07.219079: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b8ad00 of size 1024 next 504\n", + "2024-12-07 08:59:07.219086: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b8b100 of size 32768 next 136\n", + "2024-12-07 08:59:07.219092: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b93100 of size 1792 next 475\n", + "2024-12-07 08:59:07.219100: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b93800 of size 65536 next 67\n", + "2024-12-07 08:59:07.219106: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ba3800 of size 131072 next 672\n", + "2024-12-07 08:59:07.219114: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7bc3800 of size 512 next 580\n", + "2024-12-07 08:59:07.219121: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bc3a00 of size 1024 next 707\n", + "2024-12-07 08:59:07.219129: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bc3e00 of size 198656 next 600\n", + "2024-12-07 08:59:07.219136: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf4600 of size 1792 next 679\n", + "2024-12-07 08:59:07.219144: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf4d00 of size 1024 next 441\n", + "2024-12-07 08:59:07.219151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf5100 of size 101376 next 106\n", + "2024-12-07 08:59:07.219158: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c0dd00 of size 65536 next 183\n", + "2024-12-07 08:59:07.219165: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c1dd00 of size 65536 next 184\n", + "2024-12-07 08:59:07.219172: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c2dd00 of size 65536 next 187\n", + "2024-12-07 08:59:07.219178: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c3dd00 of size 65536 next 112\n", + "2024-12-07 08:59:07.219185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c4dd00 of size 65536 next 740\n", + "2024-12-07 08:59:07.219191: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c5dd00 of size 131072 next 436\n", + "2024-12-07 08:59:07.219198: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c7dd00 of size 131072 next 430\n", + "2024-12-07 08:59:07.219204: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c9dd00 of size 65536 next 526\n", + "2024-12-07 08:59:07.219211: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cadd00 of size 65536 next 678\n", + "2024-12-07 08:59:07.219218: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cbdd00 of size 65536 next 508\n", + "2024-12-07 08:59:07.219225: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ccdd00 of size 65536 next 731\n", + "2024-12-07 08:59:07.219231: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cddd00 of size 65536 next 627\n", + "2024-12-07 08:59:07.219238: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cedd00 of size 131072 next 537\n", + "2024-12-07 08:59:07.219245: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d0dd00 of size 65536 next 478\n", + "2024-12-07 08:59:07.219252: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d1dd00 of size 65536 next 503\n", + "2024-12-07 08:59:07.219258: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d2dd00 of size 65536 next 594\n", + "2024-12-07 08:59:07.219266: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d3dd00 of size 181248 next 153\n", + "2024-12-07 08:59:07.219273: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d6a100 of size 65536 next 190\n", + "2024-12-07 08:59:07.219281: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d7a100 of size 65536 next 191\n", + "2024-12-07 08:59:07.219288: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d8a100 of size 65536 next 194\n", + "2024-12-07 08:59:07.219295: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d9a100 of size 65536 next 195\n", + "2024-12-07 08:59:07.219302: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7daa100 of size 65536 next 198\n", + "2024-12-07 08:59:07.219309: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dba100 of size 65536 next 199\n", + "2024-12-07 08:59:07.219316: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca100 of size 512 next 203\n", + "2024-12-07 08:59:07.219322: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca300 of size 512 next 204\n", + "2024-12-07 08:59:07.219329: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca500 of size 131072 next 205\n", + "2024-12-07 08:59:07.219336: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dea500 of size 261120 next 147\n", + "2024-12-07 08:59:07.219343: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a100 of size 256 next 620\n", + "2024-12-07 08:59:07.219350: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a200 of size 256 next 612\n", + "2024-12-07 08:59:07.219356: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a300 of size 131072 next 682\n", + "2024-12-07 08:59:07.219376: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e4a300 of size 32768 next 709\n", + "2024-12-07 08:59:07.219383: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e52300 of size 32768 next 698\n", + "2024-12-07 08:59:07.219392: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e5a300 of size 115712 next 88\n", + "2024-12-07 08:59:07.219398: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e76700 of size 1280 next 456\n", + "2024-12-07 08:59:07.219406: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e76c00 of size 64256 next 125\n", + "2024-12-07 08:59:07.219412: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e86700 of size 32768 next 75\n", + "2024-12-07 08:59:07.219419: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8e700 of size 1024 next 535\n", + "2024-12-07 08:59:07.219426: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8eb00 of size 1024 next 562\n", + "2024-12-07 08:59:07.219433: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8ef00 of size 1024 next 461\n", + "2024-12-07 08:59:07.219441: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8f300 of size 1024 next 656\n", + "2024-12-07 08:59:07.219449: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8f700 of size 43520 next 146\n", + "2024-12-07 08:59:07.219456: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a100 of size 1024 next 206\n", + "2024-12-07 08:59:07.219463: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a500 of size 1024 next 207\n", + "2024-12-07 08:59:07.219470: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a900 of size 131072 next 208\n", + "2024-12-07 08:59:07.219475: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eba900 of size 131072 next 209\n", + "2024-12-07 08:59:07.219482: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eda900 of size 512 next 210\n", + "2024-12-07 08:59:07.219488: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edab00 of size 512 next 211\n", + "2024-12-07 08:59:07.219495: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edad00 of size 512 next 212\n", + "2024-12-07 08:59:07.219501: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edaf00 of size 512 next 213\n", + "2024-12-07 08:59:07.219508: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb100 of size 512 next 214\n", + "2024-12-07 08:59:07.219514: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb300 of size 512 next 215\n", + "2024-12-07 08:59:07.219521: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb500 of size 65536 next 216\n", + "2024-12-07 08:59:07.219528: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eeb500 of size 65536 next 217\n", + "2024-12-07 08:59:07.219535: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb500 of size 512 next 218\n", + "2024-12-07 08:59:07.219543: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb700 of size 512 next 219\n", + "2024-12-07 08:59:07.219550: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb900 of size 65536 next 220\n", + "2024-12-07 08:59:07.219557: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f0b900 of size 65536 next 221\n", + "2024-12-07 08:59:07.219565: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1b900 of size 512 next 222\n", + "2024-12-07 08:59:07.219572: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1bb00 of size 512 next 223\n", + "2024-12-07 08:59:07.219580: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1bd00 of size 65536 next 224\n", + "2024-12-07 08:59:07.219587: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f2bd00 of size 65536 next 225\n", + "2024-12-07 08:59:07.219594: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3bd00 of size 512 next 226\n", + "2024-12-07 08:59:07.219602: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3bf00 of size 512 next 227\n", + "2024-12-07 08:59:07.219609: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3c100 of size 65536 next 228\n", + "2024-12-07 08:59:07.219616: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f4c100 of size 65536 next 229\n", + "2024-12-07 08:59:07.219624: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c100 of size 512 next 230\n", + "2024-12-07 08:59:07.219631: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c300 of size 512 next 231\n", + "2024-12-07 08:59:07.219639: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c500 of size 65536 next 232\n", + "2024-12-07 08:59:07.219646: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f6c500 of size 65536 next 233\n", + "2024-12-07 08:59:07.219653: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c500 of size 512 next 234\n", + "2024-12-07 08:59:07.219660: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c700 of size 512 next 235\n", + "2024-12-07 08:59:07.219668: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c900 of size 512 next 236\n", + "2024-12-07 08:59:07.219674: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cb00 of size 512 next 237\n", + "2024-12-07 08:59:07.219680: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cd00 of size 512 next 238\n", + "2024-12-07 08:59:07.219687: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cf00 of size 512 next 239\n", + "2024-12-07 08:59:07.219694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7d100 of size 131072 next 240\n", + "2024-12-07 08:59:07.219700: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f9d100 of size 131072 next 241\n", + "2024-12-07 08:59:07.219707: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd100 of size 1024 next 242\n", + "2024-12-07 08:59:07.219714: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd500 of size 1024 next 243\n", + "2024-12-07 08:59:07.219721: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd900 of size 131072 next 244\n", + "2024-12-07 08:59:07.219728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fdd900 of size 131072 next 245\n", + "2024-12-07 08:59:07.219734: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffd900 of size 512 next 246\n", + "2024-12-07 08:59:07.219741: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdb00 of size 512 next 247\n", + "2024-12-07 08:59:07.219748: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdd00 of size 512 next 248\n", + "2024-12-07 08:59:07.219755: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdf00 of size 512 next 249\n", + "2024-12-07 08:59:07.219762: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe100 of size 512 next 250\n", + "2024-12-07 08:59:07.219770: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe300 of size 512 next 251\n", + "2024-12-07 08:59:07.219776: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe500 of size 65536 next 252\n", + "2024-12-07 08:59:07.219784: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e800e500 of size 65536 next 253\n", + "2024-12-07 08:59:07.219791: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e500 of size 512 next 254\n", + "2024-12-07 08:59:07.219798: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e700 of size 512 next 255\n", + "2024-12-07 08:59:07.219806: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e900 of size 65536 next 256\n", + "2024-12-07 08:59:07.219813: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e802e900 of size 65536 next 257\n", + "2024-12-07 08:59:07.219820: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803e900 of size 512 next 258\n", + "2024-12-07 08:59:07.219828: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803eb00 of size 512 next 259\n", + "2024-12-07 08:59:07.219835: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803ed00 of size 65536 next 260\n", + "2024-12-07 08:59:07.219842: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e804ed00 of size 65536 next 261\n", + "2024-12-07 08:59:07.219850: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805ed00 of size 512 next 262\n", + "2024-12-07 08:59:07.219857: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805ef00 of size 512 next 263\n", + "2024-12-07 08:59:07.219864: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805f100 of size 65536 next 264\n", + "2024-12-07 08:59:07.219872: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e806f100 of size 65536 next 265\n", + "2024-12-07 08:59:07.219879: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f100 of size 512 next 266\n", + "2024-12-07 08:59:07.219886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f300 of size 512 next 267\n", + "2024-12-07 08:59:07.219894: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f500 of size 65536 next 268\n", + "2024-12-07 08:59:07.219901: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e808f500 of size 65536 next 269\n", + "2024-12-07 08:59:07.219907: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f500 of size 512 next 270\n", + "2024-12-07 08:59:07.219914: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f700 of size 512 next 271\n", + "2024-12-07 08:59:07.219921: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f900 of size 512 next 272\n", + "2024-12-07 08:59:07.219927: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809fb00 of size 512 next 273\n", + "2024-12-07 08:59:07.219933: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809fd00 of size 512 next 274\n", + "2024-12-07 08:59:07.219940: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809ff00 of size 512 next 275\n", + "2024-12-07 08:59:07.219947: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80a0100 of size 131072 next 276\n", + "2024-12-07 08:59:07.219953: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80c0100 of size 131072 next 277\n", + "2024-12-07 08:59:07.219960: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0100 of size 1024 next 278\n", + "2024-12-07 08:59:07.219968: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0500 of size 1024 next 279\n", + "2024-12-07 08:59:07.219975: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0900 of size 131072 next 280\n", + "2024-12-07 08:59:07.219982: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8100900 of size 131072 next 281\n", + "2024-12-07 08:59:07.219990: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120900 of size 512 next 282\n", + "2024-12-07 08:59:07.219997: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120b00 of size 512 next 283\n", + "2024-12-07 08:59:07.220005: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120d00 of size 512 next 284\n", + "2024-12-07 08:59:07.220012: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120f00 of size 512 next 285\n", + "2024-12-07 08:59:07.220020: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121100 of size 512 next 286\n", + "2024-12-07 08:59:07.220027: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121300 of size 512 next 287\n", + "2024-12-07 08:59:07.220034: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121500 of size 65536 next 288\n", + "2024-12-07 08:59:07.220041: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8131500 of size 65536 next 289\n", + "2024-12-07 08:59:07.220048: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141500 of size 512 next 290\n", + "2024-12-07 08:59:07.220054: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141700 of size 512 next 291\n", + "2024-12-07 08:59:07.220061: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141900 of size 65536 next 292\n", + "2024-12-07 08:59:07.220068: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8151900 of size 65536 next 293\n", + "2024-12-07 08:59:07.220075: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161900 of size 512 next 294\n", + "2024-12-07 08:59:07.220081: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161b00 of size 512 next 295\n", + "2024-12-07 08:59:07.220088: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161d00 of size 65536 next 296\n", + "2024-12-07 08:59:07.220095: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8171d00 of size 65536 next 297\n", + "2024-12-07 08:59:07.220102: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8181d00 of size 512 next 298\n", + "2024-12-07 08:59:07.220111: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8181f00 of size 512 next 299\n", + "2024-12-07 08:59:07.220118: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8182100 of size 65536 next 300\n", + "2024-12-07 08:59:07.220125: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8192100 of size 65536 next 301\n", + "2024-12-07 08:59:07.220131: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2100 of size 512 next 302\n", + "2024-12-07 08:59:07.220138: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2300 of size 512 next 303\n", + "2024-12-07 08:59:07.220146: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2500 of size 65536 next 304\n", + "2024-12-07 08:59:07.220153: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81b2500 of size 65536 next 305\n", + "2024-12-07 08:59:07.220161: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2500 of size 512 next 306\n", + "2024-12-07 08:59:07.220169: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2700 of size 512 next 307\n", + "2024-12-07 08:59:07.220178: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2900 of size 512 next 308\n", + "2024-12-07 08:59:07.220185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2b00 of size 512 next 309\n", + "2024-12-07 08:59:07.220192: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2d00 of size 512 next 310\n", + "2024-12-07 08:59:07.220200: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2f00 of size 512 next 311\n", + "2024-12-07 08:59:07.220207: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c3100 of size 131072 next 312\n", + "2024-12-07 08:59:07.220214: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81e3100 of size 131072 next 313\n", + "2024-12-07 08:59:07.220221: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203100 of size 1024 next 314\n", + "2024-12-07 08:59:07.220229: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203500 of size 1024 next 315\n", + "2024-12-07 08:59:07.220236: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203900 of size 131072 next 316\n", + "2024-12-07 08:59:07.220245: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8223900 of size 131072 next 317\n", + "2024-12-07 08:59:07.220253: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243900 of size 512 next 318\n", + "2024-12-07 08:59:07.220261: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243b00 of size 512 next 319\n", + "2024-12-07 08:59:07.220268: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243d00 of size 512 next 320\n", + "2024-12-07 08:59:07.220276: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243f00 of size 512 next 321\n", + "2024-12-07 08:59:07.220282: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244100 of size 512 next 322\n", + "2024-12-07 08:59:07.220289: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244300 of size 512 next 323\n", + "2024-12-07 08:59:07.220296: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244500 of size 115712 next 324\n", + "2024-12-07 08:59:07.220303: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8260900 of size 115712 next 325\n", + "2024-12-07 08:59:07.220310: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827cd00 of size 1024 next 326\n", + "2024-12-07 08:59:07.220317: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d100 of size 1024 next 327\n", + "2024-12-07 08:59:07.220323: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d500 of size 512 next 328\n", + "2024-12-07 08:59:07.220330: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d700 of size 512 next 329\n", + "2024-12-07 08:59:07.220337: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d900 of size 512 next 330\n", + "2024-12-07 08:59:07.220344: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827db00 of size 512 next 331\n", + "2024-12-07 08:59:07.220351: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827dd00 of size 131072 next 332\n", + "2024-12-07 08:59:07.220371: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e829dd00 of size 131072 next 333\n", + "2024-12-07 08:59:07.220381: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82bdd00 of size 512 next 334\n", + "2024-12-07 08:59:07.220388: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82bdf00 of size 512 next 335\n", + "2024-12-07 08:59:07.220395: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82be100 of size 131072 next 336\n", + "2024-12-07 08:59:07.220402: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82de100 of size 131072 next 337\n", + "2024-12-07 08:59:07.220409: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe100 of size 1024 next 338\n", + "2024-12-07 08:59:07.220416: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe500 of size 1024 next 339\n", + "2024-12-07 08:59:07.220424: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe900 of size 131072 next 340\n", + "2024-12-07 08:59:07.220429: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e831e900 of size 131072 next 341\n", + "2024-12-07 08:59:07.220437: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833e900 of size 1024 next 342\n", + "2024-12-07 08:59:07.220443: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833ed00 of size 1024 next 343\n", + "2024-12-07 08:59:07.220450: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833f100 of size 131072 next 344\n", + "2024-12-07 08:59:07.220457: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e835f100 of size 131072 next 345\n", + "2024-12-07 08:59:07.220464: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f100 of size 1024 next 346\n", + "2024-12-07 08:59:07.220471: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f500 of size 1024 next 347\n", + "2024-12-07 08:59:07.220478: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f900 of size 131072 next 348\n", + "2024-12-07 08:59:07.220485: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e839f900 of size 131072 next 349\n", + "2024-12-07 08:59:07.220492: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bf900 of size 512 next 350\n", + "2024-12-07 08:59:07.220500: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bfb00 of size 512 next 351\n", + "2024-12-07 08:59:07.220507: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bfd00 of size 32768 next 352\n", + "2024-12-07 08:59:07.220513: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83c7d00 of size 32768 next 353\n", + "2024-12-07 08:59:07.220520: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cfd00 of size 256 next 354\n", + "2024-12-07 08:59:07.220529: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cfe00 of size 256 next 355\n", + "2024-12-07 08:59:07.220536: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cff00 of size 1792 next 356\n", + "2024-12-07 08:59:07.220544: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d0600 of size 1792 next 357\n", + "2024-12-07 08:59:07.220551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d0d00 of size 1792 next 358\n", + "2024-12-07 08:59:07.220558: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d1400 of size 1792 next 359\n", + "2024-12-07 08:59:07.220566: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d1b00 of size 458752 next 360\n", + "2024-12-07 08:59:07.220573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8441b00 of size 458752 next 361\n", + "2024-12-07 08:59:07.220580: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b1b00 of size 1024 next 362\n", + "2024-12-07 08:59:07.220589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b1f00 of size 1024 next 363\n", + "2024-12-07 08:59:07.220595: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2300 of size 1024 next 364\n", + "2024-12-07 08:59:07.220602: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2700 of size 1024 next 365\n", + "2024-12-07 08:59:07.220609: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2b00 of size 1024 next 366\n", + "2024-12-07 08:59:07.220616: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2f00 of size 1024 next 367\n", + "2024-12-07 08:59:07.220623: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b3300 of size 131072 next 368\n", + "2024-12-07 08:59:07.220630: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84d3300 of size 131072 next 369\n", + "2024-12-07 08:59:07.220637: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3300 of size 512 next 370\n", + "2024-12-07 08:59:07.220643: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3500 of size 512 next 371\n", + "2024-12-07 08:59:07.220650: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3700 of size 512 next 372\n", + "2024-12-07 08:59:07.220657: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3900 of size 512 next 373\n", + "2024-12-07 08:59:07.220664: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3b00 of size 512 next 374\n", + "2024-12-07 08:59:07.220672: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3d00 of size 512 next 375\n", + "2024-12-07 08:59:07.220680: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3f00 of size 32768 next 376\n", + "2024-12-07 08:59:07.220687: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84fbf00 of size 32768 next 377\n", + "2024-12-07 08:59:07.220694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8503f00 of size 256 next 378\n", + "2024-12-07 08:59:07.220701: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504000 of size 256 next 379\n", + "2024-12-07 08:59:07.220708: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504100 of size 1280 next 380\n", + "2024-12-07 08:59:07.220714: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504600 of size 1280 next 381\n", + "2024-12-07 08:59:07.220724: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504b00 of size 256 next 382\n", + "2024-12-07 08:59:07.220732: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504c00 of size 256 next 383\n", + "2024-12-07 08:59:07.220739: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504d00 of size 256 next 384\n", + "2024-12-07 08:59:07.220746: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504e00 of size 256 next 385\n", + "2024-12-07 08:59:07.220753: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504f00 of size 256 next 386\n", + "2024-12-07 08:59:07.220761: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505000 of size 256 next 387\n", + "2024-12-07 08:59:07.220769: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505100 of size 256 next 388\n", + "2024-12-07 08:59:07.220775: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505200 of size 256 next 389\n", + "2024-12-07 08:59:07.220782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505300 of size 256 next 390\n", + "2024-12-07 08:59:07.220790: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505400 of size 256 next 391\n", + "2024-12-07 08:59:07.220798: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505500 of size 256 next 392\n", + "2024-12-07 08:59:07.220805: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505600 of size 256 next 393\n", + "2024-12-07 08:59:07.220812: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505700 of size 256 next 394\n", + "2024-12-07 08:59:07.220818: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505800 of size 256 next 395\n", + "2024-12-07 08:59:07.220824: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505900 of size 256 next 396\n", + "2024-12-07 08:59:07.220831: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505a00 of size 256 next 397\n", + "2024-12-07 08:59:07.220838: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505b00 of size 256 next 398\n", + "2024-12-07 08:59:07.220845: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505c00 of size 256 next 399\n", + "2024-12-07 08:59:07.220852: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505d00 of size 256 next 400\n", + "2024-12-07 08:59:07.220860: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505e00 of size 256 next 401\n", + "2024-12-07 08:59:07.220867: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505f00 of size 256 next 402\n", + "2024-12-07 08:59:07.220874: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506000 of size 256 next 403\n", + "2024-12-07 08:59:07.220882: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506100 of size 256 next 404\n", + "2024-12-07 08:59:07.220888: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506200 of size 256 next 405\n", + "2024-12-07 08:59:07.220895: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506300 of size 256 next 406\n", + "2024-12-07 08:59:07.220901: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506400 of size 256 next 407\n", + "2024-12-07 08:59:07.220908: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506500 of size 256 next 408\n", + "2024-12-07 08:59:07.220914: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506600 of size 256 next 409\n", + "2024-12-07 08:59:07.220921: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506700 of size 256 next 410\n", + "2024-12-07 08:59:07.220928: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506800 of size 256 next 411\n", + "2024-12-07 08:59:07.220935: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506900 of size 256 next 412\n", + "2024-12-07 08:59:07.220942: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506a00 of size 256 next 413\n", + "2024-12-07 08:59:07.220950: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506b00 of size 256 next 414\n", + "2024-12-07 08:59:07.220958: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506c00 of size 256 next 415\n", + "2024-12-07 08:59:07.220966: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506d00 of size 256 next 416\n", + "2024-12-07 08:59:07.220975: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506e00 of size 65536 next 597\n", + "2024-12-07 08:59:07.220983: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8516e00 of size 186368 next 447\n", + "2024-12-07 08:59:07.220990: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8544600 of size 262144 next 701\n", + "2024-12-07 08:59:07.220998: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8584600 of size 251904 next 718\n", + "2024-12-07 08:59:07.221004: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e85c1e00 of size 262144 next 524\n", + "2024-12-07 08:59:07.221011: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8601e00 of size 5373952 next 570\n", + "2024-12-07 08:59:07.221018: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8b21e00 of size 5373952 next 649\n", + "2024-12-07 08:59:07.221025: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9041e00 of size 2686976 next 725\n", + "2024-12-07 08:59:07.221031: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e92d1e00 of size 2686976 next 107\n", + "2024-12-07 08:59:07.221038: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9561e00 of size 1343488 next 429\n", + "2024-12-07 08:59:07.221045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e96a9e00 of size 12410880 next 700\n", + "2024-12-07 08:59:07.221053: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea27fe00 of size 131072 next 602\n", + "2024-12-07 08:59:07.221061: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4ea29fe00 of size 512 next 500\n", + "2024-12-07 08:59:07.221069: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea2a0000 of size 720384 next 427\n", + "2024-12-07 08:59:07.221077: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea34fe00 of size 458752 next 661\n", + "2024-12-07 08:59:07.221085: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea3bfe00 of size 10747904 next 476\n", + "2024-12-07 08:59:07.221093: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eadffe00 of size 10747904 next 582\n", + "2024-12-07 08:59:07.221102: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eb83fe00 of size 10747904 next 83\n", + "2024-12-07 08:59:07.221108: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ec27fe00 of size 10747904 next 445\n", + "2024-12-07 08:59:07.221116: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eccbfe00 of size 10747904 next 727\n", + "2024-12-07 08:59:07.221124: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ed6ffe00 of size 10747904 next 684\n", + "2024-12-07 08:59:07.221131: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ee13fe00 of size 10747904 next 63\n", + "2024-12-07 08:59:07.221137: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eeb7fe00 of size 10747904 next 662\n", + "2024-12-07 08:59:07.221144: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ef5bfe00 of size 10747904 next 665\n", + "2024-12-07 08:59:07.221150: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4effffe00 of size 10747904 next 50\n", + "2024-12-07 08:59:07.221156: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0a3fe00 of size 5373952 next 587\n", + "2024-12-07 08:59:07.221163: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f5fe00 of size 83968 next 683\n", + "2024-12-07 08:59:07.221170: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f74600 of size 83968 next 626\n", + "2024-12-07 08:59:07.221176: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f88e00 of size 5206016 next 536\n", + "2024-12-07 08:59:07.221183: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f147fe00 of size 12059136 next 18446744073709551615\n", + "2024-12-07 08:59:07.221190: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296\n", + "2024-12-07 08:59:07.221197: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad61e000000 of size 2303392000 next 1\n", + "2024-12-07 08:59:07.221206: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74af900 of size 1280 next 2\n", + "2024-12-07 08:59:07.221212: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74afe00 of size 354368000 next 534\n", + "2024-12-07 08:59:07.221220: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6bc6a3800 of size 385728000 next 630\n", + "2024-12-07 08:59:07.221227: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6d367f600 of size 354368000 next 498\n", + "2024-12-07 08:59:07.221234: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6e8873000 of size 385728000 next 426\n", + "2024-12-07 08:59:07.221240: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6ff84ee00 of size 354368000 next 632\n", + "2024-12-07 08:59:07.221247: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a42800 of size 1280 next 553\n", + "2024-12-07 08:59:07.221254: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a42d00 of size 32768 next 549\n", + "2024-12-07 08:59:07.221261: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a4ad00 of size 97024 next 94\n", + "2024-12-07 08:59:07.221269: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a62800 of size 65536 next 729\n", + "2024-12-07 08:59:07.221275: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a72800 of size 65536 next 556\n", + "2024-12-07 08:59:07.221282: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a82800 of size 65536 next 519\n", + "2024-12-07 08:59:07.221289: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a92800 of size 65536 next 631\n", + "2024-12-07 08:59:07.221297: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714aa2800 of size 65536 next 477\n", + "2024-12-07 08:59:07.221304: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ab2800 of size 131072 next 473\n", + "2024-12-07 08:59:07.221311: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ad2800 of size 65536 next 495\n", + "2024-12-07 08:59:07.221319: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ae2800 of size 65536 next 490\n", + "2024-12-07 08:59:07.221328: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714af2800 of size 65536 next 670\n", + "2024-12-07 08:59:07.221336: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b02800 of size 131072 next 509\n", + "2024-12-07 08:59:07.221343: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b22800 of size 65536 next 54\n", + "2024-12-07 08:59:07.221350: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b32800 of size 131072 next 440\n", + "2024-12-07 08:59:07.221356: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b52800 of size 131072 next 618\n", + "2024-12-07 08:59:07.221374: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b72800 of size 65536 next 547\n", + "2024-12-07 08:59:07.221381: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b82800 of size 99584 next 738\n", + "2024-12-07 08:59:07.221388: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b9ad00 of size 65536 next 105\n", + "2024-12-07 08:59:07.221396: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714baad00 of size 131072 next 561\n", + "2024-12-07 08:59:07.221403: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bcad00 of size 65536 next 511\n", + "2024-12-07 08:59:07.221410: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bdad00 of size 65536 next 516\n", + "2024-12-07 08:59:07.221417: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bead00 of size 65536 next 485\n", + "2024-12-07 08:59:07.221423: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bfad00 of size 131072 next 551\n", + "2024-12-07 08:59:07.221431: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c1ad00 of size 65536 next 104\n", + "2024-12-07 08:59:07.221438: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c2ad00 of size 131072 next 720\n", + "2024-12-07 08:59:07.221444: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c4ad00 of size 65536 next 739\n", + "2024-12-07 08:59:07.221451: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c5ad00 of size 65536 next 708\n", + "2024-12-07 08:59:07.221457: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c6ad00 of size 65536 next 87\n", + "2024-12-07 08:59:07.221464: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c7ad00 of size 131072 next 694\n", + "2024-12-07 08:59:07.221471: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c9ad00 of size 246784 next 425\n", + "2024-12-07 08:59:07.221479: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714cd7100 of size 65536 next 765\n", + "2024-12-07 08:59:07.221487: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ce7100 of size 131072 next 655\n", + "2024-12-07 08:59:07.221495: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07100 of size 1792 next 422\n", + "2024-12-07 08:59:07.221502: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07800 of size 1024 next 530\n", + "2024-12-07 08:59:07.221511: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07c00 of size 65536 next 454\n", + "2024-12-07 08:59:07.221519: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d17c00 of size 165888 next 62\n", + "2024-12-07 08:59:07.221528: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d40400 of size 131072 next 448\n", + "2024-12-07 08:59:07.221537: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d60400 of size 131072 next 120\n", + "2024-12-07 08:59:07.221545: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80400 of size 1024 next 671\n", + "2024-12-07 08:59:07.221551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80800 of size 256 next 687\n", + "2024-12-07 08:59:07.221559: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80900 of size 256 next 449\n", + "2024-12-07 08:59:07.221566: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80a00 of size 2048 next 513\n", + "2024-12-07 08:59:07.221573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d81200 of size 231424 next 434\n", + "2024-12-07 08:59:07.221582: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714db9a00 of size 83968 next 733\n", + "2024-12-07 08:59:07.221589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dce200 of size 2048 next 591\n", + "2024-12-07 08:59:07.221596: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dcea00 of size 272640 next 59\n", + "2024-12-07 08:59:07.221604: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e11300 of size 65536 next 585\n", + "2024-12-07 08:59:07.221611: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e21300 of size 131072 next 636\n", + "2024-12-07 08:59:07.221618: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e41300 of size 131072 next 638\n", + "2024-12-07 08:59:07.221625: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e61300 of size 458752 next 512\n", + "2024-12-07 08:59:07.221632: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed1300 of size 10240 next 85\n", + "2024-12-07 08:59:07.221639: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed3b00 of size 83968 next 61\n", + "2024-12-07 08:59:07.221645: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ee8300 of size 131072 next 483\n", + "2024-12-07 08:59:07.221656: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f08300 of size 131072 next 572\n", + "2024-12-07 08:59:07.221664: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f28300 of size 32768 next 622\n", + "2024-12-07 08:59:07.221673: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f30300 of size 124928 next 101\n", + "2024-12-07 08:59:07.221680: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f4eb00 of size 231424 next 520\n", + "2024-12-07 08:59:07.221687: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f87300 of size 251904 next 668\n", + "2024-12-07 08:59:07.221694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714fc4b00 of size 524288 next 128\n", + "2024-12-07 08:59:07.221701: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad715044b00 of size 604672 next 71\n", + "2024-12-07 08:59:07.221708: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150d8500 of size 131072 next 573\n", + "2024-12-07 08:59:07.221715: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150f8500 of size 141056 next 444\n", + "2024-12-07 08:59:07.221722: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71511ac00 of size 27541504 next 608\n", + "2024-12-07 08:59:07.221728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad716b5ec00 of size 21495808 next 640\n", + "2024-12-07 08:59:07.221736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad717fdec00 of size 21495808 next 84\n", + "2024-12-07 08:59:07.221742: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71945ec00 of size 10747904 next 133\n", + "2024-12-07 08:59:07.221750: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad719e9ec00 of size 10747904 next 744\n", + "2024-12-07 08:59:07.221757: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71a8dec00 of size 10747904 next 77\n", + "2024-12-07 08:59:07.221763: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71b31ec00 of size 10747904 next 510\n", + "2024-12-07 08:59:07.221769: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71bd5ec00 of size 10747904 next 150\n", + "2024-12-07 08:59:07.221776: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71c79ec00 of size 10747904 next 673\n", + "2024-12-07 08:59:07.221782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71d1dec00 of size 14816256 next 18446744073709551615\n", + "2024-12-07 08:59:07.221789: I tensorflow/tsl/framework/bfc_allocator.cc:1100] Summary of in-use Chunks by size: \n", + "2024-12-07 08:59:07.221801: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 196 Chunks of size 256 totalling 49.0KiB\n", + "2024-12-07 08:59:07.221809: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 148 Chunks of size 512 totalling 74.0KiB\n", + "2024-12-07 08:59:07.221817: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 18 Chunks of size 768 totalling 13.5KiB\n", + "2024-12-07 08:59:07.221825: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 37 Chunks of size 1024 totalling 37.0KiB\n", + "2024-12-07 08:59:07.221833: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1280 totalling 12.5KiB\n", + "2024-12-07 08:59:07.221841: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 1536 totalling 3.0KiB\n", + "2024-12-07 08:59:07.221849: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1792 totalling 17.5KiB\n", + "2024-12-07 08:59:07.221857: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 2048 totalling 6.0KiB\n", + "2024-12-07 08:59:07.221866: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 10240 totalling 10.0KiB\n", + "2024-12-07 08:59:07.221875: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 20992 totalling 20.5KiB\n", + "2024-12-07 08:59:07.221882: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 13 Chunks of size 32768 totalling 416.0KiB\n", + "2024-12-07 08:59:07.221890: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 43520 totalling 42.5KiB\n", + "2024-12-07 08:59:07.221898: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 59648 totalling 58.2KiB\n", + "2024-12-07 08:59:07.221907: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 64256 totalling 62.8KiB\n", + "2024-12-07 08:59:07.221916: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 77 Chunks of size 65536 totalling 4.81MiB\n", + "2024-12-07 08:59:07.221923: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 12 Chunks of size 83968 totalling 984.0KiB\n", + "2024-12-07 08:59:07.221930: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 97024 totalling 94.8KiB\n", + "2024-12-07 08:59:07.221938: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 99584 totalling 97.2KiB\n", + "2024-12-07 08:59:07.221945: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 101376 totalling 99.0KiB\n", + "2024-12-07 08:59:07.221954: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 115712 totalling 452.0KiB\n", + "2024-12-07 08:59:07.221962: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 124928 totalling 122.0KiB\n", + "2024-12-07 08:59:07.221971: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 130560 totalling 127.5KiB\n", + "2024-12-07 08:59:07.221978: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 53 Chunks of size 131072 totalling 6.62MiB\n", + "2024-12-07 08:59:07.221986: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 141056 totalling 137.8KiB\n", + "2024-12-07 08:59:07.221993: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 164608 totalling 160.8KiB\n", + "2024-12-07 08:59:07.222001: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 165888 totalling 162.0KiB\n", + "2024-12-07 08:59:07.222008: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 181248 totalling 177.0KiB\n", + "2024-12-07 08:59:07.222015: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 186368 totalling 182.0KiB\n", + "2024-12-07 08:59:07.222023: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 198656 totalling 194.0KiB\n", + "2024-12-07 08:59:07.222030: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 231424 totalling 452.0KiB\n", + "2024-12-07 08:59:07.222038: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 246784 totalling 482.0KiB\n", + "2024-12-07 08:59:07.222045: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 251904 totalling 492.0KiB\n", + "2024-12-07 08:59:07.222052: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 261120 totalling 255.0KiB\n", + "2024-12-07 08:59:07.222060: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 262144 totalling 512.0KiB\n", + "2024-12-07 08:59:07.222071: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 272640 totalling 266.2KiB\n", + "2024-12-07 08:59:07.222078: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 458752 totalling 1.75MiB\n", + "2024-12-07 08:59:07.222086: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 524288 totalling 512.0KiB\n", + "2024-12-07 08:59:07.222094: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 604672 totalling 590.5KiB\n", + "2024-12-07 08:59:07.222102: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 720384 totalling 703.5KiB\n", + "2024-12-07 08:59:07.222110: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 1343488 totalling 1.28MiB\n", + "2024-12-07 08:59:07.222118: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 5 Chunks of size 2686976 totalling 12.81MiB\n", + "2024-12-07 08:59:07.222125: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 5206016 totalling 4.96MiB\n", + "2024-12-07 08:59:07.222132: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 5373952 totalling 20.50MiB\n", + "2024-12-07 08:59:07.222140: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 8060928 totalling 7.69MiB\n", + "2024-12-07 08:59:07.222148: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 39 Chunks of size 10747904 totalling 399.75MiB\n", + "2024-12-07 08:59:07.222155: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12059136 totalling 11.50MiB\n", + "2024-12-07 08:59:07.222162: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12410880 totalling 11.84MiB\n", + "2024-12-07 08:59:07.222170: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 14816256 totalling 14.13MiB\n", + "2024-12-07 08:59:07.222177: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 16793600 totalling 32.03MiB\n", + "2024-12-07 08:59:07.222185: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 6 Chunks of size 21495808 totalling 123.00MiB\n", + "2024-12-07 08:59:07.222192: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 27541504 totalling 78.80MiB\n", + "2024-12-07 08:59:07.222199: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 31360000 totalling 29.91MiB\n", + "2024-12-07 08:59:07.222206: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 36157952 totalling 34.48MiB\n", + "2024-12-07 08:59:07.222212: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 101920000 totalling 97.20MiB\n", + "2024-12-07 08:59:07.222220: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 354368000 totalling 1.32GiB\n", + "2024-12-07 08:59:07.222228: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 385728000 totalling 1.44GiB\n", + "2024-12-07 08:59:07.222236: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 708736000 totalling 675.90MiB\n", + "2024-12-07 08:59:07.222243: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 771456000 totalling 735.72MiB\n", + "2024-12-07 08:59:07.222250: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 2303392000 totalling 2.14GiB\n", + "2024-12-07 08:59:07.222259: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 2507232000 totalling 2.33GiB\n", + "2024-12-07 08:59:07.222267: I tensorflow/tsl/framework/bfc_allocator.cc:1107] Sum Total of in-use chunks: 9.50GiB\n", + "2024-12-07 08:59:07.222275: I tensorflow/tsl/framework/bfc_allocator.cc:1109] Total bytes in pool: 10198122496 memory_limit_: 10198122496 available bytes: 0 curr_region_allocation_bytes_: 17179869184\n", + "2024-12-07 08:59:07.222287: I tensorflow/tsl/framework/bfc_allocator.cc:1114] Stats: \n", + "Limit: 10198122496\n", + "InUse: 10195919872\n", + "MaxInUse: 10195920896\n", + "NumAllocs: 43119401\n", + "MaxAllocSize: 2507232000\n", + "Reserved: 0\n", + "PeakReserved: 0\n", + "LargestFreeBlock: 0\n", + "\n", + "2024-12-07 08:59:07.222309: W tensorflow/tsl/framework/bfc_allocator.cc:497] ****************************************************************************************************\n", + "2024-12-07 08:59:07.222350: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at einsum_op_impl.h:604 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[512,8,41,41] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", + "2024-12-07 08:59:07.222417: W tensorflow/tsl/framework/bfc_allocator.cc:485] Allocator (GPU_0_bfc) ran out of memory trying to allocate 10.25MiB (rounded to 10747904)requested by op OilTransformer/dense_11/Tensordot/MatMul\n", + "If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \n", + "Current allocation summary follows.\n", + "Current allocation summary follows.\n", + "2024-12-07 08:59:07.222532: I tensorflow/tsl/framework/bfc_allocator.cc:1039] BFCAllocator dump for GPU_0_bfc\n", + "2024-12-07 08:59:07.222560: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (256): \tTotal Chunks: 197, Chunks in use: 196. 49.2KiB allocated for chunks. 49.0KiB in use in bin. 3.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222573: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (512): \tTotal Chunks: 171, Chunks in use: 166. 90.5KiB allocated for chunks. 87.5KiB in use in bin. 83.4KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222584: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1024): \tTotal Chunks: 63, Chunks in use: 59. 75.2KiB allocated for chunks. 70.0KiB in use in bin. 66.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222596: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2048): \tTotal Chunks: 6, Chunks in use: 3. 12.5KiB allocated for chunks. 6.0KiB in use in bin. 5.8KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222607: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4096): \tTotal Chunks: 1, Chunks in use: 0. 4.0KiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-12-07 08:59:07.222620: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8192): \tTotal Chunks: 1, Chunks in use: 1. 10.0KiB allocated for chunks. 10.0KiB in use in bin. 10.0KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222630: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16384): \tTotal Chunks: 1, Chunks in use: 1. 20.5KiB allocated for chunks. 20.5KiB in use in bin. 20.5KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222641: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (32768): \tTotal Chunks: 16, Chunks in use: 16. 579.5KiB allocated for chunks. 579.5KiB in use in bin. 512.0KiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222651: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (65536): \tTotal Chunks: 98, Chunks in use: 98. 6.74MiB allocated for chunks. 6.74MiB in use in bin. 6.54MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222661: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (131072): \tTotal Chunks: 66, Chunks in use: 66. 9.26MiB allocated for chunks. 9.26MiB in use in bin. 8.52MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222671: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (262144): \tTotal Chunks: 7, Chunks in use: 7. 2.51MiB allocated for chunks. 2.51MiB in use in bin. 2.47MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222681: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (524288): \tTotal Chunks: 3, Chunks in use: 3. 1.76MiB allocated for chunks. 1.76MiB in use in bin. 1.44MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222691: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (1048576): \tTotal Chunks: 1, Chunks in use: 1. 1.28MiB allocated for chunks. 1.28MiB in use in bin. 1.28MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222701: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (2097152): \tTotal Chunks: 6, Chunks in use: 5. 14.89MiB allocated for chunks. 12.81MiB in use in bin. 12.81MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222711: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (4194304): \tTotal Chunks: 6, Chunks in use: 6. 33.15MiB allocated for chunks. 33.15MiB in use in bin. 28.19MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222721: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (8388608): \tTotal Chunks: 42, Chunks in use: 42. 437.22MiB allocated for chunks. 437.22MiB in use in bin. 430.50MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222731: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (16777216): \tTotal Chunks: 12, Chunks in use: 12. 263.74MiB allocated for chunks. 263.74MiB in use in bin. 252.20MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222741: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (33554432): \tTotal Chunks: 1, Chunks in use: 1. 34.48MiB allocated for chunks. 34.48MiB in use in bin. 26.27MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222751: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (67108864): \tTotal Chunks: 1, Chunks in use: 1. 97.20MiB allocated for chunks. 97.20MiB in use in bin. 97.20MiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222761: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (134217728): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-12-07 08:59:07.222770: I tensorflow/tsl/framework/bfc_allocator.cc:1046] Bin (268435456): \tTotal Chunks: 12, Chunks in use: 12. 8.62GiB allocated for chunks. 8.62GiB in use in bin. 8.62GiB client-requested in use in bin.\n", + "2024-12-07 08:59:07.222783: I tensorflow/tsl/framework/bfc_allocator.cc:1062] Bin for 10.25MiB was 8.00MiB, Chunk State: \n", + "2024-12-07 08:59:07.222796: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 1608187904\n", + "2024-12-07 08:59:07.222809: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdd6000000 of size 385728000 next 704\n", + "2024-12-07 08:59:07.222818: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abdecfdbe00 of size 354368000 next 653\n", + "2024-12-07 08:59:07.222828: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe021cf800 of size 385728000 next 455\n", + "2024-12-07 08:59:07.222837: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe191ab600 of size 10747904 next 89\n", + "2024-12-07 08:59:07.222847: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe19beb600 of size 10747904 next 563\n", + "2024-12-07 08:59:07.222856: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1a62b600 of size 16793600 next 544\n", + "2024-12-07 08:59:07.222865: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1b62f600 of size 27541504 next 40\n", + "2024-12-07 08:59:07.222876: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d073600 of size 83968 next 667\n", + "2024-12-07 08:59:07.222884: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1d087e00 of size 10747904 next 615\n", + "2024-12-07 08:59:07.222893: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dac7e00 of size 83968 next 759\n", + "2024-12-07 08:59:07.222902: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dadc600 of size 5373952 next 103\n", + "2024-12-07 08:59:07.222911: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1dffc600 of size 2686976 next 713\n", + "2024-12-07 08:59:07.222920: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e28c600 of size 83968 next 525\n", + "2024-12-07 08:59:07.222929: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2a0e00 of size 83968 next 705\n", + "2024-12-07 08:59:07.222938: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2b5600 of size 83968 next 471\n", + "2024-12-07 08:59:07.222947: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2c9e00 of size 83968 next 645\n", + "2024-12-07 08:59:07.222955: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2de600 of size 83968 next 458\n", + "2024-12-07 08:59:07.222963: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e2f2e00 of size 83968 next 621\n", + "2024-12-07 08:59:07.222972: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7abe1e307600 of size 2183168 next 703\n", + "2024-12-07 08:59:07.222980: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1e51c600 of size 10747904 next 715\n", + "2024-12-07 08:59:07.222989: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe1ef5c600 of size 21495808 next 675\n", + "2024-12-07 08:59:07.222997: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe203dc600 of size 21495808 next 750\n", + "2024-12-07 08:59:07.223006: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2185c600 of size 10747904 next 588\n", + "2024-12-07 08:59:07.223015: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2229c600 of size 10747904 next 497\n", + "2024-12-07 08:59:07.223023: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe22cdc600 of size 10747904 next 722\n", + "2024-12-07 08:59:07.223032: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2371c600 of size 10747904 next 479\n", + "2024-12-07 08:59:07.223041: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2415c600 of size 10747904 next 121\n", + "2024-12-07 08:59:07.223049: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe24b9c600 of size 10747904 next 419\n", + "2024-12-07 08:59:07.223056: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe255dc600 of size 10747904 next 507\n", + "2024-12-07 08:59:07.223065: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2601c600 of size 2686976 next 721\n", + "2024-12-07 08:59:07.223074: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe262ac600 of size 8060928 next 599\n", + "2024-12-07 08:59:07.223082: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe26a5c600 of size 10747904 next 541\n", + "2024-12-07 08:59:07.223091: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2749c600 of size 16793600 next 669\n", + "2024-12-07 08:59:07.223100: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe284a0600 of size 27541504 next 719\n", + "2024-12-07 08:59:07.223108: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe29ee4600 of size 10747904 next 517\n", + "2024-12-07 08:59:07.223118: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2a924600 of size 10747904 next 681\n", + "2024-12-07 08:59:07.223126: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2b364600 of size 10747904 next 432\n", + "2024-12-07 08:59:07.223135: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2bda4600 of size 10747904 next 95\n", + "2024-12-07 08:59:07.223144: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2c7e4600 of size 21495808 next 565\n", + "2024-12-07 08:59:07.223152: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2dc64600 of size 21495808 next 724\n", + "2024-12-07 08:59:07.223160: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2f0e4600 of size 10747904 next 66\n", + "2024-12-07 08:59:07.223168: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fb24600 of size 2686976 next 747\n", + "2024-12-07 08:59:07.223177: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe2fdb4600 of size 10747904 next 533\n", + "2024-12-07 08:59:07.223185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe307f4600 of size 10747904 next 571\n", + "2024-12-07 08:59:07.223193: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31234600 of size 10747904 next 488\n", + "2024-12-07 08:59:07.223201: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe31c74600 of size 10747904 next 710\n", + "2024-12-07 08:59:07.223210: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe326b4600 of size 10747904 next 552\n", + "2024-12-07 08:59:07.223219: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe330f4600 of size 10747904 next 46\n", + "2024-12-07 08:59:07.223227: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7abe33b34600 of size 36157952 next 18446744073709551615\n", + "2024-12-07 08:59:07.223236: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296\n", + "2024-12-07 08:59:07.223244: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad3f2000000 of size 2507232000 next 4\n", + "2024-12-07 08:59:07.223253: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad487715300 of size 101920000 next 5\n", + "2024-12-07 08:59:07.223265: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848000 of size 256 next 6\n", + "2024-12-07 08:59:07.223273: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848100 of size 256 next 7\n", + "2024-12-07 08:59:07.223281: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848200 of size 256 next 8\n", + "2024-12-07 08:59:07.223290: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848300 of size 256 next 9\n", + "2024-12-07 08:59:07.223299: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848400 of size 256 next 10\n", + "2024-12-07 08:59:07.223308: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad48d848500 of size 708736000 next 11\n", + "2024-12-07 08:59:07.223318: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4b7c2f900 of size 771456000 next 12\n", + "2024-12-07 08:59:07.223327: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e5be7500 of size 31360000 next 13\n", + "2024-12-07 08:59:07.223336: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79cf900 of size 256 next 14\n", + 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256 next 20\n", + "2024-12-07 08:59:07.223424: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0200 of size 256 next 586\n", + "2024-12-07 08:59:07.223433: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0300 of size 256 next 27\n", + "2024-12-07 08:59:07.223441: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0400 of size 256 next 23\n", + "2024-12-07 08:59:07.223450: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0500 of size 256 next 26\n", + "2024-12-07 08:59:07.223459: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0600 of size 256 next 30\n", + "2024-12-07 08:59:07.223468: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0700 of size 256 next 24\n", + "2024-12-07 08:59:07.223475: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0800 of size 256 next 79\n", + "2024-12-07 08:59:07.223484: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0900 of size 256 next 592\n", + "2024-12-07 08:59:07.223493: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0a00 of size 256 next 25\n", + "2024-12-07 08:59:07.223502: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0b00 of size 256 next 31\n", + "2024-12-07 08:59:07.223510: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0c00 of size 256 next 32\n", + "2024-12-07 08:59:07.223519: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0d00 of size 256 next 57\n", + "2024-12-07 08:59:07.223527: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0e00 of size 256 next 24960\n", + "2024-12-07 08:59:07.223536: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d0f00 of size 256 next 24951\n", + "2024-12-07 08:59:07.223545: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1000 of size 256 next 24929\n", + "2024-12-07 08:59:07.223553: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1100 of size 256 next 584\n", + "2024-12-07 08:59:07.223561: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1200 of size 256 next 41\n", + "2024-12-07 08:59:07.223569: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1300 of size 256 next 35\n", + "2024-12-07 08:59:07.223578: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1400 of size 256 next 36\n", + "2024-12-07 08:59:07.223586: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1500 of size 256 next 49\n", + "2024-12-07 08:59:07.223595: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1600 of size 256 next 45\n", + "2024-12-07 08:59:07.223603: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1700 of size 256 next 43\n", + "2024-12-07 08:59:07.223610: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1800 of size 256 next 44\n", + "2024-12-07 08:59:07.223619: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1900 of size 256 next 24946\n", + "2024-12-07 08:59:07.223627: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1a00 of size 256 next 604\n", + "2024-12-07 08:59:07.223636: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1b00 of size 256 next 435\n", + "2024-12-07 08:59:07.223646: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1c00 of size 256 next 564\n", + "2024-12-07 08:59:07.223655: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1d00 of size 256 next 523\n", + "2024-12-07 08:59:07.223664: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1e00 of size 256 next 48\n", + "2024-12-07 08:59:07.223672: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d1f00 of size 256 next 51\n", + "2024-12-07 08:59:07.223681: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d2000 of size 256 next 52\n", + "2024-12-07 08:59:07.223689: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d2100 of size 512 next 634\n", + "2024-12-07 08:59:07.223698: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d2300 of size 1024 next 514\n", + "2024-12-07 08:59:07.223707: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d2700 of size 1792 next 96\n", + "2024-12-07 08:59:07.223715: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d2e00 of size 1024 next 647\n", + "2024-12-07 08:59:07.223724: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3200 of size 256 next 589\n", + "2024-12-07 08:59:07.223732: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3300 of size 1280 next 506\n", + "2024-12-07 08:59:07.223742: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3800 of size 256 next 611\n", + "2024-12-07 08:59:07.223751: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3900 of size 256 next 172\n", + "2024-12-07 08:59:07.223760: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3a00 of size 256 next 677\n", + "2024-12-07 08:59:07.223768: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3b00 of size 256 next 613\n", + "2024-12-07 08:59:07.223776: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3c00 of size 256 next 489\n", + "2024-12-07 08:59:07.223785: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3d00 of size 256 next 24973\n", + "2024-12-07 08:59:07.223792: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3e00 of size 256 next 124\n", + "2024-12-07 08:59:07.223801: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d3f00 of size 256 next 24979\n", + "2024-12-07 08:59:07.223810: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4000 of size 256 next 453\n", + "2024-12-07 08:59:07.223819: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4100 of size 256 next 651\n", + "2024-12-07 08:59:07.223829: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4200 of size 256 next 658\n", + "2024-12-07 08:59:07.223847: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4300 of size 256 next 642\n", + "2024-12-07 08:59:07.223855: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4400 of size 768 next 135\n", + "2024-12-07 08:59:07.223864: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4700 of size 768 next 691\n", + "2024-12-07 08:59:07.223873: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4a00 of size 768 next 560\n", + "2024-12-07 08:59:07.223881: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d4d00 of size 768 next 428\n", + "2024-12-07 08:59:07.223890: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5000 of size 512 next 64\n", + "2024-12-07 08:59:07.223898: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5200 of size 512 next 527\n", + "2024-12-07 08:59:07.223907: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5400 of size 256 next 143\n", + "2024-12-07 08:59:07.223916: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5500 of size 512 next 486\n", + "2024-12-07 08:59:07.223925: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5700 of size 512 next 501\n", + "2024-12-07 08:59:07.223934: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5900 of size 768 next 539\n", + "2024-12-07 08:59:07.223943: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5c00 of size 768 next 60\n", + "2024-12-07 08:59:07.223951: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d5f00 of size 512 next 423\n", + "2024-12-07 08:59:07.223960: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6100 of size 256 next 173\n", + "2024-12-07 08:59:07.223968: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6200 of size 256 next 590\n", + "2024-12-07 08:59:07.223977: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6300 of size 512 next 98\n", + "2024-12-07 08:59:07.223986: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6500 of size 512 next 531\n", + "2024-12-07 08:59:07.223999: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6700 of size 768 next 663\n", + "2024-12-07 08:59:07.224008: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6a00 of size 768 next 548\n", + "2024-12-07 08:59:07.224016: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6d00 of size 512 next 131\n", + "2024-12-07 08:59:07.224025: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d6f00 of size 512 next 482\n", + "2024-12-07 08:59:07.224035: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7100 of size 512 next 109\n", + "2024-12-07 08:59:07.224043: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7300 of size 256 next 115\n", + "2024-12-07 08:59:07.224052: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7400 of size 256 next 116\n", + "2024-12-07 08:59:07.224061: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7500 of size 256 next 741\n", + "2024-12-07 08:59:07.224069: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7600 of size 512 next 86\n", + "2024-12-07 08:59:07.224078: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79d7800 of size 768 next 491\n", + "2024-12-07 08:59:07.224088: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7b00 of size 256 next 502\n", + "2024-12-07 08:59:07.224099: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7c00 of size 256 next 24995\n", + "2024-12-07 08:59:07.224108: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7d00 of size 256 next 641\n", + "2024-12-07 08:59:07.224117: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7e00 of size 256 next 81\n", + "2024-12-07 08:59:07.224126: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d7f00 of size 256 next 577\n", + "2024-12-07 08:59:07.224133: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8000 of size 256 next 123\n", + "2024-12-07 08:59:07.224142: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8100 of size 256 next 117\n", + "2024-12-07 08:59:07.224151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8200 of size 256 next 118\n", + "2024-12-07 08:59:07.224164: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8300 of size 256 next 438\n", + "2024-12-07 08:59:07.224173: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8400 of size 256 next 505\n", + "2024-12-07 08:59:07.224182: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8500 of size 256 next 628\n", + "2024-12-07 08:59:07.224190: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8600 of size 256 next 127\n", + "2024-12-07 08:59:07.224199: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8700 of size 256 next 113\n", + "2024-12-07 08:59:07.224208: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8800 of size 256 next 126\n", + "2024-12-07 08:59:07.224218: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8900 of size 256 next 129\n", + "2024-12-07 08:59:07.224227: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8a00 of size 256 next 130\n", + "2024-12-07 08:59:07.224235: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8b00 of size 256 next 164\n", + "2024-12-07 08:59:07.224244: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8c00 of size 256 next 499\n", + "2024-12-07 08:59:07.224253: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8d00 of size 256 next 114\n", + "2024-12-07 08:59:07.224262: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8e00 of size 256 next 451\n", + "2024-12-07 08:59:07.224271: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d8f00 of size 256 next 542\n", + "2024-12-07 08:59:07.224279: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9000 of size 256 next 593\n", + "2024-12-07 08:59:07.224288: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9100 of size 256 next 450\n", + "2024-12-07 08:59:07.224297: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9200 of size 256 next 480\n", + "2024-12-07 08:59:07.224306: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9300 of size 256 next 550\n", + "2024-12-07 08:59:07.224315: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9400 of size 512 next 102\n", + "2024-12-07 08:59:07.224323: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9600 of size 512 next 554\n", + "2024-12-07 08:59:07.224332: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9800 of size 1280 next 643\n", + "2024-12-07 08:59:07.224340: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79d9d00 of size 768 next 132\n", + "2024-12-07 08:59:07.224349: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79da000 of size 1792 next 139\n", + "2024-12-07 08:59:07.224374: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79da700 of size 1792 next 140\n", + "2024-12-07 08:59:07.224384: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dae00 of size 256 next 141\n", + "2024-12-07 08:59:07.224393: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79daf00 of size 256 next 142\n", + "2024-12-07 08:59:07.224402: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db000 of size 256 next 598\n", + "2024-12-07 08:59:07.224410: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db100 of size 256 next 463\n", + "2024-12-07 08:59:07.224419: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db200 of size 256 next 692\n", + "2024-12-07 08:59:07.224428: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db300 of size 256 next 467\n", + "2024-12-07 08:59:07.224436: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db400 of size 256 next 690\n", + "2024-12-07 08:59:07.224445: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db500 of size 256 next 466\n", + "2024-12-07 08:59:07.224454: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db600 of size 256 next 616\n", + "2024-12-07 08:59:07.224462: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db700 of size 256 next 24947\n", + "2024-12-07 08:59:07.224471: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db800 of size 256 next 559\n", + "2024-12-07 08:59:07.224479: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79db900 of size 256 next 633\n", + "2024-12-07 08:59:07.224488: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dba00 of size 256 next 24980\n", + "2024-12-07 08:59:07.224497: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dbb00 of size 256 next 144\n", + "2024-12-07 08:59:07.224506: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dbc00 of size 1024 next 148\n", + "2024-12-07 08:59:07.224516: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc000 of size 1024 next 149\n", + "2024-12-07 08:59:07.224524: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc400 of size 1280 next 676\n", + "2024-12-07 08:59:07.224533: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dc900 of size 256 next 151\n", + "2024-12-07 08:59:07.224542: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dca00 of size 512 next 154\n", + "2024-12-07 08:59:07.224551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dcc00 of size 512 next 155\n", + "2024-12-07 08:59:07.224559: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dce00 of size 256 next 156\n", + "2024-12-07 08:59:07.224567: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dcf00 of size 256 next 157\n", + "2024-12-07 08:59:07.224576: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd000 of size 256 next 158\n", + "2024-12-07 08:59:07.224584: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd100 of size 256 next 161\n", + "2024-12-07 08:59:07.224593: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd200 of size 256 next 162\n", + "2024-12-07 08:59:07.224601: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd300 of size 256 next 160\n", + "2024-12-07 08:59:07.224609: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd400 of size 256 next 169\n", + "2024-12-07 08:59:07.224618: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd500 of size 256 next 166\n", + "2024-12-07 08:59:07.224627: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd600 of size 256 next 167\n", + "2024-12-07 08:59:07.224635: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd700 of size 256 next 168\n", + "2024-12-07 08:59:07.224644: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd800 of size 256 next 163\n", + "2024-12-07 08:59:07.224652: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dd900 of size 256 next 170\n", + "2024-12-07 08:59:07.224661: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dda00 of size 256 next 171\n", + "2024-12-07 08:59:07.224670: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79ddb00 of size 2048 next 165\n", + "2024-12-07 08:59:07.224678: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de300 of size 256 next 174\n", + "2024-12-07 08:59:07.224687: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de400 of size 256 next 175\n", + "2024-12-07 08:59:07.224696: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de500 of size 256 next 176\n", + "2024-12-07 08:59:07.224704: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de600 of size 768 next 177\n", + "2024-12-07 08:59:07.224713: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79de900 of size 768 next 178\n", + "2024-12-07 08:59:07.224722: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dec00 of size 256 next 179\n", + "2024-12-07 08:59:07.224731: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79ded00 of size 256 next 180\n", + "2024-12-07 08:59:07.224739: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dee00 of size 512 next 181\n", + "2024-12-07 08:59:07.224748: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df000 of size 512 next 182\n", + "2024-12-07 08:59:07.224756: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df200 of size 512 next 185\n", + "2024-12-07 08:59:07.224765: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df400 of size 512 next 186\n", + "2024-12-07 08:59:07.224774: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df600 of size 512 next 188\n", + "2024-12-07 08:59:07.224782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79df800 of size 512 next 189\n", + "2024-12-07 08:59:07.224790: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfa00 of size 512 next 192\n", + "2024-12-07 08:59:07.224798: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfc00 of size 512 next 193\n", + "2024-12-07 08:59:07.224807: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79dfe00 of size 512 next 196\n", + "2024-12-07 08:59:07.224815: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0000 of size 512 next 197\n", + "2024-12-07 08:59:07.224823: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0200 of size 512 next 200\n", + "2024-12-07 08:59:07.224832: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0400 of size 512 next 201\n", + "2024-12-07 08:59:07.224840: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0600 of size 512 next 202\n", + "2024-12-07 08:59:07.224848: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0800 of size 768 next 29\n", + "2024-12-07 08:59:07.224857: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0b00 of size 512 next 78\n", + "2024-12-07 08:59:07.224865: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0d00 of size 512 next 558\n", + "2024-12-07 08:59:07.224874: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e0f00 of size 512 next 657\n", + "2024-12-07 08:59:07.224883: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1100 of size 768 next 528\n", + "2024-12-07 08:59:07.224891: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1400 of size 512 next 33\n", + "2024-12-07 08:59:07.224899: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1600 of size 768 next 664\n", + "2024-12-07 08:59:07.224907: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1900 of size 256 next 515\n", + "2024-12-07 08:59:07.224916: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1a00 of size 256 next 723\n", + "2024-12-07 08:59:07.224924: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1b00 of size 256 next 24991\n", + "2024-12-07 08:59:07.224933: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1c00 of size 256 next 99\n", + "2024-12-07 08:59:07.224941: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e1d00 of size 512 next 152\n", + "2024-12-07 08:59:07.224950: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e1f00 of size 1792 next 92\n", + "2024-12-07 08:59:07.224966: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2600 of size 256 next 462\n", + "2024-12-07 08:59:07.224974: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2700 of size 256 next 614\n", + "2024-12-07 08:59:07.224982: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2800 of size 256 next 557\n", + "2024-12-07 08:59:07.224991: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2900 of size 256 next 521\n", + "2024-12-07 08:59:07.224999: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2a00 of size 256 next 659\n", + "2024-12-07 08:59:07.225007: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2b00 of size 256 next 24934\n", + "2024-12-07 08:59:07.225016: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2c00 of size 256 next 543\n", + "2024-12-07 08:59:07.225026: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e2d00 of size 768 next 568\n", + "2024-12-07 08:59:07.225034: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e3000 of size 768 next 639\n", + "2024-12-07 08:59:07.225042: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3300 of size 512 next 53\n", + "2024-12-07 08:59:07.225051: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3500 of size 1024 next 606\n", + "2024-12-07 08:59:07.225060: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3900 of size 256 next 575\n", + "2024-12-07 08:59:07.225069: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3a00 of size 256 next 607\n", + "2024-12-07 08:59:07.225078: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e3b00 of size 1280 next 420\n", + "2024-12-07 08:59:07.225087: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4000 of size 256 next 689\n", + "2024-12-07 08:59:07.225099: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4100 of size 2048 next 465\n", + "2024-12-07 08:59:07.225108: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4900 of size 256 next 712\n", + "2024-12-07 08:59:07.225116: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4a00 of size 256 next 468\n", + "2024-12-07 08:59:07.225124: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4b00 of size 256 next 742\n", + "2024-12-07 08:59:07.225133: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4c00 of size 256 next 578\n", + "2024-12-07 08:59:07.225142: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4d00 of size 512 next 39\n", + "2024-12-07 08:59:07.225151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e4f00 of size 512 next 635\n", + "2024-12-07 08:59:07.225159: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5100 of size 256 next 446\n", + "2024-12-07 08:59:07.225168: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e5200 of size 2048 next 540\n", + "2024-12-07 08:59:07.225176: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5a00 of size 768 next 34\n", + "2024-12-07 08:59:07.225185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5d00 of size 256 next 623\n", + "2024-12-07 08:59:07.225194: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e5e00 of size 512 next 492\n", + "2024-12-07 08:59:07.225203: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e6000 of size 512 next 702\n", + 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size 512 next 576\n", + "2024-12-07 08:59:07.225280: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e79e7300 of size 512 next 583\n", + "2024-12-07 08:59:07.225289: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7500 of size 256 next 487\n", + "2024-12-07 08:59:07.225297: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7600 of size 256 next 522\n", + "2024-12-07 08:59:07.225306: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7700 of size 256 next 459\n", + "2024-12-07 08:59:07.225314: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7800 of size 256 next 654\n", + "2024-12-07 08:59:07.225323: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7900 of size 768 next 596\n", + "2024-12-07 08:59:07.225335: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7c00 of size 512 next 648\n", + "2024-12-07 08:59:07.225344: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e7e00 of size 512 next 625\n", + "2024-12-07 08:59:07.225354: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8000 of size 1280 next 735\n", + "2024-12-07 08:59:07.225373: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8500 of size 512 next 652\n", + "2024-12-07 08:59:07.225382: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8700 of size 512 next 457\n", + "2024-12-07 08:59:07.225391: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8900 of size 512 next 28\n", + "2024-12-07 08:59:07.225400: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79e8b00 of size 20992 next 37\n", + "2024-12-07 08:59:07.225409: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79edd00 of size 32768 next 159\n", + "2024-12-07 08:59:07.225418: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e79f5d00 of size 246784 next 619\n", + "2024-12-07 08:59:07.225426: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a32100 of size 164608 next 566\n", + "2024-12-07 08:59:07.225434: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5a400 of size 256 next 42\n", + "2024-12-07 08:59:07.225448: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5a500 of size 256 next 546\n", + "2024-12-07 08:59:07.225457: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5a600 of size 4096 next 644\n", + "2024-12-07 08:59:07.225466: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5b600 of size 512 next 569\n", + "2024-12-07 08:59:07.225474: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5b800 of size 1536 next 421\n", + "2024-12-07 08:59:07.225482: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5be00 of size 512 next 474\n", + "2024-12-07 08:59:07.225490: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5c000 of size 512 next 624\n", + 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size 512 next 431\n", + "2024-12-07 08:59:07.225561: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5db00 of size 512 next 145\n", + "2024-12-07 08:59:07.225567: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5dd00 of size 512 next 481\n", + "2024-12-07 08:59:07.225573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5df00 of size 512 next 464\n", + "2024-12-07 08:59:07.225579: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e100 of size 1536 next 442\n", + "2024-12-07 08:59:07.225585: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e700 of size 512 next 730\n", + "2024-12-07 08:59:07.225591: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5e900 of size 256 next 518\n", + "2024-12-07 08:59:07.225597: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ea00 of size 512 next 110\n", + "2024-12-07 08:59:07.225603: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a5ec00 of size 256 next 494\n", + "2024-12-07 08:59:07.225609: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ed00 of size 256 next 100\n", + "2024-12-07 08:59:07.225615: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a5ee00 of size 32768 next 693\n", + "2024-12-07 08:59:07.225621: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a66e00 of size 1024 next 696\n", + "2024-12-07 08:59:07.225627: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a67200 of size 512 next 470\n", + "2024-12-07 08:59:07.225634: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a67400 of size 59648 next 138\n", + "2024-12-07 08:59:07.225641: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a75d00 of size 32768 next 56\n", + "2024-12-07 08:59:07.225648: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a7dd00 of size 65536 next 629\n", + "2024-12-07 08:59:07.225653: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7a8dd00 of size 1024 next 666\n", + "2024-12-07 08:59:07.225660: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7a8e100 of size 130560 next 493\n", + "2024-12-07 08:59:07.225667: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7aadf00 of size 65536 next 80\n", + "2024-12-07 08:59:07.225673: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7abdf00 of size 131072 next 38\n", + "2024-12-07 08:59:07.225683: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7addf00 of size 512 next 545\n", + "2024-12-07 08:59:07.225689: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ade100 of size 65536 next 697\n", + "2024-12-07 08:59:07.225696: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7aee100 of size 65536 next 637\n", + "2024-12-07 08:59:07.225702: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7afe100 of size 115712 next 58\n", + "2024-12-07 08:59:07.225709: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b1a500 of size 512 next 108\n", + "2024-12-07 08:59:07.225715: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b1a700 of size 131072 next 439\n", + "2024-12-07 08:59:07.225721: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b3a700 of size 131072 next 74\n", + "2024-12-07 08:59:07.225728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b5a700 of size 1024 next 601\n", + "2024-12-07 08:59:07.225733: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b5ab00 of size 65536 next 424\n", + "2024-12-07 08:59:07.225739: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b6ab00 of size 512 next 609\n", + "2024-12-07 08:59:07.225746: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b6ad00 of size 131072 next 685\n", + "2024-12-07 08:59:07.225752: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b8ad00 of size 1024 next 504\n", + "2024-12-07 08:59:07.225758: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b8b100 of size 32768 next 136\n", + "2024-12-07 08:59:07.225764: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b93100 of size 1792 next 475\n", + "2024-12-07 08:59:07.225770: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7b93800 of size 65536 next 67\n", + "2024-12-07 08:59:07.225777: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ba3800 of size 131072 next 672\n", + "2024-12-07 08:59:07.225782: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4e7bc3800 of size 512 next 580\n", + "2024-12-07 08:59:07.225789: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bc3a00 of size 1024 next 707\n", + "2024-12-07 08:59:07.225795: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bc3e00 of size 198656 next 600\n", + "2024-12-07 08:59:07.225802: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf4600 of size 1792 next 679\n", + "2024-12-07 08:59:07.225808: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf4d00 of size 1024 next 441\n", + "2024-12-07 08:59:07.225814: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7bf5100 of size 101376 next 106\n", + "2024-12-07 08:59:07.225820: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c0dd00 of size 65536 next 183\n", + "2024-12-07 08:59:07.225827: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c1dd00 of size 65536 next 184\n", + "2024-12-07 08:59:07.225833: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c2dd00 of size 65536 next 187\n", + "2024-12-07 08:59:07.225840: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c3dd00 of size 65536 next 112\n", + "2024-12-07 08:59:07.225846: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c4dd00 of size 65536 next 740\n", + "2024-12-07 08:59:07.225852: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c5dd00 of size 131072 next 436\n", + "2024-12-07 08:59:07.225861: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c7dd00 of size 131072 next 430\n", + "2024-12-07 08:59:07.225868: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7c9dd00 of size 65536 next 526\n", + "2024-12-07 08:59:07.225874: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cadd00 of size 65536 next 678\n", + "2024-12-07 08:59:07.225880: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cbdd00 of size 65536 next 508\n", + "2024-12-07 08:59:07.225886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ccdd00 of size 65536 next 731\n", + "2024-12-07 08:59:07.225892: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cddd00 of size 65536 next 627\n", + "2024-12-07 08:59:07.225899: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7cedd00 of size 131072 next 537\n", + "2024-12-07 08:59:07.225905: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d0dd00 of size 65536 next 478\n", + "2024-12-07 08:59:07.225911: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d1dd00 of size 65536 next 503\n", + "2024-12-07 08:59:07.225917: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d2dd00 of size 65536 next 594\n", + "2024-12-07 08:59:07.225923: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d3dd00 of size 181248 next 153\n", + "2024-12-07 08:59:07.225930: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d6a100 of size 65536 next 190\n", + "2024-12-07 08:59:07.225937: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d7a100 of size 65536 next 191\n", + "2024-12-07 08:59:07.225942: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d8a100 of size 65536 next 194\n", + "2024-12-07 08:59:07.225949: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7d9a100 of size 65536 next 195\n", + "2024-12-07 08:59:07.225954: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7daa100 of size 65536 next 198\n", + "2024-12-07 08:59:07.225961: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dba100 of size 65536 next 199\n", + "2024-12-07 08:59:07.225967: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca100 of size 512 next 203\n", + "2024-12-07 08:59:07.225972: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca300 of size 512 next 204\n", + "2024-12-07 08:59:07.225979: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dca500 of size 131072 next 205\n", + "2024-12-07 08:59:07.225985: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7dea500 of size 261120 next 147\n", + "2024-12-07 08:59:07.225993: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a100 of size 256 next 620\n", + "2024-12-07 08:59:07.225999: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a200 of size 256 next 612\n", + "2024-12-07 08:59:07.226005: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e2a300 of size 131072 next 682\n", + "2024-12-07 08:59:07.226011: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e4a300 of size 32768 next 709\n", + "2024-12-07 08:59:07.226017: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e52300 of size 32768 next 698\n", + "2024-12-07 08:59:07.226023: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e5a300 of size 115712 next 88\n", + "2024-12-07 08:59:07.226030: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e76700 of size 1280 next 456\n", + "2024-12-07 08:59:07.226036: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e76c00 of size 64256 next 125\n", + "2024-12-07 08:59:07.226042: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e86700 of size 32768 next 75\n", + "2024-12-07 08:59:07.226052: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8e700 of size 1024 next 535\n", + "2024-12-07 08:59:07.226058: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8eb00 of size 1024 next 562\n", + "2024-12-07 08:59:07.226065: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8ef00 of size 1024 next 461\n", + "2024-12-07 08:59:07.226071: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8f300 of size 1024 next 656\n", + "2024-12-07 08:59:07.226077: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e8f700 of size 43520 next 146\n", + "2024-12-07 08:59:07.226083: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a100 of size 1024 next 206\n", + "2024-12-07 08:59:07.226090: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a500 of size 1024 next 207\n", + "2024-12-07 08:59:07.226096: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7e9a900 of size 131072 next 208\n", + "2024-12-07 08:59:07.226102: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eba900 of size 131072 next 209\n", + "2024-12-07 08:59:07.226111: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eda900 of size 512 next 210\n", + "2024-12-07 08:59:07.226117: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edab00 of size 512 next 211\n", + "2024-12-07 08:59:07.226123: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edad00 of size 512 next 212\n", + "2024-12-07 08:59:07.226129: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edaf00 of size 512 next 213\n", + "2024-12-07 08:59:07.226135: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb100 of size 512 next 214\n", + "2024-12-07 08:59:07.226141: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb300 of size 512 next 215\n", + "2024-12-07 08:59:07.226147: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7edb500 of size 65536 next 216\n", + "2024-12-07 08:59:07.226154: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7eeb500 of size 65536 next 217\n", + "2024-12-07 08:59:07.226160: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb500 of size 512 next 218\n", + "2024-12-07 08:59:07.226166: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb700 of size 512 next 219\n", + "2024-12-07 08:59:07.226172: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7efb900 of size 65536 next 220\n", + "2024-12-07 08:59:07.226178: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f0b900 of size 65536 next 221\n", + "2024-12-07 08:59:07.226185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1b900 of size 512 next 222\n", + "2024-12-07 08:59:07.226191: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1bb00 of size 512 next 223\n", + "2024-12-07 08:59:07.226197: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f1bd00 of size 65536 next 224\n", + "2024-12-07 08:59:07.226204: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f2bd00 of size 65536 next 225\n", + "2024-12-07 08:59:07.226210: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3bd00 of size 512 next 226\n", + "2024-12-07 08:59:07.226220: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3bf00 of size 512 next 227\n", + "2024-12-07 08:59:07.226226: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f3c100 of size 65536 next 228\n", + "2024-12-07 08:59:07.226233: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f4c100 of size 65536 next 229\n", + "2024-12-07 08:59:07.226239: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c100 of size 512 next 230\n", + "2024-12-07 08:59:07.226245: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c300 of size 512 next 231\n", + "2024-12-07 08:59:07.226250: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f5c500 of size 65536 next 232\n", + "2024-12-07 08:59:07.226257: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f6c500 of size 65536 next 233\n", + "2024-12-07 08:59:07.226263: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c500 of size 512 next 234\n", + "2024-12-07 08:59:07.226270: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c700 of size 512 next 235\n", + "2024-12-07 08:59:07.226275: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7c900 of size 512 next 236\n", + "2024-12-07 08:59:07.226282: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cb00 of size 512 next 237\n", + "2024-12-07 08:59:07.226288: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cd00 of size 512 next 238\n", + "2024-12-07 08:59:07.226294: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7cf00 of size 512 next 239\n", + "2024-12-07 08:59:07.226300: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f7d100 of size 131072 next 240\n", + "2024-12-07 08:59:07.226306: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7f9d100 of size 131072 next 241\n", + "2024-12-07 08:59:07.226313: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd100 of size 1024 next 242\n", + "2024-12-07 08:59:07.226319: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd500 of size 1024 next 243\n", + "2024-12-07 08:59:07.226325: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fbd900 of size 131072 next 244\n", + "2024-12-07 08:59:07.226332: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7fdd900 of size 131072 next 245\n", + "2024-12-07 08:59:07.226338: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffd900 of size 512 next 246\n", + "2024-12-07 08:59:07.226344: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdb00 of size 512 next 247\n", + "2024-12-07 08:59:07.226350: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdd00 of size 512 next 248\n", + "2024-12-07 08:59:07.226356: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffdf00 of size 512 next 249\n", + "2024-12-07 08:59:07.226372: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe100 of size 512 next 250\n", + "2024-12-07 08:59:07.226378: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe300 of size 512 next 251\n", + "2024-12-07 08:59:07.226384: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e7ffe500 of size 65536 next 252\n", + "2024-12-07 08:59:07.226391: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e800e500 of size 65536 next 253\n", + "2024-12-07 08:59:07.226397: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e500 of size 512 next 254\n", + "2024-12-07 08:59:07.226403: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e700 of size 512 next 255\n", + "2024-12-07 08:59:07.226409: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e801e900 of size 65536 next 256\n", + "2024-12-07 08:59:07.226415: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e802e900 of size 65536 next 257\n", + "2024-12-07 08:59:07.226421: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803e900 of size 512 next 258\n", + "2024-12-07 08:59:07.226427: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803eb00 of size 512 next 259\n", + "2024-12-07 08:59:07.226434: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e803ed00 of size 65536 next 260\n", + "2024-12-07 08:59:07.226440: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e804ed00 of size 65536 next 261\n", + "2024-12-07 08:59:07.226446: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805ed00 of size 512 next 262\n", + "2024-12-07 08:59:07.226452: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805ef00 of size 512 next 263\n", + "2024-12-07 08:59:07.226458: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e805f100 of size 65536 next 264\n", + "2024-12-07 08:59:07.226464: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e806f100 of size 65536 next 265\n", + "2024-12-07 08:59:07.226470: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f100 of size 512 next 266\n", + "2024-12-07 08:59:07.226477: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f300 of size 512 next 267\n", + "2024-12-07 08:59:07.226482: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e807f500 of size 65536 next 268\n", + "2024-12-07 08:59:07.226489: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e808f500 of size 65536 next 269\n", + "2024-12-07 08:59:07.226497: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f500 of size 512 next 270\n", + "2024-12-07 08:59:07.226505: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f700 of size 512 next 271\n", + "2024-12-07 08:59:07.226513: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809f900 of size 512 next 272\n", + "2024-12-07 08:59:07.226522: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809fb00 of size 512 next 273\n", + "2024-12-07 08:59:07.226530: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809fd00 of size 512 next 274\n", + "2024-12-07 08:59:07.226539: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e809ff00 of size 512 next 275\n", + "2024-12-07 08:59:07.226548: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80a0100 of size 131072 next 276\n", + "2024-12-07 08:59:07.226556: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80c0100 of size 131072 next 277\n", + "2024-12-07 08:59:07.226565: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0100 of size 1024 next 278\n", + "2024-12-07 08:59:07.226573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0500 of size 1024 next 279\n", + "2024-12-07 08:59:07.226580: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e80e0900 of size 131072 next 280\n", + "2024-12-07 08:59:07.226589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8100900 of size 131072 next 281\n", + "2024-12-07 08:59:07.226598: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120900 of size 512 next 282\n", + "2024-12-07 08:59:07.226606: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120b00 of size 512 next 283\n", + "2024-12-07 08:59:07.226615: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120d00 of size 512 next 284\n", + "2024-12-07 08:59:07.226629: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8120f00 of size 512 next 285\n", + "2024-12-07 08:59:07.226637: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121100 of size 512 next 286\n", + "2024-12-07 08:59:07.226645: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121300 of size 512 next 287\n", + "2024-12-07 08:59:07.226653: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8121500 of size 65536 next 288\n", + "2024-12-07 08:59:07.226662: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8131500 of size 65536 next 289\n", + "2024-12-07 08:59:07.226671: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141500 of size 512 next 290\n", + "2024-12-07 08:59:07.226680: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141700 of size 512 next 291\n", + "2024-12-07 08:59:07.226688: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8141900 of size 65536 next 292\n", + "2024-12-07 08:59:07.226696: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8151900 of size 65536 next 293\n", + "2024-12-07 08:59:07.226705: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161900 of size 512 next 294\n", + "2024-12-07 08:59:07.226714: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161b00 of size 512 next 295\n", + "2024-12-07 08:59:07.226723: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8161d00 of size 65536 next 296\n", + "2024-12-07 08:59:07.226736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8171d00 of size 65536 next 297\n", + "2024-12-07 08:59:07.226745: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8181d00 of size 512 next 298\n", + "2024-12-07 08:59:07.226753: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8181f00 of size 512 next 299\n", + "2024-12-07 08:59:07.226760: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8182100 of size 65536 next 300\n", + "2024-12-07 08:59:07.226769: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8192100 of size 65536 next 301\n", + "2024-12-07 08:59:07.226778: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2100 of size 512 next 302\n", + "2024-12-07 08:59:07.226786: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2300 of size 512 next 303\n", + "2024-12-07 08:59:07.226795: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81a2500 of size 65536 next 304\n", + "2024-12-07 08:59:07.226803: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81b2500 of size 65536 next 305\n", + "2024-12-07 08:59:07.226811: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2500 of size 512 next 306\n", + "2024-12-07 08:59:07.226820: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2700 of size 512 next 307\n", + "2024-12-07 08:59:07.226828: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2900 of size 512 next 308\n", + "2024-12-07 08:59:07.226835: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2b00 of size 512 next 309\n", + "2024-12-07 08:59:07.226844: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2d00 of size 512 next 310\n", + "2024-12-07 08:59:07.226853: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c2f00 of size 512 next 311\n", + "2024-12-07 08:59:07.226862: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81c3100 of size 131072 next 312\n", + "2024-12-07 08:59:07.226870: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e81e3100 of size 131072 next 313\n", + "2024-12-07 08:59:07.226879: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203100 of size 1024 next 314\n", + "2024-12-07 08:59:07.226887: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203500 of size 1024 next 315\n", + "2024-12-07 08:59:07.226896: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8203900 of size 131072 next 316\n", + "2024-12-07 08:59:07.226904: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8223900 of size 131072 next 317\n", + "2024-12-07 08:59:07.226912: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243900 of size 512 next 318\n", + "2024-12-07 08:59:07.226921: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243b00 of size 512 next 319\n", + "2024-12-07 08:59:07.226929: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243d00 of size 512 next 320\n", + "2024-12-07 08:59:07.226937: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8243f00 of size 512 next 321\n", + "2024-12-07 08:59:07.226945: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244100 of size 512 next 322\n", + "2024-12-07 08:59:07.226953: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244300 of size 512 next 323\n", + "2024-12-07 08:59:07.226962: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8244500 of size 115712 next 324\n", + "2024-12-07 08:59:07.226970: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8260900 of size 115712 next 325\n", + "2024-12-07 08:59:07.226978: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827cd00 of size 1024 next 326\n", + "2024-12-07 08:59:07.226987: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d100 of size 1024 next 327\n", + "2024-12-07 08:59:07.226995: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d500 of size 512 next 328\n", + "2024-12-07 08:59:07.227004: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d700 of size 512 next 329\n", + "2024-12-07 08:59:07.227012: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827d900 of size 512 next 330\n", + "2024-12-07 08:59:07.227020: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827db00 of size 512 next 331\n", + "2024-12-07 08:59:07.227029: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e827dd00 of size 131072 next 332\n", + "2024-12-07 08:59:07.227037: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e829dd00 of size 131072 next 333\n", + "2024-12-07 08:59:07.227045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82bdd00 of size 512 next 334\n", + "2024-12-07 08:59:07.227054: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82bdf00 of size 512 next 335\n", + "2024-12-07 08:59:07.227062: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82be100 of size 131072 next 336\n", + "2024-12-07 08:59:07.227071: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82de100 of size 131072 next 337\n", + "2024-12-07 08:59:07.227079: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe100 of size 1024 next 338\n", + "2024-12-07 08:59:07.227087: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe500 of size 1024 next 339\n", + "2024-12-07 08:59:07.227096: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e82fe900 of size 131072 next 340\n", + "2024-12-07 08:59:07.227110: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e831e900 of size 131072 next 341\n", + "2024-12-07 08:59:07.227118: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833e900 of size 1024 next 342\n", + "2024-12-07 08:59:07.227126: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833ed00 of size 1024 next 343\n", + "2024-12-07 08:59:07.227134: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e833f100 of size 131072 next 344\n", + "2024-12-07 08:59:07.227143: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e835f100 of size 131072 next 345\n", + "2024-12-07 08:59:07.227151: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f100 of size 1024 next 346\n", + "2024-12-07 08:59:07.227158: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f500 of size 1024 next 347\n", + "2024-12-07 08:59:07.227167: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e837f900 of size 131072 next 348\n", + "2024-12-07 08:59:07.227176: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e839f900 of size 131072 next 349\n", + "2024-12-07 08:59:07.227185: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bf900 of size 512 next 350\n", + "2024-12-07 08:59:07.227193: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bfb00 of size 512 next 351\n", + "2024-12-07 08:59:07.227201: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83bfd00 of size 32768 next 352\n", + "2024-12-07 08:59:07.227209: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83c7d00 of size 32768 next 353\n", + "2024-12-07 08:59:07.227218: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cfd00 of size 256 next 354\n", + "2024-12-07 08:59:07.227226: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cfe00 of size 256 next 355\n", + "2024-12-07 08:59:07.227235: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83cff00 of size 1792 next 356\n", + "2024-12-07 08:59:07.227243: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d0600 of size 1792 next 357\n", + "2024-12-07 08:59:07.227251: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d0d00 of size 1792 next 358\n", + "2024-12-07 08:59:07.227260: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d1400 of size 1792 next 359\n", + "2024-12-07 08:59:07.227269: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e83d1b00 of size 458752 next 360\n", + "2024-12-07 08:59:07.227277: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8441b00 of size 458752 next 361\n", + "2024-12-07 08:59:07.227286: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b1b00 of size 1024 next 362\n", + "2024-12-07 08:59:07.227295: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b1f00 of size 1024 next 363\n", + "2024-12-07 08:59:07.227303: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2300 of size 1024 next 364\n", + "2024-12-07 08:59:07.227311: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2700 of size 1024 next 365\n", + "2024-12-07 08:59:07.227320: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2b00 of size 1024 next 366\n", + "2024-12-07 08:59:07.227329: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b2f00 of size 1024 next 367\n", + "2024-12-07 08:59:07.227337: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84b3300 of size 131072 next 368\n", + "2024-12-07 08:59:07.227349: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84d3300 of size 131072 next 369\n", + "2024-12-07 08:59:07.227367: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3300 of size 512 next 370\n", + "2024-12-07 08:59:07.227377: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3500 of size 512 next 371\n", + "2024-12-07 08:59:07.227385: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3700 of size 512 next 372\n", + "2024-12-07 08:59:07.227393: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3900 of size 512 next 373\n", + "2024-12-07 08:59:07.227402: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3b00 of size 512 next 374\n", + "2024-12-07 08:59:07.227410: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3d00 of size 512 next 375\n", + "2024-12-07 08:59:07.227418: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84f3f00 of size 32768 next 376\n", + "2024-12-07 08:59:07.227427: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e84fbf00 of size 32768 next 377\n", + "2024-12-07 08:59:07.227436: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8503f00 of size 256 next 378\n", + "2024-12-07 08:59:07.227445: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504000 of size 256 next 379\n", + "2024-12-07 08:59:07.227453: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504100 of size 1280 next 380\n", + "2024-12-07 08:59:07.227461: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504600 of size 1280 next 381\n", + "2024-12-07 08:59:07.227469: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504b00 of size 256 next 382\n", + "2024-12-07 08:59:07.227478: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504c00 of size 256 next 383\n", + "2024-12-07 08:59:07.227487: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504d00 of size 256 next 384\n", + "2024-12-07 08:59:07.227495: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504e00 of size 256 next 385\n", + "2024-12-07 08:59:07.227503: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8504f00 of size 256 next 386\n", + "2024-12-07 08:59:07.227513: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505000 of size 256 next 387\n", + "2024-12-07 08:59:07.227522: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505100 of size 256 next 388\n", + "2024-12-07 08:59:07.227531: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505200 of size 256 next 389\n", + "2024-12-07 08:59:07.227540: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505300 of size 256 next 390\n", + "2024-12-07 08:59:07.227547: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505400 of size 256 next 391\n", + "2024-12-07 08:59:07.227556: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505500 of size 256 next 392\n", + "2024-12-07 08:59:07.227565: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505600 of size 256 next 393\n", + "2024-12-07 08:59:07.227573: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505700 of size 256 next 394\n", + "2024-12-07 08:59:07.227581: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505800 of size 256 next 395\n", + "2024-12-07 08:59:07.227589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505900 of size 256 next 396\n", + "2024-12-07 08:59:07.227598: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505a00 of size 256 next 397\n", + "2024-12-07 08:59:07.227606: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505b00 of size 256 next 398\n", + "2024-12-07 08:59:07.227615: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505c00 of size 256 next 399\n", + "2024-12-07 08:59:07.227624: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505d00 of size 256 next 400\n", + "2024-12-07 08:59:07.227632: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505e00 of size 256 next 401\n", + "2024-12-07 08:59:07.227639: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8505f00 of size 256 next 402\n", + "2024-12-07 08:59:07.227648: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506000 of size 256 next 403\n", + "2024-12-07 08:59:07.227657: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506100 of size 256 next 404\n", + "2024-12-07 08:59:07.227666: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506200 of size 256 next 405\n", + "2024-12-07 08:59:07.227674: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506300 of size 256 next 406\n", + "2024-12-07 08:59:07.227682: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506400 of size 256 next 407\n", + "2024-12-07 08:59:07.227690: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506500 of size 256 next 408\n", + "2024-12-07 08:59:07.227699: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506600 of size 256 next 409\n", + "2024-12-07 08:59:07.227708: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506700 of size 256 next 410\n", + "2024-12-07 08:59:07.227717: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506800 of size 256 next 411\n", + "2024-12-07 08:59:07.227730: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506900 of size 256 next 412\n", + "2024-12-07 08:59:07.227738: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506a00 of size 256 next 413\n", + "2024-12-07 08:59:07.227747: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506b00 of size 256 next 414\n", + "2024-12-07 08:59:07.227755: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506c00 of size 256 next 415\n", + "2024-12-07 08:59:07.227764: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506d00 of size 256 next 416\n", + "2024-12-07 08:59:07.227772: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8506e00 of size 65536 next 597\n", + "2024-12-07 08:59:07.227781: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8516e00 of size 186368 next 447\n", + "2024-12-07 08:59:07.227790: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8544600 of size 262144 next 701\n", + "2024-12-07 08:59:07.227799: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8584600 of size 251904 next 718\n", + "2024-12-07 08:59:07.227808: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e85c1e00 of size 262144 next 524\n", + "2024-12-07 08:59:07.227816: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8601e00 of size 5373952 next 570\n", + "2024-12-07 08:59:07.227824: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e8b21e00 of size 5373952 next 649\n", + "2024-12-07 08:59:07.227832: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9041e00 of size 2686976 next 725\n", + "2024-12-07 08:59:07.227841: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e92d1e00 of size 2686976 next 107\n", + "2024-12-07 08:59:07.227849: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e9561e00 of size 1343488 next 429\n", + "2024-12-07 08:59:07.227857: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4e96a9e00 of size 12410880 next 700\n", + "2024-12-07 08:59:07.227867: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea27fe00 of size 131072 next 602\n", + "2024-12-07 08:59:07.227877: I tensorflow/tsl/framework/bfc_allocator.cc:1095] Free at 7ad4ea29fe00 of size 512 next 500\n", + "2024-12-07 08:59:07.227886: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea2a0000 of size 720384 next 427\n", + "2024-12-07 08:59:07.227898: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea34fe00 of size 458752 next 661\n", + "2024-12-07 08:59:07.227906: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ea3bfe00 of size 10747904 next 476\n", + "2024-12-07 08:59:07.227914: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eadffe00 of size 10747904 next 582\n", + "2024-12-07 08:59:07.227923: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eb83fe00 of size 10747904 next 83\n", + "2024-12-07 08:59:07.227932: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ec27fe00 of size 10747904 next 445\n", + "2024-12-07 08:59:07.227940: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eccbfe00 of size 10747904 next 727\n", + "2024-12-07 08:59:07.227948: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ed6ffe00 of size 10747904 next 684\n", + "2024-12-07 08:59:07.227957: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ee13fe00 of size 10747904 next 63\n", + "2024-12-07 08:59:07.227966: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4eeb7fe00 of size 10747904 next 662\n", + "2024-12-07 08:59:07.227975: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4ef5bfe00 of size 10747904 next 665\n", + "2024-12-07 08:59:07.227982: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4effffe00 of size 10747904 next 50\n", + "2024-12-07 08:59:07.227990: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0a3fe00 of size 5373952 next 587\n", + "2024-12-07 08:59:07.227999: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f5fe00 of size 83968 next 683\n", + "2024-12-07 08:59:07.228008: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f74600 of size 83968 next 626\n", + "2024-12-07 08:59:07.228017: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f0f88e00 of size 5206016 next 536\n", + "2024-12-07 08:59:07.228027: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad4f147fe00 of size 12059136 next 18446744073709551615\n", + "2024-12-07 08:59:07.228035: I tensorflow/tsl/framework/bfc_allocator.cc:1075] Next region of size 4294967296\n", + "2024-12-07 08:59:07.228045: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad61e000000 of size 2303392000 next 1\n", + "2024-12-07 08:59:07.228054: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74af900 of size 1280 next 2\n", + "2024-12-07 08:59:07.228062: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6a74afe00 of size 354368000 next 534\n", + "2024-12-07 08:59:07.228071: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6bc6a3800 of size 385728000 next 630\n", + "2024-12-07 08:59:07.228079: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6d367f600 of size 354368000 next 498\n", + "2024-12-07 08:59:07.228089: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6e8873000 of size 385728000 next 426\n", + "2024-12-07 08:59:07.228096: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad6ff84ee00 of size 354368000 next 632\n", + "2024-12-07 08:59:07.228105: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a42800 of size 1280 next 553\n", + "2024-12-07 08:59:07.228114: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a42d00 of size 32768 next 549\n", + "2024-12-07 08:59:07.228122: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a4ad00 of size 97024 next 94\n", + "2024-12-07 08:59:07.228130: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a62800 of size 65536 next 729\n", + "2024-12-07 08:59:07.228138: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a72800 of size 65536 next 556\n", + "2024-12-07 08:59:07.228147: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a82800 of size 65536 next 519\n", + "2024-12-07 08:59:07.228156: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714a92800 of size 65536 next 631\n", + "2024-12-07 08:59:07.228164: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714aa2800 of size 65536 next 477\n", + "2024-12-07 08:59:07.228173: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ab2800 of size 131072 next 473\n", + "2024-12-07 08:59:07.228182: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ad2800 of size 65536 next 495\n", + "2024-12-07 08:59:07.228190: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ae2800 of size 65536 next 490\n", + "2024-12-07 08:59:07.228197: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714af2800 of size 65536 next 670\n", + "2024-12-07 08:59:07.228206: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b02800 of size 131072 next 509\n", + "2024-12-07 08:59:07.228215: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b22800 of size 65536 next 54\n", + "2024-12-07 08:59:07.228223: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b32800 of size 131072 next 440\n", + "2024-12-07 08:59:07.228232: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b52800 of size 131072 next 618\n", + "2024-12-07 08:59:07.228240: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b72800 of size 65536 next 547\n", + "2024-12-07 08:59:07.228249: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b82800 of size 99584 next 738\n", + "2024-12-07 08:59:07.228258: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714b9ad00 of size 65536 next 105\n", + "2024-12-07 08:59:07.228266: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714baad00 of size 131072 next 561\n", + "2024-12-07 08:59:07.228274: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bcad00 of size 65536 next 511\n", + "2024-12-07 08:59:07.228283: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bdad00 of size 65536 next 516\n", + "2024-12-07 08:59:07.228292: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bead00 of size 65536 next 485\n", + "2024-12-07 08:59:07.228300: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714bfad00 of size 131072 next 551\n", + "2024-12-07 08:59:07.228309: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c1ad00 of size 65536 next 104\n", + "2024-12-07 08:59:07.228318: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c2ad00 of size 131072 next 720\n", + "2024-12-07 08:59:07.228327: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c4ad00 of size 65536 next 739\n", + "2024-12-07 08:59:07.228335: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c5ad00 of size 65536 next 708\n", + "2024-12-07 08:59:07.228344: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c6ad00 of size 65536 next 87\n", + "2024-12-07 08:59:07.228352: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c7ad00 of size 131072 next 694\n", + "2024-12-07 08:59:07.228371: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714c9ad00 of size 246784 next 425\n", + "2024-12-07 08:59:07.228381: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714cd7100 of size 65536 next 765\n", + "2024-12-07 08:59:07.228389: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ce7100 of size 131072 next 655\n", + "2024-12-07 08:59:07.228398: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07100 of size 1792 next 422\n", + "2024-12-07 08:59:07.228405: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07800 of size 1024 next 530\n", + "2024-12-07 08:59:07.228414: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d07c00 of size 65536 next 454\n", + "2024-12-07 08:59:07.228423: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d17c00 of size 165888 next 62\n", + "2024-12-07 08:59:07.228431: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d40400 of size 131072 next 448\n", + "2024-12-07 08:59:07.228440: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d60400 of size 131072 next 120\n", + "2024-12-07 08:59:07.228448: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80400 of size 1024 next 671\n", + "2024-12-07 08:59:07.228456: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80800 of size 256 next 687\n", + "2024-12-07 08:59:07.228465: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80900 of size 256 next 449\n", + "2024-12-07 08:59:07.228473: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d80a00 of size 2048 next 513\n", + "2024-12-07 08:59:07.228482: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714d81200 of size 231424 next 434\n", + "2024-12-07 08:59:07.228491: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714db9a00 of size 83968 next 733\n", + "2024-12-07 08:59:07.228500: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dce200 of size 2048 next 591\n", + "2024-12-07 08:59:07.228509: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714dcea00 of size 272640 next 59\n", + "2024-12-07 08:59:07.228517: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e11300 of size 65536 next 585\n", + "2024-12-07 08:59:07.228525: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e21300 of size 131072 next 636\n", + "2024-12-07 08:59:07.228534: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e41300 of size 131072 next 638\n", + "2024-12-07 08:59:07.228542: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714e61300 of size 458752 next 512\n", + "2024-12-07 08:59:07.228551: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed1300 of size 10240 next 85\n", + "2024-12-07 08:59:07.228559: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ed3b00 of size 83968 next 61\n", + "2024-12-07 08:59:07.228568: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714ee8300 of size 131072 next 483\n", + "2024-12-07 08:59:07.228576: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f08300 of size 131072 next 572\n", + "2024-12-07 08:59:07.228589: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f28300 of size 32768 next 622\n", + "2024-12-07 08:59:07.228598: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f30300 of size 124928 next 101\n", + "2024-12-07 08:59:07.228607: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f4eb00 of size 231424 next 520\n", + "2024-12-07 08:59:07.228615: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714f87300 of size 251904 next 668\n", + "2024-12-07 08:59:07.228624: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad714fc4b00 of size 524288 next 128\n", + "2024-12-07 08:59:07.228633: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad715044b00 of size 604672 next 71\n", + "2024-12-07 08:59:07.228642: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150d8500 of size 131072 next 573\n", + "2024-12-07 08:59:07.228651: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad7150f8500 of size 141056 next 444\n", + "2024-12-07 08:59:07.228660: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71511ac00 of size 27541504 next 608\n", + "2024-12-07 08:59:07.228668: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad716b5ec00 of size 21495808 next 640\n", + "2024-12-07 08:59:07.228677: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad717fdec00 of size 21495808 next 84\n", + "2024-12-07 08:59:07.228686: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71945ec00 of size 10747904 next 133\n", + "2024-12-07 08:59:07.228694: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad719e9ec00 of size 10747904 next 744\n", + "2024-12-07 08:59:07.228703: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71a8dec00 of size 10747904 next 77\n", + "2024-12-07 08:59:07.228711: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71b31ec00 of size 10747904 next 510\n", + "2024-12-07 08:59:07.228719: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71bd5ec00 of size 10747904 next 150\n", + "2024-12-07 08:59:07.228728: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71c79ec00 of size 10747904 next 673\n", + "2024-12-07 08:59:07.228736: I tensorflow/tsl/framework/bfc_allocator.cc:1095] InUse at 7ad71d1dec00 of size 14816256 next 18446744073709551615\n", + "2024-12-07 08:59:07.228744: I tensorflow/tsl/framework/bfc_allocator.cc:1100] Summary of in-use Chunks by size: \n", + "2024-12-07 08:59:07.228755: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 196 Chunks of size 256 totalling 49.0KiB\n", + "2024-12-07 08:59:07.228764: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 148 Chunks of size 512 totalling 74.0KiB\n", + "2024-12-07 08:59:07.228773: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 18 Chunks of size 768 totalling 13.5KiB\n", + "2024-12-07 08:59:07.228781: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 37 Chunks of size 1024 totalling 37.0KiB\n", + "2024-12-07 08:59:07.228790: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1280 totalling 12.5KiB\n", + "2024-12-07 08:59:07.228798: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 1536 totalling 3.0KiB\n", + "2024-12-07 08:59:07.228807: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 10 Chunks of size 1792 totalling 17.5KiB\n", + "2024-12-07 08:59:07.228815: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 3 Chunks of size 2048 totalling 6.0KiB\n", + "2024-12-07 08:59:07.228824: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 10240 totalling 10.0KiB\n", + "2024-12-07 08:59:07.228833: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 20992 totalling 20.5KiB\n", + "2024-12-07 08:59:07.228842: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 13 Chunks of size 32768 totalling 416.0KiB\n", + "2024-12-07 08:59:07.228855: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 43520 totalling 42.5KiB\n", + "2024-12-07 08:59:07.228863: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 59648 totalling 58.2KiB\n", + "2024-12-07 08:59:07.228872: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 64256 totalling 62.8KiB\n", + "2024-12-07 08:59:07.228880: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 77 Chunks of size 65536 totalling 4.81MiB\n", + "2024-12-07 08:59:07.228889: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 12 Chunks of size 83968 totalling 984.0KiB\n", + "2024-12-07 08:59:07.228897: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 97024 totalling 94.8KiB\n", + "2024-12-07 08:59:07.228906: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 99584 totalling 97.2KiB\n", + "2024-12-07 08:59:07.228914: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 101376 totalling 99.0KiB\n", + "2024-12-07 08:59:07.228923: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 115712 totalling 452.0KiB\n", + "2024-12-07 08:59:07.228931: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 124928 totalling 122.0KiB\n", + "2024-12-07 08:59:07.228940: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 130560 totalling 127.5KiB\n", + "2024-12-07 08:59:07.228948: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 53 Chunks of size 131072 totalling 6.62MiB\n", + "2024-12-07 08:59:07.228957: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 141056 totalling 137.8KiB\n", + "2024-12-07 08:59:07.228966: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 164608 totalling 160.8KiB\n", + "2024-12-07 08:59:07.228974: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 165888 totalling 162.0KiB\n", + "2024-12-07 08:59:07.228982: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 181248 totalling 177.0KiB\n", + "2024-12-07 08:59:07.228991: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 186368 totalling 182.0KiB\n", + "2024-12-07 08:59:07.228999: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 198656 totalling 194.0KiB\n", + "2024-12-07 08:59:07.229008: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 231424 totalling 452.0KiB\n", + "2024-12-07 08:59:07.229017: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks of size 246784 totalling 482.0KiB\n", + "2024-12-07 08:59:07.229025: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 2 Chunks 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tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 1343488 totalling 1.28MiB\n", + "2024-12-07 08:59:07.229101: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 5 Chunks of size 2686976 totalling 12.81MiB\n", + "2024-12-07 08:59:07.229109: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 5206016 totalling 4.96MiB\n", + "2024-12-07 08:59:07.229117: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 4 Chunks of size 5373952 totalling 20.50MiB\n", + "2024-12-07 08:59:07.229126: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 8060928 totalling 7.69MiB\n", + "2024-12-07 08:59:07.229134: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 39 Chunks of size 10747904 totalling 399.75MiB\n", + "2024-12-07 08:59:07.229143: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12059136 totalling 11.50MiB\n", + "2024-12-07 08:59:07.229151: I tensorflow/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 12410880 totalling 11.84MiB\n", 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curr_region_allocation_bytes_: 17179869184\n", + "2024-12-07 08:59:07.229298: I tensorflow/tsl/framework/bfc_allocator.cc:1114] Stats: \n", + "Limit: 10198122496\n", + "InUse: 10195919872\n", + "MaxInUse: 10195920896\n", + "NumAllocs: 43119401\n", + "MaxAllocSize: 2507232000\n", + "Reserved: 0\n", + "PeakReserved: 0\n", + "LargestFreeBlock: 0\n", + "\n", + "2024-12-07 08:59:07.229328: W tensorflow/tsl/framework/bfc_allocator.cc:497] ****************************************************************************************************\n", + "2024-12-07 08:59:07.229377: W tensorflow/core/framework/op_kernel.cc:1839] OP_REQUIRES failed at matmul_op_impl.h:908 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[20992,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Memoria GPU esaurita, riprovo con batch size più piccolo...\n", + "Epoch 1/150\n", + " 5/Unknown - 1s 38ms/step - loss: 0.0238 - mae: 0.1432WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0342s vs `on_train_batch_end` time: 0.0350s). Check your callbacks.\n", + " 9953/Unknown - 294s 29ms/step - loss: 0.0258 - mae: 0.1480" + ] + } + ], + "source": [ + "model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e2fb5a5341dac92", + "metadata": {}, + "outputs": [], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4af58aa9bbc156f5", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model_performance(model, data, targets, set_name=\"\"):\n", + " \"\"\"\n", + " Valuta le performance del modello su un set di dati specifico.\n", + " \"\"\"\n", + " predictions = model.predict(data, verbose=0)\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + " metrics = {}\n", + "\n", + " for i, name in enumerate(target_names):\n", + " mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n", + " mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n", + " rmse = np.sqrt(mse)\n", + " mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n", + "\n", + " metrics[f\"{name}_mae\"] = mae\n", + " metrics[f\"{name}_rmse\"] = rmse\n", + " metrics[f\"{name}_mape\"] = mape\n", + "\n", + " if set_name:\n", + " print(f\"\\nPerformance sul set {set_name}:\")\n", + " for metric, value in metrics.items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def retrain_model(base_model, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Implementa il retraining del modello con i dati combinati.\n", + " \"\"\"\n", + " print(\"Valutazione performance iniziali del modello...\")\n", + " initial_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Combina i dati per il retraining\n", + " combined_data = {\n", + " 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n", + " 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n", + " }\n", + " combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n", + "\n", + " # Crea una nuova suddivisione per la validazione\n", + " indices = np.arange(len(combined_targets))\n", + " np.random.shuffle(indices)\n", + "\n", + " split_idx = int(len(indices) * 0.9)\n", + " train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n", + "\n", + " # Prepara i dati per il retraining\n", + " retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n", + " retrain_targets = combined_targets[train_idx]\n", + " retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n", + " retrain_val_targets = combined_targets[val_idx]\n", + "\n", + " # Configura callbacks\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=10,\n", + " restore_best_weights=True,\n", + " min_delta=0.0001\n", + " ),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_retrained_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " )\n", + " ]\n", + "\n", + " # Imposta learning rate per il fine-tuning\n", + " optimizer = tf.keras.optimizers.AdamW(\n", + " learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n", + " initial_learning_rate=1e-4,\n", + " decay_steps=1000,\n", + " decay_rate=0.9\n", + " ),\n", + " weight_decay=0.01\n", + " )\n", + "\n", + " # Ricompila il modello con il nuovo optimizer\n", + " base_model.compile(\n", + " optimizer=optimizer,\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " print(\"\\nAvvio retraining...\")\n", + " history = base_model.fit(\n", + " retrain_data,\n", + " retrain_targets,\n", + " validation_data=(retrain_val_data, retrain_val_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " print(\"\\nValutazione performance finali...\")\n", + " final_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Salva il modello finale\n", + " save_path = f'{execute_name}_retrained_model.keras'\n", + " os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n", + " \n", + " base_model.save_weights(f'{execute_name}_retrained/weights')\n", + " base_model.save(save_path, save_format='keras')\n", + " print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n", + "\n", + " # Report miglioramenti\n", + " print(\"\\nMiglioramenti delle performance:\")\n", + " for dataset in ['train', 'val', 'test']:\n", + " print(f\"\\nSet {dataset}:\")\n", + " for metric in initial_metrics[dataset].keys():\n", + " initial = initial_metrics[dataset][metric]\n", + " final = final_metrics[dataset][metric]\n", + " improvement = ((initial - final) / initial) * 100\n", + " print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n", + "\n", + " return base_model, history, final_metrics\n", + "\n", + "\n", + "def start_retraining(model_path, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Avvia il processo di retraining in modo sicuro.\n", + " \"\"\"\n", + " try:\n", + " print(\"Caricamento del modello...\")\n", + " base_model = tf.keras.models.load_model(model_path, compile=False)\n", + " print(\"Modello caricato con successo!\")\n", + "\n", + " return retrain_model(\n", + " base_model=base_model,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=epochs,\n", + " batch_size=batch_size\n", + " )\n", + " except Exception as e:\n", + " print(f\"Errore durante il retraining: {str(e)}\")\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "588c7e49371f4a0c", + "metadata": {}, + "outputs": [], + "source": [ + "model_path = f'{execute_name}_final_model.keras'\n", + "\n", + "retrained_model, retrain_history, final_metrics = start_retraining(\n", + " model_path=model_path,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=50,\n", + " batch_size=256\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", + "metadata": {}, + "outputs": [], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from typing import List, Dict, Tuple, Union\n", + "\n", + "def analyze_feature_importance(model: tf.keras.Model, \n", + " test_data: dict, \n", + " feature_names: List[str]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Analizza l'importanza delle feature usando perturbazione.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n", + " feature_names: Lista dei nomi delle feature\n", + " \n", + " Returns:\n", + " dict: Dizionario con l'importanza relativa di ogni feature\n", + " \"\"\"\n", + " # Estrai i dati temporali e statici\n", + " temporal_data = test_data['temporal']\n", + " static_data = test_data['static']\n", + " \n", + " # Ottieni la predizione base\n", + " base_prediction = model.predict(test_data)\n", + " feature_importance = {}\n", + " \n", + " # Per ogni feature temporale\n", + " for i, feature in enumerate(feature_names):\n", + " if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature temporale\n", + " temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n", + " \n", + " # Crea rumore per la feature temporale\n", + " feature_values = temporal_data[..., temp_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature temporale\n", + " perturbed_temporal = perturbed_data['temporal'].copy()\n", + " perturbed_temporal[..., temp_idx] = feature_values + noise\n", + " perturbed_data['temporal'] = perturbed_temporal\n", + " \n", + " else: # Feature statiche\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature statica\n", + " static_idx = ['ha'].index(feature)\n", + " \n", + " # Crea rumore per la feature statica\n", + " feature_values = static_data[..., static_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature statica\n", + " perturbed_static = perturbed_data['static'].copy()\n", + " perturbed_static[..., static_idx] = feature_values + noise\n", + " perturbed_data['static'] = perturbed_static\n", + " \n", + " # Calcola nuova predizione\n", + " perturbed_prediction = model.predict(perturbed_data)\n", + " \n", + " # Calcola impatto della perturbazione\n", + " impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n", + " feature_importance[feature] = float(impact)\n", + " \n", + " # Normalizza le importanze\n", + " total_importance = sum(feature_importance.values())\n", + " feature_importance = {k: v/total_importance \n", + " for k, v in feature_importance.items()}\n", + " \n", + " return feature_importance\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor o array dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calcola varianza manualmente\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Ordina il tensor per il calcolo della mediana\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data: np.ndarray, \n", + " bins: int = 50, \n", + " title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Imposta il titolo generale\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf\n", + "\n", + "def analyze_model_predictions(model: tf.keras.Model, \n", + " test_data: np.ndarray,\n", + " test_targets: np.ndarray,\n", + " scaler_y) -> None:\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Esegue un'analisi completa del modello includendo errori,\n", + " importanza delle feature e distribuzioni.\n", + " \"\"\"\n", + " print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n", + " \n", + " # 1. Analisi degli errori\n", + " print(\"\\n1. ANALISI DEGLI ERRORI\")\n", + " print(\"-\" * 50)\n", + " analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n", + " \n", + " # 2. Analisi dell'importanza delle feature\n", + " print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Definisci i nomi delle feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha']\n", + " \n", + " all_features = temporal_features + static_features\n", + " importance = analyze_feature_importance(retrained_model, test_data, all_features)\n", + " \n", + " print(\"\\nImportanza relativa delle feature:\")\n", + " for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n", + " print(f\"{feature}: {imp:.4f}\")\n", + " \n", + " # 3. Analisi distribuzionale\n", + " print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n", + " print(\"-\" * 50)\n", + " \n", + " prob = ProbabilityFunctions()\n", + " predictions = retrained_model.predict(test_data)\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi distribuzionale per {target}\")\n", + " \n", + " # Statistiche\n", + " stats_pred = prob.calculate_statistics(predictions_real[:, i])\n", + " stats_true = prob.calculate_statistics(targets_real[:, i])\n", + " \n", + " print(\"\\nStatistiche Predizioni:\")\n", + " for key, value in stats_pred.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " print(\"\\nStatistiche Target Reali:\")\n", + " for key, value in stats_true.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza distribuzioni\n", + " prob.plot_distributions(predictions_real[:, i], bins=50,\n", + " title=f\"Distribuzione Predizioni - {target}\")\n", + " prob.plot_distributions(targets_real[:, i], bins=50,\n", + " title=f\"Distribuzione Target Reali - {target}\")\n", + "\n", + "def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data):\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calculate variance manually\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Sort the tensor for median calculation\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data, bins=50):\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf):\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Set overall title\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812", + "metadata": {}, + "outputs": [], + "source": [ + "run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/olive_oli/.ipynb_checkpoints/olive_oil-512-checkpoint.ipynb b/models/olive_oli/.ipynb_checkpoints/olive_oil-512-checkpoint.ipynb new file mode 100644 index 0000000..acde314 --- /dev/null +++ b/models/olive_oli/.ipynb_checkpoints/olive_oil-512-checkpoint.ipynb @@ -0,0 +1,3418 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\n", + "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1581 B]\n", + "Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [1192 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy/restricted amd64 Packages [164 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy/universe amd64 Packages [17.5 MB]\n", + "Get:9 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [45.2 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu jammy/multiverse amd64 Packages [266 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 Packages [1792 kB] \n", + "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", + "Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [3323 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [3446 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1514 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [53.3 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [33.8 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [81.4 kB]\n", + "Get:19 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2454 kB]\n", + "Get:20 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1225 kB]\n", + "Fetched 36.5 MB in 2s (18.2 MB/s) \n", + "Reading package lists... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pandas\n", + " Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Collecting pytz>=2020.1 (from pandas)\n", + " Obtaining dependency information for pytz>=2020.1 from https://files.pythonhosted.org/packages/11/c3/005fcca25ce078d2cc29fd559379817424e94885510568bc1bc53d7d5846/pytz-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading pytz-2024.2-py2.py3-none-any.whl.metadata (22 kB)\n", + "Collecting tzdata>=2022.7 (from pandas)\n", + " Obtaining dependency information for tzdata>=2022.7 from https://files.pythonhosted.org/packages/a6/ab/7e5f53c3b9d14972843a647d8d7a853969a58aecc7559cb3267302c94774/tzdata-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m74.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m:01\u001b[0m\n", + "\u001b[?25hDownloading pytz-2024.2-py2.py3-none-any.whl (508 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m508.0/508.0 kB\u001b[0m \u001b[31m106.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading tzdata-2024.2-py2.py3-none-any.whl (346 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m103.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pytz, tzdata, pandas\n", + "Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.2\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting scikit-learn\n", + " Obtaining dependency information for scikit-learn from https://files.pythonhosted.org/packages/49/21/3723de321531c9745e40f1badafd821e029d346155b6c79704e0b7197552/scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Collecting scipy>=1.6.0 (from scikit-learn)\n", + " Obtaining dependency information for scipy>=1.6.0 from https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting joblib>=1.2.0 (from scikit-learn)\n", + " Obtaining dependency information for joblib>=1.2.0 from https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl.metadata\n", + " Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n", + " Obtaining dependency information for threadpoolctl>=3.1.0 from https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl.metadata\n", + " Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n", + "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading joblib-1.4.2-py3-none-any.whl (301 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m104.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m55.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n", + "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n", + "Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-3.5.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pyarrow\n", + " Obtaining dependency information for pyarrow from https://files.pythonhosted.org/packages/5e/b5/9e14e9f7590e0eaa435ecea84dabb137284a4dbba7b3c337b58b65b76d95/pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata\n", + " Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", + "Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (40.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.1/40.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pyarrow\n", + "Successfully installed pyarrow-18.1.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting fastparquet\n", + " Obtaining dependency information for fastparquet from https://files.pythonhosted.org/packages/8d/e8/e1ede861bea68394a755d8be1aa2e2d60a3b9f6b551bfd56aeca74987e2e/fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Collecting cramjam>=2.3 (from fastparquet)\n", + " Obtaining dependency information for cramjam>=2.3 from https://files.pythonhosted.org/packages/79/1d/180f2ca168625073f0df80b16c795926deed91b7e89dbfc045263ba7444b/cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n", + "Collecting fsspec (from fastparquet)\n", + " Obtaining dependency information for fsspec from https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl.metadata\n", + " Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hDownloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading fsspec-2024.10.0-py3-none-any.whl (179 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.6/179.6 kB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: fsspec, cramjam, fastparquet\n", + "Successfully installed cramjam-2.9.0 fastparquet-2024.11.0 fsspec-2024.10.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting seaborn\n", + " Obtaining dependency information for seaborn from https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl.metadata\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting tqdm\n", + " Obtaining dependency information for tqdm from https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl.metadata\n", + " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: tqdm\n", + "Successfully installed tqdm-4.67.1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pydot\n", + " Obtaining dependency information for pydot from https://files.pythonhosted.org/packages/3e/1b/ef569ac44598b6b24bc0f80d5ac4f811af59d3f0d0d23b0216e014c0ec33/pydot-3.0.3-py3-none-any.whl.metadata\n", + " Downloading pydot-3.0.3-py3-none-any.whl.metadata (10 kB)\n", + "Collecting pyparsing>=3.0.9 (from pydot)\n", + " Obtaining dependency information for pyparsing>=3.0.9 from https://files.pythonhosted.org/packages/be/ec/2eb3cd785efd67806c46c13a17339708ddc346cbb684eade7a6e6f79536a/pyparsing-3.2.0-py3-none-any.whl.metadata\n", + " Downloading pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)\n", + "Downloading pydot-3.0.3-py3-none-any.whl (35 kB)\n", + "Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.9/106.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pyparsing, pydot\n", + " Attempting uninstall: pyparsing\n", + " Found existing installation: pyparsing 2.4.7\n", + " Uninstalling pyparsing-2.4.7:\n", + " Successfully uninstalled pyparsing-2.4.7\n", + "Successfully installed pydot-3.0.3 pyparsing-3.2.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting tensorflow-io\n", + " Obtaining dependency information for tensorflow-io from https://files.pythonhosted.org/packages/f0/5e/f47443a14a00816fab54caf74599e2fcb34c05d6059e91f82126f8f4c68d/tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n", + "Collecting tensorflow-io-gcs-filesystem==0.37.1 (from tensorflow-io)\n", + " Obtaining dependency information for tensorflow-io-gcs-filesystem==0.37.1 from https://files.pythonhosted.org/packages/66/7f/e36ae148c2f03d61ca1bff24bc13a0fef6d6825c966abef73fc6f880a23b/tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n", + "Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.6 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 MB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: tensorflow-io-gcs-filesystem, tensorflow-io\n", + " Attempting uninstall: tensorflow-io-gcs-filesystem\n", + " Found existing installation: tensorflow-io-gcs-filesystem 0.34.0\n", + " Uninstalling tensorflow-io-gcs-filesystem-0.34.0:\n", + " Successfully uninstalled tensorflow-io-gcs-filesystem-0.34.0\n", + "Successfully installed tensorflow-io-0.37.1 tensorflow-io-gcs-filesystem-0.37.1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 10:36:10.368632: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-12-06 10:36:10.368679: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-12-06 10:36:10.368726: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-12-06 10:36:10.377750: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keras version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "CUDA available: True\n", + "GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n", + "1 Physical GPUs, 1 Logical GPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 10:36:13.233242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import keras\n", + "\n", + "print(f\"Keras version: {keras.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n", + "print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n", + "\n", + "# GPU configuration\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Limita la crescita della memoria GPU\n", + "gpus = tf.config.experimental.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " # Imposta la crescita di memoria dinamica\n", + " for gpu in gpus:\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + " \n", + " # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)\n", + " # tf.config.experimental.set_virtual_device_configuration(\n", + " # gpus[0],\n", + " # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)] # 4GB\n", + " # )\n", + " \n", + " logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n", + " print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n", + " except RuntimeError as e:\n", + " print(e)\n", + " \n", + "# Imposta le opzioni di logging\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Riduce i messaggi di log\n", + " \n", + "# Configura la modalità mista di precisione\n", + "tf.keras.mixed_precision.set_global_policy('float32')\n", + "\n", + "# Imposta il seed per la riproducibilità\n", + "##tf.random.set_seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c0155cde4740b0a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tensorflow_addons as tfa\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "import re\n", + "from typing import List\n", + "\n", + "random_state_value = None\n", + "execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "base_project_dir = './'\n", + "data_dir = '../../sources/'\n", + "models_project_dir = base_project_dir\n", + "\n", + "os.makedirs(base_project_dir, exist_ok=True)\n", + "os.makedirs(models_project_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1347fb59-50cc-4aa8-b805-ca9403037af5", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_column_name(name: str) -> str:\n", + " \"\"\"\n", + " Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.\n", + "\n", + " Parameters\n", + " ----------\n", + " name : str\n", + " Nome della colonna da pulire\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " Nome della colonna pulito\n", + " \"\"\"\n", + " # Rimuove caratteri speciali\n", + " name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n", + " # Converte in snake_case\n", + " name = name.lower().replace(' ', '_')\n", + "\n", + " # Abbreviazioni comuni\n", + " abbreviations = {\n", + " 'production': 'prod',\n", + " 'percentage': 'pct',\n", + " 'hectare': 'ha',\n", + " 'tonnes': 't',\n", + " 'litres': 'l',\n", + " 'minimum': 'min',\n", + " 'maximum': 'max',\n", + " 'average': 'avg'\n", + " }\n", + "\n", + " for full, abbr in abbreviations.items():\n", + " name = name.replace(full, abbr)\n", + "\n", + " return name\n", + "\n", + "\n", + "def clean_column_names(df: pd.DataFrame) -> List[str]:\n", + " \"\"\"\n", + " Pulisce tutti i nomi delle colonne in un DataFrame.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : pd.DataFrame\n", + " DataFrame con le colonne da pulire\n", + "\n", + " Returns\n", + " -------\n", + " list\n", + " Lista dei nuovi nomi delle colonne puliti\n", + " \"\"\"\n", + " new_columns = []\n", + "\n", + " for col in df.columns:\n", + " # Usa regex per separare le varietà\n", + " varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n", + " if varieties:\n", + " new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n", + " else:\n", + " new_columns.append(col)\n", + "\n", + " return new_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4da1f1bb67343e3e", + "metadata": {}, + "outputs": [], + "source": [ + "def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n", + " filename = filename.replace(' ', '_').lower()\n", + " filepath = os.path.join(output_dir, f\"{filename}.png\")\n", + " plt.savefig(filepath, bbox_inches='tight', dpi=300)\n", + " print(f\"Plot salvato come: {filepath}\")\n", + "\n", + "\n", + "def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + "\n", + " # Trova tutte le colonne delle tecniche\n", + " tech_columns = [col for col in df.columns if col.endswith('_tech')]\n", + "\n", + " # Applica il mapping a tutte le colonne delle tecniche\n", + " for col in tech_columns:\n", + " df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n", + "\n", + " return df\n", + "\n", + "\n", + "def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + " reverse_mapping = {v: k for k, v in technique_mapping.items()}\n", + " reverse_mapping[0] = ''\n", + "\n", + " return reverse_mapping.get(technique_value, '')\n", + "\n", + "\n", + "def prepare_comparison_data(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + "\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + " comparison_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n", + "\n", + " if not variety_data.empty:\n", + " avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " comparison_data.append({\n", + " 'Variety': variety,\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(comparison_data)\n", + "\n", + "\n", + "def plot_variety_comparison(comparison_data, metric):\n", + " plt.figure(figsize=(12, 6))\n", + " bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n", + " plt.title(f'Comparison of {metric} across Olive Varieties')\n", + " plt.xlabel('Variety')\n", + " plt.ylabel(metric)\n", + " plt.xticks(rotation=45, ha='right')\n", + "\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width() / 2., height,\n", + " f'{height:.2f}',\n", + " ha='center', va='bottom')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Oil Efficiency (L/kg)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Oil Efficiency (L oil / kg olives)')\n", + " plt.tight_layout()\n", + " save_plot(plt, 'efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Water Efficiency (L oil/m³ water)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Water Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Water Efficiency (L oil / m³ water)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_need_vs_oil_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n", + " comparison_data['Avg Oil Production (L/ha)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Production vs Water Need by Variety')\n", + " plt.xlabel('Average Water Need (m³/ha)')\n", + " plt.ylabel('Average Oil Production (L/ha)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_need_vs_oil_production')\n", + " plt.close()\n", + "\n", + "\n", + "def analyze_by_technique(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " technique_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0]\n", + "\n", + " if not variety_data.empty:\n", + " for tech in variety_data[tech_col].unique():\n", + " tech_data = variety_data[variety_data[tech_col] == tech]\n", + "\n", + " avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n", + "\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " technique_data.append({\n", + " 'Variety': variety,\n", + " 'Technique': tech,\n", + " 'Technique String': decode_single_technique(tech),\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(technique_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9aa4bf176c4affb9", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_real_error(model, test_data, test_targets, scaler_y):\n", + " # Fare predizioni\n", + " predictions = model.predict(test_data)\n", + "\n", + " # Denormalizzare predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + "\n", + " # Calcolare errore percentuale per ogni target\n", + " percentage_errors = []\n", + " absolute_errors = []\n", + "\n", + " for i in range(predictions_real.shape[1]):\n", + " mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n", + " mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n", + " percentage_errors.append(mape)\n", + " absolute_errors.append(mae)\n", + "\n", + " # Stampa risultati per ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " print(\"\\nErrori per target:\")\n", + " print(\"-\" * 50)\n", + " for i, target in enumerate(target_names):\n", + " print(f\"{target}:\")\n", + " print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n", + " print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n", + " print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n", + " print(\"-\" * 50)\n", + "\n", + " return percentage_errors, absolute_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3ba2b96ba678389", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_olive_production_kg_ha.png\n" + ] + }, + { + "data": { + "image/png": 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IOnbsiEqVKmk5UsoLnj59mmumU1RUFI4cOYL4+HgsXboUwOuBe//+/REdHY2xY8diwIAB0NHRQWJiIgwMDGBubq6t8CkfGDduHF6+fIl169YhKyvrrZd1t2/fVoqdDx48GLq6urn+WCQCcq9ccHV1hbe3N6ytrZGcnAwjIyP8/PPPqFmzJvz9/TF69GioVCoYGRnB0tISR44c4Xic/pVvvvkGQUFB2LRpEzp16oTHjx/j+vXrCAgIQNmyZdG6dWtYW1trO0z6yJiYojxl/fr1CAoKQokSJbBy5UoAQGJiInx8fODl5YVBgwZh7ty5AF7XSMheIsPBFUVERKBhw4bKz2FhYRg1ahTKly+P+fPnw9bWFgCQkJCA3r17486dOzhy5AgqVqyorZApH8hOcp49exaXLl1CZmYmmjRpAhsbGyQkJGD+/PmIiIhA9+7d4eLiou1wKQ9asmQJHj58iPnz5yvLppo3b46IiAh8+eWXuZahp6enw9HREbGxsXB0dMSQIUNgZGSkxegpL8r5Mg94PQayt7dH6dKlsWPHDgB/JhjS09Nx6dIl1KtXL9dnZCeviLLlTCjdvHkTCxcuxOjRo1GnTh0cPXoUy5YtQ3R0NHx9fWFra4uEhAQ8evQImZmZqFOnjlJziuNxAv68T50/fx7Xrl2Dvr4+ypUrBzs7OwC5k1Pt2rVjnWBijSnSrpx50YyMDISHh+PIkSOIi4tT2i0tLTF48GCMGjUKmzdvxoQJEwBASUqJCB+CBdzBgwfRtm3bXDt7mJqaQq1WIzAwUBloaTQaWFlZwdfXF5UqVUK9evVw+/ZtLUVN+YFKpcKuXbvQpk0bLF26FIsWLULt2rWxevVqWFlZYdq0aWjQoAECAgIwe/ZsbYdLeZCxsTGcnJygp6eHV69eQU9PDwcOHEC7du0QHR2N3bt3KzU49PX1sWnTJlhYWMDPzw+vXr3ScvSU12g0GiUpdevWLSQnJ0NXVxfdunVDVFQUgoODAfxZdyouLg4LFixAZGRkrs9hUoqy7dmzB8CffWbHjh1KLakyZcoAeL3TrIuLC6ytrdG7d29ERUXBysoKtra2qFevnpJ053icsmXXUWzZsiWWLVuGoUOHYvDgwZg5cyaA1zWnvvzySwwdOhR79+7lRjEECJGWXL9+XV68eCEiIrNnz5ZLly7JrVu3ZPz48WJiYiKrVq3KdX5iYqK4ublJ165dRaPRaCNkysMSEhJERCQ+Pl5pO3/+vNSsWVPq1asnqampIiJK37l37558/fXXEh0d/fGDpXzj4sWLUrx4cfH29pbk5GRJSkqSefPmia6urqxbt05EROLi4sTR0VG+/PJLefLkiZYjprwqLCxMxowZI1evXhURkSdPnkizZs2kSZMmEhAQIFlZWcq5r169krt372orVMqjcvaRmTNnSocOHSQ4OFhERMLDw6V58+bSvXt32b9/v4iI3L59Wzp16iRNmzaVzMxMrcRMedvatWulcePGkpWVpfSRTZs2SYsWLcTc3Fzu3buX6/wTJ05Ily5dpGjRonLz5k1thEx5WM77zMWLF6VYsWLi5eUlaWlpcv36dXF3d5eyZcvKrFmzlPPatWsn5cuXl+TkZC1ETHkJE1P00Wk0Grl48aKoVCpZv369jB49WoyMjOTKlSsiInLz5k0ZP368VKtWTVavXp3r2qdPnyqJBSan6E03btwQlUolP/30k9J2/vx5qVatmjRo0EBJhGb3HQ7U6U1v3leCg4OlRo0akpCQkOvY7NmzxdjYWElsJiQkKMlRKthyJg/S09OVf69bt04qVaokEydOlOvXr4uIyOPHj6Vp06bSpEkT2b9/f65rif6Kq6urlChRQnbv3i2PHj1S2o8cOSIdO3YUCwsLKVOmjFSvXl3s7OyUfsj+RW9KTExUxkKnT59W2v39/cXOzk6aNWsmd+7cyXXNkSNHxNnZmWMoUqxcufKt+8vu3bulevXq8vz5c6XtwYMHMnv2bLGzs1Ne0ogIX8SQiDAxRVq0aNEiMTQ0FGNjYwkPDxeRP/8ovHHjhpKcWrt27VvXMilFf2Xy5MliZGQkP//8s9KWnZxq3LixMnOKKFv2YCrnoOrx48ei0WgkICBA1Gq18tb41atXIiJy//59KV++vPj5+X38gCnPu337tqSkpIiIyJ49e2TOnDkiIrJixQqpW7eujBs3LldyqkWLFlK9enU5dOiQ1mKm/OHYsWNStmxZ+f3330VEJC0tTW7fvi0HDx6Uhw8fSnp6uoSHh4uHh4fs27dPSR5kZGRoM2zKY1xdXZXnmYhISEiIqFQqWbFihdK2a9cucXBwEHt7e4mLi3vn5zA5RVevXpWGDRu+tQIhKChILC0t5cyZM7nao6KixMjISAIDAz9mmJQPsMYUfXRZWVkAgPLlyyMjIwNpaWm4ePEikpKSlLoJ1tbWGD16NNq3b48pU6Zg7969uT4jZ9FPKrjk/2qURUREwNfXFxqNBosXL4azszO+/fZbeHt7AwBq164NX19f3Lx5Ex07dtRmyJQHqdVq3LlzB9OnTwcA+Pn5oX379njy5AlatmyJJk2aYOzYsXj48KFSnFNfXx+Ghoasp0FvSUtLQ+/evdGgQQNs3rwZ3bp1U3ZnHDduHAYOHIiwsDCsWrUKN27cgIWFBXbt2oWyZcuiSpUqWo6e8jqVSgULCwsUKlQI58+fx/Tp0+Hg4IARI0bA3t4eV69exeeff44xY8agY8eO0NHRQVZWFu9VpLh16xZWrVoFBwcHpa5PhQoVMHXqVMyePRuenp4AgO7du2PkyJFQq9UYPHjwO2tyslYZVa5cGUFBQahcuTJ+//13aDQaAK9rBBcpUgS//PILHjx4oJxfpkwZVKtWTTmPSKHtzBgVHG9O8czMzJTMzEyZP3++qNVqWblypSQlJeU65/79+7J06VK+kaG3ZM+a27VrlxQvXlxcXV3l4sWLyvFZs2aJjo5OrplTFy9elJiYmI8eK+VtGo1GFi1aJLVr15ZOnTqJrq6ubNq0STm+YcMGadGihXTq1EliYmLkxo0bMmPGDPnss8/eWuJAJPJ6WcJnn30mBgYGsmbNGhF5PbMl2/Lly6Vu3boyceJEZRk7l1nRm95VuuDMmTNSvnx5sbe3FxMTExk2bJhs27ZNjh8/LtWqVRN/f39thUv5yNmzZ6Vq1arSuHFjZann3bt3ZcaMGWJqaioeHh7KuX5+flKrVi0ZM2aMtsKlfODRo0diY2Mj9erVU55na9asERMTE5k0aZIcO3ZM7t+/Ly4uLmJlZZWrJiyRCJfy0UeSc8B96tQpOXTokISEhChtM2fOFLVaLV5eXkpyatCgQcqAXYTTheltJ0+elMKFC8uaNWveuUxh1qxZYmRk9FYhfaJ3cXR0FJVKJe3atcvVrtFoZOPGjWJvby8qlUpsbGykfPnycvbsWS1FSnldQkKCFC5cWCwsLOTzzz9XlvXlXDqzYsUKKVu2rLi4uEh6ejqXqFMuOcdNjx49kuTkZCWBEBoaKj/99JPs379fKRj88uVLqVu3ruzZs0cb4VI+dPbsWbG2tpbPP/9c6Vvx8fFKcsrT01M59+jRoxyH09/KyMiQgIAAqVOnTq4NF9atWye1a9eWokWLio2NjZQtW1bOnTun5WgpL1KJ/N9aGKKPwNXVFQEBAXj58iVKlCgBXV1dhIWFQaVSYd68eXB3d0e/fv1w/fp1JCQk4MaNG5x+Tm8REahUKixYsADh4eHYt2+fciwrKyvX1PLJkydj48aNiImJQeHChbURLuVxGRkZUKlUcHV1RXx8PO7fvw87Ozt8//33MDY2Vs7TaDQ4fvw4TExMYGVlBSsrKy1GTXldfHw8Xr16hY4dO8LExAQhISEoVKgQ0tPTlSWhW7ZsQZMmTVCxYkUtR0t5iUajgVr9utrGokWLsG/fPqSnp6NkyZLYvHkzihQpgszMTOjq6uLVq1dITk6Go6Mjnjx5gpMnT3J5Fb1T9tgp58+RkZHo2bMnihcvjmPHjkFPTw93797F2rVr4eHhAVdXV7i4uCjXvDnGIsrp1atXOHr0KCZPnowiRYogNDQUOjo6iImJwdOnT5Gamopq1apx/ETvxMQUfTA5B1YAsHz5cri7u2P//v1o2LAhFi5ciGnTpuHgwYNo06YNAMDDwwMRERHQ09PDmjVroKenx4cg/aWJEyfi3LlzOHr0aK6+BryuO1W/fn2o1Wo8evQIxYsX11KUlFe9OUjPbps9ezYOHTqEzz//PFdy6t69e7CysnqrrxFl96VHjx4pzy0LCwtoNBpcvHgRvXv3RuHChREcHIxChQph2bJlSE1NVeqaEWXLeV+aNm0afv75Z7i7u6No0aJwc3ODvr4+Dhw4gNKlSyMtLQ2LFy/G0aNH8eLFC4SFhXHcRO+Uc0yu0WiQnp4OQ0NDAEBkZCS++eYblChRQklO3bt3D4sXL8bly5cRGBgIgPVd6U/Z96kzZ87gzJkzUKlUaNy4MWxtbXMlp8zNzREaGspxE/0r7CX0QTx69AhqtVopdJ6VlYULFy5g/vz5aNSoEfbt24eFCxdizZo1aNOmDZKTkwEAY8eOxdq1a+Ht7Q09PT1kZmZycEUA/ix0fvfuXeXfpUqVwuXLl98qyJmWlobNmzfj119/BQAmpegt2YOq4OBgjB07FtOnT8fRo0ehUqng4uKCdu3aISIiAq6urvjjjz8wa9Ys9OzZEy9fvtR26JTHZPelX3/9FR07dkSLFi3QuHFjBAcHQ61Wo3bt2tixYwdSUlJQpUoV9OnTB87OzujUqZO2Q6c85ObNmwD+/OM/MDAQBw4cgJ+fH4YOHQo9PT0kJCTg6dOnaN68Oe7evQtDQ0N8+eWX+Prrr3HixAmOm+idcialli5dir59+6JRo0ZYtGgRzp49i7p168LPzw8PHz5EixYtkJGRgVKlSmHatGkIDAxkQopyyX7m7d69G506dcKGDRvg6+sLe3t7BAcHw8DAAA4ODliyZAlSUlJQt25dFjqnf+ejLx6kT97s2bPF2NhYYmNjReR1nYSsrCxp1qyZrFmzRg4ePCgmJibi5eUlIq9rR/3444+yefPmXJ/DehuULbsv7N27V2rVqiUbNmxQjjVs2FBq1aol169fl9TUVElLS5OpU6dKmTJl5Pbt21qKmPKDgIAAMTQ0lNatW0v9+vXF3Nxctm7dKiIiL168EHd3d7G1tZVy5cqJpaWlnDp1SssRU14VEBAgJiYm8sMPP8jx48fFyclJChUqlOu59uDBAxk9erSMGDFCLl26pMVoKa/p2rWrzJgxI1dbUFCQzJ07V0REfvvtNylWrJisWrVKLl68KMWKFZM6derIrVu3cl3DGkCU05vjaFdXV7GwsJBp06bJyJEjpUqVKvL111/LoUOHRETk3LlzUrVqValUqVKuup0cj9Objh07JsWLF5e1a9eKyOt6ZSqVSvT09MTPz09EXtdU3Lt3rzRu3JjjcfpXmJii9+748ePStm1bqVSpkjJoyszMlKlTp0rLli3FzMxMfvrpJ+X8xMRE+eqrr2TlypXaCpnygV9//VUMDQ1lxYoVuf6oi46OlmbNmomFhYXY2tpKixYtpHjx4iysSH/r8ePHsmrVKmVQFRsbK1OmTBGVSiVbtmwRkde7qJ06dUp8fX3f+gOQKNudO3fE3t5efvzxRxERiYuLk4oVK0r16tVFV1dXfHx8chWyzi4yTJTtxIkTSmH8xMREpf3u3bvy6tUradWqlUybNk1ERJKSkqRJkyaiVqulU6dOIsLEAf217HtPVFSUWFtb59p4KCQkRDp16iRdunSR+Ph40Wg0Eh4eLr169WKSk/7WnDlzZPr06SLyumB+2bJlZciQITJs2DDR1dWVgwcPisjr5FT25h9E/4SJKXpvfvnlF+XfERER0rp1a6lQoYLcvHlTREROnz4tpUqVknr16snly5clKytL7t27J+3bt5fGjRvzIUh/KSUlRVq3bi1ubm5/eY63t7csXLhQPDw8lD5H9C4XL14UMzMzqV69ujJ4Enm981V2cip75hTRu2QnAp49eybp6ekyf/58efr0qdy/f1+qVasmTk5Okp6eLj169BBzc3P5+eefmTygf7Ry5Urp1q1brhcrd+7ckXLlyklgYKCIiDx9+lR69eolp0+fzpXwJMrm4uIiU6dOzdV2+fJlKVmypISGhuZqP3LkiBQpUkTpXzlxXE7Zsp9fgYGBcvnyZYmOjpYTJ05IcnKyNG7cWL799lsReb3zukqlEpVKJfv27dNmyJQPscYUvReHDh1Cr1694O7uDgBo0KAB5s+fD2tra7Rq1QoxMTGoX78+tm3bhvv376N///6wtrZGjx498OjRI2XXhuyaVEQ5paWlITo6GjVr1gSAXGvV5f/qTQ0ePBguLi4YM2YMd7iiv6Wnp4fu3bvj5s2bePbsGYDX/ahYsWJwcXGBq6sr+vfvDz8/Py1HSnmVSqWCr68v7Ozs8PLlSwwZMgTm5ubw8PBAxYoVsXTpUujp6aFMmTJQq9VwcXFBUlKStsOmPEbe2H+oTJkyOHXqFDw9PXHhwgUAQNmyZVGiRAm4urpi586d6NatG+7du4d69erlquVJBAB//PEHEhIScPToUfzwww9Ke2ZmJlQqFe7du6f8DAAtW7ZE2bJlERER8dZnsVYZZVOpVAgLC0OXLl0QGRmJypUro0mTJrh+/TrS09Mxfvx4AEDhwoXRq1cvuLm5oXLlylqOmvIbJqbovbC3t4eXlxfmzp2LefPmAXidnHJ3d0eVKlXQunVrREdH44svvkBwcDCmT5+OESNGwNXVFadOnWLBTvpbpqamsLCwwJkzZwAg12D8zJkzWL9+vXLumwN9ojdVrVoVU6ZMQc+ePeHk5ITg4GCluKuFhQUmTpyImTNnwsbGRsuRUl517949bNmyBZMmTYKZmRksLS0BANeuXUOpUqVgZmYG4PXGHxs3bsSNGzdQuHBhbYZMeVD2fef333/Hixcv0KVLF6xfvx5BQUFYtmwZoqKiAAArV66EgYEB5s2bBwMDAxw5cgRqtRoajYbjJsqlcOHCWLx4MRo2bIgDBw5g/vz5AABbW1t88803GD58OE6dOgVdXV0AwPPnzyEi+Oyzz7QZNuVxd+7cwYEDBzB9+nT069dPaX/27BnOnTuHFy9eAAC2bt2KZ8+eYerUqRxD0X+n1fla9El59eqVeHl5iY6OjlKwU+T1sr42bdpI+fLlJSYmRkTerofA6cKULbtvZGRkyIsXL5T2iRMnSu3atZWiitmcnZ2lcePG8uzZs48ZJuUT2f0pNjZWrl+/LhcuXFCOXbt2TQYNGiTm5uYSFBSU63wukaG/cubMGenbt6+0bdtWEhMTcz2/Zs6cKYUKFZIFCxaIo6OjmJuby/Xr17UYLeVFOe8vAQEBYmtrK0uXLpW0tDQREdm/f7+ULVtWBg4cKFeuXFHOjYuLy/WMJMop570oKChIevbsKZUrV1bq34mI9O7dW/T19cXZ2VnmzJkjbdq0kVq1arE/0V+6cuWKNGnSRMqXLy9r1qwRkT/72qtXr6Rnz56iUqmkfv36YmpqKufPn9dmuJSPqUQ4vYD+d/J/W4ZmS0tLg7e3N8aOHYvZs2djxowZAIDTp09jxowZuHnzJg4cOABra2tthUx5WHZ/OnDgADZv3oyoqCh8/fXXaN++PZo0aYKuXbvi6dOnqFOnDmxtbXH69Gns3r0bx48fh62trbbDpzwmuz/t3bsXM2bMQHJyMoyNjdGmTRssW7YMwOsZLj/88AMOHDiAjRs3ol27dlqOmvK6uXPnYuPGjXjx4gVu3LgBU1NTZGRkQE9PD6mpqXBzc0NISAjMzc2xfPly1KlTR9shUx6i0WigVr9esLBlyxZcuHABa9euhbm5OSZNmoShQ4fC0NAQBw4cwKhRo+Dg4IBRo0ahfv367/wMojdNmjQJUVFRUKvVOH/+PIyNjTFy5Ei4uLgAAL7//nscOXIE6enpKF++PH7++Wfo6ekhKyuLM/DoncaOHYvNmzejVatW8PHxgampqTLGevLkCQ4cOIA//vgD7dq14xI++t9pMytG+VvON34ZGRm5ZkF5eHiIWq1+a+ZUvXr15JtvvvmocVL+kN1/9u7dK8bGxjJjxgzZvHmz2NvbS4UKFeT27dvy/PlzmT59utjb20utWrWkc+fOuWbAEL3pwIEDYmJiIqtWrZIbN27IqlWrRKVSyYgRI5Rzrl27Jt27d5cKFSpIamoqi1TT30pPT5clS5ZIqVKlZMCAAfL8+XMRyT0T+OnTp5KamqqtECkfmD59ulIYf9OmTdKsWTOpXbu2LFu2TF6+fCkir+9fBgYG4u7uruVoKb/YsWOHFClSRCIiIuTly5fy4MEDGThwoNjZ2cmiRYuU8/74449cM6w4Y4qy/dUYaNKkSVK9enWZN2+e/PHHHx85KioIOGOK/ic539YtX74c58+fR0xMDLp164YuXbqgYsWK8PLywrhx4zBr1ixl5tTVq1dRtWpVvukjAMCBAwdQunRp2NraQkTw+PFj9OjRA126dMGECRPw8uVLlCtXDv3798eSJUty9ZuUlBTo6+tDX19fi9+A8rLHjx9j6NCh+OKLL/Ddd98hISEBTZo0QZUqVXDixAn07t1bqU8WHR0NExMTWFlZaTlqykvk/94IP3jwQJkRVaZMGWRmZuLHH3/E7t270bBhQ3z//fcwNTVFZmamUruF6F1EBPfu3UOrVq0wY8YM9O/fHwCQmpqKYcOG4fTp0xg/frwycyo8PBwNGzbkTBb6VxYtWoTt27fj9OnTyr3o7t27GDlyJM6ePYspU6Zg4sSJua6RN1Y/UMGV3RciIiIQHh4OfX19VKxYEW3btgUATJgwAcePH0fXrl0xduxYmJmZcQYnvTfsRfQ/yb4Bubq6Yv78+WjQoAEcHBywfv16DB8+HC9evICTkxM8PDzg7u4OZ2dnAICNjY1SsJMKtgcPHmDMmDFYvnw5rl69CpVKBWNjY6SkpKB9+/aIjY2FtbU1unTpgqVLl0KtVuPQoUOIjo4GAJiYmDApRbmIiFL8Pjo6GsWKFcOXX36JTp064eHDh2jTpg3atm2Lffv2YeLEifD29lb+KLS2tmZSinLJHqD7+/ujffv2aNSoEVq2bAl3d3fo6upi8uTJ6NKlC86cOYPp06cjKSmJSSl6p5zvgFUqFQoVKgS1Wq0UDM7MzEShQoWwadMmqNVqeHl5Ye3atXj16hUaN27MXYvpH2WPq0uUKAGNRqPsvqfRaFC6dGm4ubnhxYsXWLlyJXx8fHJdy6QUZVOpVPDz80Pr1q2xc+dOeHl5oUOHDsrfccuXL0fjxo3x66+/4ocffkBycjKTUvTesCfRf5Y9wIqIiMC+ffsQEBCA0aNHo1mzZoiNjUW/fv1gbGwMAwMDjBw5EvPmzUN4eHiugRlvYlSyZEns2rULly5dwtKlS3Hp0iXo6Ojg5cuXCAkJQZs2bdC+fXv89NNPAIDbt2/Dx8cHMTExWo6c8prk5GQArwdUKpUK+/btQ4sWLXDlyhUMHz4clStXxs6dO1GyZEnMmTMHBgYGKFWqFOzs7BAeHq4M4IlyUqlUCAoKQu/evTFo0CDMmTMHY8eOxZw5c+Dk5AQdHR1MnjwZnTp1QmBgINzd3bkrKL0l52yUx48fAwD09PRgZmaGoKAgAICuri6ysrKgq6uLunXrQl9fH7t27cLx48eVz+GMKcrpzRe82ePqBg0aIDY2FitWrMCLFy+U9oyMDDRv3hwTJ06Eo6PjR4+X8ofo6GiMGTMGCxcuxPHjxxEaGgofHx94enrC1dUVAODh4YEaNWogPDwc6enpWo6YPilaWD5I+dCsWbMkICAgV9vRo0fFxsZGRER27dolpqam8tNPP4mISEpKivj7+0tqaqpkZWUp65VZu4XedO7cOalXr544OTnJ/fv3xdPTU1QqlXz11Ve5znNzc5OaNWtKXFycliKlvGjYsGEyePBgSU9PFxGRO3fuSK9evWT16tW5zhs5cqQ0bNhQ+XnKlCmyYMGCXDs/EmXLflaNHDlS+vbtm+vY0aNHRa1Wyw8//CAir3cl+vHHHyU2NvZjh0l5XM5anHv27JFWrVrJ5cuXRUTk999/F2NjYxkzZoxSp1Oj0UifPn3kwIEDUrduXenRo4e2Qqc8LOdYevXq1fLdd9/JzJkz5c6dOyLyekyuo6Mjw4cPl/3798vly5elXbt2MnLkSOVa7oZN73Ly5EmpWrWq3L17N1f7xo0bxcjISEJCQpS2xMTEjx0efeI455z+0aVLl3D48GGcOHEChoaG+PLLLwG8fgtoYWGB7du3Y8SIEfjhhx8wYsQIAMCpU6ewd+9e1KhRQ9mdQbiGnd6hbt26WL9+PYYMGYKZM2eid+/emDRpEpYtW4bFixcDAGJjY7FlyxYcO3YMZcqU0XLElFfs2LED/v7+CAwMhJ6eHiIjI+Hl5YV79+7BwcEBwJ/18Lp06YINGzaga9eu0NfXx6FDhxAeHg4jIyMtfwvKS7KfUy9evEChQoUQGxsLc3Nz5VhGRgbs7e0xb948bN26FQMHDkTJkiXx3XffaTlyymty1l05cuQI/Pz8cO7cOcyePRtz5sxBw4YNsWXLFvTr1w+RkZEoWbIk7t+/j6dPn2Lbtm04efIkjh49yvotlEvO/uDq6gpvb2/Url0bDx8+hLe3N4KCgtC9e3fs27cPU6ZMwf79+6Gjo4NixYph3759UKlUEBHOwKN30tPTQ3R0NKKjo1GqVCnlmejg4AArKyskJCQo55YsWVKLkdKniE86+kc1a9bE/PnzYWhoiEWLFuHQoUMAgJYtW+KPP/5Av3798P3332PkyJEAgLS0NCxduhTJycmoWLGi8jlMStFfqVu3Lry9vREZGYmdO3eiTZs2WL58OTZu3Ag/Pz88f/4cJ0+e5LbrlEt8fDwsLCxQp04dHDx4EAMHDkRYWBjOnDmD2NhYAH8ub2jSpAk2bNiA1NRUqNVqHDt2DDY2NtoMn/KY7AF4UFAQZs6cibi4OHTu3BlHjx7FmTNnoFKpoKenBwAwNzeHSqWCmZmZlqOmvCr73vPdd99h7NixKFq0KJo3b45jx45h+vTpuHbtGrp27YqoqCjUqVMHZmZmaNSoES5dugTg9WYxFStW5PJQyiW7Xz18+BAvXrzAoUOHcPjwYWzbtg22trb4/PPPce3aNXz11VcIDAxEcHAwfH19ERERAT09PWRmZnI8TgD+LM1y9epVhIWFITY2FvXq1UPHjh2xatUqnD9/XukrxYsXR5EiRbh0jz4o7spHfysjI0MZiO/YsQObN29GSkoKZs+ejZYtW+Lq1avo3LkzLCwsMHz4cGRmZsLX1xeJiYmIjIyErq4u3/bRv3bu3DkMHz4cderUwdy5c2FpaQmVSoW0tDQYGhpqOzzKY06fPo0BAwbgs88+Q2hoKAIDA5GRkYHJkyejYsWKmDlzJurXr5/rGo1Gg4yMDBgYGGgpasrLdu/ejf79+2Pq1Kn46quvYGhoiKlTpyIrKwtz586FnZ0dAGDy5Mk4e/Ys9u3bB1NTUy1HTXlVaGgoevXqhT179qBx48YAgPXr18PHxwclS5aEu7s7bGxskJWVpcxgefjwIRYvXgwfHx+EhoaievXq2vwKlAdt2bIFI0eORPXq1bFr1y5lJnlMTAzGjx+P8PBwhIeHo2rVqrmuy9nPiADA398fAwYMgKWlJeLj47F+/Xq8fPkS27dvh5mZGYYPH47y5ctj48aN2LBhA37//XeUL19e22HTp0pLSwgpn5k1a5b07NlTbG1tRa1WS7NmzSQoKEhERKKjo6VVq1ZSs2ZNadq0qQwcOFCp98I17PRfnTt3Tho0aCC9evWSS5cuiQhrk9FfGzVqlKhUKmnUqJHStm3bNqlfv74MGDBAzp49q7TnrPdC9Kbr169LhQoVxMvLK1e7v7+/dOzYUSwsLOSrr76Stm3bipmZmURGRmonUMo3goODxcLCQnmWZVu5cqUYGBhI9+7dlZpTIiLx8fHy/fffS5UqVdi/6C8dOXJE2rZtKyYmJkpdqexxUkxMjHTs2FFUKpXEx8drM0zKw7KysuTJkyfStGlTWbNmjURHR8u8efNEV1dXVq1aJevWrZNevXqJWq2WatWqSeXKleXcuXPaDps+cZwxRf/Iy8sLrq6uCAgIQOXKlREeHg5PT0+o1WpMnz5dqeXy6NEjGBsbo1ChQgBeb3/MrbPpf3H69GlMmTIF27dvh5WVlbbDoTzq5cuX6NChAypWrIiTJ0/C1tYW27dvBwBs27YNy5YtQ82aNTFy5Eg0bNhQy9FSXhcUFITRo0cjMDAQ5cqVyzXb99q1azh79iwCAwNRunRpDBgwANWqVdNyxJRXyf8tCz116hT69euHlStX4uuvv1b6VFZWFmxtbWFsbIwaNWpgwYIFsLKygkajQUJCAnR1dVm/hQDgnasORARnzpzByJEjkZSUhBMnTqB48eJKv7t+/TrWr1+PBQsWcBxOuWT3kbS0NIgI3N3dMXnyZKWW4rJly+Ds7IwlS5agT58+SE5ORnp6OiwsLFCiRAktR0+fOt6t6B9FRESgU6dOaNGiBQDgm2++gYmJCSZNmoSZM2dCrVbD3t4exYsXV64RET4M6X/WoEEDHDx4kMv36G8ZGRkhICAAxsbG8Pb2xqJFi9C3b19s27YNffv2VZLnhoaGqF27Npfv0d9KSUnBy5cvc7VlL31JTExE06ZN0a9fPy1FR3nZm8mD7Losn3/+OapUqYLx48ejTJkysLW1BQAkJiaiVq1asLGxwaZNm3DlyhVYWVlBrVajVKlSWvkOlPfk7Fd79uzB/fv3odFo0Lp1azRo0ABr167FuHHjYG9vj6NHj6JEiRIQEVStWlXZPIYviSknlUqFvXv34qeffkJ8fDw0Gg169eqlJKYmTpwIlUoFZ2dnPHz4EG5ubsqEA6IPjYV/6B8VLVoUT548yTVgb9euHRwdHXH27FlMmDABv//+e65rWFiR/n8xKUX/hrGxMQCgZ8+ecHFxQWRkJPr27QsA6N27NxYuXAhnZ2cmpegf1a5dG48fP8batWsBvC4ynF2Pxd/fHxs2bGDhV3pLzuTBzp07MWvWLHh4eCA0NBQAcODAARQvXhydOnXC999/Dx8fHwwcOBApKSmYNWsWRAS//fabNr8C5VHZ/crZ2RmjR49GSEgIvL290bdvX3h7e6NevXpYtGgRLCws8OWXXyIxMfGt8TeTUpTTmTNn4OjoiAoVKqBhw4a4efMmvL29cefOHeWcCRMmYO7cufDy8kJaWpoWo6WChokp+ke2trY4efIkgoKCcu0OU7JkSTRp0gTffPMNGjRooMUIiaigMzExQc+ePeHs7IyLFy+iQ4cOAF7P8KxQoYKWo6P8oEKFCvD09MTixYvh7OyMS5cu4erVq3BxccHGjRvRp08f6OvraztMykNEJFfyYMKECTh79iz27NmDKVOmYPPmzVCpVAgPD8eXX36J/fv3Y8GCBdDX18fOnTsBAFZWVqhSpYo2vwblYdu3b8f27duxb98+7Ny5E+PGjcPly5dRpEgRAK93nF2yZAnS09MxefJk7QZLedrNmzcREBCAqVOn4qeffsKGDRuwYsUK+Pn5YfXq1bmSUy4uLrh16xYsLCy0GDEVNEyj0z8aNGgQTpw4gf79+2P16tWoV68eLC0tsXv3bjg4OMDNzQ0qlYq77xGRVhUqVAg9e/ZEWloafHx8cO/ePS6Lof9k0KBBMDU1xfDhw7F9+3YYGhpCR0cHR44cYU0pyiXnmGfVqlX45Zdf4Ofnh88//xxeXl6YOHEiZs2ahRcvXmD48OFYv349nj9/DhFRls3MnDkTsbGxaNWqlTa/CuVhMTEx+OKLL1C/fn3s3LkTEyZMwIoVK9CtWzekpKTg4cOHaNiwIXbt2gUbGxtth0t5VFJSEnr37o3bt2/j22+/VdpHjhwJjUaDBQsWQEdHB05OTsrLvOzkJ9HHwuLn9LdyDrzGjBkDf39/ZGVlwdTUFDo6Orh48SJ0dXWVYnpERNr24sULZGRkoHDhwtoOhfKp+/fv486dO1CpVKhQoQILUVMuOcc8SUlJcHNzQ/ny5TF58mTs27cPjo6OGD9+PKKjo3Hs2DEsXLgQ/fv3V66Pjo7GzJkzERoaiv3796Nu3bra+iqUh7zrBa+rqyt0dHTQsWNHtG7dGosXL8aIESMgIvDx8cHTp08xbtw46OnpAfizLh7RmyIjI9GrVy+UKFECq1evRs2aNZVjq1evxsSJEzF16lS4ublxCShpBRNT9I9yDsDCw8Px+PFjpKamokePHtDR0eFDkIiIiAqEo0eP4v79++jXrx+GDx8Oc3NzjB8/Hi9fvkRWVha++uorjB49GhMmTMCePXvQp08f6OnpYdOmTejatSsAIC0tDYcPH4aNjQ0qV66s5W9EeUHOpNTNmzdhZGSE4sWL4/Tp02jWrBkAwNfXFz169AAApKamolu3bqhZsyZ+/PFHrcVN+cuFCxcwcOBANGzYEOPGjUONGjWUYz///DO++OILWFtbazFCKsiYmKJ/5a+W6TEpRURERJ86EUFKSgq6d++O9PR0mJmZITQ0FGFhYcpue1u2bIGHhwcCAwNRuHBhBAYGYs2aNWjfvj0GDx7M8RK9U84XwK6urti7dy8ePXqEGjVqKLXtRo0aBW9vbzRt2hRJSUmYMmUKHj58iIiICM5uof8kMjISQ4cORb169TBx4kRUr15d2yERAWDx8wJLo9G8s/2v8pTZSak3r+Mgi4iIiD51KpUKpqam2LFjBxITE/Hrr7/Czc1NSUoBgJ6eHuLi4hAWFoYXL17Aw8MD5cuXh5OTkzLDnCgnjUajJKV27NiBjRs3YuHChfjxxx/RqFEjTJgwAadPn8aiRYvg5OSExo0bw9HREenp6fj999+hq6vLfkX/Sd26dbF+/XpcuHAB8+bNw7Vr17QdEhEAFj8vkHLOfrp06RJevHiBEiVKoHz58lCpVH85Cyrn7jPXrl1D2bJlla3aiYiIiD51arUalSpVQsmSJREcHIzSpUujX79+AIDq1avjiy++gKOjI4oUKYJChQph9+7dUKlUEBG+zKO3ZI+rQ0JCEBwcDGdnZ3Tu3BnA6/pl5cuXh6urK7Zv347Lly8jPj4eZmZmqF27NtRqNTIzMzljiv6zunXrwtPTE1OmTGE9TsozuJSvgMk5XXjatGnYt28f4uLi0KhRIzRs2BDu7u4A3l6il/M6Dw8PLFiwACdPnkT58uU/+ncgIiIi0qbExEQ4OTnh5cuXcHJyUpJT169fx7Vr15CcnIw+ffpAR0eHyQP6W4mJiWjWrBkePnwIFxcXTJs2TTn25MkTODk5oUyZMvDw8Mh1HXfDpv9faWlpMDQ01HYYRAC4lK/AyU4uubu7Y/369VixYgViYmJQqlQpeHp6YsyYMQCQa8p5zqTUmjVrMHv2bCxdupRJKSIiIiqQLC0t4enpCWNjY2zcuBHe3t7IysrCqFGjcPHiRfTv318ZSzEpRX/H0tISu3fvRokSJbB7925ERkYqxywsLFCsWDHcvHnzreuYlKL/X0xKUV7CO1oBkXNi3JUrV7Bnzx5s3boVDg4OiIqKwi+//II2bdrg4MGDmDBhAoDXyamMjIxcSSlnZ2esXbsWvXv31sbXICIiIsoTKlSoAA8PD5iammLJkiWwtrbGw4cP4ezsrJzD5Xv0b9ja2mL37t3IysrC8uXLcf78eQBAcnIyrl69itKlS2s3QCKiD4xL+QqAnDOeoqKiYGtri/Xr16Nbt264dOkSevfujXnz5mHIkCHo0KEDjhw5gm7dumHbtm3KZ6xduxbOzs74+eef0b17d219FSIiIqI8JSEhAWfPnsWDBw8wcOBA6Orqcvke/U8iIyPRv39/PH36FPXr14e+vj5iY2Nx6tQp6Ovr5xrTExF9SpiY+sS9uQXtqVOnsGPHDpQsWRIqlQojR46Erq4uli5dCj09PUyZMgWnT59G9erV4enpCbVajX379qFLly7YtWsXunXrpuVvRERERJR3/dUmMkT/xqVLl9CpUyeULl0affv2xYgRIwAAGRkZ0NPT03J0REQfBpfyfeKyk1LXrl1DeHg45s+fD0tLS6U9NjYWd+/ehZ6eHrKysnDnzh0MGDAAq1atUtaud+jQAUePHmVSioiIiOgfMClF/z9q1qyJ3bt3Iz09HefOnUNMTAwAMClFRJ80zpgqABYsWICQkBAYGhpiy5YtMDU1hUajAQAsX74cmzdvhpWVFZKSkvD8+XNERUVBR0cHIsKinUREREREH1lkZCRGjBiBihUrYtasWahWrZq2QyIi+mA4Y6oAsLGxweHDh3H8+HHcvn0bwOudPNRqNfr06QNHR0cULlwYNWrUQGRkpLKLjEqlYlKKiIiIiOgjq1u3Ljw9PZGQkIDChQtrOxwiog+KM6Y+MX9VFDE4OBht2rTBoEGDlOV8f4UFO4mIiIiItC8tLQ2GhobaDoOI6IPijKlPiEajUZJSDx8+RFxcnHKsVatW8Pf3h4+PD9zd3fHgwYNc12UTESaliIiIiIjyACaliKggYGLqE6HRaJRi5XPnzkX79u3RoEEDtGvXDiEhIUhLS0PHjh3h7++P1atXY/78+UhISAAA5ToA3IKWiIiIiIiIiD4aJqY+ASKiJJdmzZqF1atXY8KECQgPD8etW7cwffp0BAQE5EpOeXp6Yvv27VqOnIiIiIiIiIgKMq7ZyseuXr0KGxsb5ecTJ05g79692LJlCxwcHBAWFoZ79+5BRDB9+nTo6Ojgq6++QocOHRAWFoZGjRppMXoiIiIiIiIiKug4YyqfWrJkiZJ8UqlUEBGYm5tjzJgxcHBwQHBwMLp164ZVq1YhOjoaaWlpWLp0KXx9fZGeno6mTZtCV1cXmZmZ2v4qRERERERERFRAMTGVT9WqVQtffPEFxo8frySnrK2t0bFjR2RkZGD58uUYNmwYHB0dISKwtrZGVFQUTpw4AX19feVzWOiciIiIiIiIiLSFial8Zt26dQCAtm3bYtSoUahcuTLGjh2LY8eOQU9PDyVLlkR6ejoeP34MCwsLpfZU2bJlERISgtWrV2szfCIiIiIiIiIiBafL5CNBQUEYPnw4oqKi4OnpiRYtWkBE4OXlhXHjxsHDwwPNmzeHWq2Grq4udu3ahaSkJISFheHJkyeoW7cu1Go1srKyoKOjo+2vQ0REREREREQFHGdM5SMNGjTA2rVrsWvXLowePRoAYG9vj1GjRqFKlSoYO3YsQkJCYGRkBD8/PxgbG+PEiRMwNTXFmTNnoFarodFomJQiIiIiIiIiojxBJSKi7SDo30tOTsaOHTswbdo09OjRA6tWrQIAhISEwMvLCzdu3MDSpUvh4OCAtLQ0iAgMDQ2hUqmQmZnJmlJERERERERElGcwS5EPiAhUKhUAwNTUFD169AAAuLm5AQBWrVoFe3t7AICXlxemTJmCBQsWoE2bNrk+g0kpIiIiIiIiIspLmKnI4zQajVLAXKPRIDMzE0WKFMHAgQMBAFOnTgXwZ3JKpVJh3rx52LZtW67EVHZii4iIiIiIiIgor2BiKg/LmZT68ccfERUVhXPnzmH48OFo2bIlhg0bBgCYNm0aVCqVUhDdzMwMtWvX1mboRERERERERET/iDWm8oGpU6fi559/xsyZM5GSkoL169ejWrVq2LFjB7KysrBz505Mnz4drVq1wtatW5Xrcia2iIiIiIiIiIjyGmYt8riIiAj4+/sjICAAY8aMQbNmzRAXF4eePXvCxMQEhQsXxoABAzB16lQ8f/4cGo1GuZZJKSIiIiIiIiLKy5i5yOM0Gg0MDQ3RqFEj/PLLL2jfvj1WrlwJR0dHpKam4sCBAwCAb7/9Fr/++ivUanWu5BQRERERERERUV7FxFQe8q6EUkpKCtLS0rBjxw58++23WLhwIUaMGAEAOHnyJLZt24a4uDgYGRlBpVJBRDhTioiIiIiIiIjyBdaYyiNy1oNavXo1ACgJqLZt2+Lw4cPw8PDA6NGjAQBpaWn45ptvYGRkBF9fXyajiIiIiIiIiCjf4a58eUR2YmnKlCnw9fXFwIEDcffuXZQuXRrff/89/vjjDyxbtgyFCxfGs2fPEBAQgPv37+P8+fPK8j0mp4iIiIiIiIgoP+GMqTxky5Yt+O677/Dbb7/Bzs5OaddoNLh27Rrmzp2LqKgolChRAtbW1vjpp5+gp6eHzMxM6Ooyx0hERERERERE+QsTU3mIm5sb7t27h40bNyIrKws6OjpvJZ0ePHgACwsLpY1JKSIiIiIiIiLKr7j2Kw+5d+8eYmNjAQA6OjoQEejq6iItLQ1BQUEAgJIlSyqJqOzjRERERERERET5ERNTWvCu3fcAoG7dunjw4AGOHj2K9PR0qFQqAEBSUhLmzJmD3377Ldf52ceJiIiIiIiIiPIjLuX7yHIWKT99+jQ0Gg10dHRQv359vHr1Ck2bNgUATJ06FU2bNkVKSgomTJiAZ8+e4dixY9DR0dFm+ERERERERERE7w0TUx+RiCiznFxcXLB9+3aoVCo8ePAAffr0waJFi2BqaorOnTvj3r17iImJQfXq1aGnp4fjx49DT09PqT1FRERERERERJTfMTGlBZ6enpgzZw727t0LCwsLxMfHY8CAAWjUqBG2bt0KfX19XLlyBdevX0fJkiXRrFmzdxZCJyIiIiIiIiLKz5iY0oKBAwfCyMgIq1evVmZRnT9/Hl988QXGjh2L+fPnv3UNZ0oRERERERER0aeGxc8/sDfzfhkZGbh37x7S0tKU4+np6ahTpw5mz56NnTt34tmzZ8jKysp1HZNSRERERERERPSpYWLqA9JoNEpNqVu3buHhw4fQ09ODo6Mjdu3aheDgYKjVaujp6QEADAwMUKxYMRQqVIiJKCIiIiIiIiL65DEx9QFl777n5uaGTp06oXr16nB2doaJiQmGDBmC0aNH4+DBg9BoNPjjjz/w66+/olSpUkqiioiIiIiIiIjoU8ZK2h+ARqNRklI7d+7Epk2b4OnpiQsXLuDgwYOIi4vD559/jo4dO6JDhw6oWLEidHR0YGBggNOnT0OlUuXawY+IiIiIiIiI6FPE4ucf0LFjx+Dn54fatWtjyJAhAIB9+/bBw8MD5ubmGDZsGEqUKIHff/8dJiYm6NWrF3ffIyIiIiIiIqICg4mpDyQxMRHNmjXDo0ePMGfOHEyYMEE5FhAQgOXLl8PMzAxTp05Fw4YNlWPcfY+IiIiIiIiICgrWmPpALC0tsXv3blhaWuLAgQO4ePGicqxjx46YNGkSYmJisGfPnlzXMSlFRERERERERAUFZ0x9YFFRURg8eDDq16+P8ePHo0aNGsqxkydPolGjRkxGEREREREREVGBxMTURxAZGYmhQ4fCzs4OEyZMQPXq1XMd5/I9IiIiIiIiIiqImJj6SCIjIzF8+HCUK1cOixYtQoUKFbQdEhERERERERGRVrHG1EdSt25deHp6wtTUFOXKldN2OEREREREREREWscZUx+ZiEClUkGj0UCtZl6QiIiIiIiIiAouJqa0IDs5RURERERERERUkHHKjhYwKUVERERERERExMQUERERERERERFpCRNTRERERERERESkFUxMERERERERERGRVjAxRUREREREREREWsHEFBERERERERERaQUTU0RERET5lEqlgr+/v7bDICIiIvqfMTFFRERE9AF17NgR7dq1e+exsLAwqFQqXLhw4X/67ISEBLRv3/5fnz9o0CB06dLlf/pdRERERB8CE1NEREREH5CTkxMOHz6Mu3fvvnVsw4YNqF+/Pmxtbf/TZ6anpwMALC0tYWBg8F7iJCIiItIGJqaIiIiIPqAOHTqgePHi8PHxydWekpKCnTt3okuXLujTpw9KlSoFY2Nj1KpVC9u3b891rr29PcaMGYMJEyagWLFiaNu2LYC3l/LFx8ejZ8+eKFKkCIoWLYrOnTvj9u3bAIDZs2dj48aN2Lt3L1QqFVQqFUJCQuDg4IAxY8bk+n2PHj2Cvr4+goOD3/v/BxEREVFOTEwRERERfUC6urpwdHSEj48PRERp37lzJ7KystC/f3/Y2dlh//79uHTpEr799lsMGDAAERERuT5n48aN0NfXx4kTJ7B69eq3fk9GRgbatm0LU1NThIWF4cSJEzAxMUG7du2Qnp6OyZMno2fPnmjXrh0SEhKQkJCAJk2aYOjQodi2bRtevXqlfNaWLVtQqlQpODg4fLj/GCIiIiIwMUVERET0wQ0ZMgQ3b95EaGio0rZhwwZ0794d5cqVw+TJk1GnTh1UrFgRY8eORbt27fDLL7/k+gxra2ssWrQIVatWRdWqVd/6Hb6+vtBoNFi/fj1q1aoFGxsbbNiwAXFxcQgJCYGJiQmMjIxgYGAAS0tLWFpaQl9fH926dQMA7N27V/ksHx8fDBo0CCqV6gP9jxARERG9xsQUERER0QdWrVo1NGnSBN7e3gCAmJgYhIWFwcnJCVlZWZg3bx5q1aqFokWLwsTEBIcOHUJcXFyuz7Czs/vb3xEVFYWYmBiYmprCxMQEJiYmKFq0KNLS0nDz5s2/vM7Q0BADBgxQYjt37hwuXbqEQYMG/f99aSIiIqJ/QVfbARAREREVBE5OThg7dixWrVqFDRs2oFKlSmjRogV++OEHrFixAsuXL0etWrVQqFAhTJgwQSlwnq1QoUJ/+/kpKSmws7PD1q1b3zpWvHjxv7126NChqFOnDu7evYsNGzbAwcEB5cqV++9fkoiIiOg/YmKKiIiI6CPo2bMnxo8fj23btmHTpk0YOXIkVCoVTpw4gc6dO6N///4AAI1Ggxs3bqB69er/6fPr1asHX19flChRAmZmZu88R19fH1lZWW+116pVC/Xr18e6deuwbds2eHp6/vcvSERERPQ/4FI+IiIioo/AxMQEvXr1wtSpU5GQkKAslbO2tsbhw4dx8uRJXL16FcOHD8eDBw/+8+f369cPxYoVQ+fOnREWFobY2FiEhIRg3LhxuHv3LgCgfPnyuHDhAq5fv47Hjx8jIyNDuX7o0KFYuHAhRARdu3Z9L9+ZiIiI6J8wMUVERET0kTg5OeHZs2do27YtPvvsMwDA9OnTUa9ePbRt2xb29vawtLREly5d/vNnGxsb49ixYyhbtiy6desGGxsbODk5IS0tTZlBNWzYMFStWhX169dH8eLFceLECeX6Pn36QFdXF3369IGhoeF7+b5ERERE/0QlOfctJiIiIqIC6fbt26hUqRJOnz6NevXqaTscIiIiKiCYmCIiIiIqwDIyMvDkyRNMnjwZsbGxuWZREREREX1oXMpHREREVICdOHECVlZWOH36NFavXq3tcIiIiKiA4YwpIiIiIiIiIiLSCs6YIiIiIiIiIiIirWBiioiIiIiIiIiItIKJKSIiIiIiIiIi0gompoiIiIiIiIiISCuYmCIiIiIiIiIiIq1gYoqIiIiIiIiIiLSCiSkiIiIiIiIiItIKJqaIiIiIiIiIiEgrmJgiIiIiIiIiIiKt+H9QO1u8MBXrZAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-06_10-36_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/water_need_vs_oil_production.png\n", + " Variety Technique Technique String \\\n", + "0 nocellara_delletna 3 tradizionale \n", + "1 nocellara_delletna 1 intensiva \n", + "2 nocellara_delletna 2 superintensiva \n", + "3 leccino 1 intensiva \n", + "4 leccino 2 superintensiva \n", + "5 leccino 3 tradizionale \n", + "6 frantoio 2 superintensiva \n", + "7 frantoio 3 tradizionale \n", + "8 frantoio 1 intensiva \n", + "9 coratina 1 intensiva \n", + "10 coratina 2 superintensiva \n", + "11 coratina 3 tradizionale \n", + "12 taggiasca 3 tradizionale \n", + "13 taggiasca 2 superintensiva \n", + "14 taggiasca 1 intensiva \n", + "15 pendolino 1 intensiva \n", + "16 pendolino 2 superintensiva \n", + "17 pendolino 3 tradizionale \n", + "18 moraiolo 2 superintensiva \n", + "19 moraiolo 1 intensiva \n", + "20 moraiolo 3 tradizionale \n", + "\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "0 9564.638687 2088.362004 \n", + "1 13699.079622 2991.183032 \n", + "2 17826.710664 3892.059753 \n", + "3 16432.379678 3229.053194 \n", + "4 20528.499013 4033.942398 \n", + "5 10937.982122 2149.449585 \n", + "6 24621.040119 6047.876212 \n", + "7 13740.739760 3375.103688 \n", + "8 20550.900635 5047.942655 \n", + "9 16429.706879 4215.265516 \n", + "10 19164.700743 4916.649709 \n", + "11 12318.510310 3160.037128 \n", + "12 6839.506230 1381.247995 \n", + "13 16433.741502 3319.210170 \n", + "14 10968.603159 2215.371493 \n", + "15 13705.431414 2468.678455 \n", + "16 19183.689269 3455.879324 \n", + "17 10960.549241 1974.357984 \n", + "18 17793.971752 3885.415851 \n", + "19 13144.222436 2870.020002 \n", + "20 8765.195655 1913.745255 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "0 32997.227891 0.218342 \n", + "1 33079.012125 0.218349 \n", + "2 33118.708645 0.218327 \n", + "3 25013.303736 0.196506 \n", + "4 24989.459147 0.196504 \n", + "5 24981.219100 0.196512 \n", + "6 28874.473543 0.245639 \n", + "7 29003.452741 0.245628 \n", + "8 28921.261327 0.245631 \n", + "9 38270.638622 0.256564 \n", + "10 38264.650562 0.256547 \n", + "11 38253.676395 0.256528 \n", + "12 26219.134374 0.201951 \n", + "13 26253.317778 0.201975 \n", + "14 26284.027794 0.201974 \n", + "15 26154.359691 0.180124 \n", + "16 26153.199618 0.180147 \n", + "17 26152.823801 0.180133 \n", + "18 32561.911109 0.218356 \n", + "19 32577.899255 0.218348 \n", + "20 32594.860153 0.218335 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "0 0.063289 \n", + "1 0.090425 \n", + "2 0.117518 \n", + "3 0.129093 \n", + "4 0.161426 \n", + "5 0.086043 \n", + "6 0.209454 \n", + "7 0.116369 \n", + "8 0.174541 \n", + "9 0.110144 \n", + "10 0.128491 \n", + "11 0.082607 \n", + "12 0.052681 \n", + "13 0.126430 \n", + "14 0.084286 \n", + "15 0.094389 \n", + "16 0.132140 \n", + "17 0.075493 \n", + "18 0.119324 \n", + "19 0.088097 \n", + "20 0.058713 \n", + "Comparison by Variety:\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "Variety \n", + "nocellara_delletna 13696.683690 2990.507461 \n", + "leccino 15971.162702 3138.439782 \n", + "frantoio 19648.631813 4826.360700 \n", + "coratina 15974.164423 4098.136472 \n", + "taggiasca 11412.636779 2305.011278 \n", + "pendolino 14617.432649 2633.129635 \n", + "moraiolo 13232.961913 2889.399172 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "Variety \n", + "nocellara_delletna 33064.983905 0.218338 \n", + "leccino 24994.676451 0.196507 \n", + "frantoio 28932.932409 0.245633 \n", + "coratina 38262.995517 0.256548 \n", + "taggiasca 26252.184893 0.201970 \n", + "pendolino 26153.461822 0.180136 \n", + "moraiolo 32578.228327 0.218349 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "Variety \n", + "nocellara_delletna 0.090443 \n", + "leccino 0.125564 \n", + "frantoio 0.166812 \n", + "coratina 0.107104 \n", + "taggiasca 0.087803 \n", + "pendolino 0.100680 \n", + "moraiolo 0.088691 \n", + "\n", + "Best Varieties by Water Efficiency:\n", + " Variety Avg Olive Production (kg/ha) \\\n", + "2 frantoio 19648.631813 \n", + "1 leccino 15971.162702 \n", + "3 coratina 15974.164423 \n", + "5 pendolino 14617.432649 \n", + "0 nocellara_delletna 13696.683690 \n", + "\n", + " Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "2 4826.360700 28932.932409 0.245633 \n", + "1 3138.439782 24994.676451 0.196507 \n", + "3 4098.136472 38262.995517 0.256548 \n", + "5 2633.129635 26153.461822 0.180136 \n", + "0 2990.507461 33064.983905 0.218338 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "2 0.166812 \n", + "1 0.125564 \n", + "3 0.107104 \n", + "5 0.100680 \n", + "0 0.090443 \n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "# Esecuzione dell'analisi\n", + "comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n", + "\n", + "# Genera i grafici\n", + "plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n", + "plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n", + "plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n", + "plot_efficiency_vs_production(comparison_data)\n", + "plot_water_efficiency_vs_production(comparison_data)\n", + "plot_water_need_vs_oil_production(comparison_data)\n", + "\n", + "# Analisi per tecnica\n", + "technique_data = analyze_by_technique(simulated_data, olive_varieties)\n", + "\n", + "print(technique_data)\n", + "\n", + "# Stampa un sommario statistico\n", + "print(\"Comparison by Variety:\")\n", + "print(comparison_data.set_index('Variety'))\n", + "print(\"\\nBest Varieties by Water Efficiency:\")\n", + "print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bbe87b415168368", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_transformer_data(df, olive_varieties_df):\n", + " # Crea una copia del DataFrame per evitare modifiche all'originale\n", + " df = df.copy()\n", + "\n", + " # Ordina per zona e anno\n", + " df = df.sort_values(['zone', 'year'])\n", + "\n", + " # Definisci le feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha'] # Feature statiche base\n", + " target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Ottieni le varietà pulite\n", + " all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " # Crea la struttura delle feature per ogni varietà\n", + " variety_features = [\n", + " 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n", + " 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n", + " 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n", + " ]\n", + "\n", + " # Prepara dizionari per le nuove colonne\n", + " new_columns = {}\n", + "\n", + " # Prepara le feature per ogni varietà\n", + " for variety in varieties:\n", + " # Feature esistenti\n", + " for feature in variety_features:\n", + " col_name = f\"{variety}_{feature}\"\n", + " if col_name in df.columns:\n", + " if feature != 'tech': # Non includere la colonna tech direttamente\n", + " static_features.append(col_name)\n", + "\n", + " # Feature binarie per le tecniche di coltivazione\n", + " for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n", + " col_name = f\"{variety}_{technique}\"\n", + " new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n", + " df[f\"{variety}_tech\"].str.lower() == technique\n", + " ).fillna(False)\n", + " static_features.append(col_name)\n", + "\n", + " # Aggiungi tutte le nuove colonne in una volta sola\n", + " new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n", + "\n", + " # Ordiniamo per zona e anno per mantenere la continuità temporale\n", + " df_sorted = new_df.sort_values(['zone', 'year'])\n", + "\n", + " # Definiamo la dimensione della finestra temporale\n", + " window_size = 41\n", + "\n", + " # Liste per raccogliere i dati\n", + " temporal_sequences = []\n", + " static_features_list = []\n", + " targets_list = []\n", + "\n", + " # Iteriamo per ogni zona\n", + " for zone in df_sorted['zone'].unique():\n", + " zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)\n", + "\n", + " if len(zone_data) >= window_size: # Verifichiamo che ci siano abbastanza dati\n", + " # Creiamo sequenze temporali scorrevoli\n", + " for i in range(len(zone_data) - window_size + 1):\n", + " # Sequenza temporale\n", + " temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values\n", + " # Verifichiamo che non ci siano valori NaN\n", + " if not np.isnan(temporal_window).any():\n", + " temporal_sequences.append(temporal_window)\n", + "\n", + " # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)\n", + " static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)\n", + "\n", + " # Target (prendiamo quelli dell'ultimo timestep della finestra)\n", + " targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)\n", + "\n", + " # Convertiamo in array numpy\n", + " X_temporal = np.array(temporal_sequences)\n", + " X_static = np.array(static_features_list)\n", + " y = np.array(targets_list)\n", + "\n", + " print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n", + "\n", + " # Split dei dati (usando indici casuali per una migliore distribuzione)\n", + " indices = np.random.permutation(len(X_temporal))\n", + "\n", + " #train_idx = int(len(indices) * 0.7) # 70% training\n", + " #val_idx = int(len(indices) * 0.85) # 15% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_idx = int(len(indices) * 0.65) # 65% training\n", + " val_idx = int(len(indices) * 0.85) # 20% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " #train_idx = int(len(indices) * 0.60) # 60% training\n", + " #val_idx = int(len(indices) * 0.85) # 25% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_indices = indices[:train_idx]\n", + " val_indices = indices[train_idx:val_idx]\n", + " test_indices = indices[val_idx:]\n", + "\n", + " # Split dei dati\n", + " X_temporal_train = X_temporal[train_indices]\n", + " X_temporal_val = X_temporal[val_indices]\n", + " X_temporal_test = X_temporal[test_indices]\n", + "\n", + " X_static_train = X_static[train_indices]\n", + " X_static_val = X_static[val_indices]\n", + " X_static_test = X_static[test_indices]\n", + "\n", + " y_train = y[train_indices]\n", + " y_val = y[val_indices]\n", + " y_test = y[test_indices]\n", + "\n", + " # Standardizzazione\n", + " scaler_temporal = StandardScaler()\n", + " scaler_static = StandardScaler()\n", + " scaler_y = StandardScaler()\n", + "\n", + " # Standardizzazione dei dati temporali\n", + " X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n", + " X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n", + " X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n", + "\n", + " # Standardizzazione dei dati statici\n", + " X_static_train = scaler_static.fit_transform(X_static_train)\n", + " X_static_val = scaler_static.transform(X_static_val)\n", + " X_static_test = scaler_static.transform(X_static_test)\n", + "\n", + " # Standardizzazione dei target\n", + " y_train = scaler_y.fit_transform(y_train)\n", + " y_val = scaler_y.transform(y_val)\n", + " y_test = scaler_y.transform(y_test)\n", + "\n", + " print(\"\\nShape dopo lo split e standardizzazione:\")\n", + " print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n", + " print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n", + " print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n", + "\n", + " # Prepara i dizionari di input\n", + " train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n", + " val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n", + " test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n", + "\n", + " joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n", + " joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n", + " joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n", + "\n", + " return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9c4d5f0f3fafdc2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset completo - Temporal: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)\n", + "\n", + "Shape dopo lo split e standardizzazione:\n", + "Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)\n", + "Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)\n", + "Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)\n", + "Temporal data shape: (2548000, 41, 3)\n", + "Static data shape: (2548000, 113)\n", + "Target shape: (2548000, 5)\n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "\n", + "(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n", + "\n", + "scaler_temporal, scaler_static, scaler_y = scalers\n", + "\n", + "print(\"Temporal data shape:\", train_data['temporal'].shape)\n", + "print(\"Static data shape:\", train_data['static'].shape)\n", + "print(\"Target shape:\", train_targets.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "604c952c7195f40c", + "metadata": {}, + "outputs": [], + "source": [ + "@keras.saving.register_keras_serializable()\n", + "class DataAugmentation(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n", + "\n", + " def __init__(self, noise_stddev=0.03, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.noise_stddev = noise_stddev\n", + "\n", + " def call(self, inputs, training=None):\n", + " if training:\n", + " return inputs + tf.random.normal(\n", + " shape=tf.shape(inputs),\n", + " mean=0.0,\n", + " stddev=self.noise_stddev\n", + " )\n", + " return inputs\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"noise_stddev\": self.noise_stddev})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class PositionalEncoding(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'encoding posizionale\"\"\"\n", + "\n", + " def __init__(self, d_model, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.d_model = d_model\n", + "\n", + " def build(self, input_shape):\n", + " _, seq_length, _ = input_shape\n", + "\n", + " # Crea la matrice di encoding posizionale\n", + " position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n", + " div_term = tf.exp(\n", + " tf.range(0, self.d_model, 2, dtype=tf.float32) *\n", + " (-tf.math.log(10000.0) / self.d_model)\n", + " )\n", + "\n", + " # Calcola sin e cos\n", + " pos_encoding = tf.zeros((1, seq_length, self.d_model))\n", + " pos_encoding_even = tf.sin(position * div_term)\n", + " pos_encoding_odd = tf.cos(position * div_term)\n", + "\n", + " # Assegna i valori alle posizioni pari e dispari\n", + " pos_encoding = tf.concat(\n", + " [tf.expand_dims(pos_encoding_even, -1),\n", + " tf.expand_dims(pos_encoding_odd, -1)],\n", + " axis=-1\n", + " )\n", + " pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n", + " pos_encoding = pos_encoding[:, :, :self.d_model]\n", + "\n", + " # Salva l'encoding come peso non trainabile\n", + " self.pos_encoding = self.add_weight(\n", + " shape=(1, seq_length, self.d_model),\n", + " initializer=tf.keras.initializers.Constant(pos_encoding),\n", + " trainable=False,\n", + " name='positional_encoding'\n", + " )\n", + "\n", + " super().build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " # Broadcast l'encoding posizionale sul batch\n", + " batch_size = tf.shape(inputs)[0]\n", + " pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n", + " return inputs + pos_encoding_tiled\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"d_model\": self.d_model})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n", + " warmup_lr = self.initial_learning_rate * warmup_pct\n", + " decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n", + " decayed_lr = self.initial_learning_rate * decay_factor\n", + " return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'initial_learning_rate': self.initial_learning_rate,\n", + " 'warmup_steps': self.warmup_steps,\n", + " 'decay_steps': self.decay_steps\n", + " }\n", + "\n", + "\n", + "def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n", + " d_model=128, num_heads=8, ff_dim=256,\n", + " num_transformer_blocks=4, mlp_units=None,\n", + " dropout=0.2):\n", + " \"\"\"\n", + " Crea un transformer per la predizione della produzione di olio d'oliva.\n", + " \"\"\"\n", + " # Input layers\n", + " if mlp_units is None:\n", + " mlp_units = [256, 128, 64]\n", + "\n", + " temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n", + " static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n", + "\n", + " # === TEMPORAL PATH ===\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n", + " x = DataAugmentation()(x)\n", + "\n", + " # Temporal projection\n", + " x = tf.keras.layers.Dense(\n", + " d_model // 2,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + " x = tf.keras.layers.Dense(\n", + " d_model,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Positional encoding\n", + " x = PositionalEncoding(d_model)(x)\n", + "\n", + " # Transformer blocks\n", + " skip_connection = x\n", + " for _ in range(num_transformer_blocks):\n", + " # Self-attention\n", + " attention_output = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // num_heads,\n", + " value_dim=d_model // num_heads\n", + " )(x, x)\n", + " attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n", + "\n", + " # Residual connection con pesi addestrabili\n", + " residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Feed-forward network\n", + " ffn = tf.keras.layers.Dense(ff_dim, activation=\"swish\")(x)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + " ffn = tf.keras.layers.Dense(d_model)(ffn)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + "\n", + " # Second residual connection\n", + " x = tfa.layers.StochasticDepth()([x, ffn])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Add final skip connection\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])\n", + "\n", + " # Temporal pooling\n", + " attention_pooled = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // 4\n", + " )(x, x)\n", + " attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)\n", + "\n", + " # Additional pooling operations\n", + " avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)\n", + " max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)\n", + "\n", + " # Combine pooling results\n", + " temporal_features = tf.keras.layers.Concatenate()(\n", + " [attention_pooled, avg_pooled, max_pooled]\n", + " )\n", + "\n", + " # === STATIC PATH ===\n", + " static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n", + " for units in [256, 128, 64]:\n", + " static_features = tf.keras.layers.Dense(\n", + " units,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(static_features)\n", + " static_features = tf.keras.layers.Dropout(dropout)(static_features)\n", + "\n", + " # === FEATURE FUSION ===\n", + " combined = tf.keras.layers.Concatenate()([temporal_features, static_features])\n", + "\n", + " # === MLP HEAD ===\n", + " x = combined\n", + " for units in mlp_units:\n", + " x = tf.keras.layers.BatchNormalization()(x)\n", + " x = tf.keras.layers.Dense(\n", + " units,\n", + " activation=\"swish\",\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + "\n", + " # Output layer\n", + " outputs = tf.keras.layers.Dense(\n", + " num_outputs,\n", + " activation='linear',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Create model\n", + " model = tf.keras.Model(\n", + " inputs={'temporal': temporal_input, 'static': static_input},\n", + " outputs=outputs,\n", + " name='OilTransformer'\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def create_transformer_callbacks(target_names, val_data, val_targets):\n", + " \"\"\"\n", + " Crea i callbacks per il training del modello.\n", + " \n", + " Parameters:\n", + " -----------\n", + " target_names : list\n", + " Lista dei nomi dei target per il monitoraggio specifico\n", + " val_data : dict\n", + " Dati di validazione\n", + " val_targets : array\n", + " Target di validazione\n", + " \n", + " Returns:\n", + " --------\n", + " list\n", + " Lista dei callbacks configurati\n", + " \"\"\"\n", + "\n", + " # Custom Metric per target specifici\n", + " class TargetSpecificMetric(tf.keras.callbacks.Callback):\n", + " def __init__(self, validation_data, target_names):\n", + " super().__init__()\n", + " self.validation_data = validation_data\n", + " self.target_names = target_names\n", + "\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " x_val, y_val = self.validation_data\n", + " y_pred = self.model.predict(x_val, verbose=0)\n", + "\n", + " for i, name in enumerate(self.target_names):\n", + " mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n", + " logs[f'val_{name}_mae'] = mae\n", + "\n", + "\n", + " callbacks = [\n", + " # Early Stopping\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=20,\n", + " restore_best_weights=True,\n", + " min_delta=0.0005,\n", + " mode='min'\n", + " ),\n", + "\n", + " # Model Checkpoint\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " ),\n", + "\n", + " # Metric per target specifici\n", + " TargetSpecificMetric(\n", + " validation_data=(val_data, val_targets),\n", + " target_names=target_names\n", + " ),\n", + "\n", + " # Reduce LR on Plateau\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.5,\n", + " patience=10,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + "\n", + " # TensorBoard logging\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./logs_{execute_name}',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " )\n", + " ]\n", + "\n", + " return callbacks\n", + "\n", + "\n", + "def compile_model(model, learning_rate=1e-3):\n", + " \"\"\"\n", + " Compila il modello con le impostazioni standard.\n", + " \"\"\"\n", + " lr_schedule = WarmUpLearningRateSchedule(\n", + " initial_learning_rate=learning_rate,\n", + " warmup_steps=500,\n", + " decay_steps=5000\n", + " )\n", + "\n", + " model.compile(\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.01\n", + " ),\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n", + " \"\"\"\n", + " Configura e prepara il transformer con dimensioni dinamiche basate sui dati.\n", + " \"\"\"\n", + " # Estrai le shape dai dati\n", + " temporal_shape = (train_data['temporal'].shape[1], train_data['temporal'].shape[2])\n", + " static_shape = (train_data['static'].shape[1],)\n", + " num_outputs = train_targets.shape[1]\n", + "\n", + " print(f\"Shape rilevate:\")\n", + " print(f\"- Temporal shape: {temporal_shape}\")\n", + " print(f\"- Static shape: {static_shape}\")\n", + " print(f\"- Numero di output: {num_outputs}\")\n", + "\n", + " # Target names basati sul numero di output\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Assicurati che il numero di target names corrisponda al numero di output\n", + " assert len(target_names) == num_outputs, \\\n", + " f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n", + "\n", + " # Crea il modello con le dimensioni rilevate\n", + " model = create_olive_oil_transformer(\n", + " temporal_shape=temporal_shape,\n", + " static_shape=static_shape,\n", + " num_outputs=num_outputs\n", + " )\n", + "\n", + " # Compila il modello\n", + " model = compile_model(model)\n", + "\n", + " # Crea i callbacks\n", + " callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n", + "\n", + " return model, callbacks, target_names\n", + "\n", + "\n", + "def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):\n", + " \"\"\"\n", + " Funzione principale per l'addestramento del transformer con ottimizzazioni.\n", + " \"\"\"\n", + " # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n", + " .cache()\\\n", + " .shuffle(buffer_size=1024)\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n", + " .cache()\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " # Setup del modello\n", + " strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n", + " \n", + " with strategy.scope():\n", + " model, callbacks, target_names = setup_transformer_training(\n", + " train_data, train_targets, val_data, val_targets\n", + " )\n", + "\n", + " # Mostra il summary del modello\n", + " model.summary()\n", + " \n", + " try:\n", + " keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not create model plot: {e}\")\n", + "\n", + " # Training con gestione degli errori\n", + " try:\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " workers=4,\n", + " use_multiprocessing=True\n", + " )\n", + " except tf.errors.ResourceExhaustedError:\n", + " print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n", + " # Riprova con batch size più piccolo\n", + " batch_size = batch_size // 2\n", + " train_dataset = train_dataset.unbatch().batch(batch_size)\n", + " val_dataset = val_dataset.unbatch().batch(batch_size)\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " # Salva il modello finale\n", + " try:\n", + " save_path = f'{execute_name}_{save_name}.keras'\n", + " model.save(save_path, save_format='keras')\n", + " \n", + " os.makedirs(f'{execute_name}/weights', exist_ok=True)\n", + " model.save_weights(f'{execute_name}/weights')\n", + " print(f\"\\nModello salvato in: {save_path}\")\n", + " except Exception as e:\n", + " print(f\"Warning: Could not save model: {e}\")\n", + "\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "35490e902e494c4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape rilevate:\n", + "- Temporal shape: (41, 3)\n", + "- Static shape: (113,)\n", + "- Numero di output: 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 11:43:09.026945: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"OilTransformer\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " temporal (InputLayer) [(None, 41, 3)] 0 [] \n", + " \n", + " layer_normalization (Layer (None, 41, 3) 6 ['temporal[0][0]'] \n", + " Normalization) \n", + " \n", + " data_augmentation (DataAug (None, 41, 3) 0 ['layer_normalization[0][0]'] \n", + " mentation) \n", + " \n", + " dense (Dense) (None, 41, 64) 256 ['data_augmentation[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 41, 64) 0 ['dense[0][0]'] \n", + " \n", + " dense_1 (Dense) (None, 41, 128) 8320 ['dropout[0][0]'] \n", + " \n", + " positional_encoding (Posit (None, 41, 128) 5248 ['dense_1[0][0]'] \n", + " ionalEncoding) \n", + " \n", + " multi_head_attention (Mult (None, 41, 128) 66048 ['positional_encoding[0][0]', \n", + " iHeadAttention) 'positional_encoding[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 41, 128) 16512 ['positional_encoding[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 41, 128) 0 ['multi_head_attention[0][0]']\n", + " \n", + " tf.math.multiply (TFOpLamb (None, 41, 128) 0 ['dense_2[0][0]', \n", + " da) 'dropout_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 41, 128) 0 ['positional_encoding[0][0]', \n", + " icDepth) 'tf.math.multiply[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 41, 128) 256 ['stochastic_depth[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_3 (Dense) (None, 41, 256) 33024 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_2 (Dropout) (None, 41, 256) 0 ['dense_3[0][0]'] \n", + " \n", + " dense_4 (Dense) (None, 41, 128) 32896 ['dropout_2[0][0]'] \n", + " \n", + " dropout_3 (Dropout) (None, 41, 128) 0 ['dense_4[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 41, 128) 0 ['layer_normalization_1[0][0]'\n", + " sticDepth) , 'dropout_3[0][0]'] \n", + " \n", + " layer_normalization_2 (Lay (None, 41, 128) 256 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_1 (Mu (None, 41, 128) 66048 ['layer_normalization_2[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_2[0][0]\n", + " '] \n", + " \n", + " dense_5 (Dense) (None, 41, 128) 16512 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_4 (Dropout) (None, 41, 128) 0 ['multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_1 (TFOpLa (None, 41, 128) 0 ['dense_5[0][0]', \n", + " mbda) 'dropout_4[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 41, 128) 0 ['layer_normalization_2[0][0]'\n", + " sticDepth) , 'tf.math.multiply_1[0][0]'] \n", + " \n", + " layer_normalization_3 (Lay (None, 41, 128) 256 ['stochastic_depth_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_6 (Dense) (None, 41, 256) 33024 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dropout_5 (Dropout) (None, 41, 256) 0 ['dense_6[0][0]'] \n", + " \n", + " dense_7 (Dense) (None, 41, 128) 32896 ['dropout_5[0][0]'] \n", + " \n", + " dropout_6 (Dropout) (None, 41, 128) 0 ['dense_7[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 41, 128) 0 ['layer_normalization_3[0][0]'\n", + " sticDepth) , 'dropout_6[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 41, 128) 256 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_2 (Mu (None, 41, 128) 66048 ['layer_normalization_4[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_4[0][0]\n", + " '] \n", + " \n", + " dense_8 (Dense) (None, 41, 128) 16512 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dropout_7 (Dropout) (None, 41, 128) 0 ['multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_2 (TFOpLa (None, 41, 128) 0 ['dense_8[0][0]', \n", + " mbda) 'dropout_7[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 41, 128) 0 ['layer_normalization_4[0][0]'\n", + " sticDepth) , 'tf.math.multiply_2[0][0]'] \n", + " \n", + " layer_normalization_5 (Lay (None, 41, 128) 256 ['stochastic_depth_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_9 (Dense) (None, 41, 256) 33024 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dropout_8 (Dropout) (None, 41, 256) 0 ['dense_9[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 41, 128) 32896 ['dropout_8[0][0]'] \n", + " \n", + " dropout_9 (Dropout) (None, 41, 128) 0 ['dense_10[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 41, 128) 0 ['layer_normalization_5[0][0]'\n", + " sticDepth) , 'dropout_9[0][0]'] \n", + " \n", + " layer_normalization_6 (Lay (None, 41, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_3 (Mu (None, 41, 128) 66048 ['layer_normalization_6[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_6[0][0]\n", + " '] \n", + " \n", + " dense_11 (Dense) (None, 41, 128) 16512 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dropout_10 (Dropout) (None, 41, 128) 0 ['multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_3 (TFOpLa (None, 41, 128) 0 ['dense_11[0][0]', \n", + " mbda) 'dropout_10[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 41, 128) 0 ['layer_normalization_6[0][0]'\n", + " sticDepth) , 'tf.math.multiply_3[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 41, 128) 256 ['stochastic_depth_6[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_12 (Dense) (None, 41, 256) 33024 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " dropout_11 (Dropout) (None, 41, 256) 0 ['dense_12[0][0]'] \n", + " \n", + " dense_13 (Dense) (None, 41, 128) 32896 ['dropout_11[0][0]'] \n", + " \n", + " static (InputLayer) [(None, 113)] 0 [] \n", + " \n", + " dropout_12 (Dropout) (None, 41, 128) 0 ['dense_13[0][0]'] \n", + " \n", + " layer_normalization_9 (Lay (None, 113) 226 ['static[0][0]'] \n", + " erNormalization) \n", + " \n", + " stochastic_depth_7 (Stocha (None, 41, 128) 0 ['layer_normalization_7[0][0]'\n", + " sticDepth) , 'dropout_12[0][0]'] \n", + " \n", + " dense_14 (Dense) (None, 256) 29184 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_8 (Lay (None, 41, 128) 256 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_13 (Dropout) (None, 256) 0 ['dense_14[0][0]'] \n", + " \n", + " stochastic_depth_8 (Stocha (None, 41, 128) 0 ['layer_normalization_8[0][0]'\n", + " sticDepth) , 'positional_encoding[0][0]']\n", + " \n", + " dense_15 (Dense) (None, 128) 32896 ['dropout_13[0][0]'] \n", + " \n", + " multi_head_attention_4 (Mu (None, 41, 128) 131968 ['stochastic_depth_8[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_8[0][0]'] \n", + " \n", + " dropout_14 (Dropout) (None, 128) 0 ['dense_15[0][0]'] \n", + " \n", + " global_average_pooling1d ( (None, 128) 0 ['multi_head_attention_4[0][0]\n", + " GlobalAveragePooling1D) '] \n", + " \n", + " global_average_pooling1d_1 (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " (GlobalAveragePooling1D) \n", + " \n", + " global_max_pooling1d (Glob (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " alMaxPooling1D) \n", + " \n", + " dense_16 (Dense) (None, 64) 8256 ['dropout_14[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 384) 0 ['global_average_pooling1d[0][\n", + " 0]', \n", + " 'global_average_pooling1d_1[0\n", + " ][0]', \n", + " 'global_max_pooling1d[0][0]']\n", + " \n", + " dropout_15 (Dropout) (None, 64) 0 ['dense_16[0][0]'] \n", + " \n", + " concatenate_1 (Concatenate (None, 448) 0 ['concatenate[0][0]', \n", + " ) 'dropout_15[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 448) 1792 ['concatenate_1[0][0]'] \n", + " Normalization) \n", + " \n", + " dense_17 (Dense) (None, 256) 114944 ['batch_normalization[0][0]'] \n", + " \n", + " dropout_16 (Dropout) (None, 256) 0 ['dense_17[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 256) 1024 ['dropout_16[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_18 (Dense) (None, 128) 32896 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_17 (Dropout) (None, 128) 0 ['dense_18[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 128) 512 ['dropout_17[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_19 (Dense) (None, 64) 8256 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_18 (Dropout) (None, 64) 0 ['dense_19[0][0]'] \n", + " \n", + " dense_20 (Dense) (None, 5) 325 ['dropout_18[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 972077 (3.71 MB)\n", + "Trainable params: 965165 (3.68 MB)\n", + "Non-trainable params: 6912 (27.00 KB)\n", + "__________________________________________________________________________________________________\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 11:43:25.651745: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7d7e70d1ce40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-06 11:43:25.651778: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-06 11:43:25.659099: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-12-06 11:43:25.722749: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-06 11:43:25.861911: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9954/9954 [==============================] - 481s 46ms/step - loss: 0.0460 - mae: 0.1872 - val_loss: 0.0145 - val_mae: 0.0865 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 1.0219e-05\n", + "Epoch 2/150\n", + "9954/9954 [==============================] - 473s 47ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0603 - lr: 1.0438e-07\n", + "Epoch 3/150\n", + "9954/9954 [==============================] - 477s 48ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0861 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0602 - lr: 1.0661e-09\n", + "Epoch 4/150\n", + "9954/9954 [==============================] - 508s 51ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0867 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0616 - lr: 1.0889e-11\n", + "Epoch 5/150\n", + "9954/9954 [==============================] - 431s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0612 - lr: 1.1122e-13\n", + "Epoch 6/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0598 - lr: 1.1361e-15\n", + "Epoch 7/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0868 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0620 - lr: 1.1604e-17\n", + "Epoch 8/150\n", + "9954/9954 [==============================] - 433s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0611 - lr: 1.1852e-19\n", + "Epoch 9/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0608 - lr: 1.2106e-21\n", + "Epoch 10/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0607 - lr: 1.2365e-23\n", + "Epoch 11/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.2630e-25\n", + "Epoch 12/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0866 - val_olive_prod_mae: 0.0968 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0606 - lr: 1.2900e-27\n", + "Epoch 13/150\n", + "9954/9954 [==============================] - 425s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0144 - val_mae: 0.0868 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0619 - lr: 1.3177e-29\n", + "Epoch 14/150\n", + "9954/9954 [==============================] - 409s 41ms/step - loss: 0.0272 - mae: 0.1504 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0605 - lr: 1.3459e-31\n", + "Epoch 15/150\n", + "9954/9954 [==============================] - 439s 44ms/step - loss: 0.0273 - mae: 0.1509 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0926 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0609 - lr: 1.3747e-33\n", + "Epoch 16/150\n", + "9954/9954 [==============================] - 421s 42ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.4041e-35\n", + "Epoch 17/150\n", + "9954/9954 [==============================] - 429s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0600 - lr: 1.4342e-37\n", + "Epoch 18/150\n", + "9954/9954 [==============================] - 414s 41ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0602 - lr: 0.0000e+00\n", + "Epoch 19/150\n", + "9954/9954 [==============================] - 441s 44ms/step - loss: 0.0272 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0608 - lr: 0.0000e+00\n", + "Epoch 20/150\n", + "9954/9954 [==============================] - 440s 44ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0601 - lr: 0.0000e+00\n", + "Epoch 21/150\n", + "9954/9954 [==============================] - 448s 45ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 0.0000e+00\n", + "\n", + "Modello salvato in: 2024-12-06_10-36_final_model.keras\n" + ] + } + ], + "source": [ + "model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3e2fb5a5341dac92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 102s 4ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1585.45\n", + "Errore percentuale medio: 6.91%\n", + "Precisione: 93.09%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 319.12\n", + "Errore percentuale medio: 6.61%\n", + "Precisione: 93.39%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 387.31\n", + "Errore percentuale medio: 6.74%\n", + "Precisione: 93.26%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 337.11\n", + "Errore percentuale medio: 6.46%\n", + "Precisione: 93.54%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1775.48\n", + "Errore percentuale medio: 4.24%\n", + "Precisione: 95.76%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4af58aa9bbc156f5", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model_performance(model, data, targets, set_name=\"\"):\n", + " \"\"\"\n", + " Valuta le performance del modello su un set di dati specifico.\n", + " \"\"\"\n", + " predictions = model.predict(data, verbose=0)\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + " metrics = {}\n", + "\n", + " for i, name in enumerate(target_names):\n", + " mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n", + " mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n", + " rmse = np.sqrt(mse)\n", + " mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n", + "\n", + " metrics[f\"{name}_mae\"] = mae\n", + " metrics[f\"{name}_rmse\"] = rmse\n", + " metrics[f\"{name}_mape\"] = mape\n", + "\n", + " if set_name:\n", + " print(f\"\\nPerformance sul set {set_name}:\")\n", + " for metric, value in metrics.items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def retrain_model(base_model, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Implementa il retraining del modello con i dati combinati.\n", + " \"\"\"\n", + " print(\"Valutazione performance iniziali del modello...\")\n", + " initial_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Combina i dati per il retraining\n", + " combined_data = {\n", + " 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n", + " 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n", + " }\n", + " combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n", + "\n", + " # Crea una nuova suddivisione per la validazione\n", + " indices = np.arange(len(combined_targets))\n", + " np.random.shuffle(indices)\n", + "\n", + " split_idx = int(len(indices) * 0.9)\n", + " train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n", + "\n", + " # Prepara i dati per il retraining\n", + " retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n", + " retrain_targets = combined_targets[train_idx]\n", + " retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n", + " retrain_val_targets = combined_targets[val_idx]\n", + "\n", + " # Configura callbacks\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=10,\n", + " restore_best_weights=True,\n", + " min_delta=0.0001\n", + " ),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_retrained_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " )\n", + " ]\n", + "\n", + " # Imposta learning rate per il fine-tuning\n", + " optimizer = tf.keras.optimizers.AdamW(\n", + " learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n", + " initial_learning_rate=1e-4,\n", + " decay_steps=1000,\n", + " decay_rate=0.9\n", + " ),\n", + " weight_decay=0.01\n", + " )\n", + "\n", + " # Ricompila il modello con il nuovo optimizer\n", + " base_model.compile(\n", + " optimizer=optimizer,\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " print(\"\\nAvvio retraining...\")\n", + " history = base_model.fit(\n", + " retrain_data,\n", + " retrain_targets,\n", + " validation_data=(retrain_val_data, retrain_val_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " print(\"\\nValutazione performance finali...\")\n", + " final_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Salva il modello finale\n", + " save_path = f'{execute_name}_retrained_model.keras'\n", + " os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n", + " \n", + " base_model.save_weights(f'{execute_name}_retrained/weights')\n", + " base_model.save(save_path, save_format='keras')\n", + " print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n", + "\n", + " # Report miglioramenti\n", + " print(\"\\nMiglioramenti delle performance:\")\n", + " for dataset in ['train', 'val', 'test']:\n", + " print(f\"\\nSet {dataset}:\")\n", + " for metric in initial_metrics[dataset].keys():\n", + " initial = initial_metrics[dataset][metric]\n", + " final = final_metrics[dataset][metric]\n", + " improvement = ((initial - final) / initial) * 100\n", + " print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n", + "\n", + " return base_model, history, final_metrics\n", + "\n", + "\n", + "def start_retraining(model_path, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Avvia il processo di retraining in modo sicuro.\n", + " \"\"\"\n", + " try:\n", + " print(\"Caricamento del modello...\")\n", + " base_model = tf.keras.models.load_model(model_path, compile=False)\n", + " print(\"Modello caricato con successo!\")\n", + "\n", + " return retrain_model(\n", + " base_model=base_model,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=epochs,\n", + " batch_size=batch_size\n", + " )\n", + " except Exception as e:\n", + " print(f\"Errore durante il retraining: {str(e)}\")\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "588c7e49371f4a0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caricamento del modello...\n", + "Modello caricato con successo!\n", + "Valutazione performance iniziali del modello...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0963\n", + "olive_prod_rmse: 0.1300\n", + "olive_prod_mape: 77.2491\n", + "min_oil_prod_mae: 0.0936\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 91.4612\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 88.9396\n", + "avg_oil_prod_mae: 0.0895\n", + "avg_oil_prod_rmse: 0.1238\n", + "avg_oil_prod_mape: 89.5317\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0808\n", + "total_water_need_mape: 44.4531\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0964\n", + "olive_prod_rmse: 0.1301\n", + "olive_prod_mape: 133.2427\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1310\n", + "min_oil_prod_mape: 120.7693\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 86.2224\n", + "avg_oil_prod_mae: 0.0894\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 83.8138\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0809\n", + "total_water_need_mape: 53.9347\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0962\n", + "olive_prod_rmse: 0.1298\n", + "olive_prod_mape: 77.9806\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 95.5886\n", + "max_oil_prod_mae: 0.0934\n", + "max_oil_prod_rmse: 0.1301\n", + "max_oil_prod_mape: 76.3217\n", + "avg_oil_prod_mae: 0.0893\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 111.2211\n", + "total_water_need_mae: 0.0596\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 38.1699\n", + "\n", + "Avvio retraining...\n", + "Epoch 1/50\n", + "27563/27563 [==============================] - 851s 30ms/step - loss: 0.0261 - mae: 0.1520 - val_loss: 0.0118 - val_mae: 0.0804 - lr: 5.4806e-06\n", + "Epoch 2/50\n", + "27563/27563 [==============================] - 852s 31ms/step - loss: 0.0245 - mae: 0.1478 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 3.0034e-07\n", + "Epoch 3/50\n", + "27563/27563 [==============================] - 836s 30ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0117 - val_mae: 0.0807 - lr: 1.6459e-08\n", + "Epoch 4/50\n", + "27563/27563 [==============================] - 863s 31ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 9.0196e-10\n", + "Epoch 5/50\n", + "27563/27563 [==============================] - 854s 31ms/step - loss: 0.0243 - mae: 0.1474 - val_loss: 0.0119 - val_mae: 0.0812 - lr: 4.9428e-11\n", + "Epoch 6/50\n", + "27563/27563 [==============================] - 869s 32ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.7087e-12\n", + "Epoch 7/50\n", + "27563/27563 [==============================] - 867s 31ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0806 - lr: 1.4844e-13\n", + "Epoch 8/50\n", + "27563/27563 [==============================] - 899s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 8.1345e-15\n", + "Epoch 9/50\n", + "27563/27563 [==============================] - 966s 35ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0804 - lr: 4.4578e-16\n", + "Epoch 10/50\n", + "27563/27563 [==============================] - 930s 34ms/step - loss: 0.0244 - mae: 0.1474 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.4429e-17\n", + "Epoch 11/50\n", + "27563/27563 [==============================] - 921s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0809 - lr: 1.3387e-18\n", + "\n", + "Valutazione performance finali...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 75.7735\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1245\n", + "min_oil_prod_mape: 91.0646\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 89.5375\n", + "avg_oil_prod_mae: 0.0845\n", + "avg_oil_prod_rmse: 0.1171\n", + "avg_oil_prod_mape: 86.3355\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0678\n", + "total_water_need_mape: 41.0436\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 138.3196\n", + "min_oil_prod_mae: 0.0885\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 126.9523\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 82.7593\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1169\n", + "avg_oil_prod_mape: 84.3605\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0679\n", + "total_water_need_mape: 48.6941\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0899\n", + "olive_prod_rmse: 0.1219\n", + "olive_prod_mape: 77.0356\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 96.3498\n", + "max_oil_prod_mae: 0.0885\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 76.4509\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1167\n", + "avg_oil_prod_mape: 87.8912\n", + "total_water_need_mae: 0.0494\n", + "total_water_need_rmse: 0.0677\n", + "total_water_need_mape: 30.6997\n", + "\n", + "Modello riaddestrato salvato in: 2024-12-06_10-36_retrained_model.keras\n", + "\n", + "Miglioramenti delle performance:\n", + "\n", + "Set train:\n", + "olive_prod_mae: 6.48% di miglioramento\n", + "olive_prod_rmse: 6.00% di miglioramento\n", + "olive_prod_mape: 1.91% di miglioramento\n", + "min_oil_prod_mae: 5.29% di miglioramento\n", + "min_oil_prod_rmse: 5.12% di miglioramento\n", + "min_oil_prod_mape: 0.43% di miglioramento\n", + "max_oil_prod_mae: 5.11% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: -0.67% di miglioramento\n", + "avg_oil_prod_mae: 5.58% di miglioramento\n", + "avg_oil_prod_rmse: 5.45% di miglioramento\n", + "avg_oil_prod_mape: 3.57% di miglioramento\n", + "total_water_need_mae: 17.16% di miglioramento\n", + "total_water_need_rmse: 15.99% di miglioramento\n", + "total_water_need_mape: 7.67% di miglioramento\n", + "\n", + "Set val:\n", + "olive_prod_mae: 6.51% di miglioramento\n", + "olive_prod_rmse: 6.04% di miglioramento\n", + "olive_prod_mape: -3.81% di miglioramento\n", + "min_oil_prod_mae: 5.33% di miglioramento\n", + "min_oil_prod_rmse: 5.16% di miglioramento\n", + "min_oil_prod_mape: -5.12% di miglioramento\n", + "max_oil_prod_mae: 5.13% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: 4.02% di miglioramento\n", + "avg_oil_prod_mae: 5.62% di miglioramento\n", + "avg_oil_prod_rmse: 5.48% di miglioramento\n", + "avg_oil_prod_mape: -0.65% di miglioramento\n", + "total_water_need_mae: 17.23% di miglioramento\n", + "total_water_need_rmse: 16.08% di miglioramento\n", + "total_water_need_mape: 9.72% di miglioramento\n", + "\n", + "Set test:\n", + "olive_prod_mae: 6.52% di miglioramento\n", + "olive_prod_rmse: 6.09% di miglioramento\n", + "olive_prod_mape: 1.21% di miglioramento\n", + "min_oil_prod_mae: 5.32% di miglioramento\n", + "min_oil_prod_rmse: 5.22% di miglioramento\n", + "min_oil_prod_mape: -0.80% di miglioramento\n", + "max_oil_prod_mae: 5.22% di miglioramento\n", + "max_oil_prod_rmse: 4.83% di miglioramento\n", + "max_oil_prod_mape: -0.17% di miglioramento\n", + "avg_oil_prod_mae: 5.64% di miglioramento\n", + "avg_oil_prod_rmse: 5.59% di miglioramento\n", + "avg_oil_prod_mape: 20.98% di miglioramento\n", + "total_water_need_mae: 17.22% di miglioramento\n", + "total_water_need_rmse: 16.03% di miglioramento\n", + "total_water_need_mape: 19.57% di miglioramento\n" + ] + } + ], + "source": [ + "model_path = f'{execute_name}_final_model.keras'\n", + "\n", + "retrained_model, retrain_history, final_metrics = start_retraining(\n", + " model_path=model_path,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=50,\n", + " batch_size=128\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 137s 6ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1482.22\n", + "Errore percentuale medio: 5.77%\n", + "Precisione: 94.23%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 302.12\n", + "Errore percentuale medio: 5.68%\n", + "Precisione: 94.32%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 367.45\n", + "Errore percentuale medio: 5.78%\n", + "Precisione: 94.22%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 318.15\n", + "Errore percentuale medio: 5.49%\n", + "Precisione: 94.51%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1469.51\n", + "Errore percentuale medio: 3.31%\n", + "Precisione: 96.69%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from typing import List, Dict, Tuple, Union\n", + "\n", + "def analyze_feature_importance(model: tf.keras.Model, \n", + " test_data: dict, \n", + " feature_names: List[str]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Analizza l'importanza delle feature usando perturbazione.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n", + " feature_names: Lista dei nomi delle feature\n", + " \n", + " Returns:\n", + " dict: Dizionario con l'importanza relativa di ogni feature\n", + " \"\"\"\n", + " # Estrai i dati temporali e statici\n", + " temporal_data = test_data['temporal']\n", + " static_data = test_data['static']\n", + " \n", + " # Ottieni la predizione base\n", + " base_prediction = model.predict(test_data)\n", + " feature_importance = {}\n", + " \n", + " # Per ogni feature temporale\n", + " for i, feature in enumerate(feature_names):\n", + " if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature temporale\n", + " temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n", + " \n", + " # Crea rumore per la feature temporale\n", + " feature_values = temporal_data[..., temp_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature temporale\n", + " perturbed_temporal = perturbed_data['temporal'].copy()\n", + " perturbed_temporal[..., temp_idx] = feature_values + noise\n", + " perturbed_data['temporal'] = perturbed_temporal\n", + " \n", + " else: # Feature statiche\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature statica\n", + " static_idx = ['ha'].index(feature)\n", + " \n", + " # Crea rumore per la feature statica\n", + " feature_values = static_data[..., static_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature statica\n", + " perturbed_static = perturbed_data['static'].copy()\n", + " perturbed_static[..., static_idx] = feature_values + noise\n", + " perturbed_data['static'] = perturbed_static\n", + " \n", + " # Calcola nuova predizione\n", + " perturbed_prediction = model.predict(perturbed_data)\n", + " \n", + " # Calcola impatto della perturbazione\n", + " impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n", + " feature_importance[feature] = float(impact)\n", + " \n", + " # Normalizza le importanze\n", + " total_importance = sum(feature_importance.values())\n", + " feature_importance = {k: v/total_importance \n", + " for k, v in feature_importance.items()}\n", + " \n", + " return feature_importance\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor o array dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calcola varianza manualmente\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Ordina il tensor per il calcolo della mediana\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data: np.ndarray, \n", + " bins: int = 50, \n", + " title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Imposta il titolo generale\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf\n", + "\n", + "def analyze_model_predictions(model: tf.keras.Model, \n", + " test_data: np.ndarray,\n", + " test_targets: np.ndarray,\n", + " scaler_y) -> None:\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Esegue un'analisi completa del modello includendo errori,\n", + " importanza delle feature e distribuzioni.\n", + " \"\"\"\n", + " print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n", + " \n", + " # 1. Analisi degli errori\n", + " print(\"\\n1. ANALISI DEGLI ERRORI\")\n", + " print(\"-\" * 50)\n", + " analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n", + " \n", + " # 2. Analisi dell'importanza delle feature\n", + " print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Definisci i nomi delle feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha']\n", + " \n", + " all_features = temporal_features + static_features\n", + " importance = analyze_feature_importance(retrained_model, test_data, all_features)\n", + " \n", + " print(\"\\nImportanza relativa delle feature:\")\n", + " for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n", + " print(f\"{feature}: {imp:.4f}\")\n", + " \n", + " # 3. Analisi distribuzionale\n", + " print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n", + " print(\"-\" * 50)\n", + " \n", + " prob = ProbabilityFunctions()\n", + " predictions = retrained_model.predict(test_data)\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi distribuzionale per {target}\")\n", + " \n", + " # Statistiche\n", + " stats_pred = prob.calculate_statistics(predictions_real[:, i])\n", + " stats_true = prob.calculate_statistics(targets_real[:, i])\n", + " \n", + " print(\"\\nStatistiche Predizioni:\")\n", + " for key, value in stats_pred.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " print(\"\\nStatistiche Target Reali:\")\n", + " for key, value in stats_true.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza distribuzioni\n", + " prob.plot_distributions(predictions_real[:, i], bins=50,\n", + " title=f\"Distribuzione Predizioni - {target}\")\n", + " prob.plot_distributions(targets_real[:, i], bins=50,\n", + " title=f\"Distribuzione Target Reali - {target}\")\n", + "\n", + "def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data):\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calculate variance manually\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Sort the tensor for median calculation\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data, bins=50):\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf):\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Set overall title\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ANALISI COMPLETA DEL MODELLO ===\n", + "\n", + "1. ANALISI DEGLI ERRORI\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "\n", + "Analisi per olive_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -71.944\n", + "variance: 4009595.000\n", + "std: 2002.397\n", + "min: -18637.889\n", + "max: 12871.579\n", + "median: 48.672\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-1937.87, 1843.27]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4458.63, 3733.83]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-6979.39, 5624.40]\n", + "\n", + "Analisi per min_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -32.785\n", + "variance: 179026.016\n", + "std: 423.115\n", + "min: -4439.664\n", + "max: 3453.714\n", + "median: -12.655\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-414.04, 375.30]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1045.51, 848.90]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1519.11, 1164.63]\n", + "\n", + "Analisi per max_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -34.971\n", + "variance: 261409.344\n", + "std: 511.282\n", + "min: -5732.709\n", + "max: 4274.197\n", + "median: -11.391\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-429.05, 371.50]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1229.60, 971.92]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1830.02, 1572.33]\n", + "\n", + "Analisi per avg_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -33.549\n", + "variance: 192810.531\n", + "std: 439.102\n", + "min: -4876.229\n", + "max: 3813.953\n", + "median: -13.710\n" + ] + }, + { + "data": { + "image/png": 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X6qqrrtKCBQu0YMEC1a5dWx06dHBqKzY2VkuWLNGBAwcc5Tt37tQXX3xR4v2IiorSnDlznPrNpk2b9NVXX6lbt24laq+4Dhw4oI8//tgxn5mZqffee09RUVHnvTVfKv53sSTfEQBA0bjCDQCl8MUXX2jbtm06ffq0UlNTtWrVKiUlJal+/fr65JNP5O3tXeS648aN09q1a9W9e3fVr19faWlpmj59uurWrav27dtLOpP8BgQEaObMmfLz81O1atUUHR1d6mdma9asqfbt22vAgAFKTU3VlClT1KhRI6dXlz300ENavHixunTponvvvVe7du3SBx98oIYNGzq11bt3bwUFBalx48ZO7xuXpJtvvrnQV5QFBQUpMTFRY8eOVZcuXXT77bdr+/btmj59utq0aeM0QFpxPfDAA1q4cKEeeeQRrV69Wtdff73y8vK0bds2LVy4UF9++aVat25d6LpVqlTRSy+9pIcfflg33XST4uPjtXv3bs2aNavA1fzibqdVq1bq2bOnpkyZosOHDzteC5Z/5bEs71rYv39/gWMvnRlsrrh/BCnMSy+95HhH/GOPPSZPT0/94x//UHZ2dqHvQb9Ubr31Vr3//vuqXr26mjVrpuTkZK1YsaLI19DFx8dr1KhR8vb21sCBAwvcVj9mzBh99dVXuv766/Xoo48qLy9Pb731llq0aKGNGzeWKLZXX31VXbt2VUxMjAYOHOh4LVj16tUv+O710rriiis0cOBArVu3TiEhIXr33XeVmpqqWbNmXXDd4n4XS/IdAQCch/sGSAeA8if/VTv5k5eXlwkNDTU333yzeeONN5xevZXv3NeCrVy50txxxx0mLCzMeHl5mbCwMNO7d2/z66+/Oq23dOlS06xZM8erivJfEdaxY0fTvHnzQuMr6rVgH374oUlMTDTBwcGmatWqpnv37mbv3r0F1n/ttddMnTp1jM1mM9dff7356aefCrSpIl5JJcmsXr3a6Tid+2qot956yzRt2tRUqVLFhISEmEcffdT8+eefBfahsP3r16+fqV+/vlNZTk6O+fvf/26aN29ubDabqVGjhmnVqpUZO3asycjIKPQYnW369OkmMjLS2Gw207p1a7N27dpCX9VU3O2cOHHCDBkyxNSsWdP4+vqaHj16mO3btxtJZsKECY56rnot2NnHp1+/fqZatWqFtiHJDBkypNBlGzZsMHFxccbX19f4+PiYG2+80Xz33XdOdc73ejxXvBbszz//NAMGDDCBgYHG19fXxMXFmW3bthX5KrsdO3Y4jsk333xTaJsrV64011xzjfHy8jINGzY0//rXv8yTTz5pvL29SxzfihUrzPXXX2+qVq1q/P39zW233Wa2bNniVKcsXwvWvXt38+WXX5qrr77a2Gw207RpU7No0aJCt1fUKwyL8100pvjfEQBA4SzGnGeUEgAAcFE2btyoa665Rh988MF5HzWA+/Xo0UObN28uMObB5SQiIkItWrTQsmXL3B0KAKAYeIYbAIAycvLkyQJlU6ZMkdVqdXqOGO537me1Y8cOff755+rUqZN7AgIAVEg8ww0AQBmZOHGi1q9frxtvvFGenp764osv9MUXX2jw4MGX9evDKqMGDRqof//+jndPz5gxQ15eXnrmmWcknXm/d2F/QDnbhQYoK4n09PRCB4HL5+XlpZo1a5bZ9gAAlwYJNwAAZeS6665TUlKSXnzxRR0/flz16tXTmDFj9Le//c3doeEcXbp00YcffqiUlBTZbDbFxMTolVdecbzS7YknntCcOXPO20ZZPpXXpk0b7d27t8jlHTt21Jo1a8psewCAS4NnuAEAAM6xZcsWp9eGFaY0748vyrfffnveK+o1atRQq1atymx7AIBLg4QbAAAAAAAXYNA0AAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgBApWaxWDR06NAya2/27NmyWCz66aefLli3U6dO6tSpk2N+z549slgsmj17tqNszJgxslgsZRYfLh/nfv4AgIqHhBsAcNnJT1rzJ29vb11xxRUaOnSoUlNT3R2e273yyitasmRJmba5Zs0ax/H+4IMPCq1z/fXXy2KxqEWLFmW67bJwdn85ewoNDXVrXFu2bNGYMWO0Z88et8YBAHAPT3cHAABAUcaNG6fIyEidOnVK33zzjWbMmKHPP/9cmzZtko+Pj7vDu2hfffXVBeuMHDlSzz33nFPZK6+8orvvvls9evQo85i8vb01b9483X///U7le/bs0XfffSdvb+8y32ZZufnmm9W3b1+nsqpVq7opmjO2bNmisWPHqlOnToqIiHBaVpzPHwBQvpFwAwAuW127dlXr1q0lSQ899JBq1aqlyZMna+nSperdu3eh65w4cULVqlW7lGGWmpeX1wXreHp6ytPz0v267tatmz755BMdOnRIgYGBjvJ58+YpJCREjRs31p9//nnJ4imJK664osAfCi5nxfn8AQDlG7eUAwDKjZtuukmStHv3bklS//795evrq127dqlbt27y8/NTnz59JJ1JvJ988kmFh4fLZrOpSZMmmjRpkowxhbY9d+5cNWnSRN7e3mrVqpXWrl3rtHzv3r167LHH1KRJE1WtWlW1atXSPffcU+StwllZWXr44YdVq1Yt+fv7q2/fvgUS1eI8w3vuM9wWi0UnTpzQnDlzHLdN9+/fX6tXr5bFYtHHH39coI158+bJYrEoOTn5vNuSpDvuuEM2m02LFi0q0Ma9994rDw+PAuvMmjVLN910k4KDg2Wz2dSsWTPNmDGjQL2ffvpJcXFxCgwMVNWqVRUZGakHH3zQqc78+fPVqlUr+fn5yd/fX1dddZXeeOONC8Z9If379y9whVkq/Bn5/Of6lyxZohYtWshms6l58+Zavnx5gfX379+vgQMHKiwsTDabTZGRkXr00UeVk5Oj2bNn65577pEk3XjjjY7Pa82aNZIK//zT0tI0cOBAhYSEyNvbWy1bttScOXOc6uQ/6z9p0iS9/fbbatiwoWw2m9q0aaN169aV/iABAMocV7gBAOXGrl27JEm1atVylJ0+fVpxcXFq3769Jk2aJB8fHxljdPvtt2v16tUaOHCgoqKi9OWXX+rpp5/W/v379frrrzu1+/XXX2vBggUaNmyYbDabpk+fri5duujHH390PK+8bt06fffdd+rVq5fq1q2rPXv2aMaMGerUqZO2bNlS4Bb3oUOHKiAgQGPGjNH27ds1Y8YM7d271/GsdGm9//77euihh9S2bVsNHjxYktSwYUO1a9dO4eHhmjt3ru68806ndebOnauGDRsqJibmgu37+Pjojjvu0IcffqhHH31UkvTzzz9r8+bN+te//qVffvmlwDozZsxQ8+bNdfvtt8vT01OffvqpHnvsMdntdg0ZMkTSmUTylltuUVBQkJ577jkFBARoz549+uijjxztJCUlqXfv3urcubP+/ve/S5K2bt2qb7/9Vk888cQFYz916pQOHTrkVObn5yebzXbBdc/1zTff6KOPPtJjjz0mPz8/TZ06VT179tS+ffsc/e/AgQNq27atjh49qsGDB6tp06bav3+/Fi9erKysLHXo0EHDhg3T1KlT9fzzz+vKK6+UJMe/5zp58qQ6deqknTt3aujQoYqMjNSiRYvUv39/HT16tMAxmDdvno4dO6aHH35YFotFEydO1F133aXffvtNVapUKfE+AwBcwAAAcJmZNWuWkWRWrFhh0tPTze+//27mz59vatWqZapWrWr++OMPY4wx/fr1M5LMc88957T+kiVLjCTz0ksvOZXffffdxmKxmJ07dzrKJBlJ5qeffnKU7d2713h7e5s777zTUZaVlVUgzuTkZCPJvPfeewVib9WqlcnJyXGUT5w40UgyS5cudZR17NjRdOzY0TG/e/duI8nMmjXLUTZ69Ghz7q/ratWqmX79+hWIJzEx0dhsNnP06FFHWVpamvH09DSjR48uUP9sq1evNpLMokWLzLJly4zFYjH79u0zxhjz9NNPmwYNGjhibt68udO6hR2buLg4xzrGGPPxxx8bSWbdunVFxvDEE08Yf39/c/r06fPGWpj8z/HcKf9Y9uvXz9SvX7/AeoUdX0nGy8vLqZ/8/PPPRpJ58803HWV9+/Y1Vqu10H2y2+3GGGMWLVpkJJnVq1cXqHPu5z9lyhQjyXzwwQeOspycHBMTE2N8fX1NZmamMeavflKrVi1z5MgRR92lS5caSebTTz8t+kABAC4pbikHAFy2YmNjFRQUpPDwcPXq1Uu+vr76+OOPVadOHad6+Vdi833++efy8PDQsGHDnMqffPJJGWP0xRdfOJXHxMSoVatWjvl69erpjjvu0Jdffqm8vDxJzoNv5ebm6vDhw2rUqJECAgK0YcOGArEPHjzY6Srjo48+Kk9PT33++eclPArF17dvX2VnZ2vx4sWOsgULFuj06dMlerb5lltuUc2aNTV//nwZYzR//vwin5mXnI9NRkaGDh06pI4dO+q3335TRkaGJCkgIECStGzZMuXm5hbaTkBAgE6cOKGkpKRix3q2O+64Q0lJSU5TXFxcqdqKjY1Vw4YNHfNXX321/P399dtvv0mS7Ha7lixZottuu80xzsDZSnMXw+eff67Q0FCnY12lShUNGzZMx48f19dff+1UPz4+XjVq1HDM33DDDZLkiBEA4H7cUg4AuGxNmzZNV1xxhTw9PRUSEqImTZrIanX+W7Gnp6fq1q3rVLZ3716FhYXJz8/PqTz/Vt69e/c6lTdu3LjAtq+44gplZWUpPT1doaGhOnnypMaPH69Zs2Zp//79Ts+C5yeV52vT19dXtWvXdunroZo2bao2bdpo7ty5GjhwoKQzt5O3a9dOjRo1KnY7VapU0T333KN58+apbdu2+v3333XfffcVWf/bb7/V6NGjlZycrKysLKdlGRkZql69ujp27KiePXtq7Nixev3119WpUyf16NFD9913n+OW78cee0wLFy5U165dVadOHd1yyy2699571aVLl2LFXbduXcXGxhZ7P8+nXr16Bcpq1KjheA4/PT1dmZmZZfqKtL1796px48YF+nhR/fbcGPOT78t1UDsAqIy4wg0AuGy1bdtWsbGx6tSpk6688soCiYgk2Wy2QsvL2uOPP66XX35Z9957rxYuXKivvvpKSUlJqlWrlux2u8u3X1x9+/bV119/rT/++EO7du3S999/X6qRu++77z5t3LhRY8aMUcuWLdWsWbNC6+3atUudO3fWoUOHNHnyZH322WdKSkrSiBEjJMlxbCwWixYvXqzk5GQNHTpU+/fv14MPPqhWrVrp+PHjkqTg4GBt3LhRn3zyieMZ/K5du6pfv36lPBp/KeqKc/4dDOcqbHA4SUUOuucO5SFGAKjsSLgBABVO/fr1deDAAR07dsypfNu2bY7lZ9uxY0eBNn799Vf5+PgoKChIkrR48WL169dPr732mu6++27dfPPNat++vY4ePVpoDOe2efz4cR08eLDQkbJL6ny3K/fq1UseHh768MMPNXfuXFWpUkXx8fEl3kb79u1Vr149rVmz5rxXtz/99FNlZ2frk08+0cMPP6xu3bopNja2yPdft2vXTi+//LJ++uknzZ07V5s3b9b8+fMdy728vHTbbbdp+vTp2rVrlx5++GG999572rlzZ4n34Ww1atQo9LM696pxcQUFBcnf31+bNm06b72S3Fpev3597dixo8AfcIrqtwCAyx8JNwCgwunWrZvy8vL01ltvOZW//vrrslgs6tq1q1N5cnKy03PYv//+u5YuXapbbrnFcRXRw8OjwJXDN998s8grpG+//bbTs8ozZszQ6dOnC2y7NKpVq1Zkoh8YGKiuXbvqgw8+0Ny5c9WlSxen92kXl8Vi0dSpUzV69Gg98MADRdbLPz7n3mI/a9Ysp3p//vlngeMXFRUlScrOzpYkHT582Gm51WrV1Vdf7VSntBo2bKiMjAynUdYPHjxY6GvUisNqtapHjx769NNP9dNPPxVYnr+v+e+EL+rzOlu3bt2UkpKiBQsWOMpOnz6tN998U76+vurYsWOpYgUAuA/PcAMAKpzbbrtNN954o/72t79pz549atmypb766istXbpUw4cPdxoMS5JatGihuLg4p9eCSdLYsWMddW699Va9//77ql69upo1a6bk5GStWLHC6RVlZ8vJyVHnzp117733avv27Zo+fbrat2+v22+//aL3r1WrVlqxYoUmT56ssLAwRUZGKjo62rG8b9++uvvuuyVJL774Yqm3c8cdd+iOO+44b51bbrnFcVX64Ycf1vHjx/XPf/5TwcHBOnjwoKPenDlzNH36dN15551q2LChjh07pn/+85/y9/dXt27dJEkPPfSQjhw5optuukl169bV3r179eabbyoqKqrIV2kVV69evfTss8/qzjvv1LBhw5SVlaUZM2boiiuuKHTQu+J45ZVX9NVXX6ljx44aPHiwrrzySh08eFCLFi3SN998o4CAAEVFRcnDw0N///vflZGRIZvN5nhn+bkGDx6sf/zjH+rfv7/Wr1+viIgILV68WN9++62mTJlSYEwCAMDlj4QbAFDhWK1WffLJJxo1apQWLFigWbNmKSIiQq+++qqefPLJAvU7duyomJgYjR07Vvv27VOzZs00e/Zsx9VVSXrjjTfk4eGhuXPn6tSpU7r++uu1YsWKIkfBfuuttzR37lyNGjVKubm56t27t6ZOnXpR7+DON3nyZA0ePFgjR47UyZMn1a9fP6eE+7bbblONGjVkt9vLJME/nyZNmmjx4sUaOXKknnrqKYWGhurRRx9VUFCQHnzwQUe9jh076scff9T8+fOVmpqq6tWrq23btpo7d64iIyMlSffff7/efvttTZ8+XUePHlVoaKji4+M1ZsyYi35Ov1atWvr444+VkJCgZ555RpGRkRo/frx27NhR6oS7Tp06+uGHH/TCCy9o7ty5yszMVJ06ddS1a1fHe9lDQ0M1c+ZMjR8/XgMHDlReXp5Wr15daMJdtWpVrVmzRs8995zmzJmjzMxMNWnSRLNmzVL//v0vZvcBAG5iMYysAQBAhXL69GmFhYXptttu0zvvvOPucAAAqLR4hhsAgApmyZIlSk9PV9++fd0dCgAAlRpXuAEAqCB++OEH/fLLL3rxxRcVGBhY6lulAQBA2eAKNwAAFcSMGTP06KOPKjg4WO+99567wwEAoNLjCjcAAAAAAC7AFW4AAAAAAFyAhBsAAAAAABfgPdylZLfbdeDAAfn5+ZXJO1UBAAAAAJcnY4yOHTumsLAwWa3Fv25Nwl1KBw4cUHh4uLvDAAAAAABcIr///rvq1q1b7Pok3KXk5+cn6cwB9/f3d3M0cCW73a709HQFBQWV6K9ZQHlDX0dlQn9HZUFfR2Xiyv6emZmp8PBwRx5YXCTcpZR/G7m/vz8JdwVnt9t16tQp+fv784sKFRp9HZUJ/R2VBX0dlcml6O8lfZyYbx0AAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AI8ww0AAAAAFZAxRqdPn1ZeXp67Q7kk7Ha7cnNzderUqRI/w+3h4SFPT88yf+UzCTcAAAAAVDA5OTk6ePCgsrKy3B3KJWOMkd1u17Fjx0qVOPv4+Kh27dry8vIqs5hIuAEAAACgArHb7dq9e7c8PDwUFhYmLy+vMr9yeznKv6Jf0ivVxhjl5OQoPT1du3fvVuPGjctslHMSbgAAAACoQHJycmS32xUeHi4fHx93h3PJlDbhlqSqVauqSpUq2rt3r3JycuTt7V0mMTFoGgAAAABUQLx7vWRccbz4BAAAAAAAcIHLIuGeNm2aIiIi5O3trejoaP34449F1v3nP/+pG264QTVq1FCNGjUUGxtboL4xRqNGjVLt2rVVtWpVxcbGaseOHU51jhw5oj59+sjf318BAQEaOHCgjh8/7pL9AwAAAABUPm5/hnvBggVKSEjQzJkzFR0drSlTpiguLk7bt29XcHBwgfpr1qxR7969dd1118nb21t///vfdcstt2jz5s2qU6eOJGnixImaOnWq5syZo8jISL3wwguKi4vTli1bHPfi9+nTRwcPHlRSUpJyc3M1YMAADR48WPPmzbuk+w8AAAAAl8LrSb9e0u2NuPmKS7q9y5HFGGPcGUB0dLTatGmjt956S5IcD/c//vjjeu655y64fl5enmrUqKG33npLffv2lTFGYWFhevLJJ/XUU09JkjIyMhQSEqLZs2erV69e2rp1q5o1a6Z169apdevWkqTly5erW7du+uOPPxQWFlZgO9nZ2crOznbMZ2ZmKjw8XH/++af8/f3L4lDgMmW325Wenq6goCCeg0GFRl9HZfJG0q/yyTuuLA9fqRQj9z4R29gFUQFlj3N75XTq1Cnt2bNHkZGRToN/TVlxaRPu4bElT7gHDBigOXPmSJKqVKmievXq6YEHHtDzzz+vb775RjfddJMCAgJ04MABp31bt26doqOjJZ3p99KZi7U33XRTgW08//zzeumllwqUnzp1Srt373bcfX22zMxM1ahRQxkZGSXK/9x6hTsnJ0fr169XYmKio8xqtSo2NlbJycnFaiMrK0u5ubmqWbOmJGn37t1KSUlRbGyso0716tUVHR2t5ORk9erVS8nJyQoICHAk25IUGxsrq9WqH374QXfeeWeB7YwfP15jx44tUJ6enq5Tp04Ve59R/tjtdmVkZMgYwy8qVGj0dVQmPvbjsplTkl2SSp5wp6WllXlMgCtwbq+ccnNzZbfbdfr0aZ0+fdpRnp+IXipnb7u47Ha74uLi9M9//lPZ2dlavny5hg0bJg8PD7Vr106S5Ofnp8WLF6tXr16O9f71r3+pXr162rdvn3Jzc2WxWJSXlydJ2rRpk1OS7OvrW2hsp0+flt1u1+HDh1WlShWnZceOHSvxvkhuTrgPHTqkvLw8hYSEOJWHhIRo27ZtxWrj2WefVVhYmCPBTklJcbRxbpv5y1JSUgrcru7p6amaNWs66pwrMTFRCQkJjvn8K9xBQUFc4a7g7Ha7LBYLfxlGhUdfR2WSZT0qGSnLWror3IU99gZcjji3V06nTp3SsWPH5OnpKU/Pv1K+S90Hzt52cVmtVnl7e6tu3bqSpCFDhuiTTz7RZ599puuvv16S1LdvX7333nu6//77JUknT57UwoUL9fjjj+ull15yJMseHh6SpLCwMAUEBBQrXqvVqlq1ahW4wl3a14S5/RnuizFhwgTNnz9fa9asKbP3pBXFZrPJZrMVKLdarZy8KgGLxcJnjUqBvo5Kw2JxnkqI7wjKE87tlY/VapXFYnFMfyn5+e5ilPRd2EWtW7VqVR0+fNhR1rdvX02aNEm///676tWrp48++kgRERG69tprndbPr1/wOBS9zaK+L6X9/rj1WxcYGCgPDw+lpqY6laempio0NPS8606aNEkTJkzQV199pauvvtpRnr/e+doMDQ0tcCvY6dOndeTIkQtuFwAAAADgesYYrVixQl9++aXTs9jBwcHq2rWrZs+eLUl699139eCDDxbZTt26deXr6+uYDh8+7OrQHdyacHt5ealVq1ZauXKlo8xut2vlypWKiYkpcr2JEyfqxRdf1PLly52ew5akyMhIhYaGOrWZmZmpH374wdFmTEyMjh49qvXr1zvqrFq1Sna73fGgPQAAAADg0lu2bJl8fX3l7e2trl27Kj4+XmPGjHGq8+CDD2r27Nn67bfflJycrD59+hTZ3n/+8x9t3LjRMdWoUcPFe/AXt99SnpCQoH79+ql169Zq27atpkyZohMnTmjAgAGSztwuUKdOHY0fP16S9Pe//12jRo3SvHnzFBER4XjmOv+vFRaLRcOHD9dLL72kxo0bO14LFhYWph49ekiSrrzySnXp0kWDBg3SzJkzlZubq6FDh6pXr16FjlAOAAAAALg0brzxRs2YMUNeXl4KCwsr9Fnwrl27avDgwRo4cKBuu+021apVq8j2IiMji/UMtyu4PeGOj49Xenq6Ro0apZSUFEVFRWn58uWOQc/27dvndL/8jBkzlJOTo7vvvtupndGjRzv+6vHMM8/oxIkTGjx4sI4ePar27dtr+fLlTs95z507V0OHDlXnzp1ltVrVs2dPTZ061fU7DAAAAAAoUrVq1dSoUaPz1vH09FTfvn01ceJEffHFF5cospJze8ItSUOHDtXQoUMLXbZmzRqn+T179lywPYvFonHjxmncuHFF1qlZs6bmzZtXkjABAAAAAJeJF198UU8//fR5r26722WRcAMAAAAAXGvEzVe4O4Qy5eXlpcDAQHeHcV4k3AAAAACAy0L+yOOF6dSpk4wxRS7v0aOHcnJyil3/UuBlfAAAAAAAuAAJNwAAAAAALkDCDQAAAACAC/AMNwAAKHdeT/rV3SEAAHBBXOEGAAAAgArI3QOGlTeuOF4k3AAAAABQgVSpUkWSlJWV5eZIypf845V//MoCt5QDAAAAQAXi4eGhgIAApaWlSZJ8fHxksVjcHJXrGWN0+vRpeXp6lmh/jTHKyspSWlqaAgIC5OHhUWYxkXADAAAAQAUTGhoqSY6kuzIwxshut8tqtZbqDwwBAQGO41ZWSLgBAAAAoIKxWCyqXbu2goODlZub6+5wLgm73a7Dhw+rVq1aslpL9vR0lSpVyvTKdj4SbgAAAACooDw8PFySSF6O7Ha7qlSpIm9v7xIn3K5yeUQBAAAAAEAFQ8INAAAAAIALkHADAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuAAJNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4AAk3AAAAAAAu4PaEe9q0aYqIiJC3t7eio6P1448/Fll38+bN6tmzpyIiImSxWDRlypQCdfKXnTsNGTLEUadTp04Flj/yyCOu2D0AAAAAQCXl1oR7wYIFSkhI0OjRo7Vhwwa1bNlScXFxSktLK7R+VlaWGjRooAkTJig0NLTQOuvWrdPBgwcdU1JSkiTpnnvucao3aNAgp3oTJ04s250DAAAAAFRqnu7c+OTJkzVo0CANGDBAkjRz5kx99tlnevfdd/Xcc88VqN+mTRu1adNGkgpdLklBQUFO8xMmTFDDhg3VsWNHp3IfH58ik/bCZGdnKzs72zGfmZkpSbLb7bLb7cVuB+WP3W6XMYbPGRUefR3lijEXv37+VAp8T1BecG5HZeLK/l7aNt2WcOfk5Gj9+vVKTEx0lFmtVsXGxio5ObnMtvHBBx8oISFBFovFadncuXP1wQcfKDQ0VLfddpteeOEF+fj4FNnW+PHjNXbs2ALl6enpOnXqVJnEi8uT3W5XRkaGjDGyWt3+FAbgMvR1lCc+eccvsgUjmzkl2SXJcqHKBRR1Nx5wueHcjsrElf392LFjpVrPbQn3oUOHlJeXp5CQEKfykJAQbdu2rUy2sWTJEh09elT9+/d3Kr/vvvtUv359hYWF6ZdfftGzzz6r7du366OPPiqyrcTERCUkJDjmMzMzFR4erqCgIPn7+5dJvLg82e12WSwWBQUF8YsKFRp9HeVJlkfGxTVgjGSkLKuvZCl5wh0cHHxx2wcuEc7tqExc2d+9vb1LtZ5bbyl3tXfeeUddu3ZVWFiYU/ngwYMdP1911VWqXbu2OnfurF27dqlhw4aFtmWz2WSz2QqUW61WTl6VgMVi4bNGpUBfR7lRiiS50DbypxLiO4LyhHM7KhNX9ffStue2b11gYKA8PDyUmprqVJ6amlqiZ6uLsnfvXq1YsUIPPfTQBetGR0dLknbu3HnR2wUAAAAAQHJjwu3l5aVWrVpp5cqVjjK73a6VK1cqJibmotufNWuWgoOD1b179wvW3bhxoySpdu3aF71dAAAAAAAkN99SnpCQoH79+ql169Zq27atpkyZohMnTjhGLe/bt6/q1Kmj8ePHSzozCNqWLVscP+/fv18bN26Ur6+vGjVq5GjXbrdr1qxZ6tevnzw9nXdx165dmjdvnrp166ZatWrpl19+0YgRI9ShQwddffXVl2jPAQAAAAAVnVsT7vj4eKWnp2vUqFFKSUlRVFSUli9f7hhIbd++fU73yh84cEDXXHONY37SpEmaNGmSOnbsqDVr1jjKV6xYoX379unBBx8ssE0vLy+tWLHCkdyHh4erZ8+eGjlypOt2FAAAAABQ6ViMudgXWVZOmZmZql69ujIyMhilvIKz2+1KS0tTcHAwg42gQqOvozx5PenXi2vAGPnkHVeWR+lGKR9x8xUXt33gEuHcjsrElf29tPkf3zoAAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFzA7Qn3tGnTFBERIW9vb0VHR+vHH38ssu7mzZvVs2dPRUREyGKxaMqUKQXqjBkzRhaLxWlq2rSpU51Tp05pyJAhqlWrlnx9fdWzZ0+lpqaW9a4BAAAAACoxtybcCxYsUEJCgkaPHq0NGzaoZcuWiouLU1paWqH1s7Ky1KBBA02YMEGhoaFFttu8eXMdPHjQMX3zzTdOy0eMGKFPP/1UixYt0tdff60DBw7orrvuKtN9AwAAAABUbm5NuCdPnqxBgwZpwIABatasmWbOnCkfHx+9++67hdZv06aNXn31VfXq1Us2m63Idj09PRUaGuqYAgMDHcsyMjL0zjvvaPLkybrpppvUqlUrzZo1S999952+//77Mt9HAAAAAEDl5OmuDefk5Gj9+vVKTEx0lFmtVsXGxio5Ofmi2t6xY4fCwsLk7e2tmJgYjR8/XvXq1ZMkrV+/Xrm5uYqNjXXUb9q0qerVq6fk5GS1a9eu0Dazs7OVnZ3tmM/MzJQk2e122e32i4oXlze73S5jDJ8zKjz6OsoVYy5+/fypFPieoLzg3I7KxJX9vbRtui3hPnTokPLy8hQSEuJUHhISom3btpW63ejoaM2ePVtNmjTRwYMHNXbsWN1www3atGmT/Pz8lJKSIi8vLwUEBBTYbkpKSpHtjh8/XmPHji1Qnp6erlOnTpU6Xlz+7Ha7MjIyZIyR1er2YQ8Al6GvozzxyTt+kS0Y2cwpyS5JlhKvXdTjb8DlhnM7KhNX9vdjx46Vaj23Jdyu0rVrV8fPV199taKjo1W/fn0tXLhQAwcOLHW7iYmJSkhIcMxnZmYqPDxcQUFB8vf3v6iYcXmz2+2yWCwKCgriFxUqNPo6ypMsj4yLa8AYyUhZVl/JUvKEOzg4+OK2D1winNtRmbiyv3t7e5dqPbcl3IGBgfLw8CgwOnhqaup5B0QrqYCAAF1xxRXauXOnJCk0NFQ5OTk6evSo01XuC23XZrMV+ty41Wrl5FUJWCwWPmtUCvR1lBulSJILbSN/KiG+IyhPOLejMnFVfy9te2771nl5ealVq1ZauXKlo8xut2vlypWKiYkps+0cP35cu3btUu3atSVJrVq1UpUqVZy2u337du3bt69MtwsAAAAAqNzcekt5QkKC+vXrp9atW6tt27aaMmWKTpw4oQEDBkiS+vbtqzp16mj8+PGSzgy0tmXLFsfP+/fv18aNG+Xr66tGjRpJkp566inddtttql+/vg4cOKDRo0fLw8NDvXv3liRVr15dAwcOVEJCgmrWrCl/f389/vjjiomJKXLANAAAAAAASsqtCXd8fLzS09M1atQopaSkKCoqSsuXL3cMpLZv3z6nS/cHDhzQNddc45ifNGmSJk2apI4dO2rNmjWSpD/++EO9e/fW4cOHFRQUpPbt2+v7779XUFCQY73XX39dVqtVPXv2VHZ2tuLi4jR9+vRLs9MAAAAAgErBYszFvlejcsrMzFT16tWVkZHBoGkVnN1uV1pamoKDg3n2CRUafR3lyetJv15cA8bIJ++4sjxKN2jaiJuvuLjtA5cI53ZUJq7s76XN//jWAQAAAADgAiTcAAAAAAC4AAk3AAAAAAAuQMINAAAAAIALkHADAAAAAOACJNwAAAAAALiAW9/DDQAAUB5d7GvJeK0YAFQOXOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcwNPdAQAAgMrl9aRf3R0CAACXBFe4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHCBUiXcq1evLus4AAAAAACoUEqVcHfp0kUNGzbUSy+9pN9///2iApg2bZoiIiLk7e2t6Oho/fjjj0XW3bx5s3r27KmIiAhZLBZNmTKlQJ3x48erTZs28vPzU3BwsHr06KHt27c71enUqZMsFovT9Mgjj1zUfgAAAAAAcLZSJdz79+/X0KFDtXjxYjVo0EBxcXFauHChcnJyStTOggULlJCQoNGjR2vDhg1q2bKl4uLilJaWVmj9rKwsNWjQQBMmTFBoaGihdb7++msNGTJE33//vZKSkpSbm6tbbrlFJ06ccKo3aNAgHTx40DFNnDixRLEDAAAAAHA+pUq4AwMDNWLECG3cuFE//PCDrrjiCj322GMKCwvTsGHD9PPPPxerncmTJ2vQoEEaMGCAmjVrppkzZ8rHx0fvvvtuofXbtGmjV199Vb169ZLNZiu0zvLly9W/f381b95cLVu21OzZs7Vv3z6tX7/eqZ6Pj49CQ0Mdk7+/f8kOAgAAAAAA5+F5sQ1ce+21Cg0NVa1atTRhwgS9++67mj59umJiYjRz5kw1b9680PVycnK0fv16JSYmOsqsVqtiY2OVnJx8sWE5ZGRkSJJq1qzpVD537lx98MEHCg0N1W233aYXXnhBPj4+RbaTnZ2t7Oxsx3xmZqYkyW63y263l1m8uPzY7XYZY/icUeHR13HJGOPuCM7EkD+5Ad8zXCqc21GZuLK/l7bNUifcubm5Wrp0qd59910lJSWpdevWeuutt9S7d2+lp6dr5MiRuueee7Rly5ZC1z906JDy8vIUEhLiVB4SEqJt27aVNiwndrtdw4cP1/XXX68WLVo4yu+77z7Vr19fYWFh+uWXX/Tss89q+/bt+uijj4psa/z48Ro7dmyB8vT0dJ06dapM4sXlyW63KyMjQ8YYWa0M7I+Ki76OS8Un77i7Q5BkZDOnJLskWS751ot6fA4oa5zbUZm4sr8fO3asVOuVKuF+/PHH9eGHH8oYowceeEATJ050SmirVaumSZMmKSwsrFRBlZUhQ4Zo06ZN+uabb5zKBw8e7Pj5qquuUu3atdW5c2ft2rVLDRs2LLStxMREJSQkOOYzMzMVHh6uoKAgbkev4Ox2uywWi4KCgvhFhQqNvo5LJcsjw90h/P/VbSnL6itZLn3CHRwcfMm3icqJczsqE1f2d29v71KtV6qEe8uWLXrzzTd11113FfksdWBg4HlfHxYYGCgPDw+lpqY6laemphY5IFpJDB06VMuWLdPatWtVt27d89aNjo6WJO3cubPIhNtmsxW6r1arlZNXJWCxWPisUSnQ13FJuCHBLZTF8td0ifEdw6XEuR2Viav6e2nbK9Vao0eP1j333FMgAT19+rTWrl0rSfL09FTHjh2LbMPLy0utWrXSypUrHWV2u10rV65UTExMacKSJBljNHToUH388cdatWqVIiMjL7jOxo0bJUm1a9cu9XYBAAAAADhbqa5w33jjjTp48GCB26EyMjJ04403Ki8vr1jtJCQkqF+/fmrdurXatm2rKVOm6MSJExowYIAkqW/fvqpTp47Gjx8v6cxAa/nPhOfk5Gj//v3auHGjfH191ahRI0lnbiOfN2+eli5dKj8/P6WkpEiSqlevrqpVq2rXrl2aN2+eunXrplq1aumXX37RiBEj1KFDB1199dWlORwAAAAAABRQqoTbGCNLIbdfHT58WNWqVSt2O/Hx8UpPT9eoUaOUkpKiqKgoLV++3DGQ2r59+5wu3R84cEDXXHONY37SpEmaNGmSOnbsqDVr1kiSZsyYIUnq1KmT07ZmzZql/v37y8vLSytWrHAk9+Hh4erZs6dGjhxZ7LgBAAAAALiQEiXcd911l6Qz98X379/f6ZbyvLw8/fLLL7ruuutKFMDQoUM1dOjQQpflJ9H5IiIiZC7w+o4LLQ8PD9fXX39dohgBAAAAACipEiXc1atXl3QmqfXz81PVqlUdy7y8vNSuXTsNGjSobCMEAAAAAKAcKlHCPWvWLElnrjQ/9dRTJbp9HAAAAACAyqRUz3CPHj26rOMAAAAAAKBCKXbCfe2112rlypWqUaOGrrnmmkIHTcu3YcOGMgkOAAAAAIDyqtgJ9x133OEYJK1Hjx6uigcAAAAAgAqh2An32beRc0s5AAAAAADnZ71wFQAAAAAAUFLFvsJdo0aN8z63fbYjR46UOiAAAAAAACqCYifcU6ZMcWEYAAAAAABULMVOuPv16+fKOAAAAAAAqFCKnXBnZmbK39/f8fP55NcDAAAAAKCyKtEz3AcPHlRwcLACAgIKfZ7bGCOLxaK8vLwyDRIAAAAAgPKm2An3qlWrVLNmTUnS6tWrXRYQAAAAAAAVQbET7o4dOxb6MwAAAAAAKKjYCfe5/vzzT73zzjvaunWrJKlZs2YaMGCA4yo4AAAAAACVmbU0K61du1YRERGaOnWq/vzzT/3555+aOnWqIiMjtXbt2rKOEQAAAACAcqdUV7iHDBmi+Ph4zZgxQx4eHpKkvLw8PfbYYxoyZIj+97//lWmQAAAAAACUN6W6wr1z5049+eSTjmRbkjw8PJSQkKCdO3eWWXAAAAAAAJRXpUq4r732Wsez22fbunWrWrZsedFBAQAAAABQ3hX7lvJffvnF8fOwYcP0xBNPaOfOnWrXrp0k6fvvv9e0adM0YcKEso8SAAAAAIByptgJd1RUlCwWi4wxjrJnnnmmQL377rtP8fHxZRMdAAAAAADlVLET7t27d7syDgAAAAAAKpRiJ9z169d3ZRwAAAAAAFQopXotWL4tW7Zo3759ysnJcSq//fbbLyooAAAAAADKu1Il3L/99pvuvPNO/e9//3N6rttisUg6805uAAAAAAAqs1K9FuyJJ55QZGSk0tLS5OPjo82bN2vt2rVq3bq11qxZU6K2pk2bpoiICHl7eys6Olo//vhjkXU3b96snj17KiIiQhaLRVOmTClVm6dOndKQIUNUq1Yt+fr6qmfPnkpNTS1R3AAAAAAAnE+pEu7k5GSNGzdOgYGBslqtslqtat++vcaPH69hw4YVu50FCxYoISFBo0eP1oYNG9SyZUvFxcUpLS2t0PpZWVlq0KCBJkyYoNDQ0FK3OWLECH366adatGiRvv76ax04cEB33XVXyQ4CAAAAAADnUaqEOy8vT35+fpKkwMBAHThwQNKZgdW2b99e7HYmT56sQYMGacCAAWrWrJlmzpwpHx8fvfvuu4XWb9OmjV599VX16tVLNputVG1mZGTonXfe0eTJk3XTTTepVatWmjVrlr777jt9//33JTkMAAAAAAAUqVTPcLdo0UI///yzIiMjFR0drYkTJ8rLy0tvv/22GjRoUKw2cnJytH79eiUmJjrKrFarYmNjlZycXJqwitXm+vXrlZubq9jYWEedpk2bql69ekpOTla7du0KbTs7O1vZ2dmO+czMTEmS3W6X3W4vVbwoH+x2u4wxfM6o8OjruGT+f+wXt8eQP7kB3zNcKpzbUZm4sr+Xts1SJdwjR47UiRMnJEnjxo3TrbfeqhtuuEG1atXSggULitXGoUOHlJeXp5CQEKfykJAQbdu2rTRhFavNlJQUeXl5KSAgoECdlJSUItseP368xo4dW6A8PT1dp06dKlW8KB/sdrsyMjJkjJHVWqqbQoBygb6OS8Un77i7Q5BkZDOnJLskWS751ot6fA4oa5zbUZm4sr8fO3asVOuVKuGOi4tz/NyoUSNt27ZNR44cUY0aNRwjlVc0iYmJSkhIcMxnZmYqPDxcQUFB8vf3d2NkcDW73S6LxaKgoCB+UaFCo6/jUsnyyHB3CP9/dVvKsvpKbvi/S3Bw8CXfJionzu2oTFzZ3729vUu13kW9h1uSfv/9d0lSeHh4idYLDAyUh4dHgdHBU1NTixwQrSzaDA0NVU5Ojo4ePep0lftC27XZbIU+N54/aBwqNovFwmeNSoG+jkvicvnjvMXy13SJ8R3DpcS5HZWJq/p7adsr1VqnT5/WCy+8oOrVqysiIkIRERGqXr26Ro4cqdzc3GK14eXlpVatWmnlypWOMrvdrpUrVyomJqY0YRWrzVatWqlKlSpOdbZv3659+/aVersAAAAAAJyrVFe4H3/8cX300UeaOHGiI0lNTk7WmDFjdPjwYc2YMaNY7SQkJKhfv35q3bq12rZtqylTpujEiRMaMGCAJKlv376qU6eOxo8fL+nMoGhbtmxx/Lx//35t3LhRvr6+atSoUbHarF69ugYOHKiEhATVrFlT/v7+evzxxxUTE1PkgGkAAAAAAJRUqRLuefPmaf78+eratauj7Oqrr1Z4eLh69+5d7IQ7Pj5e6enpGjVqlFJSUhQVFaXly5c7Bj3bt2+f06X7AwcO6JprrnHMT5o0SZMmTVLHjh21Zs2aYrUpSa+//rqsVqt69uyp7OxsxcXFafr06aU5FAAAAAAAFMpiTMnfhxEcHKyvv/5aV155pVP51q1b1aFDB6Wnp5dZgJerzMxMVa9eXRkZGQyaVsHZ7XalpaUpODiYZ59QodHXcam8nvSru0OQjJFP3nFlebhn0LQRN19xybeJyolzOyoTV/b30uZ/pYpi6NChevHFF53eS52dna2XX35ZQ4cOLU2TAAAAAABUKMW+pfyuu+5yml+xYoXq1q2rli1bSpJ+/vln5eTkqHPnzmUbIQAAAAAA5VCxE+7q1as7zffs2dNpvqSvBQMAAAAAoCIrdsI9a9YsV8YBAAAAAECFUqpRyvOlp6dr+/btkqQmTZooKCioTIICAAAAAKC8K9WgaSdOnNCDDz6o2rVrq0OHDurQoYPCwsI0cOBAZWVllXWMAAAAAACUO6VKuBMSEvT111/r008/1dGjR3X06FEtXbpUX3/9tZ588smyjhEAAAAAgHKnVLeU//vf/9bixYvVqVMnR1m3bt1UtWpV3XvvvZoxY0ZZxQcAAAAAQLlUqivcWVlZCgkJKVAeHBzMLeUAAAAAAKiUCXdMTIxGjx6tU6dOOcpOnjypsWPHKiYmpsyCAwAAAACgvCrVLeVTpkxRly5dVLduXbVs2VKS9PPPP8vb21tffvllmQYIAAAAAEB5VKqE+6qrrtKOHTs0d+5cbdu2TZLUu3dv9enTR1WrVi3TAAEAAAAAKI9KnHDn5uaqadOmWrZsmQYNGuSKmAAAAAAAKPdK/Ax3lSpVnJ7dBgAAAAAABZVq0LQhQ4bo73//u06fPl3W8QAAAAAAUCGU6hnudevWaeXKlfrqq6901VVXqVq1ak7LP/roozIJDgAAAACA8qpUCXdAQIB69uxZ1rEAAAAAAFBhlCjhttvtevXVV/Xrr78qJydHN910k8aMGcPI5AAAAAAAnKNEz3C//PLLev755+Xr66s6depo6tSpGjJkiKtiAwAAAACg3CpRwv3ee+9p+vTp+vLLL7VkyRJ9+umnmjt3rux2u6viAwAAAACgXCpRwr1v3z5169bNMR8bGyuLxaIDBw6UeWAAAAAAAJRnJUq4T58+LW9vb6eyKlWqKDc3t0yDAgAAAACgvCvRoGnGGPXv3182m81RdurUKT3yyCNOrwbjtWAAAAAAgMquRAl3v379CpTdf//9ZRYMAAAAAAAVRYkS7lmzZrkqDgAAAAAAKpQSJdwAAACvJ/3q7hAAACgXSjRomqtMmzZNERER8vb2VnR0tH788cfz1l+0aJGaNm0qb29vXXXVVfr888+dllsslkKnV1991VEnIiKiwPIJEya4ZP8AAAAAAJWP2xPuBQsWKCEhQaNHj9aGDRvUsmVLxcXFKS0trdD63333nXr37q2BAwfqv//9r3r06KEePXpo06ZNjjoHDx50mt59911ZLBb17NnTqa1x48Y51Xv88cdduq8AAAAAgMrD7beUT548WYMGDdKAAQMkSTNnztRnn32md999V88991yB+m+88Ya6dOmip59+WpL04osvKikpSW+99ZZmzpwpSQoNDXVaZ+nSpbrxxhvVoEEDp3I/P78CdYuSnZ2t7Oxsx3xmZqYkyW63y263F3NvUR7Z7XYZY/icUeHR11Fsxrg7gotnzF+TG/A9w6XCuR2ViSv7e2nbdGvCnZOTo/Xr1ysxMdFRZrVaFRsbq+Tk5ELXSU5OVkJCglNZXFyclixZUmj91NRUffbZZ5ozZ06BZRMmTNCLL76oevXq6b777tOIESPk6Vn4IRk/frzGjh1boDw9PV2nTp0qahdRAdjtdmVkZMgYI6vV7TeFAC5DX0dx+eQdd3cIZcDIZk5JdkmyXPKtF3UnH1DWOLejMnFlfz927Fip1nNrwn3o0CHl5eUpJCTEqTwkJETbtm0rdJ2UlJRC66ekpBRaf86cOfLz89Ndd93lVD5s2DBde+21qlmzpr777jslJibq4MGDmjx5cqHtJCYmOiX6mZmZCg8PV1BQkPz9/S+4ryi/7Ha7LBaLgoKC+EWFCo2+juLK8shwdwgXzxjJSFlWX8ly6RPuD3+5+GP4RGzjMogEFR3ndlQmruzv3t7epVrP7beUu9q7776rPn36FDhAZyfPV199tby8vPTwww9r/PjxstlsBdqx2WyFllutVk5elYDFYuGzRqVAX0exuCFBdQmL5a+pHOJ7iuLi3I7KxFX9vbTtufVbFxgYKA8PD6WmpjqVp6amFvlsdWhoaLHr/+c//9H27dv10EMPXTCW6OhonT59Wnv27Cn+DgAAAAAAUAS3JtxeXl5q1aqVVq5c6Siz2+1auXKlYmJiCl0nJibGqb4kJSUlFVr/nXfeUatWrdSyZcsLxrJx40ZZrVYFBweXcC8AAAAAACjI7beUJyQkqF+/fmrdurXatm2rKVOm6MSJE45Ry/v27as6depo/PjxkqQnnnhCHTt21Guvvabu3btr/vz5+umnn/T22287tZuZmalFixbptddeK7DN5ORk/fDDD7rxxhvl5+en5ORkjRgxQvfff79q1Kjh+p0GAAAAAFR4bk+44+PjlZ6erlGjRiklJUVRUVFavny5Y2C0ffv2Od0vf91112nevHkaOXKknn/+eTVu3FhLlixRixYtnNqdP3++jDHq3bt3gW3abDbNnz9fY8aMUXZ2tiIjIzVixIgCo58DAAAAAFBaFmMqwss0L73MzExVr15dGRkZjFJewdntdqWlpSk4OJjBRlCh0ddRXK8n/eruEC6eMfLJO64sD/eMUl4WRtx8hbtDQDnAuR2ViSv7e2nzP751AAAAAAC4AAk3AAAAAAAuQMINAAAAAIALkHADAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuAAJNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4AAk3AAAAAAAuQMINAAAAAIALkHADAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuAAJNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4wGWRcE+bNk0RERHy9vZWdHS0fvzxx/PWX7RokZo2bSpvb29dddVV+vzzz52W9+/fXxaLxWnq0qWLU50jR46oT58+8vf3V0BAgAYOHKjjx4+X+b4BAAAAAContyfcCxYsUEJCgkaPHq0NGzaoZcuWiouLU1paWqH1v/vuO/Xu3VsDBw7Uf//7X/Xo0UM9evTQpk2bnOp16dJFBw8edEwffvih0/I+ffpo8+bNSkpK0rJly7R27VoNHjzYZfsJAAAAAKhcLMYY484AoqOj1aZNG7311luSJLvdrvDwcD3++ON67rnnCtSPj4/XiRMntGzZMkdZu3btFBUVpZkzZ0o6c4X76NGjWrJkSaHb3Lp1q5o1a6Z169apdevWkqTly5erW7du+uOPPxQWFlZgnezsbGVnZzvmMzMzFR4erj///FP+/v6l3n9c/ux2u9LT0xUUFCSr1e1/owJchr6O4npjxQ53h3DxjJFP3nFlefhKFou7oymVJ2IbuzsElAOc21GZuLK/Z2ZmqkaNGsrIyChR/udZplGUUE5OjtavX6/ExERHmdVqVWxsrJKTkwtdJzk5WQkJCU5lcXFxBZLrNWvWKDg4WDVq1NBNN92kl156SbVq1XK0ERAQ4Ei2JSk2NlZWq1U//PCD7rzzzgLbHT9+vMaOHVugPD09XadOnSr2PqP8sdvtysjIkDGGX1So0OjrKC6fvIrwCJaRzZyS7JJUPhPuou4GBM7GuR2ViSv7+7Fjx0q1nlsT7kOHDikvL08hISFO5SEhIdq2bVuh66SkpBRaPyUlxTHfpUsX3XXXXYqMjNSuXbv0/PPPq2vXrkpOTpaHh4dSUlIUHBzs1Ianp6dq1qzp1M7ZEhMTnRL9/CvcQUFBXOGu4Ox2uywWC38ZRoVHX0dxZXlkuDuEi2eMZKQsa/m9wn3u/2WAwnBuR2Xiyv7u7e1dqvXcmnC7Sq9evRw/X3XVVbr66qvVsGFDrVmzRp07dy5VmzabTTabrUC51Wrl5FUJWCwWPmtUCvR1FEs5TVALsFj+msohvqcoLs7tqExc1d9L255bv3WBgYHy8PBQamqqU3lqaqpCQ0MLXSc0NLRE9SWpQYMGCgwM1M6dOx1tnHsb1unTp3XkyJHztgMAAAAAQHG5NeH28vJSq1attHLlSkeZ3W7XypUrFRMTU+g6MTExTvUlKSkpqcj6kvTHH3/o8OHDql27tqONo0ePav369Y46q1atkt1uV3R09MXsEgAAAAAAki6D14IlJCTon//8p+bMmaOtW7fq0Ucf1YkTJzRgwABJUt++fZ0GVXviiSe0fPlyvfbaa9q2bZvGjBmjn376SUOHDpUkHT9+XE8//bS+//577dmzRytXrtQdd9yhRo0aKS4uTpJ05ZVXqkuXLho0aJB+/PFHffvttxo6dKh69epV6AjlAAAAAACUlNuf4Y6Pj1d6erpGjRqllJQURUVFafny5Y6B0fbt2+d0v/x1112nefPmaeTIkXr++efVuHFjLVmyRC1atJAkeXh46JdfftGcOXN09OhRhYWF6ZZbbtGLL77o9Az23LlzNXToUHXu3FlWq1U9e/bU1KlTL+3OAwAAAAAqLLe/h7u8yszMVPXq1Uv8HjaUP3a7XWlpaQoODmawEVRo9HUU1+tJv7o7hItXAd7DPeLmK9wdAsoBzu2oTFzZ30ub//GtAwAAAADABUi4AQAAAABwAbc/ww0AAC6tCnFLOAAA5QBXuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgNeCAQAAlEMX+3q3ETdfUUaRAACKwhVuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFLouEe9q0aYqIiJC3t7eio6P1448/nrf+okWL1LRpU3l7e+uqq67S559/7liWm5urZ599VldddZWqVaumsLAw9e3bVwcOHHBqIyIiQhaLxWmaMGGCS/YPAAAAAFD5uD3hXrBggRISEjR69Ght2LBBLVu2VFxcnNLS0gqt/91336l3794aOHCg/vvf/6pHjx7q0aOHNm3aJEnKysrShg0b9MILL2jDhg366KOPtH37dt1+++0F2ho3bpwOHjzomB5//HGX7isAAAAAoPKwGGOMOwOIjo5WmzZt9NZbb0mS7Ha7wsPD9fjjj+u5554rUD8+Pl4nTpzQsmXLHGXt2rVTVFSUZs6cWeg21q1bp7Zt22rv3r2qV6+epDNXuIcPH67hw4cXK87s7GxlZ2c75jMzMxUeHq4///xT/v7+xd1dlEN2u13p6ekKCgqS1er2v1EBLkNfrzzeWLHD3SG4nzHyyTuuLA9fyWJxdzRu8URsY3eHgEuAczsqE1f298zMTNWoUUMZGRklyv88yzSKEsrJydH69euVmJjoKLNarYqNjVVycnKh6yQnJyshIcGpLC4uTkuWLClyOxkZGbJYLAoICHAqnzBhgl588UXVq1dP9913n0aMGCFPz8IPyfjx4zV27NgC5enp6Tp16lSR20b5Z7fblZGRIWMMv6hQodHXKw+fvOPuDuEyYGQzpyS7JFXOhLuouwlRsXBuR2Xiyv5+7NixUq3n1oT70KFDysvLU0hIiFN5SEiItm3bVug6KSkphdZPSUkptP6pU6f07LPPqnfv3k5/iRg2bJiuvfZa1axZU999950SExN18OBBTZ48udB2EhMTnRL9/CvcQUFBXOGu4Ox2uywWC38ZRoVHXy8fyuTqtIfvxbdR3hkjGSnLWnmvcAcHB7s7BFwCnNtRmbiyv3t7e5dqPbcm3K6Wm5ure++9V8YYzZgxw2nZ2cnz1VdfLS8vLz388MMaP368bDZbgbZsNluh5VarlZNXJWCxWPisUSnQ18uBSpocuoTF8tdUCfE9rzw4t6MycVV/L217bv3WBQYGysPDQ6mpqU7lqampCg0NLXSd0NDQYtXPT7b37t2rpKSkC16Fjo6O1unTp7Vnz56S7wgAAAAAAOdwa8Lt5eWlVq1aaeXKlY4yu92ulStXKiYmptB1YmJinOpLUlJSklP9/GR7x44dWrFihWrVqnXBWDZu3Cir1crtVQAAAACAMuH2W8oTEhLUr18/tW7dWm3bttWUKVN04sQJDRgwQJLUt29f1alTR+PHj5ckPfHEE+rYsaNee+01de/eXfPnz9dPP/2kt99+W9KZZPvuu+/Whg0btGzZMuXl5Tme765Zs6a8vLyUnJysH374QTfeeKP8/PyUnJysESNG6P7771eNGjXccyAAAAAAABWK2xPu+Ph4paena9SoUUpJSVFUVJSWL1/uGBht3759TvfLX3fddZo3b55Gjhyp559/Xo0bN9aSJUvUokULSdL+/fv1ySefSJKioqKctrV69Wp16tRJNptN8+fP15gxY5Sdna3IyEiNGDGiwOjnAAAAAACUltvfw11eZWZmqnr16iV+DxvKH7vdrrS0NAUHBzPYCCo0+nr58HrSr+4OoWLgPdwacfMV7g4BlwDndlQmruzvpc3/3H6FGwAAAJfexf7xhoQdAC6MP3MBAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuAAJNwAAAAAALkDCDQAAAACAC3i6OwAAACqT15N+dXcIAADgEuEKNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4AIOmAQAAoMTKYgDAETdfUQaRAMDliyvcAAAAAAC4AAk3AAAAAAAuwC3lAACUAO/RBgAAxcUVbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABXiGGwBQafD8NXB5udjvJK8VA3C54wo3AAAAAAAuQMINAAAAAIALcEs5AKDc4JZwAABQnlwWCfe0adP06quvKiUlRS1bttSbb76ptm3bFll/0aJFeuGFF7Rnzx41btxYf//739WtWzfHcmOMRo8erX/+8586evSorr/+es2YMUONGzd21Dly5Igef/xxffrpp7JarerZs6feeOMN+fr6unRfAQAAUDZ4BhzA5c7tCfeCBQuUkJCgmTNnKjo6WlOmTFFcXJy2b9+u4ODgAvW/++479e7dW+PHj9ett96qefPmqUePHtqwYYNatGghSZo4caKmTp2qOXPmKDIyUi+88ILi4uK0ZcsWeXt7S5L69OmjgwcPKikpSbm5uRowYIAGDx6sefPmXdL9B4DKhCvUAACgMrEYY4w7A4iOjlabNm301ltvSZLsdrvCw8P1+OOP67nnnitQPz4+XidOnNCyZcscZe3atVNUVJRmzpwpY4zCwsL05JNP6qmnnpIkZWRkKCQkRLNnz1avXr20detWNWvWTOvWrVPr1q0lScuXL1e3bt30xx9/KCws7IJxZ2Zmqnr16srIyJC/v39ZHApcpux2u9LS0hQcHCyrlWEPUHEVp6+TMKPCMEY+eceV5eErWSzujgbl2OV+lZz/x6AycWV/L23+59Yr3Dk5OVq/fr0SExMdZVarVbGxsUpOTi50neTkZCUkJDiVxcXFacmSJZKk3bt3KyUlRbGxsY7l1atXV3R0tJKTk9WrVy8lJycrICDAkWxLUmxsrKxWq3744QfdeeedBbabnZ2t7Oxsx3xGRoYk6ejRo7Lb7SXfeZQbdrtdmZmZ8vLy4hdVOTZj9S53h3D5M0ZV7cd10voHCQgqPmNksR/XKauhv+OijP94vbtDOL9ycG5/9MaG7g4BFYQr/9+emZkp6czjyyXh1oT70KFDysvLU0hIiFN5SEiItm3bVug6KSkphdZPSUlxLM8vO1+dc29X9/T0VM2aNR11zjV+/HiNHTu2QHn9+vWL2j0AAAAAF/C8uwMASuDYsWOqXr16seu7/Rnu8iIxMdHpyrrdbteRI0dUq1YtWS7TvxaibGRmZio8PFy///47jw+gQqOvozKhv6OyoK+jMnFlfzfG6NixY8V6/Phsbk24AwMD5eHhodTUVKfy1NRUhYaGFrpOaGjoeevn/5uamqratWs71YmKinLUSUtLc2rj9OnTOnLkSJHbtdlsstlsTmUBAQHn30FUKP7+/vyiQqVAX0dlQn9HZUFfR2Xiqv5ekivb+dz6QKqXl5datWqllStXOsrsdrtWrlypmJiYQteJiYlxqi9JSUlJjvqRkZEKDQ11qpOZmakffvjBUScmJkZHjx7V+vV/PXOzatUq2e12RUdHl9n+AQAAAAAqL7ffUp6QkKB+/fqpdevWatu2raZMmaITJ05owIABkqS+ffuqTp06Gj9+vCTpiSeeUMeOHfXaa6+pe/fumj9/vn766Se9/fbbkiSLxaLhw4frpZdeUuPGjR2vBQsLC1OPHj0kSVdeeaW6dOmiQYMGaebMmcrNzdXQoUPVq1evEt8iAAAAAABAYdyecMfHxys9PV2jRo1SSkqKoqKitHz5csegZ/v27XMaYe66667TvHnzNHLkSD3//PNq3LixlixZ4ngHtyQ988wzOnHihAYPHqyjR4+qffv2Wr58ueMd3JI0d+5cDR06VJ07d5bValXPnj01derUS7fjKDdsNptGjx5d4JECoKKhr6Myob+jsqCvozK5HPu729/DDQAAAABARcRLhQEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4UellZ2crKipKFotFGzdudFr2yy+/6IYbbpC3t7fCw8M1ceLEAusvWrRITZs2lbe3t6666ip9/vnnTsuNMRo1apRq166tqlWrKjY2Vjt27HDlLgEOe/bs0cCBAxUZGamqVauqYcOGGj16tHJycpzq0ddRmUybNk0RERHy9vZWdHS0fvzxR3eHBJzX+PHj1aZNG/n5+Sk4OFg9evTQ9u3bneqcOnVKQ4YMUa1ateTr66uePXsqNTXVqc6+ffvUvXt3+fj4KDg4WE8//bROnz7tVGfNmjW69tprZbPZ1KhRI82ePdvVuwcUacKECY7XPucrd33dAJXcsGHDTNeuXY0k89///tdRnpGRYUJCQkyfPn3Mpk2bzIcffmiqVq1q/vGPfzjqfPvtt8bDw8NMnDjRbNmyxYwcOdJUqVLF/O9//3PUmTBhgqlevbpZsmSJ+fnnn83tt99uIiMjzcmTJy/lbqKS+uKLL0z//v3Nl19+aXbt2mWWLl1qgoODzZNPPumoQ19HZTJ//nzj5eVl3n33XbN582YzaNAgExAQYFJTU90dGlCkuLg4M2vWLLNp0yazceNG061bN1OvXj1z/PhxR51HHnnEhIeHm5UrV5qffvrJtGvXzlx33XWO5adPnzYtWrQwsbGx5r///a/5/PPPTWBgoElMTHTU+e2334yPj49JSEgwW7ZsMW+++abx8PAwy5cvv6T7CxhjzI8//mgiIiLM1VdfbZ544glHeXnr6yTcqNQ+//xz07RpU7N58+YCCff06dNNjRo1THZ2tqPs2WefNU2aNHHM33vvvaZ79+5ObUZHR5uHH37YGGOM3W43oaGh5tVXX3UsP3r0qLHZbObDDz900V4B5zdx4kQTGRnpmKevozJp27atGTJkiGM+Ly/PhIWFmfHjx7sxKqBk0tLSjCTz9ddfG2POnG+rVKliFi1a5KizdetWI8kkJycbY878n8dqtZqUlBRHnRkzZhh/f3/H+f+ZZ54xzZs3d9pWfHy8iYuLc/UuAU6OHTtmGjdubJKSkkzHjh0dCXd57OvcUo5KKzU1VYMGDdL7778vHx+fAsuTk5PVoUMHeXl5Ocri4uK0fft2/fnnn446sbGxTuvFxcUpOTlZkrR7926lpKQ41alevbqio6MddYBLLSMjQzVr1nTM09dRWeTk5Gj9+vVO/dRqtSo2NpZ+inIlIyNDkhzn8vXr1ys3N9epbzdt2lT16tVz9O3k5GRdddVVCgkJcdSJi4tTZmamNm/e7KhzvnM9cKkMGTJE3bt3L9Afy2NfJ+FGpWSMUf/+/fXII4+odevWhdZJSUlx+qJKcsynpKSct87Zy89er7A6wKW0c+dOvfnmm3r44YcdZfR1VBaHDh1SXl4e/RTlmt1u1/Dhw3X99derRYsWks6cg728vBQQEOBU99zzdGnP9ZmZmTp58qQrdgcoYP78+dqwYYPGjx9fYFl57Osk3KhQnnvuOVkslvNO27Zt05tvvqljx44pMTHR3SEDpVLcvn62/fv3q0uXLrrnnns0aNAgN0UOALgYQ4YM0aZNmzR//nx3hwKUud9//11PPPGE5s6dK29vb3eHUyY83R0AUJaefPJJ9e/f/7x1GjRooFWrVik5OVk2m81pWevWrdWnTx/NmTNHoaGhBUY8zJ8PDQ11/FtYnbOX55fVrl3bqU5UVFSJ9w/IV9y+nu/AgQO68cYbdd111+ntt992qkdfR2URGBgoDw+P8/Zl4HI2dOhQLVu2TGvXrlXdunUd5aGhocrJydHRo0edrvyde54+d0T+4p7r/f39VbVqVVfsEuBk/fr1SktL07XXXusoy8vL09q1a/XWW2/pyy+/LHd9nSvcqFCCgoLUtGnT805eXl6aOnWqfv75Z23cuFEbN250vN5owYIFevnllyVJMTExWrt2rXJzcx3tJyUlqUmTJqpRo4ajzsqVK51iSEpKUkxMjCQpMjJSoaGhTnUyMzP1ww8/OOoApVHcvi6dubLdqVMntWrVSrNmzZLV6nzqp6+jsvDy8lKrVq2c+qndbtfKlSvpp7isGWM0dOhQffzxx1q1apUiIyOdlrdq1UpVqlRx6tvbt2/Xvn37HH07JiZG//vf/5SWluaok5SUJH9/fzVr1sxR53znesDVOnfurP/973+O/6Nv3LjRcUEs/+dy19fLfBg2oBzavXt3gVHKjx49akJCQswDDzxgNm3aZObPn298fHwKvCrJ09PTTJo0yWzdutWMHj260FclBQQEmKVLl5pffvnF3HHHHbwqCZfMH3/8YRo1amQ6d+5s/vjjD3Pw4EHHlI++jspk/vz5xmazmdmzZ5stW7aYwYMHm4CAAKfRbIHLzaOPPmqqV69u1qxZ43Qez8rKctR55JFHTL169cyqVavMTz/9ZGJiYkxMTIxjef6rkm655RazceNGs3z5chMUFFToq5Kefvpps3XrVjNt2jReCwa3O3uUcmPKX18n4QZM4Qm3Mcb8/PPPpn379sZms5k6deqYCRMmFFh34cKF5oorrjBeXl6mefPm5rPPPnNabrfbzQsvvGBCQkKMzWYznTt3Ntu3b3fl7gAOs2bNMpIKnc5GX0dl8uabb5p69eoZLy8v07ZtW/P999+7OyTgvIo6j8+aNctR5+TJk+axxx4zNWrUMD4+PubOO+90+uOqMcbs2bPHdO3a1VStWtUEBgaaJ5980uTm5jrVWb16tYmKijJeXl6mQYMGTtsA3OHchLu89XWLMcaU/XVzAAAAAAAqN57hBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXICEGwAAAAAAFyDhBgAAAADABUi4AQAAAABwARJuAAAAAABcgIQbAAAAAAAXIOEGAAAAAMAFSLgBACgD/fv3V0RERJm2OXv2bFksFu3Zs6dM28XlJyIiQv3793d3GACAMkbCDQC4bOzatUsPP/ywGjRoIG9vb/n7++v666/XG2+8oZMnT7o7PJd55ZVXtGTJEneH4ZCf6FssFn3zzTcFlhtjFB4eLovFoltvvdUNERZtz549jtjPndq1a+fW2L777juNGTNGR48edWscAIBLx9PdAQAAIEmfffaZ7rnnHtlsNvXt21ctWrRQTk6OvvnmGz399NPavHmz3n77bXeH6RKvvPKK7r77bvXo0cOp/IEHHlCvXr1ks9ncEpe3t7fmzZun9u3bO5V//fXX+uOPP9wWV3H07t1b3bp1cyoLCgpyUzRnfPfddxo7dqz69++vgIAAp2Xbt2+X1cp1EACoaEi4AQBut3v3bvXq1Uv169fXqlWrVLt2bceyIUOGaOfOnfrss8/cGKF7eHh4yMPDw23b79atmxYtWqSpU6fK0/Ov/zLMmzdPrVq10qFDh9wW24Vce+21uv/++90dRrFdzn+8AACUHn9KBQC43cSJE3X8+HG98847Tsl2vkaNGumJJ56Q9Nctw7Nnzy5Qz2KxaMyYMY75MWPGyGKx6Ndff9X999+v6tWrKygoSC+88IKMMfr99991xx13yN/fX6GhoXrttdec2ivqGeo1a9bIYrFozZo1592vSZMm6brrrlOtWrVUtWpVtWrVSosXLy4Q84kTJzRnzhzHrc/5z/Keu/1bb71VDRo0KHRbMTExat26tVPZBx98oFatWqlq1aqqWbOmevXqpd9///28MZ+td+/eOnz4sJKSkhxlOTk5Wrx4se67775S77MkJSUlqX379goICJCvr6+aNGmi559/3qnOm2++qebNm8vHx0c1atRQ69atNW/evGLHX5ROnTqpU6dOBcrPfQ4/v69NmjRJb7/9tho2bCibzaY2bdpo3bp1Bdbftm2b7r33XgUFBalq1apq0qSJ/va3v0k60xeffvppSVJkZKTjs87/bAt7hvu3337TPffco5o1a8rHx0ft2rUr8Ien/L64cOFCvfzyy6pbt668vb3VuXNn7dy5s/QHCQBQJki4AQBu9+mnn6pBgwa67rrrXNJ+fHy87Ha7JkyYoOjoaL300kuaMmWKbr75ZtWpU0d///vf1ahRIz311FNau3ZtmW33jTfe0DXXXKNx48bplVdekaenp+655x6npOn999+XzWbTDTfcoPfff1/vv/++Hn744SL3Y/fu3QWSvb179+r7779Xr169HGUvv/yy+vbtq8aNG2vy5MkaPny4Vq5cqQ4dOhT7GeKIiAjFxMToww8/dJR98cUXysjIcNpWSfd58+bNuvXWW5Wdna1x48bptdde0+23365vv/3WUeef//ynhg0bpmbNmmnKlCkaO3asoqKi9MMPPxQr9qysLB06dMhpys3NLda655o3b55effVVPfzww3rppZe0Z88e3XXXXU7t/fLLL4qOjtaqVas0aNAgvfHGG+rRo4c+/fRTSdJdd92l3r17S5Jef/11x2dd1G3uqampuu666/Tll1/qscce08svv6xTp07p9ttv18cff1yg/oQJE/Txxx/rqaeeUmJior7//nv16dOnVPsLAChDBgAAN8rIyDCSzB133FGs+rt37zaSzKxZswosk2RGjx7tmB89erSRZAYPHuwoO336tKlbt66xWCxmwoQJjvI///zTVK1a1fTr189RNmvWLCPJ7N6922k7q1evNpLM6tWrHWX9+vUz9evXd6qXlZXlNJ+Tk2NatGhhbrrpJqfyatWqOW23qO1nZGQYm81mnnzySad6EydONBaLxezdu9cYY8yePXuMh4eHefnll53q/e9//zOenp4Fyova7rp168xbb71l/Pz8HPtyzz33mBtvvNEYY0z9+vVN9+7dS7zPr7/+upFk0tPTi4zhjjvuMM2bNz9vnIXJ7x+FTfmfV8eOHU3Hjh0LrHvuZ5jfVq1atcyRI0cc5UuXLjWSzKeffuoo69Chg/Hz83N8Bvnsdrvj51dffbXQ/mTMmWN5dh8YPny4kWT+85//OMqOHTtmIiMjTUREhMnLyzPG/NUXr7zySpOdne2o+8YbbxhJ5n//+995jxcAwLW4wg0AcKvMzExJkp+fn8u28dBDDzl+9vDwUOvWrWWM0cCBAx3lAQEBatKkiX777bcy227VqlUdP//555/KyMjQDTfcoA0bNpSqPX9/f3Xt2lULFy6UMcZRvmDBArVr10716tWTJH300Uey2+269957na7whoaGqnHjxlq9enWxt3nvvffq5MmTWrZsmY4dO6Zly5YVeTu5VLx9zh8wbOnSpbLb7YW2ExAQoD/++KPQW7eLY/DgwUpKSnKaWrZsWaq24uPjVaNGDcf8DTfcIEmOvpKenq61a9fqwQcfdHwG+SwWS6m2+fnnn6tt27ZOA9b5+vpq8ODB2rNnj7Zs2eJUf8CAAfLy8ioyRgCAezBoGgDArfz9/SVJx44dc9k2zk2CqlevLm9vbwUGBhYoP3z4cJltd9myZXrppZe0ceNGZWdnO8pLm4RJZ5K/JUuWKDk5Wdddd5127dql9evXa8qUKY46O3bskDFGjRs3LrSNKlWqFHt7QUFBio2N1bx585SVlaW8vDzdfffdRdYvzj7Hx8frX//6lx566CE999xz6ty5s+666y7dfffdjpG6n332Wa1YsUJt27ZVo0aNdMstt+i+++7T9ddfX6y4GzdurNjY2GLv5/mc23/yk+8///xT0l9JbYsWLcpke9KZxwSio6MLlF955ZWO5Wdv70IxAgDcg4QbAOBW/v7+CgsL06ZNm4pVv6hkNS8vr8h1Chvpu6jRv8++clyabeX7z3/+o9tvv10dOnTQ9OnTVbt2bVWpUkWzZs26qIG/brvtNvn4+GjhwoW67rrrtHDhQlmtVt1zzz2OOna7XRaLRV988UWh++nr61uibd53330aNGiQUlJS1LVr1wKvtMpX3H2uWrWq1q5dq9WrV+uzzz7T8uXLtWDBAt1000366quv5OHhoSuvvFLbt2/XsmXLtHz5cv373//W9OnTNWrUKI0dO7ZE8Z/LYrE4fc75ivpci9NX3K08xAgAlREJNwDA7W699Va9/fbbSk5OVkxMzHnr5l+5O3fgr71795Z5XBezrX//+9/y9vbWl19+6fTKp1mzZhWoW5Ir3tWqVdOtt96qRYsWafLkyVqwYIFuuOEGhYWFOeo0bNhQxhhFRkbqiiuuKHbbRbnzzjv18MMP6/vvv9eCBQuKrFeSfbZarercubM6d+6syZMn65VXXtHf/vY3rV692nFlulq1aoqPj1d8fLxycnJ011136eWXX1ZiYqK8vb1LvT81atQo9Fbr0vah/JHjL/RHo5J8zvXr19f27dsLlG/bts2xHABw+eMZbgCA2z3zzDOqVq2aHnroIaWmphZYvmvXLr3xxhuSzlwRDwwMLDCa+PTp08s8roYNG0qS07by8vL09ttvX3BdDw8PWSwWp6ume/bs0ZIlSwrUrVatWrFHDpfO3JJ94MAB/etf/9LPP/+s+Ph4p+V33XWXPDw8NHbs2AJXOI0xJb5t3tfXVzNmzNCYMWN02223FVmvuPt85MiRAutGRUVJkuM29HNj9PLyUrNmzWSMKfVo4/kaNmyobdu2KT093VH2888/O42SXhJBQUHq0KGD3n33Xe3bt89p2dnHv1q1apIK/gGnMN26ddOPP/6o5ORkR9mJEyf09ttvKyIiQs2aNStVrACAS4sr3AAAt2vYsKHmzZun+Ph4XXnllerbt69atGihnJwcfffdd1q0aJHTO4ofeughTZgwQQ899JBat26ttWvX6tdffy3zuJo3b6527dopMTFRR44cUc2aNTV//nydPn36gut2795dkydPVpcuXXTfffcpLS1N06ZNU6NGjfTLL7841W3VqpVWrFihyZMnKywsTJGRkYU+v5uvW7du8vPz01NPPSUPDw/17NnTaXnDhg310ksvKTExUXv27FGPHj3k5+en3bt36+OPP9bgwYP11FNPlehY9OvXr8z2edy4cVq7dq26d++u+vXrKy0tTdOnT1fdunUdg4TdcsstCg0N1fXXX6+QkBBt3bpVb731lrp3737RA+w9+OCDmjx5suLi4jRw4EClpaVp5syZat68uWMQv5KaOnWq2rdvr2uvvVaDBw9WZGSk9uzZo88++0wbN26UdOZzlqS//e1v6tWrl6pUqaLbbrvNkYif7bnnntOHH36orl27atiwYapZs6bmzJmj3bt369///rfjWXcAwGXOPYOjAwBQ0K+//moGDRpkIiIijJeXl/Hz8zPXX3+9efPNN82pU6cc9bKysszAgQNN9erVjZ+fn7n33ntNWlpaka8FO/f1U/369TPVqlUrsP2OHTsWeBXVrl27TGxsrLHZbCYkJMQ8//zzJikpqVivBXvnnXdM48aNjc1mM02bNjWzZs1yxHS2bdu2mQ4dOpiqVasaSY7XQxX1WjJjjOnTp4+RZGJjY4s8nv/+979N+/btTbVq1Uy1atVM06ZNzZAhQ8z27duLXOfs7a5bt+689Qp7LVhx9nnlypXmjjvuMGFhYcbLy8uEhYWZ3r17m19//dVR5x//+Ifp0KGDqVWrlrHZbKZhw4bm6aefNhkZGeeNKf9VXq+++up5633wwQemQYMGxsvLy0RFRZkvv/yyyNeCFdbWuX3NGGM2bdpk7rzzThMQEGC8vb1NkyZNzAsvvOBU58UXXzR16tQxVqvV6bM997Vgxpzpe3fffbejvbZt25ply5Y51cl/LdiiRYsKPQ6FvT4PAHDpWIxhNA0AAAAAAMoa9yMBAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuICnuwMor+x2uw4cOCA/Pz9ZLBZ3hwMAAAAAcBFjjI4dO6awsDBZrcW/bk3CXUoHDhxQeHi4u8MAAAAAAFwiv//+u+rWrVvs+iTcpeTn5yfpzAH39/d3czS4WHa7Xenp6QoKCirRX6yA8o6+j8qM/o/Kir6Pyqy0/T8zM1Ph4eGOPLC4SLhLKf82cn9/fxLuCsBut+vUqVPy9/fnFw8qFfo+KjP6Pyor+j4qs4vt/yV9nJhvGAAAAAAALkDCDQAAAACAC5BwAwAAAADgAjzD7ULGGJ0+fVp5eXnuDqXcqFKlijw8PNwdBgAAAABcNBJuF8nJydHBgweVlZXl7lDKFYvForp168rX19fdoQAAAADARSHhdgG73a7du3fLw8NDYWFh8vLyKvFodpWRMUbp6en6448/1LhxY650AwAAACjXSLhdICcnR3a7XeHh4fLx8XF3OOVKUFCQ9uzZo9zcXBJuAAAAAOVahRg0be3atbrtttsUFhYmi8WiJUuWXHCdNWvW6Nprr5XNZlOjRo00e/bsMo+L9xqWHHcCAAAAAKgoKkRGeOLECbVs2VLTpk0rVv3du3ere/fuuvHGG7Vx40YNHz5cDz30kL788ksXRwoAAAAAqCwqxC3lXbt2VdeuXYtdf+bMmYqMjNRrr70mSbryyiv1zTff6PXXX1dcXJyrwgQAAEBFYLdLeXmFT6dPF70sL+/MuvmTMc7zJVl2MVNenrwzMyU/Pyn/7kJjLvxvcerku5i6xfn57LLC6pVm/kLtlbaOK9cvqzYuFzffLLVr5+4oylSFSLhLKjk5WbGxsU5lcXFxGj58eJHrZGdnKzs72zGfmZkp6cwAaXa73amu3W6XMcYxofjyj1lhx9WV8j+zS7lN4HJA30dlRv8vp/LypBMnnKfjxwuWZWVJJ07IcnZZTs6ZKTf3r3/zp3PLi/jXUgFe92qVFODuIIBC2H18pLZtXbuNUp77S/u7olIm3CkpKQoJCXEqCwkJUWZmpk6ePKmqVasWWGf8+PEaO3ZsgfL09HSdOnXKqSw3N1d2u12nT5/W6dOnyzb4SyAlJUUTJkzQF198of379ys4OFhXX321hg0bpptuukmNGzfW3r179f777ys+Pt5p3ZYtW2rr1q3617/+pb59+0qSo/7Z6tSpo927dxfY9unTp2W323X48GFVqVLFdTt5DrvdroyMDBljePYelQp9H5UZ/f8yYLfLcvSorIcOyePQIVkPHZI1Pd353/wpM1OWrCxZzroAcrkynp6Sh4eM1Sp5eJyZrFaZs36WxSJZLGfqnD0VVm6xnFn/rOVOk1SgzBRVT5KxWHQ6L0+eVarIkr/u/5c7tXd2u2crqt7Z/+a3d7465xu75+xlxfm5iDJTWOznm79Q+cXWdcX6ZdXGZeBUeLhy0tJcuo3SnvuPHTtWqu1VyoS7NBITE5WQkOCYz8zMVHh4uIKCguTv7+9U99SpUzp27Jg8PT3l6Vm+DvGePXvUvn17BQQEaOLEibrqqquUm5urL7/8Uk888YS2bt0qSQoPD9f777+vPn36ONb9/vvvlZqaqmrVqslqtTrt+9ixYzVo0CDHvIeHR6HHxtPTU1arVbVq1ZK3t7cL99SZ3W6XxWJRUFAQ/+lCpULfR2VG/3exvDzpt9+kTZukXbtkSU2VUlOltLS/pvR0WUp5ccJYrVK1agUnH5+/fvb1dfxsfHwkm03y8pI8Pc/8W6VK4f8WtaxKlb8S6KKm8/SlwlIid6RJdrtdf6an0/dx2bkU//sv7bm/tLlJ+coGy0hoaKhSU1OdylJTU+Xv71/o1W1JstlsstlsBcqtVmuBD8pqtcpisTgmGXPmtiZ38PEp0V+8hgwZIovFoh9//FHVqlVzlLdo0UIDBw50jCLep08fvf766/rjjz8UHh4uSZo1a5b69Omj99577699/3/+/v6qXbv2Bbefv15hx9XV3LVdwN3o+6jM6P9lwG6X9u6VNm8+M23adObfrVulc+4CLFJAgBQScmYKDi7834AApwTaYrOV6P84FeP6X9mh76MyK03/L+13pVIm3DExMfr888+dypKSkhQTE+OaDWZlnfkF4Q7Hj5/5xVQMR44c0fLly/Xyyy87Jdv5AgICHD+HhIQoLi5Oc+bM0ciRI5WVlaUFCxbo66+/1nvvvVdW0QMAgMuFMdL+/c5J9aZN0pYtZ56PLoy3t9SsmdS0qVS7duHJdHDwmSvIAFABVYiE+/jx49q5c6djfvfu3dq4caNq1qypevXqKTExUfv373ckgo888ojeeustPfPMM3rwwQe1atUqLVy4UJ999pm7duGysHPnThlj1LRp02LVf/DBB/Xkk0/qb3/7mxYvXqyGDRsqKiqq0LrPPvusRo4c6Zh/5ZVXNGzYsLIIGwAAuEJurrRqlfTJJ9LGjWcS7IyMwut6eUlNmkgtWkjNm//1b2TkmdusAaCSqhAJ908//aQbb7zRMZ//rHW/fv00e/ZsHTx4UPv27XMsj4yM1GeffaYRI0bojTfeUN26dfWvf/3Lda8E8/E5c6XZHXx8il21pCOqd+/eXQ8//LDWrl2rd999Vw8++GCRdZ9++mn179/fMR8YGFiibQEAgEsgJ0dauVJatEhaskT680/n5R4e0hVXOCfVzZtLjRqdeb4ZAOCkQiTcnTp1Om+yOHv27ELX+e9//+vCqM5isRT7tm53aty4sSwWi7Zt21as+p6ennrggQc0evRo/fDDD/r444+LrBsYGKhGjRqVVagAAKCs5ORISUlnkuylS6WjR/9aFhIi3Xmn1KHDmQT7iivODDwGACiWCpFwo2zUrFlTcXFxmjZtmoYNG1bgOe6jR486PcctnbmtfNKkSYqPj1eNGjUuYbQAAKDUsrOlr746k2R/8onzreKhoVLPntI990jt23NLOABcBBJuOJk2bZquv/56tW3bVuPGjdP/tXfncVGW+//H38MuIkKxuKG4lVkaiUpaHS0tLE3t1Ils0cwsza3QSsrleDqlbWop5S9LzcpcWizTYxnmkkt+c81ST5keM2XxmIAbIHP//pjDJIEkyMzFMK/n4zGPue/r3j63Xszw4bru62rdurXOnDmjFStW6PXXX3dOC1bksssu05EjRxRcjq7rAADAgNOnpc8/dyTZS5ZIOTm/b6tX7/cku2NHkmwAqCQk3CimSZMm2rJli5599lmNHDlShw8fVmRkpOLj4/X666+XeszFF1/s5igBAMB5OXVKWr789yT77DFl6teX7rjDkWR36FDm/NEAgIoh4UYJdevW1fTp0zV9+vRSt+/fv7/M44+d/ezXeewPAABc4JNPpEcekQ4d+r0sJub3JDshgSQbAFyMhBsAAKA6yciQhg1ztGpLUoMGUlKSI8lu144kGwDciIQbAACgOrAsac4caeRIx3Revr7SE09IY8dKNWqYjg4AvBIJNwAAgKf7+Wfp4YelL790rLdpI731lhQXZzQsAPB29CkCAADwVIWF0uTJUqtWjmQ7KEh64QXpm29ItgGgCqCF24UsyzIdgsfh3wwAgPO0Y4f04IPS//2fY/3666U33pCaNTMbFwDAiRZuF/D395cknTx50nAknic/P1+S5Mv8nwAAlO70aWnMGCk+3pFs164tzZwppaWRbANAFUMLtwv4+voqLCxMmZmZkqTg4GDZbDbDUVV9drtdWVlZCg4Olp8fVRMAgBK+/trRqr1nj2P9r3+Vpk+X6tY1GxcAoFRkNS5Sp04dSXIm3Tg/Pj4+atiwIX+gAADgbDk5UkqK9NprjvU6daTUVEfCDQCoski4XcRms6lu3bqKiopSQUGB6XA8RkBAgHyYHxQAgN999pk0eLB08KBj/cEHHQOjhYebjQsA8KdIuF3M19eX55EBAED5HT0qDRkizZ/vWG/SxPGs9g03mI0LAHDeSLgBAACqmrw8qXt3aeNGycdHGjlS+vvfpeBg05EBAMqBhBsAAKAqsSxHy/bGjVJYmPTFF1K7dqajAgBUAA/LAgAAVCUzZkhvveVo2Z4/n2QbADwYCTcAAEBVsXatNHy4Y3nSJCkx0Ww8AIALQsINAABQFfzyi3THHdKZM9Jdd0mjRpmOCABwgUi4AQAATDt1SrrtNikzU4qLc3Qpt9lMRwUAuEAk3AAAACZZlvTQQ9LmzdLFF0sff8xo5ABQTZBwAwAAmPTKK9K770q+vtKiRVJsrOmIAACVhIQbAADAlLS035/VnjxZuv56s/EAACoVCTcAAIAJ+/ZJSUlSYaHUr580bJjpiAAAlYyEGwAAwN1OnJB695b++1/HPNszZjBIGgBUQyTcAAAA7mRZ0gMPSDt2SFFR0kcfSUFBpqMCALgACTcAAIA7vfCCtHCh5Ocnffih1KCB6YgAAC5Cwg0AAOAuy5dLKSmO5WnTpGuvNRsPAMClSLgBAADc4ccfpT59fp93e9Ag0xEBAFyMhBsAAMDVcnMdg6QdOyZ17Ci9+qrpiAAAbkDCDQAA4Ep2u9S3r/TDD1K9etIHH0iBgaajAgC4AQk3AACAK/3zn9LixVJAgGNE8rp1TUcEAHATEm4AAABX+fRTafx4x/KMGVJCgtl4AABuRcINAADgCrt2Sffe61geOlTq399sPAAAtyPhBgAAqGzHjzsGScvNlTp1kiZPNh0RAMAAEm4AAIDKNmuW9O9/Sw0aSIsWSf7+piMCABhAwg0AAFCZ7HZp+nTH8lNPSZGRZuMBABhDwg0AAFCZvvhC+vFHqXZt6b77TEcDADCIhBsAAKAyvfqq471/fykkxGwsAACjSLgBAAAqy48/Sv/6l2SzSUOGmI4GAGAYCTcAAEBlSU11vN9yi9SsmdlYAADGkXADAABUhtxcx+jkkjRsmNlYAABVAgk3AABAZZg715F0X3qpdOONpqMBAFQBJNwAAAAX6uypwIYOlXz4FQsAQMINAABw4b78Utq9W6pVS+rXz3Q0AIAqotok3KmpqYqNjVVQUJASEhK0adOmMvefOnWqLr30UtWoUUMxMTF67LHHdPr0aTdFCwAAqpVp0xzv99/vSLoBAFA1SbgXLFig5ORkjR8/Xlu2bNGVV16pxMREZWZmlrr/vHnzNHr0aI0fP167du3SW2+9pQULFuipp55yc+QAAMDj7d0rLV3qWB461GwsAIAqxc90AJVh8uTJGjhwoPr37y9JmjFjhpYuXapZs2Zp9OjRJfZfv369rrnmGt19992SpNjYWPXp00fffPPNOa+Rl5envLw853pOTo4kyW63y263V+btwAC73S7Lsvi/hNeh7sObVVb9t02fLptlyUpMlNWsmeN5bqAK47Mf3qyi9b+iPy8en3Dn5+dr8+bNSklJcZb5+Pioa9eu2rBhQ6nHdOzYUe+++642bdqk9u3b6+eff9ayZct03333nfM6EydO1IQJE0qUZ2Vl0RW9GrDb7crOzpZlWfJhoBt4Eeo+vFll1H/biROKnDVLNkm/3Xef8s/Ruw6oSvjshzeraP3Pzc2t0PU8PuE+cuSICgsLFR0dXaw8Ojpau3fvLvWYu+++W0eOHNG1114ry7J05swZDRo0qMwu5SkpKUpOTnau5+TkKCYmRpGRkQoNDa2cm4ExdrtdNptNkZGRfPHAq1D34c0qpf7PmCGfnBxZzZopLCmJ0cnhEfjshzeraP0PCgqq0PU8PuGuiFWrVum5557Ta6+9poSEBP30008aMWKEnnnmGY0dO7bUYwIDAxUYGFii3MfHhw+qasJms/H/Ca9E3Yc3u6D6b1lSaqrjPEOHyubnlb9WwUPx2Q9vVpH6X9GfFY//ZoiIiJCvr68yMjKKlWdkZKhOnTqlHjN27Fjdd999evDBByVJrVq10okTJ/TQQw/p6aef5oMHAAD8uZUrpR9+kGrWdIxODgDAH3h8ZhkQEKD4+HilpaU5y+x2u9LS0tShQ4dSjzl58mSJpNrX11eSZFmW64IFAADVx9lTgdWubTQUAEDV5PEt3JKUnJysfv36qW3btmrfvr2mTp2qEydOOEct79u3r+rXr6+JEydKkm699VZNnjxZV111lbNL+dixY3Xrrbc6E28AAIBz2rdPWrLEscxUYACAczCScJ84cUI1a9astPMlJSUpKytL48aNU3p6uuLi4rR8+XLnQGoHDhwo1qI9ZswY2Ww2jRkzRr/++qsiIyN166236tlnn620mAAAQDX22muO6b9uvFFq0cJ0NACAKspmGehDHRISojvvvFMPPPCArr32WndfvlLk5OSodu3ays7OZpTyasButyszM1NRUVE8ww+vQt2HN6tw/T95UmrQQPrtN0crd48ergsScAE+++HNKlr/K5r/GfkJe/fdd3X06FHdcMMNuuSSSzRp0iQdOnTIRCgAAADl8957jmS7SRPp5ptNRwMAqMKMJNy9e/fW4sWL9euvv2rQoEGaN2+eGjVqpB49euijjz7SmTNnTIQFAABQNsuSXn3VsTxkiMTYLwCAMhjtQxIZGank5GTt2LFDkydP1pdffqk77rhD9erV07hx43Ty5EmT4QEAABS3erW0c6cUHCw98IDpaAAAVZzRUcozMjL09ttva86cOfrPf/6jO+64QwMGDNDBgwf1/PPPa+PGjfriiy9MhggAAPC7oqnA+vaVwsKMhgIAqPqMJNwfffSRZs+erc8//1wtW7bUI488onvvvVdhZ31xdezYUZdddpmJ8AAAAEr6z3+kxYsdy0wFBgA4D0YS7v79++uuu+7SunXr1K5du1L3qVevnp5++mk3RwYAAHAOr7/umAqsSxfp8stNRwMA8ABGEu7Dhw8rODi4zH1q1Kih8ePHuykiAACAMpw6Jc2c6VgeNsxsLAAAj2Fk0LRatWopMzOzRPl///tf+TLaJwAAqGrmzZOOHpViY5l3GwBw3owk3JZllVqel5engIAAN0cDAABQBsv6fbA0pgIDAJSDW7uUv/q/eSttNpvefPNNhYSEOLcVFhZqzZo1atGihTtDAgAAKNvXX0vbt0s1ajAVGACgXNyacE+ZMkWSo4V7xowZxbqPBwQEKDY2VjNmzHBnSAAAAGX7X4OB7r1Xuugis7EAADyKWxPuffv2SZKuv/56ffTRRwoPD3fn5QEAAMrnl1+kjz92LDNYGgCgnIyMUv7VV1+ZuCwAAED5zJghFRZKnTtLrVqZjgYA4GHclnAnJyfrmWeeUc2aNZWcnFzmvpMnT3ZTVAAAAOdw+rT0xhuOZVq3AQAV4LaEe+vWrSooKHAun4vNZnNXSAAAAOc2f7505IgUEyP17Gk6GgCAB3Jbwn12N3K6lAMAgCrNsn4fLG3IEMnPyFN4AAAPZ2QebgAAgCpt/Xpp61YpKEh68EHT0QAAPJTb/lz717/+9bz3/eijj1wYCQAAwJ+YNs3xfvfd0sUXm40FAOCx3JZw165d212XAgAAqLgTJ6TFix3LjzxiNBQAgGdzW8I9e/Zsd10KAACg4laulPLypEaNpDZtTEcDAPBgPMMNAABwtiVLHO+33ioxewoA4AK4rYW7TZs2SktLU3h4uK666qoyp//asmWLu8ICAAD4nWVJn33mWO7Rw2wsAACP57aEu1evXgoMDJQk9e7d212XBQAAOH9btkiHD0s1a0qdOpmOBgDg4dyWcI8fP77UZQAAgCqjqHX7ppscU4IBAHAB3JZwl+bbb7/Vrl27JEktW7ZUfHy8yXAAAIC3K3p+m+7kAIBKYCThPnjwoPr06aN169YpLCxMknTs2DF17NhR8+fPV4MGDUyEBQAAvNmhQ9LmzY7l7t3NxgIAqBaMjFL+4IMPqqCgQLt27dLRo0d19OhR7dq1S3a7XQ8++KCJkAAAgLdbutTx3r69FB1tNhYAQLVgpIV79erVWr9+vS699FJn2aWXXqpp06bpuuuuMxESAADwdkXPb996q9k4AADVhpEW7piYGBUUFJQoLywsVL169QxEBAAAvNqpU9KXXzqWeX4bAFBJjCTcL774ooYNG6Zvv/3WWfbtt99qxIgReumll0yEBAAAvNlXX0knT0oNGkhXXmk6GgBANeG2LuXh4eGy2WzO9RMnTighIUF+fo4Qzpw5Iz8/Pz3wwAPM0w0AANyrqDt5jx7SWb+vAABwIdyWcE+dOtVdlwIAADh/lsXz2wAAl3Bbwt2vXz93XQoAAOD87dgh/fKLVKOGdP31pqMBAFQjRkYpP9vp06eVn59frCw0NNRQNAAAwOsUtW537epIugEAqCRGBk07ceKEhg4dqqioKNWsWVPh4eHFXgAAAO5iK5p/m+7kAIBKZiThfuKJJ7Ry5Uq9/vrrCgwM1JtvvqkJEyaoXr16mjt3romQAACAF/LJypI2bXKsdO9uNhgAQLVjpEv5kiVLNHfuXHXu3Fn9+/fXddddp2bNmqlRo0Z67733dM8995gICwAAeJnAtDTZLEuKj5fq1TMdDgCgmjHSwn306FE1adJEkuN57aNHj0qSrr32Wq1Zs8ZESAAAwAsFrljhWOjRw2wgAIBqyUjC3aRJE+3bt0+S1KJFCy1cuFCSo+U7LCzMREgAAMDb5OUpYPVqxzLPbwMAXMBIwt2/f39t375dkjR69GilpqYqKChIjz32mB5//HETIQEAAG+zerV8TpyQVbeudNVVpqMBAFRDRp7hfuyxx5zLXbt21a5du7RlyxY1a9ZMrVu3NhESAADwMrai6cC6d5d8jLRBAACqOePzcEtSbGysYmNjTYcBAAC8hWVJ/5sOzOreXTbD4QAAqidjf85NS0tTjx491LRpUzVt2lQ9evTQl19+aSocAADgTb7/Xrb9+2UFBkpdupiOBgBQTRlJuF977TV169ZNtWrV0ogRIzRixAiFhobqlltuUWpqqomQAACAN/lfd/K8a6+VatY0HAwAoLoyknA/99xzmjJlit5//30NHz5cw4cP17x58zRlyhQ999xzFTpnamqqYmNjFRQUpISEBG3atKnM/Y8dO6YhQ4aobt26CgwM1CWXXKJly5ZV6NoAAMDDLFkiScq78UbDgQAAqjMjCfexY8fUrVu3EuU33XSTsrOzy32+BQsWKDk5WePHj9eWLVt05ZVXKjExUZmZmaXun5+frxtvvFH79+/XBx98oD179mjmzJmqX79+ua8NAAA8zJEj0oYNkqS8rl0NBwMAqM6MDJrWs2dPffzxxyWmAPvkk0/Uo0ePcp9v8uTJGjhwoPr37y9JmjFjhpYuXapZs2Zp9OjRJfafNWuWjh49qvXr18vf31+S/nTQtry8POXl5TnXc3JyJEl2u112u73cMaNqsdvtsiyL/0t4Heo+vNLSpfKxLFmtW6uwXj3qP7wOn/3wZhWt/xX9eXFbwv3qq686l1u2bKlnn31Wq1atUocOHSRJGzdu1Lp16zRy5MhynTc/P1+bN29WSkqKs8zHx0ddu3bVhv/99fqPPv30U3Xo0EFDhgzRJ598osjISN1999168skn5evrW+oxEydO1IQJE0qUZ2Vl6fTp0+WKGVWP3W5Xdna2LMuSD1PDwItQ9+GNan/4oWpIOn799Tp27Bj1H16Hz354s4rW/9zc3Apdz2ZZllWhI8upcePG57WfzWbTzz//fN7nPXTokOrXr6/169c7k3dJeuKJJ7R69Wp98803JY5p0aKF9u/fr3vuuUePPPKIfvrpJz3yyCMaPny4xo8fX+p1SmvhjomJ0W+//abQ0NDzjhdVk91uV1ZWliIjI/nigVeh7sPr5OfLFh0tW06Oznz9tbKaNKH+w+vw2Q9vVtH6n5OTo/DwcGVnZ5cr/3NbC/e+ffvcdak/ZbfbFRUVpTfeeEO+vr6Kj4/Xr7/+qhdffPGcCXdgYKACAwNLlPv4+PBBVU3YbDb+P+GVqPvwKuvWSTk5UlSUfBISZDtyhPoPr8RnP7xZRep/RX9WjDzDfbaiBnabzVah4yMiIuTr66uMjIxi5RkZGapTp06px9StW1f+/v7Fuo9fdtllSk9PV35+vgICAioUCwAAqOL+Nx2YuneXSDQAAC5m7Jtm7ty5atWqlWrUqKEaNWqodevWeuedd8p9noCAAMXHxystLc1ZZrfblZaWVqyL+dmuueYa/fTTT8UefP/3v/+tunXrkmwDAFBdWZZzOjBVYJBWAADKy0jCPXnyZA0ePFi33HKLFi5cqIULF6pbt24aNGiQpkyZUu7zJScna+bMmXr77be1a9cuDR48WCdOnHCOWt63b99ig6oNHjxYR48e1YgRI/Tvf/9bS5cu1XPPPachQ4ZU2j0CAIAqZs8eae9eKSBAYv5tAIAbGOlSPm3aNL3++uvq27evs6xnz566/PLL9fe//12PPfZYuc6XlJSkrKwsjRs3Tunp6YqLi9Py5csVHR0tSTpw4ECxPvcxMTH6/PPP9dhjj6l169aqX7++RowYoSeffLJybhAAAFQ9Ra3bnTtLtWpJTIkEAHAxIwn34cOH1bFjxxLlHTt21OHDhyt0zqFDh2ro0KGlblu1alWJsg4dOmjjxo0VuhYAAPBARc9v33qr2TgAAF7DSJfyZs2aaeHChSXKFyxYoObNmxuICAAAVGtHjzpGKJd4fhsA4DZGWrgnTJigpKQkrVmzRtdcc40kad26dUpLSys1EQcAALggy5dLhYXSFVdIsbGmowEAeAkjLdy33367Nm3apIiICC1evFiLFy9WRESENm3apNtuu81ESAAAoDor6k5O6zYAwI3c3sJdUFCghx9+WGPHjtW7777r7ssDAABvU1Ag/etfjmWe3wYAuJHbW7j9/f314YcfuvuyAADAW61fLx07JkVESAkJpqMBAHgRI13Ke/furcWLF5u4NAAA8DZF04Hdcovk62s2FgCAVzEyaFrz5s31j3/8Q+vWrVN8fLxq1qxZbPvw4cNNhAUAAKojnt8GABhiJOF+6623FBYWps2bN2vz5s3FttlsNhJuAABQOX78UdqzR/Lzk266yXQ0AAAvYyTh3rdvn4nLAgAAb1PUut2pk1S7ttlYAABex+0J98aNG7VkyRLl5+erS5cu6tatm7tDAAAA3oLu5AAAg9yacH/wwQdKSkpSjRo15O/vr8mTJ+v555/XqFGj3BkGAADwBtnZ0po1jmWmAwMAGODWUconTpyogQMHKjs7W7/99pv++c9/6rnnnnNnCAAAwFt8/rl05ozUooXUtKnpaAAAXsitCfeePXs0atQo+f5vSo6RI0cqNzdXmZmZ7gwDAAB4g6LpwGjdBgAY4taE++TJkwoNDXWuBwQEKCgoSMePH3dnGAAAoLorLJSWLXMs8/w2AMAQtw+a9uabbyokJMS5fubMGc2ZM0cRERHOMqYFAwAAF2TDBunoUSk8XOrY0XQ0AAAv5daEu2HDhpo5c2axsjp16uidd95xrjMPNwAAuGBFo5PffLNjDm4AAAxw6zfQ/v373Xk5AADgrZgODABQBbj1GW4AAACX27dP+v57yddX6tbNdDQAAC9Gwg0AAKqXotHJr73W8Qw3AACGkHADAIDq5ZNPHO9MBwYAMIyEGwAAVB///a+0erVj+bbbzMYCAPB6JNwAAKD6WLrUMQd369ZSkyamowEAeDljCffevXs1ZswY9enTR5mZmZKkf/3rX/r+++9NhQQAADzd4sWO9969TUYBAIAkQwn36tWr1apVK33zzTf66KOPdPz4cUnS9u3bNX78eBMhAQAAT3fypLR8uWOZhBsAUAUYSbhHjx6tf/7zn1qxYoUCAgKc5TfccIM2btxoIiQAAODpVqyQTp2SGjWS4uJMRwMAgJmE+7vvvtNtpQxkEhUVpSNHjhiICAAAeLyzu5PbbCYjAQBAkqGEOywsTIcPHy5RvnXrVtWvX99ARAAAwKOdOfP7/Nt0JwcAVBFGEu677rpLTz75pNLT02Wz2WS327Vu3TqNGjVKffv2NRESAADwZF9/7ZgS7OKLpWuvNR0NAACSDCXczz33nFq0aKGYmBgdP35cLVu21F/+8hd17NhRY8aMMRESAADwZEXdyW+9VfLzMxoKAABFjHwjBQQEaObMmRo7dqx27typ48eP66qrrlLz5s1NhAMAADyZZTEdGACgSjKScH/99de69tpr1bBhQzVs2NBECAAAoLrYtk36z3+kGjWkG280HQ0AAE5GupTfcMMNaty4sZ566in98MMPJkIAAADVRVHrdrduUnCw0VAAADibkYT70KFDGjlypFavXq0rrrhCcXFxevHFF3Xw4EET4QAAAE9Gd3IAQBVlJOGOiIjQ0KFDtW7dOu3du1d/+9vf9Pbbbys2NlY33HCDiZAAAIAn+vlnaccOyddX6tHDdDQAABRjJOE+W+PGjTV69GhNmjRJrVq10urVq02HBAAAPEVR63anTtJFFxkNBQCAPzKacK9bt06PPPKI6tatq7vvvltXXHGFli5dajIkAADgSehODgCowoyMUp6SkqL58+fr0KFDuvHGG/XKK6+oV69eCmagEwAAcL4yM6Wvv3Ys9+plNhYAAEphJOFes2aNHn/8cd15552KiIgwEQIAAPB0S5Y45uCOj5eYZhQAUAUZSbjXrVtn4rIAAKA6oTs5AKCKc1vC/emnn+rmm2+Wv7+/Pv300zL37dmzp5uiAgAAHik3V1qxwrFMwg0AqKLclnD37t1b6enpioqKUu8yvhhtNpsKCwvdFRYAAPBEn38u5eVJzZpJl19uOhoAAErltoTbbreXugwAAFBuZ3cnt9lMRgIAwDkZmRZs7ty5ysvLK1Gen5+vuXPnGogIAAB4jPx86bPPHMt0JwcAVGFGEu7+/fsrOzu7RHlubq769+9vICIAAOAxVq+WsrOl6Gjp6qtNRwMAwDkZSbgty5KtlO5fBw8eVO3atSt0ztTUVMXGxiooKEgJCQnatGnTeR03f/582Wy2Mp8rBwAAVUhRd/KePSVfX6OhAABQFrdOC3bVVVfJZrPJZrOpS5cu8vP7/fKFhYXat2+funXrVu7zLliwQMnJyZoxY4YSEhI0depUJSYmas+ePYqKijrncfv379eoUaN03XXXVeh+AACAm9ntTAcGAPAYbk24i1qRt23bpsTERIWEhDi3BQQEKDY2Vrfffnu5zzt58mQNHDjQ2R19xowZWrp0qWbNmqXRo0eXekxhYaHuueceTZgwQWvXrtWxY8fKfV0AAOBm334rHTokhYRIXbqYjgYAgDK5NeEeP368JCk2NlZJSUkKCgq64HPm5+dr8+bNSklJcZb5+Pioa9eu2rBhwzmP+8c//qGoqCgNGDBAa9eu/dPr5OXlFRvoLScnR5JjxHVGXfd8drtdlmXxfwmvQ92Hp7F9/LFskqybb5bl7+9o8a4g6j+8FXUf3qyi9b+iPy9uTbiL9OvXr9LOdeTIERUWFio6OrpYeXR0tHbv3l3qMV9//bXeeustbdu27byvM3HiRE2YMKFEeVZWlk6fPl2umFH12O12ZWdny7Is+fgYGdoAMIK6D08T8cEH8pOUff31Op2ZeUHnov7DW1H34c0qWv9zc3MrdD0jCXdhYaGmTJmihQsX6sCBA8rPzy+2/ejRoy67dm5uru677z7NnDlTERER531cSkqKkpOTnes5OTmKiYlRZGSkQkNDXREq3Mhut8tmsykyMpIvHngV6j48yu7d8vnpJ1n+/gq96y6FVnCg1SLUf3gr6j68WUXrf0V7ZxtJuCdMmKA333xTI0eO1JgxY/T0009r//79Wrx4scaNG1euc0VERMjX11cZGRnFyjMyMlSnTp0S++/du1f79+/Xrbfe6iwr6h7g5+enPXv2qGnTpiWOCwwMVGBgYIlyHx8fPqiqCZvNxv8nvBJ1Hx5jyRJJku2GG2QLD6+UU1L/4a2o+/BmFan/Ff1ZMfIT9t5772nmzJkaOXKk/Pz81KdPH7355psaN26cNm7cWK5zBQQEKD4+Xmlpac4yu92utLQ0dejQocT+LVq00Hfffadt27Y5Xz179tT111+vbdu2KSYm5oLvDwAAuACjkwMAPIyRFu709HS1atVKkhQSEqLs7GxJUo8ePTR27Nhyny85OVn9+vVT27Zt1b59e02dOlUnTpxwjlret29f1a9fXxMnTlRQUJCuuOKKYseHhYVJUolyAABQRRw6JBX9Ub5XL7OxAABwnowk3A0aNNDhw4fVsGFDNW3aVF988YXatGmj//u//yu12/afSUpKUlZWlsaNG6f09HTFxcVp+fLlzoHUDhw4QHcZAAA82aefOt6vvlqqW9dsLAAAnCcjCfdtt92mtLQ0JSQkaNiwYbr33nv11ltv6cCBA3rssccqdM6hQ4dq6NChpW5btWpVmcfOmTOnQtcEAABuQndyAIAHMpJwT5o0ybmclJSkhg0basOGDWrevHmxwcwAAACUnS2tXOlYvu02s7EAAFAORhLuP+rQoUOpA5wBAABo2TKpoEC67DLpkktMRwMAwHlzW8L9adGzV+ehZ8+eLowEAAB4FLqTAwA8lNsS7t7n+SVps9lUWFjo2mAAAIBnOH3a0cItkXADADyO2xJuu93urksBAIDqYuVK6fhxqX59qW1b09EAAFAuzJUFAACqrqLu5L16SUzxCQDwMEYGTfvHP/5R5vZx48a5KRIAAFBlFRZKn3ziWKY7OQDAAxlJuD/++ONi6wUFBdq3b5/8/PzUtGlTEm4AACBt3ChlZkq1a0udO5uOBgCAcjOScG/durVEWU5Oju6//37dxvyaAABA+r07eY8ekr+/0VAAAKiIKvMwVGhoqCZMmKCxY8eaDgUAAJhmWVJRjzi6kwMAPFSVSbglKTs7W9nZ2abDAAAApn3/vbR3rxQYKHXrZjoaAAAqxEiX8ldffbXYumVZOnz4sN555x3dfPPNJkICAABVSVF38htvlEJCjIYCAEBFGUm4p0yZUmzdx8dHkZGR6tevn1JSUkyEBAAAqhK6kwMAqgEjCfe+fftMXBYAAHiCAwekLVsc82737Gk6GgAAKqxKPcMNAADgnHv7mmukyEizsQAAcAGMtHCfPn1a06ZN01dffaXMzEzZ7fZi27ds2WIiLAAAUBXQnRwAUE0YSbgHDBigL774QnfccYfat28vm81mIgwAAFDVZGZKa9Y4lkm4AQAezkjC/dlnn2nZsmW65pprTFweAABUVa+9JhUWSgkJUpMmpqMBAOCCGHmGu379+qpVq5aJSwMAgKrq5EkpNdWxPHKk2VgAAKgERhLul19+WU8++aT+85//mLg8AACoiubOlY4ckRo3lm67zXQ0AABcMCNdytu2bavTp0+rSZMmCg4Olr+/f7HtR48eNREWAAAwxW6XJk92LD/6qORn5FcUAAAqlZFvsz59+ujXX3/Vc889p+joaAZNAwDA2y1ZIv34oxQWJj3wgOloAACoFEYS7vXr12vDhg268sorTVweAABUNS+95HgfPFgKCTEbCwAAlcTIM9wtWrTQqVOnTFwaAABUNRs3Sl9/Lfn7S8OGmY4GAIBKYyThnjRpkkaOHKlVq1bpv//9r3Jycoq9AACAF3n5Zcf7vfdKdeuajQUAgEpkpEt5t27dJEldunQpVm5Zlmw2mwoLC02EBQAA3O3nn6WPPnIsJyebjQUAgEpmJOH+6quvTFwWAABUNVOnOkYo79ZNuuIK09EAAFCpjCTcnTp1MnFZAABQlRw9Kr31lmN51CizsQAA4AJGEu41a9aUuf0vf/mLmyIBAADGzJghnTwpxcVJN9xgOhoAACqdkYS7c+fOJcrOnoubZ7gBAKjm8vKkadMcyyNHSmf9HgAAQHVhZJTy3377rdgrMzNTy5cvV7t27fTFF1+YCAkAALjTvHlSerpUv76UlGQ6GgAAXMJIC3ft2rVLlN14440KCAhQcnKyNm/ebCAqAADgFpYlvfSSY/nRRx3zbwMAUA0ZaeE+l+joaO3Zs8d0GAAAwJWWL5d++EGqVUsaONB0NAAAuIyRFu4dO3YUW7csS4cPH9akSZMUFxdnIiQAAOAuRa3bDz0kldLrDQCA6sJIwh0XFyebzSbLsoqVX3311Zo1a5aJkAAAgDts3SqtXCn5+krDh5uOBgAAlzKScO/bt6/Yuo+PjyIjIxUUFGQiHAAA4C4vv+x4T0qSGjY0GwsAAC5mJOFu1KiRicsCAACTfvlFmj/fsTxypNlYAABwA7cOmrZy5Uq1bNlSOTk5JbZlZ2fr8ssv19q1a90ZEgAAcJdXXpEKC6UbbpDatDEdDQAALufWhHvq1KkaOHCgQkNDS2yrXbu2Hn74YU2ePNmdIQEAAHfIzpbeeMOxPGqU2VgAAHATtybc27dvV7du3c65/aabbmIObgAAqqOZM6XcXKllS6mM3wUAAKhO3JpwZ2RkyN/f/5zb/fz8lJWV5caIAACAyxUUOLqTS45nt202s/EAAOAmbk2469evr507d55z+44dO1S3bl03RgQAAFxu4ULp4EEpOlq65x7T0QAA4DZuTbhvueUWjR07VqdPny6x7dSpUxo/frx69OjhzpAAAIArWZb00kuO5eHDpcBAs/EAAOBGbp0WbMyYMfroo490ySWXaOjQobr00kslSbt371ZqaqoKCwv19NNPuzMkAADgSitXStu2ScHB0qBBpqMBAMCt3JpwR0dHa/369Ro8eLBSUlJkWZYkyWazKTExUampqYqOjnZnSAAAwJWKWrcfeEC66CKzsQAA4GZu7VIuSY0aNdKyZct05MgRffPNN9q4caOOHDmiZcuWqXHjxhU+b2pqqmJjYxUUFKSEhARt2rTpnPvOnDlT1113ncLDwxUeHq6uXbuWuT8AAKiAnTul5cslHx/p0UdNRwMAgNu5PeEuEh4ernbt2ql9+/YKDw+/oHMtWLBAycnJGj9+vLZs2aIrr7xSiYmJyszMLHX/VatWqU+fPvrqq6+0YcMGxcTE6KabbtKvv/56QXEAAICzTJ7seP/rX6WmTc3GAgCAATarqF+3B0tISFC7du00ffp0SZLdbldMTIyGDRum0aNH/+nxhYWFCg8P1/Tp09W3b99S98nLy1NeXp5zPScnRzExMfrtt98UGhpaOTcCY+x2u7KyshQZGSkfH2N/hwLcjroPlzl8WLbGjWUrKJB9/XopIcF0RCVQ/+GtqPvwZhWt/zk5OQoPD1d2dna58j+3PsPtCvn5+dq8ebNSUlKcZT4+Puratas2bNhwXuc4efKkCgoKdFEZz5ZNnDhREyZMKFGelZVV6qjr8Cx2u13Z2dmyLIsvHngV6j5cJeT55xVSUKD8du10tHFj6Ry9zkyi/sNbUffhzSpa/3Nzcyt0PY9PuI8cOaLCwsISg61FR0dr9+7d53WOJ598UvXq1VPXrl3PuU9KSoqSk5Od60Ut3JGRkbRwVwN2u102m42/9MLrUPfhEsePy/bOO5Ikv9GjFRUVZTig0lH/4a2o+/BmFa3/QUFBFbqexyfcF2rSpEmaP3++Vq1aVeY/YmBgoAJLmTvUx8eHD6pqwmaz8f8Jr0TdR6V7+23p2DGpeXP59OrlGDStiqL+w1tR9+HNKlL/K/qz4vEJd0REhHx9fZWRkVGsPCMjQ3Xq1Cnz2JdeekmTJk3Sl19+qdatW7syTAAAvMOZM9KUKY7l5GTJ19dsPAAAGOTxf9IKCAhQfHy80tLSnGV2u11paWnq0KHDOY974YUX9Mwzz2j58uVq27atO0IFAKD6+/hjad8+KSJCOsdApAAAeAuPb+GWpOTkZPXr109t27ZV+/btNXXqVJ04cUL9+/eXJPXt21f169fXxIkTJUnPP/+8xo0bp3nz5ik2Nlbp6emSpJCQEIWEhBi7DwAAPFpOjvT0047lRx6RgoPNxgMAgGHVIuFOSkpSVlaWxo0bp/T0dMXFxWn58uXOgdQOHDhQrM/966+/rvz8fN1xxx3FzjN+/Hj9/e9/d2foAABUD5YlDRgg/fijFBMjjRhhOiIAAIyrFgm3JA0dOlRDhw4tdduqVauKre/fv9/1AQEA4E2mTpU++EDy95cWLZLKmGoTAABv4fHPcAMAAMO+/lp6/HHH8pQpUkKC2XgAAKgiSLgBAEDFZWRId94pFRZKffo4nt0GAACSSLgBAEBFnTkj3XWXdPiw1LKl9MYbks1mOioAAKoMEm4AAFAxY8dKq1ZJISHShx863gEAgBMJNwAAKL9PP5UmTXIsv/WW1KKF2XgAAKiCSLgBAED57N0r9e3rWB4xwvEMNwAAKIGEGwAAnL9Tp6Tbb5eys6WOHaUXXjAdEQAAVRYJNwAAOH9Dh0rbt0uRkdLChVJAgOmIAACoski4AQDA+XnrLWnWLMnHR3r/fal+fdMRAQBQpZFwAwCAP7d1qzRkiGP5mWekLl3MxgMAgAcg4QYAAGX77TfHc9t5eVKPHtLo0aYjAgDAI5BwAwCAc7PbpX79pH37pMaNpblzHV3KAQDAn+IbEwAAnNvzz0tLlkiBgdIHH0jh4aYjAgDAY5BwAwCA0q1cKY0Z41iePl1q08ZsPAAAeBgSbgAAUNKvv0p33eXoUt6/vzRggOmIAADwOCTcAACguIIC6c47paws6corpdRUyWYzHRUAAB6HhBsAABT3xBPS+vVS7dqO57Zr1DAdEQAAHomEGwAA/G7RImnqVMfy229LzZoZDQcAAE9Gwg0AABy+/lp64AHH8pNPSr16mY0HAAAPR8INAIC3O3NGmjBB6tRJOn5c6txZ+uc/TUcFAIDH8zMdAAAAMOjAAenee6W1ax3rffs6pgDz41cEAAAuFC3cAAB4qw8/dIxCvnatVKuW9O67jue2a9UyHRkAANUCf74GAMDbnDwpPfaY9MYbjvX27aV586SmTc3GBQBANUMLNwAA3mTHDqltW0eybbNJo0c7Bksj2QYAoNLRwg0AgDewLCk1VRo1SsrLk+rUkd55R+ra1XRkAABUWyTcAABUd0eOOKb7WrLEsd69uzR7thQZaTYuAACqObqUAwBQna1cKbVu7Ui2AwKkV15xLJNsAwDgciTcAABURwUF0lNPObqMHz4stWghbdokDR/ueHYbAAC4HF3KAQCobn7+Wbr7bumbbxzrAwdKU6ZINWuajQsAAC9DCzcAANXJvHlSXJwj2Q4LkxYtcoxITrINAIDb0cINAICnsyxHd/GXX3Yk2JJ0zTXSe+9JjRqZjQ0AAC9Gwg0AgKc6cUJ6/33ptdekrVsdZT4+0pgx0tixkh9f8wAAmMQ3MQAAnmb3bun116W335aysx1lgYFSUpI0YoTUpo3Z+AAAgCQSbgAAPENBgfTJJ47W7K+++r28SRNp8GCpf3/p4ovNxQcAAEog4QYAoCo7eFCaOdPxOnzYUebjI/XoIT3yiHTjjY51AABQ5ZBwAwBQ1djtUlqao9v4p59KhYWO8uho6cEHpYcekho2NBsjAAD4UyTcAABUFUePSnPmSDNmSD/++Ht5p06ObuO33SYFBBgLDwAAlA8JNwAAphw/7pgve906x2vNGun0ace2WrWkfv2kQYOkyy83GycAAKgQEm4AANzll19+T67XrZO2b3d0Hz/blVc6ns2++24pJMRMnAAAoFKQcAMA4AqFhdKOHcUT7F9+Kblfw4bSNdc4XtddJ7VqJdls7o8XAABUOhJuAAAqQ05O8e7hGzc6uoyfzdfX0YJdlGBfc43UoIGZeAEAgMuRcAMAcD4syzGo2d690k8/lXzPyCh5TGio1KGDI7Hu2FFKSKCbOAAAXoSEGwCAIpblmOu6tIR6717p2LGyj4+NLd56ffnljlZtAADglUi4AQDV34kTUmamlJXleBUtn/1+8KAjqT51quxz1asnNWsmNW1a8j0szC23AwAAPEO1SbhTU1P14osvKj09XVdeeaWmTZum9u3bn3P/RYsWaezYsdq/f7+aN2+u559/XrfccosbIwYAlMuZM1JubumvnJyyk+k/S6LP5usrNWpUekLdpIkUHOy6ewQAANVKtUi4FyxYoOTkZM2YMUMJCQmaOnWqEhMTtWfPHkVFRZXYf/369erTp48mTpyoHj16aN68eerdu7e2bNmiK664wsAdAIAHsizHSNz5+VJenmP+6NOnf18uq+yP244fP3cyXfQqmp+6ooKCpKgoKTLS8SpaLnqvW9eRWDdqJPn7V86/EQAA8Go2y7Is00FcqISEBLVr107Tp0+XJNntdsXExGjYsGEaPXp0if2TkpJ04sQJffbZZ86yq6++WnFxcZoxY8Z5XTMnJ0e1a9dWdna2QkNDK+dGKtPhw45WnQvl+dXjvNjtdh09elQXXXSRfHx8Su5QGf8OF3oOV8ZQWvn57luZ6+daPte2ssrOZ/8/e53vfpblmEv5fNdLWy7rvaxthYW/v1fgZZ05o/yTJxXg4yNbQYFUnteZMzIiIECqVav4KzRUiogomUSf/V6zJtNtoRi73a7MzExFRUWV/tkPVFPUfXizitb/iuZ/Ht/CnZ+fr82bNyslJcVZ5uPjo65du2rDhg2lHrNhwwYlJycXK0tMTNTixYvPeZ28vDzl5eU513NyciQ5/sPsdvsF3IFr2KZNk23iRNNheAwfSRGmgwAMsEkKrKRzWT4+jlbkwEDH+9nLRe8BAaVvq1lTVlHyHBJSMqE++xUQUMEAz/pDCiDHd7hlWVXyexxwJeo+vFlF639Ff148PuE+cuSICgsLFR0dXaw8Ojpau3fvLvWY9PT0UvdPT08/53UmTpyoCRMmlCjPysrS6Qvt5ugCNW02Bf/hHr3en7Rs2e32sv/KVRktY1Whde1cMZRSbpVj3/Kct1jZuZb/ZLszttK2n8+2s8v/97LOWna+/nhsafv7+BTf53/rpW4vWi46R9F6Ud07a906+9izz/vHMl9fWb6+zuU/rpe1zW6z6WR+vmqEhsoWECD5+8vy8yvzXX5+xcsCAmQFBkp+Lv5KKSz881HCgXKw2+3Kzs6WZVm08sGrUPfhzSpa/3Nzcyt0PY9PuN0lJSWlWKt4Tk6OYmJiFBkZWTW7lD/zjOOF82K323UkK0uRkZF88ZylCvx5AC5mt9t1OitLodR9eCG73S6bzcZnP7wOdR/erKL1PygoqELX8/iEOyIiQr6+vsrIyChWnpGRoTp16pR6TJ06dcq1vyQFBgYqMLBkx0sfHx8+qKoJm83G/ye8EnUf3oz6D29F3Yc3q0j9r+jPisf/hAUEBCg+Pl5paWnOMrvdrrS0NHXo0KHUYzp06FBsf0lasWLFOfcHAAAAAKC8PL6FW5KSk5PVr18/tW3bVu3bt9fUqVN14sQJ9e/fX5LUt29f1a9fXxP/N4jYiBEj1KlTJ7388svq3r275s+fr2+//VZvvPGGydsAAAAAAFQj1SLhTkpKUlZWlsaNG6f09HTFxcVp+fLlzoHRDhw4UKwLQMeOHTVv3jyNGTNGTz31lJo3b67FixczBzcAAAAAoNJUi3m4Tajy83CjXJiPEt6Kug9vRv2Ht6Luw5u5ex5ufsIAAAAAAHABEm4AAAAAAFyAhBsAAAAAABcg4QYAAAAAwAVIuAEAAAAAcAESbgAAAAAAXKBazMNtQtFsajk5OYYjQWWw2+3Kzc1VUFAQ02PAq1D34c2o//BW1H14s4rW/6K8r7yzapNwV1Bubq4kKSYmxnAkAAAAAAB3yM3NVe3atc97f5tV3hQdkhx/GTl06JBq1aolm81mOhxcoJycHMXExOiXX34p10T2gKej7sObUf/hraj78GYVrf+WZSk3N1f16tUrV8s4LdwV5OPjowYNGpgOA5UsNDSULx54Jeo+vBn1H96Kug9vVpH6X56W7SI8tAEAAAAAgAuQcAMAAAAA4AIk3ICkwMBAjR8/XoGBgaZDAdyKug9vRv2Ht6Luw5u5u/4zaBoAAAAAAC5ACzcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AIk3AAAAAAAuAAJN7xCXl6e4uLiZLPZtG3btmLbduzYoeuuu05BQUGKiYnRCy+8UOL4RYsWqUWLFgoKClKrVq20bNmyYtsty9K4ceNUt25d1ahRQ127dtWPP/7oylsCyrR//34NGDBAjRs3Vo0aNdS0aVONHz9e+fn5xfaj/sNbpaamKjY2VkFBQUpISNCmTZtMhwSUy8SJE9WuXTvVqlVLUVFR6t27t/bs2VNsn9OnT2vIkCG6+OKLFRISottvv10ZGRnF9jlw4IC6d++u4OBgRUVF6fHHH9eZM2eK7bNq1Sq1adNGgYGBatasmebMmePq2wPO26RJk2Sz2fToo486y6pU3bcALzB8+HDr5ptvtiRZW7dudZZnZ2db0dHR1j333GPt3LnTev/9960aNWpY/+///T/nPuvWrbN8fX2tF154wfrhhx+sMWPGWP7+/tZ3333n3GfSpElW7dq1rcWLF1vbt2+3evbsaTVu3Ng6deqUO28TcPrXv/5l3X///dbnn39u7d271/rkk0+sqKgoa+TIkc59qP/wVvPnz7cCAgKsWbNmWd9//701cOBAKywszMrIyDAdGnDeEhMTrdmzZ1s7d+60tm3bZt1yyy1Ww4YNrePHjzv3GTRokBUTE2OlpaVZ3377rXX11VdbHTt2dG4/c+aMdcUVV1hdu3a1tm7dai1btsyKiIiwUlJSnPv8/PPPVnBwsJWcnGz98MMP1rRp0yxfX19r+fLlbr1foDSbNm2yYmNjrdatW1sjRoxwlleluk/CjWpv2bJlVosWLazvv/++RML92muvWeHh4VZeXp6z7Mknn7QuvfRS5/qdd95pde/evdg5ExISrIcfftiyLMuy2+1WnTp1rBdffNG5/dixY1ZgYKD1/vvvu+iugPJ74YUXrMaNGzvXqf/wVu3bt7eGDBniXC8sLLTq1atnTZw40WBUwIXJzMy0JFmrV6+2LMvxWezv728tWrTIuc+uXbssSdaGDRssy3L8juTj42Olp6c793n99det0NBQ53fDE088YV1++eXFrpWUlGQlJia6+paAMuXm5lrNmze3VqxYYXXq1MmZcFe1uk+XclRrGRkZGjhwoN555x0FBweX2L5hwwb95S9/UUBAgLMsMTFRe/bs0W+//ebcp2vXrsWOS0xM1IYNGyRJ+/btU3p6erF9ateurYSEBOc+QFWQnZ2tiy66yLlO/Yc3ys/P1+bNm4vVWR8fH3Xt2pU6C4+WnZ0tSc7P+c2bN6ugoKBYXW/RooUaNmzorOsbNmxQq1atFB0d7dwnMTFROTk5+v777537lPU9AJgyZMgQde/evUT9rGp1n4Qb1ZZlWbr//vs1aNAgtW3bttR90tPTi/2gSXKup6enl7nP2dvPPq60fQDTfvrpJ02bNk0PP/yws4z6D2905MgRFRYWUmdRrdjtdj366KO65pprdMUVV0hyfD4HBAQoLCys2L5//Ayv6PdATk6OTp065YrbAf7U/PnztWXLFk2cOLHEtqpW90m44XFGjx4tm81W5mv37t2aNm2acnNzlZKSYjpkoNKcb/0/26+//qpu3brpb3/7mwYOHGgocgCAqwwZMkQ7d+7U/PnzTYcCuNwvv/yiESNG6L333lNQUJDpcP6Un+kAgPIaOXKk7r///jL3adKkiVauXKkNGzYoMDCw2La2bdvqnnvu0dtvv606deqUGLGwaL1OnTrO99L2OXt7UVndunWL7RMXF1fu+wPKcr71v8ihQ4d0/fXXq2PHjnrjjTeK7Uf9hzeKiIiQr69vmfUa8CRDhw7VZ599pjVr1qhBgwbO8jp16ig/P1/Hjh0r1tL3x8/wP47Qf77fA6GhoapRo4Yrbgko0+bNm5WZmak2bdo4ywoLC7VmzRpNnz5dn3/+eZWq+7Rww+NERkaqRYsWZb4CAgL06quvavv27dq2bZu2bdvmnMpowYIFevbZZyVJHTp00Jo1a1RQUOA8/4oVK3TppZcqPDzcuU9aWlqxGFasWKEOHTpIkho3bqw6deoU2ycnJ0fffPONcx+gspxv/ZccLdudO3dWfHy8Zs+eLR+f4h/51H94o4CAAMXHxxers3a7XWlpadRZeBTLsjR06FB9/PHHWrlypRo3blxse3x8vPz9/YvV9T179ujAgQPOut6hQwd99913yszMdO6zYsUKhYaGqmXLls59yvoeANytS5cu+u6775y/42/bts3ZoFa0XKXqfvnHgwM80759+0qMUn7s2DErOjrauu+++6ydO3da8+fPt4KDg0tMi+Tn52e99NJL1q5du6zx48eXOi1SWFiY9cknn1g7duywevXqxbRIMOrgwYNWs2bNrC5dulgHDx60Dh8+7HwVof7DW82fP98KDAy05syZY/3www/WQw89ZIWFhRUbrRao6gYPHmzVrl3bWrVqVbHP+JMnTzr3GTRokNWwYUNr5cqV1rfffmt16NDB6tChg3N70dRIN910k7Vt2zZr+fLlVmRkZKlTIz3++OPWrl27rNTUVKYFQ5Vz9ijlllW16j4JN7xGaQm3ZVnW9u3brWuvvdYKDAy06tevb02aNKnEsQsXLrQuueQSKyAgwLr88sutpUuXFttut9utsWPHWtHR0VZgYKDVpUsXa8+ePa68HaBMs2fPtiSV+job9R/eatq0aVbDhg2tgIAAq3379tbGjRtNhwSUy7k+42fPnu3c59SpU9YjjzxihYeHW8HBwdZtt91W7A+vlmVZ+/fvt26++WarRo0aVkREhDVy5EiroKCg2D5fffWVFRcXZwUEBFhNmjQpdg2gKvhjwl2V6r7NsiyrfG3iAAAAAADgz/AMNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4AAk3AAAAAAAuQMINAAAAAIALkHADAAAAAOACJNwAAAAAALgACTcAADgvnTt31qOPPmo6DAAAPAYJNwAAXuDWW29Vt27dSt22du1a2Ww27dixw81RAQBQvZFwAwDgBQYMGKAVK1bo4MGDJbbNnj1bbdu2VevWrQ1EBgBA9UXCDQCAF+jRo4ciIyM1Z86cYuXHjx/XokWL1Lt3b/Xp00f169dXcHCwWrVqpffff7/Mc9psNi1evLhYWVhYWLFr/PLLL7rzzjsVFhamiy66SL169dL+/fsr56YAAKjiSLgBAPACfn5+6tu3r+bMmSPLspzlixYtUmFhoe69917Fx8dr6dKl2rlzpx566CHdd9992rRpU4WvWVBQoMTERNWqVUtr167VunXrFBISom7duik/P78ybgsAgCqNhBsAAC/xwAMPaO/evVq9erWzbPbs2br99tvVqFEjjRo1SnFxcWrSpImGDRumbt26aeHChRW+3oIFC2S32/Xmm2+qVatWuuyyyzR79mwdOHBAq1atqoQ7AgCgaiPhBgDAS7Ro0UIdO3bUrFmzJEk//fST1q5dqwEDBqiwsFDPPPOMWrVqpYsuukghISH6/PPPdeDAgQpfb/v27frpp59Uq1YthYSEKCQkRBdddJFOnz6tvXv3VtZtAQBQZfmZDgAAALjPgAEDNGzYMKWmpmr27Nlq2rSpOnXqpOeff16vvPKKpk6dqlatWqlmzZp69NFHy+z6bbPZinVPlxzdyIscP35c8fHxeu+990ocGxkZWXk3BQBAFUXCDQCAF7nzzjs1YsQIzZs3T3PnztXgwYNls9m0bt069erVS/fee68kyW6369///rdatmx5znNFRkbq8OHDzvUff/xRJ0+edK63adNGCxYsUFRUlEJDQ113UwAAVFF0KQcAwIuEhIQoKSlJKSkpOnz4sO6//35JUvPmzbVixQqtX79eu3bt0sMPP6yMjIwyz3XDDTdo+vTp2rp1q7799lsNGjRI/v7+zu333HOPIiIi1KtXL61du1b79u3TqlWrNHz48FKnJwMAoLoh4QYAwMsMGDBAv/32mxITE1WvXj1J0pgxY9SmTRslJiaqc+fOqlOnjnr37l3meV5++WXFxMTouuuu0913361Ro0YpODjYuT04OFhr1qxRw4YN9de//lWXXXaZBgwYoNOnT9PiDQDwCjbrjw9fAQAAAACAC0YLNwAAAAAALkDCDQAAAACAC5BwAwAAAADgAiTcAAAAAAC4AAk3AAAAAAAuQMINAAAAAIALkHADAAAAAOACJNwAAAAAALgACTcAAAAAAC5Awg0AAAAAgAuQcAMAAAAA4AL/H4lRhdkm2ByIAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-444.24, 250.98]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-965.65, 772.39]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1487.06, 1293.80]\n", + "\n", + "Analisi per total_water_need\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -216.226\n", + "variance: 3987062.750\n", + "std: 1996.763\n", + "min: -22812.350\n", + "max: 13374.520\n", + "median: -119.823\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-2185.83, 1432.85]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4357.05, 3604.06]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-7252.00, 5051.54]\n", + "\n", + "2. IMPORTANZA DELLE FEATURE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 79s 4ms/step\n", + "18375/18375 [==============================] - 80s 4ms/step\n", + "18375/18375 [==============================] - 99s 5ms/step\n", + "18375/18375 [==============================] - 96s 5ms/step\n", + "13976/18375 [=====================>........] - ETA: 21s" + ] + } + ], + "source": [ + "run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/olive_oli/.ipynb_checkpoints/olive_oil-checkpoint.ipynb b/models/olive_oli/.ipynb_checkpoints/olive_oil-checkpoint.ipynb new file mode 100644 index 0000000..acde314 --- /dev/null +++ b/models/olive_oli/.ipynb_checkpoints/olive_oil-checkpoint.ipynb @@ -0,0 +1,3418 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\n", + "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1581 B]\n", + "Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [1192 kB]\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy/restricted amd64 Packages [164 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy/universe amd64 Packages [17.5 MB]\n", + "Get:9 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [45.2 kB]\n", + "Get:10 http://archive.ubuntu.com/ubuntu jammy/multiverse amd64 Packages [266 kB]\n", + "Get:11 http://archive.ubuntu.com/ubuntu jammy/main amd64 Packages [1792 kB] \n", + "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", + "Get:13 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [3323 kB]\n", + "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [3446 kB]\n", + "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1514 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [53.3 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [33.8 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [81.4 kB]\n", + "Get:19 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2454 kB]\n", + "Get:20 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1225 kB]\n", + "Fetched 36.5 MB in 2s (18.2 MB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pandas\n", + " Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/cd/5f/4dba1d39bb9c38d574a9a22548c540177f78ea47b32f99c0ff2ec499fac5/pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Collecting pytz>=2020.1 (from pandas)\n", + " Obtaining dependency information for pytz>=2020.1 from https://files.pythonhosted.org/packages/11/c3/005fcca25ce078d2cc29fd559379817424e94885510568bc1bc53d7d5846/pytz-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading pytz-2024.2-py2.py3-none-any.whl.metadata (22 kB)\n", + "Collecting tzdata>=2022.7 (from pandas)\n", + " Obtaining dependency information for tzdata>=2022.7 from https://files.pythonhosted.org/packages/a6/ab/7e5f53c3b9d14972843a647d8d7a853969a58aecc7559cb3267302c94774/tzdata-2024.2-py2.py3-none-any.whl.metadata\n", + " Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m74.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m:01\u001b[0m\n", + "\u001b[?25hDownloading pytz-2024.2-py2.py3-none-any.whl (508 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m508.0/508.0 kB\u001b[0m \u001b[31m106.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading tzdata-2024.2-py2.py3-none-any.whl (346 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m103.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pytz, tzdata, pandas\n", + "Successfully installed pandas-2.2.3 pytz-2024.2 tzdata-2024.2\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting scikit-learn\n", + " Obtaining dependency information for scikit-learn from https://files.pythonhosted.org/packages/49/21/3723de321531c9745e40f1badafd821e029d346155b6c79704e0b7197552/scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Collecting scipy>=1.6.0 (from scikit-learn)\n", + " Obtaining dependency information for scipy>=1.6.0 from https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting joblib>=1.2.0 (from scikit-learn)\n", + " Obtaining dependency information for joblib>=1.2.0 from https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl.metadata\n", + " Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n", + " Obtaining dependency information for threadpoolctl>=3.1.0 from https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl.metadata\n", + " Downloading threadpoolctl-3.5.0-py3-none-any.whl.metadata (13 kB)\n", + "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading joblib-1.4.2-py3-none-any.whl (301 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m104.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.2/41.2 MB\u001b[0m \u001b[31m55.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading threadpoolctl-3.5.0-py3-none-any.whl (18 kB)\n", + "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n", + "Successfully installed joblib-1.4.2 scikit-learn-1.5.2 scipy-1.14.1 threadpoolctl-3.5.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pyarrow\n", + " Obtaining dependency information for pyarrow from https://files.pythonhosted.org/packages/5e/b5/9e14e9f7590e0eaa435ecea84dabb137284a4dbba7b3c337b58b65b76d95/pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata\n", + " Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n", + "Downloading pyarrow-18.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (40.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.1/40.1 MB\u001b[0m \u001b[31m58.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pyarrow\n", + "Successfully installed pyarrow-18.1.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting fastparquet\n", + " Obtaining dependency information for fastparquet from https://files.pythonhosted.org/packages/8d/e8/e1ede861bea68394a755d8be1aa2e2d60a3b9f6b551bfd56aeca74987e2e/fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Collecting cramjam>=2.3 (from fastparquet)\n", + " Obtaining dependency information for cramjam>=2.3 from https://files.pythonhosted.org/packages/79/1d/180f2ca168625073f0df80b16c795926deed91b7e89dbfc045263ba7444b/cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n", + "Collecting fsspec (from fastparquet)\n", + " Obtaining dependency information for fsspec from https://files.pythonhosted.org/packages/c6/b2/454d6e7f0158951d8a78c2e1eb4f69ae81beb8dca5fee9809c6c99e9d0d0/fsspec-2024.10.0-py3-none-any.whl.metadata\n", + " Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hDownloading cramjam-2.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25hDownloading fsspec-2024.10.0-py3-none-any.whl (179 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.6/179.6 kB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: fsspec, cramjam, fastparquet\n", + "Successfully installed cramjam-2.9.0 fastparquet-2024.11.0 fsspec-2024.10.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting seaborn\n", + " Obtaining dependency information for seaborn from https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl.metadata\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: seaborn\n", + "Successfully installed seaborn-0.13.2\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting tqdm\n", + " Obtaining dependency information for tqdm from https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl.metadata\n", + " Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: tqdm\n", + "Successfully installed tqdm-4.67.1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting pydot\n", + " Obtaining dependency information for pydot from https://files.pythonhosted.org/packages/3e/1b/ef569ac44598b6b24bc0f80d5ac4f811af59d3f0d0d23b0216e014c0ec33/pydot-3.0.3-py3-none-any.whl.metadata\n", + " Downloading pydot-3.0.3-py3-none-any.whl.metadata (10 kB)\n", + "Collecting pyparsing>=3.0.9 (from pydot)\n", + " Obtaining dependency information for pyparsing>=3.0.9 from https://files.pythonhosted.org/packages/be/ec/2eb3cd785efd67806c46c13a17339708ddc346cbb684eade7a6e6f79536a/pyparsing-3.2.0-py3-none-any.whl.metadata\n", + " Downloading pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)\n", + "Downloading pydot-3.0.3-py3-none-any.whl (35 kB)\n", + "Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.9/106.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pyparsing, pydot\n", + " Attempting uninstall: pyparsing\n", + " Found existing installation: pyparsing 2.4.7\n", + " Uninstalling pyparsing-2.4.7:\n", + " Successfully uninstalled pyparsing-2.4.7\n", + "Successfully installed pydot-3.0.3 pyparsing-3.2.0\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Collecting tensorflow-io\n", + " Obtaining dependency information for tensorflow-io from https://files.pythonhosted.org/packages/f0/5e/f47443a14a00816fab54caf74599e2fcb34c05d6059e91f82126f8f4c68d/tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n", + "Collecting tensorflow-io-gcs-filesystem==0.37.1 (from tensorflow-io)\n", + " Obtaining dependency information for tensorflow-io-gcs-filesystem==0.37.1 from https://files.pythonhosted.org/packages/66/7f/e36ae148c2f03d61ca1bff24bc13a0fef6d6825c966abef73fc6f880a23b/tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\n", + " Downloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (14 kB)\n", + "Downloading tensorflow_io-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.6 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 MB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading tensorflow_io_gcs_filesystem-0.37.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hInstalling collected packages: tensorflow-io-gcs-filesystem, tensorflow-io\n", + " Attempting uninstall: tensorflow-io-gcs-filesystem\n", + " Found existing installation: tensorflow-io-gcs-filesystem 0.34.0\n", + " Uninstalling tensorflow-io-gcs-filesystem-0.34.0:\n", + " Successfully uninstalled tensorflow-io-gcs-filesystem-0.34.0\n", + "Successfully installed tensorflow-io-0.37.1 tensorflow-io-gcs-filesystem-0.37.1\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 10:36:10.368632: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-12-06 10:36:10.368679: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-12-06 10:36:10.368726: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-12-06 10:36:10.377750: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keras version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "CUDA available: True\n", + "GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n", + "1 Physical GPUs, 1 Logical GPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 10:36:13.233242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import keras\n", + "\n", + "print(f\"Keras version: {keras.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n", + "print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n", + "\n", + "# GPU configuration\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Limita la crescita della memoria GPU\n", + "gpus = tf.config.experimental.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " # Imposta la crescita di memoria dinamica\n", + " for gpu in gpus:\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + " \n", + " # Opzionalmente, limita la memoria GPU massima (uncomment se necessario)\n", + " # tf.config.experimental.set_virtual_device_configuration(\n", + " # gpus[0],\n", + " # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*4)] # 4GB\n", + " # )\n", + " \n", + " logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n", + " print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n", + " except RuntimeError as e:\n", + " print(e)\n", + " \n", + "# Imposta le opzioni di logging\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Riduce i messaggi di log\n", + " \n", + "# Configura la modalità mista di precisione\n", + "tf.keras.mixed_precision.set_global_policy('float32')\n", + "\n", + "# Imposta il seed per la riproducibilità\n", + "##tf.random.set_seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c0155cde4740b0a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tensorflow_addons as tfa\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "import re\n", + "from typing import List\n", + "\n", + "random_state_value = None\n", + "execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "base_project_dir = './'\n", + "data_dir = '../../sources/'\n", + "models_project_dir = base_project_dir\n", + "\n", + "os.makedirs(base_project_dir, exist_ok=True)\n", + "os.makedirs(models_project_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1347fb59-50cc-4aa8-b805-ca9403037af5", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_column_name(name: str) -> str:\n", + " \"\"\"\n", + " Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia.\n", + "\n", + " Parameters\n", + " ----------\n", + " name : str\n", + " Nome della colonna da pulire\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " Nome della colonna pulito\n", + " \"\"\"\n", + " # Rimuove caratteri speciali\n", + " name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n", + " # Converte in snake_case\n", + " name = name.lower().replace(' ', '_')\n", + "\n", + " # Abbreviazioni comuni\n", + " abbreviations = {\n", + " 'production': 'prod',\n", + " 'percentage': 'pct',\n", + " 'hectare': 'ha',\n", + " 'tonnes': 't',\n", + " 'litres': 'l',\n", + " 'minimum': 'min',\n", + " 'maximum': 'max',\n", + " 'average': 'avg'\n", + " }\n", + "\n", + " for full, abbr in abbreviations.items():\n", + " name = name.replace(full, abbr)\n", + "\n", + " return name\n", + "\n", + "\n", + "def clean_column_names(df: pd.DataFrame) -> List[str]:\n", + " \"\"\"\n", + " Pulisce tutti i nomi delle colonne in un DataFrame.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : pd.DataFrame\n", + " DataFrame con le colonne da pulire\n", + "\n", + " Returns\n", + " -------\n", + " list\n", + " Lista dei nuovi nomi delle colonne puliti\n", + " \"\"\"\n", + " new_columns = []\n", + "\n", + " for col in df.columns:\n", + " # Usa regex per separare le varietà\n", + " varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n", + " if varieties:\n", + " new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n", + " else:\n", + " new_columns.append(col)\n", + "\n", + " return new_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4da1f1bb67343e3e", + "metadata": {}, + "outputs": [], + "source": [ + "def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n", + " filename = filename.replace(' ', '_').lower()\n", + " filepath = os.path.join(output_dir, f\"{filename}.png\")\n", + " plt.savefig(filepath, bbox_inches='tight', dpi=300)\n", + " print(f\"Plot salvato come: {filepath}\")\n", + "\n", + "\n", + "def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + "\n", + " # Trova tutte le colonne delle tecniche\n", + " tech_columns = [col for col in df.columns if col.endswith('_tech')]\n", + "\n", + " # Applica il mapping a tutte le colonne delle tecniche\n", + " for col in tech_columns:\n", + " df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n", + "\n", + " return df\n", + "\n", + "\n", + "def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + " reverse_mapping = {v: k for k, v in technique_mapping.items()}\n", + " reverse_mapping[0] = ''\n", + "\n", + " return reverse_mapping.get(technique_value, '')\n", + "\n", + "\n", + "def prepare_comparison_data(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + "\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + " comparison_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n", + "\n", + " if not variety_data.empty:\n", + " avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " comparison_data.append({\n", + " 'Variety': variety,\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(comparison_data)\n", + "\n", + "\n", + "def plot_variety_comparison(comparison_data, metric):\n", + " plt.figure(figsize=(12, 6))\n", + " bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n", + " plt.title(f'Comparison of {metric} across Olive Varieties')\n", + " plt.xlabel('Variety')\n", + " plt.ylabel(metric)\n", + " plt.xticks(rotation=45, ha='right')\n", + "\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width() / 2., height,\n", + " f'{height:.2f}',\n", + " ha='center', va='bottom')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Oil Efficiency (L/kg)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Oil Efficiency (L oil / kg olives)')\n", + " plt.tight_layout()\n", + " save_plot(plt, 'efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Water Efficiency (L oil/m³ water)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Water Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Water Efficiency (L oil / m³ water)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_need_vs_oil_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n", + " comparison_data['Avg Oil Production (L/ha)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Production vs Water Need by Variety')\n", + " plt.xlabel('Average Water Need (m³/ha)')\n", + " plt.ylabel('Average Oil Production (L/ha)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_need_vs_oil_production')\n", + " plt.close()\n", + "\n", + "\n", + "def analyze_by_technique(simulated_data, olive_varieties):\n", + " # Pulisci i nomi delle colonne\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " technique_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0]\n", + "\n", + " if not variety_data.empty:\n", + " for tech in variety_data[tech_col].unique():\n", + " tech_data = variety_data[variety_data[tech_col] == tech]\n", + "\n", + " avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n", + "\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " technique_data.append({\n", + " 'Variety': variety,\n", + " 'Technique': tech,\n", + " 'Technique String': decode_single_technique(tech),\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(technique_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9aa4bf176c4affb9", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_real_error(model, test_data, test_targets, scaler_y):\n", + " # Fare predizioni\n", + " predictions = model.predict(test_data)\n", + "\n", + " # Denormalizzare predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + "\n", + " # Calcolare errore percentuale per ogni target\n", + " percentage_errors = []\n", + " absolute_errors = []\n", + "\n", + " for i in range(predictions_real.shape[1]):\n", + " mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n", + " mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n", + " percentage_errors.append(mape)\n", + " absolute_errors.append(mae)\n", + "\n", + " # Stampa risultati per ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " print(\"\\nErrori per target:\")\n", + " print(\"-\" * 50)\n", + " for i, target in enumerate(target_names):\n", + " print(f\"{target}:\")\n", + " print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n", + " print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n", + " print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n", + " print(\"-\" * 50)\n", + "\n", + " return percentage_errors, absolute_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3ba2b96ba678389", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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A6Onpibt370rlixcvVvqf6NwvQQMHDpTKcnJyRMuWLYWOjo548uSJVP7q1SuFeDIzM0W1atXE999/r1AOQGhoaIgrV64o7dv7X16MjY0VvtS+LzMzU1hYWIhq1aqJ9PR0qXz37t0CgBg/frzSvkycOFGhD1dX149+8Xz8+LHQ0dERzZs3V0jazZs3TwAQK1askMpyv5S9e2w+ZPPmzQKAuHnzphBCiJSUFKGrq6uQhMn9bC5duqSwbZUqVRSO78CBA4VMJpOSfEK8/cJlamr60aRU7nn17heZvDg7OwtTU1PpvSpJqYCAAKGtrS2SkpKksoyMDFGyZEnh5+cnlfn7+wtra2vx9OlThf58fHyEsbGx0jn2PltbW9G8eXPx5MkT8eTJE3Hx4kXh4+OjcA7nJjGMjIzE48ePFbZ3cXERFhYW4tmzZ1LZxYsXhYaGhvSFVQghvL29ha6ursLvz9WrV4WmpuYnJ6W6d+8uNDQ0xLlz55Ta5iZMNm3alGeiRgjlpNSsWbMEAIUEbmZmpqhbt64wMDAQKSkpCjGWKlVK4fPZsWOHACB27dqlNNa7li1blue5mbuPub+/w4cPFxoaGmLlypUKbWbMmCEAiO3bt0tl6enpwtHRMc997datm8J+qhN/XufPunXrBABx4sQJpbo+ffoIPT29D+5/fv1OnjxZyGQyhXOkYcOGwtDQUKFMCKGQEMv9fercubNCG1Wv+9u2bRMA8jyPcg0ePFgYGRmJN2/efHTf3uXt7S0AfDD5s3XrVgFAzJkzRyr7WFIqOTlZyOVyMXz4cIW+pk2bpnAM79y5IzQ1NcXvv/+u0O7SpUtCS0tLqfx9b968EdbW1qJu3boK5YsWLRIAxIEDB4QQb5Pv717nhXh7nsnlcoV/P3ITTw4ODkrnwPtJqZycHFGxYkXh4eGh8Hm/evVK2Nvbi2bNmkllwcHB+V6v37+2Tpo0Sejr64sbN24otBs9erTQ1NQU9+7dE0J8+mdORFTQePseEdF/UEpKCgCovLD13r17AQDDhg1TKM9d3Pn9taeqVKmCunXrSu9r164NAPj+++9Rrlw5pfLbt28rjfnuY91zb7/LzMzEoUOHpHI9PT3p5+fPnyM5ORkNGjRQutUOABo1aoQqVap8ZE/frst05swZPHjwIM/6yMhIPH78GP369VNYv6Nly5ZwdHTMcx2u99cVatCgQZ77/K5Dhw4hMzMTQ4YMUVhktnfv3jAyMvpX632FhYWhZs2a0qLohoaGaNmypcItfO3atYOWlhY2bNgglV2+fBlXr15VeFrf/v37UbduXbi4uEhlpqamKq2TkvtUqo+dh4aGhtI5q6pOnTohKysLW7dulcoOHjyIFy9eSPELIbBlyxa0bt0aQgg8ffpUenl4eCA5OTnPc+l9Bw8ehLm5OczNzVGjRg1s2rQJ3bp1w9SpUxXatW/fHubm5tL7hw8fIjo6Gj169ICpqalU7uzsjGbNmkm/d9nZ2Thw4AC8vb0Vfn+cnJzg4eGh1nHJlZOTg+3bt6N169ZKtyoB+KS1k/bu3QsrKyt07txZKtPW1sagQYOQmpqK48ePK7Tv1KmTwjphDRo0AJD39eBdube+5bfGmBACAwYMwOzZs7FmzRqltdL279+P0qVLK9wOpquri969eyv1lZOTg/379+d5654q8b97jXr9+jWePn2KOnXqAECe55aJiQnS09Px6tWrPPctr37T0tLw9OlT1KtXD0II6ZboJ0+e4MSJE/Dz81M4b4C8P9/3r1OqXvdz17LbvXs3srKy8oy3ZMmSSEtLQ3h4+Af3632qXCNy69S5RuTeHrdx40aF28I3bNiAOnXqSMdr69atyMnJQceOHRWuD1ZWVqhYsSKOHj36wXE0NTXh4+ODiIgIhVuZ165dC0tLSzRt2hTA23Wbcq/z2dnZePbsGQwMDFC5cuU8zxNfX1+FcyAv0dHRuHnzJrp06YJnz55JsaelpaFp06Y4ceKE0u12qti0aRMaNGgAExMThWPi7u6O7OxsnDhxAsCnf+ZERAWNSSkiov8gIyMjAPjgo6rfdffuXWhoaCg92c3KygolS5ZUehrV+1+AjI2NAQBly5bNs/z58+cK5RoaGkrrfVSqVAkAFP7Hfvfu3ahTpw50dXVhamoKc3NzLFy4EMnJyUr7YG9v/7HdBPB2ra3Lly+jbNmy+PbbbxEYGKjwJTN3XytXrqy0raOjo9Kx0NXVVUhEAG+/eL6/z+/LbxwdHR04ODh88hPAXrx4gb1796JRo0aIi4uTXvXr10dkZCRu3LgBADAzM0PTpk2xceNGadsNGzZAS0sL7dq1U4gzryf+qfIUwNwvkx87Dz/lyYA1atSAo6OjQlJtw4YNMDMzkxaRfvLkCV68eIElS5ZISaXcV8+ePQEgz7Vg3le7dm2Eh4fj0KFDOH36NJ4+fYrVq1crfWl8/xz80Lnk5OQkfYF88uQJ0tPTUbFiRaV2eW2riidPniAlJQXVqlX7pO3zcvfuXVSsWFHpSV1OTk5S/bvev07kJng+9ruR691kwrtWr16N+fPnY+7cuQoJsnfjLF++vFJiJq9z9ty5c3jy5EmeSSlV4k9KSsLgwYNhaWkJPT09mJubS+dBXtep3H36WFLw3r17UjIzd626Ro0aKfSbe91S9TPO6/xU5brfqFEjtG/fHkFBQTAzM0Pbtm0RGhqqsO5Uv379UKlSJXh5eaFMmTLw8/OT1vX7EFWuEaomt9/XqVMn/PPPP4iIiAAA3Lp1C1FRUQpJ95s3b0IIgYoVKypdI65du6bS9SE3Qb927VoAwP3793Hy5En4+PhIi6/n5OQgJCQEFStWhFwuh5mZGczNzRETE/PJ/57dvHkTwNsE1vuxL1u2DBkZGXn2rUq/+/fvV+ozd4233GPyqZ85EVFB49P3iIj+g4yMjGBjY4PLly+rtZ2qsyfye8pRfuX5fbn8kJMnT6JNmzZo2LAhFixYAGtra2hrayM0NFT6n/93feyvyrk6duyIBg0aYNu2bTh48CCCg4MxdepUbN26FV5eXmrHWdRPfPqYTZs2ISMjAzNmzMCMGTOU6sPCwqQFzn18fNCzZ09ER0fDxcUFGzduRNOmTWFmZlYgsVSoUAFaWlqIiYnJt01GRgZiY2PznM3zMZ06dcLvv/+Op0+fwtDQEDt37kTnzp2hpfX2f09yZwn8+OOP+T550NnZ+aPjmJmZqbTotqrn4L+R3++oqgvrF6VPvR6UKlUKwNvkT5kyZZTq69evj+joaMybNw8dO3ZUmIWmrr1798LOzi7PWZaqxN+xY0ecPn0aI0aMgIuLCwwMDJCTkwNPT888Z6k8f/4cJUqU+OC5kp2djWbNmiEpKQmjRo2Co6Mj9PX1kZCQgB49enzS7Bcg//PzY9d9mUyGzZs34++//8auXbtw4MAB+Pn5YcaMGfj7779hYGAACwsLREdH48CBA9i3bx/27duH0NBQdO/ePc8HEuRycnLC9u3bERMTg4YNG+bZJvf6ocpM2He1bt0aJUqUwMaNG1GvXj1s3LgRGhoa0uL+wNtrhEwmw759+/L8vA0MDD46jpubGxwdHbFu3TqMGTMG69atgxBCYTbpH3/8gV9//RV+fn6YNGkSTE1NoaGhgSFDhuT5eapyLcndLjg4WGEmq7rx59Vvs2bNMHLkyDzrc/+A9KmfORFRQWNSiojoP6pVq1ZYsmQJIiIiFG61y4utrS1ycnJw8+ZNadYD8PYpbS9evICtrW2BxpaTk4Pbt29L/3MLQJrBY2dnBwDYsmULdHV1ceDAAYVHVoeGhv7r8a2trdGvXz/069cPjx8/xjfffIPff/8dXl5e0r7GxsYqPbY9Nja2wI7Fu+O8O2ssMzMT8fHxKj957H1hYWGoVq2a9MS+dy1evBhr166VklLe3t746aefpNlGN27cQEBAgFKccXFxSn3lVfY+fX19NGnSBEeOHMHdu3fzPHYbN25ERkYGWrVqpdL+vatTp04ICgrCli1bYGlpiZSUFPj4+Ej15ubmMDQ0RHZ29icfz3/j3c/4fdevX4eZmRn09fWhq6sLPT09aebDu97fNne2zrtPaASUZymZm5vDyMjoo4lpdW7js7W1RUxMDHJychRmS12/fl2qLwiOjo4AgPj4eFSvXl2pvkKFCpg2bRoaN24MT09PHD58WGEWja2tLa5evQohhML+5XXO7tmzBy1atPikOJ8/f47Dhw8jKCgI48ePl8rz+hxzxcfHK1xj83Lp0iXcuHEDq1atQvfu3aXy92+Tyr1uqPvHh1zqXvfr1KmDOnXq4Pfff8fatWvRtWtXrF+/Hr169QLwdpZn69at0bp1a+Tk5KBfv35YvHgxfv3113xnVrZq1QqTJ0/G6tWr80xKZWdnY+3atTAxMUH9+vXV2j99fX20atUKmzZtwsyZM7FhwwY0aNAANjY2Upvy5ctDCAF7e3uFf4/U1bVrV/z666+IiYnB2rVrUbFiRdSqVUuq37x5M5o0aYLly5crbPfixYtP/iNA+fLlAbz9I9THrm/q/J6XL18eqampKl0zP+UzJyIqaLx9j4joP2rkyJHQ19dHr1698OjRI6X6W7duSY9uzv1SNmvWLIU2M2fOBIA8b235t+bNmyf9LITAvHnzoK2tLa3BoampCZlMpjAD5M6dO9i+ffsnj5mdna10O4OFhQVsbGykW1Fq1qwJCwsLLFq0SOH2lH379uHatWsFdizc3d2ho6ODOXPmKMy8WL58OZKTkz9pnH/++QcnTpxAx44d8cMPPyi9evbsibi4OJw5cwbA2zVBPDw8sHHjRqxfvx46Ojrw9vZW6NPDwwMRERGIjo6WypKSkhTWp/qQcePGQQiBHj16KD3SPT4+HiNHjoS1tTV++ukntffXyckJ1atXx4YNG7BhwwZYW1srfLHV1NRE+/btsWXLljy/uD958kTtMdVhbW0NFxcXrFq1SiGJdPnyZRw8eFD6vdPU1ISHhwe2b9+Oe/fuSe2uXbuGAwcOKPRpZGQEMzMzaV2XXAsWLFB4r6GhAW9vb+zatQuRkZFKseWec/r6+gCUk1x5adGiBRITExVumXzz5g3mzp0LAwMD6fayf8vNzQ06Ojp5xp3L2dkZe/fuxbVr19C6dWuFc8vDwwMJCQnYuXOnVPb69WssXbpUoY9Hjx7h/Pnzn/w7nTuz5v2ZX+9fR991/vx51KtXT+1+hRDS9TqXubk5GjZsiBUrViicN3nFlBdVr/vPnz9X6i93Zk7uNTJ3HbBcGhoa0izEd6+j76tXrx7c3d0RGhqK3bt3K9WPHTsWN27cwMiRIz9pJmKnTp3w4MEDLFu2DBcvXlS4dQ94u7aepqYmgoKClPZRCKG0X/nJnRU1fvx4REdHK625p6mpqdT/pk2bkJCQoO4uSdzc3FC+fHlMnz4dqampSvXvXt/U+T3v2LEjIiIilK49udu/efMGwKd/5kREBY0zpYiI/qPKly+PtWvXolOnTnByckL37t1RrVo1ZGZm4vTp09i0aRN69OgB4O36PL6+vliyZAlevHiBRo0a4ezZs1i1ahW8vb3RpEmTAo1NV1cX+/fvh6+vL2rXro19+/Zhz549GDNmjLQ+U8uWLTFz5kx4enqiS5cuePz4MebPn48KFSp88HawD3n58iXKlCmDH374ATVq1ICBgQEOHTqEc+fOSbe6aWtrY+rUqejZsycaNWqEzp0749GjR5g9ezbs7OwwdOjQAjkG5ubmCAgIQFBQEDw9PdGmTRvExsZiwYIFqFWrFn788Ue1+1y7di2EEAoLPL+rRYsW0NLSQlhYmLQIfadOnfDjjz9iwYIF8PDwkBY1zjVy5EisWbMGzZo1w8CBA6Gvr49ly5ahXLlySEpK+uhf4Bs2bIjp06dj2LBhcHZ2Ro8ePWBtbY3r169j6dKlyMnJwd69e/Nd1PpjOnXqhPHjx0NXVxf+/v5K6x1NmTIFR48eRe3atdG7d29UqVIFSUlJOH/+PA4dOoSkpKRPGldVwcHB8PLyQt26deHv74/09HTMnTsXxsbGCAwMlNoFBQVh//79aNCgAfr16ycle6pWrap0vvfq1QtTpkxBr169ULNmTZw4cUKaafiuP/74AwcPHkSjRo3Qp08fODk54eHDh9i0aRNOnTqFkiVLwsXFBZqampg6dSqSk5Mhl8vx/fffw8LCQqm/Pn36YPHixejRoweioqJgZ2eHzZs346+//sKsWbPUXvMnP7q6umjevDkOHTqEiRMn5tuuTp062LFjB1q0aIEffvgB27dvh7a2Nn766SfMmzcPnTt3xuDBg2FtbY2wsDDpwQW55+zevXuhq6v7ydc3IyMjNGzYENOmTUNWVhZKly6NgwcPIj4+Ps/2UVFRSEpKQtu2bT/Yr6OjI8qXL49ffvkFCQkJMDIywpYtW/Jci2vOnDn47rvv8M0336BPnz6wt7fHnTt3sGfPHoVEcl5Uve6vWrUKCxYswP/+9z+UL18eL1++xNKlS2FkZCQltnr16oWkpCR8//33KFOmDO7evYu5c+fCxcXlozPDVq9ejaZNm6Jt27bo0qULGjRogIyMDGzduhXHjh1Dp06dMGLEiA/2kZ8WLVrA0NAQv/zyi5Skflf58uXx22+/ISAgAHfu3IG3tzcMDQ0RHx+Pbdu2oU+fPvjll18+Oo69vT3q1auHHTt2AIBSUqpVq1aYOHEievbsiXr16uHSpUsICwtTWltRHRoaGli2bBm8vLxQtWpV9OzZE6VLl0ZCQgKOHj0KIyMj7Nq1C8DbBBbwNsnn4+MDbW1ttG7dWkpWvWvEiBHYuXMnWrVqhR49esDNzQ1paWm4dOkSNm/ejDt37sDMzOxffeZERAWqiJ7yR0REn+jGjRuid+/ews7OTujo6AhDQ0NRv359MXfuXPH69WupXVZWlggKChL29vZCW1tblC1bVgQEBCi0EeLt46NbtmypNA7eeVR7rtxHqwcHB0tlvr6+Ql9fX9y6dUs0b95clChRQlhaWooJEyYoPTJ7+fLlomLFikIulwtHR0cRGhoqPd78Y2O/W5f76PCMjAwxYsQIUaNGDWFoaCj09fVFjRo1xIIFC5S227Bhg3B1dRVyuVyYmpqKrl27ivv37yu0yd2X9+UVY37mzZsnHB0dhba2trC0tBQ///yz0uPRc/t78uTJB/uqXr26KFeu3AfbNG7cWFhYWIisrCwhhBApKSlCT09PABBr1qzJc5sLFy6IBg0aCLlcLsqUKSMmT54s5syZIwCIxMRElfbzxIkTom3btsLMzExoa2uLcuXKid69e4s7d+4otc3r+L3/2PJcN2/eFAAEAHHq1Kk8x3706JHo37+/KFu2rNDW1hZWVlaiadOmYsmSJR+NO7/z/V15nefvOnTokKhfv77Q09MTRkZGonXr1uLq1atK7Y4fPy7c3NyEjo6OcHBwEIsWLcrzWLx69Ur4+/sLY2NjYWhoKDp27CgeP36scK7nunv3rujevbswNzcXcrlcODg4iP79+4uMjAypzdKlS4WDg4PQ1NRUeOR8o0aNRKNGjRT6e/TokejZs6cwMzMTOjo6onr16iI0NFTl45FXjHnZunWrkMlk0uPn393+/d/1HTt2CC0tLdGpUyfpGnL79m3RsmVLoaenJ8zNzcXw4cPFli1bBADx999/CyGE+OGHH0SLFi2UxlYn/vv374v//e9/omTJksLY2Fh06NBBPHjwIM/9HDVqlChXrpzIycn56P5fvXpVuLu7CwMDA2FmZiZ69+4tLl68KAAoHe/Lly9LMejq6orKlSuLX3/9Var/0PVDlev++fPnRefOnUW5cuWEXC4XFhYWolWrViIyMlJqs3nzZtG8eXNhYWEhdHR0RLly5cRPP/0kHj58+NF9FUKIly9fisDAQFG1alWhp6cn/Tu1cuXKPI/X+8c3NDRUABDx8fFKbbt27SoACHd393zH37Jli/juu++Evr6+0NfXF46OjqJ///4iNjZWpfiFEGL+/PkCgPj222+V6l6/fi2GDx8urK2thZ6enqhfv76IiIhQ+h07evSoACA2bdqk1EduXe7vZ64LFy6Idu3aiVKlSgm5XC5sbW1Fx44dxeHDhxXaTZo0SZQuXVpoaGgoHKu8rq0vX74UAQEBokKFCkJHR0eYmZmJevXqienTp4vMzEwhxL//zImICopMiE9YvZaIiL5aPXr0wObNm/O83YC+HEOGDMHixYuRmpr6n1vsnb582dnZqFKlCjp27IhJkyYVSJ+zZs3C0KFDcf/+fVhaWqJUqVKYPHky+vXrVyD9f0hGRgbs7OwwevRoDB48uNDHIyIi+lpwTSkiIqJi7v21oJ49e4Y///wT3333HRNSVCg0NTUxceJEzJ8//5MS2O+fs69fv8bixYtRsWJFlC5dGklJSRg6dCj+97//FVTIHxQaGgptbW307du3SMYjIiL6WnCmFBERqYUzpb48Li4uaNy4MZycnPDo0SMsX74cDx48wOHDh/N9jDvR5+Tl5YVy5crBxcUFycnJWLNmDa5cuYKwsDB06dLlc4dHREREBYQLnRMRERVzLVq0wObNm7FkyRLIZDJ88803WL58ORNS9J/l4eGBZcuWISwsTLoVcP369UpPXyMiIqIvG2dKERERERERERFRkeOaUkREREREREREVOSYlCIiIiIiIiIioiLHNaUKSE5ODh48eABDQ0PIZLLPHQ4RERERERER0WchhMDLly9hY2MDDY3850MxKVVAHjx4gLJly37uMIiIiIiIiIiI/hP++ecflClTJt96JqUKiKGhIYC3B9zIyOgzR0NERERERERE9HmkpKSgbNmyUq4kP0xKFZDcW/aMjIyYlCIiIiIiIiKir97HljfiQudERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURE9P+dOHECrVu3ho2NDWQyGbZv365Q/+jRI/To0QM2NjYoUaIEPD09cfPmTaV+IiIi8P3330NfXx9GRkZo2LAh0tPTldplZGTAxcUFMpkM0dHRCnUHDhxAnTp1YGhoCHNzc7Rv3x537tz56D7s2bMHtWvXhp6eHkxMTODt7S3VPXv2DJ6enrCxsYFcLkfZsmUxYMAApKSkqHJ4iIiIiIgKFJNSRERE/19aWhpq1KiB+fPnK9UJIeDt7Y3bt29jx44duHDhAmxtbeHu7o60tDSpXUREBDw9PdG8eXOcPXsW586dw4ABA6ChofxP7siRI2FjY6NUHh8fj7Zt2+L7779HdHQ0Dhw4gKdPn6Jdu3YfjH/Lli3o1q0bevbsiYsXL+Kvv/5Cly5dpHoNDQ20bdsWO3fuxI0bN7By5UocOnQIffv2VecwEREREREVDPEZ/fHHH6JmzZrCwMBAmJubi7Zt24rr168rtElPTxf9+vUTpqamQl9fX7Rr104kJiYqtLl7965o0aKF0NPTE+bm5uKXX34RWVlZCm2OHj0qXF1dhY6OjihfvrwIDQ1VimfevHnC1tZWyOVy8e2334ozZ86ovC/JyckCgEhOTlb9ABAR0X8WALFt2zbpfWxsrAAgLl++LJVlZ2cLc3NzsXTpUqmsdu3aYty4cR/tf+/evcLR0VFcuXJFABAXLlyQ6jZt2iS0tLREdna2VLZz504hk8lEZmZmnv1lZWWJ0qVLi2XLlqmxl0LMnj1blClTRq1tiIiIiIg+RNUcyWedKXX8+HH0798ff//9N8LDw5GVlYXmzZsr/MV56NCh2LVrFzZt2oTjx4/jwYMHCn8pzs7ORsuWLZGZmYnTp09j1apVWLlyJcaPHy+1iY+PR8uWLdGkSRNER0djyJAh6NWrFw4cOCC12bBhA4YNG4YJEybg/PnzqFGjBjw8PPD48eOiORhERPSflpGRAQDQ1dWVyjQ0NCCXy3Hq1CkAwOPHj3HmzBlYWFigXr16sLS0RKNGjaT6XI8ePULv3r3x559/okSJEkpjubm5QUNDA6GhocjOzkZycjL+/PNPuLu7Q1tbO8/4zp8/j4SEBGhoaMDV1RXW1tbw8vLC5cuX892nBw8eYOvWrWjUqJHax4OIiIiI6F8roiSZSh4/fiwAiOPHjwshhHjx4oXQ1tYWmzZtktpcu3ZNABARERFCiLd/adbQ0FCYPbVw4UJhZGQkMjIyhBBCjBw5UlStWlVhrE6dOgkPDw/p/bfffiv69+8vvc/OzhY2NjZi8uTJKsXOmVJERMUL3psplZmZKcqVKyc6dOggkpKSREZGhpgyZYoAIJo3by6EECIiIkIAEKampmLFihXi/PnzYsiQIUJHR0fcuHFDCCFETk6O8PT0FJMmTRJCCBEfH680U0oIIY4dOyYsLCyEpqamACDq1q0rnj9/nm+869atEwBEuXLlxObNm0VkZKTo3LmzKFWqlHj27JlCWx8fH6GnpycAiNatW4v09PR/f8CIiIiIiP6/L2Km1PuSk5MBAKampgCAqKgoZGVlwd3dXWrj6OiIcuXKISIiAsDbtTuqV68OS0tLqY2HhwdSUlJw5coVqc27feS2ye0jMzMTUVFRCm00NDTg7u4utXlfRkYGUlJSFF5ERFR8aWtrY+vWrbhx4wZMTU1RokQJHD16FF5eXtJ6UTk5OQCAn376CT179oSrqytCQkJQuXJlrFixAgAwd+5cvHz5EgEBAfmOlZiYiN69e8PX1xfnzp3D8ePHoaOjgx9++AFCiDy3yR177NixaN++Pdzc3BAaGgqZTIZNmzYptA0JCcH58+exY8cO3Lp1C8OGDfvXx4eIiIiISF1anzuAXDk5ORgyZAjq16+PatWqAXj7P+U6OjooWbKkQltLS0skJiZKbd5NSOXW59Z9qE1KSgrS09Px/PlzZGdn59nm+vXrecY7efJkBAUFfdrOEhHRF8nNzQ3R0dFITk5GZmYmzM3NUbt2bdSsWRMAYG1tDQCoUqWKwnZOTk64d+8eAODIkSOIiIiAXC5XaFOzZk107doVq1atwvz582FsbIxp06ZJ9WvWrEHZsmVx5swZ1KlTRym2vMaWy+VwcHCQxs5lZWUFKysrODo6wtTUFA0aNMCvv/4q9UFEREREVBT+MzOl+vfvj8uXL2P9+vWfOxSVBAQEIDk5WXr9888/nzskIiIqIsbGxjA3N8fNmzcRGRmJtm3bAgDs7OxgY2OD2NhYhfY3btyAra0tAGDOnDm4ePEioqOjER0djb179wJ4u7bh77//DgB49eqV0tP6NDU1AfzfjKj3ubm5QS6XK4ydlZWFO3fuSGPnJbe/3DWziIiIiIiKyn9iptSAAQOwe/dunDhxAmXKlJHKrayskJmZiRcvXijMlnr06BGsrKykNmfPnlXo79GjR1Jd7n9zy95tY2RkBD09PWhqakJTUzPPNrl9vE8ulyv9lZuIiL5sqampiIuLk97Hx8cjOjoapqamKFeuHDZt2gRzc3OUK1cOly5dwuDBg+Ht7Y3mzZsDAGQyGUaMGIEJEyagRo0acHFxwapVq3D9+nVs3rwZAFCuXDmFMQ0MDAAA5cuXl/4NbNmyJUJCQjBx4kR07twZL1++xJgxY2BrawtXV1cAwNmzZ9G9e3ccPnwYpUuXhpGREfr27YsJEyagbNmysLW1RXBwMACgQ4cOAIC9e/fi0aNHqFWrFgwMDHDlyhWMGDEC9evXh52dXeEdWCIiIiKiPHzWmVJCCAwYMADbtm3DkSNHYG9vr1Dv5uYGbW1tHD58WCqLjY3FvXv3ULduXQBA3bp1cenSJYWn5IWHh8PIyEi6haFu3boKfeS2ye1DR0cHbm5uCm1ycnJw+PBhqQ0RERV/kZGRcHV1lRI/w4YNg6urq/RE14cPH6Jbt25wdHTEoEGD0K1bN6xbt06hjyFDhiAgIABDhw5FjRo1cPjwYYSHh6N8+fIqx/H9999j7dq12L59O1xdXeHp6Qm5XI79+/dDT08PwNvZVLGxscjKypK2Cw4Oho+PD7p164ZatWrh7t27OHLkCExMTAAAenp6WLp0Kb777js4OTlh6NChaNOmDXbv3v2vjhsRERER0aeQifxWTC0C/fr1w9q1a7Fjxw5UrlxZKjc2Npb+p/vnn3/G3r17sXLlShgZGWHgwIEAgNOnTwMAsrOz4eLiAhsbG0ybNg2JiYno1q0bevXqhT/++APA2790V6tWDf3794efnx+OHDmCQYMGYc+ePfDw8ADw9rYJX19fLF68GN9++y1mzZqFjRs34vr160prTeUlJSUFxsbGSE5OhpGRUYEeJyIiIiIiIiKiL4WqOZLPmpSSyWR5loeGhqJHjx4AgNevX2P48OFYt24dMjIy4OHhgQULFijcVnf37l38/PPPOHbsGPT19eHr64spU6ZAS+v/7k48duwYhg4diqtXr6JMmTL49ddfpTFyzZs3D8HBwUhMTISLiwvmzJmD2rVrq7QvTEoREREREREREX0hSanihEkpIiIiIiIiIiLVcyT/iYXOiYiI8mM3es/nDoEKwZ0pLT93CERERET0mX3Whc6JiIiIiIiIiOjrxKQUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZFjUoqIiIiIiIiIiIock1JERERERERERFTkmJQiIiIiIiIiIqIix6QUEREREREREREVOSaliIiIiIiIiIioyDEpRURERERERERERY5JKSIiIiIiIiIiKnJMShERERERERERUZH7rEmpEydOoHXr1rCxsYFMJsP27dsV6mUyWZ6v4OBgqY2dnZ1S/ZQpUxT6iYmJQYMGDaCrq4uyZcti2rRpSrFs2rQJjo6O0NXVRfXq1bF3795C2WciIiIiIiIiIvrMSam0tDTUqFED8+fPz7P+4cOHCq8VK1ZAJpOhffv2Cu0mTpyo0G7gwIFSXUpKCpo3bw5bW1tERUUhODgYgYGBWLJkidTm9OnT6Ny5M/z9/XHhwgV4e3vD29sbly9fLpwdJyIiIiIiIiL6yml9zsG9vLzg5eWVb72VlZXC+x07dqBJkyZwcHBQKDc0NFRqmyssLAyZmZlYsWIFdHR0ULVqVURHR2PmzJno06cPAGD27Nnw9PTEiBEjAACTJk1CeHg45s2bh0WLFv2bXSQiIiIiIiIiojx8MWtKPXr0CHv27IG/v79S3ZQpU1CqVCm4uroiODgYb968keoiIiLQsGFD6OjoSGUeHh6IjY3F8+fPpTbu7u4KfXp4eCAiIqKQ9oaIiIiIiIiI6Ov2WWdKqWPVqlUwNDREu3btFMoHDRqEb775Bqampjh9+jQCAgLw8OFDzJw5EwCQmJgIe3t7hW0sLS2lOhMTEyQmJkpl77ZJTEzMN56MjAxkZGRI71NSUv7V/hERERERERERfU2+mKTUihUr0LVrV+jq6iqUDxs2TPrZ2dkZOjo6+OmnnzB58mTI5fJCi2fy5MkICgoqtP6JiIiIiIiIiIqzL+L2vZMnTyI2Nha9evX6aNvatWvjzZs3uHPnDoC361I9evRIoU3u+9x1qPJrk986VQAQEBCA5ORk6fXPP/+os0tERERERERERF+1LyIptXz5cri5uaFGjRofbRsdHQ0NDQ1YWFgAAOrWrYsTJ04gKytLahMeHo7KlSvDxMREanP48GGFfsLDw1G3bt18x5HL5TAyMlJ4ERERERERERGRaj5rUio1NRXR0dGIjo4GAMTHxyM6Ohr37t2T2qSkpGDTpk15zpKKiIjArFmzcPHiRdy+fRthYWEYOnQofvzxRynh1KVLF+jo6MDf3x9XrlzBhg0bMHv2bIXb/gYPHoz9+/djxowZuH79OgIDAxEZGYkBAwYU7gEgIiIiIiIiIvpKfdY1pSIjI9GkSRPpfW6iyNfXFytXrgQArF+/HkIIdO7cWWl7uVyO9evXIzAwEBkZGbC3t8fQoUMVEk7GxsY4ePAg+vfvDzc3N5iZmWH8+PHo06eP1KZevXpYu3Ytxo0bhzFjxqBixYrYvn07qlWrVkh7TkRERERERET0dZMJIcTnDqI4SElJgbGxMZKTk3krHxFRAbIbvedzh0CF4M6Ulp87BCIiIiIqJKrmSL6INaWIiIiIiIiIiKh4YVKKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBEVCydOnEDr1q1hY2MDmUyG7du3K9T36NEDMplM4eXp6anQ5vz582jWrBlKliyJUqVKoU+fPkhNTZXqV65cqdRH7uvx48cAgIcPH6JLly6oVKkSNDQ0MGTIEJXiHzRoENzc3CCXy+Hi4pJnGyEEpk+fjkqVKkEul6N06dL4/fffVT5GRFR0/ivXpHf99ddf0NLSyvcak+v169fo0aMHqlevDi0tLXh7e+fZLiMjA2PHjoWtrS3kcjns7OywYsUKlY4PEREREcCkFBEVE2lpaahRowbmz5+fbxtPT088fPhQeq1bt06qe/DgAdzd3VGhQgWcOXMG+/fvx5UrV9CjRw+pTadOnRS2f/jwITw8PNCoUSNYWFgAePslzdzcHOPGjUONGjXU2gc/Pz906tQp3/rBgwdj2bJlmD59Oq5fv46dO3fi22+/VWsMIioa/5VrUq4XL16ge/fuaNq06Udjz87Ohp6eHgYNGgR3d/d823Xs2BGHDx/G8uXLERsbi3Xr1qFy5cof7Z+IiIgol9bnDoCIqCB4eXnBy8vrg23kcjmsrKzyrNu9eze0tbUxf/58aGi8zdcvWrQIzs7OiIuLQ4UKFaCnpwc9PT1pmydPnuDIkSNYvny5VGZnZ4fZs2cDgFozBubMmSP1GRMTo1R/7do1LFy4EJcvX5a+9Nnb26vcPxEVrf/KNSlX37590aVLF2hqairN2nqfvr4+Fi5cCODt7KoXL14otdm/fz+OHz+O27dvw9TUFMDb6x8RERGROjhTioi+GseOHYOFhQUqV66Mn3/+Gc+ePZPqMjIyoKOjI335AyB92Tt16lSe/a1evRolSpTADz/8ULiBA9i1axccHBywe/du2Nvbw87ODr169UJSUlKhj01EhaOorkmhoaG4ffs2JkyYUGCx79y5EzVr1sS0adNQunRpVKpUCb/88gvS09MLbAwiIiIq/piUIqKvgqenJ1avXo3Dhw9j6tSpOH78OLy8vJCdnQ0A+P7775GYmIjg4GBkZmbi+fPnGD16NIC360TlZfny5ejSpYvCTIXCcvv2bdy9exebNm3C6tWrsXLlSkRFRRVJQoyICl5RXZNu3ryJ0aNHY82aNdDSKrgJ8rdv38apU6dw+fJlbNu2DbNmzcLmzZvRr1+/AhuDiIiIij/evkdEXwUfHx/p5+rVq8PZ2Rnly5fHsWPH0LRpU1StWhWrVq3CsGHDEBAQAE1NTQwaNAiWlpYKMxVyRURE4Nq1a/jzzz+LJP6cnBxkZGRg9erVqFSpEoC3X0Dd3NwQGxvLdVyIvjBFcU3Kzs5Gly5dEBQUJF03CkpOTg5kMhnCwsJgbGwMAJg5cyZ++OEHLFiwoEiS9URERPTl40wpIvoqOTg4wMzMDHFxcVJZly5dkJiYiISEBDx79gyBgYF48uQJHBwclLZftmwZXFxc4ObmViTxWltbQ0tLS+GLpZOTEwDg3r17RRIDERWewrgmvXz5EpGRkRgwYAC0tLSgpaWFiRMn4uLFi9DS0sKRI0c+OV5ra2uULl1aSkgBb69JQgjcv3//k/slIiKirwuTUkT0Vbp//z6ePXsGa2trpTpLS0sYGBhgw4YN0NXVRbNmzRTqU1NTsXHjRvj7+xdVuKhfvz7evHmDW7duSWU3btwAANja2hZZHERUOArjmmRkZIRLly4hOjpaevXt2xeVK1dGdHQ0ateu/cnx1q9fHw8ePEBqaqpUduPGDWhoaKBMmTKf3C8RERF9XXj7HhEVC6mpqQozDOLj4xEdHQ1TU1OYmpoiKCgI7du3h5WVFW7duoWRI0eiQoUK8PDwkLaZN28e6tWrBwMDA4SHh2PEiBGYMmUKSpYsqTDWhg0b8ObNG/z44495xhIdHS3F9OTJE0RHR0NHRwdVqlQBAGzbtg0BAQG4fv26tE1cXBxSU1ORmJiI9PR0qY8qVapAR0cH7u7u+Oabb+Dn54dZs2YhJycH/fv3R7NmzQr8thwi+vf+C9ckDQ0NVKtWTaHMwsICurq6CuXz5s3Dtm3bcPjwYans6tWryMzMRFJSEl6+fCldk1xcXAC8ncU1adIk9OzZE0FBQXj69ClGjBgBPz8/3rpHREREKmNSioiKhcjISDRp0kR6P2zYMACAr68vFi5ciJiYGKxatQovXryAjY0NmjdvjkmTJkEul0vbnD17FhMmTEBqaiocHR2xePFidOvWTWms5cuXo127dkpfDHO5urpKP0dFRWHt2rWwtbXFnTt3AADJycmIjY1V2KZXr144fvy4Uh/x8fGws7ODhoYGdu3ahYEDB6Jhw4bQ19eHl5cXZsyYod6BIqIi8V+6Jn3M06dPFWZhAkCLFi1w9+5d6X3uNUkIAQBSomzgwIGoWbMmSpUqhY4dO+K33377pBiIiIjo6yQTuf93Qf9KSkoKjI2NkZycDCMjo88dDhFRsWE3es/nDoEKwZ0pLT93CERERERUSFTNkXBNKSIiIiIiIiIiKnJMShERERERERERUZHjmlJEVGh421Xxw1uu6EvGa1Lxw2sSERHRl40zpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5JiUIiIiIiIiIiKiIsekFBERERERERERFTkmpYiIiIiIiIiIqMgxKUVEREREREREREWOSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERExdiJEyfQunVr2NjYQCaTYfv27fm27du3L2QyGWbNmqVUt2fPHtSuXRt6enowMTGBt7e3Qv2gQYPg5uYGuVwOFxcXpe0DAwMhk8mUXvr6+irtx7Nnz1CmTBnIZDK8ePFCKj916hTq16+PUqVKQU9PD46OjggJCVGpTyL6vD5rUupjF8cePXooXbA8PT0V2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3Vvg+0tERERERFTU0tLSUKNGDcyfP/+D7bZt24a///4bNjY2SnVbtmxBt27d0LNnT1y8eBF//fUXunTpotTOz88PnTp1yrP/X375BQ8fPlR4ValSBR06dFBpP/z9/eHs7KxUrq+vjwEDBuDEiRO4du0axo0bh3HjxmHJkiUq9UtEn4/W5xw89+Lo5+eHdu3a5dnG09MToaGh0nu5XK5Q37VrVzx8+BDh4eHIyspCz5490adPH6xduxYAkJKSgubNm8Pd3R2LFi3CpUuX4Ofnh5IlS6JPnz4AgNOnT6Nz586YPHkyWrVqhbVr18Lb2xvnz59HtWrVCmnviYiIiIiICp+Xlxe8vLw+2CYhIQEDBw7EgQMH0LJlS4W6N2/eYPDgwQgODoa/v79UXqVKFYV2c+bMAQA8efIEMTExSmMYGBjAwMBAen/x4kVcvXoVixYt+ug+LFy4EC9evMD48eOxb98+hTpXV1e4urpK7+3s7LB161acPHlS+s5HRP9N/yoplZGRoZQkUocqF0e5XA4rK6s8665du4b9+/fj3LlzqFmzJgBg7ty5aNGiBaZPnw4bGxuEhYUhMzMTK1asgI6ODqpWrYro6GjMnDlTukDNnj0bnp6eGDFiBABg0qRJCA8Px7x581S6QBIREREREX2pcnJy0K1bN4wYMQJVq1ZVqj9//jwSEhKgoaEBV1dXJCYmwsXFBcHBwf/qj/jLli1DpUqV0KBBgw+2u3r1KiZOnIgzZ87g9u3bH+33woULOH36NH777bdPjo2IioZat+/t27cPvr6+cHBwgLa2NkqUKAEjIyM0atQIv//+Ox48eFDgAR47dgwWFhaoXLkyfv75Zzx79kyqi4iIQMmSJaWEFAC4u7tDQ0MDZ86ckdo0bNgQOjo6UhsPDw/Exsbi+fPnUht3d3eFcT08PBAREZFvXBkZGUhJSVF4ERERERERfWmmTp0KLS0tDBo0KM/63ERQYGAgxo0bh927d8PExASNGzdGUlLSJ435+vVrhIWFKcy8yktGRgY6d+6M4OBglCtX7oNty5QpA7lcjpo1a6J///7o1avXJ8VGREVHpaTUtm3bUKlSJfj5+UFLSwujRo3C1q1bceDAASxbtgyNGjXCoUOH4ODggL59++LJkycFEpynpydWr16Nw4cPY+rUqTh+/Di8vLyQnZ0NAEhMTISFhYXCNlpaWjA1NUViYqLUxtLSUqFN7vuPtcmtz8vkyZNhbGwsvcqWLfvvdpaIiIiIiKiIRUVFYfbs2Vi5ciVkMlmebXJycgAAY8eORfv27eHm5obQ0FDIZDJs2rTpk8bdtm0bXr58CV9f3w+2CwgIgJOTE3788ceP9nny5ElERkZi0aJFmDVrFtatW/dJsRFR0VHp9r1p06YhJCQEXl5e0NBQzmN17NgRwNv7kOfOnYs1a9Zg6NCh/zo4Hx8f6efq1avD2dkZ5cuXx7Fjx9C0adN/3f+/ERAQgGHDhknvU1JSmJgiIiIiIqIvysmTJ/H48WOFWUjZ2dkYPnw4Zs2ahTt37sDa2hqA4hpScrkcDg4OuHfv3ieNu2zZMrRq1UppcsD7jhw5gkuXLmHz5s0AACEEAMDMzAxjx45FUFCQ1Nbe3h7A2++Ojx49QmBgIDp37vxJ8RFR0VApKfWh29jeVbp0aUyZMuVfBfQhDg4OMDMzQ1xcHJo2bQorKys8fvxYoc2bN2+QlJQkrUNlZWWFR48eKbTJff+xNvmtZQW8vQj/m/W0iIiIiIiIPrdu3brluZRJ7pP2AMDNzQ1yuRyxsbH47rvvAABZWVm4c+cObG1t1R4zPj4eR48exc6dOz/adsuWLUhPT5fenzt3Dn5+fjh58iTKly+f73Y5OTnIyMhQOzYiKlqf9el76rp//z6ePXsmZerr1q2LFy9eICoqCm5ubgDeZtJzcnJQu3Ztqc3YsWORlZUFbW1tAEB4eDgqV64MExMTqc3hw4cxZMgQaazw8HDUrVu3CPeOiIiIiIio4KWmpiIuLk56Hx8fj+joaJiamqJcuXIoVaqUQnttbW1YWVmhcuXKAAAjIyP07dsXEyZMQNmyZWFra4vg4GAAQIcOHaTt4uLikJqaisTERKSnpyM6OhrA2xlW767xu2LFClhbW+f50Ktt27YhICAA169fBwClxNPTp08BAE5OTihZsiQAYP78+ShXrhwcHR0BACdOnMD06dPzXSOLiP47Pikpdf/+fezcuRP37t1DZmamQt3MmTNV7udDF0dTU1MEBQWhffv2sLKywq1btzBy5EhUqFABHh4eAN5eiDw9PdG7d28sWrQIWVlZGDBgAHx8fGBjYwMA6NKlC4KCguDv749Ro0bh8uXLmD17NkJCQqRxBw8ejEaNGmHGjBlo2bIl1q9fj8jISCxZsuRTDg8REREREdF/RmRkJJo0aSK9z12GxNfXFytXrlSpj+DgYGhpaaFbt25IT09H7dq1ceTIEekP/QDQq1cvHD9+XHrv6uoK4O33PDs7OwBvZzCtXLkSPXr0gKamptI4ycnJiI2NVWv/cnJyEBAQgPj4eGhpaaF8+fKYOnUqfvrpJ7X6IaKiJxO5N+Wq6PDhw2jTpg0cHBxw/fp1VKtWDXfu3IEQAt988w2OHDmicl/Hjh1TuDjm8vX1xcKFC+Ht7Y0LFy7gxYsXsLGxQfPmzTFp0iSF+46TkpIwYMAA7Nq1CxoaGmjfvj3mzJkDAwMDqU1MTAz69++Pc+fOwczMDAMHDsSoUaMUxty0aRPGjRuHO3fuoGLFipg2bRpatGih8r6kpKTA2NgYycnJMDIyUnk7ouLMbvSezx0CFbA7U1oW+Zg8j4onnktUED7HeUREREQfp2qORO2ZUgEBAfjll18QFBQEQ0NDbNmyBRYWFujatSs8PT3V6qtx48b4UE7swIEDH+3D1NQUa9eu/WAbZ2dnnDx58oNtOnTooDD1lIiIiIiIiIiICo/yo/Q+4tq1a+jevTsAQEtLC+np6TAwMMDEiRMxderUAg+QiIiIiIiIiIiKH7VnSunr60vrSFlbW+PWrVuoWrUqgP9bdI6IiIiIiIjyx1uKiyfeVkykHrWTUnXq1MGpU6fg5OSEFi1aYPjw4bh06RK2bt2KOnXqFEaMRERERERERERUzKidlJo5cyZSU1MBAEFBQUhNTcWGDRtQsWJFtZ68R0REREREREREXy+1k1IODg7Sz/r6+li0aFGBBkRERERERERERMWf2kmpXJmZmXj8+DFycnIUysuVK/evgyIiIiIiIiIiouJN7aTUjRs34O/vj9OnTyuUCyEgk8mQnZ1dYMEREREREREREVHxpKHuBj179oSGhgZ2796NqKgonD9/HufPn8eFCxdw/vz5woiRirETJ06gdevWsLGxgUwmw/bt2xXqAwMD4ejoCH19fZiYmMDd3R1nzpxR6mfPnj2oXbs29PT0YGJiAm9vb4X6w4cPo169ejA0NISVlRVGjRqFN2/eKLQRQmD69OmoVKkS5HI5Spcujd9///2j+/Cxsc+dO4emTZuiZMmSMDExgYeHBy5evKjS8SEiIiIiIiIqrtSeKRUdHY2oqCg4OjoWRjz0lUlLS0ONGjXg5+eHdu3aKdVXqlQJ8+bNg4ODA9LT0xESEoLmzZsjLi4O5ubmAIAtW7agd+/e+OOPP/D999/jzZs3uHz5stTHxYsX0aJFC4wdOxarV69GQkIC+vbti+zsbEyfPl1qN3jwYBw8eBDTp09H9erVkZSUhKSkpA/G/7GxU1NT4enpiTZt2mDBggV48+YNJkyYAA8PD/zzzz/Q1tb+t4eQiIiIiIiI6IukdlKqSpUqePr0aWHEQl8hLy8veHl55VvfpUsXhfczZ87E8uXLERMTg6ZNm+LNmzcYPHgwgoOD4e/vL7WrUqWK9POGDRvg7OyM8ePHAwAqVKiAadOmoWPHjpgwYQIMDQ1x7do1LFy4EJcvX0blypUBAPb29h+MXZWxr1+/jqSkJEycOBFly5YFAEyYMAHOzs64e/cuKlSo8LFDRERERERERFQsqXT7XkpKivSaOnUqRo4ciWPHjuHZs2cKdSkpKYUdL33FMjMzsWTJEhgbG6NGjRoAgPPnzyMhIQEaGhpwdXWFtbU1vLy8FGYrZWRkQFdXV6EvPT09vH79GlFRUQCAXbt2wcHBAbt374a9vT3s7OzQq1evD86UUmXsypUro1SpUli+fDkyMzORnp6O5cuXw8nJCXZ2dgV4dIiIiIiIiIi+LColpXLXwjExMUGzZs3w999/o2nTprCwsJDKc9sQFbTdu3fDwMAAurq6CAkJQXh4OMzMzAAAt2/fBvB27alx48Zh9+7dMDExQePGjaWEkoeHB06fPo1169YhOzsbCQkJmDhxIgDg4cOHUj93797Fpk2bsHr1aqxcuRJRUVH44Ycf8o1LlbENDQ1x7NgxrFmzBnp6ejAwMMD+/fuxb98+aGl98sMviYiIiIiIiL54Kn0rPnr0aGHHQZSvJk2aIDo6Gk+fPsXSpUvRsWNHnDlzBhYWFsjJyQEAjB07Fu3btwcAhIaGokyZMti0aRN++uknNG/eHMHBwejbty+6desGuVyOX3/9FSdPnoSGxtu8bE5ODjIyMrB69WpUqlQJALB8+XK4ubkhNjZWuqXvXaqMnZ6eDn9/f9SvX19Kik2fPh0tW7bEuXPnoKenV+jHj4iIiIiIiOi/SKWkVKNGjbBixQq0bt1aWlyaqKjo6+ujQoUKqFChAurUqYOKFSti+fLlCAgIgLW1NQDFdZzkcjkcHBxw7949qWzYsGEYOnQoHj58CBMTE9y5cwcBAQFwcHAAAFhbW0NLS0tKSAGAk5MTAODevXt5JqVUGXvt2rW4c+cOIiIipATY2rVrYWJigh07dsDHx6dAjhERERERERHRl0al2/cAYM2aNShbtizq1auHqVOn4tq1a4UZF1G+cmc1AYCbmxvkcjliY2Ol+qysLNy5cwe2trYK28lkMtjY2EBPTw/r1q1D2bJl8c033wAA6tevjzdv3uDWrVtS+xs3bgCAUj+5VBn71atX0NDQgEwmk9rkvs+daUVERERERET0NVI5KXXkyBE8fPgQ/fr1Q1RUFGrXro2KFSti+PDhOHHiBL9g0ydJTU1FdHQ0oqOjAQDx8fGIjo7GvXv3kJaWhjFjxuDvv//G3bt3ERUVBT8/PyQkJKBDhw4AACMjI/Tt2xcTJkzAwYMHERsbi59//hkApDYAEBwcjEuXLuHKlSuYNGkSpkyZgjlz5kBTUxMA4O7ujm+++QZ+fn64cOECoqKi8NNPP6FZs2bS7KmzZ8/C0dERCQkJKo/drFkzPH/+HP3798e1a9dw5coV9OzZE1paWmjSpEnhH2AiIiIiIiKi/yiVk1IAYGJigh9//BEbN27E06dPMXfuXKSnp6Nr166wsLBA9+7dsXnzZqSlpRVWvFTMREZGwtXVFa6urgDe3mbn6uqK8ePHQ1NTE9evX0f79u1RqVIltG7dGs+ePcPJkydRtWpVqY/g4GD4+PigW7duqFWrFu7evYsjR44oLLy/b98+NGjQADVr1sSePXuwY8cOeHt7S/UaGhrYtWsXzMzM0LBhQ7Rs2RJOTk5Yv3691ObVq1eIjY1FVlaWymM7Ojpi165diImJQd26ddGgQQM8ePAA+/fvl27/IyIiIiIi+hKcOHECrVu3ho2NDWQyGbZv365QHxgYCEdHR+jr68PExATu7u44c+aMVH/nzh34+/vD3t4eenp6KF++PCZMmIDMzEypTWxsLJo0aQJLS0vo6urCwcEB48aNU/getnTpUjRo0EB68Jq7uzvOnj370fjnz58PJycn6OnpoXLlyli9erVSmxcvXqB///6wtraGXC5HpUqVsHfv3k84WqSKT378l46ODjw9PeHp6YkFCxYgMjISO3fuxKRJk3Dt2jX8+uuvBRknFVONGzeGECLf+q1bt360D21tbUyfPh3Tp0/Pt82RI0c+2o+NjQ22bNmSb31esaoydrNmzdCsWbOPjk9ERERERPRflpaWhho1asDPzw/t2rVTqq9UqRLmzZsHBwcHpKenIyQkBM2bN0dcXBzMzc1x/fp15OTkYPHixahQoQIuX76M3r17Iy0tTfpOpa2tje7du+Obb75ByZIlcfHiRfTu3Rs5OTn4448/AADHjh1D586dUa9ePejq6mLq1Klo3rw5rly5gtKlS+cZ+8KFCxEQEIClS5eiVq1aOHv2LHr37g0TExO0bt0aAJCZmYlmzZrBwsICmzdvRunSpXH37l2ULFmycA4oQSY+lBH4RFlZWdDW1i7obv/TUlJSYGxsjOTkZBgZGX3ucIj+E+xG7/ncIVABuzOlZZGPyfOoeOK5RAXhc5xHRAWF16Ti6Wu6LslkMmzbtk3hDpT35X5PPnToEJo2bZpnm+DgYCxcuBC3b9/Ot59hw4bh3LlzOHnyZJ712dnZMDExwbx589C9e/c829SrVw/169dHcHCwVDZ8+HCcOXMGp06dAgAsWrQIwcHBuH79+leX0yhoquZI1J4pNWzYsDzLZTIZdHV1UbFiRbRp0wampqbqdk1ERERERERExUBmZiaWLFkCY2Nj1KhRI992ycnJH8wfxMXFYf/+/XnOzMr16tUrZGVlfbCfjIwM6OrqKpTp6enh7Nmz0sSanTt3om7duujfvz927NgBc3NzdOnSBaNGjZLWI6aCpXZS6sKFCzh//jyys7NRuXJlAG+fUqapqQlHR0csWLAAw4YNw6lTp1ClSpUCD5gKH/9qU/x8TX+xISIiIiKiz2f37t3w8fHBq1evYG1tjfDwcJiZmeXZNi4uDnPnzs1zOZR69erh/PnzyMjIQJ8+fTBx4sR8xxw1ahRsbGzg7u6ebxsPDw8sW7YM3t7e+OabbxAVFYVly5YhKysLT58+hbW1NW7fvo0jR46ga9eu2Lt3L+Li4tCvXz9kZWVhwoQJ6h8M+ii1FjoHgLZt28Ld3R0PHjxAVFQUoqKicP/+fTRr1gydO3dGQkICGjZsiKFDhxZGvERERERERET0H9WkSRNER0fj9OnT8PT0RMeOHfH48WOldgkJCfD09ESHDh3Qu3dvpfoNGzbg/PnzWLt2Lfbs2ZPvOr5TpkzB+vXrsW3bNqWZUO/69ddf4eXlhTp16kBbWxtt27aFr68vgLcPvgKAnJwcWFhYYMmSJXBzc0OnTp0wduxYLFq06FMOBalA7aRUcHAwJk2apHBPoLGxMQIDAzFt2jSUKFEC48ePR1RUVIEGSkRERERERET/bfr6+qhQoQLq1KmD5cuXQ0tLC8uXL1do8+DBAzRp0gT16tXDkiVL8uynbNmyqFKlCjp37owpU6YgMDAQ2dnZCm2mT5+OKVOm4ODBg3B2dv5gXHp6elixYgVevXqFO3fu4N69e7Czs4OhoSHMzc0BANbW1qhUqZLCrXpOTk5ITExUeEIgFRy1k1LJycl5ZjmfPHmClJQUAEDJkiX5gRERERERERF95XJycpCRkSG9T0hIQOPGjeHm5obQ0FBpltLH+sjKykJOTo5UNm3aNEyaNAn79+9HzZo1VY5HW1sbZcqUgaamJtavX49WrVpJMdSvXx9xcXEK49y4cQPW1tbQ0dFReQxSndprSrVt2xZ+fn6YMWMGatWqBQA4d+4cfvnlF2nV/bNnz6JSpUoFGigRERERERERfT6pqamIi4uT3sfHxyM6OhqmpqYoVaoUfv/9d7Rp0wbW1tZ4+vQp5s+fj4SEBHTo0AHA/yWkbG1tMX36dDx58kTqy8rKCgAQFhYGbW1tVK9eHXK5HJGRkQgICECnTp2kJ+JNnToV48ePx9q1a2FnZ4fExEQAgIGBAQwMDAAAAQEBSEhIwOrVqwG8TS6dPXsWtWvXxvPnzzFz5kxcvnwZq1atkmL4+eefMW/ePAwePBgDBw7EzZs38ccff2DQoEGFeFS/bmonpRYvXoyhQ4fCx8cHb968eduJlhZ8fX0xc+ZMAICjoyOWLVtWsJESERERERER0WcTGRmJJk2aSO+HDRsGAPD19cWiRYtw/fp1rFq1Ck+fPkWpUqVQq1YtnDx5ElWrVgUAhIeHIy4uDnFxcShTpoxC30IIAG/zC1OnTsWNGzcghICtrS0GDBigsG71woULkZmZiR9++EGhjwkTJiAwMBAA8PDhQ9y7d0+qy87OxowZMxAbGwttbW00adIEp0+fhp2dndSmbNmyOHDgAIYOHQpnZ2eULl0agwcPxqhRo/79waM8yUTuJ6+m1NRU3L59GwDg4OAgZSO/VikpKTA2NkZycrLCeltfIj59r/j5XE/f47lU/HyOc4nnUfHEc4kKAp8uS18yXpOKJ16XiN5SNUei9ppSR48eBfB2WpyzszOcnZ2lhNT8+fM/MVwiIiIiIiIiIvqaqJ2UateuXZ5P1ps9ezYCAgIKJCgiIiIiIiIiIire1F5TKjg4GF5eXjhx4gQcHR0BADNmzMDEiROxZw+noBIREREREREVFd4KWvx8TbeBqp2U6tWrF5KSkuDu7o5Tp05hw4YN+OOPP7B3717Ur1+/MGIkIiIiIiIiIqJiRu2kFACMHDkSz549Q82aNZGdnY0DBw6gTp06BR0bEREREREREREVUyolpebMmaNUVrp0aZQoUQINGzbE2bNncfbsWQDAoEGDCjZCIiIiIiIiIiIqdlRKSoWEhORZrqmpib/++gt//fUXAEAmkzEpRUREREREREREH6VSUio+Pr6w4yAiIiIiIiIioq+IxucOgIiIiIiIiIiIvj4qJaWmTJmCV69eqdThmTNnsGcPH0lJRERERERERET5UykpdfXqVdja2qJfv37Yt28fnjx5ItW9efMGMTExWLBgAerVq4dOnTrB0NCw0AImIiIiIiIiIqIvn0prSq1evRoXL17EvHnz0KVLF6SkpEBTUxNyuVyaQeXq6opevXqhR48e0NXVLdSgiYiIiIiIiIjoy6ZSUgoAatSogaVLl2Lx4sWIiYnB3bt3kZ6eDjMzM7i4uMDMzKww4yQiIiIiIiIiomJE7YXONTQ04OLigrZt28LHxwfu7u6fnJA6ceIEWrduDRsbG8hkMmzfvl2qy8rKwqhRo1C9enXo6+vDxsYG3bt3x4MHDxT6sLOzg0wmU3hNmTJFoU1MTAwaNGgAXV1dlC1bFtOmTVOKZdOmTXB0dISuri6qV6+OvXv3ftI+ERERERERERHRx33Wp++lpaWhRo0amD9/vlLdq1evcP78efz66684f/48tm7ditjYWLRp00ap7cSJE/Hw4UPpNXDgQKkuJSUFzZs3h62tLaKiohAcHIzAwEAsWbJEanP69Gl07twZ/v7+uHDhAry9veHt7Y3Lly8Xzo4TEREREREREX3lVL59rzB4eXnBy8srzzpjY2OEh4crlM2bNw/ffvst7t27h3LlyknlhoaGsLKyyrOfsLAwZGZmYsWKFdDR0UHVqlURHR2NmTNnok+fPgCA2bNnw9PTEyNGjAAATJo0CeHh4Zg3bx4WLVpUELtKRERERERERETv+KwzpdSVnJwMmUyGkiVLKpRPmTIFpUqVgqurK4KDg/HmzRupLiIiAg0bNoSOjo5U5uHhgdjYWDx//lxq4+7urtCnh4cHIiIi8o0lIyMDKSkpCi8iIiIiIiIiIlLNZ50ppY7Xr19j1KhR6Ny5M4yMjKTyQYMG4ZtvvoGpqSlOnz6NgIAAPHz4EDNnzgQAJCYmwt7eXqEvS0tLqc7ExASJiYlS2bttEhMT841n8uTJCAoKKqjdIyIiIiIiIiL6qnwRSamsrCx07NgRQggsXLhQoW7YsGHSz87OztDR0cFPP/2EyZMnQy6XF1pMAQEBCmOnpKSgbNmyhTYeEREREREREVFxonZSKi0tDVOmTMHhw4fx+PFj5OTkKNTfvn27wIID/i8hdffuXRw5ckRhllReateujTdv3uDOnTuoXLkyrKys8OjRI4U2ue9z16HKr01+61QBgFwuL9SkFxERERERERFRcaZ2UqpXr144fvw4unXrBmtra8hkssKIC8D/JaRu3ryJo0ePolSpUh/dJjo6GhoaGrCwsAAA1K1bF2PHjkVWVha0tbUBAOHh4ahcuTJMTEykNocPH8aQIUOkfsLDw1G3bt2C3ykiIiIiIiIiIlI/KbVv3z7s2bMH9evX/9eDp6amIi4uTnofHx+P6OhomJqawtraGj/88APOnz+P3bt3Izs7W1rjydTUFDo6OoiIiMCZM2fQpEkTGBoaIiIiAkOHDsWPP/4oJZy6dOmCoKAg+Pv7Y9SoUbh8+TJmz56NkJAQadzBgwejUaNGmDFjBlq2bIn169cjMjISS5Ys+df7SEREREREREREytROSpmYmMDU1LRABo+MjESTJk2k97lrNPn6+iIwMBA7d+4EALi4uChsd/ToUTRu3BhyuRzr169HYGAgMjIyYG9vj6FDhyqs9WRsbIyDBw+if//+cHNzg5mZGcaPH48+ffpIberVq4e1a9di3LhxGDNmDCpWrIjt27ejWrVqBbKfRERERERERESkSO2k1KRJkzB+/HisWrUKJUqU+FeDN27cGEKIfOs/VAcA33zzDf7++++PjuPs7IyTJ09+sE2HDh3QoUOHj/ZFRERERERERET/ntpJqRkzZuDWrVuwtLSEnZ2dtE5TrvPnzxdYcEREREREREREVDypnZTy9vYuhDCIiIiIiIiIiOhroqHuBhMmTPjgi4iIiIiICsaJEyfQunVr2NjYQCaTYfv27Qr1W7duRfPmzVGqVCnIZDJER0fn25cQAl5eXnn2M2jQILi5uUEulyut5woAx44dQ9u2bWFtbQ19fX24uLggLCxMpX1YuXIlnJ2doaurCwsLC/Tv3z/PdnFxcTA0NETJkiVV6peIiL58aielckVFRWHNmjVYs2YNLly4UJAxERERERERgLS0NNSoUQPz58/Pt/67777D1KlTP9rXrFmzIJPJ8q338/NDp06d8qw7ffo0nJ2dsWXLFsTExKBnz57o3r07du/e/cExZ86cibFjx2L06NG4cuUKDh06BA8PD6V2WVlZ6Ny5Mxo0aPDR/SAiouJD7dv3Hj9+DB8fHxw7dkz6K8aLFy/QpEkTrF+/Hubm5gUdIxERERHRV8nLywteXl751nfr1g0AcOfOnQ/2Ex0djRkzZiAyMhLW1tZK9XPmzAEAPHnyBDExMUr1Y8aMUXg/ePBgHDx4EFu3bkWrVq3yHPP58+cYN24cdu3ahaZNm0rlzs7OSm3HjRsHR0dHNG3aFKdPn/7gvhARUfGh9kypgQMH4uXLl7hy5QqSkpKQlJSEy5cvIyUlBYMGDSqMGImIiIiI6BO9evUKXbp0wfz582FlZVVg/SYnJ8PU1DTf+vDwcOTk5CAhIQFOTk4oU6YMOnbsiH/++Ueh3ZEjR7Bp06Z8Z4MREVHxpXZSav/+/ViwYAGcnJyksipVqmD+/PnYt29fgQZHRERERET/ztChQ1GvXj20bdu2wPrcuHEjzp07h549e+bb5vbt28jJycEff/yBWbNmYfPmzUhKSkKzZs2QmZkJAHj27Bl69OiBlStXwsjIqMDiIyKiL4Pat+/l5ORAW1tbqVxbWxs5OTkFEhQREREREf17O3fuxJEjRwp0DdijR4+iZ8+eWLp0KapWrZpvu5ycHGRlZWHOnDlo3rw5AGDdunWwsrLC0aNH4eHhgd69e6NLly5o2LBhgcVHRERfDrVnSn3//fcYPHgwHjx4IJUlJCRg6NChCveKExERERHR53XkyBHcunULJUuWhJaWFrS03v5Nun379mjcuLHa/R0/fhytW7dGSEgIunfv/sG2uWtXValSRSozNzeHmZkZ7t27J8U3ffp0KTZ/f38kJydDS0sLK1asUDs+IiL6sqg9U2revHlo06YN7OzsULZsWQDAP//8g2rVqmHNmjUFHiAREREREX2a0aNHo1evXgpl1atXR0hICFq3bq1WX8eOHUOrVq0wdepU9OnT56Pt69evDwCIjY1FmTJlAABJSUl4+vQpbG1tAQARERHIzs6WttmxYwemTp2K06dPo3Tp0mrFR0REXx61k1Jly5bF+fPncejQIVy/fh0A4OTkBHd39wIPjoiIiIjoa5aamoq4uDjpfXx8PKKjo2Fqaopy5cohKSkJ9+7dk+5iiI2NBQBYWVkpvN5Xrlw52NvbS+/j4uKQmpqKxMREpKenIzo6GsDbWU46Ojo4evQoWrVqhcGDB6N9+/ZITEwEAOjo6EiLnW/btg0BAQHSd4RKlSqhbdu2GDx4MJYsWQIjIyMEBATA0dERTZo0AQCFdWoBIDIyEhoaGqhWrVpBHD4iIvqPUzspBQAymQzNmjVDs2bNCjoeIiIiIiL6/yIjI6UEDgAMGzYMAODr64uVK1di586dCouN+/j4AAAmTJiAwMBAlcfp1asXjh8/Lr13dXUF8DYJZmdnh1WrVuHVq1eYPHkyJk+eLLVr1KgRjh07BuDt0/hyk2K5Vq9ejaFDh6Jly5bQ0NBAo0aNsH///jzXqCUioq+PTAghPtZozpw56NOnD3R1dTFnzpwPth00aFCBBfclSUlJgbGxMZKTk7/4J4fYjd7zuUOgAnZnSsvPMi7PpeLnc5xLPI+KJ55LVBA+179vRAWB16Tiif++UUEoDv++qZojUWmmVEhICLp27QpdXV2EhITk204mk321SSkiIiIiIiIiIlKdSkmp+Pj4PH8mIiIiIiIiIiL6FGqvKTVx4kT88ssvKFGihEJ5eno6goODMX78+AILjoiIiIjov4a3yhQ/xeFWGSKiL5GGuhsEBQUhNTVVqfzVq1cICgoqkKCIiIiIiIiIiKh4UzspJYSATCZTKr948aL0OFgiIiIiIiIiIqIPUfn2PRMTE8hkMshkMlSqVEkhMZWdnY3U1FT07du3UIIkIiIiIiIiIqLiReWk1KxZsyCEgJ+fH4KCgmBsbCzV6ejowM7ODnXr1i2UIImIiIiIiIiIqHhROSnl6+sLALC3t0f9+vWhpaX2GulEREREREREREQAPmFNqbS0NBw+fFip/MCBA9i3b1+BBEVERERERERERMWb2kmp0aNHIzs7W6lcCIHRo0cXSFBERERERERERFS8qZ2UunnzJqpUqaJU7ujoiLi4uAIJioiIiIiIiIiIije1k1LGxsa4ffu2UnlcXBz09fULJCgiIiIiIiIiIire1E5KtW3bFkOGDMGtW7eksri4OAwfPhxt2rQp0OCIiIiIiIiIiKh4UjspNW3aNOjr68PR0RH29vawt7eHk5MTSpUqhenTpxdGjEREREREREREVMxoqbuBsbExTp8+jfDwcFy8eBF6enpwdnZGw4YNCyM+IiIiIiIiIiIqhtROSgGATCZD8+bN0bx584KOh4iIiIiIiIiIvgJqJ6UmTpz4wfrx48d/cjBERERERERERPR1UDsptW3bNoX3WVlZiI+Ph5aWFsqXL8+kFBERERERERERfZTaSakLFy4olaWkpKBHjx743//+VyBBERERERERERFR8ab20/fyYmRkhKCgIPz6668F0R0RERERERERERVzBZKUAoDk5GQkJycXVHdERERERERERFSMqX373pw5cxTeCyHw8OFD/Pnnn/Dy8iqwwIiIiIiIiIiIqPhSOykVEhKi8F5DQwPm5ubw9fVFQEBAgQVGRERERERERETFl9pJqfj4+MKIg4iIiIiIiIiIviIFtqYUERERERERERGRqlSaKdWuXTuVO9y6desnB0NERERERERERF8HlWZKGRsbSy8jIyMcPnwYkZGRUn1UVBQOHz4MY2NjtQY/ceIEWrduDRsbG8hkMmzfvl2hXgiB8ePHw9raGnp6enB3d8fNmzcV2iQlJaFr164wMjJCyZIl4e/vj9TUVIU2MTExaNCgAXR1dVG2bFlMmzZNKZZNmzbB0dERurq6qF69Ovbu3avWvhARERERERERkepUSkqFhoZKL0tLS3Ts2BHx8fHYunUrtm7ditu3b8PHxwdmZmZqDZ6WloYaNWpg/vz5edZPmzYNc+bMwaJFi3DmzBno6+vDw8MDr1+/ltp07doVV65cQXh4OHbv3o0TJ06gT58+Un1KSgqaN28OW1tbREVFITg4GIGBgViyZInU5vTp0+jcuTP8/f1x4cIFeHt7w9vbG5cvX1Zrf4iIiIiIiIiISDVqL3S+YsUKnDp1CpqamlKZpqYmhg0bhnr16iE4OFjlvry8vODl5ZVnnRACs2bNwrhx49C2bVsAwOrVq2FpaYnt27fDx8cH165dw/79+3Hu3DnUrFkTADB37ly0aNEC06dPh42NDcLCwpCZmYkVK1ZAR0cHVatWRXR0NGbOnCklr2bPng1PT0+MGDECADBp0iSEh4dj3rx5WLRokbqHiIiIiIiIiIiIPkLthc7fvHmD69evK5Vfv34dOTk5BRIU8PYpf4mJiXB3d5fKjI2NUbt2bURERAAAIiIiULJkSSkhBQDu7u7Q0NDAmTNnpDYNGzaEjo6O1MbDwwOxsbF4/vy51ObdcXLb5I5DREREREREREQFS+2ZUj179oS/vz9u3bqFb7/9FgBw5swZTJkyBT179iywwBITEwEAlpaWCuWWlpZSXWJiIiwsLBTqtbS0YGpqqtDG3t5eqY/cOhMTEyQmJn5wnLxkZGQgIyNDep+SkqLO7hERERERERERfdXUTkpNnz4dVlZWmDFjBh4+fAgAsLa2xogRIzB8+PACD/C/avLkyQgKCvrcYRARERERERERfZHUvn1PQ0MDI0eOREJCAl68eIEXL14gISEBI0eOVFhn6t+ysrICADx69Eih/NGjR1KdlZUVHj9+rFD/5s0bJCUlKbTJq493x8ivTW59XgICApCcnCy9/vnnH3V3kYiIiIiIiIjoq6V2UirXkydPEBMTg5iYGDx9+rQgYwIA2Nvbw8rKCocPH5bKUlJScObMGdStWxcAULduXbx48QJRUVFSmyNHjiAnJwe1a9eW2pw4cQJZWVlSm/DwcFSuXBkmJiZSm3fHyW2TO05e5HI5jIyMFF5ERERERERERKQatZNSaWlp8PPzg7W1NRo2bIiGDRvC2toa/v7+ePXqlVp9paamIjo6GtHR0QDeLm4eHR2Ne/fuQSaTYciQIfjtt9+wc+dOXLp0Cd27d4eNjQ28vb0BAE5OTvD09ETv3r1x9uxZ/PXXXxgwYAB8fHxgY2MDAOjSpQt0dHTg7++PK1euYMOGDZg9ezaGDRsmxTF48GDs378fM2bMwPXr1xEYGIjIyEgMGDBA3cNDREREREREREQqUDspNWzYMBw/fhy7du2Sbt/bsWMHjh8/rvaaUpGRkXB1dYWrq6vUt6urK8aPHw8AGDlyJAYOHIg+ffqgVq1aSE1Nxf79+6Grqyv1ERYWBkdHRzRt2hQtWrTAd999hyVLlkj1xsbGOHjwIOLj4+Hm5obhw4dj/Pjx6NOnj9SmXr16WLt2LZYsWYIaNWpg8+bN2L59O6pVq6bu4SEiIiIiIiIiIhWovdD5li1bsHnzZjRu3Fgqa9GiBfT09NCxY0csXLhQ5b4aN24MIUS+9TKZDBMnTsTEiRPzbWNqaoq1a9d+cBxnZ2ecPHnyg206dOiADh06fDhgIiIiIiIiIiIqEGrPlHr16hUsLS2Vyi0sLNS+fY+IiIiIiIiIiL5Oaiel6tatiwkTJuD169dSWXp6OoKCgj64MDgREREREREREVEutW/fmzVrFjw9PVGmTBnUqFEDAHDx4kXo6uriwIEDBR4gEREREREREREVP2onpapXr46bN28iLCwM169fBwB07twZXbt2hZ6eXoEHSERERERERERExY9aSamsrCw4Ojpi9+7d6N27d2HFRERERERERERExZxaa0ppa2srrCVFRERERERERET0KdRe6Lx///6YOnUq3rx5UxjxEBERERERERHRV0DtNaXOnTuHw4cP4+DBg6hevTr09fUV6rdu3VpgwRERERERERERUfGkdlKqZMmSaN++fWHEQkREREREREREXwm1k1KhoaGFEQcREREREREREX1FVF5TKicnB1OnTkX9+vVRq1YtjB49Gunp6YUZGxERERERERERFVMqJ6V+//13jBkzBgYGBihdujRmz56N/v37F2ZsRERERERERERUTKmclFq9ejUWLFiAAwcOYPv27di1axfCwsKQk5NTmPEREREREREREVExpHJS6t69e2jRooX03t3dHTKZDA8ePCiUwIiIiIiIiIiIqPhSOSn15s0b6OrqKpRpa2sjKyurwIMiIiIiIiIiIqLiTeWn7wkh0KNHD8jlcqns9evX6Nu3L/T19aWyrVu3FmyERERERERERERU7KiclPL19VUq+/HHHws0GCIiIiIiIiIi+jqonJQKDQ0tzDiIiIiIiIiIiOgrovKaUkRERERERERERAWFSSkiIiIiIiIiIipyTEoREREREREREVGRY1KKiIiIiIiIiIiKHJNSRERERERERERU5D4pKfXnn3+ifv36sLGxwd27dwEAs2bNwo4dOwo0OCIiIiIiIiIiKp7UTkotXLgQw4YNQ4sWLfDixQtkZ2cDAEqWLIlZs2YVdHxERERERERERFQMqZ2Umjt3LpYuXYqxY8dCU1NTKq9ZsyYuXbpUoMEREREREREREVHxpHZSKj4+Hq6urkrlcrkcaWlpBRIUEREREREREREVb2onpezt7REdHa1Uvn//fjg5ORVETEREREREREREVMxpqbvBsGHD0L9/f7x+/RpCCJw9exbr1q3D5MmTsWzZssKIkYiIiIiIiIiIihm1k1K9evWCnp4exo0bh1evXqFLly6wsbHB7Nmz4ePjUxgxEhERERERERFRMaN2UgoAunbtiq5du+LVq1dITU2FhYVFQcdFRERERERERETFmNprSv3222+Ij48HAJQoUYIJKSIiIiIiIiIiUpvaSalNmzahQoUKqFevHhYsWICnT58WRlxERERERERERFSMqZ2UunjxImJiYtC4cWNMnz4dNjY2aNmyJdauXYtXr14VRoxERERERERERFTMqJ2UAoCqVavijz/+wO3bt3H06FHY2dlhyJAhsLKyKuj4iIiIiIiIiIioGPqkpNS79PX1oaenBx0dHWRlZRVETEREREREREREVMx9UlIqPj4ev//+O6pWrYqaNWviwoULCAoKQmJiYkHHR0RERERERERExZCWuhvUqVMH586dg7OzM3r27InOnTujdOnShREbEREREREREREVU2o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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_olive_production_kg_ha.png\n" + ] + }, + { + "data": { + "image/png": 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ExOjWrVtmn1mzZmn37t06efKkvv76a7344ouKiIiwmrE0ZcoUHTx4UD///LOmTp2qXr16aeTIkcqePbuke7OugoKC9Morr+iHH37QmjVrNHToUIWHh5uX5O3du1fFihXTuXPnJN1bMH3ixIn64Ycf9Ouvv2ru3LmKiIjQyy+/rBw5cpj7PnbsmKKionTlyhXFxsYqKipKUVFRmf/iAQAAAMC/xMiRI/X888/Lw8ND3t7eat68uaKjo636xMTE6JVXXpGvr6/c3NxUrlw5LVmyxKrPzz//rGbNmil37tzy9PRU1apVtWnTJqs+GzZsUOXKlc07qA8cODDNN6kyDEMNGzaUxWLRt99+a7UtLRNdkPke+1K+jPD3u/d98MEHmj59unbv3q18+fJp5syZmjdvnmrXri3pXmARGBio3bt3q1KlSlq7dq2OHTum9evXy8fHR2XKlNF7772ngQMHavjw4XJyctKMGTMUEBCgjz76SJIUGBio7du3a8KECQoJCfnHj/lpN336dElSzZo1rdpnzZqljh07SpKio6M1ePBgXblyRQULFtRbb72liIgIq/579+7VO++8o+vXr6tYsWL65JNP9Morr5jb7e3tFRkZqZ49eyo4OFhubm4KCwvTiBEjzD43b95UdHS07ty5I+neOmrz58/X8OHDFR8fr4CAAEVERFitOyVJjRo10m+//WY+Llu2rKR7P/AAAAAA4GmwZcsWhYeH6/nnn9fdu3c1ZMgQ1a9fX8eOHZObm5skqUOHDrp69apWrFih3Llza968eWrdurX2799v/h3VuHFjPfvss9q4caNcXV01ceJENW7cWCdPnpSvr69++OEHNWrUSG+99Za+/PJLnTt3Tj169FBiYqLGjRv3yDonTpwoi8WSoj15oouvr6927typCxcuqEOHDnJ0dNSHH36YsS8WHspi/Ev+mk5MTNSiRYsUFhamQ4cOKSYmRnXq1NFff/1lzoKRJH9/f/Xp00cREREaNmyYVqxYYTVb5dSpUypUqJAOHjyosmXLqnr16ipXrpwmTpxo9pk1a5b69Olj3lnwUeLi4uTl5aXY2Fh5enpm0BED/30FB620dQnIJKdHhf7j++R8yro4n5BRbHEuAQDS5o8//pC3t7e2bNmi6tWrS5Lc3d01ffp0q0kEuXLl0ujRo9W1a1ddvnxZefLk0datW1WtWjVJ0rVr1+Tp6al169apbt26GjJkiNatW6d9+/aZY3z33Xdq3bq1Ll26JA8PjwfWFBUVpcaNG2v//v3y8/PTsmXL1Lx5c0nS999/r8aNG+v8+fPmHdxnzJihgQMH6o8//pCTk1NGv0RPlfTkKI99KV9CQoJ+//13nTlzxuorvQ4fPix3d3c5OzurR48eWrZsmYKCghQTEyMnJyerUEq6t6ZQTEyMpHvTApNPoPu3J297WJ+4uDiry8fuFx8fr7i4OKsvAAAAAACQuuSJHzlz5jTbKleurAULFujKlStKSkrS/Pnzdfv2bfMKmly5cqlo0aL68ssvdePGDd29e1effPKJvL29Vb58eUn3/j53cXGx2perq6tu376tAwcOPLCemzdv6qWXXtLUqVNTvQHWrl27VLJkSau8ICQkRHFxcebyQvhnpPtSvl9++UWdO3fWzp07rdoNw5DFYlFiYmK6xitatKiioqIUGxurxYsXKywsTFu2bElvWRlq5MiRevfdd21aAwAAAAAA/wVJSUnq06ePqlSpohIlSpjtCxcuVJs2bZQrVy45ODgoW7ZsWrZsmYoUKSJJslgsWr9+vZo3by4PDw/zxlOrV6821/kNCQnRxIkT9c0336h169aKiYkxl2i5cOHCA2uKiIhQ5cqV1axZs1S3p2WiC/4Z6Q6mOnbsKAcHB0VGRsrPzy/VazXTw8nJyTwpy5cvr3379mnSpElq06aNEhISdPXqVatZUxcvXjTTTl9fX+3du9dqvOS79t3f5+938rt48aI8PT3l6uqaak2DBw+2WlcoLi5O+fPnf6Lj/Dfh8oasicsbAAAAANhCeHi4jhw5ou3bt1u1v/3227p69arWr1+v3Llz69tvv1Xr1q21bds2lSxZUoZhKDw8XN7e3tq2bZtcXV31+eefq0mTJtq3b5/8/PxUv359jR07Vj169NArr7wiZ2dnvf3229q2bZvs7FK/CGzFihXauHGjDh069E8cPp5QuoOpqKgoHThwQMWKFcuMepSUlKT4+HiVL19ejo6O2rBhg1q2bCnp3qLYZ86cUXBwsCQpODhYH3zwgS5duiRvb29J0rp16+Tp6amgoCCzz6pVq6z2sW7dOnOM1Dg7O5t3bgMAAAAAAKnr1auXIiMjtXXrVuXLl89sP3nypKZMmaIjR46oePHikqTSpUtr27Ztmjp1qmbMmKGNGzcqMjJSf/31l7kO0bRp07Ru3TrNmTNHgwYNkiT17dtXERERunDhgnLkyKHTp09r8ODBKlSoUKo1bdy4USdPnkyxNFDLli1VrVo1bd68OU0TXfDPSHcwFRQUpMuXL2fIzgcPHqyGDRuqQIECunbtmubNm6fNmzdrzZo18vLyUpcuXdS3b1/lzJlTnp6eev311xUcHKxKlSpJkurXr6+goCC98sorGjNmjGJiYjR06FCFh4ebwVKPHj00ZcoUDRgwQJ07d9bGjRu1cOFCrVzJrCEAAAAAAB6HYRh6/fXXtWzZMm3evFkBAQFW22/evClJKWY12dvbKykp6aF97OzszD7JLBaL8ubNK0n65ptvlD9/fpUrVy7V2gYNGqSuXbtatZUsWVITJkxQkyZNJKVtogv+GekOpkaPHq0BAwboww8/VMmSJeXo6Gi1PT13rbt06ZI6dOigCxcuyMvLS6VKldKaNWtUr149SdKECRNkZ2enli1bKj4+XiEhIZo2bZr5fHt7e0VGRqpnz54KDg6Wm5ubwsLCzOtNJSkgIEArV65URESEJk2apHz58unzzz9XSEhIeg8dAAAAAADo3uV78+bN0/Lly+Xh4WGuy+Tl5SVXV1cVK1ZMRYoUUffu3TVu3DjlypVL3377rdatW6fIyEhJ98KhHDlyKCwsTMOGDZOrq6s+++wznTp1SqGh/7dUydixY9WgQQPZ2dlp6dKlGjVqlBYuXCh7e3tJ0rlz51SnTh19+eWXeuGFF+Tr65vqrKcCBQqYAVpaJrrgn5HuYKpu3bqSpDp16li1P87i5zNnznzodhcXF02dOlVTp059YB9/f/8Ul+r9Xc2aNbm2FAAAAACADDJ9+nRJMu+wl2zWrFnq2LGjHB0dtWrVKg0aNEhNmjTR9evXVaRIEc2ZM0eNGjWSJOXOnVurV6/WW2+9pdq1a+vOnTsqXry4li9frtKlS5tjfv/99/rggw8UHx+v0qVLa/ny5WrYsKG5/c6dO4qOjjZnYKVFWia64J+R7mBq06ZNmVEHAAAAAAD4jzAM45F9nn32WS1ZsuShfSpUqKA1a9Y8tM/GjRsfur1gwYKPrCe17WmZ6ILMl+5gqkaNGplRBwAAAAAAAJ4y6Q6mJOnq1auaOXOmjh8/LkkqXry4OnfuLC8vrwwtDgAAAAAAAFlXuoOp/fv3KyQkRK6urnrhhRckSePHj9cHH3ygtWvXPnBVfAAAAAAAkDEKDuJO81nV6VGhj+6UhaQ7mIqIiFDTpk312WefycHh3tPv3r2rrl27qk+fPtq6dWuGFwkAAAAAAICs57FmTN0fSkmSg4ODBgwYoAoVKmRocQAAAAAAAMi67NL7BE9PT505cyZF+9mzZ+Xh4ZEhRQEAAAAAACDrS3cw1aZNG3Xp0kULFizQ2bNndfbsWc2fP19du3ZVu3btMqNGAAAAAAAAZEHpvpRv3Lhxslgs6tChg+7evStJcnR0VM+ePTVq1KgMLxAAAAAAAABZU7qDKScnJ02aNEkjR47UyZMnJUmFCxdWtmzZMrw4AAAAAAAAZF3pDqaSZcuWTSVLlszIWgAAAAAAAPAUSdMaUy1atFBcXJz5/cO+AAAAAAD/PiNHjtTzzz8vDw8PeXt7q3nz5oqOjk7Rb9euXapdu7bc3Nzk6emp6tWr69atW+b2pk2bqkCBAnJxcZGfn59eeeUVnT9/3tweHR2tWrVqycfHRy4uLipUqJCGDh2qO3fuPLS+3r17q3z58nJ2dlaZMmVSbH/ccQH8u6VpxpSXl5csFouke3flS/4eAAAAAPDfsGXLFoWHh+v555/X3bt3NWTIENWvX1/Hjh2Tm5ubpHuhVIMGDTR48GBNnjxZDg4O+uGHH2Rn939zGmrVqqUhQ4bIz89P586dU79+/dSqVSvt3LlT0r01iDt06KBy5cope/bs+uGHH9StWzclJSXpww8/fGiNnTt31p49e/Tjjz+m2PYk4wL490pTMDVr1izz+9mzZ2dWLQAAAACATLJ69Wqrx7Nnz5a3t7cOHDig6tWrS5IiIiLUu3dvDRo0yOxXtGhRq+dFRESY3/v7+2vQoEFq3ry57ty5I0dHRxUqVEiFChWy6rN582Zt27btofV9/PHHkqQ//vgj1WDqcccF8O+Wpkv57le7dm1dvXo1RXtcXJxq166dETUBAAAAADJZbGysJClnzpySpEuXLmnPnj3y9vZW5cqV5ePjoxo1amj79u0PHOPKlSuaO3euKleuLEdHx1T7nDhxQqtXr1aNGjUytP7MGhfAPyvdwdTmzZuVkJCQov327dsk1QAAAADwH5CUlKQ+ffqoSpUqKlGihCTp119/lSQNHz5c3bp10+rVq1WuXDnVqVNHv/zyi9XzBw4cKDc3N+XKlUtnzpzR8uXLU+yjcuXKcnFx0bPPPqtq1appxIgRGVJ7Zo0LwDbSHEz9+OOP5nTKY8eOmY9//PFHHTp0SDNnztQzzzyTaYUCAAAAADJGeHi4jhw5ovnz55ttSUlJkqTu3burU6dOKlu2rCZMmKCiRYvqiy++sHp+//79dejQIa1du1b29vbq0KGDDMOw6rNgwQIdPHhQ8+bN08q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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-06_10-36_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-06_10-36_plots/water_need_vs_oil_production.png\n", + " Variety Technique Technique String \\\n", + "0 nocellara_delletna 3 tradizionale \n", + "1 nocellara_delletna 1 intensiva \n", + "2 nocellara_delletna 2 superintensiva \n", + "3 leccino 1 intensiva \n", + "4 leccino 2 superintensiva \n", + "5 leccino 3 tradizionale \n", + "6 frantoio 2 superintensiva \n", + "7 frantoio 3 tradizionale \n", + "8 frantoio 1 intensiva \n", + "9 coratina 1 intensiva \n", + "10 coratina 2 superintensiva \n", + "11 coratina 3 tradizionale \n", + "12 taggiasca 3 tradizionale \n", + "13 taggiasca 2 superintensiva \n", + "14 taggiasca 1 intensiva \n", + "15 pendolino 1 intensiva \n", + "16 pendolino 2 superintensiva \n", + "17 pendolino 3 tradizionale \n", + "18 moraiolo 2 superintensiva \n", + "19 moraiolo 1 intensiva \n", + "20 moraiolo 3 tradizionale \n", + "\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "0 9564.638687 2088.362004 \n", + "1 13699.079622 2991.183032 \n", + "2 17826.710664 3892.059753 \n", + "3 16432.379678 3229.053194 \n", + "4 20528.499013 4033.942398 \n", + "5 10937.982122 2149.449585 \n", + "6 24621.040119 6047.876212 \n", + "7 13740.739760 3375.103688 \n", + "8 20550.900635 5047.942655 \n", + "9 16429.706879 4215.265516 \n", + "10 19164.700743 4916.649709 \n", + "11 12318.510310 3160.037128 \n", + "12 6839.506230 1381.247995 \n", + "13 16433.741502 3319.210170 \n", + "14 10968.603159 2215.371493 \n", + "15 13705.431414 2468.678455 \n", + "16 19183.689269 3455.879324 \n", + "17 10960.549241 1974.357984 \n", + "18 17793.971752 3885.415851 \n", + "19 13144.222436 2870.020002 \n", + "20 8765.195655 1913.745255 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "0 32997.227891 0.218342 \n", + "1 33079.012125 0.218349 \n", + "2 33118.708645 0.218327 \n", + "3 25013.303736 0.196506 \n", + "4 24989.459147 0.196504 \n", + "5 24981.219100 0.196512 \n", + "6 28874.473543 0.245639 \n", + "7 29003.452741 0.245628 \n", + "8 28921.261327 0.245631 \n", + "9 38270.638622 0.256564 \n", + "10 38264.650562 0.256547 \n", + "11 38253.676395 0.256528 \n", + "12 26219.134374 0.201951 \n", + "13 26253.317778 0.201975 \n", + "14 26284.027794 0.201974 \n", + "15 26154.359691 0.180124 \n", + "16 26153.199618 0.180147 \n", + "17 26152.823801 0.180133 \n", + "18 32561.911109 0.218356 \n", + "19 32577.899255 0.218348 \n", + "20 32594.860153 0.218335 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "0 0.063289 \n", + "1 0.090425 \n", + "2 0.117518 \n", + "3 0.129093 \n", + "4 0.161426 \n", + "5 0.086043 \n", + "6 0.209454 \n", + "7 0.116369 \n", + "8 0.174541 \n", + "9 0.110144 \n", + "10 0.128491 \n", + "11 0.082607 \n", + "12 0.052681 \n", + "13 0.126430 \n", + "14 0.084286 \n", + "15 0.094389 \n", + "16 0.132140 \n", + "17 0.075493 \n", + "18 0.119324 \n", + "19 0.088097 \n", + "20 0.058713 \n", + "Comparison by Variety:\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "Variety \n", + "nocellara_delletna 13696.683690 2990.507461 \n", + "leccino 15971.162702 3138.439782 \n", + "frantoio 19648.631813 4826.360700 \n", + "coratina 15974.164423 4098.136472 \n", + "taggiasca 11412.636779 2305.011278 \n", + "pendolino 14617.432649 2633.129635 \n", + "moraiolo 13232.961913 2889.399172 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "Variety \n", + "nocellara_delletna 33064.983905 0.218338 \n", + "leccino 24994.676451 0.196507 \n", + "frantoio 28932.932409 0.245633 \n", + "coratina 38262.995517 0.256548 \n", + "taggiasca 26252.184893 0.201970 \n", + "pendolino 26153.461822 0.180136 \n", + "moraiolo 32578.228327 0.218349 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "Variety \n", + "nocellara_delletna 0.090443 \n", + "leccino 0.125564 \n", + "frantoio 0.166812 \n", + "coratina 0.107104 \n", + "taggiasca 0.087803 \n", + "pendolino 0.100680 \n", + "moraiolo 0.088691 \n", + "\n", + "Best Varieties by Water Efficiency:\n", + " Variety Avg Olive Production (kg/ha) \\\n", + "2 frantoio 19648.631813 \n", + "1 leccino 15971.162702 \n", + "3 coratina 15974.164423 \n", + "5 pendolino 14617.432649 \n", + "0 nocellara_delletna 13696.683690 \n", + "\n", + " Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "2 4826.360700 28932.932409 0.245633 \n", + "1 3138.439782 24994.676451 0.196507 \n", + "3 4098.136472 38262.995517 0.256548 \n", + "5 2633.129635 26153.461822 0.180136 \n", + "0 2990.507461 33064.983905 0.218338 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "2 0.166812 \n", + "1 0.125564 \n", + "3 0.107104 \n", + "5 0.100680 \n", + "0 0.090443 \n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "# Esecuzione dell'analisi\n", + "comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n", + "\n", + "# Genera i grafici\n", + "plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n", + "plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n", + "plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n", + "plot_efficiency_vs_production(comparison_data)\n", + "plot_water_efficiency_vs_production(comparison_data)\n", + "plot_water_need_vs_oil_production(comparison_data)\n", + "\n", + "# Analisi per tecnica\n", + "technique_data = analyze_by_technique(simulated_data, olive_varieties)\n", + "\n", + "print(technique_data)\n", + "\n", + "# Stampa un sommario statistico\n", + "print(\"Comparison by Variety:\")\n", + "print(comparison_data.set_index('Variety'))\n", + "print(\"\\nBest Varieties by Water Efficiency:\")\n", + "print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bbe87b415168368", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_transformer_data(df, olive_varieties_df):\n", + " # Crea una copia del DataFrame per evitare modifiche all'originale\n", + " df = df.copy()\n", + "\n", + " # Ordina per zona e anno\n", + " df = df.sort_values(['zone', 'year'])\n", + "\n", + " # Definisci le feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha'] # Feature statiche base\n", + " target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Ottieni le varietà pulite\n", + " all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " # Crea la struttura delle feature per ogni varietà\n", + " variety_features = [\n", + " 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n", + " 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n", + " 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n", + " ]\n", + "\n", + " # Prepara dizionari per le nuove colonne\n", + " new_columns = {}\n", + "\n", + " # Prepara le feature per ogni varietà\n", + " for variety in varieties:\n", + " # Feature esistenti\n", + " for feature in variety_features:\n", + " col_name = f\"{variety}_{feature}\"\n", + " if col_name in df.columns:\n", + " if feature != 'tech': # Non includere la colonna tech direttamente\n", + " static_features.append(col_name)\n", + "\n", + " # Feature binarie per le tecniche di coltivazione\n", + " for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n", + " col_name = f\"{variety}_{technique}\"\n", + " new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n", + " df[f\"{variety}_tech\"].str.lower() == technique\n", + " ).fillna(False)\n", + " static_features.append(col_name)\n", + "\n", + " # Aggiungi tutte le nuove colonne in una volta sola\n", + " new_df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n", + "\n", + " # Ordiniamo per zona e anno per mantenere la continuità temporale\n", + " df_sorted = new_df.sort_values(['zone', 'year'])\n", + "\n", + " # Definiamo la dimensione della finestra temporale\n", + " window_size = 41\n", + "\n", + " # Liste per raccogliere i dati\n", + " temporal_sequences = []\n", + " static_features_list = []\n", + " targets_list = []\n", + "\n", + " # Iteriamo per ogni zona\n", + " for zone in df_sorted['zone'].unique():\n", + " zone_data = df_sorted[df_sorted['zone'] == zone].reset_index(drop=True)\n", + "\n", + " if len(zone_data) >= window_size: # Verifichiamo che ci siano abbastanza dati\n", + " # Creiamo sequenze temporali scorrevoli\n", + " for i in range(len(zone_data) - window_size + 1):\n", + " # Sequenza temporale\n", + " temporal_window = zone_data.iloc[i:i + window_size][temporal_features].values\n", + " # Verifichiamo che non ci siano valori NaN\n", + " if not np.isnan(temporal_window).any():\n", + " temporal_sequences.append(temporal_window)\n", + "\n", + " # Feature statiche (prendiamo quelle dell'ultimo timestep della finestra)\n", + " static_features_list.append(zone_data.iloc[i + window_size - 1][static_features].values)\n", + "\n", + " # Target (prendiamo quelli dell'ultimo timestep della finestra)\n", + " targets_list.append(zone_data.iloc[i + window_size - 1][target_features].values)\n", + "\n", + " # Convertiamo in array numpy\n", + " X_temporal = np.array(temporal_sequences)\n", + " X_static = np.array(static_features_list)\n", + " y = np.array(targets_list)\n", + "\n", + " print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n", + "\n", + " # Split dei dati (usando indici casuali per una migliore distribuzione)\n", + " indices = np.random.permutation(len(X_temporal))\n", + "\n", + " #train_idx = int(len(indices) * 0.7) # 70% training\n", + " #val_idx = int(len(indices) * 0.85) # 15% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_idx = int(len(indices) * 0.65) # 65% training\n", + " val_idx = int(len(indices) * 0.85) # 20% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " #train_idx = int(len(indices) * 0.60) # 60% training\n", + " #val_idx = int(len(indices) * 0.85) # 25% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_indices = indices[:train_idx]\n", + " val_indices = indices[train_idx:val_idx]\n", + " test_indices = indices[val_idx:]\n", + "\n", + " # Split dei dati\n", + " X_temporal_train = X_temporal[train_indices]\n", + " X_temporal_val = X_temporal[val_indices]\n", + " X_temporal_test = X_temporal[test_indices]\n", + "\n", + " X_static_train = X_static[train_indices]\n", + " X_static_val = X_static[val_indices]\n", + " X_static_test = X_static[test_indices]\n", + "\n", + " y_train = y[train_indices]\n", + " y_val = y[val_indices]\n", + " y_test = y[test_indices]\n", + "\n", + " # Standardizzazione\n", + " scaler_temporal = StandardScaler()\n", + " scaler_static = StandardScaler()\n", + " scaler_y = StandardScaler()\n", + "\n", + " # Standardizzazione dei dati temporali\n", + " X_temporal_train = scaler_temporal.fit_transform(X_temporal_train.reshape(-1, len(temporal_features))).reshape(X_temporal_train.shape)\n", + " X_temporal_val = scaler_temporal.transform(X_temporal_val.reshape(-1, len(temporal_features))).reshape(X_temporal_val.shape)\n", + " X_temporal_test = scaler_temporal.transform(X_temporal_test.reshape(-1, len(temporal_features))).reshape(X_temporal_test.shape)\n", + "\n", + " # Standardizzazione dei dati statici\n", + " X_static_train = scaler_static.fit_transform(X_static_train)\n", + " X_static_val = scaler_static.transform(X_static_val)\n", + " X_static_test = scaler_static.transform(X_static_test)\n", + "\n", + " # Standardizzazione dei target\n", + " y_train = scaler_y.fit_transform(y_train)\n", + " y_val = scaler_y.transform(y_val)\n", + " y_test = scaler_y.transform(y_test)\n", + "\n", + " print(\"\\nShape dopo lo split e standardizzazione:\")\n", + " print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n", + " print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n", + " print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n", + "\n", + " # Prepara i dizionari di input\n", + " train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n", + " val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n", + " test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n", + "\n", + " joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n", + " joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n", + " joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n", + "\n", + " return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9c4d5f0f3fafdc2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset completo - Temporal: (3920000, 41, 3), Static: (3920000, 113), Target: (3920000, 5)\n", + "\n", + "Shape dopo lo split e standardizzazione:\n", + "Train - Temporal: (2548000, 41, 3), Static: (2548000, 113), Target: (2548000, 5)\n", + "Val - Temporal: (784000, 41, 3), Static: (784000, 113), Target: (784000, 5)\n", + "Test - Temporal: (588000, 41, 3), Static: (588000, 113), Target: (588000, 5)\n", + "Temporal data shape: (2548000, 41, 3)\n", + "Static data shape: (2548000, 113)\n", + "Target shape: (2548000, 5)\n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "\n", + "(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n", + "\n", + "scaler_temporal, scaler_static, scaler_y = scalers\n", + "\n", + "print(\"Temporal data shape:\", train_data['temporal'].shape)\n", + "print(\"Static data shape:\", train_data['static'].shape)\n", + "print(\"Target shape:\", train_targets.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "604c952c7195f40c", + "metadata": {}, + "outputs": [], + "source": [ + "@keras.saving.register_keras_serializable()\n", + "class DataAugmentation(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n", + "\n", + " def __init__(self, noise_stddev=0.03, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.noise_stddev = noise_stddev\n", + "\n", + " def call(self, inputs, training=None):\n", + " if training:\n", + " return inputs + tf.random.normal(\n", + " shape=tf.shape(inputs),\n", + " mean=0.0,\n", + " stddev=self.noise_stddev\n", + " )\n", + " return inputs\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"noise_stddev\": self.noise_stddev})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class PositionalEncoding(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'encoding posizionale\"\"\"\n", + "\n", + " def __init__(self, d_model, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.d_model = d_model\n", + "\n", + " def build(self, input_shape):\n", + " _, seq_length, _ = input_shape\n", + "\n", + " # Crea la matrice di encoding posizionale\n", + " position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n", + " div_term = tf.exp(\n", + " tf.range(0, self.d_model, 2, dtype=tf.float32) *\n", + " (-tf.math.log(10000.0) / self.d_model)\n", + " )\n", + "\n", + " # Calcola sin e cos\n", + " pos_encoding = tf.zeros((1, seq_length, self.d_model))\n", + " pos_encoding_even = tf.sin(position * div_term)\n", + " pos_encoding_odd = tf.cos(position * div_term)\n", + "\n", + " # Assegna i valori alle posizioni pari e dispari\n", + " pos_encoding = tf.concat(\n", + " [tf.expand_dims(pos_encoding_even, -1),\n", + " tf.expand_dims(pos_encoding_odd, -1)],\n", + " axis=-1\n", + " )\n", + " pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n", + " pos_encoding = pos_encoding[:, :, :self.d_model]\n", + "\n", + " # Salva l'encoding come peso non trainabile\n", + " self.pos_encoding = self.add_weight(\n", + " shape=(1, seq_length, self.d_model),\n", + " initializer=tf.keras.initializers.Constant(pos_encoding),\n", + " trainable=False,\n", + " name='positional_encoding'\n", + " )\n", + "\n", + " super().build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " # Broadcast l'encoding posizionale sul batch\n", + " batch_size = tf.shape(inputs)[0]\n", + " pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n", + " return inputs + pos_encoding_tiled\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"d_model\": self.d_model})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n", + " warmup_lr = self.initial_learning_rate * warmup_pct\n", + " decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n", + " decayed_lr = self.initial_learning_rate * decay_factor\n", + " return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'initial_learning_rate': self.initial_learning_rate,\n", + " 'warmup_steps': self.warmup_steps,\n", + " 'decay_steps': self.decay_steps\n", + " }\n", + "\n", + "\n", + "def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n", + " d_model=128, num_heads=8, ff_dim=256,\n", + " num_transformer_blocks=4, mlp_units=None,\n", + " dropout=0.2):\n", + " \"\"\"\n", + " Crea un transformer per la predizione della produzione di olio d'oliva.\n", + " \"\"\"\n", + " # Input layers\n", + " if mlp_units is None:\n", + " mlp_units = [256, 128, 64]\n", + "\n", + " temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n", + " static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n", + "\n", + " # === TEMPORAL PATH ===\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n", + " x = DataAugmentation()(x)\n", + "\n", + " # Temporal projection\n", + " x = tf.keras.layers.Dense(\n", + " d_model // 2,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + " x = tf.keras.layers.Dense(\n", + " d_model,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Positional encoding\n", + " x = PositionalEncoding(d_model)(x)\n", + "\n", + " # Transformer blocks\n", + " skip_connection = x\n", + " for _ in range(num_transformer_blocks):\n", + " # Self-attention\n", + " attention_output = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // num_heads,\n", + " value_dim=d_model // num_heads\n", + " )(x, x)\n", + " attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n", + "\n", + " # Residual connection con pesi addestrabili\n", + " residual_weights = tf.keras.layers.Dense(d_model, activation='sigmoid')(x)\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.3)([x, residual_weights * attention_output])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Feed-forward network\n", + " ffn = tf.keras.layers.Dense(ff_dim, activation=\"swish\")(x)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + " ffn = tf.keras.layers.Dense(d_model)(ffn)\n", + " ffn = tf.keras.layers.Dropout(dropout)(ffn)\n", + "\n", + " # Second residual connection\n", + " x = tfa.layers.StochasticDepth()([x, ffn])\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Add final skip connection\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.5)([x, skip_connection])\n", + "\n", + " # Temporal pooling\n", + " attention_pooled = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // 4\n", + " )(x, x)\n", + " attention_pooled = tf.keras.layers.GlobalAveragePooling1D()(attention_pooled)\n", + "\n", + " # Additional pooling operations\n", + " avg_pooled = tf.keras.layers.GlobalAveragePooling1D()(x)\n", + " max_pooled = tf.keras.layers.GlobalMaxPooling1D()(x)\n", + "\n", + " # Combine pooling results\n", + " temporal_features = tf.keras.layers.Concatenate()(\n", + " [attention_pooled, avg_pooled, max_pooled]\n", + " )\n", + "\n", + " # === STATIC PATH ===\n", + " static_features = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n", + " for units in [256, 128, 64]:\n", + " static_features = tf.keras.layers.Dense(\n", + " units,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(static_features)\n", + " static_features = tf.keras.layers.Dropout(dropout)(static_features)\n", + "\n", + " # === FEATURE FUSION ===\n", + " combined = tf.keras.layers.Concatenate()([temporal_features, static_features])\n", + "\n", + " # === MLP HEAD ===\n", + " x = combined\n", + " for units in mlp_units:\n", + " x = tf.keras.layers.BatchNormalization()(x)\n", + " x = tf.keras.layers.Dense(\n", + " units,\n", + " activation=\"swish\",\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + "\n", + " # Output layer\n", + " outputs = tf.keras.layers.Dense(\n", + " num_outputs,\n", + " activation='linear',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " # Create model\n", + " model = tf.keras.Model(\n", + " inputs={'temporal': temporal_input, 'static': static_input},\n", + " outputs=outputs,\n", + " name='OilTransformer'\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def create_transformer_callbacks(target_names, val_data, val_targets):\n", + " \"\"\"\n", + " Crea i callbacks per il training del modello.\n", + " \n", + " Parameters:\n", + " -----------\n", + " target_names : list\n", + " Lista dei nomi dei target per il monitoraggio specifico\n", + " val_data : dict\n", + " Dati di validazione\n", + " val_targets : array\n", + " Target di validazione\n", + " \n", + " Returns:\n", + " --------\n", + " list\n", + " Lista dei callbacks configurati\n", + " \"\"\"\n", + "\n", + " # Custom Metric per target specifici\n", + " class TargetSpecificMetric(tf.keras.callbacks.Callback):\n", + " def __init__(self, validation_data, target_names):\n", + " super().__init__()\n", + " self.validation_data = validation_data\n", + " self.target_names = target_names\n", + "\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " x_val, y_val = self.validation_data\n", + " y_pred = self.model.predict(x_val, verbose=0)\n", + "\n", + " for i, name in enumerate(self.target_names):\n", + " mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n", + " logs[f'val_{name}_mae'] = mae\n", + "\n", + "\n", + " callbacks = [\n", + " # Early Stopping\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=20,\n", + " restore_best_weights=True,\n", + " min_delta=0.0005,\n", + " mode='min'\n", + " ),\n", + "\n", + " # Model Checkpoint\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " ),\n", + "\n", + " # Metric per target specifici\n", + " TargetSpecificMetric(\n", + " validation_data=(val_data, val_targets),\n", + " target_names=target_names\n", + " ),\n", + "\n", + " # Reduce LR on Plateau\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.5,\n", + " patience=10,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + "\n", + " # TensorBoard logging\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./logs_{execute_name}',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " )\n", + " ]\n", + "\n", + " return callbacks\n", + "\n", + "\n", + "def compile_model(model, learning_rate=1e-3):\n", + " \"\"\"\n", + " Compila il modello con le impostazioni standard.\n", + " \"\"\"\n", + " lr_schedule = WarmUpLearningRateSchedule(\n", + " initial_learning_rate=learning_rate,\n", + " warmup_steps=500,\n", + " decay_steps=5000\n", + " )\n", + "\n", + " model.compile(\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.01\n", + " ),\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n", + " \"\"\"\n", + " Configura e prepara il transformer con dimensioni dinamiche basate sui dati.\n", + " \"\"\"\n", + " # Estrai le shape dai dati\n", + " temporal_shape = (train_data['temporal'].shape[1], train_data['temporal'].shape[2])\n", + " static_shape = (train_data['static'].shape[1],)\n", + " num_outputs = train_targets.shape[1]\n", + "\n", + " print(f\"Shape rilevate:\")\n", + " print(f\"- Temporal shape: {temporal_shape}\")\n", + " print(f\"- Static shape: {static_shape}\")\n", + " print(f\"- Numero di output: {num_outputs}\")\n", + "\n", + " # Target names basati sul numero di output\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Assicurati che il numero di target names corrisponda al numero di output\n", + " assert len(target_names) == num_outputs, \\\n", + " f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n", + "\n", + " # Crea il modello con le dimensioni rilevate\n", + " model = create_olive_oil_transformer(\n", + " temporal_shape=temporal_shape,\n", + " static_shape=static_shape,\n", + " num_outputs=num_outputs\n", + " )\n", + "\n", + " # Compila il modello\n", + " model = compile_model(model)\n", + "\n", + " # Crea i callbacks\n", + " callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n", + "\n", + " return model, callbacks, target_names\n", + "\n", + "\n", + "def train_transformer(train_data, train_targets, val_data, val_targets, epochs=150, batch_size=64, save_name='final_model'):\n", + " \"\"\"\n", + " Funzione principale per l'addestramento del transformer con ottimizzazioni.\n", + " \"\"\"\n", + " # Conversione dei dati in tf.data.Dataset per una gestione più efficiente della memoria\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n", + " .cache()\\\n", + " .shuffle(buffer_size=1024)\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n", + " .cache()\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " # Setup del modello\n", + " strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n", + " \n", + " with strategy.scope():\n", + " model, callbacks, target_names = setup_transformer_training(\n", + " train_data, train_targets, val_data, val_targets\n", + " )\n", + "\n", + " # Mostra il summary del modello\n", + " model.summary()\n", + " \n", + " try:\n", + " keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not create model plot: {e}\")\n", + "\n", + " # Training con gestione degli errori\n", + " try:\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " workers=4,\n", + " use_multiprocessing=True\n", + " )\n", + " except tf.errors.ResourceExhaustedError:\n", + " print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n", + " # Riprova con batch size più piccolo\n", + " batch_size = batch_size // 2\n", + " train_dataset = train_dataset.unbatch().batch(batch_size)\n", + " val_dataset = val_dataset.unbatch().batch(batch_size)\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " # Salva il modello finale\n", + " try:\n", + " save_path = f'{execute_name}_{save_name}.keras'\n", + " model.save(save_path, save_format='keras')\n", + " \n", + " os.makedirs(f'{execute_name}/weights', exist_ok=True)\n", + " model.save_weights(f'{execute_name}/weights')\n", + " print(f\"\\nModello salvato in: {save_path}\")\n", + " except Exception as e:\n", + " print(f\"Warning: Could not save model: {e}\")\n", + "\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "35490e902e494c4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape rilevate:\n", + "- Temporal shape: (41, 3)\n", + "- Static shape: (113,)\n", + "- Numero di output: 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 11:43:09.026945: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"OilTransformer\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " temporal (InputLayer) [(None, 41, 3)] 0 [] \n", + " \n", + " layer_normalization (Layer (None, 41, 3) 6 ['temporal[0][0]'] \n", + " Normalization) \n", + " \n", + " data_augmentation (DataAug (None, 41, 3) 0 ['layer_normalization[0][0]'] \n", + " mentation) \n", + " \n", + " dense (Dense) (None, 41, 64) 256 ['data_augmentation[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 41, 64) 0 ['dense[0][0]'] \n", + " \n", + " dense_1 (Dense) (None, 41, 128) 8320 ['dropout[0][0]'] \n", + " \n", + " positional_encoding (Posit (None, 41, 128) 5248 ['dense_1[0][0]'] \n", + " ionalEncoding) \n", + " \n", + " multi_head_attention (Mult (None, 41, 128) 66048 ['positional_encoding[0][0]', \n", + " iHeadAttention) 'positional_encoding[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 41, 128) 16512 ['positional_encoding[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 41, 128) 0 ['multi_head_attention[0][0]']\n", + " \n", + " tf.math.multiply (TFOpLamb (None, 41, 128) 0 ['dense_2[0][0]', \n", + " da) 'dropout_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 41, 128) 0 ['positional_encoding[0][0]', \n", + " icDepth) 'tf.math.multiply[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 41, 128) 256 ['stochastic_depth[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_3 (Dense) (None, 41, 256) 33024 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_2 (Dropout) (None, 41, 256) 0 ['dense_3[0][0]'] \n", + " \n", + " dense_4 (Dense) (None, 41, 128) 32896 ['dropout_2[0][0]'] \n", + " \n", + " dropout_3 (Dropout) (None, 41, 128) 0 ['dense_4[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 41, 128) 0 ['layer_normalization_1[0][0]'\n", + " sticDepth) , 'dropout_3[0][0]'] \n", + " \n", + " layer_normalization_2 (Lay (None, 41, 128) 256 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_1 (Mu (None, 41, 128) 66048 ['layer_normalization_2[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_2[0][0]\n", + " '] \n", + " \n", + " dense_5 (Dense) (None, 41, 128) 16512 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_4 (Dropout) (None, 41, 128) 0 ['multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_1 (TFOpLa (None, 41, 128) 0 ['dense_5[0][0]', \n", + " mbda) 'dropout_4[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 41, 128) 0 ['layer_normalization_2[0][0]'\n", + " sticDepth) , 'tf.math.multiply_1[0][0]'] \n", + " \n", + " layer_normalization_3 (Lay (None, 41, 128) 256 ['stochastic_depth_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_6 (Dense) (None, 41, 256) 33024 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dropout_5 (Dropout) (None, 41, 256) 0 ['dense_6[0][0]'] \n", + " \n", + " dense_7 (Dense) (None, 41, 128) 32896 ['dropout_5[0][0]'] \n", + " \n", + " dropout_6 (Dropout) (None, 41, 128) 0 ['dense_7[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 41, 128) 0 ['layer_normalization_3[0][0]'\n", + " sticDepth) , 'dropout_6[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 41, 128) 256 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_2 (Mu (None, 41, 128) 66048 ['layer_normalization_4[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_4[0][0]\n", + " '] \n", + " \n", + " dense_8 (Dense) (None, 41, 128) 16512 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dropout_7 (Dropout) (None, 41, 128) 0 ['multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_2 (TFOpLa (None, 41, 128) 0 ['dense_8[0][0]', \n", + " mbda) 'dropout_7[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 41, 128) 0 ['layer_normalization_4[0][0]'\n", + " sticDepth) , 'tf.math.multiply_2[0][0]'] \n", + " \n", + " layer_normalization_5 (Lay (None, 41, 128) 256 ['stochastic_depth_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_9 (Dense) (None, 41, 256) 33024 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dropout_8 (Dropout) (None, 41, 256) 0 ['dense_9[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 41, 128) 32896 ['dropout_8[0][0]'] \n", + " \n", + " dropout_9 (Dropout) (None, 41, 128) 0 ['dense_10[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 41, 128) 0 ['layer_normalization_5[0][0]'\n", + " sticDepth) , 'dropout_9[0][0]'] \n", + " \n", + " layer_normalization_6 (Lay (None, 41, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_3 (Mu (None, 41, 128) 66048 ['layer_normalization_6[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_6[0][0]\n", + " '] \n", + " \n", + " dense_11 (Dense) (None, 41, 128) 16512 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dropout_10 (Dropout) (None, 41, 128) 0 ['multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_3 (TFOpLa (None, 41, 128) 0 ['dense_11[0][0]', \n", + " mbda) 'dropout_10[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 41, 128) 0 ['layer_normalization_6[0][0]'\n", + " sticDepth) , 'tf.math.multiply_3[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 41, 128) 256 ['stochastic_depth_6[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_12 (Dense) (None, 41, 256) 33024 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " dropout_11 (Dropout) (None, 41, 256) 0 ['dense_12[0][0]'] \n", + " \n", + " dense_13 (Dense) (None, 41, 128) 32896 ['dropout_11[0][0]'] \n", + " \n", + " static (InputLayer) [(None, 113)] 0 [] \n", + " \n", + " dropout_12 (Dropout) (None, 41, 128) 0 ['dense_13[0][0]'] \n", + " \n", + " layer_normalization_9 (Lay (None, 113) 226 ['static[0][0]'] \n", + " erNormalization) \n", + " \n", + " stochastic_depth_7 (Stocha (None, 41, 128) 0 ['layer_normalization_7[0][0]'\n", + " sticDepth) , 'dropout_12[0][0]'] \n", + " \n", + " dense_14 (Dense) (None, 256) 29184 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_8 (Lay (None, 41, 128) 256 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_13 (Dropout) (None, 256) 0 ['dense_14[0][0]'] \n", + " \n", + " stochastic_depth_8 (Stocha (None, 41, 128) 0 ['layer_normalization_8[0][0]'\n", + " sticDepth) , 'positional_encoding[0][0]']\n", + " \n", + " dense_15 (Dense) (None, 128) 32896 ['dropout_13[0][0]'] \n", + " \n", + " multi_head_attention_4 (Mu (None, 41, 128) 131968 ['stochastic_depth_8[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_8[0][0]'] \n", + " \n", + " dropout_14 (Dropout) (None, 128) 0 ['dense_15[0][0]'] \n", + " \n", + " global_average_pooling1d ( (None, 128) 0 ['multi_head_attention_4[0][0]\n", + " GlobalAveragePooling1D) '] \n", + " \n", + " global_average_pooling1d_1 (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " (GlobalAveragePooling1D) \n", + " \n", + " global_max_pooling1d (Glob (None, 128) 0 ['stochastic_depth_8[0][0]'] \n", + " alMaxPooling1D) \n", + " \n", + " dense_16 (Dense) (None, 64) 8256 ['dropout_14[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 384) 0 ['global_average_pooling1d[0][\n", + " 0]', \n", + " 'global_average_pooling1d_1[0\n", + " ][0]', \n", + " 'global_max_pooling1d[0][0]']\n", + " \n", + " dropout_15 (Dropout) (None, 64) 0 ['dense_16[0][0]'] \n", + " \n", + " concatenate_1 (Concatenate (None, 448) 0 ['concatenate[0][0]', \n", + " ) 'dropout_15[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 448) 1792 ['concatenate_1[0][0]'] \n", + " Normalization) \n", + " \n", + " dense_17 (Dense) (None, 256) 114944 ['batch_normalization[0][0]'] \n", + " \n", + " dropout_16 (Dropout) (None, 256) 0 ['dense_17[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 256) 1024 ['dropout_16[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_18 (Dense) (None, 128) 32896 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_17 (Dropout) (None, 128) 0 ['dense_18[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 128) 512 ['dropout_17[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_19 (Dense) (None, 64) 8256 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_18 (Dropout) (None, 64) 0 ['dense_19[0][0]'] \n", + " \n", + " dense_20 (Dense) (None, 5) 325 ['dropout_18[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 972077 (3.71 MB)\n", + "Trainable params: 965165 (3.68 MB)\n", + "Non-trainable params: 6912 (27.00 KB)\n", + "__________________________________________________________________________________________________\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-06 11:43:25.651745: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7d7e70d1ce40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-06 11:43:25.651778: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-06 11:43:25.659099: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-12-06 11:43:25.722749: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-06 11:43:25.861911: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9954/9954 [==============================] - 481s 46ms/step - loss: 0.0460 - mae: 0.1872 - val_loss: 0.0145 - val_mae: 0.0865 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 1.0219e-05\n", + "Epoch 2/150\n", + "9954/9954 [==============================] - 473s 47ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0603 - lr: 1.0438e-07\n", + "Epoch 3/150\n", + "9954/9954 [==============================] - 477s 48ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0861 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0927 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0602 - lr: 1.0661e-09\n", + "Epoch 4/150\n", + "9954/9954 [==============================] - 508s 51ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0867 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0616 - lr: 1.0889e-11\n", + "Epoch 5/150\n", + "9954/9954 [==============================] - 431s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0612 - lr: 1.1122e-13\n", + "Epoch 6/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0598 - lr: 1.1361e-15\n", + "Epoch 7/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0868 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0620 - lr: 1.1604e-17\n", + "Epoch 8/150\n", + "9954/9954 [==============================] - 433s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0611 - lr: 1.1852e-19\n", + "Epoch 9/150\n", + "9954/9954 [==============================] - 413s 41ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0608 - lr: 1.2106e-21\n", + "Epoch 10/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0889 - val_total_water_need_mae: 0.0607 - lr: 1.2365e-23\n", + "Epoch 11/150\n", + "9954/9954 [==============================] - 438s 44ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.2630e-25\n", + "Epoch 12/150\n", + "9954/9954 [==============================] - 430s 43ms/step - loss: 0.0273 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0866 - val_olive_prod_mae: 0.0968 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0606 - lr: 1.2900e-27\n", + "Epoch 13/150\n", + "9954/9954 [==============================] - 425s 43ms/step - loss: 0.0273 - mae: 0.1507 - val_loss: 0.0144 - val_mae: 0.0868 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0932 - val_max_oil_prod_mae: 0.0930 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0619 - lr: 1.3177e-29\n", + "Epoch 14/150\n", + "9954/9954 [==============================] - 409s 41ms/step - loss: 0.0272 - mae: 0.1504 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0932 - val_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0605 - lr: 1.3459e-31\n", + "Epoch 15/150\n", + "9954/9954 [==============================] - 439s 44ms/step - loss: 0.0273 - mae: 0.1509 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0929 - val_max_oil_prod_mae: 0.0926 - val_avg_oil_prod_mae: 0.0886 - val_total_water_need_mae: 0.0609 - lr: 1.3747e-33\n", + "Epoch 16/150\n", + "9954/9954 [==============================] - 421s 42ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0887 - val_total_water_need_mae: 0.0604 - lr: 1.4041e-35\n", + "Epoch 17/150\n", + "9954/9954 [==============================] - 429s 43ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0863 - val_olive_prod_mae: 0.0966 - val_min_oil_prod_mae: 0.0931 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0600 - lr: 1.4342e-37\n", + "Epoch 18/150\n", + "9954/9954 [==============================] - 414s 41ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0144 - val_mae: 0.0865 - val_olive_prod_mae: 0.0967 - val_min_oil_prod_mae: 0.0933 - val_max_oil_prod_mae: 0.0931 - val_avg_oil_prod_mae: 0.0890 - val_total_water_need_mae: 0.0602 - lr: 0.0000e+00\n", + "Epoch 19/150\n", + "9954/9954 [==============================] - 441s 44ms/step - loss: 0.0272 - mae: 0.1506 - val_loss: 0.0143 - val_mae: 0.0864 - val_olive_prod_mae: 0.0965 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0928 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0608 - lr: 0.0000e+00\n", + "Epoch 20/150\n", + "9954/9954 [==============================] - 440s 44ms/step - loss: 0.0272 - mae: 0.1505 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0963 - val_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0888 - val_total_water_need_mae: 0.0601 - lr: 0.0000e+00\n", + "Epoch 21/150\n", + "9954/9954 [==============================] - 448s 45ms/step - loss: 0.0273 - mae: 0.1508 - val_loss: 0.0143 - val_mae: 0.0862 - val_olive_prod_mae: 0.0964 - val_min_oil_prod_mae: 0.0935 - val_max_oil_prod_mae: 0.0936 - val_avg_oil_prod_mae: 0.0894 - val_total_water_need_mae: 0.0598 - lr: 0.0000e+00\n", + "\n", + "Modello salvato in: 2024-12-06_10-36_final_model.keras\n" + ] + } + ], + "source": [ + "model, history = train_transformer(train_data, train_targets, val_data, val_targets, 150, 512)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3e2fb5a5341dac92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 102s 4ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1585.45\n", + "Errore percentuale medio: 6.91%\n", + "Precisione: 93.09%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 319.12\n", + "Errore percentuale medio: 6.61%\n", + "Precisione: 93.39%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 387.31\n", + "Errore percentuale medio: 6.74%\n", + "Precisione: 93.26%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 337.11\n", + "Errore percentuale medio: 6.46%\n", + "Precisione: 93.54%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1775.48\n", + "Errore percentuale medio: 4.24%\n", + "Precisione: 95.76%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4af58aa9bbc156f5", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model_performance(model, data, targets, set_name=\"\"):\n", + " \"\"\"\n", + " Valuta le performance del modello su un set di dati specifico.\n", + " \"\"\"\n", + " predictions = model.predict(data, verbose=0)\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + " metrics = {}\n", + "\n", + " for i, name in enumerate(target_names):\n", + " mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n", + " mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n", + " rmse = np.sqrt(mse)\n", + " mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n", + "\n", + " metrics[f\"{name}_mae\"] = mae\n", + " metrics[f\"{name}_rmse\"] = rmse\n", + " metrics[f\"{name}_mape\"] = mape\n", + "\n", + " if set_name:\n", + " print(f\"\\nPerformance sul set {set_name}:\")\n", + " for metric, value in metrics.items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def retrain_model(base_model, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Implementa il retraining del modello con i dati combinati.\n", + " \"\"\"\n", + " print(\"Valutazione performance iniziali del modello...\")\n", + " initial_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Combina i dati per il retraining\n", + " combined_data = {\n", + " 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n", + " 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n", + " }\n", + " combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n", + "\n", + " # Crea una nuova suddivisione per la validazione\n", + " indices = np.arange(len(combined_targets))\n", + " np.random.shuffle(indices)\n", + "\n", + " split_idx = int(len(indices) * 0.9)\n", + " train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n", + "\n", + " # Prepara i dati per il retraining\n", + " retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n", + " retrain_targets = combined_targets[train_idx]\n", + " retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n", + " retrain_val_targets = combined_targets[val_idx]\n", + "\n", + " # Configura callbacks\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=10,\n", + " restore_best_weights=True,\n", + " min_delta=0.0001\n", + " ),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_retrained_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " )\n", + " ]\n", + "\n", + " # Imposta learning rate per il fine-tuning\n", + " optimizer = tf.keras.optimizers.AdamW(\n", + " learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n", + " initial_learning_rate=1e-4,\n", + " decay_steps=1000,\n", + " decay_rate=0.9\n", + " ),\n", + " weight_decay=0.01\n", + " )\n", + "\n", + " # Ricompila il modello con il nuovo optimizer\n", + " base_model.compile(\n", + " optimizer=optimizer,\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " print(\"\\nAvvio retraining...\")\n", + " history = base_model.fit(\n", + " retrain_data,\n", + " retrain_targets,\n", + " validation_data=(retrain_val_data, retrain_val_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " print(\"\\nValutazione performance finali...\")\n", + " final_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Salva il modello finale\n", + " save_path = f'{execute_name}_retrained_model.keras'\n", + " os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n", + " \n", + " base_model.save_weights(f'{execute_name}_retrained/weights')\n", + " base_model.save(save_path, save_format='keras')\n", + " print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n", + "\n", + " # Report miglioramenti\n", + " print(\"\\nMiglioramenti delle performance:\")\n", + " for dataset in ['train', 'val', 'test']:\n", + " print(f\"\\nSet {dataset}:\")\n", + " for metric in initial_metrics[dataset].keys():\n", + " initial = initial_metrics[dataset][metric]\n", + " final = final_metrics[dataset][metric]\n", + " improvement = ((initial - final) / initial) * 100\n", + " print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n", + "\n", + " return base_model, history, final_metrics\n", + "\n", + "\n", + "def start_retraining(model_path, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Avvia il processo di retraining in modo sicuro.\n", + " \"\"\"\n", + " try:\n", + " print(\"Caricamento del modello...\")\n", + " base_model = tf.keras.models.load_model(model_path, compile=False)\n", + " print(\"Modello caricato con successo!\")\n", + "\n", + " return retrain_model(\n", + " base_model=base_model,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=epochs,\n", + " batch_size=batch_size\n", + " )\n", + " except Exception as e:\n", + " print(f\"Errore durante il retraining: {str(e)}\")\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "588c7e49371f4a0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caricamento del modello...\n", + "Modello caricato con successo!\n", + "Valutazione performance iniziali del modello...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0963\n", + "olive_prod_rmse: 0.1300\n", + "olive_prod_mape: 77.2491\n", + "min_oil_prod_mae: 0.0936\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 91.4612\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 88.9396\n", + "avg_oil_prod_mae: 0.0895\n", + "avg_oil_prod_rmse: 0.1238\n", + "avg_oil_prod_mape: 89.5317\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0808\n", + "total_water_need_mape: 44.4531\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0964\n", + "olive_prod_rmse: 0.1301\n", + "olive_prod_mape: 133.2427\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1310\n", + "min_oil_prod_mape: 120.7693\n", + "max_oil_prod_mae: 0.0936\n", + "max_oil_prod_rmse: 0.1304\n", + "max_oil_prod_mape: 86.2224\n", + "avg_oil_prod_mae: 0.0894\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 83.8138\n", + "total_water_need_mae: 0.0598\n", + "total_water_need_rmse: 0.0809\n", + "total_water_need_mape: 53.9347\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0962\n", + "olive_prod_rmse: 0.1298\n", + "olive_prod_mape: 77.9806\n", + "min_oil_prod_mae: 0.0935\n", + "min_oil_prod_rmse: 0.1312\n", + "min_oil_prod_mape: 95.5886\n", + "max_oil_prod_mae: 0.0934\n", + "max_oil_prod_rmse: 0.1301\n", + "max_oil_prod_mape: 76.3217\n", + "avg_oil_prod_mae: 0.0893\n", + "avg_oil_prod_rmse: 0.1237\n", + "avg_oil_prod_mape: 111.2211\n", + "total_water_need_mae: 0.0596\n", + "total_water_need_rmse: 0.0806\n", + "total_water_need_mape: 38.1699\n", + "\n", + "Avvio retraining...\n", + "Epoch 1/50\n", + "27563/27563 [==============================] - 851s 30ms/step - loss: 0.0261 - mae: 0.1520 - val_loss: 0.0118 - val_mae: 0.0804 - lr: 5.4806e-06\n", + "Epoch 2/50\n", + "27563/27563 [==============================] - 852s 31ms/step - loss: 0.0245 - mae: 0.1478 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 3.0034e-07\n", + "Epoch 3/50\n", + "27563/27563 [==============================] - 836s 30ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0117 - val_mae: 0.0807 - lr: 1.6459e-08\n", + "Epoch 4/50\n", + "27563/27563 [==============================] - 863s 31ms/step - loss: 0.0244 - mae: 0.1476 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 9.0196e-10\n", + "Epoch 5/50\n", + "27563/27563 [==============================] - 854s 31ms/step - loss: 0.0243 - mae: 0.1474 - val_loss: 0.0119 - val_mae: 0.0812 - lr: 4.9428e-11\n", + "Epoch 6/50\n", + "27563/27563 [==============================] - 869s 32ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.7087e-12\n", + "Epoch 7/50\n", + "27563/27563 [==============================] - 867s 31ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0806 - lr: 1.4844e-13\n", + "Epoch 8/50\n", + "27563/27563 [==============================] - 899s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0803 - lr: 8.1345e-15\n", + "Epoch 9/50\n", + "27563/27563 [==============================] - 966s 35ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0117 - val_mae: 0.0804 - lr: 4.4578e-16\n", + "Epoch 10/50\n", + "27563/27563 [==============================] - 930s 34ms/step - loss: 0.0244 - mae: 0.1474 - val_loss: 0.0118 - val_mae: 0.0807 - lr: 2.4429e-17\n", + "Epoch 11/50\n", + "27563/27563 [==============================] - 921s 33ms/step - loss: 0.0244 - mae: 0.1475 - val_loss: 0.0118 - val_mae: 0.0809 - lr: 1.3387e-18\n", + "\n", + "Valutazione performance finali...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 75.7735\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1245\n", + "min_oil_prod_mape: 91.0646\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 89.5375\n", + "avg_oil_prod_mae: 0.0845\n", + "avg_oil_prod_rmse: 0.1171\n", + "avg_oil_prod_mape: 86.3355\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0678\n", + "total_water_need_mape: 41.0436\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0901\n", + "olive_prod_rmse: 0.1222\n", + "olive_prod_mape: 138.3196\n", + "min_oil_prod_mae: 0.0885\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 126.9523\n", + "max_oil_prod_mae: 0.0888\n", + "max_oil_prod_rmse: 0.1243\n", + "max_oil_prod_mape: 82.7593\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1169\n", + "avg_oil_prod_mape: 84.3605\n", + "total_water_need_mae: 0.0495\n", + "total_water_need_rmse: 0.0679\n", + "total_water_need_mape: 48.6941\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0899\n", + "olive_prod_rmse: 0.1219\n", + "olive_prod_mape: 77.0356\n", + "min_oil_prod_mae: 0.0886\n", + "min_oil_prod_rmse: 0.1243\n", + "min_oil_prod_mape: 96.3498\n", + "max_oil_prod_mae: 0.0885\n", + "max_oil_prod_rmse: 0.1238\n", + "max_oil_prod_mape: 76.4509\n", + "avg_oil_prod_mae: 0.0843\n", + "avg_oil_prod_rmse: 0.1167\n", + "avg_oil_prod_mape: 87.8912\n", + "total_water_need_mae: 0.0494\n", + "total_water_need_rmse: 0.0677\n", + "total_water_need_mape: 30.6997\n", + "\n", + "Modello riaddestrato salvato in: 2024-12-06_10-36_retrained_model.keras\n", + "\n", + "Miglioramenti delle performance:\n", + "\n", + "Set train:\n", + "olive_prod_mae: 6.48% di miglioramento\n", + "olive_prod_rmse: 6.00% di miglioramento\n", + "olive_prod_mape: 1.91% di miglioramento\n", + "min_oil_prod_mae: 5.29% di miglioramento\n", + "min_oil_prod_rmse: 5.12% di miglioramento\n", + "min_oil_prod_mape: 0.43% di miglioramento\n", + "max_oil_prod_mae: 5.11% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: -0.67% di miglioramento\n", + "avg_oil_prod_mae: 5.58% di miglioramento\n", + "avg_oil_prod_rmse: 5.45% di miglioramento\n", + "avg_oil_prod_mape: 3.57% di miglioramento\n", + "total_water_need_mae: 17.16% di miglioramento\n", + "total_water_need_rmse: 15.99% di miglioramento\n", + "total_water_need_mape: 7.67% di miglioramento\n", + "\n", + "Set val:\n", + "olive_prod_mae: 6.51% di miglioramento\n", + "olive_prod_rmse: 6.04% di miglioramento\n", + "olive_prod_mape: -3.81% di miglioramento\n", + "min_oil_prod_mae: 5.33% di miglioramento\n", + "min_oil_prod_rmse: 5.16% di miglioramento\n", + "min_oil_prod_mape: -5.12% di miglioramento\n", + "max_oil_prod_mae: 5.13% di miglioramento\n", + "max_oil_prod_rmse: 4.70% di miglioramento\n", + "max_oil_prod_mape: 4.02% di miglioramento\n", + "avg_oil_prod_mae: 5.62% di miglioramento\n", + "avg_oil_prod_rmse: 5.48% di miglioramento\n", + "avg_oil_prod_mape: -0.65% di miglioramento\n", + "total_water_need_mae: 17.23% di miglioramento\n", + "total_water_need_rmse: 16.08% di miglioramento\n", + "total_water_need_mape: 9.72% di miglioramento\n", + "\n", + "Set test:\n", + "olive_prod_mae: 6.52% di miglioramento\n", + "olive_prod_rmse: 6.09% di miglioramento\n", + "olive_prod_mape: 1.21% di miglioramento\n", + "min_oil_prod_mae: 5.32% di miglioramento\n", + "min_oil_prod_rmse: 5.22% di miglioramento\n", + "min_oil_prod_mape: -0.80% di miglioramento\n", + "max_oil_prod_mae: 5.22% di miglioramento\n", + "max_oil_prod_rmse: 4.83% di miglioramento\n", + "max_oil_prod_mape: -0.17% di miglioramento\n", + "avg_oil_prod_mae: 5.64% di miglioramento\n", + "avg_oil_prod_rmse: 5.59% di miglioramento\n", + "avg_oil_prod_mape: 20.98% di miglioramento\n", + "total_water_need_mae: 17.22% di miglioramento\n", + "total_water_need_rmse: 16.03% di miglioramento\n", + "total_water_need_mape: 19.57% di miglioramento\n" + ] + } + ], + "source": [ + "model_path = f'{execute_name}_final_model.keras'\n", + "\n", + "retrained_model, retrain_history, final_metrics = start_retraining(\n", + " model_path=model_path,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=50,\n", + " batch_size=128\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24500/24500 [==============================] - 137s 6ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1482.22\n", + "Errore percentuale medio: 5.77%\n", + "Precisione: 94.23%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 302.12\n", + "Errore percentuale medio: 5.68%\n", + "Precisione: 94.32%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 367.45\n", + "Errore percentuale medio: 5.78%\n", + "Precisione: 94.22%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 318.15\n", + "Errore percentuale medio: 5.49%\n", + "Precisione: 94.51%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1469.51\n", + "Errore percentuale medio: 3.31%\n", + "Precisione: 96.69%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from typing import List, Dict, Tuple, Union\n", + "\n", + "def analyze_feature_importance(model: tf.keras.Model, \n", + " test_data: dict, \n", + " feature_names: List[str]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Analizza l'importanza delle feature usando perturbazione.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n", + " feature_names: Lista dei nomi delle feature\n", + " \n", + " Returns:\n", + " dict: Dizionario con l'importanza relativa di ogni feature\n", + " \"\"\"\n", + " # Estrai i dati temporali e statici\n", + " temporal_data = test_data['temporal']\n", + " static_data = test_data['static']\n", + " \n", + " # Ottieni la predizione base\n", + " base_prediction = model.predict(test_data)\n", + " feature_importance = {}\n", + " \n", + " # Per ogni feature temporale\n", + " for i, feature in enumerate(feature_names):\n", + " if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature temporale\n", + " temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n", + " \n", + " # Crea rumore per la feature temporale\n", + " feature_values = temporal_data[..., temp_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature temporale\n", + " perturbed_temporal = perturbed_data['temporal'].copy()\n", + " perturbed_temporal[..., temp_idx] = feature_values + noise\n", + " perturbed_data['temporal'] = perturbed_temporal\n", + " \n", + " else: # Feature statiche\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature statica\n", + " static_idx = ['ha'].index(feature)\n", + " \n", + " # Crea rumore per la feature statica\n", + " feature_values = static_data[..., static_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature statica\n", + " perturbed_static = perturbed_data['static'].copy()\n", + " perturbed_static[..., static_idx] = feature_values + noise\n", + " perturbed_data['static'] = perturbed_static\n", + " \n", + " # Calcola nuova predizione\n", + " perturbed_prediction = model.predict(perturbed_data)\n", + " \n", + " # Calcola impatto della perturbazione\n", + " impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n", + " feature_importance[feature] = float(impact)\n", + " \n", + " # Normalizza le importanze\n", + " total_importance = sum(feature_importance.values())\n", + " feature_importance = {k: v/total_importance \n", + " for k, v in feature_importance.items()}\n", + " \n", + " return feature_importance\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor o array dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calcola varianza manualmente\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Ordina il tensor per il calcolo della mediana\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data: np.ndarray, \n", + " bins: int = 50, \n", + " title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Imposta il titolo generale\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf\n", + "\n", + "def analyze_model_predictions(model: tf.keras.Model, \n", + " test_data: np.ndarray,\n", + " test_targets: np.ndarray,\n", + " scaler_y) -> None:\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Esegue un'analisi completa del modello includendo errori,\n", + " importanza delle feature e distribuzioni.\n", + " \"\"\"\n", + " print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n", + " \n", + " # 1. Analisi degli errori\n", + " print(\"\\n1. ANALISI DEGLI ERRORI\")\n", + " print(\"-\" * 50)\n", + " analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n", + " \n", + " # 2. Analisi dell'importanza delle feature\n", + " print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Definisci i nomi delle feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha']\n", + " \n", + " all_features = temporal_features + static_features\n", + " importance = analyze_feature_importance(retrained_model, test_data, all_features)\n", + " \n", + " print(\"\\nImportanza relativa delle feature:\")\n", + " for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n", + " print(f\"{feature}: {imp:.4f}\")\n", + " \n", + " # 3. Analisi distribuzionale\n", + " print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n", + " print(\"-\" * 50)\n", + " \n", + " prob = ProbabilityFunctions()\n", + " predictions = retrained_model.predict(test_data)\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi distribuzionale per {target}\")\n", + " \n", + " # Statistiche\n", + " stats_pred = prob.calculate_statistics(predictions_real[:, i])\n", + " stats_true = prob.calculate_statistics(targets_real[:, i])\n", + " \n", + " print(\"\\nStatistiche Predizioni:\")\n", + " for key, value in stats_pred.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " print(\"\\nStatistiche Target Reali:\")\n", + " for key, value in stats_true.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza distribuzioni\n", + " prob.plot_distributions(predictions_real[:, i], bins=50,\n", + " title=f\"Distribuzione Predizioni - {target}\")\n", + " prob.plot_distributions(targets_real[:, i], bins=50,\n", + " title=f\"Distribuzione Target Reali - {target}\")\n", + "\n", + "def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data):\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calculate variance manually\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Sort the tensor for median calculation\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data, bins=50):\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf):\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Set overall title\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ANALISI COMPLETA DEL MODELLO ===\n", + "\n", + "1. ANALISI DEGLI ERRORI\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 78s 4ms/step\n", + "\n", + "Analisi per olive_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -71.944\n", + "variance: 4009595.000\n", + "std: 2002.397\n", + "min: -18637.889\n", + "max: 12871.579\n", + "median: 48.672\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-1937.87, 1843.27]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4458.63, 3733.83]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-6979.39, 5624.40]\n", + "\n", + "Analisi per min_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -32.785\n", + "variance: 179026.016\n", + "std: 423.115\n", + "min: -4439.664\n", + "max: 3453.714\n", + "median: -12.655\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-414.04, 375.30]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1045.51, 848.90]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1519.11, 1164.63]\n", + "\n", + "Analisi per max_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -34.971\n", + "variance: 261409.344\n", + "std: 511.282\n", + "min: -5732.709\n", + "max: 4274.197\n", + "median: -11.391\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-429.05, 371.50]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1229.60, 971.92]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1830.02, 1572.33]\n", + "\n", + "Analisi per avg_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -33.549\n", + "variance: 192810.531\n", + "std: 439.102\n", + "min: -4876.229\n", + "max: 3813.953\n", + "median: -13.710\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-444.24, 250.98]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-965.65, 772.39]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1487.06, 1293.80]\n", + "\n", + "Analisi per total_water_need\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -216.226\n", + "variance: 3987062.750\n", + "std: 1996.763\n", + "min: -22812.350\n", + "max: 13374.520\n", + "median: -119.823\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 68.0%:\n", + "Range: [-2185.83, 1432.85]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4357.05, 3604.06]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-7252.00, 5051.54]\n", + "\n", + "2. IMPORTANZA DELLE FEATURE\n", + "--------------------------------------------------\n", + "18375/18375 [==============================] - 79s 4ms/step\n", + "18375/18375 [==============================] - 80s 4ms/step\n", + "18375/18375 [==============================] - 99s 5ms/step\n", + "18375/18375 [==============================] - 96s 5ms/step\n", + "13976/18375 [=====================>........] - ETA: 21s" + ] + } + ], + "source": [ + "run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/olive_oli/2024-12-07_09-08/checkpoint b/models/olive_oli/2024-12-07_09-08/checkpoint new file mode 100644 index 0000000..efd0621 --- /dev/null +++ b/models/olive_oli/2024-12-07_09-08/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "weights" +all_model_checkpoint_paths: "weights" diff --git a/models/olive_oli/2024-12-07_09-08/weights.data-00000-of-00001 b/models/olive_oli/2024-12-07_09-08/weights.data-00000-of-00001 new file mode 100644 index 0000000..22e5b55 Binary files /dev/null and b/models/olive_oli/2024-12-07_09-08/weights.data-00000-of-00001 differ diff --git a/models/olive_oli/2024-12-07_09-08/weights.index b/models/olive_oli/2024-12-07_09-08/weights.index new file mode 100644 index 0000000..d6eef67 Binary files /dev/null and b/models/olive_oli/2024-12-07_09-08/weights.index differ diff --git a/models/olive_oli/2024-12-07_09-08_logs/train/events.out.tfevents.1733566470.eefaa5e3ffd5.93.0.v2 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"output_type": "stream", + "text": [ + "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", + "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease \n", + "Fetched 257 kB in 1s (347 kB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from 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(0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.3)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n" + ] + } + ], + "source": [ + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a467d3f0dfd9beab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 14:47:22.527266: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-12-08 14:47:22.527310: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-12-08 14:47:22.527375: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-12-08 14:47:22.536734: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Keras version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "TensorFlow version: 2.14.0\n", + "CUDA available: True\n", + "GPU devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n", + "1 Physical GPUs, 1 Logical GPUs\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 14:47:25.041282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:81:00.0, compute capability: 8.9\n" + ] + } + ], + "source": [ + "import os\n", + "import tensorflow as tf\n", + "import keras\n", + "\n", + "# Print versions and system information\n", + "print(f\"Keras version: {keras.__version__}\")\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "print(f\"CUDA available: {tf.test.is_built_with_cuda()}\")\n", + "print(f\"GPU devices: {tf.config.list_physical_devices('GPU')}\")\n", + "\n", + "# GPU memory configuration\n", + "gpus = tf.config.experimental.list_physical_devices('GPU')\n", + "if gpus:\n", + " try:\n", + " for gpu in gpus:\n", + " tf.config.experimental.set_memory_growth(gpu, True)\n", + "\n", + " logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n", + " print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n", + " except RuntimeError as e:\n", + " print(e)\n", + "\n", + "# Reduce TensorFlow logging verbosity\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "\n", + "# Set global precision policy\n", + "tf.keras.mixed_precision.set_global_policy('float32')\n", + "\n", + "# Uncomment to set seed for reproducibility\n", + "#tf.random.set_seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c0155cde4740b0a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from datetime import datetime\n", + "import os\n", + "from typing import List\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "import tensorflow_addons as tfa\n", + "import joblib\n", + "import re\n", + "\n", + "# Set random state value (None for non-deterministic behavior)\n", + "random_state_value = None\n", + "\n", + "# Create execution timestamp for model versioning and logging\n", + "execute_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "# Define directory paths\n", + "base_project_dir = './'\n", + "data_dir = '../../sources/'\n", + "models_project_dir = base_project_dir\n", + "\n", + "# Create required directories if they don't exist\n", + "os.makedirs(base_project_dir, exist_ok=True)\n", + "os.makedirs(models_project_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1347fb59-50cc-4aa8-b805-ca9403037af5", + "metadata": {}, + "outputs": [], + "source": [ + "def clean_column_name(name: str) -> str:\n", + " \"\"\"\n", + " Cleans column names by removing special characters and converting to snake_case with abbreviations.\n", + "\n", + " Parameters\n", + " ----------\n", + " name : str\n", + " Column name to clean\n", + "\n", + " Returns\n", + " -------\n", + " str\n", + " Cleaned column name\n", + " \"\"\"\n", + " # Remove special characters\n", + " name = re.sub(r'[^a-zA-Z0-9\\s]', '', name)\n", + "\n", + " # Convert to snake_case\n", + " name = name.lower().replace(' ', '_')\n", + "\n", + " # Common abbreviations mapping\n", + " abbreviations = {\n", + " 'production': 'prod',\n", + " 'percentage': 'pct',\n", + " 'hectare': 'ha',\n", + " 'tonnes': 't',\n", + " 'litres': 'l',\n", + " 'minimum': 'min',\n", + " 'maximum': 'max',\n", + " 'average': 'avg'\n", + " }\n", + "\n", + " for full, abbr in abbreviations.items():\n", + " name = name.replace(full, abbr)\n", + "\n", + " return name\n", + "\n", + "\n", + "def clean_column_names(df: pd.DataFrame) -> List[str]:\n", + " \"\"\"\n", + " Cleans all column names in a DataFrame.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : pd.DataFrame\n", + " DataFrame whose columns need cleaning\n", + "\n", + " Returns\n", + " -------\n", + " list\n", + " List of cleaned column names\n", + " \"\"\"\n", + " new_columns = []\n", + "\n", + " for col in df.columns:\n", + " # Extract variety patterns using regex\n", + " varieties = re.findall(r'([a-z]+)_([a-z_]+)', col)\n", + " if varieties:\n", + " new_columns.append(f\"{varieties[0][0]}_{varieties[0][1]}\")\n", + " else:\n", + " new_columns.append(col)\n", + "\n", + " return new_columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4da1f1bb67343e3e", + "metadata": {}, + "outputs": [], + "source": [ + "def save_plot(plt, title, output_dir=f'{base_project_dir}/{execute_name}_plots'):\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " filename = \"\".join(x for x in title if x.isalnum() or x in [' ', '-', '_']).rstrip()\n", + " filename = filename.replace(' ', '_').lower()\n", + " filepath = os.path.join(output_dir, f\"{filename}.png\")\n", + " plt.savefig(filepath, bbox_inches='tight', dpi=300)\n", + " print(f\"Plot salvato come: {filepath}\")\n", + "\n", + "\n", + "def encode_techniques(df, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}. Run create_technique_mapping first.\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + "\n", + " tech_columns = [col for col in df.columns if col.endswith('_tech')]\n", + "\n", + " # Mapping apply to all tech columns\n", + " for col in tech_columns:\n", + " df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int)\n", + "\n", + " return df\n", + "\n", + "\n", + "def decode_single_technique(technique_value, mapping_path=f'{data_dir}technique_mapping.joblib'):\n", + " if not os.path.exists(mapping_path):\n", + " raise FileNotFoundError(f\"Mapping not found at {mapping_path}\")\n", + "\n", + " technique_mapping = joblib.load(mapping_path)\n", + " reverse_mapping = {v: k for k, v in technique_mapping.items()}\n", + " reverse_mapping[0] = ''\n", + "\n", + " return reverse_mapping.get(technique_value, '')\n", + "\n", + "\n", + "def prepare_comparison_data(simulated_data, olive_varieties):\n", + "\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + "\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + " comparison_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0] # Esclude le righe dove la tecnica è 0\n", + "\n", + " if not variety_data.empty:\n", + " avg_olive_prod = pd.to_numeric(variety_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(variety_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(variety_data[water_need_col], errors='coerce').mean()\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " comparison_data.append({\n", + " 'Variety': variety,\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(comparison_data)\n", + "\n", + "\n", + "def plot_variety_comparison(comparison_data, metric):\n", + " plt.figure(figsize=(12, 6))\n", + " bars = plt.bar(comparison_data['Variety'], comparison_data[metric])\n", + " plt.title(f'Comparison of {metric} across Olive Varieties')\n", + " plt.xlabel('Variety')\n", + " plt.ylabel(metric)\n", + " plt.xticks(rotation=45, ha='right')\n", + "\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width() / 2., height,\n", + " f'{height:.2f}',\n", + " ha='center', va='bottom')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, f'variety_comparison_{metric.lower().replace(\" \", \"_\").replace(\"/\", \"_\").replace(\"(\", \"\").replace(\")\", \"\")}')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Oil Efficiency (L/kg)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Oil Efficiency (L/kg)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Oil Efficiency (L oil / kg olives)')\n", + " plt.tight_layout()\n", + " save_plot(plt, 'efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_efficiency_vs_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Olive Production (kg/ha)'],\n", + " comparison_data['Water Efficiency (L oil/m³ water)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Olive Production (kg/ha)'], row['Water Efficiency (L oil/m³ water)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Water Efficiency vs Olive Production by Variety')\n", + " plt.xlabel('Average Olive Production (kg/ha)')\n", + " plt.ylabel('Water Efficiency (L oil / m³ water)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_efficiency_vs_production')\n", + " plt.close()\n", + "\n", + "\n", + "def plot_water_need_vs_oil_production(comparison_data):\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " plt.scatter(comparison_data['Avg Water Need (m³/ha)'],\n", + " comparison_data['Avg Oil Production (L/ha)'],\n", + " s=100)\n", + "\n", + " for i, row in comparison_data.iterrows():\n", + " plt.annotate(row['Variety'],\n", + " (row['Avg Water Need (m³/ha)'], row['Avg Oil Production (L/ha)']),\n", + " xytext=(5, 5), textcoords='offset points')\n", + "\n", + " plt.title('Oil Production vs Water Need by Variety')\n", + " plt.xlabel('Average Water Need (m³/ha)')\n", + " plt.ylabel('Average Oil Production (L/ha)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + " save_plot(plt, 'water_need_vs_oil_production')\n", + " plt.close()\n", + "\n", + "\n", + "def analyze_by_technique(simulated_data, olive_varieties):\n", + "\n", + " df = simulated_data.copy()\n", + "\n", + " df.columns = clean_column_names(df)\n", + " df = encode_techniques(df)\n", + " all_varieties = olive_varieties['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " technique_data = []\n", + "\n", + " for variety in varieties:\n", + " olive_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_olive_prod')), None)\n", + " oil_prod_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_avg_oil_prod')), None)\n", + " tech_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_tech')), None)\n", + " water_need_col = next((col for col in df.columns if col.startswith(f'{variety}_') and col.endswith('_water_need')), None)\n", + "\n", + " if olive_prod_col and oil_prod_col and tech_col and water_need_col:\n", + " variety_data = df[[olive_prod_col, oil_prod_col, tech_col, water_need_col]]\n", + " variety_data = variety_data[variety_data[tech_col] != 0]\n", + "\n", + " if not variety_data.empty:\n", + " for tech in variety_data[tech_col].unique():\n", + " tech_data = variety_data[variety_data[tech_col] == tech]\n", + "\n", + " avg_olive_prod = pd.to_numeric(tech_data[olive_prod_col], errors='coerce').mean()\n", + " avg_oil_prod = pd.to_numeric(tech_data[oil_prod_col], errors='coerce').mean()\n", + " avg_water_need = pd.to_numeric(tech_data[water_need_col], errors='coerce').mean()\n", + "\n", + " efficiency = avg_oil_prod / avg_olive_prod if avg_olive_prod > 0 else 0\n", + " water_efficiency = avg_oil_prod / avg_water_need if avg_water_need > 0 else 0\n", + "\n", + " technique_data.append({\n", + " 'Variety': variety,\n", + " 'Technique': tech,\n", + " 'Technique String': decode_single_technique(tech),\n", + " 'Avg Olive Production (kg/ha)': avg_olive_prod,\n", + " 'Avg Oil Production (L/ha)': avg_oil_prod,\n", + " 'Avg Water Need (m³/ha)': avg_water_need,\n", + " 'Oil Efficiency (L/kg)': efficiency,\n", + " 'Water Efficiency (L oil/m³ water)': water_efficiency\n", + " })\n", + "\n", + " return pd.DataFrame(technique_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9aa4bf176c4affb9", + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_real_error(model, test_data, test_targets, scaler_y):\n", + "\n", + " predictions = model.predict(test_data)\n", + "\n", + " # Denormalize predictions and target values\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + "\n", + " # Calculate percentage error for each target\n", + " percentage_errors = []\n", + " absolute_errors = []\n", + "\n", + " for i in range(predictions_real.shape[1]):\n", + " mae = np.mean(np.abs(predictions_real[:, i] - targets_real[:, i]))\n", + " mape = np.mean(np.abs((predictions_real[:, i] - targets_real[:, i]) / targets_real[:, i])) * 100\n", + " percentage_errors.append(mape)\n", + " absolute_errors.append(mae)\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " print(\"\\nErrori per target:\")\n", + " print(\"-\" * 50)\n", + " for i, target in enumerate(target_names):\n", + " print(f\"{target}:\")\n", + " print(f\"MAE assoluto: {absolute_errors[i]:.2f}\")\n", + " print(f\"Errore percentuale medio: {percentage_errors[i]:.2f}%\")\n", + " print(f\"Precisione: {100 - percentage_errors[i]:.2f}%\")\n", + " print(\"-\" * 50)\n", + "\n", + " return percentage_errors, absolute_errors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3ba2b96ba678389", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_olive_production_kg_ha.png\n" + ] + }, + { + "data": { + "image/png": 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IOnbsiEqVKmk5UsoLnj59mmumU1RUFI4cOYL4+HgsXboUwOuBe//+/REdHY2xY8diwIAB0NHRQWJiIgwMDGBubq6t8CkfGDduHF6+fIl169YhKyvrrZd1t2/fVoqdDx48GLq6urn+WCQCcq9ccHV1hbe3N6ytrZGcnAwjIyP8/PPPqFmzJvz9/TF69GioVCoYGRnB0tISR44c4Xic/pVvvvkGQUFB2LRpEzp16oTHjx/j+vXrCAgIQNmyZdG6dWtYW1trO0z6yJiYojxl/fr1CAoKQokSJbBy5UoAQGJiInx8fODl5YVBgwZh7ty5AF7XSMheIsPBFUVERKBhw4bKz2FhYRg1ahTKly+P+fPnw9bWFgCQkJCA3r17486dOzhy5AgqVqyorZApH8hOcp49exaXLl1CZmYmmjRpAhsbGyQkJGD+/PmIiIhA9+7d4eLiou1wKQ9asmQJHj58iPnz5yvLppo3b46IiAh8+eWXuZahp6enw9HREbGxsXB0dMSQIUNgZGSkxegpL8r5Mg94PQayt7dH6dKlsWPHDgB/JhjS09Nx6dIl1KtXL9dnZCeviLLlTCjdvHkTCxcuxOjRo1GnTh0cPXoUy5YtQ3R0NHx9fWFra4uEhAQ8evQImZmZqFOnjlJziuNxAv68T50/fx7Xrl2Dvr4+ypUrBzs7OwC5k1Pt2rVjnWBijSnSrpx50YyMDISHh+PIkSOIi4tT2i0tLTF48GCMGjUKmzdvxoQJEwBASUqJCB+CBdzBgwfRtm3bXDt7mJqaQq1WIzAwUBloaTQaWFlZwdfXF5UqVUK9evVw+/ZtLUVN+YFKpcKuXbvQpk0bLF26FIsWLULt2rWxevVqWFlZYdq0aWjQoAECAgIwe/ZsbYdLeZCxsTGcnJygp6eHV69eQU9PDwcOHEC7du0QHR2N3bt3KzU49PX1sWnTJlhYWMDPzw+vXr3ScvSU12g0GiUpdevWLSQnJ0NXVxfdunVDVFQUgoODAfxZdyouLg4LFixAZGRkrs9hUoqy7dmzB8CffWbHjh1KLakyZcoAeL3TrIuLC6ytrdG7d29ERUXBysoKtra2qFevnpJ053icsmXXUWzZsiWWLVuGoUOHYvDgwZg5cyaA1zWnvvzySwwdOhR79+7lRjEECJGWXL9+XV68eCEiIrNnz5ZLly7JrVu3ZPz48WJiYiKrVq3KdX5iYqK4ublJ165dRaPRaCNkysMSEhJERCQ+Pl5pO3/+vNSsWVPq1asnqampIiJK37l37558/fXXEh0d/fGDpXzj4sWLUrx4cfH29pbk5GRJSkqSefPmia6urqxbt05EROLi4sTR0VG+/PJLefLkiZYjprwqLCxMxowZI1evXhURkSdPnkizZs2kSZMmEhAQIFlZWcq5r169krt372orVMqjcvaRmTNnSocOHSQ4OFhERMLDw6V58+bSvXt32b9/v4iI3L59Wzp16iRNmzaVzMxMrcRMedvatWulcePGkpWVpfSRTZs2SYsWLcTc3Fzu3buX6/wTJ05Ily5dpGjRonLz5k1thEx5WM77zMWLF6VYsWLi5eUlaWlpcv36dXF3d5eyZcvKrFmzlPPatWsn5cuXl+TkZC1ETHkJE1P00Wk0Grl48aKoVCpZv369jB49WoyMjOTKlSsiInLz5k0ZP368VKtWTVavXp3r2qdPnyqJBSan6E03btwQlUolP/30k9J2/vx5qVatmjRo0EBJhGb3HQ7U6U1v3leCg4OlRo0akpCQkOvY7NmzxdjYWElsJiQkKMlRKthyJg/S09OVf69bt04qVaokEydOlOvXr4uIyOPHj6Vp06bSpEkT2b9/f65rif6Kq6urlChRQnbv3i2PHj1S2o8cOSIdO3YUCwsLKVOmjFSvXl3s7OyUfsj+RW9KTExUxkKnT59W2v39/cXOzk6aNWsmd+7cyXXNkSNHxNnZmWMoUqxcufKt+8vu3bulevXq8vz5c6XtwYMHMnv2bLGzs1Ne0ogIX8SQiDAxRVq0aNEiMTQ0FGNjYwkPDxeRP/8ovHHjhpKcWrt27VvXMilFf2Xy5MliZGQkP//8s9KWnZxq3LixMnOKKFv2YCrnoOrx48ei0WgkICBA1Gq18tb41atXIiJy//59KV++vPj5+X38gCnPu337tqSkpIiIyJ49e2TOnDkiIrJixQqpW7eujBs3LldyqkWLFlK9enU5dOiQ1mKm/OHYsWNStmxZ+f3330VEJC0tTW7fvi0HDx6Uhw8fSnp6uoSHh4uHh4fs27dPSR5kZGRoM2zKY1xdXZXnmYhISEiIqFQqWbFihdK2a9cucXBwEHt7e4mLi3vn5zA5RVevXpWGDRu+tQIhKChILC0t5cyZM7nao6KixMjISAIDAz9mmJQPsMYUfXRZWVkAgPLlyyMjIwNpaWm4ePEikpKSlLoJ1tbWGD16NNq3b48pU6Zg7969uT4jZ9FPKrjk/2qURUREwNfXFxqNBosXL4azszO+/fZbeHt7AwBq164NX19f3Lx5Ex07dtRmyJQHqdVq3LlzB9OnTwcA+Pn5oX379njy5AlatmyJJk2aYOzYsXj48KFSnFNfXx+Ghoasp0FvSUtLQ+/evdGgQQNs3rwZ3bp1U3ZnHDduHAYOHIiwsDCsWrUKN27cgIWFBXbt2oWyZcuiSpUqWo6e8jqVSgULCwsUKlQI58+fx/Tp0+Hg4IARI0bA3t4eV69exeeff44xY8agY8eO0NHRQVZWFu9VpLh16xZWrVoFBwcHpa5PhQoVMHXqVMyePRuenp4AgO7du2PkyJFQq9UYPHjwO2tyslYZVa5cGUFBQahcuTJ+//13aDQaAK9rBBcpUgS//PILHjx4oJxfpkwZVKtWTTmPSKHtzBgVHG9O8czMzJTMzEyZP3++qNVqWblypSQlJeU65/79+7J06VK+kaG3ZM+a27VrlxQvXlxcXV3l4sWLyvFZs2aJjo5OrplTFy9elJiYmI8eK+VtGo1GFi1aJLVr15ZOnTqJrq6ubNq0STm+YcMGadGihXTq1EliYmLkxo0bMmPGDPnss8/eWuJAJPJ6WcJnn30mBgYGsmbNGhF5PbMl2/Lly6Vu3boyceJEZRk7l1nRm95VuuDMmTNSvnx5sbe3FxMTExk2bJhs27ZNjh8/LtWqVRN/f39thUv5yNmzZ6Vq1arSuHFjZann3bt3ZcaMGWJqaioeHh7KuX5+flKrVi0ZM2aMtsKlfODRo0diY2Mj9erVU55na9asERMTE5k0aZIcO3ZM7t+/Ly4uLmJlZZWrJiyRCJfy0UeSc8B96tQpOXTokISEhChtM2fOFLVaLV5eXkpyatCgQcqAXYTTheltJ0+elMKFC8uaNWveuUxh1qxZYmRk9FYhfaJ3cXR0FJVKJe3atcvVrtFoZOPGjWJvby8qlUpsbGykfPnycvbsWS1FSnldQkKCFC5cWCwsLOTzzz9XlvXlXDqzYsUKKVu2rLi4uEh6ejqXqFMuOcdNjx49kuTkZCWBEBoaKj/99JPs379fKRj88uVLqVu3ruzZs0cb4VI+dPbsWbG2tpbPP/9c6Vvx8fFKcsrT01M59+jRoxyH09/KyMiQgIAAqVOnTq4NF9atWye1a9eWokWLio2NjZQtW1bOnTun5WgpL1KJ/N9aGKKPwNXVFQEBAXj58iVKlCgBXV1dhIWFQaVSYd68eXB3d0e/fv1w/fp1JCQk4MaNG5x+Tm8REahUKixYsADh4eHYt2+fciwrKyvX1PLJkydj48aNiImJQeHChbURLuVxGRkZUKlUcHV1RXx8PO7fvw87Ozt8//33MDY2Vs7TaDQ4fvw4TExMYGVlBSsrKy1GTXldfHw8Xr16hY4dO8LExAQhISEoVKgQ0tPTlSWhW7ZsQZMmTVCxYkUtR0t5iUajgVr9utrGokWLsG/fPqSnp6NkyZLYvHkzihQpgszMTOjq6uLVq1dITk6Go6Mjnjx5gpMnT3J5Fb1T9tgp58+RkZHo2bMnihcvjmPHjkFPTw93797F2rVr4eHhAVdXV7i4uCjXvDnGIsrp1atXOHr0KCZPnowiRYogNDQUOjo6iImJwdOnT5Gamopq1apx/ETvxMQUfTA5B1YAsHz5cri7u2P//v1o2LAhFi5ciGnTpuHgwYNo06YNAMDDwwMRERHQ09PDmjVroKenx4cg/aWJEyfi3LlzOHr0aK6+BryuO1W/fn2o1Wo8evQIxYsX11KUlFe9OUjPbps9ezYOHTqEzz//PFdy6t69e7CysnqrrxFl96VHjx4pzy0LCwtoNBpcvHgRvXv3RuHChREcHIxChQph2bJlSE1NVeqaEWXLeV+aNm0afv75Z7i7u6No0aJwc3ODvr4+Dhw4gNKlSyMtLQ2LFy/G0aNH8eLFC4SFhXHcRO+Uc0yu0WiQnp4OQ0NDAEBkZCS++eYblChRQklO3bt3D4sXL8bly5cRGBgIgPVd6U/Z96kzZ87gzJkzUKlUaNy4MWxtbXMlp8zNzREaGspxE/0r7CX0QTx69AhqtVopdJ6VlYULFy5g/vz5aNSoEfbt24eFCxdizZo1aNOmDZKTkwEAY8eOxdq1a+Ht7Q09PT1kZmZycEUA/ix0fvfuXeXfpUqVwuXLl98qyJmWlobNmzfj119/BQAmpegt2YOq4OBgjB07FtOnT8fRo0ehUqng4uKCdu3aISIiAq6urvjjjz8wa9Ys9OzZEy9fvtR26JTHZPelX3/9FR07dkSLFi3QuHFjBAcHQ61Wo3bt2tixYwdSUlJQpUoV9OnTB87OzujUqZO2Q6c85ObNmwD+/OM/MDAQBw4cgJ+fH4YOHQo9PT0kJCTg6dOnaN68Oe7evQtDQ0N8+eWX+Prrr3HixAmOm+idcialli5dir59+6JRo0ZYtGgRzp49i7p168LPzw8PHz5EixYtkJGRgVKlSmHatGkIDAxkQopyyX7m7d69G506dcKGDRvg6+sLe3t7BAcHw8DAAA4ODliyZAlSUlJQt25dFjqnf+ejLx6kT97s2bPF2NhYYmNjReR1nYSsrCxp1qyZrFmzRg4ePCgmJibi5eUlIq9rR/3444+yefPmXJ/DehuULbsv7N27V2rVqiUbNmxQjjVs2FBq1aol169fl9TUVElLS5OpU6dKmTJl5Pbt21qKmPKDgIAAMTQ0lNatW0v9+vXF3Nxctm7dKiIiL168EHd3d7G1tZVy5cqJpaWlnDp1SssRU14VEBAgJiYm8sMPP8jx48fFyclJChUqlOu59uDBAxk9erSMGDFCLl26pMVoKa/p2rWrzJgxI1dbUFCQzJ07V0REfvvtNylWrJisWrVKLl68KMWKFZM6derIrVu3cl3DGkCU05vjaFdXV7GwsJBp06bJyJEjpUqVKvL111/LoUOHRETk3LlzUrVqValUqVKuup0cj9Objh07JsWLF5e1a9eKyOt6ZSqVSvT09MTPz09EXtdU3Lt3rzRu3JjjcfpXmJii9+748ePStm1bqVSpkjJoyszMlKlTp0rLli3FzMxMfvrpJ+X8xMRE+eqrr2TlypXaCpnygV9//VUMDQ1lxYoVuf6oi46OlmbNmomFhYXY2tpKixYtpHjx4iysSH/r8ePHsmrVKmVQFRsbK1OmTBGVSiVbtmwRkde7qJ06dUp8fX3f+gOQKNudO3fE3t5efvzxRxERiYuLk4oVK0r16tVFV1dXfHx8chWyzi4yTJTtxIkTSmH8xMREpf3u3bvy6tUradWqlUybNk1ERJKSkqRJkyaiVqulU6dOIsLEAf217HtPVFSUWFtb59p4KCQkRDp16iRdunSR+Ph40Wg0Eh4eLr169WKSk/7WnDlzZPr06SLyumB+2bJlZciQITJs2DDR1dWVgwcPisjr5FT25h9E/4SJKXpvfvnlF+XfERER0rp1a6lQoYLcvHlTREROnz4tpUqVknr16snly5clKytL7t27J+3bt5fGjRvzIUh/KSUlRVq3bi1ubm5/eY63t7csXLhQPDw8lD5H9C4XL14UMzMzqV69ujJ4Enm981V2cip75hTRu2QnAp49eybp6ekyf/58efr0qdy/f1+qVasmTk5Okp6eLj169BBzc3P5+eefmTygf7Ry5Urp1q1brhcrd+7ckXLlyklgYKCIiDx9+lR69eolp0+fzpXwJMrm4uIiU6dOzdV2+fJlKVmypISGhuZqP3LkiBQpUkTpXzlxXE7Zsp9fgYGBcvnyZYmOjpYTJ05IcnKyNG7cWL799lsReb3zukqlEpVKJfv27dNmyJQPscYUvReHDh1Cr1694O7uDgBo0KAB5s+fD2tra7Rq1QoxMTGoX78+tm3bhvv376N///6wtrZGjx498OjRI2XXhuyaVEQ5paWlITo6GjVr1gSAXGvV5f/qTQ0ePBguLi4YM2YMd7iiv6Wnp4fu3bvj5s2bePbsGYDX/ahYsWJwcXGBq6sr+vfvDz8/Py1HSnmVSqWCr68v7Ozs8PLlSwwZMgTm5ubw8PBAxYoVsXTpUujp6aFMmTJQq9VwcXFBUlKStsOmPEbe2H+oTJkyOHXqFDw9PXHhwgUAQNmyZVGiRAm4urpi586d6NatG+7du4d69erlquVJBAB//PEHEhIScPToUfzwww9Ke2ZmJlQqFe7du6f8DAAtW7ZE2bJlERER8dZnsVYZZVOpVAgLC0OXLl0QGRmJypUro0mTJrh+/TrS09Mxfvx4AEDhwoXRq1cvuLm5oXLlylqOmvIbJqbovbC3t4eXlxfmzp2LefPmAXidnHJ3d0eVKlXQunVrREdH44svvkBwcDCmT5+OESNGwNXVFadOnWLBTvpbpqamsLCwwJkzZwAg12D8zJkzWL9+vXLumwN9ojdVrVoVU6ZMQc+ePeHk5ITg4GCluKuFhQUmTpyImTNnwsbGRsuRUl517949bNmyBZMmTYKZmRksLS0BANeuXUOpUqVgZmYG4PXGHxs3bsSNGzdQuHBhbYZMeVD2fef333/Hixcv0KVLF6xfvx5BQUFYtmwZoqKiAAArV66EgYEB5s2bBwMDAxw5cgRqtRoajYbjJsqlcOHCWLx4MRo2bIgDBw5g/vz5AABbW1t88803GD58OE6dOgVdXV0AwPPnzyEi+Oyzz7QZNuVxd+7cwYEDBzB9+nT069dPaX/27BnOnTuHFy9eAAC2bt2KZ8+eYerUqRxD0X+n1fla9El59eqVeHl5iY6OjlKwU+T1sr42bdpI+fLlJSYmRkTerofA6cKULbtvZGRkyIsXL5T2iRMnSu3atZWiitmcnZ2lcePG8uzZs48ZJuUT2f0pNjZWrl+/LhcuXFCOXbt2TQYNGiTm5uYSFBSU63wukaG/cubMGenbt6+0bdtWEhMTcz2/Zs6cKYUKFZIFCxaIo6OjmJuby/Xr17UYLeVFOe8vAQEBYmtrK0uXLpW0tDQREdm/f7+ULVtWBg4cKFeuXFHOjYuLy/WMJMop570oKChIevbsKZUrV1bq34mI9O7dW/T19cXZ2VnmzJkjbdq0kVq1arE/0V+6cuWKNGnSRMqXLy9r1qwRkT/72qtXr6Rnz56iUqmkfv36YmpqKufPn9dmuJSPqUQ4vYD+d/J/W4ZmS0tLg7e3N8aOHYvZs2djxowZAIDTp09jxowZuHnzJg4cOABra2tthUx5WHZ/OnDgADZv3oyoqCh8/fXXaN++PZo0aYKuXbvi6dOnqFOnDmxtbXH69Gns3r0bx48fh62trbbDpzwmuz/t3bsXM2bMQHJyMoyNjdGmTRssW7YMwOsZLj/88AMOHDiAjRs3ol27dlqOmvK6uXPnYuPGjXjx4gVu3LgBU1NTZGRkQE9PD6mpqXBzc0NISAjMzc2xfPly1KlTR9shUx6i0WigVr9esLBlyxZcuHABa9euhbm5OSZNmoShQ4fC0NAQBw4cwKhRo+Dg4IBRo0ahfv367/wMojdNmjQJUVFRUKvVOH/+PIyNjTFy5Ei4uLgAAL7//nscOXIE6enpKF++PH7++Wfo6ekhKyuLM/DoncaOHYvNmzejVatW8PHxgampqTLGevLkCQ4cOIA//vgD7dq14xI++t9pMytG+VvON34ZGRm5ZkF5eHiIWq1+a+ZUvXr15JtvvvmocVL+kN1/9u7dK8bGxjJjxgzZvHmz2NvbS4UKFeT27dvy/PlzmT59utjb20utWrWkc+fOuWbAEL3pwIEDYmJiIqtWrZIbN27IqlWrRKVSyYgRI5Rzrl27Jt27d5cKFSpIamoqi1TT30pPT5clS5ZIqVKlZMCAAfL8+XMRyT0T+OnTp5KamqqtECkfmD59ulIYf9OmTdKsWTOpXbu2LFu2TF6+fCkir+9fBgYG4u7uruVoKb/YsWOHFClSRCIiIuTly5fy4MEDGThwoNjZ2cmiRYuU8/74449cM6w4Y4qy/dUYaNKkSVK9enWZN2+e/PHHHx85KioIOGOK/ic539YtX74c58+fR0xMDLp164YuXbqgYsWK8PLywrhx4zBr1ixl5tTVq1dRtWpVvukjAMCBAwdQunRp2NraQkTw+PFj9OjRA126dMGECRPw8uVLlCtXDv3798eSJUty9ZuUlBTo6+tDX19fi9+A8rLHjx9j6NCh+OKLL/Ddd98hISEBTZo0QZUqVXDixAn07t1bqU8WHR0NExMTWFlZaTlqykvk/94IP3jwQJkRVaZMGWRmZuLHH3/E7t270bBhQ3z//fcwNTVFZmamUruF6F1EBPfu3UOrVq0wY8YM9O/fHwCQmpqKYcOG4fTp0xg/frwycyo8PBwNGzbkTBb6VxYtWoTt27fj9OnTyr3o7t27GDlyJM6ePYspU6Zg4sSJua6RN1Y/UMGV3RciIiIQHh4OfX19VKxYEW3btgUATJgwAcePH0fXrl0xduxYmJmZcQYnvTfsRfQ/yb4Bubq6Yv78+WjQoAEcHBywfv16DB8+HC9evICTkxM8PDzg7u4OZ2dnAICNjY1SsJMKtgcPHmDMmDFYvnw5rl69CpVKBWNjY6SkpKB9+/aIjY2FtbU1unTpgqVLl0KtVuPQoUOIjo4GAJiYmDApRbmIiFL8Pjo6GsWKFcOXX36JTp064eHDh2jTpg3atm2Lffv2YeLEifD29lb+KLS2tmZSinLJHqD7+/ujffv2aNSoEVq2bAl3d3fo6upi8uTJ6NKlC86cOYPp06cjKSmJSSl6p5zvgFUqFQoVKgS1Wq0UDM7MzEShQoWwadMmqNVqeHl5Ye3atXj16hUaN27MXYvpH2WPq0uUKAGNRqPsvqfRaFC6dGm4ubnhxYsXWLlyJXx8fHJdy6QUZVOpVPDz80Pr1q2xc+dOeHl5oUOHDsrfccuXL0fjxo3x66+/4ocffkBycjKTUvTesCfRf5Y9wIqIiMC+ffsQEBCA0aNHo1mzZoiNjUW/fv1gbGwMAwMDjBw5EvPmzUN4eHiugRlvYlSyZEns2rULly5dwtKlS3Hp0iXo6Ojg5cuXCAkJQZs2bdC+fXv89NNPAIDbt2/Dx8cHMTExWo6c8prk5GQArwdUKpUK+/btQ4sWLXDlyhUMHz4clStXxs6dO1GyZEnMmTMHBgYGKFWqFOzs7BAeHq4M4IlyUqlUCAoKQu/evTFo0CDMmTMHY8eOxZw5c+Dk5AQdHR1MnjwZnTp1QmBgINzd3bkrKL0l52yUx48fAwD09PRgZmaGoKAgAICuri6ysrKgq6uLunXrQl9fH7t27cLx48eVz+GMKcrpzRe82ePqBg0aIDY2FitWrMCLFy+U9oyMDDRv3hwTJ06Eo6PjR4+X8ofo6GiMGTMGCxcuxPHjxxEaGgofHx94enrC1dUVAODh4YEaNWogPDwc6enpWo6YPilaWD5I+dCsWbMkICAgV9vRo0fFxsZGRER27dolpqam8tNPP4mISEpKivj7+0tqaqpkZWUp65VZu4XedO7cOalXr544OTnJ/fv3xdPTU1QqlXz11Ve5znNzc5OaNWtKXFycliKlvGjYsGEyePBgSU9PFxGRO3fuSK9evWT16tW5zhs5cqQ0bNhQ+XnKlCmyYMGCXDs/EmXLflaNHDlS+vbtm+vY0aNHRa1Wyw8//CAir3cl+vHHHyU2NvZjh0l5XM5anHv27JFWrVrJ5cuXRUTk999/F2NjYxkzZoxSp1Oj0UifPn3kwIEDUrduXenRo4e2Qqc8LOdYevXq1fLdd9/JzJkz5c6dOyLyekyuo6Mjw4cPl/3798vly5elXbt2MnLkSOVa7oZN73Ly5EmpWrWq3L17N1f7xo0bxcjISEJCQpS2xMTEjx0efeI455z+0aVLl3D48GGcOHEChoaG+PLLLwG8fgtoYWGB7du3Y8SIEfjhhx8wYsQIAMCpU6ewd+9e1KhRQ9mdQbiGnd6hbt26WL9+PYYMGYKZM2eid+/emDRpEpYtW4bFixcDAGJjY7FlyxYcO3YMZcqU0XLElFfs2LED/v7+CAwMhJ6eHiIjI+Hl5YV79+7BwcEBwJ/18Lp06YINGzaga9eu0NfXx6FDhxAeHg4jIyMtfwvKS7KfUy9evEChQoUQGxsLc3Nz5VhGRgbs7e0xb948bN26FQMHDkTJkiXx3XffaTlyymty1l05cuQI/Pz8cO7cOcyePRtz5sxBw4YNsWXLFvTr1w+RkZEoWbIk7t+/j6dPn2Lbtm04efIkjh49yvotlEvO/uDq6gpvb2/Url0bDx8+hLe3N4KCgtC9e3fs27cPU6ZMwf79+6Gjo4NixYph3759UKlUEBHOwKN30tPTQ3R0NKKjo1GqVCnlmejg4AArKyskJCQo55YsWVKLkdKniE86+kc1a9bE/PnzYWhoiEWLFuHQoUMAgJYtW+KPP/5Av3798P3332PkyJEAgLS0NCxduhTJycmoWLGi8jlMStFfqVu3Lry9vREZGYmdO3eiTZs2WL58OTZu3Ag/Pz88f/4cJ0+e5LbrlEt8fDwsLCxQp04dHDx4EAMHDkRYWBjOnDmD2NhYAH8ub2jSpAk2bNiA1NRUqNVqHDt2DDY2NtoMn/KY7AF4UFAQZs6cibi4OHTu3BlHjx7FmTNnoFKpoKenBwAwNzeHSqWCmZmZlqOmvCr73vPdd99h7NixKFq0KJo3b45jx45h+vTpuHbtGrp27YqoqCjUqVMHZmZmaNSoES5dugTg9WYxFStW5PJQyiW7Xz18+BAvXrzAoUOHcPjwYWzbtg22trb4/PPPce3aNXz11VcIDAxEcHAwfH19ERERAT09PWRmZnI8TgD+LM1y9epVhIWFITY2FvXq1UPHjh2xatUqnD9/XukrxYsXR5EiRbh0jz4o7spHfysjI0MZiO/YsQObN29GSkoKZs+ejZYtW+Lq1avo3LkzLCwsMHz4cGRmZsLX1xeJiYmIjIyErq4u3/bRv3bu3DkMHz4cderUwdy5c2FpaQmVSoW0tDQYGhpqOzzKY06fPo0BAwbgs88+Q2hoKAIDA5GRkYHJkyejYsWKmDlzJurXr5/rGo1Gg4yMDBgYGGgpasrLdu/ejf79+2Pq1Kn46quvYGhoiKlTpyIrKwtz586FnZ0dAGDy5Mk4e/Ys9u3bB1NTUy1HTXlVaGgoevXqhT179qBx48YAgPXr18PHxwclS5aEu7s7bGxskJWVpcxgefjwIRYvXgwfHx+EhoaievXq2vwKlAdt2bIFI0eORPXq1bFr1y5lJnlMTAzGjx+P8PBwhIeHo2rVqrmuy9nPiADA398fAwYMgKWlJeLj47F+/Xq8fPkS27dvh5mZGYYPH47y5ctj48aN2LBhA37//XeUL19e22HTp0pLSwgpn5k1a5b07NlTbG1tRa1WS7NmzSQoKEhERKKjo6VVq1ZSs2ZNadq0qQwcOFCp98I17PRfnTt3Tho0aCC9evWSS5cuiQhrk9FfGzVqlKhUKmnUqJHStm3bNqlfv74MGDBAzp49q7TnrPdC9Kbr169LhQoVxMvLK1e7v7+/dOzYUSwsLOSrr76Stm3bipmZmURGRmonUMo3goODxcLCQnmWZVu5cqUYGBhI9+7dlZpTIiLx8fHy/fffS5UqVdi/6C8dOXJE2rZtKyYmJkpdqexxUkxMjHTs2FFUKpXEx8drM0zKw7KysuTJkyfStGlTWbNmjURHR8u8efNEV1dXVq1aJevWrZNevXqJWq2WatWqSeXKleXcuXPaDps+cZwxRf/Iy8sLrq6uCAgIQOXKlREeHg5PT0+o1WpMnz5dqeXy6NEjGBsbo1ChQgBeb3/MrbPpf3H69GlMmTIF27dvh5WVlbbDoTzq5cuX6NChAypWrIiTJ0/C1tYW27dvBwBs27YNy5YtQ82aNTFy5Eg0bNhQy9FSXhcUFITRo0cjMDAQ5cqVyzXb99q1azh79iwCAwNRunRpDBgwANWqVdNyxJRXyf8tCz116hT69euHlStX4uuvv1b6VFZWFmxtbWFsbIwaNWpgwYIFsLKygkajQUJCAnR1dVm/hQDgnasORARnzpzByJEjkZSUhBMnTqB48eJKv7t+/TrWr1+PBQsWcBxOuWT3kbS0NIgI3N3dMXnyZKWW4rJly+Ds7IwlS5agT58+SE5ORnp6OiwsLFCiRAktR0+fOt6t6B9FRESgU6dOaNGiBQDgm2++gYmJCSZNmoSZM2dCrVbD3t4exYsXV64RET4M6X/WoEEDHDx4kMv36G8ZGRkhICAAxsbG8Pb2xqJFi9C3b19s27YNffv2VZLnhoaGqF27Npfv0d9KSUnBy5cvc7VlL31JTExE06ZN0a9fPy1FR3nZm8mD7Losn3/+OapUqYLx48ejTJkysLW1BQAkJiaiVq1asLGxwaZNm3DlyhVYWVlBrVajVKlSWvkOlPfk7Fd79uzB/fv3odFo0Lp1azRo0ABr167FuHHjYG9vj6NHj6JEiRIQEVStWlXZPIYviSknlUqFvXv34qeffkJ8fDw0Gg169eqlJKYmTpwIlUoFZ2dnPHz4EG5ubsqEA6IPjYV/6B8VLVoUT548yTVgb9euHRwdHXH27FlMmDABv//+e65rWFiR/n8xKUX/hrGxMQCgZ8+ecHFxQWRkJPr27QsA6N27NxYuXAhnZ2cmpegf1a5dG48fP8batWsBvC4ynF2Pxd/fHxs2bGDhV3pLzuTBzp07MWvWLHh4eCA0NBQAcODAARQvXhydOnXC999/Dx8fHwwcOBApKSmYNWsWRAS//fabNr8C5VHZ/crZ2RmjR49GSEgIvL290bdvX3h7e6NevXpYtGgRLCws8OWXXyIxMfGt8TeTUpTTmTNn4OjoiAoVKqBhw4a4efMmvL29cefOHeWcCRMmYO7cufDy8kJaWpoWo6WChokp+ke2trY4efIkgoKCcu0OU7JkSTRp0gTffPMNGjRooMUIiaigMzExQc+ePeHs7IyLFy+iQ4cOAF7P8KxQoYKWo6P8oEKFCvD09MTixYvh7OyMS5cu4erVq3BxccHGjRvRp08f6OvraztMykNEJFfyYMKECTh79iz27NmDKVOmYPPmzVCpVAgPD8eXX36J/fv3Y8GCBdDX18fOnTsBAFZWVqhSpYo2vwblYdu3b8f27duxb98+7Ny5E+PGjcPly5dRpEgRAK93nF2yZAnS09MxefJk7QZLedrNmzcREBCAqVOn4qeffsKGDRuwYsUK+Pn5YfXq1bmSUy4uLrh16xYsLCy0GDEVNEyj0z8aNGgQTpw4gf79+2P16tWoV68eLC0tsXv3bjg4OMDNzQ0qlYq77xGRVhUqVAg9e/ZEWloafHx8cO/ePS6Lof9k0KBBMDU1xfDhw7F9+3YYGhpCR0cHR44cYU0pyiXnmGfVqlX45Zdf4Ofnh88//xxeXl6YOHEiZs2ahRcvXmD48OFYv349nj9/DhFRls3MnDkTsbGxaNWqlTa/CuVhMTEx+OKLL1C/fn3s3LkTEyZMwIoVK9CtWzekpKTg4cOHaNiwIXbt2gUbGxtth0t5VFJSEnr37o3bt2/j22+/VdpHjhwJjUaDBQsWQEdHB05OTsrLvOzkJ9HHwuLn9LdyDrzGjBkDf39/ZGVlwdTUFDo6Orh48SJ0dXWVYnpERNr24sULZGRkoHDhwtoOhfKp+/fv486dO1CpVKhQoQILUVMuOcc8SUlJcHNzQ/ny5TF58mTs27cPjo6OGD9+PKKjo3Hs2DEsXLgQ/fv3V66Pjo7GzJkzERoaiv3796Nu3bra+iqUh7zrBa+rqyt0dHTQsWNHtG7dGosXL8aIESMgIvDx8cHTp08xbtw46OnpAfizLh7RmyIjI9GrVy+UKFECq1evRs2aNZVjq1evxsSJEzF16lS4ublxCShpBRNT9I9yDsDCw8Px+PFjpKamokePHtDR0eFDkIiIiAqEo0eP4v79++jXrx+GDx8Oc3NzjB8/Hi9fvkRWVha++uorjB49GhMmTMCePXvQp08f6OnpYdOmTejatSsAIC0tDYcPH4aNjQ0qV66s5W9EeUHOpNTNmzdhZGSE4sWL4/Tp02jWrBkAwNfXFz169AAApKamolu3bqhZsyZ+/PFHrcVN+cuFCxcwcOBANGzYEOPGjUONGjWUYz///DO++OILWFtbazFCKsiYmKJ/5a+W6TEpRURERJ86EUFKSgq6d++O9PR0mJmZITQ0FGFhYcpue1u2bIGHhwcCAwNRuHBhBAYGYs2aNWjfvj0GDx7M8RK9U84XwK6urti7dy8ePXqEGjVqKLXtRo0aBW9vbzRt2hRJSUmYMmUKHj58iIiICM5uof8kMjISQ4cORb169TBx4kRUr15d2yERAWDx8wJLo9G8s/2v8pTZSak3r+Mgi4iIiD51KpUKpqam2LFjBxITE/Hrr7/Czc1NSUoBgJ6eHuLi4hAWFoYXL17Aw8MD5cuXh5OTkzLDnCgnjUajJKV27NiBjRs3YuHChfjxxx/RqFEjTJgwAadPn8aiRYvg5OSExo0bw9HREenp6fj999+hq6vLfkX/Sd26dbF+/XpcuHAB8+bNw7Vr17QdEhEAFj8vkHLOfrp06RJevHiBEiVKoHz58lCpVH85Cyrn7jPXrl1D2bJlla3aiYiIiD51arUalSpVQsmSJREcHIzSpUujX79+AIDq1avjiy++gKOjI4oUKYJChQph9+7dUKlUEBG+zKO3ZI+rQ0JCEBwcDGdnZ3Tu3BnA6/pl5cuXh6urK7Zv347Lly8jPj4eZmZmqF27NtRqNTIzMzljiv6zunXrwtPTE1OmTGE9TsozuJSvgMk5XXjatGnYt28f4uLi0KhRIzRs2BDu7u4A3l6il/M6Dw8PLFiwACdPnkT58uU/+ncgIiIi0qbExEQ4OTnh5cuXcHJyUpJT169fx7Vr15CcnIw+ffpAR0eHyQP6W4mJiWjWrBkePnwIFxcXTJs2TTn25MkTODk5oUyZMvDw8Mh1HXfDpv9faWlpMDQ01HYYRAC4lK/AyU4uubu7Y/369VixYgViYmJQqlQpeHp6YsyYMQCQa8p5zqTUmjVrMHv2bCxdupRJKSIiIiqQLC0t4enpCWNjY2zcuBHe3t7IysrCqFGjcPHiRfTv318ZSzEpRX/H0tISu3fvRokSJbB7925ERkYqxywsLFCsWDHcvHnzreuYlKL/X0xKUV7CO1oBkXNi3JUrV7Bnzx5s3boVDg4OiIqKwi+//II2bdrg4MGDmDBhAoDXyamMjIxcSSlnZ2esXbsWvXv31sbXICIiIsoTKlSoAA8PD5iammLJkiWwtrbGw4cP4ezsrJzD5Xv0b9ja2mL37t3IysrC8uXLcf78eQBAcnIyrl69itKlS2s3QCKiD4xL+QqAnDOeoqKiYGtri/Xr16Nbt264dOkSevfujXnz5mHIkCHo0KEDjhw5gm7dumHbtm3KZ6xduxbOzs74+eef0b17d219FSIiIqI8JSEhAWfPnsWDBw8wcOBA6Orqcvke/U8iIyPRv39/PH36FPXr14e+vj5iY2Nx6tQp6Ovr5xrTExF9SpiY+sS9uQXtqVOnsGPHDpQsWRIqlQojR46Erq4uli5dCj09PUyZMgWnT59G9erV4enpCbVajX379qFLly7YtWsXunXrpuVvRERERJR3/dUmMkT/xqVLl9CpUyeULl0affv2xYgRIwAAGRkZ0NPT03J0REQfBpfyfeKyk1LXrl1DeHg45s+fD0tLS6U9NjYWd+/ehZ6eHrKysnDnzh0MGDAAq1atUtaud+jQAUePHmVSioiIiOgfMClF/z9q1qyJ3bt3Iz09HefOnUNMTAwAMClFRJ80zpgqABYsWICQkBAYGhpiy5YtMDU1hUajAQAsX74cmzdvhpWVFZKSkvD8+XNERUVBR0cHIsKinUREREREH1lkZCRGjBiBihUrYtasWahWrZq2QyIi+mA4Y6oAsLGxweHDh3H8+HHcvn0bwOudPNRqNfr06QNHR0cULlwYNWrUQGRkpLKLjEqlYlKKiIiIiOgjq1u3Ljw9PZGQkIDChQtrOxwiog+KM6Y+MX9VFDE4OBht2rTBoEGDlOV8f4UFO4mIiIiItC8tLQ2GhobaDoOI6IPijKlPiEajUZJSDx8+RFxcnHKsVatW8Pf3h4+PD9zd3fHgwYNc12UTESaliIiIiIjyACaliKggYGLqE6HRaJRi5XPnzkX79u3RoEEDtGvXDiEhIUhLS0PHjh3h7++P1atXY/78+UhISAAA5ToA3IKWiIiIiIiIiD4aJqY+ASKiJJdmzZqF1atXY8KECQgPD8etW7cwffp0BAQE5EpOeXp6Yvv27VqOnIiIiIiIiIgKMq7ZyseuXr0KGxsb5ecTJ05g79692LJlCxwcHBAWFoZ79+5BRDB9+nTo6Ojgq6++QocOHRAWFoZGjRppMXoiIiIiIiIiKug4YyqfWrJkiZJ8UqlUEBGYm5tjzJgxcHBwQHBwMLp164ZVq1YhOjoaaWlpWLp0KXx9fZGeno6mTZtCV1cXmZmZ2v4qRERERERERFRAMTGVT9WqVQtffPEFxo8frySnrK2t0bFjR2RkZGD58uUYNmwYHB0dISKwtrZGVFQUTpw4AX19feVzWOiciIiIiIiIiLSFial8Zt26dQCAtm3bYtSoUahcuTLGjh2LY8eOQU9PDyVLlkR6ejoeP34MCwsLpfZU2bJlERISgtWrV2szfCIiIiIiIiIiBafL5CNBQUEYPnw4oqKi4OnpiRYtWkBE4OXlhXHjxsHDwwPNmzeHWq2Grq4udu3ahaSkJISFheHJkyeoW7cu1Go1srKyoKOjo+2vQ0REREREREQFHGdM5SMNGjTA2rVrsWvXLowePRoAYG9vj1GjRqFKlSoYO3YsQkJCYGRkBD8/PxgbG+PEiRMwNTXFmTNnoFarodFomJQiIiIiIiIiojxBJSKi7SDo30tOTsaOHTswbdo09OjRA6tWrQIAhISEwMvLCzdu3MDSpUvh4OCAtLQ0iAgMDQ2hUqmQmZnJmlJERERERERElGcwS5EPiAhUKhUAwNTUFD169AAAuLm5AQBWrVoFe3t7AICXlxemTJmCBQsWoE2bNrk+g0kpIiIiIiIiIspLmKnI4zQajVLAXKPRIDMzE0WKFMHAgQMBAFOnTgXwZ3JKpVJh3rx52LZtW67EVHZii4iIiIiIiIgor2BiKg/LmZT68ccfERUVhXPnzmH48OFo2bIlhg0bBgCYNm0aVCqVUhDdzMwMtWvX1mboRERERERERET/iDWm8oGpU6fi559/xsyZM5GSkoL169ejWrVq2LFjB7KysrBz505Mnz4drVq1wtatW5Xrcia2iIiIiIiIiIjyGmYt8riIiAj4+/sjICAAY8aMQbNmzRAXF4eePXvCxMQEhQsXxoABAzB16lQ8f/4cGo1GuZZJKSIiIiIiIiLKy5i5yOM0Gg0MDQ3RqFEj/PLLL2jfvj1WrlwJR0dHpKam4sCBAwCAb7/9Fr/++ivUanWu5BQRERERERERUV7FxFQe8q6EUkpKCtLS0rBjxw58++23WLhwIUaMGAEAOHnyJLZt24a4uDgYGRlBpVJBRDhTioiIiIiIiIjyBdaYyiNy1oNavXo1ACgJqLZt2+Lw4cPw8PDA6NGjAQBpaWn45ptvYGRkBF9fXyajiIiIiIiIiCjf4a58eUR2YmnKlCnw9fXFwIEDcffuXZQuXRrff/89/vjjDyxbtgyFCxfGs2fPEBAQgPv37+P8+fPK8j0mp4iIiIiIiIgoP+GMqTxky5Yt+O677/Dbb7/Bzs5OaddoNLh27Rrmzp2LqKgolChRAtbW1vjpp5+gp6eHzMxM6Ooyx0hERERERERE+QsTU3mIm5sb7t27h40bNyIrKws6OjpvJZ0ePHgACwsLpY1JKSIiIiIiIiLKr7j2Kw+5d+8eYmNjAQA6OjoQEejq6iItLQ1BQUEAgJIlSyqJqOzjRERERERERET5ERNTWvCu3fcAoG7dunjw4AGOHj2K9PR0qFQqAEBSUhLmzJmD3377Ldf52ceJiIiIiIiIiPIjLuX7yHIWKT99+jQ0Gg10dHRQv359vHr1Ck2bNgUATJ06FU2bNkVKSgomTJiAZ8+e4dixY9DR0dFm+ERERERERERE7w0TUx+RiCiznFxcXLB9+3aoVCo8ePAAffr0waJFi2BqaorOnTvj3r17iImJQfXq1aGnp4fjx49DT09PqT1FRERERERERJTfMTGlBZ6enpgzZw727t0LCwsLxMfHY8CAAWjUqBG2bt0KfX19XLlyBdevX0fJkiXRrFmzdxZCJyIiIiIiIiLKz5iY0oKBAwfCyMgIq1evVmZRnT9/Hl988QXGjh2L+fPnv3UNZ0oRERERERER0aeGxc8/sDfzfhkZGbh37x7S0tKU4+np6ahTpw5mz56NnTt34tmzZ8jKysp1HZNSRERERERERPSpYWLqA9JoNEpNqVu3buHhw4fQ09ODo6Mjdu3aheDgYKjVaujp6QEADAwMUKxYMRQqVIiJKCIiIiIiIiL65DEx9QFl777n5uaGTp06oXr16nB2doaJiQmGDBmC0aNH4+DBg9BoNPjjjz/w66+/olSpUkqiioiIiIiIiIjoU8ZK2h+ARqNRklI7d+7Epk2b4OnpiQsXLuDgwYOIi4vD559/jo4dO6JDhw6oWLEidHR0YGBggNOnT0OlUuXawY+IiIiIiIiI6FPE4ucf0LFjx+Dn54fatWtjyJAhAIB9+/bBw8MD5ubmGDZsGEqUKIHff/8dJiYm6NWrF3ffIyIiIiIiIqICg4mpDyQxMRHNmjXDo0ePMGfOHEyYMEE5FhAQgOXLl8PMzAxTp05Fw4YNlWPcfY+IiIiIiIiICgrWmPpALC0tsXv3blhaWuLAgQO4ePGicqxjx46YNGkSYmJisGfPnlzXMSlFRERERERERAUFZ0x9YFFRURg8eDDq16+P8ePHo0aNGsqxkydPolGjRkxGEREREREREVGBxMTURxAZGYmhQ4fCzs4OEyZMQPXq1XMd5/I9IiIiIiIiIiqImJj6SCIjIzF8+HCUK1cOixYtQoUKFbQdEhERERERERGRVrHG1EdSt25deHp6wtTUFOXKldN2OEREREREREREWscZUx+ZiEClUkGj0UCtZl6QiIiIiIiIiAouJqa0IDs5RURERERERERUkHHKjhYwKUVERERERERExMQUERERERERERFpCRNTRERERERERESkFUxMERERERERERGRVjAxRUREREREREREWsHEFBERERERERERaQUTU0RERET5lEqlgr+/v7bDICIiIvqfMTFFRERE9AF17NgR7dq1e+exsLAwqFQqXLhw4X/67ISEBLRv3/5fnz9o0CB06dLlf/pdRERERB8CE1NEREREH5CTkxMOHz6Mu3fvvnVsw4YNqF+/Pmxtbf/TZ6anpwMALC0tYWBg8F7iJCIiItIGJqaIiIiIPqAOHTqgePHi8PHxydWekpKCnTt3okuXLujTpw9KlSoFY2Nj1KpVC9u3b891rr29PcaMGYMJEyagWLFiaNu2LYC3l/LFx8ejZ8+eKFKkCIoWLYrOnTvj9u3bAIDZs2dj48aN2Lt3L1QqFVQqFUJCQuDg4IAxY8bk+n2PHj2Cvr4+goOD3/v/BxEREVFOTEwRERERfUC6urpwdHSEj48PRERp37lzJ7KystC/f3/Y2dlh//79uHTpEr799lsMGDAAERERuT5n48aN0NfXx4kTJ7B69eq3fk9GRgbatm0LU1NThIWF4cSJEzAxMUG7du2Qnp6OyZMno2fPnmjXrh0SEhKQkJCAJk2aYOjQodi2bRtevXqlfNaWLVtQqlQpODg4fLj/GCIiIiIwMUVERET0wQ0ZMgQ3b95EaGio0rZhwwZ0794d5cqVw+TJk1GnTh1UrFgRY8eORbt27fDLL7/k+gxra2ssWrQIVatWRdWqVd/6Hb6+vtBoNFi/fj1q1aoFGxsbbNiwAXFxcQgJCYGJiQmMjIxgYGAAS0tLWFpaQl9fH926dQMA7N27V/ksHx8fDBo0CCqV6gP9jxARERG9xsQUERER0QdWrVo1NGnSBN7e3gCAmJgYhIWFwcnJCVlZWZg3bx5q1aqFokWLwsTEBIcOHUJcXFyuz7Czs/vb3xEVFYWYmBiYmprCxMQEJiYmKFq0KNLS0nDz5s2/vM7Q0BADBgxQYjt37hwuXbqEQYMG/f99aSIiIqJ/QVfbARAREREVBE5OThg7dixWrVqFDRs2oFKlSmjRogV++OEHrFixAsuXL0etWrVQqFAhTJgwQSlwnq1QoUJ/+/kpKSmws7PD1q1b3zpWvHjxv7126NChqFOnDu7evYsNGzbAwcEB5cqV++9fkoiIiOg/YmKKiIiI6CPo2bMnxo8fj23btmHTpk0YOXIkVCoVTpw4gc6dO6N///4AAI1Ggxs3bqB69er/6fPr1asHX19flChRAmZmZu88R19fH1lZWW+116pVC/Xr18e6deuwbds2eHp6/vcvSERERPQ/4FI+IiIioo/AxMQEvXr1wtSpU5GQkKAslbO2tsbhw4dx8uRJXL16FcOHD8eDBw/+8+f369cPxYoVQ+fOnREWFobY2FiEhIRg3LhxuHv3LgCgfPnyuHDhAq5fv47Hjx8jIyNDuX7o0KFYuHAhRARdu3Z9L9+ZiIiI6J8wMUVERET0kTg5OeHZs2do27YtPvvsMwDA9OnTUa9ePbRt2xb29vawtLREly5d/vNnGxsb49ixYyhbtiy6desGGxsbODk5IS0tTZlBNWzYMFStWhX169dH8eLFceLECeX6Pn36QFdXF3369IGhoeF7+b5ERERE/0QlOfctJiIiIqIC6fbt26hUqRJOnz6NevXqaTscIiIiKiCYmCIiIiIqwDIyMvDkyRNMnjwZsbGxuWZREREREX1oXMpHREREVICdOHECVlZWOH36NFavXq3tcIiIiKiA4YwpIiIiIiIiIiLSCs6YIiIiIiIiIiIirWBiioiIiIiIiIiItIKJKSIiIiIiIiIi0gompoiIiIiIiIiISCuYmCIiIiIiIiIiIq1gYoqIiIiIiIiIiLSCiSkiIiIiIiIiItIKJqaIiIiIiIiIiEgrmJgiIiIiIiIiIiKt+H9QO1u8MBXrZAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_oil_production_l_ha.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_avg_water_need_m³_ha.png\n" + ] + }, + { + "data": { + "image/png": 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WkiVLjCTz7rvv3rBfkSJFTO3ata1/d+3aNdPje+2+q1OnTqZkyZLm8uXL1rajR48aJycnm8cwu9v89bz22mtGktm+fbu1LTEx0bi7u5tOnTpZ267dPxtjTHh4uClfvrxNW8Z7RnR0dKb+177OR40aZQoWLGh27Nhh02/AgAHG2dnZHDhwwBhjzPHjxzM9Phmu3Z/t27fPODs7mzfffNOm3+bNm02+fPms7bf6nAMAjOFIKQCAjaSkJElS4cKFs9U/47LuGb9EZ3j11VclKdORSVWrVlX9+vWtf4eEhEiSHnroIZUpUyZT+549ezLd59VHn2ScfpeamqoVK1ZY2/Pnz2/9/+nTp5WYmKjGjRtnOtVOkkJDQ1W1atWbrOmVMY3Wrl1rc3TG1f744w8dO3ZMvXr1sjmapUWLFgoMDMzyKK1rx/5p3Lhxlut8tRUrVig1NVUvv/yyzUC/PXr0UJEiRW5pvK+zZ89KuvnzXrhwYes2khtat26tH3/8MdPt3wzSfa3FixerZs2a1iOurna9Uw9PnTqllStX6sknn9TZs2etR3CdPHlS4eHh2rlzZ6ZTMnv27GmzvMaNGystLU379++XdOXqY2fPntWAAQMyHe109XwRERFau3at9ZQzSVqwYIH8/f0VGhp6w3U9efKkdbyvW/H333/rwIEDatGiRaZphw4dUmhoqC5duqSffvpJZcuWtU47cuSINm/erIiICBUqVMjaHhoaqurVq2d5Xxl1ZpxCeT1ubm7W7TwtLU0nT55UoUKFdN9999m8nhcvXiwvLy/17ds30zKufZ7btWtnPY1Lko4ePaqNGzeqW7duKlasmLW9Ro0aatq0qXU/J918P+Dh4SFJ+uGHH3ThwoUbrtvVbudrsEOHDjp27JjNFecWLVqk9PR0dejQQdKtbfPXyhgf7uqjpRYvXqzk5GTrqXuS7f45MTFRJ06cUGhoqPbs2ZPpdLdy5copPDz8puv4xRdfqHHjxvL09LQ56jIsLExpaWn66aefbrqMa3355ZdKT0/Xk08+abNMHx8fVapUSatWrZJ06885AIDT9wAA18g4bSTjC9LN7N+/X05OTpmu7Obj46OiRYtav5BnuDp4kv73Yd7f3z/L9tOnT9u0Ozk5qXz58jZtGVdqu/oUm2+//Vb/+c9/5O7urmLFillP28hqfI/rna52rfHjx+vvv/+Wv7+/6tWrpxEjRtgESBnrmtWVsQIDAzM9Fu7u7jZfjKUrX9SvXedrXe9+XF1dVb58+Uz3kx0ZX4Rv9ryfPXs224FldpQuXVphYWGZbtc7dexW7N6923p6Unbt2rVLxhgNHTpUJUqUsLllnCqaMQB7hmu37YzQJeP5zAiZblZLxmmSCxYskHTlS/u3336rzp07Z2v8LnOTMZpuZNmyZfL29ladOnUyTfvvf/+rY8eOafXq1SpVqpTNtIxtLqsrPF7vqo8Zdd5sndLT0/Xuu++qUqVKcnNzk5eXl0qUKKG//vrL5vW8e/du3XfffdkaqPra1/yNXrtVqlTRiRMndP78eUk33w+UK1dOkZGR+vDDD+Xl5aXw8HBNnz79pmML3c7XYPPmzeXh4WFzWmhUVJSCgoKs+89b2eavVaNGDVWrVk2ffvqptW3hwoXWxyHDr7/+qrCwMOvYXSVKlNCgQYMkKctQKjt27typ6OjoTLVnjK92s9qvt0xjjCpVqpRpudu2bbMu81afcwAAV98DAFyjSJEi8vPzyzQGy81kd7DrawfvvVn7rXzB/vnnn9WqVSs98MADmjFjhnx9feXi4qI5c+ZkOd7J1b/a38iTTz6pxo0b66uvvtLy5cv19ttva9y4cfryyy9vOCj09VxvnR2hYsWKypcvn/7666/r9klJSdH27duzDCzymoyxzPr163fdozSuDVtyaxv29PTUY489pgULFmjYsGFatGiRUlJSbnqVQkkqXrz4TUPNG/nuu+/UvHnzLF/Pbdu21bx58zR58mSNGTPmlu8jQ0adXl5eN+z31ltvaejQoXr66ac1atQoFStWTE5OTnr55ZczjTmXXdl9zWclO/uBd955R926ddPXX3+t5cuX68UXX9SYMWP022+/qXTp0lkut0qVKpJ0w9fg/v37lZSUlK0jO6/m5uZmHRdqxowZSkhI0K+//qq33nrL2udWtvmsdOnSRQMGDNAff/yh0qVLa9WqVXruueesYeHu3bv18MMPKzAwUBMnTpS/v79cXV313Xff6d133830nGb3uUpPT1fTpk31+uuvZzk9I3zLifT0dFksFn3//fdZvr6vPirwVp5zAAChFAAgC4899pg++OADxcXF2Zxql5WyZcsqPT1dO3futH6pkqSEhASdOXPG5hSf3JCenq49e/bYfMHYsWOHJFkHKF+8eLHc3d31ww8/2Fw2fM6cOf/6/n19fdWrVy/16tVLx44dU+3atfXmm2/qkUcesa7r9u3bM13mfvv27bn2WFx9P1cfNZaamqq9e/fe0pXXChYsqAcffFArV67U/v37s6z1888/V0pKih577LFbL94BKlSokOOQNeNxdXFx+VdXsru2DunKKXI3+3IfERGh1q1b6/fff9eCBQtUq1Yt3X///Te9j8DAQC1YsECJiYnWow2z68yZM1qzZk2mwdkz9O3bVxUrVtSwYcPk4eGhAQMGWKdlbC+7du3KNF9WbZK0d+9eSbLZb2Rl0aJFevDBB/XRRx9lqvfqQKtChQpau3atLl26JBcXlxsu81pXv6au9c8//8jLy0sFCxa0tt1oP5ChevXqql69uoYMGWIdMH3mzJkaPXp0ljVUrlxZlStX1pIlSzR58uQsj4aaN2+eJN3Sa7BDhw6aO3euYmJitG3bNhljrKfuSbm3zXfq1EkDBw7UwoULVbZsWaWlpdmcuvfNN98oJSVFS5cutTm6MONUuFtVoUIFnTt37qa15+RqoRUqVJAxRuXKlctWqJXT5xwAwOl7AIAsvP766ypYsKCeffZZJSQkZJq+e/duTZ48WZL06KOPSrpylbSrTZw4UZKyHJvm35o2bZr1/8YYTZs2TS4uLnr44YclXTlixWKxKC0tzdpv3759WrJkyS3fZ1paWqZTMUqWLCk/Pz+lpKRIkurUqaOSJUtq5syZ1jZJ+v7777Vt27ZceyzCwsLk6uqqKVOm2ByF89FHHykxMfGW72fIkCEyxqhbt266ePGizbS9e/fq9ddfl6+vr5577rl/Vb+9tWvXTps2bcry6mHXO4qpZMmSatKkid5//30dPXo00/Tjx4/nuI5mzZqpcOHCGjNmjJKTk29YxyOPPCIvLy+NGzdOq1evztZRUpJUv359GWOue8W7G1m+fLm1zusZOnSo+vXrp4EDB1qvYiZJfn5+qlatmubNm6dz585Z21evXq3Nmzdnuaz169fLw8PjpmGbs7Nzpsfniy++yDS+Ubt27XTixAmb/UOGmx2t5uvrq6CgIM2dO9fmaml///23li9fbt3PZWc/kJSUpMuXL9v0qV69upycnGz2C1kZNmyYTp8+reeff95m/yVdebzGjRunatWqqV27djdcTlbCwsJUrFgxRUVFKSoqSvXq1bM5NS63tvkyZcqocePGioqK0ieffKJy5crZXMk144ijq5+TxMTEf/2jwZNPPqm4uDj98MMPmaadOXPG+pwUKFDA2nYzbdu2lbOzs0aOHJlpGzLG6OTJk5L+3XMOAPc6jpQCAGRSoUIFLVy4UB06dFCVKlUUERGhatWqKTU1VWvWrNEXX3yhbt26SZJq1qyprl276oMPPtCZM2cUGhqqdevWae7cuWrTpk2uDlgtXRmHKTo6Wl27dlVISIi+//57LVu2TIMGDbKOz9SiRQtNnDhRzZs311NPPaVjx45p+vTpqlix4g1PjbmRs2fPqnTp0nriiSdUs2ZNFSpUSCtWrNDvv/+ud955R9KVIwzGjRun7t27KzQ0VJ06dVJCQoImT56sgIAAvfLKK7nyGJQoUUIDBw7UyJEj1bx5c7Vq1Urbt2/XjBkzVLdu3WwHGNd64IEHNGHCBEVGRqpGjRrq1q2bfH199c8//2jWrFlKT0/Xd999968G0r7Wjh079Mknn2Rq9/b2VtOmTXPlPl577TUtWrRI7du319NPP63g4GCdOnVKS5cu1cyZM1WzZs0s55s+fboaNWqk6tWrq0ePHipfvrwSEhIUFxenQ4cOadOmTTmqo0iRInr33Xf17LPPqm7dunrqqafk6empTZs26cKFC5o7d661r4uLizp27Khp06bJ2dlZnTp1ytZ9NGrUSMWLF9eKFSsyHa0nSTExMZkCMUlq06aNli1bpkaNGt30CKu3335biYmJ6t27twoXLmzd3t566y21bt1aDRs2VPfu3XX69GlNmzZN1apVswmqMvz4449q2bLlTY9ceeyxx/TGG2+oe/fuatCggTZv3qwFCxZkGlsuIiJC8+bNU2RkpNatW6fGjRvr/PnzWrFihXr16qXWrVvfdL0eeeQR1a9fX88884wuXryoqVOnysPDQyNGjJCUvf3AypUr1adPH7Vv316VK1fW5cuXNX/+fDk7O980TOrcubN+//13TZ48WVu3blXnzp3l6empDRs2aPbs2SpevLgWLVqU4yPBpCvbVNu2bfXZZ5/p/PnzmjBhQqY+ubXNd+nSRT179tSRI0c0ePBgm2nNmjWTq6urWrZsqeeee07nzp3TrFmzVLJkySzDsOx67bXXtHTpUj322GPq1q2bgoODdf78eW3evFmLFi3Svn375OXlpfz586tq1aqKiopS5cqVVaxYMVWrVi3Lsd4qVKig0aNHa+DAgdq3b5/atGmjwoULa+/evfrqq6/Us2dP9evX71895wBwz7Pvxf4AAHeTHTt2mB49epiAgADj6upqChcubBo2bGimTp1qc8nwS5cumZEjR5py5coZFxcX4+/vbwYOHGjTx5grl/Bu0aJFpvuRlOkS61ldwr5r166mYMGCZvfu3aZZs2a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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_oil_efficiency_l_kg.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/variety_comparison_water_efficiency_l_oil_m³_water.png\n", + "Plot salvato come: .//2024-12-08_14-47_plots/efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/water_efficiency_vs_production.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plot salvato come: .//2024-12-08_14-47_plots/water_need_vs_oil_production.png\n", + " Variety Technique Technique String \\\n", + "0 nocellara_delletna 3 tradizionale \n", + "1 nocellara_delletna 1 intensiva \n", + "2 nocellara_delletna 2 superintensiva \n", + "3 leccino 1 intensiva \n", + "4 leccino 2 superintensiva \n", + "5 leccino 3 tradizionale \n", + "6 frantoio 2 superintensiva \n", + "7 frantoio 3 tradizionale \n", + "8 frantoio 1 intensiva \n", + "9 coratina 1 intensiva \n", + "10 coratina 2 superintensiva \n", + "11 coratina 3 tradizionale \n", + "12 taggiasca 3 tradizionale \n", + "13 taggiasca 2 superintensiva \n", + "14 taggiasca 1 intensiva \n", + "15 pendolino 1 intensiva \n", + "16 pendolino 2 superintensiva \n", + "17 pendolino 3 tradizionale \n", + "18 moraiolo 2 superintensiva \n", + "19 moraiolo 1 intensiva \n", + "20 moraiolo 3 tradizionale \n", + "\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "0 9564.638687 2088.362004 \n", + "1 13699.079622 2991.183032 \n", + "2 17826.710664 3892.059753 \n", + "3 16432.379678 3229.053194 \n", + "4 20528.499013 4033.942398 \n", + "5 10937.982122 2149.449585 \n", + "6 24621.040119 6047.876212 \n", + "7 13740.739760 3375.103688 \n", + "8 20550.900635 5047.942655 \n", + "9 16429.706879 4215.265516 \n", + "10 19164.700743 4916.649709 \n", + "11 12318.510310 3160.037128 \n", + "12 6839.506230 1381.247995 \n", + "13 16433.741502 3319.210170 \n", + "14 10968.603159 2215.371493 \n", + "15 13705.431414 2468.678455 \n", + "16 19183.689269 3455.879324 \n", + "17 10960.549241 1974.357984 \n", + "18 17793.971752 3885.415851 \n", + "19 13144.222436 2870.020002 \n", + "20 8765.195655 1913.745255 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "0 32997.227891 0.218342 \n", + "1 33079.012125 0.218349 \n", + "2 33118.708645 0.218327 \n", + "3 25013.303736 0.196506 \n", + "4 24989.459147 0.196504 \n", + "5 24981.219100 0.196512 \n", + "6 28874.473543 0.245639 \n", + "7 29003.452741 0.245628 \n", + "8 28921.261327 0.245631 \n", + "9 38270.638622 0.256564 \n", + "10 38264.650562 0.256547 \n", + "11 38253.676395 0.256528 \n", + "12 26219.134374 0.201951 \n", + "13 26253.317778 0.201975 \n", + "14 26284.027794 0.201974 \n", + "15 26154.359691 0.180124 \n", + "16 26153.199618 0.180147 \n", + "17 26152.823801 0.180133 \n", + "18 32561.911109 0.218356 \n", + "19 32577.899255 0.218348 \n", + "20 32594.860153 0.218335 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "0 0.063289 \n", + "1 0.090425 \n", + "2 0.117518 \n", + "3 0.129093 \n", + "4 0.161426 \n", + "5 0.086043 \n", + "6 0.209454 \n", + "7 0.116369 \n", + "8 0.174541 \n", + "9 0.110144 \n", + "10 0.128491 \n", + "11 0.082607 \n", + "12 0.052681 \n", + "13 0.126430 \n", + "14 0.084286 \n", + "15 0.094389 \n", + "16 0.132140 \n", + "17 0.075493 \n", + "18 0.119324 \n", + "19 0.088097 \n", + "20 0.058713 \n", + "Comparison by Variety:\n", + " Avg Olive Production (kg/ha) Avg Oil Production (L/ha) \\\n", + "Variety \n", + "nocellara_delletna 13696.683690 2990.507461 \n", + "leccino 15971.162702 3138.439782 \n", + "frantoio 19648.631813 4826.360700 \n", + "coratina 15974.164423 4098.136472 \n", + "taggiasca 11412.636779 2305.011278 \n", + "pendolino 14617.432649 2633.129635 \n", + "moraiolo 13232.961913 2889.399172 \n", + "\n", + " Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "Variety \n", + "nocellara_delletna 33064.983905 0.218338 \n", + "leccino 24994.676451 0.196507 \n", + "frantoio 28932.932409 0.245633 \n", + "coratina 38262.995517 0.256548 \n", + "taggiasca 26252.184893 0.201970 \n", + "pendolino 26153.461822 0.180136 \n", + "moraiolo 32578.228327 0.218349 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "Variety \n", + "nocellara_delletna 0.090443 \n", + "leccino 0.125564 \n", + "frantoio 0.166812 \n", + "coratina 0.107104 \n", + "taggiasca 0.087803 \n", + "pendolino 0.100680 \n", + "moraiolo 0.088691 \n", + "\n", + "Best Varieties by Water Efficiency:\n", + " Variety Avg Olive Production (kg/ha) \\\n", + "2 frantoio 19648.631813 \n", + "1 leccino 15971.162702 \n", + "3 coratina 15974.164423 \n", + "5 pendolino 14617.432649 \n", + "0 nocellara_delletna 13696.683690 \n", + "\n", + " Avg Oil Production (L/ha) Avg Water Need (m³/ha) Oil Efficiency (L/kg) \\\n", + "2 4826.360700 28932.932409 0.245633 \n", + "1 3138.439782 24994.676451 0.196507 \n", + "3 4098.136472 38262.995517 0.256548 \n", + "5 2633.129635 26153.461822 0.180136 \n", + "0 2990.507461 33064.983905 0.218338 \n", + "\n", + " Water Efficiency (L oil/m³ water) \n", + "2 0.166812 \n", + "1 0.125564 \n", + "3 0.107104 \n", + "5 0.100680 \n", + "0 0.090443 \n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "# Esecuzione dell'analisi\n", + "comparison_data = prepare_comparison_data(simulated_data, olive_varieties)\n", + "\n", + "# Genera i grafici\n", + "plot_variety_comparison(comparison_data, 'Avg Olive Production (kg/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Oil Production (L/ha)')\n", + "plot_variety_comparison(comparison_data, 'Avg Water Need (m³/ha)')\n", + "plot_variety_comparison(comparison_data, 'Oil Efficiency (L/kg)')\n", + "plot_variety_comparison(comparison_data, 'Water Efficiency (L oil/m³ water)')\n", + "plot_efficiency_vs_production(comparison_data)\n", + "plot_water_efficiency_vs_production(comparison_data)\n", + "plot_water_need_vs_oil_production(comparison_data)\n", + "\n", + "technique_data = analyze_by_technique(simulated_data, olive_varieties)\n", + "\n", + "print(technique_data)\n", + "\n", + "# Stampa un sommario statistico\n", + "print(\"Comparison by Variety:\")\n", + "print(comparison_data.set_index('Variety'))\n", + "print(\"\\nBest Varieties by Water Efficiency:\")\n", + "print(comparison_data.sort_values('Water Efficiency (L oil/m³ water)', ascending=False).head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bbe87b415168368", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_transformer_data(df, olive_varieties_df):\n", + " # Crea una copia del DataFrame per evitare modifiche all'originale\n", + " df = df.copy()\n", + "\n", + " # Definisci le feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha'] # Feature statiche base\n", + " target_features = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " # Ottieni le varietà pulite\n", + " all_varieties = olive_varieties_df['Varietà di Olive'].unique()\n", + " varieties = [clean_column_name(variety) for variety in all_varieties]\n", + "\n", + " # Crea la struttura delle feature per ogni varietà\n", + " variety_features = [\n", + " 'tech', 'pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha',\n", + " 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', 'max_oil_prod_l_ha',\n", + " 'avg_oil_prod_l_ha', 'l_per_t', 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'\n", + " ]\n", + "\n", + " # Prepara dizionari per le nuove colonne\n", + " new_columns = {}\n", + "\n", + " # Prepara le feature per ogni varietà\n", + " for variety in varieties:\n", + " # Feature esistenti\n", + " for feature in variety_features:\n", + " col_name = f\"{variety}_{feature}\"\n", + " if col_name in df.columns:\n", + " if feature != 'tech': # Non includere la colonna tech direttamente\n", + " static_features.append(col_name)\n", + "\n", + " # Feature binarie per le tecniche di coltivazione\n", + " for technique in ['tradizionale', 'intensiva', 'superintensiva']:\n", + " col_name = f\"{variety}_{technique}\"\n", + " new_columns[col_name] = df[f\"{variety}_tech\"].notna() & (\n", + " df[f\"{variety}_tech\"].str.lower() == technique\n", + " ).fillna(False)\n", + " static_features.append(col_name)\n", + "\n", + " # Aggiungi tutte le nuove colonne in una volta sola\n", + " df = pd.concat([df] + [pd.Series(v, name=k) for k, v in new_columns.items()], axis=1)\n", + "\n", + " # Prepara X e y\n", + " X_temporal = df[temporal_features].values\n", + " X_static = df[static_features].values\n", + " y = df[target_features].values\n", + "\n", + " print(f\"Dataset completo - Temporal: {X_temporal.shape}, Static: {X_static.shape}, Target: {y.shape}\")\n", + "\n", + " # Split dei dati (usando indici casuali per una migliore distribuzione)\n", + " indices = np.random.permutation(len(X_temporal))\n", + " train_idx = int(len(indices) * 0.65) # 65% training\n", + " val_idx = int(len(indices) * 0.85) # 20% validation\n", + " # Il resto rimane 15% test\n", + "\n", + " train_indices = indices[:train_idx]\n", + " val_indices = indices[train_idx:val_idx]\n", + " test_indices = indices[val_idx:]\n", + "\n", + " # Split dei dati\n", + " X_temporal_train = X_temporal[train_indices]\n", + " X_temporal_val = X_temporal[val_indices]\n", + " X_temporal_test = X_temporal[test_indices]\n", + "\n", + " X_static_train = X_static[train_indices]\n", + " X_static_val = X_static[val_indices]\n", + " X_static_test = X_static[test_indices]\n", + "\n", + " y_train = y[train_indices]\n", + " y_val = y[val_indices]\n", + " y_test = y[test_indices]\n", + "\n", + " # Standardizzazione\n", + " scaler_temporal = StandardScaler()\n", + " scaler_static = StandardScaler()\n", + " scaler_y = StandardScaler()\n", + "\n", + " # Standardizzazione dei dati\n", + " X_temporal_train = scaler_temporal.fit_transform(X_temporal_train)\n", + " X_temporal_val = scaler_temporal.transform(X_temporal_val)\n", + " X_temporal_test = scaler_temporal.transform(X_temporal_test)\n", + "\n", + " X_static_train = scaler_static.fit_transform(X_static_train)\n", + " X_static_val = scaler_static.transform(X_static_val)\n", + " X_static_test = scaler_static.transform(X_static_test)\n", + "\n", + " y_train = scaler_y.fit_transform(y_train)\n", + " y_val = scaler_y.transform(y_val)\n", + " y_test = scaler_y.transform(y_test)\n", + "\n", + " print(\"\\nShape dopo lo split e standardizzazione:\")\n", + " print(f\"Train - Temporal: {X_temporal_train.shape}, Static: {X_static_train.shape}, Target: {y_train.shape}\")\n", + " print(f\"Val - Temporal: {X_temporal_val.shape}, Static: {X_static_val.shape}, Target: {y_val.shape}\")\n", + " print(f\"Test - Temporal: {X_temporal_test.shape}, Static: {X_static_test.shape}, Target: {y_test.shape}\")\n", + "\n", + " # Reshape per il transformer (aggiunge la dimensione del sequence length = 1)\n", + " X_temporal_train = X_temporal_train.reshape(X_temporal_train.shape[0], 1, -1)\n", + " X_temporal_val = X_temporal_val.reshape(X_temporal_val.shape[0], 1, -1)\n", + " X_temporal_test = X_temporal_test.reshape(X_temporal_test.shape[0], 1, -1)\n", + "\n", + " # Prepara i dizionari di input\n", + " train_data = {'temporal': X_temporal_train, 'static': X_static_train}\n", + " val_data = {'temporal': X_temporal_val, 'static': X_static_val}\n", + " test_data = {'temporal': X_temporal_test, 'static': X_static_test}\n", + "\n", + " # Salva gli scaler\n", + " joblib.dump(scaler_temporal, os.path.join(base_project_dir, f'{execute_name}_scaler_temporal.joblib'))\n", + " joblib.dump(scaler_static, os.path.join(base_project_dir, f'{execute_name}_scaler_static.joblib'))\n", + " joblib.dump(scaler_y, os.path.join(base_project_dir, f'{execute_name}_scaler_y.joblib'))\n", + "\n", + " return (train_data, y_train), (val_data, y_val), (test_data, y_test), (scaler_temporal, scaler_static, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9c4d5f0f3fafdc2d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset completo - Temporal: (4000000, 3), Static: (4000000, 113), Target: (4000000, 5)\n", + "\n", + "Shape dopo lo split e standardizzazione:\n", + "Train - Temporal: (2600000, 3), Static: (2600000, 113), Target: (2600000, 5)\n", + "Val - Temporal: (800000, 3), Static: (800000, 113), Target: (800000, 5)\n", + "Test - Temporal: (600000, 3), Static: (600000, 113), Target: (600000, 5)\n", + "Temporal data shape: (2600000, 1, 3)\n", + "Static data shape: (2600000, 113)\n", + "Target shape: (2600000, 5)\n" + ] + } + ], + "source": [ + "simulated_data = pd.read_parquet(f\"{data_dir}olive_training_dataset.parquet\")\n", + "olive_varieties = pd.read_parquet(f\"{data_dir}olive_varieties.parquet\")\n", + "\n", + "(train_data, train_targets), (val_data, val_targets), (test_data, test_targets), scalers = prepare_transformer_data(simulated_data, olive_varieties)\n", + "\n", + "scaler_temporal, scaler_static, scaler_y = scalers\n", + "\n", + "print(\"Temporal data shape:\", train_data['temporal'].shape)\n", + "print(\"Static data shape:\", train_data['static'].shape)\n", + "print(\"Target shape:\", train_targets.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "604c952c7195f40c", + "metadata": {}, + "outputs": [], + "source": [ + "@keras.saving.register_keras_serializable()\n", + "class DataAugmentation(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'augmentation dei dati\"\"\"\n", + "\n", + " def __init__(self, noise_stddev=0.03, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.noise_stddev = noise_stddev\n", + "\n", + " def call(self, inputs, training=None):\n", + " if training:\n", + " return inputs + tf.random.normal(\n", + " shape=tf.shape(inputs),\n", + " mean=0.0,\n", + " stddev=self.noise_stddev\n", + " )\n", + " return inputs\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"noise_stddev\": self.noise_stddev})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class PositionalEncoding(tf.keras.layers.Layer):\n", + " \"\"\"Custom layer per l'encoding posizionale\"\"\"\n", + "\n", + " def __init__(self, d_model, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.d_model = d_model\n", + "\n", + " def build(self, input_shape):\n", + " _, seq_length, _ = input_shape\n", + "\n", + " # Crea la matrice di encoding posizionale\n", + " position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]\n", + " div_term = tf.exp(\n", + " tf.range(0, self.d_model, 2, dtype=tf.float32) *\n", + " (-tf.math.log(10000.0) / self.d_model)\n", + " )\n", + "\n", + " # Calcola sin e cos\n", + " pos_encoding = tf.zeros((1, seq_length, self.d_model))\n", + " pos_encoding_even = tf.sin(position * div_term)\n", + " pos_encoding_odd = tf.cos(position * div_term)\n", + "\n", + " # Assegna i valori alle posizioni pari e dispari\n", + " pos_encoding = tf.concat(\n", + " [tf.expand_dims(pos_encoding_even, -1),\n", + " tf.expand_dims(pos_encoding_odd, -1)],\n", + " axis=-1\n", + " )\n", + " pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))\n", + " pos_encoding = pos_encoding[:, :, :self.d_model]\n", + "\n", + " # Salva l'encoding come peso non trainabile\n", + " self.pos_encoding = self.add_weight(\n", + " shape=(1, seq_length, self.d_model),\n", + " initializer=tf.keras.initializers.Constant(pos_encoding),\n", + " trainable=False,\n", + " name='positional_encoding'\n", + " )\n", + "\n", + " super().build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " # Broadcast l'encoding posizionale sul batch\n", + " batch_size = tf.shape(inputs)[0]\n", + " pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])\n", + " return inputs + pos_encoding_tiled\n", + "\n", + " def get_config(self):\n", + " config = super().get_config()\n", + " config.update({\"d_model\": self.d_model})\n", + " return config\n", + "\n", + "\n", + "@keras.saving.register_keras_serializable()\n", + "class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " \"\"\"Custom learning rate schedule with linear warmup and exponential decay.\"\"\"\n", + "\n", + " def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_learning_rate = initial_learning_rate\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps\n", + " warmup_lr = self.initial_learning_rate * warmup_pct\n", + " decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)\n", + " decayed_lr = self.initial_learning_rate * decay_factor\n", + " return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)\n", + "\n", + " def get_config(self):\n", + " return {\n", + " 'initial_learning_rate': self.initial_learning_rate,\n", + " 'warmup_steps': self.warmup_steps,\n", + " 'decay_steps': self.decay_steps\n", + " }\n", + "\n", + "\n", + "def create_olive_oil_transformer(temporal_shape, static_shape, num_outputs,\n", + " d_model=128, num_heads=8, ff_dim=256,\n", + " num_transformer_blocks=4, mlp_units=None,\n", + " dropout=0.2):\n", + " if mlp_units is None:\n", + " mlp_units = [256, 128, 64]\n", + "\n", + " temporal_input = tf.keras.layers.Input(shape=temporal_shape, name='temporal')\n", + " static_input = tf.keras.layers.Input(shape=static_shape, name='static')\n", + "\n", + " # === TEMPORAL PATH ===\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(temporal_input)\n", + " x = DataAugmentation()(x)\n", + "\n", + " # Temporal projection con dimensione aumentata per compensare la sequenza corta\n", + " x = tf.keras.layers.Dense(\n", + " d_model,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + " x = tf.keras.layers.Dropout(dropout)(x)\n", + "\n", + " # Additional feature extraction prima del transformer\n", + " x = tf.keras.layers.Dense(\n", + " d_model * 2,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(x)\n", + "\n", + " x = PositionalEncoding(d_model * 2)(x)\n", + "\n", + " skip_connection = x\n", + " for _ in range(num_transformer_blocks):\n", + " # Multi-head self-attention con più heads per compensare la sequenza corta\n", + " attention_output = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads * 2,\n", + " key_dim=d_model // num_heads,\n", + " value_dim=d_model // num_heads\n", + " )(x, x)\n", + " attention_output = tf.keras.layers.Dropout(dropout)(attention_output)\n", + "\n", + " # Residual connection con gating mechanism\n", + " gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n", + " x = x + gate * attention_output\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Feed-forward network potenziato\n", + " ffn = tf.keras.Sequential([\n", + " tf.keras.layers.Dense(ff_dim * 2, activation=\"swish\"), # Raddoppiato\n", + " tf.keras.layers.Dropout(dropout),\n", + " tf.keras.layers.Dense(ff_dim, activation=\"swish\"),\n", + " tf.keras.layers.Dropout(dropout),\n", + " tf.keras.layers.Dense(d_model * 2)\n", + " ])\n", + " ffn_output = ffn(x)\n", + "\n", + " # Gated residual connection\n", + " gate = tf.keras.layers.Dense(d_model * 2, activation='sigmoid')(x)\n", + " x = x + gate * ffn_output\n", + " x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(x)\n", + "\n", + " # Global feature attention\n", + " x = tf.keras.layers.MultiHeadAttention(\n", + " num_heads=num_heads,\n", + " key_dim=d_model // num_heads\n", + " )(x, x)\n", + "\n", + " # Feature pooling\n", + " x = tf.keras.layers.GlobalAveragePooling1D()(x)\n", + "\n", + " # === STATIC PATH ===\n", + " s = tf.keras.layers.LayerNormalization(epsilon=1e-6)(static_input)\n", + " for units in [512, 256, 128]: # Aumentate le dimensioni\n", + " s = tf.keras.layers.Dense(\n", + " units,\n", + " activation='swish',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(s)\n", + " s = tf.keras.layers.BatchNormalization()(s)\n", + " s = tf.keras.layers.Dropout(dropout)(s)\n", + "\n", + "\n", + " # === FEATURE FUSION con attention ===\n", + " # Project features to same dimensionality\n", + " x = tf.keras.layers.Dense(256)(x)\n", + " s = tf.keras.layers.Dense(256)(s)\n", + "\n", + " # Cross-attention between temporal and static features\n", + " combined = tf.keras.layers.Concatenate()([x, s])\n", + " combined = tf.keras.layers.Dense(256, activation='swish')(combined)\n", + "\n", + " # MLP head with residual connections\n", + " for units in mlp_units:\n", + " skip = combined\n", + " combined = tf.keras.layers.BatchNormalization()(combined)\n", + " combined = tf.keras.layers.Dense(units, activation=\"swish\")(combined)\n", + " combined = tf.keras.layers.Dropout(dropout)(combined)\n", + " if units == skip.shape[-1]: # Se le dimensioni combaciano\n", + " combined = combined + skip\n", + "\n", + " # Apply final normalization to output\n", + " outputs = tf.keras.layers.BatchNormalization()(combined)\n", + " outputs = tf.keras.layers.Dense(\n", + " num_outputs,\n", + " activation='linear',\n", + " kernel_regularizer=tf.keras.regularizers.l2(1e-5)\n", + " )(outputs)\n", + "\n", + " # Create model\n", + " model = tf.keras.Model(\n", + " inputs={'temporal': temporal_input, 'static': static_input},\n", + " outputs=outputs,\n", + " name='OilTransformer'\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def create_transformer_callbacks(target_names, val_data, val_targets):\n", + " \"\"\"\n", + " Crea i callbacks per il training del modello single-step.\n", + " \"\"\"\n", + " class TargetSpecificMetric(tf.keras.callbacks.Callback):\n", + " def __init__(self, validation_data, target_names):\n", + " super().__init__()\n", + " self.validation_data = validation_data\n", + " self.target_names = target_names\n", + " self.best_metrics = {name: float('inf') for name in target_names}\n", + " \n", + " def on_epoch_end(self, epoch, logs=None):\n", + " logs = logs or {}\n", + " \n", + " # Esegui il calcolo solo ogni 5 epoche\n", + " if epoch % 5 == 0:\n", + " x_val, y_val = self.validation_data\n", + " y_pred = self.model.predict(x_val, verbose=0)\n", + " \n", + " # Calcola e logga le metriche per ogni target\n", + " for i, name in enumerate(self.target_names):\n", + " mae = np.mean(np.abs(y_val[:, i] - y_pred[:, i]))\n", + " mape = np.mean(np.abs((y_val[:, i] - y_pred[:, i]) / np.clip(np.abs(y_val[:, i]), 1e-7, None))) * 100\n", + " logs[f'val_{name}_mae'] = mae\n", + " logs[f'val_{name}_mape'] = mape\n", + " \n", + " # Traccia i migliori risultati\n", + " if mae < self.best_metrics[name]:\n", + " self.best_metrics[name] = mae\n", + " logs[f'best_{name}_mae'] = mae\n", + "\n", + "\n", + " callbacks = [\n", + " # Early Stopping ottimizzato\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=25, # Aumentato per dare più chance al modello\n", + " restore_best_weights=True,\n", + " min_delta=0.0001, # Più sensibile ai miglioramenti\n", + " mode='min'\n", + " ),\n", + "\n", + " # Model Checkpoint con monitoraggio multiplo\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " ),\n", + "\n", + " # Metric per target specifici\n", + " TargetSpecificMetric(\n", + " validation_data=(val_data, val_targets),\n", + " target_names=target_names\n", + " ),\n", + "\n", + " # LR reduction ottimizzato per single-step\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2, # Riduzione più aggressiva\n", + " patience=15,\n", + " min_lr=1e-7,\n", + " verbose=1,\n", + " cooldown=5 # Periodo di cool-down per stabilizzazione\n", + " ),\n", + "\n", + " # TensorBoard con più metriche\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./logs_{execute_name}',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " write_images=True,\n", + " update_freq='epoch',\n", + " profile_batch='500,520' # Profile per ottimizzazione\n", + " )\n", + " ]\n", + "\n", + " return callbacks\n", + "\n", + "def compile_model(model, learning_rate=5e-4): # Learning rate ridotto\n", + " \"\"\"\n", + " Compila il modello con ottimizzazioni per single-step.\n", + " \"\"\"\n", + " lr_schedule = WarmUpLearningRateSchedule(\n", + " initial_learning_rate=learning_rate,\n", + " warmup_steps=1000, # Aumentato per stabilità\n", + " decay_steps=7000 # Aumentato per permettere più esplorazione\n", + " )\n", + "\n", + " def weighted_huber_loss(y_true, y_pred):\n", + " # Pesi per diversi output\n", + " weights = tf.constant([1.0, 0.8, 0.8, 1.0, 0.6], dtype=tf.float32)\n", + " huber = tf.keras.losses.Huber(delta=1.0)\n", + " loss = huber(y_true, y_pred)\n", + " weighted_loss = tf.reduce_mean(loss * weights)\n", + " return weighted_loss\n", + "\n", + " model.compile(\n", + " optimizer=tf.keras.optimizers.AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0, # Gradient clipping\n", + " epsilon=1e-7 # Aumentato per stabilità numerica\n", + " ),\n", + " loss=weighted_huber_loss,\n", + " metrics=['mae', 'mape']\n", + " )\n", + "\n", + " return model\n", + "\n", + "def setup_transformer_training(train_data, train_targets, val_data, val_targets):\n", + " \"\"\"\n", + " Configura il single-step transformer.\n", + " \"\"\"\n", + " # Estrai le shape dai dati\n", + " temporal_shape = (1, train_data['temporal'].shape[2])\n", + " static_shape = (train_data['static'].shape[1],)\n", + " num_outputs = train_targets.shape[1]\n", + "\n", + " print(f\"Shape rilevate:\")\n", + " print(f\"- Temporal shape: {temporal_shape}\")\n", + " print(f\"- Static shape: {static_shape}\")\n", + " print(f\"- Numero di output: {num_outputs}\")\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + "\n", + " assert len(target_names) == num_outputs, \\\n", + " f\"Il numero di target names ({len(target_names)}) non corrisponde al numero di output ({num_outputs})\"\n", + "\n", + " # Crea il modello con il nuovo transformer\n", + " model = create_olive_oil_transformer(\n", + " temporal_shape=temporal_shape,\n", + " static_shape=static_shape,\n", + " num_outputs=num_outputs,\n", + " d_model=256,\n", + " num_heads=8,\n", + " ff_dim=512,\n", + " num_transformer_blocks=6,\n", + " dropout=0.1\n", + " )\n", + "\n", + " model = compile_model(model)\n", + " callbacks = create_transformer_callbacks(target_names, val_data, val_targets)\n", + "\n", + " return model, callbacks, target_names\n", + "\n", + "def train_transformer(train_data, train_targets, val_data, val_targets, epochs=200, batch_size=128, save_name='final_model'):\n", + " \"\"\"\n", + " Training ottimizzato per single-step transformer.\n", + " \"\"\"\n", + " # Dataset con augmentation\n", + " def augment(x, y):\n", + " # Ottieni il dtype dei dati originali\n", + " original_dtype = x['temporal'].dtype\n", + " # Genera il rumore con lo stesso dtype\n", + " noise = tf.random.normal(\n", + " tf.shape(x['temporal']), \n", + " mean=0.0, \n", + " stddev=0.01,\n", + " dtype=original_dtype\n", + " )\n", + " x['temporal'] += noise\n", + " return x, y\n", + "\n", + " train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_targets))\\\n", + " .map(augment, num_parallel_calls=tf.data.AUTOTUNE)\\\n", + " .cache()\\\n", + " .shuffle(buffer_size=10000)\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets))\\\n", + " .cache()\\\n", + " .batch(batch_size)\\\n", + " .prefetch(tf.data.AUTOTUNE)\n", + "\n", + " strategy = tf.distribute.MirroredStrategy() if len(tf.config.list_physical_devices('GPU')) > 1 else tf.distribute.get_strategy()\n", + "\n", + " with strategy.scope():\n", + " model, callbacks, target_names = setup_transformer_training(\n", + " train_data, train_targets, val_data, val_targets\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " try:\n", + " keras.utils.plot_model(model, f\"{execute_name}_{save_name}.png\", show_shapes=True)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not create model plot: {e}\")\n", + "\n", + " # Training con gestione errori e memory saving\n", + " try:\n", + " with tf.device('/GPU:0'): # Forza l'uso della GPU principale\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " workers=8,\n", + " use_multiprocessing=True\n", + " )\n", + " except tf.errors.ResourceExhaustedError:\n", + " print(\"Memoria GPU esaurita, riprovo con batch size più piccolo...\")\n", + " batch_size = batch_size // 2\n", + " train_dataset = train_dataset.unbatch().batch(batch_size)\n", + " val_dataset = val_dataset.unbatch().batch(batch_size)\n", + " history = model.fit(\n", + " train_dataset,\n", + " validation_data=val_dataset,\n", + " epochs=epochs,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " # Salvataggio modello\n", + " try:\n", + " save_path = f'{execute_name}_{save_name}.keras'\n", + " model.save(save_path, save_format='keras')\n", + "\n", + " os.makedirs(f'{execute_name}/weights', exist_ok=True)\n", + " model.save_weights(f'{execute_name}/weights')\n", + " print(f\"\\nModello salvato in: {save_path}\")\n", + "\n", + " # Salva anche la storia del training\n", + " with open(f'{execute_name}_training_history.json', 'w') as f:\n", + " json.dump(history.history, f)\n", + " except Exception as e:\n", + " print(f\"Warning: Could not save model: {e}\")\n", + "\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "35490e902e494c4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape rilevate:\n", + "- Temporal shape: (1, 3)\n", + "- Static shape: (113,)\n", + "- Numero di output: 5\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 14:50:03.536829: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"OilTransformer\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " temporal (InputLayer) [(None, 1, 3)] 0 [] \n", + " \n", + " layer_normalization (Layer (None, 1, 3) 6 ['temporal[0][0]'] \n", + " Normalization) \n", + " \n", + " data_augmentation (DataAug (None, 1, 3) 0 ['layer_normalization[0][0]'] \n", + " mentation) \n", + " \n", + " dense (Dense) (None, 1, 256) 1024 ['data_augmentation[0][0]'] \n", + " \n", + " dropout (Dropout) (None, 1, 256) 0 ['dense[0][0]'] \n", + " \n", + " dense_1 (Dense) (None, 1, 512) 131584 ['dropout[0][0]'] \n", + " \n", + " positional_encoding (Posit (None, 1, 512) 512 ['dense_1[0][0]'] \n", + " ionalEncoding) \n", + " \n", + " multi_head_attention (Mult (None, 1, 512) 1050624 ['positional_encoding[0][0]', \n", + " iHeadAttention) 'positional_encoding[0][0]'] \n", + " \n", + " dense_2 (Dense) (None, 1, 512) 262656 ['positional_encoding[0][0]'] \n", + " \n", + " dropout_1 (Dropout) (None, 1, 512) 0 ['multi_head_attention[0][0]']\n", + " \n", + " tf.math.multiply (TFOpLamb (None, 1, 512) 0 ['dense_2[0][0]', \n", + " da) 'dropout_1[0][0]'] \n", + " \n", + " tf.__operators__.add (TFOp (None, 1, 512) 0 ['positional_encoding[0][0]', \n", + " Lambda) 'tf.math.multiply[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 1, 512) 1024 ['tf.__operators__.add[0][0]']\n", + " erNormalization) \n", + " \n", + " dense_6 (Dense) (None, 1, 512) 262656 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " sequential (Sequential) (None, 1, 512) 1312768 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " tf.math.multiply_1 (TFOpLa (None, 1, 512) 0 ['dense_6[0][0]', \n", + " mbda) 'sequential[0][0]'] \n", + " \n", + " tf.__operators__.add_1 (TF (None, 1, 512) 0 ['layer_normalization_1[0][0]'\n", + " OpLambda) , 'tf.math.multiply_1[0][0]'] \n", + " \n", + " layer_normalization_2 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_1[0][0]\n", + " erNormalization) '] \n", + " \n", + " multi_head_attention_1 (Mu (None, 1, 512) 1050624 ['layer_normalization_2[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_2[0][0]\n", + " '] \n", + " \n", + " dense_7 (Dense) (None, 1, 512) 262656 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_4 (Dropout) (None, 1, 512) 0 ['multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_2 (TFOpLa (None, 1, 512) 0 ['dense_7[0][0]', \n", + " mbda) 'dropout_4[0][0]'] \n", + " \n", + " tf.__operators__.add_2 (TF (None, 1, 512) 0 ['layer_normalization_2[0][0]'\n", + " OpLambda) , 'tf.math.multiply_2[0][0]'] \n", + " \n", + " layer_normalization_3 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_2[0][0]\n", + " erNormalization) '] \n", + " \n", + " dense_11 (Dense) (None, 1, 512) 262656 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " sequential_1 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " tf.math.multiply_3 (TFOpLa (None, 1, 512) 0 ['dense_11[0][0]', \n", + " mbda) 'sequential_1[0][0]'] \n", + " \n", + " tf.__operators__.add_3 (TF (None, 1, 512) 0 ['layer_normalization_3[0][0]'\n", + " OpLambda) , 'tf.math.multiply_3[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_3[0][0]\n", + " erNormalization) '] \n", + " \n", + " multi_head_attention_2 (Mu (None, 1, 512) 1050624 ['layer_normalization_4[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_4[0][0]\n", + " '] \n", + " \n", + " dense_12 (Dense) (None, 1, 512) 262656 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dropout_7 (Dropout) (None, 1, 512) 0 ['multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_4 (TFOpLa (None, 1, 512) 0 ['dense_12[0][0]', \n", + " mbda) 'dropout_7[0][0]'] \n", + " \n", + " tf.__operators__.add_4 (TF (None, 1, 512) 0 ['layer_normalization_4[0][0]'\n", + " OpLambda) , 'tf.math.multiply_4[0][0]'] \n", + " \n", + " layer_normalization_5 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_4[0][0]\n", + " erNormalization) '] \n", + " \n", + " dense_16 (Dense) (None, 1, 512) 262656 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " sequential_2 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " tf.math.multiply_5 (TFOpLa (None, 1, 512) 0 ['dense_16[0][0]', \n", + " mbda) 'sequential_2[0][0]'] \n", + " \n", + " tf.__operators__.add_5 (TF (None, 1, 512) 0 ['layer_normalization_5[0][0]'\n", + " OpLambda) , 'tf.math.multiply_5[0][0]'] \n", + " \n", + " layer_normalization_6 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_5[0][0]\n", + " erNormalization) '] \n", + " \n", + " multi_head_attention_3 (Mu (None, 1, 512) 1050624 ['layer_normalization_6[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_6[0][0]\n", + " '] \n", + " \n", + " dense_17 (Dense) (None, 1, 512) 262656 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dropout_10 (Dropout) (None, 1, 512) 0 ['multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_6 (TFOpLa (None, 1, 512) 0 ['dense_17[0][0]', \n", + " mbda) 'dropout_10[0][0]'] \n", + " \n", + " tf.__operators__.add_6 (TF (None, 1, 512) 0 ['layer_normalization_6[0][0]'\n", + " OpLambda) , 'tf.math.multiply_6[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_6[0][0]\n", + " erNormalization) '] \n", + " \n", + " dense_21 (Dense) (None, 1, 512) 262656 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " sequential_3 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " tf.math.multiply_7 (TFOpLa (None, 1, 512) 0 ['dense_21[0][0]', \n", + " mbda) 'sequential_3[0][0]'] \n", + " \n", + " tf.__operators__.add_7 (TF (None, 1, 512) 0 ['layer_normalization_7[0][0]'\n", + " OpLambda) , 'tf.math.multiply_7[0][0]'] \n", + " \n", + " layer_normalization_8 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_7[0][0]\n", + " erNormalization) '] \n", + " \n", + " multi_head_attention_4 (Mu (None, 1, 512) 1050624 ['layer_normalization_8[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_8[0][0]\n", + " '] \n", + " \n", + " dense_22 (Dense) (None, 1, 512) 262656 ['layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " dropout_13 (Dropout) (None, 1, 512) 0 ['multi_head_attention_4[0][0]\n", + " '] \n", + " \n", + " tf.math.multiply_8 (TFOpLa (None, 1, 512) 0 ['dense_22[0][0]', \n", + " mbda) 'dropout_13[0][0]'] \n", + " \n", + " tf.__operators__.add_8 (TF (None, 1, 512) 0 ['layer_normalization_8[0][0]'\n", + " OpLambda) , 'tf.math.multiply_8[0][0]'] \n", + " \n", + " layer_normalization_9 (Lay (None, 1, 512) 1024 ['tf.__operators__.add_8[0][0]\n", + " erNormalization) '] \n", + " \n", + " dense_26 (Dense) (None, 1, 512) 262656 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " sequential_4 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " tf.math.multiply_9 (TFOpLa (None, 1, 512) 0 ['dense_26[0][0]', \n", + " mbda) 'sequential_4[0][0]'] \n", + " \n", + " tf.__operators__.add_9 (TF (None, 1, 512) 0 ['layer_normalization_9[0][0]'\n", + " OpLambda) , 'tf.math.multiply_9[0][0]'] \n", + " \n", + " layer_normalization_10 (La (None, 1, 512) 1024 ['tf.__operators__.add_9[0][0]\n", + " yerNormalization) '] \n", + " \n", + " multi_head_attention_5 (Mu (None, 1, 512) 1050624 ['layer_normalization_10[0][0]\n", + " ltiHeadAttention) ', \n", + " 'layer_normalization_10[0][0]\n", + " '] \n", + " \n", + " static (InputLayer) [(None, 113)] 0 [] \n", + " \n", + " dense_27 (Dense) (None, 1, 512) 262656 ['layer_normalization_10[0][0]\n", + " '] \n", + " \n", + " dropout_16 (Dropout) (None, 1, 512) 0 ['multi_head_attention_5[0][0]\n", + " '] \n", + " \n", + " layer_normalization_13 (La (None, 113) 226 ['static[0][0]'] \n", + " yerNormalization) \n", + " \n", + " tf.math.multiply_10 (TFOpL (None, 1, 512) 0 ['dense_27[0][0]', \n", + " ambda) 'dropout_16[0][0]'] \n", + " \n", + " dense_32 (Dense) (None, 512) 58368 ['layer_normalization_13[0][0]\n", + " '] \n", + " \n", + " tf.__operators__.add_10 (T (None, 1, 512) 0 ['layer_normalization_10[0][0]\n", + " FOpLambda) ', \n", + " 'tf.math.multiply_10[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 512) 2048 ['dense_32[0][0]'] \n", + " Normalization) \n", + " \n", + " layer_normalization_11 (La (None, 1, 512) 1024 ['tf.__operators__.add_10[0][0\n", + " yerNormalization) ]'] \n", + " \n", + " dropout_19 (Dropout) (None, 512) 0 ['batch_normalization[0][0]'] \n", + " \n", + " dense_31 (Dense) (None, 1, 512) 262656 ['layer_normalization_11[0][0]\n", + " '] \n", + " \n", + " sequential_5 (Sequential) (None, 1, 512) 1312768 ['layer_normalization_11[0][0]\n", + " '] \n", + " \n", + " dense_33 (Dense) (None, 256) 131328 ['dropout_19[0][0]'] \n", + " \n", + " tf.math.multiply_11 (TFOpL (None, 1, 512) 0 ['dense_31[0][0]', \n", + " ambda) 'sequential_5[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 256) 1024 ['dense_33[0][0]'] \n", + " chNormalization) \n", + " \n", + " tf.__operators__.add_11 (T (None, 1, 512) 0 ['layer_normalization_11[0][0]\n", + " FOpLambda) ', \n", + " 'tf.math.multiply_11[0][0]'] \n", + " \n", + " dropout_20 (Dropout) (None, 256) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_12 (La (None, 1, 512) 1024 ['tf.__operators__.add_11[0][0\n", + " yerNormalization) ]'] \n", + " \n", + " dense_34 (Dense) (None, 128) 32896 ['dropout_20[0][0]'] \n", + " \n", + " multi_head_attention_6 (Mu (None, 1, 512) 525568 ['layer_normalization_12[0][0]\n", + " ltiHeadAttention) ', \n", + " 'layer_normalization_12[0][0]\n", + " '] \n", + " \n", + " batch_normalization_2 (Bat (None, 128) 512 ['dense_34[0][0]'] \n", + " chNormalization) \n", + " \n", + " global_average_pooling1d ( (None, 512) 0 ['multi_head_attention_6[0][0]\n", + " GlobalAveragePooling1D) '] \n", + " \n", + " dropout_21 (Dropout) (None, 128) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dense_35 (Dense) (None, 256) 131328 ['global_average_pooling1d[0][\n", + " 0]'] \n", + " \n", + " dense_36 (Dense) (None, 256) 33024 ['dropout_21[0][0]'] \n", + " \n", + " concatenate (Concatenate) (None, 512) 0 ['dense_35[0][0]', \n", + " 'dense_36[0][0]'] \n", + " \n", + " dense_37 (Dense) (None, 256) 131328 ['concatenate[0][0]'] \n", + " \n", + " batch_normalization_3 (Bat (None, 256) 1024 ['dense_37[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_38 (Dense) (None, 256) 65792 ['batch_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dropout_22 (Dropout) (None, 256) 0 ['dense_38[0][0]'] \n", + " \n", + " tf.__operators__.add_12 (T (None, 256) 0 ['dropout_22[0][0]', \n", + " FOpLambda) 'dense_37[0][0]'] \n", + " \n", + " batch_normalization_4 (Bat (None, 256) 1024 ['tf.__operators__.add_12[0][0\n", + " chNormalization) ]'] \n", + " \n", + " dense_39 (Dense) (None, 128) 32896 ['batch_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dropout_23 (Dropout) (None, 128) 0 ['dense_39[0][0]'] \n", + " \n", + " batch_normalization_5 (Bat (None, 128) 512 ['dropout_23[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_40 (Dense) (None, 64) 8256 ['batch_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dropout_24 (Dropout) (None, 64) 0 ['dense_40[0][0]'] \n", + " \n", + " batch_normalization_6 (Bat (None, 64) 256 ['dropout_24[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_41 (Dense) (None, 5) 325 ['batch_normalization_6[0][0]'\n", + " ] \n", + " \n", + "==================================================================================================\n", + "Total params: 18635373 (71.09 MB)\n", + "Trainable params: 18631661 (71.07 MB)\n", + "Non-trainable params: 3712 (14.50 KB)\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 14:50:05.929229: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n", + "2024-12-08 14:50:05.929268: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n", + "2024-12-08 14:50:05.929313: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1694] Profiler found 1 GPUs\n", + "2024-12-08 14:50:05.963777: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n", + "2024-12-08 14:50:05.963883: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/200\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 14:50:28.629786: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x773b6d05ca80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-12-08 14:50:28.629849: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-12-08 14:50:28.638389: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-12-08 14:50:28.698504: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-12-08 14:50:28.846260: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80/80 [==============================] - 336s 4s/step - loss: 0.6468 - mae: 1.1652 - mape: 1425.9640 - val_loss: 0.3750 - val_mae: 0.8167 - val_mape: 357.5072 - val_olive_prod_mae: 0.8317 - val_olive_prod_mape: 540.9344 - best_olive_prod_mae: 0.8317 - val_min_oil_prod_mae: 0.8388 - val_min_oil_prod_mape: 440.9449 - best_min_oil_prod_mae: 0.8388 - val_max_oil_prod_mae: 0.7774 - val_max_oil_prod_mape: 197.5839 - best_max_oil_prod_mae: 0.7774 - val_avg_oil_prod_mae: 0.7770 - val_avg_oil_prod_mape: 158.8580 - best_avg_oil_prod_mae: 0.7770 - val_total_water_need_mae: 0.8589 - val_total_water_need_mape: 449.2141 - best_total_water_need_mae: 0.8589 - lr: 3.9500e-05\n", + "Epoch 2/200\n", + "80/80 [==============================] - 37s 447ms/step - loss: 0.2883 - mae: 0.6758 - mape: 853.8861 - val_loss: 0.3493 - val_mae: 0.7684 - val_mape: 759.7661 - lr: 7.9500e-05\n", + "Epoch 3/200\n", + "80/80 [==============================] - 37s 450ms/step - loss: 0.1705 - mae: 0.4817 - mape: 538.0516 - val_loss: 0.2085 - val_mae: 0.5459 - val_mape: 613.3998 - lr: 1.1950e-04\n", + "Epoch 4/200\n", + "80/80 [==============================] - 37s 448ms/step - loss: 0.1249 - mae: 0.3872 - mape: 533.9152 - val_loss: 0.1381 - val_mae: 0.4314 - val_mape: 558.7706 - lr: 1.5950e-04\n", + "Epoch 5/200\n", + "80/80 [==============================] - 37s 449ms/step - loss: 0.0947 - mae: 0.3178 - mape: 355.3927 - val_loss: 0.0832 - val_mae: 0.3333 - val_mape: 487.2971 - lr: 1.9950e-04\n", + "Epoch 6/200\n", + "80/80 [==============================] - 236s 3s/step - loss: 0.0720 - mae: 0.2658 - mape: 324.9050 - val_loss: 0.0496 - val_mae: 0.2558 - val_mape: 360.3815 - val_olive_prod_mae: 0.3133 - val_olive_prod_mape: 210.0914 - best_olive_prod_mae: 0.3133 - val_min_oil_prod_mae: 0.2730 - val_min_oil_prod_mape: 291.2299 - best_min_oil_prod_mae: 0.2730 - val_max_oil_prod_mae: 0.2761 - val_max_oil_prod_mape: 889.4504 - best_max_oil_prod_mae: 0.2761 - val_avg_oil_prod_mae: 0.2060 - val_avg_oil_prod_mape: 174.9547 - best_avg_oil_prod_mae: 0.2060 - val_total_water_need_mae: 0.2104 - val_total_water_need_mape: 236.1803 - best_total_water_need_mae: 0.2104 - lr: 2.3950e-04\n", + "Epoch 7/200\n", + "19/80 [======>.......................] - ETA: 20s - loss: 0.0607 - mae: 0.2401 - mape: 225.5450" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 15:02:15.855854: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.\n", + "2024-12-08 15:02:15.855911: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "39/80 [=============>................] - ETA: 14s - loss: 0.0594 - mae: 0.2364 - mape: 234.2973" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-08 15:02:23.478578: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.\n", + "2024-12-08 15:02:23.647243: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1828] CUPTI activity buffer flushed\n", + "2024-12-08 15:02:27.754232: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 110980 callback api events and 111545 activity events. \n", + "2024-12-08 15:03:20.421265: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "80/80 [==============================] - 96s 1s/step - loss: 0.0577 - mae: 0.2319 - mape: 262.5403 - val_loss: 0.1514 - val_mae: 0.4553 - val_mape: 361.8612 - lr: 2.7950e-04\n", + "Epoch 8/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0537 - mae: 0.2191 - mape: 247.8973 - val_loss: 0.0820 - val_mae: 0.3394 - val_mape: 311.3165 - lr: 3.1950e-04\n", + "Epoch 9/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0457 - mae: 0.1994 - mape: 220.3584 - val_loss: 0.0261 - val_mae: 0.1699 - val_mape: 192.3138 - lr: 3.5950e-04\n", + "Epoch 10/200\n", + "80/80 [==============================] - 35s 436ms/step - loss: 0.0398 - mae: 0.1852 - mape: 238.8312 - val_loss: 0.0632 - val_mae: 0.3179 - val_mape: 472.3796 - lr: 3.9950e-04\n", + "Epoch 11/200\n", + "80/80 [==============================] - 242s 3s/step - loss: 0.0380 - mae: 0.1785 - mape: 226.0223 - val_loss: 0.0411 - val_mae: 0.2068 - val_mape: 289.5609 - val_olive_prod_mae: 0.2628 - val_olive_prod_mape: 305.8365 - best_olive_prod_mae: 0.2628 - val_min_oil_prod_mae: 0.1618 - val_min_oil_prod_mape: 150.3432 - best_min_oil_prod_mae: 0.1618 - val_max_oil_prod_mae: 0.1877 - val_max_oil_prod_mape: 491.9082 - best_max_oil_prod_mae: 0.1877 - val_avg_oil_prod_mae: 0.2413 - val_avg_oil_prod_mape: 259.4505 - val_total_water_need_mae: 0.1803 - val_total_water_need_mape: 240.2667 - best_total_water_need_mae: 0.1803 - lr: 4.3950e-04\n", + "Epoch 12/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0367 - mae: 0.1737 - mape: 186.3838 - val_loss: 0.0223 - val_mae: 0.1476 - val_mape: 164.0920 - lr: 4.7950e-04\n", + "Epoch 13/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0327 - mae: 0.1630 - mape: 184.1294 - val_loss: 0.0191 - val_mae: 0.1254 - val_mape: 148.7276 - lr: 3.5526e-04\n", + "Epoch 14/200\n", + "80/80 [==============================] - 35s 436ms/step - loss: 0.0390 - mae: 0.1759 - mape: 202.7457 - val_loss: 0.0271 - val_mae: 0.1711 - val_mape: 249.5935 - lr: 3.4603e-04\n", + "Epoch 15/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0312 - mae: 0.1579 - mape: 155.0903 - val_loss: 0.0202 - val_mae: 0.1389 - val_mape: 188.7654 - lr: 3.3704e-04\n", + "Epoch 16/200\n", + "80/80 [==============================] - 260s 3s/step - loss: 0.0300 - mae: 0.1540 - mape: 163.7010 - val_loss: 0.0186 - val_mae: 0.1330 - val_mape: 187.7290 - val_olive_prod_mae: 0.1273 - val_olive_prod_mape: 106.2623 - best_olive_prod_mae: 0.1273 - val_min_oil_prod_mae: 0.1385 - val_min_oil_prod_mape: 108.2680 - best_min_oil_prod_mae: 0.1385 - val_max_oil_prod_mae: 0.1391 - val_max_oil_prod_mape: 505.4183 - best_max_oil_prod_mae: 0.1391 - val_avg_oil_prod_mae: 0.1402 - val_avg_oil_prod_mape: 103.5567 - best_avg_oil_prod_mae: 0.1402 - val_total_water_need_mae: 0.1197 - val_total_water_need_mape: 115.1407 - best_total_water_need_mae: 0.1197 - lr: 3.2829e-04\n", + "Epoch 17/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0287 - mae: 0.1509 - mape: 154.7843 - val_loss: 0.0143 - val_mae: 0.0994 - val_mape: 142.0511 - lr: 3.1976e-04\n", + "Epoch 18/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0285 - mae: 0.1517 - mape: 147.2274 - val_loss: 0.0308 - val_mae: 0.1751 - val_mape: 184.6624 - lr: 3.1146e-04\n", + "Epoch 19/200\n", + "80/80 [==============================] - 35s 435ms/step - loss: 0.0296 - mae: 0.1522 - mape: 166.7331 - val_loss: 0.0153 - val_mae: 0.1091 - val_mape: 158.7550 - lr: 3.0337e-04\n", + "Epoch 20/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0291 - mae: 0.1503 - mape: 166.9374 - val_loss: 0.0157 - val_mae: 0.1154 - val_mape: 130.2295 - lr: 2.9549e-04\n", + "Epoch 21/200\n", + "80/80 [==============================] - 251s 3s/step - loss: 0.0296 - mae: 0.1512 - mape: 157.1642 - val_loss: 0.0269 - val_mae: 0.1741 - val_mape: 309.7536 - val_olive_prod_mae: 0.2191 - val_olive_prod_mape: 276.2847 - val_min_oil_prod_mae: 0.1626 - val_min_oil_prod_mape: 218.7291 - val_max_oil_prod_mae: 0.1893 - val_max_oil_prod_mape: 669.6759 - val_avg_oil_prod_mae: 0.1389 - val_avg_oil_prod_mape: 156.0846 - best_avg_oil_prod_mae: 0.1389 - val_total_water_need_mae: 0.1608 - val_total_water_need_mape: 227.9932 - lr: 2.8781e-04\n", + "Epoch 22/200\n", + "80/80 [==============================] - 35s 435ms/step - loss: 0.0259 - mae: 0.1422 - mape: 166.8822 - val_loss: 0.0144 - val_mae: 0.1019 - val_mape: 137.3515 - lr: 2.8034e-04\n", + "Epoch 23/200\n", + "80/80 [==============================] - 35s 434ms/step - loss: 0.0248 - mae: 0.1394 - mape: 143.1400 - val_loss: 0.0165 - val_mae: 0.1251 - val_mape: 158.9414 - lr: 2.7306e-04\n", + "Epoch 24/200\n", + "80/80 [==============================] - 35s 433ms/step - loss: 0.0247 - mae: 0.1388 - mape: 164.7200 - val_loss: 0.0146 - val_mae: 0.1058 - val_mape: 129.0920 - lr: 2.6597e-04\n", + "Epoch 25/200\n", + "80/80 [==============================] - 35s 435ms/step - loss: 0.0241 - mae: 0.1368 - mape: 149.9482 - val_loss: 0.0144 - val_mae: 0.1076 - val_mape: 127.7412 - lr: 2.5906e-04\n", + "Epoch 26/200\n", + "80/80 [==============================] - 239s 3s/step - loss: 0.0233 - mae: 0.1353 - mape: 166.1661 - val_loss: 0.0153 - val_mae: 0.1085 - val_mape: 116.0930 - val_olive_prod_mae: 0.1049 - val_olive_prod_mape: 89.0422 - best_olive_prod_mae: 0.1049 - val_min_oil_prod_mae: 0.0967 - val_min_oil_prod_mape: 100.7810 - best_min_oil_prod_mae: 0.0967 - val_max_oil_prod_mae: 0.1026 - val_max_oil_prod_mape: 147.8183 - best_max_oil_prod_mae: 0.1026 - val_avg_oil_prod_mae: 0.0993 - val_avg_oil_prod_mape: 78.5883 - best_avg_oil_prod_mae: 0.0993 - val_total_water_need_mae: 0.1389 - val_total_water_need_mape: 164.2348 - lr: 2.5233e-04\n", + "Epoch 27/200\n", + "80/80 [==============================] - 35s 433ms/step - loss: 0.0248 - mae: 0.1380 - mape: 152.2872 - val_loss: 0.0155 - val_mae: 0.1181 - val_mape: 110.0643 - lr: 2.4578e-04\n", + "Epoch 28/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0230 - mae: 0.1342 - mape: 143.7230 - val_loss: 0.0139 - val_mae: 0.0997 - val_mape: 108.3232 - lr: 2.3939e-04\n", + "Epoch 29/200\n", + "80/80 [==============================] - 37s 449ms/step - loss: 0.0227 - mae: 0.1332 - mape: 143.9378 - val_loss: 0.0137 - val_mae: 0.0994 - val_mape: 123.0778 - lr: 2.3318e-04\n", + "Epoch 30/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0248 - mae: 0.1380 - mape: 162.3151 - val_loss: 0.0210 - val_mae: 0.1404 - val_mape: 200.2853 - lr: 2.2712e-04\n", + "Epoch 31/200\n", + "80/80 [==============================] - 235s 3s/step - loss: 0.0224 - mae: 0.1323 - mape: 156.1841 - val_loss: 0.0191 - val_mae: 0.1358 - val_mape: 241.1420 - val_olive_prod_mae: 0.1327 - val_olive_prod_mape: 126.8244 - val_min_oil_prod_mae: 0.1761 - val_min_oil_prod_mape: 250.3756 - val_max_oil_prod_mae: 0.1593 - val_max_oil_prod_mape: 618.0728 - val_avg_oil_prod_mae: 0.1147 - val_avg_oil_prod_mape: 96.8866 - val_total_water_need_mae: 0.0962 - val_total_water_need_mape: 113.5497 - best_total_water_need_mae: 0.0962 - lr: 2.2122e-04\n", + "Epoch 32/200\n", + "80/80 [==============================] - 35s 434ms/step - loss: 0.0220 - mae: 0.1309 - mape: 150.9560 - val_loss: 0.0188 - val_mae: 0.1264 - val_mape: 163.6075 - lr: 2.1548e-04\n", + "Epoch 33/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0229 - mae: 0.1333 - mape: 166.5528 - val_loss: 0.0165 - val_mae: 0.1219 - val_mape: 138.8618 - lr: 2.0988e-04\n", + "Epoch 34/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0231 - mae: 0.1332 - mape: 166.1271 - val_loss: 0.0155 - val_mae: 0.1149 - val_mape: 156.8145 - lr: 2.0443e-04\n", + "Epoch 35/200\n", + "80/80 [==============================] - 37s 449ms/step - loss: 0.0208 - mae: 0.1283 - mape: 162.5032 - val_loss: 0.0134 - val_mae: 0.1000 - val_mape: 129.9323 - lr: 1.9912e-04\n", + "Epoch 36/200\n", + "80/80 [==============================] - 255s 3s/step - loss: 0.0206 - mae: 0.1278 - mape: 157.6071 - val_loss: 0.0129 - val_mae: 0.0970 - val_mape: 124.6722 - val_olive_prod_mae: 0.1012 - val_olive_prod_mape: 78.9201 - best_olive_prod_mae: 0.1012 - val_min_oil_prod_mae: 0.1006 - val_min_oil_prod_mape: 102.2447 - val_max_oil_prod_mae: 0.0998 - val_max_oil_prod_mape: 267.4592 - best_max_oil_prod_mae: 0.0998 - val_avg_oil_prod_mae: 0.0957 - val_avg_oil_prod_mape: 76.1857 - best_avg_oil_prod_mae: 0.0957 - val_total_water_need_mae: 0.0878 - val_total_water_need_mape: 98.5509 - best_total_water_need_mae: 0.0878 - lr: 1.9395e-04\n", + "Epoch 37/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0205 - mae: 0.1267 - mape: 152.8490 - val_loss: 0.0119 - val_mae: 0.0886 - val_mape: 128.2448 - lr: 1.8891e-04\n", + "Epoch 38/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0202 - mae: 0.1257 - mape: 136.0964 - val_loss: 0.0129 - val_mae: 0.0986 - val_mape: 124.0754 - lr: 1.8400e-04\n", + "Epoch 39/200\n", + "80/80 [==============================] - 37s 451ms/step - loss: 0.0201 - mae: 0.1259 - mape: 149.9923 - val_loss: 0.0116 - val_mae: 0.0882 - val_mape: 101.0983 - lr: 1.7923e-04\n", + "Epoch 40/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0202 - mae: 0.1252 - mape: 162.2166 - val_loss: 0.0129 - val_mae: 0.0950 - val_mape: 103.8257 - lr: 1.7457e-04\n", + "Epoch 41/200\n", + "80/80 [==============================] - 249s 3s/step - loss: 0.0201 - mae: 0.1254 - mape: 167.0735 - val_loss: 0.0126 - val_mae: 0.0987 - val_mape: 110.8911 - val_olive_prod_mae: 0.1091 - val_olive_prod_mape: 76.9292 - val_min_oil_prod_mae: 0.1120 - val_min_oil_prod_mape: 92.9203 - val_max_oil_prod_mae: 0.0993 - val_max_oil_prod_mape: 259.1275 - best_max_oil_prod_mae: 0.0993 - val_avg_oil_prod_mae: 0.1080 - val_avg_oil_prod_mape: 77.5547 - val_total_water_need_mae: 0.0651 - val_total_water_need_mape: 47.9241 - best_total_water_need_mae: 0.0651 - lr: 1.7004e-04\n", + "Epoch 42/200\n", + "80/80 [==============================] - 36s 448ms/step - loss: 0.0196 - mae: 0.1243 - mape: 157.7031 - val_loss: 0.0140 - val_mae: 0.1126 - val_mape: 122.0974 - lr: 1.6562e-04\n", + "Epoch 43/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0192 - mae: 0.1233 - mape: 137.9573 - val_loss: 0.0118 - val_mae: 0.0918 - val_mape: 119.4805 - lr: 1.6132e-04\n", + "Epoch 44/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0192 - mae: 0.1232 - mape: 154.7663 - val_loss: 0.0117 - val_mae: 0.0897 - val_mape: 103.2779 - lr: 1.5713e-04\n", + "Epoch 45/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0190 - mae: 0.1226 - mape: 153.6464 - val_loss: 0.0118 - val_mae: 0.0905 - val_mape: 117.5741 - lr: 1.5305e-04\n", + "Epoch 46/200\n", + "80/80 [==============================] - 252s 3s/step - loss: 0.0191 - mae: 0.1233 - mape: 150.4706 - val_loss: 0.0131 - val_mae: 0.0959 - val_mape: 114.9800 - val_olive_prod_mae: 0.1016 - val_olive_prod_mape: 76.1410 - val_min_oil_prod_mae: 0.1050 - val_min_oil_prod_mape: 98.5945 - val_max_oil_prod_mae: 0.1024 - val_max_oil_prod_mape: 273.3916 - val_avg_oil_prod_mae: 0.1005 - val_avg_oil_prod_mape: 77.1865 - val_total_water_need_mae: 0.0697 - val_total_water_need_mape: 49.5867 - lr: 1.4907e-04\n", + "Epoch 47/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0187 - mae: 0.1218 - mape: 145.5018 - val_loss: 0.0117 - val_mae: 0.0899 - val_mape: 108.3948 - lr: 1.4520e-04\n", + "Epoch 48/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0185 - mae: 0.1212 - mape: 136.5238 - val_loss: 0.0115 - val_mae: 0.0881 - val_mape: 106.7443 - lr: 1.4143e-04\n", + "Epoch 49/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0184 - mae: 0.1209 - mape: 143.3521 - val_loss: 0.0110 - val_mae: 0.0855 - val_mape: 101.2103 - lr: 1.3776e-04\n", + "Epoch 50/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0182 - mae: 0.1199 - mape: 127.5113 - val_loss: 0.0113 - val_mae: 0.0887 - val_mape: 111.8407 - lr: 1.3418e-04\n", + "Epoch 51/200\n", + "80/80 [==============================] - 245s 3s/step - loss: 0.0180 - mae: 0.1196 - mape: 154.3594 - val_loss: 0.0114 - val_mae: 0.0906 - val_mape: 105.7354 - val_olive_prod_mae: 0.0969 - val_olive_prod_mape: 73.9075 - best_olive_prod_mae: 0.0969 - val_min_oil_prod_mae: 0.0960 - val_min_oil_prod_mape: 94.2868 - best_min_oil_prod_mae: 0.0960 - val_max_oil_prod_mae: 0.0943 - val_max_oil_prod_mape: 226.3041 - best_max_oil_prod_mae: 0.0943 - val_avg_oil_prod_mae: 0.0927 - val_avg_oil_prod_mape: 68.3263 - best_avg_oil_prod_mae: 0.0927 - val_total_water_need_mae: 0.0730 - val_total_water_need_mape: 65.8522 - lr: 1.3069e-04\n", + "Epoch 52/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0179 - mae: 0.1191 - mape: 134.7333 - val_loss: 0.0108 - val_mae: 0.0833 - val_mape: 102.4398 - lr: 1.2730e-04\n", + "Epoch 53/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0179 - mae: 0.1194 - mape: 140.6877 - val_loss: 0.0113 - val_mae: 0.0904 - val_mape: 108.1014 - lr: 1.2399e-04\n", + "Epoch 54/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0176 - mae: 0.1187 - mape: 139.4526 - val_loss: 0.0112 - val_mae: 0.0914 - val_mape: 99.9837 - lr: 1.2077e-04\n", + "Epoch 55/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0173 - mae: 0.1176 - mape: 155.5683 - val_loss: 0.0109 - val_mae: 0.0872 - val_mape: 102.1552 - lr: 1.1764e-04\n", + "Epoch 56/200\n", + "80/80 [==============================] - 233s 3s/step - loss: 0.0173 - mae: 0.1174 - mape: 147.7777 - val_loss: 0.0110 - val_mae: 0.0888 - val_mape: 94.7351 - val_olive_prod_mae: 0.0954 - val_olive_prod_mape: 75.4792 - best_olive_prod_mae: 0.0954 - val_min_oil_prod_mae: 0.0942 - val_min_oil_prod_mape: 95.7656 - best_min_oil_prod_mae: 0.0942 - val_max_oil_prod_mae: 0.0956 - val_max_oil_prod_mape: 188.4087 - val_avg_oil_prod_mae: 0.0902 - val_avg_oil_prod_mape: 69.8961 - best_avg_oil_prod_mae: 0.0902 - val_total_water_need_mae: 0.0685 - val_total_water_need_mape: 44.1256 - lr: 1.1458e-04\n", + "Epoch 57/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0171 - mae: 0.1171 - mape: 142.1757 - val_loss: 0.0109 - val_mae: 0.0869 - val_mape: 99.5790 - lr: 1.1161e-04\n", + "Epoch 58/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0170 - mae: 0.1168 - mape: 138.0693 - val_loss: 0.0106 - val_mae: 0.0847 - val_mape: 96.3503 - lr: 1.0871e-04\n", + "Epoch 59/200\n", + "80/80 [==============================] - 247s 3s/step - loss: 0.0167 - mae: 0.1159 - mape: 136.2257 - val_loss: 0.0115 - val_mae: 0.0938 - val_mape: 98.5057 - val_olive_prod_mae: 0.1011 - val_olive_prod_mape: 72.6107 - val_min_oil_prod_mae: 0.0996 - val_min_oil_prod_mape: 90.0922 - val_max_oil_prod_mae: 0.1000 - val_max_oil_prod_mape: 218.1897 - val_avg_oil_prod_mae: 0.0983 - val_avg_oil_prod_mape: 65.7479 - val_total_water_need_mae: 0.0699 - val_total_water_need_mape: 45.8883 - lr: 1.0045e-04\n", + "Epoch 62/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0167 - mae: 0.1166 - mape: 122.0693 - val_loss: 0.0107 - val_mae: 0.0880 - val_mape: 100.1896 - lr: 9.7846e-05\n", + "Epoch 63/200\n", + "80/80 [==============================] - 37s 453ms/step - loss: 0.0165 - mae: 0.1158 - mape: 135.5000 - val_loss: 0.0104 - val_mae: 0.0846 - val_mape: 95.0203 - lr: 9.5304e-05\n", + "Epoch 64/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0163 - mae: 0.1150 - mape: 142.8906 - val_loss: 0.0107 - val_mae: 0.0892 - val_mape: 98.5729 - lr: 9.2829e-05\n", + "Epoch 65/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0162 - mae: 0.1147 - mape: 139.8213 - val_loss: 0.0108 - val_mae: 0.0894 - val_mape: 112.6549 - lr: 9.0418e-05\n", + "Epoch 66/200\n", + "80/80 [==============================] - 237s 3s/step - loss: 0.0161 - mae: 0.1148 - mape: 140.7891 - val_loss: 0.0104 - val_mae: 0.0866 - val_mape: 100.1666 - val_olive_prod_mae: 0.0930 - val_olive_prod_mape: 76.0337 - best_olive_prod_mae: 0.0930 - val_min_oil_prod_mae: 0.0930 - val_min_oil_prod_mape: 93.2880 - best_min_oil_prod_mae: 0.0930 - val_max_oil_prod_mae: 0.0929 - val_max_oil_prod_mape: 222.5383 - best_max_oil_prod_mae: 0.0929 - val_avg_oil_prod_mae: 0.0891 - val_avg_oil_prod_mape: 67.5470 - best_avg_oil_prod_mae: 0.0891 - val_total_water_need_mae: 0.0648 - val_total_water_need_mape: 41.4260 - best_total_water_need_mae: 0.0648 - lr: 8.8070e-05\n", + "Epoch 67/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0161 - mae: 0.1147 - mape: 132.5854 - val_loss: 0.0104 - val_mae: 0.0870 - val_mape: 95.3056 - lr: 8.5782e-05\n", + "Epoch 68/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0159 - mae: 0.1139 - mape: 133.2579 - val_loss: 0.0104 - val_mae: 0.0879 - val_mape: 96.8682 - lr: 8.3555e-05\n", + "Epoch 69/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0159 - mae: 0.1142 - mape: 134.3016 - val_loss: 0.0110 - val_mae: 0.0905 - val_mape: 102.5002 - lr: 8.1384e-05\n", + "Epoch 70/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0158 - mae: 0.1136 - mape: 127.2419 - val_loss: 0.0104 - val_mae: 0.0881 - val_mape: 104.7385 - lr: 7.9271e-05\n", + "Epoch 71/200\n", + "80/80 [==============================] - 37s 450ms/step - loss: 0.0155 - mae: 0.1132 - mape: 129.8317 - val_loss: 0.0103 - val_mae: 0.0860 - val_mape: 99.2322 - lr: 7.3253e-05\n", + "Epoch 74/200\n", + "80/80 [==============================] - 37s 450ms/step - loss: 0.0155 - mae: 0.1131 - mape: 138.3970 - val_loss: 0.0103 - val_mae: 0.0895 - val_mape: 110.4572 - lr: 7.1351e-05\n", + "Epoch 75/200\n", + "80/80 [==============================] - 37s 456ms/step - loss: 0.0153 - mae: 0.1125 - mape: 134.7098 - val_loss: 0.0099 - val_mae: 0.0838 - val_mape: 101.4078 - lr: 6.9498e-05\n", + "Epoch 76/200\n", + "80/80 [==============================] - 209s 3s/step - loss: 0.0153 - mae: 0.1127 - mape: 133.3882 - val_loss: 0.0103 - val_mae: 0.0884 - val_mape: 101.1207 - val_olive_prod_mae: 0.0948 - val_olive_prod_mape: 73.1592 - val_min_oil_prod_mae: 0.0959 - val_min_oil_prod_mape: 92.9563 - val_max_oil_prod_mae: 0.0954 - val_max_oil_prod_mape: 230.6032 - val_avg_oil_prod_mae: 0.0924 - val_avg_oil_prod_mape: 66.7822 - val_total_water_need_mae: 0.0637 - val_total_water_need_mape: 42.1024 - lr: 6.7693e-05\n", + "Epoch 77/200\n", + "80/80 [==============================] - 37s 454ms/step - loss: 0.0151 - mae: 0.1123 - mape: 129.7830 - val_loss: 0.0099 - val_mae: 0.0844 - val_mape: 100.0219 - lr: 6.5935e-05\n", + "Epoch 78/200\n", + "80/80 [==============================] - 37s 448ms/step - loss: 0.0151 - mae: 0.1121 - mape: 128.3232 - val_loss: 0.0098 - val_mae: 0.0846 - val_mape: 104.9378 - lr: 6.4222e-05\n", + "Epoch 79/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0150 - mae: 0.1119 - mape: 136.6634 - val_loss: 0.0098 - val_mae: 0.0831 - val_mape: 94.8757 - lr: 6.2554e-05\n", + "Epoch 80/200\n", + "80/80 [==============================] - 229s 3s/step - loss: 0.0149 - mae: 0.1113 - mape: 154.3307 - val_loss: 0.0097 - val_mae: 0.0837 - val_mape: 100.8595 - val_olive_prod_mae: 0.0925 - val_olive_prod_mape: 74.3520 - best_olive_prod_mae: 0.0925 - val_min_oil_prod_mae: 0.0903 - val_min_oil_prod_mape: 93.5097 - best_min_oil_prod_mae: 0.0903 - val_max_oil_prod_mae: 0.0895 - val_max_oil_prod_mape: 224.7449 - best_max_oil_prod_mae: 0.0895 - val_avg_oil_prod_mae: 0.0861 - val_avg_oil_prod_mape: 65.9836 - best_avg_oil_prod_mae: 0.0861 - val_total_water_need_mae: 0.0603 - val_total_water_need_mape: 45.7072 - lr: 5.9347e-05\n", + "Epoch 82/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0148 - mae: 0.1116 - mape: 142.9829 - val_loss: 0.0095 - val_mae: 0.0829 - val_mape: 104.9523 - lr: 5.7806e-05\n", + "Epoch 83/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0148 - mae: 0.1117 - mape: 137.5172 - val_loss: 0.0101 - val_mae: 0.0874 - val_mape: 100.8047 - lr: 5.6304e-05\n", + "Epoch 84/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0147 - mae: 0.1109 - mape: 149.5698 - val_loss: 0.0097 - val_mae: 0.0862 - val_mape: 99.6029 - lr: 5.4842e-05\n", + "Epoch 85/200\n", + "80/80 [==============================] - 230s 3s/step - loss: 0.0146 - mae: 0.1108 - mape: 138.4030 - val_loss: 0.0096 - val_mae: 0.0849 - val_mape: 104.8251 - val_olive_prod_mae: 0.0921 - val_olive_prod_mape: 72.9428 - best_olive_prod_mae: 0.0921 - val_min_oil_prod_mae: 0.0908 - val_min_oil_prod_mape: 93.9297 - val_max_oil_prod_mae: 0.0915 - val_max_oil_prod_mape: 243.2711 - val_avg_oil_prod_mae: 0.0882 - val_avg_oil_prod_mape: 68.0993 - val_total_water_need_mae: 0.0617 - val_total_water_need_mape: 45.8819 - lr: 5.2030e-05\n", + "Epoch 87/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0143 - mae: 0.1101 - mape: 133.9900 - val_loss: 0.0095 - val_mae: 0.0845 - val_mape: 103.7470 - lr: 4.8081e-05\n", + "Epoch 90/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0143 - mae: 0.1101 - mape: 129.2636 - val_loss: 0.0094 - val_mae: 0.0834 - val_mape: 107.9905 - lr: 4.6832e-05\n", + "Epoch 91/200\n", + "80/80 [==============================] - 264s 3s/step - loss: 0.0142 - mae: 0.1103 - mape: 138.2555 - val_loss: 0.0094 - val_mae: 0.0847 - val_mape: 102.1767 - val_olive_prod_mae: 0.0921 - val_olive_prod_mape: 73.1834 - val_min_oil_prod_mae: 0.0913 - val_min_oil_prod_mape: 93.8826 - val_max_oil_prod_mae: 0.0915 - val_max_oil_prod_mape: 236.0179 - val_avg_oil_prod_mae: 0.0877 - val_avg_oil_prod_mape: 67.3626 - val_total_water_need_mae: 0.0609 - val_total_water_need_mape: 40.4373 - lr: 4.5616e-05\n", + "Epoch 92/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0142 - mae: 0.1099 - mape: 137.3376 - val_loss: 0.0094 - val_mae: 0.0840 - val_mape: 103.3979 - lr: 4.4431e-05\n", + "Epoch 93/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0141 - mae: 0.1097 - mape: 131.1958 - val_loss: 0.0094 - val_mae: 0.0827 - val_mape: 99.3171 - lr: 4.3277e-05\n", + "Epoch 94/200\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0140 - mae: 0.1094 - mape: 129.6252 - val_loss: 0.0091 - val_mae: 0.0807 - val_mape: 99.7888 - lr: 4.2153e-05\n", + "Epoch 95/200\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0141 - mae: 0.1099 - mape: 145.5369 - val_loss: 0.0092 - val_mae: 0.0828 - val_mape: 99.6861 - lr: 4.1058e-05\n", + "Epoch 96/200\n", + "80/80 [==============================] - 260s 3s/step - loss: 0.0143 - mae: 0.1101 - mape: 133.5272 - val_loss: 0.0095 - val_mae: 0.0844 - val_mape: 94.5343 - val_olive_prod_mae: 0.0933 - val_olive_prod_mape: 72.0466 - val_min_oil_prod_mae: 0.0915 - val_min_oil_prod_mape: 92.5089 - val_max_oil_prod_mae: 0.0918 - val_max_oil_prod_mape: 200.5542 - val_avg_oil_prod_mae: 0.0883 - val_avg_oil_prod_mape: 65.6080 - val_total_water_need_mae: 0.0571 - val_total_water_need_mape: 41.9541 - best_total_water_need_mae: 0.0571 - lr: 3.9992e-05\n", + "Epoch 97/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0139 - mae: 0.1095 - mape: 121.2405 - val_loss: 0.0095 - val_mae: 0.0845 - val_mape: 101.0546 - lr: 3.8953e-05\n", + "Epoch 98/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0139 - mae: 0.1094 - mape: 129.6525 - val_loss: 0.0092 - val_mae: 0.0825 - val_mape: 105.1573 - lr: 3.7941e-05\n", + "Epoch 99/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0139 - mae: 0.1095 - mape: 127.7979 - val_loss: 0.0093 - val_mae: 0.0841 - val_mape: 106.7909 - lr: 3.6956e-05\n", + "Epoch 100/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0138 - mae: 0.1093 - mape: 132.6569 - val_loss: 0.0092 - val_mae: 0.0826 - val_mape: 101.0493 - lr: 3.5996e-05\n", + "Epoch 101/200\n", + "80/80 [==============================] - 231s 3s/step - loss: 0.0138 - mae: 0.1092 - mape: 142.6844 - val_loss: 0.0091 - val_mae: 0.0822 - val_mape: 103.1497 - val_olive_prod_mae: 0.0911 - val_olive_prod_mape: 74.2005 - best_olive_prod_mae: 0.0911 - val_min_oil_prod_mae: 0.0893 - val_min_oil_prod_mape: 95.3363 - best_min_oil_prod_mae: 0.0893 - val_max_oil_prod_mae: 0.0891 - val_max_oil_prod_mape: 236.3043 - best_max_oil_prod_mae: 0.0891 - val_avg_oil_prod_mae: 0.0856 - val_avg_oil_prod_mape: 67.7334 - best_avg_oil_prod_mae: 0.0856 - val_total_water_need_mae: 0.0560 - val_total_water_need_mape: 42.1736 - best_total_water_need_mae: 0.0560 - lr: 3.5061e-05\n", + "Epoch 102/200\n", + "80/80 [==============================] - 36s 447ms/step - loss: 0.0137 - mae: 0.1087 - mape: 120.9930 - val_loss: 0.0092 - val_mae: 0.0840 - val_mape: 102.9081 - lr: 3.4151e-05\n", + "Epoch 103/200\n", + "80/80 [==============================] - 37s 453ms/step - loss: 0.0137 - mae: 0.1090 - mape: 145.3493 - val_loss: 0.0090 - val_mae: 0.0820 - val_mape: 103.3405 - lr: 3.3264e-05\n", + "Epoch 104/200\n", + "80/80 [==============================] - 37s 447ms/step - loss: 0.0137 - mae: 0.1088 - mape: 132.0474 - val_loss: 0.0088 - val_mae: 0.0788 - val_mape: 99.5223 - lr: 3.2400e-05\n", + "Epoch 105/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0136 - mae: 0.1089 - mape: 143.3882 - val_loss: 0.0090 - val_mae: 0.0820 - val_mape: 102.5941 - lr: 3.1558e-05\n", + "Epoch 106/200\n", + "80/80 [==============================] - 233s 3s/step - loss: 0.0136 - mae: 0.1085 - mape: 130.0781 - val_loss: 0.0090 - val_mae: 0.0824 - val_mape: 100.9554 - val_olive_prod_mae: 0.0908 - val_olive_prod_mape: 73.1759 - best_olive_prod_mae: 0.0908 - val_min_oil_prod_mae: 0.0893 - val_min_oil_prod_mape: 93.9239 - val_max_oil_prod_mae: 0.0895 - val_max_oil_prod_mape: 228.2562 - val_avg_oil_prod_mae: 0.0859 - val_avg_oil_prod_mape: 66.5741 - val_total_water_need_mae: 0.0565 - val_total_water_need_mape: 42.8471 - lr: 3.0739e-05\n", + "Epoch 107/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0136 - mae: 0.1088 - mape: 128.6352 - val_loss: 0.0090 - val_mae: 0.0816 - val_mape: 101.1500 - lr: 2.9940e-05\n", + "Epoch 108/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0135 - mae: 0.1085 - mape: 139.2546 - val_loss: 0.0091 - val_mae: 0.0820 - val_mape: 100.3162 - lr: 2.9163e-05\n", + "Epoch 109/200\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0134 - mae: 0.1086 - mape: 148.4389 - val_loss: 0.0087 - val_mae: 0.0794 - val_mape: 102.8431 - lr: 2.8405e-05\n", + "Epoch 110/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0135 - mae: 0.1084 - mape: 126.4040 - val_loss: 0.0088 - val_mae: 0.0805 - val_mape: 100.3828 - lr: 2.7668e-05\n", + "Epoch 111/200\n", + "80/80 [==============================] - 245s 3s/step - loss: 0.0134 - mae: 0.1080 - mape: 138.5028 - val_loss: 0.0088 - val_mae: 0.0802 - val_mape: 103.2501 - val_olive_prod_mae: 0.0886 - val_olive_prod_mape: 73.6275 - best_olive_prod_mae: 0.0886 - val_min_oil_prod_mae: 0.0870 - val_min_oil_prod_mape: 94.9520 - best_min_oil_prod_mae: 0.0870 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 239.6956 - best_max_oil_prod_mae: 0.0869 - val_avg_oil_prod_mae: 0.0836 - val_avg_oil_prod_mape: 67.5310 - best_avg_oil_prod_mae: 0.0836 - val_total_water_need_mae: 0.0550 - val_total_water_need_mape: 40.4444 - best_total_water_need_mae: 0.0550 - lr: 2.6949e-05\n", + "Epoch 112/200\n", + "80/80 [==============================] - 35s 434ms/step - loss: 0.0133 - mae: 0.1080 - mape: 140.4691 - val_loss: 0.0088 - val_mae: 0.0800 - val_mape: 100.8578 - lr: 2.6249e-05\n", + "Epoch 113/200\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0133 - mae: 0.1082 - mape: 128.5066 - val_loss: 0.0087 - val_mae: 0.0808 - val_mape: 102.7916 - lr: 2.5567e-05\n", + "Epoch 114/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0133 - mae: 0.1083 - mape: 148.3104 - val_loss: 0.0088 - val_mae: 0.0817 - val_mape: 100.1457 - lr: 2.4903e-05\n", + "Epoch 115/200\n", + "80/80 [==============================] - 247s 3s/step - loss: 0.0132 - mae: 0.1079 - mape: 145.9036 - val_loss: 0.0086 - val_mae: 0.0797 - val_mape: 102.3304 - val_olive_prod_mae: 0.0879 - val_olive_prod_mape: 74.0978 - best_olive_prod_mae: 0.0879 - val_min_oil_prod_mae: 0.0872 - val_min_oil_prod_mape: 94.1461 - val_max_oil_prod_mae: 0.0865 - val_max_oil_prod_mape: 235.5081 - best_max_oil_prod_mae: 0.0865 - val_avg_oil_prod_mae: 0.0833 - val_avg_oil_prod_mape: 67.5759 - best_avg_oil_prod_mae: 0.0833 - val_total_water_need_mae: 0.0535 - val_total_water_need_mape: 40.3236 - best_total_water_need_mae: 0.0535 - lr: 2.3626e-05\n", + "Epoch 117/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0132 - mae: 0.1078 - mape: 141.3102 - val_loss: 0.0089 - val_mae: 0.0828 - val_mape: 104.7780 - lr: 2.3013e-05\n", + "Epoch 118/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0132 - mae: 0.1081 - mape: 132.8581 - val_loss: 0.0086 - val_mae: 0.0797 - val_mape: 100.6831 - lr: 2.2415e-05\n", + "Epoch 119/200\n", + "80/80 [==============================] - 231s 3s/step - loss: 0.0131 - mae: 0.1078 - mape: 133.2317 - val_loss: 0.0086 - val_mae: 0.0792 - val_mape: 101.1063 - val_olive_prod_mae: 0.0877 - val_olive_prod_mape: 73.5447 - best_olive_prod_mae: 0.0877 - val_min_oil_prod_mae: 0.0867 - val_min_oil_prod_mape: 94.1456 - best_min_oil_prod_mae: 0.0867 - val_max_oil_prod_mae: 0.0868 - val_max_oil_prod_mape: 231.5488 - val_avg_oil_prod_mae: 0.0835 - val_avg_oil_prod_mape: 66.7244 - val_total_water_need_mae: 0.0511 - val_total_water_need_mape: 39.5675 - best_total_water_need_mae: 0.0511 - lr: 2.0714e-05\n", + "Epoch 122/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0131 - mae: 0.1077 - mape: 135.5140 - val_loss: 0.0085 - val_mae: 0.0784 - val_mape: 100.5666 - lr: 2.0176e-05\n", + "Epoch 123/200\n", + "80/80 [==============================] - 35s 435ms/step - loss: 0.0131 - mae: 0.1077 - mape: 137.8844 - val_loss: 0.0086 - val_mae: 0.0798 - val_mape: 102.1380 - lr: 1.9652e-05\n", + "Epoch 124/200\n", + "80/80 [==============================] - 37s 457ms/step - loss: 0.0130 - mae: 0.1074 - mape: 126.6170 - val_loss: 0.0084 - val_mae: 0.0780 - val_mape: 98.9292 - lr: 1.9141e-05\n", + "Epoch 125/200\n", + "80/80 [==============================] - 36s 447ms/step - loss: 0.0130 - mae: 0.1074 - mape: 124.3804 - val_loss: 0.0085 - val_mae: 0.0795 - val_mape: 99.5572 - lr: 1.8644e-05\n", + "Epoch 126/200\n", + "80/80 [==============================] - 258s 3s/step - loss: 0.0130 - mae: 0.1073 - mape: 140.2923 - val_loss: 0.0085 - val_mae: 0.0792 - val_mape: 101.3633 - val_olive_prod_mae: 0.0875 - val_olive_prod_mape: 73.1242 - best_olive_prod_mae: 0.0875 - val_min_oil_prod_mae: 0.0866 - val_min_oil_prod_mape: 93.7120 - best_min_oil_prod_mae: 0.0866 - val_max_oil_prod_mae: 0.0864 - val_max_oil_prod_mape: 233.8133 - best_max_oil_prod_mae: 0.0864 - val_avg_oil_prod_mae: 0.0832 - val_avg_oil_prod_mape: 66.9306 - best_avg_oil_prod_mae: 0.0832 - val_total_water_need_mae: 0.0522 - val_total_water_need_mape: 39.2365 - lr: 1.8160e-05\n", + "Epoch 127/200\n", + "80/80 [==============================] - 36s 434ms/step - loss: 0.0130 - mae: 0.1072 - mape: 136.5076 - val_loss: 0.0085 - val_mae: 0.0797 - val_mape: 100.7464 - lr: 1.7688e-05\n", + "Epoch 128/200\n", + "80/80 [==============================] - 35s 434ms/step - loss: 0.0129 - mae: 0.1075 - mape: 138.2281 - val_loss: 0.0086 - val_mae: 0.0807 - val_mape: 99.2860 - lr: 1.7229e-05\n", + "Epoch 129/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0130 - mae: 0.1073 - mape: 132.3837 - val_loss: 0.0085 - val_mae: 0.0796 - val_mape: 100.0050 - lr: 1.6781e-05\n", + "Epoch 130/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0128 - mae: 0.1071 - mape: 147.8209 - val_loss: 0.0085 - val_mae: 0.0797 - val_mape: 101.9830 - lr: 1.6346e-05\n", + "Epoch 131/200\n", + "80/80 [==============================] - 37s 457ms/step - loss: 0.0128 - mae: 0.1070 - mape: 132.8198 - val_loss: 0.0084 - val_mae: 0.0784 - val_mape: 100.2413 - lr: 1.5105e-05\n", + "Epoch 134/200\n", + "80/80 [==============================] - 37s 453ms/step - loss: 0.0128 - mae: 0.1071 - mape: 122.8966 - val_loss: 0.0084 - val_mae: 0.0792 - val_mape: 99.7014 - lr: 1.4712e-05\n", + "Epoch 135/200\n", + "80/80 [==============================] - 37s 451ms/step - loss: 0.0128 - mae: 0.1069 - mape: 131.0883 - val_loss: 0.0085 - val_mae: 0.0802 - val_mape: 101.1884 - lr: 1.4330e-05\n", + "Epoch 136/200\n", + "80/80 [==============================] - 194s 2s/step - loss: 0.0128 - mae: 0.1069 - mape: 125.1339 - val_loss: 0.0084 - val_mae: 0.0795 - val_mape: 100.6931 - val_olive_prod_mae: 0.0883 - val_olive_prod_mape: 72.7114 - val_min_oil_prod_mae: 0.0870 - val_min_oil_prod_mape: 93.7169 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 230.8019 - val_avg_oil_prod_mae: 0.0836 - val_avg_oil_prod_mape: 66.5721 - val_total_water_need_mae: 0.0517 - val_total_water_need_mape: 39.6633 - lr: 1.3958e-05\n", + "Epoch 137/200\n", + "80/80 [==============================] - 37s 458ms/step - loss: 0.0128 - mae: 0.1069 - mape: 141.0860 - val_loss: 0.0083 - val_mae: 0.0783 - val_mape: 99.3577 - lr: 1.3596e-05\n", + "Epoch 138/200\n", + "80/80 [==============================] - 38s 460ms/step - loss: 0.0128 - mae: 0.1068 - mape: 126.0336 - val_loss: 0.0084 - val_mae: 0.0791 - val_mape: 103.3938 - lr: 1.3242e-05\n", + "Epoch 139/200\n", + "80/80 [==============================] - 37s 460ms/step - loss: 0.0127 - mae: 0.1068 - mape: 141.5065 - val_loss: 0.0084 - val_mae: 0.0796 - val_mape: 100.4431 - lr: 1.2899e-05\n", + "Epoch 140/200\n", + "80/80 [==============================] - 37s 456ms/step - loss: 0.0127 - mae: 0.1068 - mape: 131.7737 - val_loss: 0.0084 - val_mae: 0.0797 - val_mape: 100.3351 - lr: 1.2564e-05\n", + "Epoch 141/200\n", + "80/80 [==============================] - 198s 2s/step - loss: 0.0126 - mae: 0.1066 - mape: 135.8964 - val_loss: 0.0084 - val_mae: 0.0797 - val_mape: 100.3036 - val_olive_prod_mae: 0.0880 - val_olive_prod_mape: 73.0974 - val_min_oil_prod_mae: 0.0873 - val_min_oil_prod_mape: 93.2542 - val_max_oil_prod_mae: 0.0869 - val_max_oil_prod_mape: 228.5796 - val_avg_oil_prod_mae: 0.0839 - val_avg_oil_prod_mape: 65.8285 - val_total_water_need_mae: 0.0525 - val_total_water_need_mape: 40.7585 - lr: 1.2237e-05\n", + "Epoch 142/200\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0127 - mae: 0.1069 - mape: 144.6937 - val_loss: 0.0083 - val_mae: 0.0784 - val_mape: 98.5027 - lr: 1.1919e-05\n", + "Epoch 143/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0126 - mae: 0.1066 - mape: 140.0901 - val_loss: 0.0083 - val_mae: 0.0787 - val_mape: 100.0019 - lr: 1.1610e-05\n", + "Epoch 144/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0126 - mae: 0.1066 - mape: 142.0852 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 101.1596 - lr: 1.1308e-05\n", + "Epoch 145/200\n", + "80/80 [==============================] - 239s 3s/step - loss: 0.0126 - mae: 0.1066 - mape: 132.7490 - val_loss: 0.0083 - val_mae: 0.0790 - val_mape: 100.0930 - val_olive_prod_mae: 0.0877 - val_olive_prod_mape: 73.2577 - val_min_oil_prod_mae: 0.0865 - val_min_oil_prod_mape: 93.5152 - val_max_oil_prod_mae: 0.0862 - val_max_oil_prod_mape: 226.7579 - val_avg_oil_prod_mae: 0.0830 - val_avg_oil_prod_mape: 66.1355 - val_total_water_need_mae: 0.0514 - val_total_water_need_mape: 40.7990 - lr: 1.0729e-05\n", + "Epoch 147/200\n", + "80/80 [==============================] - 36s 447ms/step - loss: 0.0126 - mae: 0.1063 - mape: 131.2485 - val_loss: 0.0083 - val_mae: 0.0786 - val_mape: 101.2258 - lr: 1.0450e-05\n", + "Epoch 148/200\n", + "80/80 [==============================] - 36s 447ms/step - loss: 0.0126 - mae: 0.1064 - mape: 117.1725 - val_loss: 0.0082 - val_mae: 0.0786 - val_mape: 102.0448 - lr: 1.0179e-05\n", + "Epoch 149/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0126 - mae: 0.1067 - mape: 155.9800 - val_loss: 0.0082 - val_mae: 0.0778 - val_mape: 98.7628 - lr: 9.9142e-06\n", + "Epoch 150/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1064 - mape: 137.1006 - val_loss: 0.0083 - val_mae: 0.0791 - val_mape: 101.1951 - lr: 9.6567e-06\n", + "Epoch 151/200\n", + "80/80 [==============================] - 231s 3s/step - loss: 0.0125 - mae: 0.1062 - mape: 133.0278 - val_loss: 0.0082 - val_mae: 0.0780 - val_mape: 101.7752 - val_olive_prod_mae: 0.0869 - val_olive_prod_mape: 73.5762 - val_min_oil_prod_mae: 0.0858 - val_min_oil_prod_mape: 94.9374 - best_min_oil_prod_mae: 0.0858 - val_max_oil_prod_mae: 0.0854 - val_max_oil_prod_mape: 233.4457 - best_max_oil_prod_mae: 0.0854 - val_avg_oil_prod_mae: 0.0825 - val_avg_oil_prod_mape: 66.9196 - best_avg_oil_prod_mae: 0.0825 - val_total_water_need_mae: 0.0496 - val_total_water_need_mape: 39.9972 - lr: 9.4059e-06\n", + "Epoch 152/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1063 - mape: 139.6716 - val_loss: 0.0082 - val_mae: 0.0778 - val_mape: 101.3717 - lr: 9.1616e-06\n", + "Epoch 153/200\n", + "80/80 [==============================] - 36s 436ms/step - loss: 0.0125 - mae: 0.1063 - mape: 136.4329 - val_loss: 0.0083 - val_mae: 0.0793 - val_mape: 101.8978 - lr: 8.9236e-06\n", + "Epoch 154/200\n", + "80/80 [==============================] - 37s 456ms/step - loss: 0.0125 - mae: 0.1065 - mape: 136.6036 - val_loss: 0.0082 - val_mae: 0.0774 - val_mape: 100.2120 - lr: 8.6919e-06\n", + "Epoch 155/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0124 - mae: 0.1063 - mape: 121.0664 - val_loss: 0.0083 - val_mae: 0.0795 - val_mape: 100.9535 - lr: 8.4661e-06\n", + "Epoch 156/200\n", + "80/80 [==============================] - 257s 3s/step - loss: 0.0125 - mae: 0.1062 - mape: 128.6301 - val_loss: 0.0082 - val_mae: 0.0785 - val_mape: 100.7175 - val_olive_prod_mae: 0.0871 - val_olive_prod_mape: 73.1359 - val_min_oil_prod_mae: 0.0860 - val_min_oil_prod_mape: 94.4131 - val_max_oil_prod_mae: 0.0856 - val_max_oil_prod_mape: 230.1177 - val_avg_oil_prod_mae: 0.0827 - val_avg_oil_prod_mape: 66.3622 - val_total_water_need_mae: 0.0511 - val_total_water_need_mape: 39.5585 - lr: 8.2462e-06\n", + "Epoch 157/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0125 - mae: 0.1066 - mape: 140.2754 - val_loss: 0.0082 - val_mae: 0.0790 - val_mape: 100.1723 - lr: 8.0321e-06\n", + "Epoch 158/200\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0125 - mae: 0.1063 - mape: 140.8587 - val_loss: 0.0082 - val_mae: 0.0784 - val_mape: 101.4369 - lr: 7.8235e-06\n", + "Epoch 159/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0124 - mae: 0.1061 - mape: 130.1687 - val_loss: 0.0082 - val_mae: 0.0785 - val_mape: 100.3944 - lr: 7.6203e-06\n", + "Epoch 160/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0124 - mae: 0.1062 - mape: 125.2505 - val_loss: 0.0082 - val_mae: 0.0787 - val_mape: 100.8615 - lr: 7.4224e-06\n", + "Epoch 161/200\n", + "80/80 [==============================] - 260s 3s/step - loss: 0.0125 - mae: 0.1064 - mape: 130.2965 - val_loss: 0.0081 - val_mae: 0.0776 - val_mape: 100.5380 - val_olive_prod_mae: 0.0866 - val_olive_prod_mape: 73.4325 - best_olive_prod_mae: 0.0866 - val_min_oil_prod_mae: 0.0857 - val_min_oil_prod_mape: 94.5307 - best_min_oil_prod_mae: 0.0857 - val_max_oil_prod_mae: 0.0854 - val_max_oil_prod_mape: 226.6531 - best_max_oil_prod_mae: 0.0854 - val_avg_oil_prod_mae: 0.0824 - val_avg_oil_prod_mape: 66.6267 - best_avg_oil_prod_mae: 0.0824 - val_total_water_need_mae: 0.0479 - val_total_water_need_mape: 41.4470 - best_total_water_need_mae: 0.0479 - lr: 7.2296e-06\n", + "Epoch 162/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0124 - mae: 0.1062 - mape: 130.7567 - val_loss: 0.0082 - val_mae: 0.0791 - val_mape: 100.3720 - lr: 7.0418e-06\n", + "Epoch 163/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0124 - mae: 0.1060 - mape: 123.9180 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 101.3976 - lr: 6.8589e-06\n", + "Epoch 164/200\n", + "80/80 [==============================] - 36s 443ms/step - loss: 0.0124 - mae: 0.1060 - mape: 143.9072 - val_loss: 0.0081 - val_mae: 0.0784 - val_mape: 102.0180 - lr: 6.6808e-06\n", + "Epoch 165/200\n", + "80/80 [==============================] - 37s 453ms/step - loss: 0.0124 - mae: 0.1061 - mape: 118.9328 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 100.5574 - lr: 6.5073e-06\n", + "Epoch 166/200\n", + "80/80 [==============================] - 249s 3s/step - loss: 0.0123 - mae: 0.1060 - mape: 135.2133 - val_loss: 0.0082 - val_mae: 0.0787 - val_mape: 102.0016 - val_olive_prod_mae: 0.0872 - val_olive_prod_mape: 72.9689 - val_min_oil_prod_mae: 0.0864 - val_min_oil_prod_mape: 94.5919 - val_max_oil_prod_mae: 0.0861 - val_max_oil_prod_mape: 234.7237 - val_avg_oil_prod_mae: 0.0830 - val_avg_oil_prod_mape: 66.6378 - val_total_water_need_mae: 0.0507 - val_total_water_need_mape: 41.0858 - lr: 6.3383e-06\n", + "Epoch 167/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0123 - mae: 0.1060 - mape: 146.5321 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 101.1286 - lr: 6.1736e-06\n", + "Epoch 168/200\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0124 - mae: 0.1060 - mape: 134.5835 - val_loss: 0.0081 - val_mae: 0.0778 - val_mape: 100.2120 - lr: 6.0133e-06\n", + "Epoch 169/200\n", + "80/80 [==============================] - ETA: 0s - loss: 0.0123 - mae: 0.1061 - mape: 133.2897\n", + "Epoch 169: ReduceLROnPlateau reducing learning rate to 1.1714245374605527e-06.\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0123 - mae: 0.1061 - mape: 133.2897 - val_loss: 0.0081 - val_mae: 0.0780 - val_mape: 100.6940 - lr: 5.8571e-06\n", + "Epoch 170/200\n", + "80/80 [==============================] - 36s 441ms/step - loss: 0.0123 - mae: 0.1060 - mape: 139.9322 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 101.5473 - lr: 5.7050e-06\n", + "Epoch 171/200\n", + "80/80 [==============================] - 260s 3s/step - loss: 0.0124 - mae: 0.1061 - mape: 126.8107 - val_loss: 0.0080 - val_mae: 0.0774 - val_mape: 100.5322 - val_olive_prod_mae: 0.0860 - val_olive_prod_mape: 73.5066 - best_olive_prod_mae: 0.0860 - val_min_oil_prod_mae: 0.0851 - val_min_oil_prod_mape: 95.2965 - best_min_oil_prod_mae: 0.0851 - val_max_oil_prod_mae: 0.0851 - val_max_oil_prod_mape: 224.2950 - best_max_oil_prod_mae: 0.0851 - val_avg_oil_prod_mae: 0.0818 - val_avg_oil_prod_mape: 66.9162 - best_avg_oil_prod_mae: 0.0818 - val_total_water_need_mae: 0.0489 - val_total_water_need_mape: 42.6473 - lr: 5.5568e-06\n", + "Epoch 172/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0123 - mae: 0.1060 - mape: 128.4193 - val_loss: 0.0081 - val_mae: 0.0784 - val_mape: 101.6377 - lr: 5.4125e-06\n", + "Epoch 173/200\n", + "80/80 [==============================] - 36s 437ms/step - loss: 0.0123 - mae: 0.1061 - mape: 125.6502 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 101.5257 - lr: 5.2719e-06\n", + "Epoch 174/200\n", + "80/80 [==============================] - 241s 3s/step - loss: 0.0123 - mae: 0.1059 - mape: 133.7707 - val_loss: 0.0081 - val_mae: 0.0782 - val_mape: 102.2110 - val_olive_prod_mae: 0.0868 - val_olive_prod_mape: 72.7026 - val_min_oil_prod_mae: 0.0861 - val_min_oil_prod_mape: 94.5739 - val_max_oil_prod_mae: 0.0856 - val_max_oil_prod_mape: 236.4975 - val_avg_oil_prod_mae: 0.0826 - val_avg_oil_prod_mape: 66.7356 - val_total_water_need_mae: 0.0498 - val_total_water_need_mape: 40.5453 - lr: 4.8717e-06\n", + "Epoch 177/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0123 - mae: 0.1059 - mape: 144.8284 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 100.9244 - lr: 4.7452e-06\n", + "Epoch 178/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0123 - mae: 0.1060 - mape: 135.6987 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.0468 - lr: 4.6220e-06\n", + "Epoch 179/200\n", + "80/80 [==============================] - 37s 450ms/step - loss: 0.0123 - mae: 0.1059 - mape: 136.3290 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.4255 - lr: 4.5019e-06\n", + "Epoch 180/200\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0123 - mae: 0.1058 - mape: 154.4108 - val_loss: 0.0080 - val_mae: 0.0772 - val_mape: 99.4628 - lr: 4.3850e-06\n", + "Epoch 181/200\n", + "80/80 [==============================] - 225s 3s/step - loss: 0.0123 - mae: 0.1057 - mape: 124.7869 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.7030 - val_olive_prod_mae: 0.0861 - val_olive_prod_mape: 73.0007 - val_min_oil_prod_mae: 0.0853 - val_min_oil_prod_mape: 94.6533 - val_max_oil_prod_mae: 0.0850 - val_max_oil_prod_mape: 227.6605 - best_max_oil_prod_mae: 0.0850 - val_avg_oil_prod_mae: 0.0820 - val_avg_oil_prod_mape: 66.6156 - val_total_water_need_mae: 0.0488 - val_total_water_need_mape: 41.5849 - lr: 4.2711e-06\n", + "Epoch 182/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0123 - mae: 0.1059 - mape: 131.7903 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.5481 - lr: 4.1602e-06\n", + "Epoch 183/200\n", + "80/80 [==============================] - 36s 442ms/step - loss: 0.0123 - mae: 0.1059 - mape: 130.2979 - val_loss: 0.0081 - val_mae: 0.0781 - val_mape: 101.2592 - lr: 4.0521e-06\n", + "Epoch 184/200\n", + "80/80 [==============================] - 35s 433ms/step - loss: 0.0123 - mae: 0.1059 - mape: 145.6990 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 99.8872 - lr: 3.9469e-06\n", + "Epoch 185/200\n", + "80/80 [==============================] - 36s 446ms/step - loss: 0.0122 - mae: 0.1057 - mape: 132.5820 - val_loss: 0.0080 - val_mae: 0.0783 - val_mape: 102.3656 - lr: 3.8444e-06\n", + "Epoch 186/200\n", + "80/80 [==============================] - 255s 3s/step - loss: 0.0122 - mae: 0.1058 - mape: 142.5915 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.3732 - val_olive_prod_mae: 0.0863 - val_olive_prod_mape: 72.9255 - val_min_oil_prod_mae: 0.0857 - val_min_oil_prod_mape: 94.1567 - val_max_oil_prod_mae: 0.0853 - val_max_oil_prod_mape: 226.7281 - val_avg_oil_prod_mae: 0.0823 - val_avg_oil_prod_mape: 66.1140 - val_total_water_need_mae: 0.0487 - val_total_water_need_mape: 41.9421 - lr: 3.7445e-06\n", + "Epoch 187/200\n", + "80/80 [==============================] - 36s 439ms/step - loss: 0.0122 - mae: 0.1058 - mape: 151.9291 - val_loss: 0.0080 - val_mae: 0.0774 - val_mape: 101.3301 - lr: 3.6473e-06\n", + "Epoch 188/200\n", + "80/80 [==============================] - ETA: 0s - loss: 0.0122 - mae: 0.1055 - mape: 142.8741\n", + "Epoch 188: ReduceLROnPlateau reducing learning rate to 7.105123586370611e-07.\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0122 - mae: 0.1055 - mape: 142.8741 - val_loss: 0.0080 - val_mae: 0.0771 - val_mape: 100.6780 - lr: 3.5526e-06\n", + "Epoch 189/200\n", + "80/80 [==============================] - 36s 445ms/step - loss: 0.0122 - mae: 0.1058 - mape: 136.2120 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 101.6683 - lr: 3.4603e-06\n", + "Epoch 190/200\n", + "80/80 [==============================] - 36s 444ms/step - loss: 0.0122 - mae: 0.1055 - mape: 140.9985 - val_loss: 0.0080 - val_mae: 0.0777 - val_mape: 101.3500 - lr: 3.3704e-06\n", + "Epoch 191/200\n", + "80/80 [==============================] - 256s 3s/step - loss: 0.0122 - mae: 0.1058 - mape: 134.9611 - val_loss: 0.0080 - val_mae: 0.0780 - val_mape: 101.2254 - val_olive_prod_mae: 0.0866 - val_olive_prod_mape: 73.1756 - val_min_oil_prod_mae: 0.0859 - val_min_oil_prod_mape: 94.5778 - val_max_oil_prod_mae: 0.0855 - val_max_oil_prod_mape: 230.5076 - val_avg_oil_prod_mae: 0.0825 - val_avg_oil_prod_mape: 66.5401 - val_total_water_need_mae: 0.0495 - val_total_water_need_mape: 41.3259 - lr: 3.2829e-06\n", + "Epoch 192/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1056 - mape: 131.2589 - val_loss: 0.0080 - val_mae: 0.0773 - val_mape: 100.6354 - lr: 3.1976e-06\n", + "Epoch 193/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1057 - mape: 138.6146 - val_loss: 0.0080 - val_mae: 0.0779 - val_mape: 101.6130 - lr: 3.1146e-06\n", + "Epoch 194/200\n", + "80/80 [==============================] - 36s 438ms/step - loss: 0.0122 - mae: 0.1059 - mape: 121.4555 - val_loss: 0.0080 - val_mae: 0.0775 - val_mape: 100.8018 - lr: 3.0337e-06\n", + "Epoch 195/200\n", + "80/80 [==============================] - 36s 440ms/step - loss: 0.0122 - mae: 0.1058 - mape: 126.8764 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.0004 - lr: 2.9549e-06\n", + "Epoch 196/200\n", + "80/80 [==============================] - 226s 3s/step - loss: 0.0122 - mae: 0.1057 - mape: 139.6645 - val_loss: 0.0080 - val_mae: 0.0776 - val_mape: 100.6636 - val_olive_prod_mae: 0.0860 - val_olive_prod_mape: 73.5066 - val_min_oil_prod_mae: 0.0851 - val_min_oil_prod_mape: 95.2965 - val_max_oil_prod_mae: 0.0851 - val_max_oil_prod_mape: 224.2950 - val_avg_oil_prod_mae: 0.0818 - val_avg_oil_prod_mape: 66.9162 - val_total_water_need_mae: 0.0489 - val_total_water_need_mape: 42.6473 - lr: 2.8781e-06\n", + "\n", + "Modello salvato in: 2024-12-08_14-47_final_model.keras\n", + "Warning: Could not save model: name 'json' is not defined\n" + ] + } + ], + "source": [ + "model, history = train_transformer(train_data, train_targets, val_data, val_targets, 200, 32768)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3e2fb5a5341dac92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25000/25000 [==============================] - 211s 8ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1413.43\n", + "Errore percentuale medio: 5.47%\n", + "Precisione: 94.53%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 290.38\n", + "Errore percentuale medio: 5.54%\n", + "Precisione: 94.46%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 352.04\n", + "Errore percentuale medio: 5.57%\n", + "Precisione: 94.43%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 308.69\n", + "Errore percentuale medio: 5.37%\n", + "Precisione: 94.63%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1450.19\n", + "Errore percentuale medio: 3.24%\n", + "Precisione: 96.76%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4af58aa9bbc156f5", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model_performance(model, data, targets, set_name=\"\"):\n", + " \"\"\"\n", + " Valuta le performance del modello su un set di dati specifico.\n", + " \"\"\"\n", + " predictions = model.predict(data, verbose=0)\n", + "\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', 'avg_oil_prod', 'total_water_need']\n", + " metrics = {}\n", + "\n", + " for i, name in enumerate(target_names):\n", + " mae = np.mean(np.abs(targets[:, i] - predictions[:, i]))\n", + " mse = np.mean(np.square(targets[:, i] - predictions[:, i]))\n", + " rmse = np.sqrt(mse)\n", + " mape = np.mean(np.abs((targets[:, i] - predictions[:, i]) / (targets[:, i] + 1e-7))) * 100\n", + "\n", + " metrics[f\"{name}_mae\"] = mae\n", + " metrics[f\"{name}_rmse\"] = rmse\n", + " metrics[f\"{name}_mape\"] = mape\n", + "\n", + " if set_name:\n", + " print(f\"\\nPerformance sul set {set_name}:\")\n", + " for metric, value in metrics.items():\n", + " print(f\"{metric}: {value:.4f}\")\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def retrain_model(base_model, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Implementa il retraining del modello con i dati combinati.\n", + " \"\"\"\n", + " print(\"Valutazione performance iniziali del modello...\")\n", + " initial_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Combina i dati per il retraining\n", + " combined_data = {\n", + " 'temporal': np.concatenate([train_data['temporal'], val_data['temporal'], test_data['temporal']]),\n", + " 'static': np.concatenate([train_data['static'], val_data['static'], test_data['static']])\n", + " }\n", + " combined_targets = np.concatenate([train_targets, val_targets, test_targets])\n", + "\n", + " # Crea una nuova suddivisione per la validazione\n", + " indices = np.arange(len(combined_targets))\n", + " np.random.shuffle(indices)\n", + "\n", + " split_idx = int(len(indices) * 0.9)\n", + " train_idx, val_idx = indices[:split_idx], indices[split_idx:]\n", + "\n", + " # Prepara i dati per il retraining\n", + " retrain_data = {k: v[train_idx] for k, v in combined_data.items()}\n", + " retrain_targets = combined_targets[train_idx]\n", + " retrain_val_data = {k: v[val_idx] for k, v in combined_data.items()}\n", + " retrain_val_targets = combined_targets[val_idx]\n", + "\n", + " # Configura callbacks\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=10,\n", + " restore_best_weights=True,\n", + " min_delta=0.0001\n", + " ),\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=5,\n", + " min_lr=1e-6,\n", + " verbose=1\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{execute_name}_retrained_best_oil_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True\n", + " )\n", + " ]\n", + "\n", + " # Imposta learning rate per il fine-tuning\n", + " optimizer = tf.keras.optimizers.AdamW(\n", + " learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(\n", + " initial_learning_rate=1e-4,\n", + " decay_steps=1000,\n", + " decay_rate=0.9\n", + " ),\n", + " weight_decay=0.01\n", + " )\n", + "\n", + " # Ricompila il modello con il nuovo optimizer\n", + " base_model.compile(\n", + " optimizer=optimizer,\n", + " loss=tf.keras.losses.Huber(),\n", + " metrics=['mae']\n", + " )\n", + "\n", + " print(\"\\nAvvio retraining...\")\n", + " history = base_model.fit(\n", + " retrain_data,\n", + " retrain_targets,\n", + " validation_data=(retrain_val_data, retrain_val_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1\n", + " )\n", + "\n", + " print(\"\\nValutazione performance finali...\")\n", + " final_metrics = {\n", + " 'train': evaluate_model_performance(base_model, train_data, train_targets, \"training\"),\n", + " 'val': evaluate_model_performance(base_model, val_data, val_targets, \"validazione\"),\n", + " 'test': evaluate_model_performance(base_model, test_data, test_targets, \"test\")\n", + " }\n", + "\n", + " # Salva il modello finale\n", + " save_path = f'{execute_name}_retrained_model.keras'\n", + " os.makedirs(f'{execute_name}_retrained/weights', exist_ok=True)\n", + " \n", + " base_model.save_weights(f'{execute_name}_retrained/weights')\n", + " base_model.save(save_path, save_format='keras')\n", + " print(f\"\\nModello riaddestrato salvato in: {save_path}\")\n", + "\n", + " # Report miglioramenti\n", + " print(\"\\nMiglioramenti delle performance:\")\n", + " for dataset in ['train', 'val', 'test']:\n", + " print(f\"\\nSet {dataset}:\")\n", + " for metric in initial_metrics[dataset].keys():\n", + " initial = initial_metrics[dataset][metric]\n", + " final = final_metrics[dataset][metric]\n", + " improvement = ((initial - final) / initial) * 100\n", + " print(f\"{metric}: {improvement:.2f}% di miglioramento\")\n", + "\n", + " return base_model, history, final_metrics\n", + "\n", + "\n", + "def start_retraining(model_path, train_data, train_targets,\n", + " val_data, val_targets,\n", + " test_data, test_targets,\n", + " epochs=50, batch_size=128):\n", + " \"\"\"\n", + " Avvia il processo di retraining in modo sicuro.\n", + " \"\"\"\n", + " try:\n", + " print(\"Caricamento del modello...\")\n", + " base_model = tf.keras.models.load_model(model_path, compile=False)\n", + " print(\"Modello caricato con successo!\")\n", + "\n", + " return retrain_model(\n", + " base_model=base_model,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=epochs,\n", + " batch_size=batch_size\n", + " )\n", + " except Exception as e:\n", + " print(f\"Errore durante il retraining: {str(e)}\")\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "588c7e49371f4a0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caricamento del modello...\n", + "Modello caricato con successo!\n", + "Valutazione performance iniziali del modello...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0860\n", + "olive_prod_rmse: 0.1161\n", + "olive_prod_mape: 78.0493\n", + "min_oil_prod_mae: 0.0851\n", + "min_oil_prod_rmse: 0.1188\n", + "min_oil_prod_mape: 97.5077\n", + "max_oil_prod_mae: 0.0852\n", + "max_oil_prod_rmse: 0.1187\n", + "max_oil_prod_mape: 153.0588\n", + "avg_oil_prod_mae: 0.0819\n", + "avg_oil_prod_rmse: 0.1132\n", + "avg_oil_prod_mape: 133.1723\n", + "total_water_need_mae: 0.0489\n", + "total_water_need_rmse: 0.0645\n", + "total_water_need_mape: 49.3876\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0860\n", + "olive_prod_rmse: 0.1160\n", + "olive_prod_mape: 73.4536\n", + "min_oil_prod_mae: 0.0851\n", + "min_oil_prod_rmse: 0.1186\n", + "min_oil_prod_mape: 96.1195\n", + "max_oil_prod_mae: 0.0851\n", + "max_oil_prod_rmse: 0.1186\n", + "max_oil_prod_mape: 284.3090\n", + "avg_oil_prod_mae: 0.0818\n", + "avg_oil_prod_rmse: 0.1130\n", + "avg_oil_prod_mape: 66.9595\n", + "total_water_need_mae: 0.0489\n", + "total_water_need_rmse: 0.0644\n", + "total_water_need_mape: 41.5436\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0859\n", + "olive_prod_rmse: 0.1158\n", + "olive_prod_mape: 85.4890\n", + "min_oil_prod_mae: 0.0851\n", + "min_oil_prod_rmse: 0.1187\n", + "min_oil_prod_mape: 147.1340\n", + "max_oil_prod_mae: 0.0850\n", + "max_oil_prod_rmse: 0.1184\n", + "max_oil_prod_mape: 181.8315\n", + "avg_oil_prod_mae: 0.0818\n", + "avg_oil_prod_rmse: 0.1130\n", + "avg_oil_prod_mape: 83.6362\n", + "total_water_need_mae: 0.0490\n", + "total_water_need_rmse: 0.0645\n", + "total_water_need_mape: 40.1340\n", + "\n", + "Avvio retraining...\n", + "Epoch 1/100\n", + "220/220 [==============================] - 65s 158ms/step - loss: 0.0145 - mae: 0.1079 - val_loss: 0.0098 - val_mae: 0.0878 - lr: 9.7719e-05\n", + "Epoch 2/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0141 - mae: 0.1066 - val_loss: 0.0094 - val_mae: 0.0835 - lr: 9.5480e-05\n", + "Epoch 3/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0138 - mae: 0.1059 - val_loss: 0.0095 - val_mae: 0.0857 - lr: 9.3292e-05\n", + "Epoch 4/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0135 - mae: 0.1055 - val_loss: 0.0097 - val_mae: 0.0856 - lr: 9.1155e-05\n", + "Epoch 5/100\n", + "220/220 [==============================] - 34s 156ms/step - loss: 0.0133 - mae: 0.1049 - val_loss: 0.0096 - val_mae: 0.0856 - lr: 8.9066e-05\n", + "Epoch 6/100\n", + "220/220 [==============================] - 35s 158ms/step - loss: 0.0131 - mae: 0.1046 - val_loss: 0.0091 - val_mae: 0.0834 - lr: 8.7025e-05\n", + "Epoch 7/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0129 - mae: 0.1040 - val_loss: 0.0092 - val_mae: 0.0827 - lr: 8.5031e-05\n", + "Epoch 8/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0127 - mae: 0.1037 - val_loss: 0.0090 - val_mae: 0.0821 - lr: 8.3083e-05\n", + "Epoch 9/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0125 - mae: 0.1034 - val_loss: 0.0093 - val_mae: 0.0860 - lr: 8.1179e-05\n", + "Epoch 10/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0124 - mae: 0.1030 - val_loss: 0.0088 - val_mae: 0.0818 - lr: 7.9319e-05\n", + "Epoch 11/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0122 - mae: 0.1026 - val_loss: 0.0091 - val_mae: 0.0821 - lr: 7.7502e-05\n", + "Epoch 12/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0121 - mae: 0.1023 - val_loss: 0.0093 - val_mae: 0.0856 - lr: 7.5726e-05\n", + "Epoch 13/100\n", + "220/220 [==============================] - 35s 157ms/step - loss: 0.0120 - mae: 0.1020 - val_loss: 0.0086 - val_mae: 0.0823 - lr: 7.3991e-05\n", + "Epoch 14/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0118 - mae: 0.1017 - val_loss: 0.0092 - val_mae: 0.0862 - lr: 7.2296e-05\n", + "Epoch 15/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0117 - mae: 0.1015 - val_loss: 0.0086 - val_mae: 0.0804 - lr: 7.0639e-05\n", + "Epoch 16/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0117 - mae: 0.1013 - val_loss: 0.0083 - val_mae: 0.0800 - lr: 6.9021e-05\n", + "Epoch 17/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0116 - mae: 0.1011 - val_loss: 0.0084 - val_mae: 0.0809 - lr: 6.7439e-05\n", + "Epoch 18/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0114 - mae: 0.1008 - val_loss: 0.0083 - val_mae: 0.0809 - lr: 6.5894e-05\n", + "Epoch 19/100\n", + "220/220 [==============================] - 34s 156ms/step - loss: 0.0114 - mae: 0.1006 - val_loss: 0.0081 - val_mae: 0.0804 - lr: 6.4384e-05\n", + "Epoch 20/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0113 - mae: 0.1004 - val_loss: 0.0088 - val_mae: 0.0837 - lr: 6.2909e-05\n", + "Epoch 21/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0112 - mae: 0.1002 - val_loss: 0.0084 - val_mae: 0.0812 - lr: 6.1468e-05\n", + "Epoch 22/100\n", + "220/220 [==============================] - 34s 156ms/step - loss: 0.0111 - mae: 0.1000 - val_loss: 0.0080 - val_mae: 0.0806 - lr: 6.0059e-05\n", + "Epoch 23/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0110 - mae: 0.0999 - val_loss: 0.0084 - val_mae: 0.0808 - lr: 5.8683e-05\n", + "Epoch 24/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0110 - mae: 0.0997 - val_loss: 0.0079 - val_mae: 0.0803 - lr: 5.7338e-05\n", + "Epoch 25/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0109 - mae: 0.0995 - val_loss: 0.0081 - val_mae: 0.0806 - lr: 5.6025e-05\n", + "Epoch 26/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0108 - mae: 0.0993 - val_loss: 0.0078 - val_mae: 0.0788 - lr: 5.4741e-05\n", + "Epoch 27/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0108 - mae: 0.0992 - val_loss: 0.0082 - val_mae: 0.0820 - lr: 5.3487e-05\n", + "Epoch 28/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0107 - mae: 0.0992 - val_loss: 0.0079 - val_mae: 0.0792 - lr: 5.2261e-05\n", + "Epoch 29/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0107 - mae: 0.0990 - val_loss: 0.0082 - val_mae: 0.0804 - lr: 5.1064e-05\n", + "Epoch 30/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0106 - mae: 0.0988 - val_loss: 0.0077 - val_mae: 0.0787 - lr: 4.9894e-05\n", + "Epoch 31/100\n", + "220/220 [==============================] - 33s 151ms/step - loss: 0.0106 - mae: 0.0987 - val_loss: 0.0078 - val_mae: 0.0806 - lr: 4.8751e-05\n", + "Epoch 32/100\n", + "220/220 [==============================] - 35s 159ms/step - loss: 0.0105 - mae: 0.0986 - val_loss: 0.0073 - val_mae: 0.0774 - lr: 4.7634e-05\n", + "Epoch 33/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0105 - mae: 0.0985 - val_loss: 0.0075 - val_mae: 0.0794 - lr: 4.6542e-05\n", + "Epoch 34/100\n", + "220/220 [==============================] - 35s 158ms/step - loss: 0.0104 - mae: 0.0983 - val_loss: 0.0072 - val_mae: 0.0776 - lr: 4.5476e-05\n", + "Epoch 35/100\n", + "220/220 [==============================] - 35s 157ms/step - loss: 0.0104 - mae: 0.0982 - val_loss: 0.0073 - val_mae: 0.0767 - lr: 4.4434e-05\n", + "Epoch 36/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0103 - mae: 0.0982 - val_loss: 0.0073 - val_mae: 0.0782 - lr: 4.3416e-05\n", + "Epoch 37/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0103 - mae: 0.0981 - val_loss: 0.0071 - val_mae: 0.0770 - lr: 4.2421e-05\n", + "Epoch 38/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0103 - mae: 0.0979 - val_loss: 0.0071 - val_mae: 0.0770 - lr: 4.1449e-05\n", + "Epoch 39/100\n", + "220/220 [==============================] - ETA: 0s - loss: 0.0102 - mae: 0.0978\n", + "Epoch 39: ReduceLROnPlateau reducing learning rate to 8.099838305497542e-06.\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0102 - mae: 0.0978 - val_loss: 0.0073 - val_mae: 0.0779 - lr: 4.0499e-05\n", + "Epoch 40/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0102 - mae: 0.0977 - val_loss: 0.0071 - val_mae: 0.0772 - lr: 3.9571e-05\n", + "Epoch 41/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0101 - mae: 0.0976 - val_loss: 0.0069 - val_mae: 0.0756 - lr: 3.8665e-05\n", + "Epoch 42/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0101 - mae: 0.0976 - val_loss: 0.0073 - val_mae: 0.0773 - lr: 3.7779e-05\n", + "Epoch 43/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0101 - mae: 0.0974 - val_loss: 0.0071 - val_mae: 0.0760 - lr: 3.6913e-05\n", + "Epoch 44/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0101 - mae: 0.0974 - val_loss: 0.0070 - val_mae: 0.0762 - lr: 3.6067e-05\n", + "Epoch 45/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0071 - val_mae: 0.0774 - lr: 3.5241e-05\n", + "Epoch 46/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0068 - val_mae: 0.0748 - lr: 3.4433e-05\n", + "Epoch 47/100\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0100 - mae: 0.0972 - val_loss: 0.0074 - val_mae: 0.0790 - lr: 3.3644e-05\n", + "Epoch 48/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0099 - mae: 0.0971 - val_loss: 0.0069 - val_mae: 0.0759 - lr: 3.2874e-05\n", + "Epoch 49/100\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0099 - mae: 0.0970 - val_loss: 0.0069 - val_mae: 0.0768 - lr: 3.2120e-05\n", + "Epoch 50/100\n", + "220/220 [==============================] - 33s 151ms/step - loss: 0.0099 - mae: 0.0969 - val_loss: 0.0070 - val_mae: 0.0758 - lr: 3.1384e-05\n", + "Epoch 51/100\n", + "220/220 [==============================] - ETA: 0s - loss: 0.0099 - mae: 0.0969\n", + "Epoch 51: ReduceLROnPlateau reducing learning rate to 6.133050192147493e-06.\n", + "220/220 [==============================] - 33s 151ms/step - loss: 0.0099 - mae: 0.0969 - val_loss: 0.0068 - val_mae: 0.0757 - lr: 3.0665e-05\n", + "Epoch 52/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0099 - mae: 0.0968 - val_loss: 0.0068 - val_mae: 0.0753 - lr: 2.9963e-05\n", + "Epoch 53/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0098 - mae: 0.0967 - val_loss: 0.0069 - val_mae: 0.0761 - lr: 2.9276e-05\n", + "Epoch 54/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0098 - mae: 0.0966 - val_loss: 0.0068 - val_mae: 0.0754 - lr: 2.8605e-05\n", + "Epoch 55/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0098 - mae: 0.0966 - val_loss: 0.0067 - val_mae: 0.0750 - lr: 2.7950e-05\n", + "Epoch 56/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0965 - val_loss: 0.0066 - val_mae: 0.0748 - lr: 2.7309e-05\n", + "Epoch 57/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0066 - val_mae: 0.0746 - lr: 2.6684e-05\n", + "Epoch 58/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0067 - val_mae: 0.0748 - lr: 2.6072e-05\n", + "Epoch 59/100\n", + "220/220 [==============================] - 33s 151ms/step - loss: 0.0097 - mae: 0.0964 - val_loss: 0.0066 - val_mae: 0.0740 - lr: 2.5475e-05\n", + "Epoch 60/100\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0097 - mae: 0.0963 - val_loss: 0.0070 - val_mae: 0.0760 - lr: 2.4891e-05\n", + "Epoch 61/100\n", + "220/220 [==============================] - ETA: 0s - loss: 0.0097 - mae: 0.0962\n", + "Epoch 61: ReduceLROnPlateau reducing learning rate to 4.864184666075744e-06.\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0097 - mae: 0.0962 - val_loss: 0.0067 - val_mae: 0.0754 - lr: 2.4321e-05\n", + "Epoch 62/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0096 - mae: 0.0962 - val_loss: 0.0067 - val_mae: 0.0751 - lr: 2.3764e-05\n", + "Epoch 63/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0962 - val_loss: 0.0065 - val_mae: 0.0746 - lr: 2.3219e-05\n", + "Epoch 64/100\n", + "220/220 [==============================] - 33s 151ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0067 - val_mae: 0.0754 - lr: 2.2687e-05\n", + "Epoch 65/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0065 - val_mae: 0.0743 - lr: 2.2167e-05\n", + "Epoch 66/100\n", + "220/220 [==============================] - 34s 153ms/step - loss: 0.0096 - mae: 0.0961 - val_loss: 0.0066 - val_mae: 0.0744 - lr: 2.1659e-05\n", + "Epoch 67/100\n", + "220/220 [==============================] - 34s 155ms/step - loss: 0.0096 - mae: 0.0960 - val_loss: 0.0064 - val_mae: 0.0739 - lr: 2.1163e-05\n", + "Epoch 68/100\n", + "220/220 [==============================] - ETA: 0s - loss: 0.0095 - mae: 0.0959\n", + "Epoch 68: ReduceLROnPlateau reducing learning rate to 4.135647031944245e-06.\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0066 - val_mae: 0.0742 - lr: 2.0678e-05\n", + "Epoch 69/100\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0065 - val_mae: 0.0750 - lr: 2.0204e-05\n", + "Epoch 70/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0066 - val_mae: 0.0749 - lr: 1.9742e-05\n", + "Epoch 71/100\n", + "220/220 [==============================] - 33s 152ms/step - loss: 0.0095 - mae: 0.0959 - val_loss: 0.0065 - val_mae: 0.0745 - lr: 1.9289e-05\n", + "Epoch 72/100\n", + "220/220 [==============================] - 34s 154ms/step - loss: 0.0095 - mae: 0.0958 - val_loss: 0.0064 - val_mae: 0.0741 - lr: 1.8847e-05\n", + "Epoch 73/100\n", + "220/220 [==============================] - ETA: 0s - loss: 0.0095 - mae: 0.0957\n", + "Epoch 73: ReduceLROnPlateau reducing learning rate to 3.683072645799257e-06.\n", + "220/220 [==============================] - 34s 152ms/step - loss: 0.0095 - mae: 0.0957 - val_loss: 0.0065 - val_mae: 0.0736 - lr: 1.8415e-05\n", + "\n", + "Valutazione performance finali...\n", + "\n", + "Performance sul set training:\n", + "olive_prod_mae: 0.0840\n", + "olive_prod_rmse: 0.1148\n", + "olive_prod_mape: 77.0417\n", + "min_oil_prod_mae: 0.0818\n", + "min_oil_prod_rmse: 0.1149\n", + "min_oil_prod_mape: 96.4133\n", + "max_oil_prod_mae: 0.0816\n", + "max_oil_prod_rmse: 0.1144\n", + "max_oil_prod_mape: 149.8516\n", + "avg_oil_prod_mae: 0.0798\n", + "avg_oil_prod_rmse: 0.1114\n", + "avg_oil_prod_mape: 125.5588\n", + "total_water_need_mae: 0.0456\n", + "total_water_need_rmse: 0.0610\n", + "total_water_need_mape: 45.2614\n", + "\n", + "Performance sul set validazione:\n", + "olive_prod_mae: 0.0839\n", + "olive_prod_rmse: 0.1146\n", + "olive_prod_mape: 72.7076\n", + "min_oil_prod_mae: 0.0817\n", + "min_oil_prod_rmse: 0.1147\n", + "min_oil_prod_mape: 95.6358\n", + "max_oil_prod_mae: 0.0815\n", + "max_oil_prod_rmse: 0.1141\n", + "max_oil_prod_mape: 271.0537\n", + "avg_oil_prod_mae: 0.0798\n", + "avg_oil_prod_rmse: 0.1112\n", + "avg_oil_prod_mape: 64.6259\n", + "total_water_need_mae: 0.0456\n", + "total_water_need_rmse: 0.0609\n", + "total_water_need_mape: 39.7287\n", + "\n", + "Performance sul set test:\n", + "olive_prod_mae: 0.0838\n", + "olive_prod_rmse: 0.1145\n", + "olive_prod_mape: 82.7762\n", + "min_oil_prod_mae: 0.0817\n", + "min_oil_prod_rmse: 0.1147\n", + "min_oil_prod_mape: 146.3828\n", + "max_oil_prod_mae: 0.0815\n", + "max_oil_prod_rmse: 0.1141\n", + "max_oil_prod_mape: 171.8919\n", + "avg_oil_prod_mae: 0.0797\n", + "avg_oil_prod_rmse: 0.1112\n", + "avg_oil_prod_mape: 78.1842\n", + "total_water_need_mae: 0.0456\n", + "total_water_need_rmse: 0.0610\n", + "total_water_need_mape: 38.6316\n", + "\n", + "Modello riaddestrato salvato in: 2024-12-08_14-47_retrained_model.keras\n", + "\n", + "Miglioramenti delle performance:\n", + "\n", + "Set train:\n", + "olive_prod_mae: 2.37% di miglioramento\n", + "olive_prod_rmse: 1.12% di miglioramento\n", + "olive_prod_mape: 1.29% di miglioramento\n", + "min_oil_prod_mae: 3.93% di miglioramento\n", + "min_oil_prod_rmse: 3.30% di miglioramento\n", + "min_oil_prod_mape: 1.12% di miglioramento\n", + "max_oil_prod_mae: 4.13% di miglioramento\n", + "max_oil_prod_rmse: 3.68% di miglioramento\n", + "max_oil_prod_mape: 2.10% di miglioramento\n", + "avg_oil_prod_mae: 2.51% di miglioramento\n", + "avg_oil_prod_rmse: 1.57% di miglioramento\n", + "avg_oil_prod_mape: 5.72% di miglioramento\n", + "total_water_need_mae: 6.84% di miglioramento\n", + "total_water_need_rmse: 5.40% di miglioramento\n", + "total_water_need_mape: 8.35% di miglioramento\n", + "\n", + "Set val:\n", + "olive_prod_mae: 2.39% di miglioramento\n", + "olive_prod_rmse: 1.17% di miglioramento\n", + "olive_prod_mape: 1.02% di miglioramento\n", + "min_oil_prod_mae: 3.93% di miglioramento\n", + "min_oil_prod_rmse: 3.33% di miglioramento\n", + "min_oil_prod_mape: 0.50% di miglioramento\n", + "max_oil_prod_mae: 4.18% di miglioramento\n", + "max_oil_prod_rmse: 3.75% di miglioramento\n", + "max_oil_prod_mape: 4.66% di miglioramento\n", + "avg_oil_prod_mae: 2.54% di miglioramento\n", + "avg_oil_prod_rmse: 1.64% di miglioramento\n", + "avg_oil_prod_mape: 3.49% di miglioramento\n", + "total_water_need_mae: 6.85% di miglioramento\n", + "total_water_need_rmse: 5.38% di miglioramento\n", + "total_water_need_mape: 4.37% di miglioramento\n", + "\n", + "Set test:\n", + "olive_prod_mae: 2.39% di miglioramento\n", + "olive_prod_rmse: 1.12% di miglioramento\n", + "olive_prod_mape: 3.17% di miglioramento\n", + "min_oil_prod_mae: 3.98% di miglioramento\n", + "min_oil_prod_rmse: 3.38% di miglioramento\n", + "min_oil_prod_mape: 0.51% di miglioramento\n", + "max_oil_prod_mae: 4.14% di miglioramento\n", + "max_oil_prod_rmse: 3.69% di miglioramento\n", + "max_oil_prod_mape: 5.47% di miglioramento\n", + "avg_oil_prod_mae: 2.54% di miglioramento\n", + "avg_oil_prod_rmse: 1.60% di miglioramento\n", + "avg_oil_prod_mape: 6.52% di miglioramento\n", + "total_water_need_mae: 6.87% di miglioramento\n", + "total_water_need_rmse: 5.43% di miglioramento\n", + "total_water_need_mape: 3.74% di miglioramento\n" + ] + } + ], + "source": [ + "model_path = f'{execute_name}_final_model.keras'\n", + "\n", + "retrained_model, retrain_history, final_metrics = start_retraining(\n", + " model_path=model_path,\n", + " train_data=train_data,\n", + " train_targets=train_targets,\n", + " val_data=val_data,\n", + " val_targets=val_targets,\n", + " test_data=test_data,\n", + " test_targets=test_targets,\n", + " epochs=100,\n", + " batch_size=16384\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a95606bc-c4bc-418a-acdb-2e24c30dfa81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25000/25000 [==============================] - 247s 10ms/step\n", + "\n", + "Errori per target:\n", + "--------------------------------------------------\n", + "olive_prod:\n", + "MAE assoluto: 1379.63\n", + "Errore percentuale medio: 5.07%\n", + "Precisione: 94.93%\n", + "--------------------------------------------------\n", + "min_oil_prod:\n", + "MAE assoluto: 278.96\n", + "Errore percentuale medio: 5.09%\n", + "Precisione: 94.91%\n", + "--------------------------------------------------\n", + "max_oil_prod:\n", + "MAE assoluto: 337.33\n", + "Errore percentuale medio: 5.13%\n", + "Precisione: 94.87%\n", + "--------------------------------------------------\n", + "avg_oil_prod:\n", + "MAE assoluto: 300.85\n", + "Errore percentuale medio: 4.98%\n", + "Precisione: 95.02%\n", + "--------------------------------------------------\n", + "total_water_need:\n", + "MAE assoluto: 1350.83\n", + "Errore percentuale medio: 2.90%\n", + "Precisione: 97.10%\n", + "--------------------------------------------------\n" + ] + } + ], + "source": [ + "percentage_errors, absolute_errors = calculate_real_error(retrained_model, val_data, val_targets, scaler_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e6f357cb-56a4-4f19-a4e8-77b73a28329d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "from typing import List, Dict, Tuple, Union\n", + "\n", + "def analyze_feature_importance(model: tf.keras.Model, \n", + " test_data: dict, \n", + " feature_names: List[str]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Analizza l'importanza delle feature usando perturbazione.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dizionario con chiavi 'temporal' e 'static' contenenti i dati\n", + " feature_names: Lista dei nomi delle feature\n", + " \n", + " Returns:\n", + " dict: Dizionario con l'importanza relativa di ogni feature\n", + " \"\"\"\n", + " # Estrai i dati temporali e statici\n", + " temporal_data = test_data['temporal']\n", + " static_data = test_data['static']\n", + " \n", + " # Ottieni la predizione base\n", + " base_prediction = model.predict(test_data)\n", + " feature_importance = {}\n", + " \n", + " # Per ogni feature temporale\n", + " for i, feature in enumerate(feature_names):\n", + " if feature in ['temp_mean', 'precip_sum', 'solar_energy_sum']:\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature temporale\n", + " temp_idx = ['temp_mean', 'precip_sum', 'solar_energy_sum'].index(feature)\n", + " \n", + " # Crea rumore per la feature temporale\n", + " feature_values = temporal_data[..., temp_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature temporale\n", + " perturbed_temporal = perturbed_data['temporal'].copy()\n", + " perturbed_temporal[..., temp_idx] = feature_values + noise\n", + " perturbed_data['temporal'] = perturbed_temporal\n", + " \n", + " else: # Feature statiche\n", + " # Crea copia perturbata dei dati\n", + " perturbed_data = {\n", + " 'temporal': temporal_data.copy(),\n", + " 'static': static_data.copy()\n", + " }\n", + " \n", + " # Trova l'indice della feature statica\n", + " static_idx = ['ha'].index(feature)\n", + " \n", + " # Crea rumore per la feature statica\n", + " feature_values = static_data[..., static_idx]\n", + " noise = np.random.normal(0, np.std(feature_values) * 0.1, \n", + " size=feature_values.shape)\n", + " \n", + " # Applica il rumore alla feature statica\n", + " perturbed_static = perturbed_data['static'].copy()\n", + " perturbed_static[..., static_idx] = feature_values + noise\n", + " perturbed_data['static'] = perturbed_static\n", + " \n", + " # Calcola nuova predizione\n", + " perturbed_prediction = model.predict(perturbed_data)\n", + " \n", + " # Calcola impatto della perturbazione\n", + " impact = np.mean(np.abs(perturbed_prediction - base_prediction))\n", + " feature_importance[feature] = float(impact)\n", + " \n", + " # Normalizza le importanze\n", + " total_importance = sum(feature_importance.values())\n", + " feature_importance = {k: v/total_importance \n", + " for k, v in feature_importance.items()}\n", + " \n", + " return feature_importance\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data: Union[np.ndarray, tf.Tensor]) -> Dict[str, float]:\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor o array dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calcola varianza manualmente\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Ordina il tensor per il calcolo della mediana\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data: np.ndarray, bins: int = 50) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data: np.ndarray, \n", + " bins: int = 50, \n", + " title: str = \"Distribuzione\") -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Imposta il titolo generale\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf\n", + "\n", + "def analyze_model_predictions(model: tf.keras.Model, \n", + " test_data: np.ndarray,\n", + " test_targets: np.ndarray,\n", + " scaler_y) -> None:\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.68, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "def run_comprehensive_analysis(retrained_model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Esegue un'analisi completa del modello includendo errori,\n", + " importanza delle feature e distribuzioni.\n", + " \"\"\"\n", + " print(\"=== ANALISI COMPLETA DEL MODELLO ===\")\n", + " \n", + " # 1. Analisi degli errori\n", + " print(\"\\n1. ANALISI DEGLI ERRORI\")\n", + " print(\"-\" * 50)\n", + " analyze_model_predictions(retrained_model, test_data, test_targets, scaler_y)\n", + " \n", + " # 2. Analisi dell'importanza delle feature\n", + " print(\"\\n2. IMPORTANZA DELLE FEATURE\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Definisci i nomi delle feature\n", + " temporal_features = ['temp_mean', 'precip_sum', 'solar_energy_sum']\n", + " static_features = ['ha']\n", + " \n", + " all_features = temporal_features + static_features\n", + " importance = analyze_feature_importance(retrained_model, test_data, all_features)\n", + " \n", + " print(\"\\nImportanza relativa delle feature:\")\n", + " for feature, imp in sorted(importance.items(), key=lambda x: x[1], reverse=True):\n", + " print(f\"{feature}: {imp:.4f}\")\n", + " \n", + " # 3. Analisi distribuzionale\n", + " print(\"\\n3. ANALISI DISTRIBUZIONALE\")\n", + " print(\"-\" * 50)\n", + " \n", + " prob = ProbabilityFunctions()\n", + " predictions = retrained_model.predict(test_data)\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi distribuzionale per {target}\")\n", + " \n", + " # Statistiche\n", + " stats_pred = prob.calculate_statistics(predictions_real[:, i])\n", + " stats_true = prob.calculate_statistics(targets_real[:, i])\n", + " \n", + " print(\"\\nStatistiche Predizioni:\")\n", + " for key, value in stats_pred.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " print(\"\\nStatistiche Target Reali:\")\n", + " for key, value in stats_true.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza distribuzioni\n", + " prob.plot_distributions(predictions_real[:, i], bins=50,\n", + " title=f\"Distribuzione Predizioni - {target}\")\n", + " prob.plot_distributions(targets_real[:, i], bins=50,\n", + " title=f\"Distribuzione Target Reali - {target}\")\n", + "\n", + "def analyze_model_predictions(model, test_data, test_targets, scaler_y):\n", + " \"\"\"\n", + " Analizza le distribuzioni di probabilità delle predizioni del modello.\n", + " \n", + " Args:\n", + " model: Modello TensorFlow addestrato\n", + " test_data: Dati di test\n", + " test_targets: Target di test\n", + " scaler_y: Scaler usato per denormalizzare i target\n", + " \"\"\"\n", + " # Ottieni le predizioni\n", + " predictions = model.predict(test_data)\n", + " \n", + " # Denormalizza predizioni e target\n", + " predictions_real = scaler_y.inverse_transform(predictions)\n", + " targets_real = scaler_y.inverse_transform(test_targets)\n", + " \n", + " # Inizializza la classe per l'analisi delle probabilità\n", + " prob = ProbabilityFunctions()\n", + " \n", + " # Analizza ogni target\n", + " target_names = ['olive_prod', 'min_oil_prod', 'max_oil_prod', \n", + " 'avg_oil_prod', 'total_water_need']\n", + " \n", + " for i, target in enumerate(target_names):\n", + " print(f\"\\nAnalisi per {target}\")\n", + " print(\"-\" * 50)\n", + " \n", + " # Calcola errori\n", + " errors = predictions_real[:, i] - targets_real[:, i]\n", + " \n", + " # Calcola statistiche degli errori\n", + " error_stats = prob.calculate_statistics(errors)\n", + " print(\"\\nStatistiche degli Errori:\")\n", + " for key, value in error_stats.items():\n", + " print(f\"{key}: {value:.3f}\")\n", + " \n", + " # Visualizza le distribuzioni degli errori\n", + " bin_centers, pmf, cmf = prob.plot_distributions(\n", + " errors, \n", + " bins=50,\n", + " title=f\"Distribuzione degli Errori - {target}\"\n", + " )\n", + " \n", + " # Calcola intervalli di confidenza\n", + " confidence_levels = [0.80,0.85, 0.90, 0.95, 0.99] # 1σ, 2σ, 3σ\n", + " for level in confidence_levels:\n", + " lower_idx = np.searchsorted(cmf, (1 - level) / 2)\n", + " upper_idx = np.searchsorted(cmf, (1 + level) / 2)\n", + " \n", + " print(f\"\\nIntervallo di Confidenza {level*100}%:\")\n", + " print(f\"Range: [{bin_centers[lower_idx]:.2f}, {bin_centers[upper_idx]:.2f}]\")\n", + "\n", + "class ProbabilityFunctions:\n", + " @staticmethod\n", + " def calculate_statistics(data):\n", + " \"\"\"\n", + " Calcola statistiche di base usando TensorFlow.\n", + " \n", + " Args:\n", + " data: Tensor dei dati\n", + " \n", + " Returns:\n", + " dict: Dizionario con le statistiche\n", + " \"\"\"\n", + " if not isinstance(data, tf.Tensor):\n", + " data = tf.convert_to_tensor(data, dtype=tf.float32)\n", + " \n", + " mean = tf.reduce_mean(data)\n", + " # Calculate variance manually\n", + " squared_deviations = tf.square(data - mean)\n", + " variance = tf.reduce_mean(squared_deviations)\n", + " std = tf.sqrt(variance)\n", + " \n", + " # Sort the tensor for median calculation\n", + " sorted_data = tf.sort(data)\n", + " size = tf.size(data)\n", + " mid_index = size // 2\n", + " median = sorted_data[mid_index]\n", + " \n", + " return {\n", + " 'mean': mean.numpy(),\n", + " 'variance': variance.numpy(),\n", + " 'std': std.numpy(),\n", + " 'min': tf.reduce_min(data).numpy(),\n", + " 'max': tf.reduce_max(data).numpy(),\n", + " 'median': median.numpy()\n", + " }\n", + "\n", + " @staticmethod\n", + " def calculate_pmf(data, bins=50):\n", + " \"\"\"\n", + " Calcola la Probability Mass Function (PMF) dei dati.\n", + " \n", + " Args:\n", + " data: Array di dati\n", + " bins: Numero di bin per l'istogramma\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, bin_edges)\n", + " \"\"\"\n", + " # Calcola l'istogramma\n", + " hist, bin_edges = np.histogram(data, bins=bins, density=True)\n", + " \n", + " # Calcola i centri dei bin\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " \n", + " # Normalizza per ottenere la PMF\n", + " pmf = hist / np.sum(hist)\n", + " \n", + " return bin_centers, pmf, bin_edges\n", + "\n", + " @staticmethod\n", + " def calculate_cmf(pmf):\n", + " \"\"\"\n", + " Calcola la Cumulative Mass Function (CMF) dalla PMF.\n", + " \n", + " Args:\n", + " pmf: Probability Mass Function\n", + " \n", + " Returns:\n", + " array: Cumulative Mass Function\n", + " \"\"\"\n", + " return np.cumsum(pmf)\n", + "\n", + " def plot_distributions(self, data, bins=50, title=\"Distribuzione\"):\n", + " \"\"\"\n", + " Calcola e visualizza PMF e CMF delle distribuzioni.\n", + " \n", + " Args:\n", + " data: Array di dati da analizzare\n", + " bins: Numero di bin per l'istogramma\n", + " title: Titolo del grafico\n", + " \n", + " Returns:\n", + " tuple: (bin_centers, pmf, cmf)\n", + " \"\"\"\n", + " # Calcola PMF e CMF\n", + " bin_centers, pmf, bin_edges = self.calculate_pmf(data, bins)\n", + " cmf = self.calculate_cmf(pmf)\n", + " \n", + " # Crea il plot\n", + " fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", + " \n", + " # Plot PMF\n", + " width = np.diff(bin_edges)\n", + " ax1.bar(bin_centers, pmf, width=width, alpha=0.5, label='PMF')\n", + " ax1.set_title('Probability Mass Function')\n", + " ax1.set_ylabel('Probability')\n", + " ax1.grid(True, alpha=0.3)\n", + " ax1.legend()\n", + " \n", + " # Plot CMF\n", + " ax2.plot(bin_centers, cmf, 'r-', label='CMF')\n", + " ax2.set_title('Cumulative Mass Function')\n", + " ax2.set_xlabel('Value')\n", + " ax2.set_ylabel('Cumulative Probability')\n", + " ax2.grid(True, alpha=0.3)\n", + " ax2.legend()\n", + " \n", + " # Set overall title\n", + " fig.suptitle(title, y=1.02)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " return bin_centers, pmf, cmf" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b98f2a45-ba6f-4519-acaa-0138b4ee7812", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ANALISI COMPLETA DEL MODELLO ===\n", + "\n", + "1. ANALISI DEGLI ERRORI\n", + "--------------------------------------------------\n", + "18750/18750 [==============================] - 191s 10ms/step\n", + "\n", + "Analisi per olive_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: 16.634\n", + "variance: 3545753.250\n", + "std: 1883.017\n", + "min: -15594.572\n", + "max: 14395.641\n", + "median: 192.294\n" + ] + }, + { + "data": { + "image/png": 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WTdLJ9yoxMVFLlizRrl27XP02bdqkzz///JzjBQB4xpltACgln332mTZv3qzc3FylpaVpxYoVSk5OVt26dfXhhx/K39/f47ITJkzQ6tWr1b17d9WtW1f79u3TjBkzVLt2bbVr107SycI3JCREs2bNUlBQkKpUqaK4uLhi3yNbvXp1tWvXToMGDVJaWpqmTZumBg0auD2e7K677tLixYvVpUsX3XLLLdq2bZvefvtt1a9f321d/fr1U1hYmBo2bOj2PHFJuuaaawp9xFBYWJhGjx6t8ePHq0uXLrr++uu1ZcsWzZgxQ5dddpnbYGhFdccdd2jhwoX617/+pZUrV+rKK69UXl6eNm/erIULF+rzzz9XmzZtCl22UqVKeuqppzRs2DBdffXV6tOnj7Zv3645c+YUOItf1O20bt1avXv31rRp03TgwAHXo7/yz15aebXC7t27C7z30smB5Yr6BUhhnnrqKdcz4O+55x75+vrqv//9r7Kzswt9zvn5Uhpx9enTRy+99JLGjh2r5s2bq0mTJm7zS5JfhfH399fSpUs1YMAAxcXF6bPPPtMnn3yiRx991ON94ad67rnn1LVrV8XHx2vw4MGuR39VrVrV7Rnu48eP19KlS3XVVVfpnnvuUW5url566SU1bdrUbcwDAEAJeXUsdACogPIfa5Q/+fn5mcjISHPNNdeYF154we3xWvlOf/TX8uXLTc+ePU1UVJTx8/MzUVFRpl+/fua3335zW+6DDz4wF198sfH19XV7DFiHDh1M06ZNC43P06O/3n33XTN69GgTHh5uKleubLp37+72aKB8zz//vKlVq5ZxOBzmyiuvND/++GOBdcrDY6d0yiOXPD3+afr06aZx48amUqVKJiIiwtx9993m77//LrAPhe3f6Y9wMubkI7z+85//mKZNmxqHw2GqVatmWrdubcaPH28yMjIKfY9ONWPGDBMbG2scDodp06aNWb16daGP4Crqdo4ePWqGDx9uqlevbgIDA02vXr3Mli1bjCQzadIkV7/SevTXqe/PgAEDTJUqVQpdhyQzfPjwQuetXbvWJCYmmsDAQBMQEGA6depkvvnmG7c+Z3oEXmk8+quocRXl0V/5nE6niY6ONpLMU089Veg2S5pfp8ZQpUoVs23bNnPttdeagIAAExERYcaOHev2KLz8R38999xzha5n2bJl5sorrzSVK1c2wcHBpkePHmbjxo0F+n355ZemdevWxs/Pz9SrV8/MmjWrwHEIAFAyNmPO42gjAACggHXr1umSSy7R22+/fcbbC1BxDRw4UIsXL9aRI0e8HQoAwCLcsw0AwHl07NixAm3Tpk2T3W5X+/btvRARAAAoDdyzDQDAefTss89qzZo16tSpk3x9ffXZZ5/ps88+09ChQ8v0I8JQPBkZGYV+wXIqT88PBwCUbxTbAACcR1dccYWSk5P15JNP6siRI6pTp47GjRtX4LFSqBjuu+8+vfHGG2fswx19AFAxcc82AABAKdm4caP27Nlzxj7FeYY8AKDso9gGAAAAAMBiDJAGAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wCAC5LNZtOIESMsW9/cuXNls9n0448/nrVvx44d1bFjR9frHTt2yGazae7cua62cePGyWazWRYfyo7TP38AQMVEsQ0AKDPyC9b8yd/fXxdddJFGjBihtLQ0b4fndc8884yWLFli6TpXrVrler/ffvvtQvtceeWVstlsatasmaXbtsKp+XLqFBkZ6dW4Nm7cqHHjxmnHjh1ejQMA4D2+3g4AAIDTTZgwQbGxsTp+/Li++uorzZw5U59++ql+/fVXBQQEeDu8Evviiy/O2mfMmDF65JFH3NqeeeYZ3XTTTerVq5flMfn7+2vevHm6/fbb3dp37Nihb775Rv7+/pZv0yrXXHON+vfv79ZWuXJlL0Vz0saNGzV+/Hh17NhRMTExbvOK8vkDAMo/im0AQJnTtWtXtWnTRpJ01113qUaNGpoyZYo++OAD9evXr9Bljh49qipVqpzPMIvNz8/vrH18fX3l63v+/kx369ZNH374ofbv36/Q0FBX+7x58xQREaGGDRvq77//Pm/xnIuLLrqowJcEZVlRPn8AQPnHZeQAgDLv6quvliRt375dkjRw4EAFBgZq27Zt6tatm4KCgnTbbbdJOll0P/DAA4qOjpbD4VCjRo00efJkGWMKXfc777yjRo0ayd/fX61bt9bq1avd5u/cuVP33HOPGjVqpMqVK6tGjRq6+eabPV4enJWVpWHDhqlGjRoKDg5W//79CxSpRbln9/R7tm02m44ePao33njDdan0wIEDtXLlStlsNr3//vsF1jFv3jzZbDalpKSccVuS1LNnTzkcDi1atKjAOm655Rb5+PgUWGbOnDm6+uqrFR4eLofDoYsvvlgzZ84s0O/HH39UYmKiQkNDVblyZcXGxurOO+906zN//ny1bt1aQUFBCg4OVvPmzfXCCy+cNe6zGThwYIEzy1Lh98Tn38e/ZMkSNWvWTA6HQ02bNtXSpUsLLL97924NHjxYUVFRcjgcio2N1d13362cnBzNnTtXN998sySpU6dOrs9r1apVkgr//Pft26fBgwcrIiJC/v7+atmypd544w23Pvn39k+ePFmvvPKK6tevL4fDocsuu0w//PBD8d8kAECp4Mw2AKDM27ZtmySpRo0arrbc3FwlJiaqXbt2mjx5sgICAmSM0fXXX6+VK1dq8ODBatWqlT7//HM99NBD2r17t6ZOneq23i+//FILFizQyJEj5XA4NGPGDHXp0kXff/+96/7kH374Qd9884369u2r2rVra8eOHZo5c6Y6duyojRs3FrisfcSIEQoJCdG4ceO0ZcsWzZw5Uzt37nTdG11cb731lu666y61bdtWQ4cOlSTVr19fl19+uaKjo/XOO+/ohhtucFvmnXfeUf369RUfH3/W9QcEBKhnz5569913dffdd0uSfv75Z23YsEGvvfaa1q9fX2CZmTNnqmnTprr++uvl6+urjz76SPfcc4+cTqeGDx8u6WQRee211yosLEyPPPKIQkJCtGPHDr333nuu9SQnJ6tfv37q3Lmz/vOf/0iSNm3apK+//lr33XffWWM/fvy49u/f79YWFBQkh8Nx1mVP99VXX+m9997TPffco6CgIL344ovq3bu3du3a5cq/PXv2qG3btjp06JCGDh2qxo0ba/fu3Vq8eLGysrLUvn17jRw5Ui+++KIeffRRNWnSRJJc/57u2LFj6tixo7Zu3aoRI0YoNjZWixYt0sCBA3Xo0KEC78G8efN0+PBhDRs2TDabTc8++6xuvPFG/fHHH6pUqdI57zMAoJQYAADKiDlz5hhJZtmyZSY9Pd38+eefZv78+aZGjRqmcuXK5q+//jLGGDNgwAAjyTzyyCNuyy9ZssRIMk899ZRb+0033WRsNpvZunWrq02SkWR+/PFHV9vOnTuNv7+/ueGGG1xtWVlZBeJMSUkxksybb75ZIPbWrVubnJwcV/uzzz5rJJkPPvjA1dahQwfToUMH1+vt27cbSWbOnDmutrFjx5rT/0xXqVLFDBgwoEA8o0ePNg6Hwxw6dMjVtm/fPuPr62vGjh1boP+pVq5caSSZRYsWmY8//tjYbDaza9cuY4wxDz30kKlXr54r5qZNm7otW9h7k5iY6FrGGGPef/99I8n88MMPHmO47777THBwsMnNzT1jrIXJ/xxPn/LfywEDBpi6desWWK6w91eS8fPzc8uTn3/+2UgyL730kqutf//+xm63F7pPTqfTGGPMokWLjCSzcuXKAn1O//ynTZtmJJm3337b1ZaTk2Pi4+NNYGCgyczMNMb8kyc1atQwBw8edPX94IMPjCTz0UcfeX6jAADnHZeRAwDKnISEBIWFhSk6Olp9+/ZVYGCg3n//fdWqVcutX/4Z2HyffvqpfHx8NHLkSLf2Bx54QMYYffbZZ27t8fHxat26tet1nTp11LNnT33++efKy8uT5D7Q1okTJ3TgwAE1aNBAISEhWrt2bYHYhw4d6nZ28e6775avr68+/fTTc3wXiq5///7Kzs7W4sWLXW0LFixQbm7uOd3LfO2116p69eqaP3++jDGaP3++x3vkJff3JiMjQ/v371eHDh30xx9/KCMjQ5IUEhIiSfr444914sSJQtcTEhKio0ePKjk5ucixnqpnz55KTk52mxITE4u1roSEBNWvX9/1ukWLFgoODtYff/whSXI6nVqyZIl69OjhGlfgVMW5euHTTz9VZGSk23tdqVIljRw5UkeOHNGXX37p1r9Pnz6qVq2a6/VVV10lSa4YAQBlA5eRAwDKnJdfflkXXXSRfH19FRERoUaNGslud/9+2NfXV7Vr13Zr27lzp6KiohQUFOTWnn/57s6dO93aGzZsWGDbF110kbKyspSenq7IyEgdO3ZMEydO1Jw5c7R79263e7/zC8ozrTMwMFA1a9Ys1UdANW7cWJdddpneeecdDR48WNLJS8gvv/xyNWjQoMjrqVSpkm6++WbNmzdPbdu21Z9//qlbb73VY/+vv/5aY8eOVUpKirKystzmZWRkqGrVqurQoYN69+6t8ePHa+rUqerYsaN69eqlW2+91XWZ9z333KOFCxeqa9euqlWrlq699lrdcsst6tKlS5Hirl27thISEoq8n2dSp06dAm3VqlVz3Xefnp6uzMxMSx+DtnPnTjVs2LBAjnvK29NjzC+8y+oAdgBwoeLMNgCgzGnbtq0SEhLUsWNHNWnSpEARIkkOh6PQdqvde++9evrpp3XLLbdo4cKF+uKLL5ScnKwaNWrI6XSW+vaLqn///vryyy/1119/adu2bfr222+LNUL3rbfeqnXr1mncuHFq2bKlLr744kL7bdu2TZ07d9b+/fs1ZcoUffLJJ0pOTtaoUaMkyfXe2Gw2LV68WCkpKRoxYoR2796tO++8U61bt9aRI0ckSeHh4Vq3bp0+/PBD1z33Xbt21YABA4r5bvzD05nm/CsXTlfYQHCSPA6w5w3lIUYAAMU2AKACqVu3rvbs2aPDhw+7tW/evNk1/1S///57gXX89ttvCggIUFhYmCRp8eLFGjBggJ5//nnddNNNuuaaa9SuXTsdOnSo0BhOX+eRI0e0d+/eQkfEPldnukS5b9++8vHx0bvvvqt33nlHlSpVUp8+fc55G+3atVOdOnW0atWqM57V/uijj5Sdna0PP/xQw4YNU7du3ZSQkODx+daXX365nn76af3444965513tGHDBs2fP98138/PTz169NCMGTO0bds2DRs2TG+++aa2bt16zvtwqmrVqhX6WZ1+triowsLCFBwcrF9//fWM/c7lcvK6devq999/L/Dljae8BQCUDxTbAIAKo1u3bsrLy9P06dPd2qdOnSqbzaauXbu6taekpLjdd/3nn3/qgw8+0LXXXus6e+jj41PgjOFLL73k8czoK6+84nZv8syZM5Wbm1tg28VRpUoVj0V+aGiounbtqrffflvvvPOOunTp4va87KKy2Wx68cUXNXbsWN1xxx0e++W/P6dfVj9nzhy3fn///XeB969Vq1aSpOzsbEnSgQMH3Obb7Xa1aNHCrU9x1a9fXxkZGW6jqe/du7fQR6UVhd1uV69evfTRRx/pxx9/LDA/f1/zn/nu6fM6Vbdu3ZSamqoFCxa42nJzc/XSSy8pMDBQHTp0KFasAADv4p5tAECF0aNHD3Xq1EmPPfaYduzYoZYtW+qLL77QBx98oPvvv99t4CtJatasmRITE90e/SVJ48ePd/W57rrr9NZbb6lq1aq6+OKLlZKSomXLlrk9huxUOTk56ty5s2655RZt2bJFM2bMULt27XT99deXeP9at26tZcuWacqUKYqKilJsbKzi4uJc8/v376+bbrpJkvTkk08Wezs9e/ZUz549z9jn2muvdZ2NHjZsmI4cOaJXX31V4eHh2rt3r6vfG2+8oRkzZuiGG25Q/fr1dfjwYb366qsKDg5Wt27dJEl33XWXDh48qKuvvlq1a9fWzp079dJLL6lVq1YeH5dVVH379tXDDz+sG264QSNHjlRWVpZmzpypiy66qNAB7orimWee0RdffKEOHTpo6NChatKkifbu3atFixbpq6++UkhIiFq1aiUfHx/95z//UUZGhhwOh+uZ5KcbOnSo/vvf/2rgwIFas2aNYmJitHjxYn399deaNm1agTEIAADlA8U2AKDCsNvt+vDDD/XEE09owYIFmjNnjmJiYvTcc8/pgQceKNC/Q4cOio+P1/jx47Vr1y5dfPHFmjt3ruusqiS98MIL8vHx0TvvvKPjx4/ryiuv1LJlyzyOdj19+nS98847euKJJ3TixAn169dPL774YomesZ1vypQpGjp0qMaMGaNjx45pwIABbsV2jx49VK1aNTmdTkuK+zNp1KiRFi9erDFjxujBBx9UZGSk7r77boWFhenOO+909evQoYO+//57zZ8/X2lpaapataratm2rd955R7GxsZKk22+/Xa+88opmzJihQ4cOKTIyUn369NG4ceNKfF9+jRo19P777yspKUn//ve/FRsbq4kTJ+r3338vdrFdq1Ytfffdd3r88cf1zjvvKDMzU7Vq1VLXrl1dz12PjIzUrFmzNHHiRA0ePFh5eXlauXJlocV25cqVtWrVKj3yyCN64403lJmZqUaNGmnOnDkaOHBgSXYfAOBFNsNoGgAAVAi5ubmKiopSjx499Prrr3s7HAAALmjcsw0AQAWxZMkSpaenq3///t4OBQCACx5ntgEAKOe+++47rV+/Xk8++aRCQ0OLfXk0AACwDme2AQAo52bOnKm7775b4eHhevPNN70dDgAAEGe2AQAAAACwHGe2AQAAAACwGMU2AAAAAAAW4znbxeR0OrVnzx4FBQVZ8uxUAAAAAEDZYYzR4cOHFRUVJbv93M9TU2wX0549exQdHe3tMAAAAAAApejPP/9U7dq1z3k5iu1iCgoKknTyjQ8ODvZyNNZzOp1KT09XWFhYsb7FQcVGfsATcgOekBs4E/IDnpAb8OR85EZmZqaio6Ndtd+5otgupvxLx4ODgytssX38+HEFBwdzYEMB5Ac8ITfgCbmBMyE/4Am5AU/OZ24U97ZhMhYAAAAAAItRbAMAAAAAYDGKbQAAAAAALMY92wAAAABQARhjlJubq7y8PG+HUuqcTqdOnDih48ePF/uebR8fH/n6+pbao5wptgEAAACgnMvJydHevXuVlZXl7VDOC2OMnE6nDh8+XKJiOSAgQDVr1pSfn5+F0Z1EsQ0AAAAA5ZjT6dT27dvl4+OjqKgo+fn5ldrZ2rIi/yx+cc9MG2OUk5Oj9PR0bd++XQ0bNrR8VHOKbQAAAAAox3JycuR0OhUdHa2AgABvh3NelLTYlqTKlSurUqVK2rlzp3JycuTv729pjAyQBgAAAAAVAM8iP3el+Z7xaQAAAAAAYDGKbQAAAAAALMY92wAAAABQQU1N/u28bm/UNRed1+2VZRTbAADgvPjgp93K8smQSjBCLv+JA4CKZeDAgXrjjTckSZUqVVKdOnXUv39/Pfroo/rqq6/UqVMnhYSEaO/evW4DmP3www+Ki4uTdHKwNElatWqVOnXqVGAbjz32mJ566qnzsDfuvH4Z+csvv6yYmBj5+/srLi5O33//vce+GzZsUO/evRUTEyObzaZp06YV6JM/7/Rp+PDhrj4dO3YsMP9f//pXaeweAAAAAOAMunTpor179+r333/XAw88oHHjxum5555zzQ8KCtL777/vtszrr7+uOnXqFLq+LVu2aO/eva7pkUceKdX4PfFqsb1gwQIlJSVp7NixWrt2rVq2bKnExETt27ev0P5ZWVmqV6+eJk2apMjIyEL7/PDDD25vbHJysiTp5ptvdus3ZMgQt37PPvustTsHAAAAADgrh8OhyMhI1a1bV3fffbcSEhL04YcfuuYPGDBAs2fPdr0+duyYFixYoNtvv73Q9YWHhysyMtI1BQYGlvo+FMarl5FPmTJFQ4YM0aBBgyRJs2bN0ieffKLZs2cX+u3DZZddpssuu0ySPH47ERYW5vZ60qRJql+/vjp06ODWHhAQ4LFgL0x2drays7NdrzMzMyWdfIC80+ks8nrKC6fTKWNMhdw3lBz5AU/IDXjidDolY05OJV0PKhyOHfCE3Cia/Pcpf3JXsuPuuSq4/XNfrnLlyjpw4ICr7fbbb9dzzz2nnTt3qk6dOlq8eLFiYmLUqlUrt2VP/beoceT3LayuK2neea3YzsnJ0Zo1azR69GhXm91uV0JCglJSUizbxttvv62kpKQCDzp/55139PbbbysyMlI9evTQ448/fsYHwE+cOFHjx48v0J6enq7jx49bEm9Z4nQ6lZGRIWMMz+tDAeQHPCE34InT6ZTDHJecklT8e7Y9Xf2G8o1jBzwhN4rmxIkTcjqdys3NVW5urtu88/1FxenbP5v8Ijc3N1fGGK1YsUKff/65hg8frry8PElS9erVlZiYqNmzZ2vMmDGaPXu2+vfv79q3/G3m94+OjnbbxtatW1WjRg2P8TqdTh04cECVKlVym3f48OFz2pfTea3Y3r9/v/Ly8hQREeHWHhERoc2bN1uyjSVLlujQoUMaOHCgW/utt96qunXrKioqSuvXr9fDDz+sLVu26L333vO4rtGjRyspKcn1OjMzU9HR0QoLC1NwcLAl8ZYlTqdTNptNYWFhHNhQAPkBT8gNeOJ0OpVt26Mse2CJBkgLDw+3MCqUFRw74Am5UTTHjx/X4cOH5evrK19f9xLvfL9vp2//bOx2uz799FNVq1bN9aXBrbfeqvHjx+uHH35wrXPw4MG6//771b9/f3377bdatGiRVq5c6bZNHx8fSdLq1asVFBTk2saZ8sfX11d2u101atRwG4BNUoHX56pCj0b++uuvq2vXroqKinJrHzp0qOvn5s2bq2bNmurcubO2bdum+vXrF7ouh8Mhh8NRoN1ut1fYX3ybzVah9w8lQ37AE3IDHtls/0zFRF5VXBw74Am5cXZ2u91t8Gd3xT/mFkfB7Z9dp06dNHPmTPn5+SkqKspVPOevy2azqVu3bho2bJjuuusu9ejRQzVq1HCbf+q/9erVU0hISJHj9ZRjJc05r2VsaGiofHx8lJaW5taelpZ2TvdSe7Jz504tW7ZMd91111n75g8Zv3Xr1hJvFwAAAABQdFWqVFGDBg1Up04dj2fGfX191b9/f61atUp33nnneY6weLxWbPv5+al169Zavny5q83pdGr58uWKj48v8frnzJmj8PBwde/e/ax9161bJ0mqWbNmibcLAAAAALDek08+qfT0dCUmJno7lCLx6mXkSUlJGjBggNq0aaO2bdtq2rRpOnr0qGt08v79+6tWrVqaOHGipJMDnm3cuNH18+7du7Vu3ToFBgaqQYMGrvU6nU7NmTNHAwYMKPDNyLZt2zRv3jx169ZNNWrU0Pr16zVq1Ci1b99eLVq0OE97DgAAAAClb9Q1F3k7BMv4+fkpNDRUUvFHPT+fvFps9+nTR+np6XriiSeUmpqqVq1aaenSpa5B03bt2uV2nfyePXt0ySWXuF5PnjxZkydPVocOHbRq1SpX+7Jly7Rr165CLy/w8/PTsmXLXIV9dHS0evfurTFjxpTejgIAAAAACpg7d67HeR07djxjUd2zZ0+30dbP1v988/oAaSNGjNCIESMKnXdqAS1JMTExRXrzrr32Wo/9oqOj9eWXX55znAAAAAAAFBVD+gEAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAKACKEuDg5UXpfmeUWwDAAAAQDlWqVIlSVJWVpaXIyl/8t+z/PfQSl4fjRwAAAAAUHw+Pj4KCQnRvn37JEkBAQGy2Wxejqp0GWOUm5srX1/fYu2rMUZZWVnat2+fQkJC5OPjY3mMFNsAAAAAUM5FRkZKkqvgruiMMXI6nbLb7SX6YiEkJMT13lmNYhsAAAAAyjmbzaaaNWsqPDxcJ06c8HY4pc7pdOrAgQOqUaOG7Pbi3R1dqVKlUjmjnY9iGwAAAAAqCB8fn1ItIMsKp9OpSpUqyd/fv9jFdmkrm1EBAAAAAFCOUWwDAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsBjFNgAAAAAAFqPYBgAAAADAYhTbAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsJivtwMAAAAoqqnJv5Vo+VHXXGRRJAAAnBlntgEAAAAAsBjFNgAAAAAAFqPYBgAAAADAYhTbAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxrxfbL7/8smJiYuTv76+4uDh9//33Hvtu2LBBvXv3VkxMjGw2m6ZNm1agz7hx42Sz2dymxo0bu/U5fvy4hg8frho1aigwMFC9e/dWWlqa1bsGAAAAALhAebXYXrBggZKSkjR27FitXbtWLVu2VGJiovbt21do/6ysLNWrV0+TJk1SZGSkx/U2bdpUe/fudU1fffWV2/xRo0bpo48+0qJFi/Tll19qz549uvHGGy3dNwAAAADAhcurxfaUKVM0ZMgQDRo0SBdffLFmzZqlgIAAzZ49u9D+l112mZ577jn17dtXDofD43p9fX0VGRnpmkJDQ13zMjIy9Prrr2vKlCm6+uqr1bp1a82ZM0fffPONvv32W8v3EQAAAABw4fH11oZzcnK0Zs0ajR492tVmt9uVkJCglJSUEq37999/V1RUlPz9/RUfH6+JEyeqTp06kqQ1a9boxIkTSkhIcPVv3Lix6tSpo5SUFF1++eWFrjM7O1vZ2dmu15mZmZIkp9Mpp9NZonjLIqfTKWNMhdw3lBz5AU/IDXjidDolY05O3o4DZQ7HDnhCbsCT85EbJV2314rt/fv3Ky8vTxEREW7tERER2rx5c7HXGxcXp7lz56pRo0bau3evxo8fr6uuukq//vqrgoKClJqaKj8/P4WEhBTYbmpqqsf1Tpw4UePHjy/Qnp6eruPHjxc73rLK6XQqIyNDxhjZ7V6/tR9lDPkBT8gNeOJ0OuUwxyWnJNm8FoenW9XgXRw74Am5AU/OR24cPny4RMt7rdguLV27dnX93KJFC8XFxalu3bpauHChBg8eXOz1jh49WklJSa7XmZmZio6OVlhYmIKDg0sUc1nkdDpls9kUFhbGgQ0FkB/whNyAJ06nU9m2PcqyB0o27xXb4eHhXts2POPYAU/IDXhyPnLD39+/RMt7rdgODQ2Vj49PgVHA09LSzjj42bkKCQnRRRddpK1bt0qSIiMjlZOTo0OHDrmd3T7bdh0OR6H3idvt9gr7i2+z2Sr0/qFkyA94Qm7AI5vtn8lLyMuyi2MHPCE34Elp50ZJ1+u1jPXz81Pr1q21fPlyV5vT6dTy5csVHx9v2XaOHDmibdu2qWbNmpKk1q1bq1KlSm7b3bJli3bt2mXpdgEAAAAAFy6vXkaelJSkAQMGqE2bNmrbtq2mTZumo0ePatCgQZKk/v37q1atWpo4caKkk4Oqbdy40fXz7t27tW7dOgUGBqpBgwaSpAcffFA9evRQ3bp1tWfPHo0dO1Y+Pj7q16+fJKlq1aoaPHiwkpKSVL16dQUHB+vee+9VfHy8x8HRAAAAAAA4F14ttvv06aP09HQ98cQTSk1NVatWrbR06VLXoGm7du1yO3W/Z88eXXLJJa7XkydP1uTJk9WhQwetWrVKkvTXX3+pX79+OnDggMLCwtSuXTt9++23CgsLcy03depU2e129e7dW9nZ2UpMTNSMGTPOz04DAAAAACo8mzFefgZHOZWZmamqVasqIyOjwg6Qtm/fPoWHh3N/DAogP+AJuQFPnE6nXv38J2X5eHeAtFHXXOS1bcMzjh3whNyAJ+cjN0pa85GxAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsBjFNgAAAAAAFqPYBgAAAADAYhTbAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxX28HAAAAyoepyb8Vf2FjFGBdKAAAlHmc2QYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWMzrxfbLL7+smJgY+fv7Ky4uTt9//73Hvhs2bFDv3r0VExMjm82madOmFegzceJEXXbZZQoKClJ4eLh69eqlLVu2uPXp2LGjbDab2/Svf/3L6l0DAAAAAFygvFpsL1iwQElJSRo7dqzWrl2rli1bKjExUfv27Su0f1ZWlurVq6dJkyYpMjKy0D5ffvmlhg8frm+//VbJyck6ceKErr32Wh09etSt35AhQ7R3717X9Oyzz1q+fwAAAACAC5OvNzc+ZcoUDRkyRIMGDZIkzZo1S5988olmz56tRx55pED/yy67TJdddpkkFTpfkpYuXer2eu7cuQoPD9eaNWvUvn17V3tAQIDHgh0AAAAAgJLwWrGdk5OjNWvWaPTo0a42u92uhIQEpaSkWLadjIwMSVL16tXd2t955x29/fbbioyMVI8ePfT4448rICDA43qys7OVnZ3tep2ZmSlJcjqdcjqdlsVbVjidThljKuS+oeTID3hCblRwxpRs2fzJi8jNsoljBzwhN+DJ+ciNkq7ba8X2/v37lZeXp4iICLf2iIgIbd682ZJtOJ1O3X///bryyivVrFkzV/utt96qunXrKioqSuvXr9fDDz+sLVu26L333vO4rokTJ2r8+PEF2tPT03X8+HFL4i1LnE6nMjIyZIyR3e71W/tRxpAf8ITcqNgC8o6UYGkjhzkuOSXJZlFE587TrWrwLo4d8ITcgCfnIzcOHz5couW9ehl5aRs+fLh+/fVXffXVV27tQ4cOdf3cvHlz1axZU507d9a2bdtUv379Qtc1evRoJSUluV5nZmYqOjpaYWFhCg4OLp0d8CKn0ymbzaawsDAObCiA/IAn5EbFluWTUfyFjZGMlGUPlGzeK7bDw8O9tm14xrEDnpAb8OR85Ia/v3+JlvdasR0aGiofHx+lpaW5taelpVlyL/WIESP08ccfa/Xq1apdu/YZ+8bFxUmStm7d6rHYdjgccjgcBdrtdnuF/cW32WwVev9QMuQHPCE3KrCSFsk22z+Tl5CXZRfHDnhCbsCT0s6Nkq7Xaxnr5+en1q1ba/ny5a42p9Op5cuXKz4+vtjrNcZoxIgRev/997VixQrFxsaedZl169ZJkmrWrFns7QIAAAAAkM+rl5EnJSVpwIABatOmjdq2batp06bp6NGjrtHJ+/fvr1q1amnixImSTg6qtnHjRtfPu3fv1rp16xQYGKgGDRpIOnnp+Lx58/TBBx8oKChIqampkqSqVauqcuXK2rZtm+bNm6du3bqpRo0aWr9+vUaNGqX27durRYsWXngXAAAAAAAVjVeL7T59+ig9PV1PPPGEUlNT1apVKy1dutQ1aNquXbvcTt3v2bNHl1xyiev15MmTNXnyZHXo0EGrVq2SJM2cOVOS1LFjR7dtzZkzRwMHDpSfn5+WLVvmKuyjo6PVu3dvjRkzpnR3FgAAAABwwfD6AGkjRozQiBEjCp2XX0Dni4mJkTnLI0PONj86OlpffvnlOcUIAAAAAMC5YJQBAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABYrVrG9cuVKq+MAAAAAAKDCKFax3aVLF9WvX19PPfWU/vzzT6tjAgAAAACgXCtWsb17926NGDFCixcvVr169ZSYmKiFCxcqJyfH6vgAAAAAACh3ilVsh4aGatSoUVq3bp2+++47XXTRRbrnnnsUFRWlkSNH6ueff7Y6TgAAAAAAyo0SD5B26aWXavTo0RoxYoSOHDmi2bNnq3Xr1rrqqqu0YcMGK2IEAAAAAKBcKXaxfeLECS1evFjdunVT3bp19fnnn2v69OlKS0vT1q1bVbduXd18881WxgoAAAAAQLngW5yF7r33Xr377rsyxuiOO+7Qs88+q2bNmrnmV6lSRZMnT1ZUVJRlgQIAAAAAUF4Uq9jeuHGjXnrpJd14441yOByF9gkNDeURYQAAAACAC1KxLiMfO3asbr755gKFdm5urlavXi1J8vX1VYcOHUoeIQAAAAAA5Uyxiu1OnTrp4MGDBdozMjLUqVOnEgcFAAAAAEB5Vqxi2xgjm81WoP3AgQOqUqVKiYMCAAAAAKA8O6d7tm+88UZJks1m08CBA90uI8/Ly9P69et1xRVXWBshAAAAAADlzDkV21WrVpV08sx2UFCQKleu7Jrn5+enyy+/XEOGDLE2QgAAAAAAyplzKrbnzJkjSYqJidGDDz7IJeMAAAAAABSiWI/+Gjt2rNVxAAAAAABQYRS52L700ku1fPlyVatWTZdcckmhA6TlW7t2rSXBAQAAAABQHhW52O7Zs6drQLRevXqVVjwAAAAAAJR7RS62T710nMvIAQAAAADwrFjP2bbSyy+/rJiYGPn7+ysuLk7ff/+9x74bNmxQ7969FRMTI5vNpmnTphVrncePH9fw4cNVo0YNBQYGqnfv3kpLS7NytwAAAAAAF7AiF9vVqlVT9erVizQV1YIFC5SUlKSxY8dq7dq1atmypRITE7Vv375C+2dlZalevXqaNGmSIiMji73OUaNG6aOPPtKiRYv05Zdfas+ePa5niAMAAAAAUFJFvozc01nkkpgyZYqGDBmiQYMGSZJmzZqlTz75RLNnz9YjjzxSoP9ll12myy67TJIKnV+UdWZkZOj111/XvHnzdPXVV0s6+UizJk2a6Ntvv9Xll19u+X4CAAAAAC4sRS62BwwYYOmGc3JytGbNGo0ePdrVZrfblZCQoJSUlFJb55o1a3TixAklJCS4+jRu3Fh16tRRSkqKx2I7Oztb2dnZrteZmZmSJKfTKafTWax4yzKn0yljTIXcN5Qc+QFPyI0KzpiSLZs/eRG5WTZx7IAn5AY8OR+5UdJ1F7nYzszMVHBwsOvnM8nvdyb79+9XXl6eIiIi3NojIiK0efPmooZ1zutMTU2Vn5+fQkJCCvRJTU31uO6JEydq/PjxBdrT09N1/PjxYsVbljmdTmVkZMgYI7vd67f2o4whP+AJuVGxBeQdKcHSRg5zXHJKkufHh5Y2T7eqwbs4dsATcgOenI/cOHz4cImWL3KxXa1aNe3du1fh4eEKCQkp9DnbxhjZbDbl5eWVKKiyaPTo0UpKSnK9zszMVHR0tMLCwor05UJ543Q6ZbPZFBYWxoENBZAf8ITcqNiyfDKKv7AxkpGy7IFSIf+HOF/Cw8O9tm14xrEDnpAb8OR85Ia/v3+Jli9ysb1ixQrX4GcrV64s0UYlKTQ0VD4+PgVGAU9LS/M4+JkV64yMjFROTo4OHTrkdnb7bNt1OByu54yfym63V9hffJvNVqH3DyVDfsATcqMCK2mRbLP9M3kJeVl2ceyAJ+QGPCnt3CjpeotcbHfo0KHQn4vLz89PrVu31vLly9WrVy9JJ7+dWL58uUaMGFFq62zdurUqVaqk5cuXq3fv3pKkLVu2aNeuXYqPjy/xfgEAAAAAUORi+3R///23Xn/9dW3atEmSdPHFF2vQoEHn9OivpKQkDRgwQG3atFHbtm01bdo0HT161DWSeP/+/VWrVi1NnDhR0skB0DZu3Oj6effu3Vq3bp0CAwPVoEGDIq2zatWqGjx4sJKSklS9enUFBwfr3nvvVXx8PCORAwAqtKnJv3k7BAAALhjFKrZXr16tHj16qGrVqmrTpo0k6cUXX9SECRP00UcfqX379kVaT58+fZSenq4nnnhCqampatWqlZYuXeoa4GzXrl1up+737NmjSy65xPV68uTJmjx5sjp06KBVq1YVaZ2SNHXqVNntdvXu3VvZ2dlKTEzUjBkzivNWAAAAAABQgM2Yc38GR/PmzRUfH6+ZM2fKx8dHkpSXl6d77rlH33zzjX755RfLAy1rMjMzVbVqVWVkZFTYAdL27dun8PBw7o9BAeQHPCE3yjavntk2RgF5R5Tl490B0kZdc5HXtg3POHbAE3IDnpyP3ChpzVesqLZu3aoHHnjAVWhLko+Pj5KSkrR169birBIAAAAAgAqjWMX2pZde6rpX+1SbNm1Sy5YtSxwUAAAAAADlWZHv2V6/fr3r55EjR+q+++7T1q1bXYOKffvtt3r55Zc1adIk66MEAAAAAKAcKXKx3apVK9lsNp16i/e///3vAv1uvfVW9enTx5roAAAAAAAoh4pcbG/fvr004wAAAAAAoMIocrFdt27d0owDAAAAAIAKo1jP2c63ceNG7dq1Szk5OW7t119/fYmCAgAAKA0lffwZjw4DABRVsYrtP/74QzfccIN++eUXt/u4bf//uZl5eXnWRQgAAAAAQDlTrEd/3XfffYqNjdW+ffsUEBCgDRs2aPXq1WrTpo1WrVplcYgAAAAAAJQvxTqznZKSohUrVig0NFR2u112u13t2rXTxIkTNXLkSP30009WxwkAAAAAQLlRrDPbeXl5CgoKkiSFhoZqz549kk4OorZlyxbrogMAAAAAoBwq1pntZs2a6eeff1ZsbKzi4uL07LPPys/PT6+88orq1atndYwAAAAAAJQrxSq2x4wZo6NHj0qSJkyYoOuuu05XXXWVatSooQULFlgaIAAAAAAA5U2xiu3ExETXzw0aNNDmzZt18OBBVatWzTUiOQAAAAAAF6oSPWdbkv78809JUnR0dImDAQAAAACgIijWAGm5ubl6/PHHVbVqVcXExCgmJkZVq1bVmDFjdOLECatjBAAAAACgXCnWme17771X7733np599lnFx8dLOvk4sHHjxunAgQOaOXOmpUECAAAAAFCeFKvYnjdvnubPn6+uXbu62lq0aKHo6Gj169ePYhsAAAAAcEEr1mXkDodDMTExBdpjY2Pl5+dX0pgAAAAAACjXilVsjxgxQk8++aSys7NdbdnZ2Xr66ac1YsQIy4IDAAAAAKA8KvJl5DfeeKPb62XLlql27dpq2bKlJOnnn39WTk6OOnfubG2EAAAAAACUM0UutqtWrer2unfv3m6vefQXAAAAAAAnFbnYnjNnTmnGAQAAAABAhVGs0cjzpaena8uWLZKkRo0aKSwszJKgAAAAAAAoz4o1QNrRo0d15513qmbNmmrfvr3at2+vqKgoDR48WFlZWVbHCAAAAABAuVKsYjspKUlffvmlPvroIx06dEiHDh3SBx98oC+//FIPPPCA1TECAAAAAFCuFOsy8v/7v//T4sWL1bFjR1dbt27dVLlyZd1yyy2aOXOmVfEBAAAAAFDuFOvMdlZWliIiIgq0h4eHcxk5AAAAAOCCV6xiOz4+XmPHjtXx48ddbceOHdP48eMVHx9vWXAAAAAAAJRHxbqMfNq0aerSpYtq166tli1bSpJ+/vln+fv76/PPP7c0QAAAAAAAyptindlu3ry5fv/9d02cOFGtWrVSq1atNGnSJP3+++9q2rTpOa/v5ZdfVkxMjPz9/RUXF6fvv//+jP0XLVqkxo0by9/fX82bN9enn37qNt9msxU6Pffcc64+MTExBeZPmjTpnGMHAAAAAOB053xm+8SJE2rcuLE+/vhjDRkypMQBLFiwQElJSZo1a5bi4uI0bdo0JSYmasuWLQoPDy/Q/5tvvlG/fv00ceJEXXfddZo3b5569eqltWvXqlmzZpKkvXv3ui3z2WefafDgwerdu7db+4QJE9z2ISgoqMT7AwAAAADAOZ/ZrlSpktu92iU1ZcoUDRkyRIMGDdLFF1+sWbNmKSAgQLNnzy60/wsvvKAuXbrooYceUpMmTfTkk0/q0ksv1fTp0119IiMj3aYPPvhAnTp1Ur169dzWFRQU5NavSpUqlu0XAAAAAODCVax7tocPH67//Oc/eu211+TrW6xVSJJycnK0Zs0ajR492tVmt9uVkJCglJSUQpdJSUlRUlKSW1tiYqKWLFlSaP+0tDR98skneuONNwrMmzRpkp588knVqVNHt956q0aNGuVxf7Kzs5Wdne16nZmZKUlyOp1yOp1n3M/yyOl0yhhTIfcNJUd+wBNyo4wzxrvbzp/KMXK7dHDsgCfkBjw5H7lR0nUXq1L+4YcftHz5cn3xxRdq3rx5gTPC7733XpHWs3//fuXl5RV4jFhERIQ2b95c6DKpqamF9k9NTS20/xtvvKGgoCDdeOONbu0jR47UpZdequrVq+ubb77R6NGjtXfvXk2ZMqXQ9UycOFHjx48v0J6enm7pmf6ywul0KiMjQ8YY2e3FurUfFRj5AU/IjbItIO+IF7du5DDHJack2bwYR8ns27fP2yFUSBw74Am5AU/OR24cPny4RMsXq9gOCQkpcP9zWTV79mzddttt8vf3d2s/9ex4ixYt5Ofnp2HDhmnixIlyOBwF1jN69Gi3ZTIzMxUdHa2wsDAFBweX3g54idPplM1mU1hYGAc2FEB+wBNyo2zL8snw3saNkYyUZQ+UbOW32C5sPBmUHMcOeEJuwJPzkRun15Dn6pyKbafTqeeee06//fabcnJydPXVV2vcuHGqXLlysTYeGhoqHx8fpaWlubWnpaUpMjKy0GUiIyOL3P9///uftmzZogULFpw1lri4OOXm5mrHjh1q1KhRgfkOh6PQItxut1fYX3ybzVah9w8lQ37AE3KjDPN2kWuz/TOVU+R16eHYAU/IDXhS2rlR0vWe09JPP/20Hn30UQUGBqpWrVp68cUXNXz48GJv3M/PT61bt9by5ctdbU6nU8uXL1d8fHyhy8THx7v1l6Tk5ORC+7/++utq3bq161ngZ7Ju3TrZ7Xa+sQYAAAAAlNg5ndl+8803NWPGDA0bNkyStGzZMnXv3l2vvfZasav+pKQkDRgwQG3atFHbtm01bdo0HT16VIMGDZIk9e/fX7Vq1dLEiRMlSffdd586dOig559/Xt27d9f8+fP1448/6pVXXnFbb2ZmphYtWqTnn3++wDZTUlL03XffqVOnTgoKClJKSopGjRql22+/XdWqVSvWfgAAAAAAkO+ciu1du3apW7durtcJCQmy2Wzas2ePateuXawA+vTpo/T0dD3xxBNKTU1Vq1attHTpUtcgaLt27XIr5K+44grNmzdPY8aM0aOPPqqGDRtqyZIlrmds55s/f76MMerXr1+BbTocDs2fP1/jxo1Tdna2YmNjNWrUqAKjnAMAAAAAUBw2Y4r+DA4fHx+lpqYqLCzM1RYUFKT169crNja2VAIsqzIzM1W1alVlZGRU2AHS9u3bp/DwcO6PQQHkBzwhN8q2qcm/eW/jxigg74iyfMr3AGmjrrnI2yFUSBw74Am5AU/OR26UtOY7pzPbxhgNHDjQbaCw48eP61//+pfb47+K+ugvAAAAAAAqonMqtgcMGFCg7fbbb7csGAAAAAAAKoJzKrbnzJlTWnEAAAAAAFBhcOMDAAAAAAAWo9gGAAAAAMBi53QZOQAA8B6vjiYOAADOCWe2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDFfbwcAAABQXkxN/q1Ey4+65iKLIgEAlHWc2QYAAAAAwGJloth++eWXFRMTI39/f8XFxen7778/Y/9FixapcePG8vf3V/PmzfXpp5+6zR84cKBsNpvb1KVLF7c+Bw8e1G233abg4GCFhIRo8ODBOnLkiOX7BgAAAAC48Hi92F6wYIGSkpI0duxYrV27Vi1btlRiYqL27dtXaP9vvvlG/fr10+DBg/XTTz+pV69e6tWrl3799Ve3fl26dNHevXtd07vvvus2/7bbbtOGDRuUnJysjz/+WKtXr9bQoUNLbT8BAAAAABcOrxfbU6ZM0ZAhQzRo0CBdfPHFmjVrlgICAjR79uxC+7/wwgvq0qWLHnroITVp0kRPPvmkLr30Uk2fPt2tn8PhUGRkpGuqVq2aa96mTZu0dOlSvfbaa4qLi1O7du300ksvaf78+dqzZ0+p7i8AAAAAoOLz6gBpOTk5WrNmjUaPHu1qs9vtSkhIUEpKSqHLpKSkKCkpya0tMTFRS5YscWtbtWqVwsPDVa1aNV199dV66qmnVKNGDdc6QkJC1KZNG1f/hIQE2e12fffdd7rhhhsKbDc7O1vZ2dmu15mZmZIkp9Mpp9N5bjteDjidThljKuS+oeTID3hCbpQyY7wdQfEZ8890AeN3o3AcO+AJuQFPzkdulHTdXi229+/fr7y8PEVERLi1R0REaPPmzYUuk5qaWmj/1NRU1+suXbroxhtvVGxsrLZt26ZHH31UXbt2VUpKinx8fJSamqrw8HC3dfj6+qp69epu6znVxIkTNX78+ALt6enpOn78eJH2tzxxOp3KyMiQMUZ2u9cvgEAZQ37AE3KjdAXkleexRYwc5rjklCSbt4PxGk+3yV3oOHbAE3IDnpyP3Dh8+HCJlq+Qj/7q27ev6+fmzZurRYsWql+/vlatWqXOnTsXa52jR492O6OemZmp6OhohYWFKTg4uMQxlzVOp1M2m01hYWEc2FAA+QFPyI3SleWT4e0Qis8YyUhZ9kDJduEW26d/2Y+TOHbAE3IDnpyP3PD39y/R8l4ttkNDQ+Xj46O0tDS39rS0NEVGRha6TGRk5Dn1l6R69eopNDRUW7duVefOnRUZGVngm+Xc3FwdPHjQ43ocDoccDkeBdrvdXmF/8W02W4XeP5QM+QFPyI1SVN6LVJvtn+kCxe+FZxw74Am5AU9KOzdKul6vZqyfn59at26t5cuXu9qcTqeWL1+u+Pj4QpeJj4936y9JycnJHvtL0l9//aUDBw6oZs2arnUcOnRIa9ascfVZsWKFnE6n4uLiSrJLAAAAAAB4fzTypKQkvfrqq3rjjTe0adMm3X333Tp69KgGDRokSerfv7/bAGr33Xefli5dqueff16bN2/WuHHj9OOPP2rEiBGSpCNHjuihhx7St99+qx07dmj58uXq2bOnGjRooMTERElSkyZN1KVLFw0ZMkTff/+9vv76a40YMUJ9+/ZVVFTU+X8TAAAAAAAVitfv2e7Tp4/S09P1xBNPKDU1Va1atdLSpUtdg6Dt2rXL7fT9FVdcoXnz5mnMmDF69NFH1bBhQy1ZskTNmjWTJPn4+Gj9+vV64403dOjQIUVFRenaa6/Vk08+6XYZ+DvvvKMRI0aoc+fOstvt6t27t1588cXzu/MAAAAAgArJZswF/gyOYsrMzFTVqlWVkZFRYQdI27dvn8LDw7k/BgWQH/CE3PBsavJv3g7Bu4xRQN4RZflc2AOkjbrmIm+HUCZx7IAn5AY8OR+5UdKaj4wFAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFiMYhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxXy9HQAAAOXB1OTfvB0CKgAr8mjUNRdZEAkAoLRxZhsAAAAAAItRbAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiZaLYfvnllxUTEyN/f3/FxcXp+++/P2P/RYsWqXHjxvL391fz5s316aefuuadOHFCDz/8sJo3b64qVaooKipK/fv31549e9zWERMTI5vN5jZNmjSpVPYPAAAAAHBh8XqxvWDBAiUlJWns2LFau3atWrZsqcTERO3bt6/Q/t9884369eunwYMH66efflKvXr3Uq1cv/frrr5KkrKwsrV27Vo8//rjWrl2r9957T1u2bNH1119fYF0TJkzQ3r17XdO9995bqvsKAAAAALgweL3YnjJlioYMGaJBgwbp4osv1qxZsxQQEKDZs2cX2v+FF15Qly5d9NBDD6lJkyZ68skndemll2r69OmSpKpVqyo5OVm33HKLGjVqpMsvv1zTp0/XmjVrtGvXLrd1BQUFKTIy0jVVqVKl1PcXAAAAAFDx+Xpz4zk5OVqzZo1Gjx7tarPb7UpISFBKSkqhy6SkpCgpKcmtLTExUUuWLPG4nYyMDNlsNoWEhLi1T5o0SU8++aTq1KmjW2+9VaNGjZKvb+FvSXZ2trKzs12vMzMzJUlOp1NOp/NMu1kuOZ1OGWMq5L6h5MgPeFKhc8MYb0dQvhnzz4QSqYi/XxX62IESITfgyfnIjZKu26vF9v79+5WXl6eIiAi39oiICG3evLnQZVJTUwvtn5qaWmj/48eP6+GHH1a/fv0UHBzsah85cqQuvfRSVa9eXd98841Gjx6tvXv3asqUKYWuZ+LEiRo/fnyB9vT0dB0/fvyM+1keOZ1OZWRkyBgju93rF0CgjCE/4ElFzo2AvCPeDqGcM3KY45JTkmzeDqZc83SrXXlWkY8dKBlyA56cj9w4fPhwiZb3arFd2k6cOKFbbrlFxhjNnDnTbd6pZ8dbtGghPz8/DRs2TBMnTpTD4SiwrtGjR7stk5mZqejoaIWFhbkV8RWF0+mUzWZTWFgYBzYUQH7Ak4qcG1k+Gd4OoXwzRjJSlj1QslFsl0R4eLi3Q7BcRT52oGTIDXhyPnLD39+/RMt7tdgODQ2Vj4+P0tLS3NrT0tIUGRlZ6DKRkZFF6p9faO/cuVMrVqw4a0EcFxen3Nxc7dixQ40aNSow3+FwFFqE2+32CvuLb7PZKvT+oWTID3hSYXODArHkbLZ/JhRbhfvd+v8q7LEDJUZuwJPSzo2SrterGevn56fWrVtr+fLlrjan06nly5crPj6+0GXi4+Pd+ktScnKyW//8Qvv333/XsmXLVKNGjbPGsm7dOtnt9gr5bTEAAAAA4Pzy+mXkSUlJGjBggNq0aaO2bdtq2rRpOnr0qAYNGiRJ6t+/v2rVqqWJEydKku677z516NBBzz//vLp376758+frxx9/1CuvvCLpZKF90003ae3atfr444+Vl5fnup+7evXq8vPzU0pKir777jt16tRJQUFBSklJ0ahRo3T77berWrVq3nkjAAAAAAAVhteL7T59+ig9PV1PPPGEUlNT1apVKy1dutQ1CNquXbvcTt9fccUVmjdvnsaMGaNHH31UDRs21JIlS9SsWTNJ0u7du/Xhhx9Kklq1auW2rZUrV6pjx45yOByaP3++xo0bp+zsbMXGxmrUqFEFRjkHAFQcU5N/83YIAADgAmIzhmdwFEdmZqaqVq2qjIyMCjtA2r59+xQeHs79MSiA/IAnZTk3KLa9zBgF5B1Rlg8DpJXUqGsu8nYIlivLxw54F7kBT85HbpS05iNjAQAAAACwGMU2AAAAAAAWo9gGAAAAAMBiFNsAAAAAAFjM66ORAwAAoOhKOthfRRxgDQDKIs5sAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABZjNHIAQLlQ0hGYAQAAzifObAMAAAAAYDGKbQAAAAAALEaxDQAAAACAxSi2AQAAAACwGMU2AAAAAAAWYzRyAACAC0hJR/Yfdc1FFkUCABUbZ7YBAAAAALAYZ7YBAOcFz8kGAAAXEs5sAwAAAABgMYptAAAAAAAsRrENAAAAAIDFKLYBAAAAALAYxTYAAAAAABZjNHIAwFkVeSRxYxSQd0RZPhmSzVa6QQHwCp7TDQBFw5ltAAAAAAAsRrENAAAAAIDFuIwcAC4AJb3sEwAAAOeGYhsAAADnTaFf/p3jeA/c9w2gPOAycgAAAAAALFYmzmy//PLLeu6555SamqqWLVvqpZdeUtu2bT32X7RokR5//HHt2LFDDRs21H/+8x9169bNNd8Yo7Fjx+rVV1/VoUOHdOWVV2rmzJlq2LChq8/Bgwd177336qOPPpLdblfv3r31wgsvKDAwsFT3FQCKg8vAAQAAyhevF9sLFixQUlKSZs2apbi4OE2bNk2JiYnasmWLwsPDC/T/5ptv1K9fP02cOFHXXXed5s2bp169emnt2rVq1qyZJOnZZ5/Viy++qDfeeEOxsbF6/PHHlZiYqI0bN8rf31+SdNttt2nv3r1KTk7WiRMnNGjQIA0dOlTz5s07r/sP4MJAsQwA1uHxYwDKA5sxxngzgLi4OF122WWaPn26JMnpdCo6Olr33nuvHnnkkQL9+/Tpo6NHj+rjjz92tV1++eVq1aqVZs2aJWOMoqKi9MADD+jBBx+UJGVkZCgiIkJz585V3759tWnTJl188cX64Ycf1KZNG0nS0qVL1a1bN/3111+Kioo6a9yZmZmqWrWqMjIyFBwcbMVbUaY4nU7t27dP4eHhstu52wDuLrT8oFA+B677LgN5zjbckRs4k3KWHxTr58+F9n8OFN35yI2S1nxePbOdk5OjNWvWaPTo0a42u92uhIQEpaSkFLpMSkqKkpKS3NoSExO1ZMkSSdL27duVmpqqhIQE1/yqVasqLi5OKSkp6tu3r1JSUhQSEuIqtCUpISFBdrtd3333nW644YYC283OzlZ2drbrdUZGhiTp0KFDcjqd577zZZzT6VRmZqb8/Pw4sJVzM1dus36lxqiy84iO2f8qF/8pwnlkjGzOIzpuN+QG3JEbOJNylh8T31/j7RDKvbs71S9SP/5PCk/OR25kZmZKOnmbcnF4tdjev3+/8vLyFBER4dYeERGhzZs3F7pMampqof1TU1Nd8/PbztTn9EvUfX19Vb16dVef002cOFHjx48v0F63bl1PuwcAAACgEI96OwDgHBw+fFhVq1Y95+W8fs92eTF69Gi3M+pOp1MHDx5UjRo1ZCsH38Ceq8zMTEVHR+vPP/+skJfJo2TID3hCbsATcgNnQn7AE3IDnpyP3DDG6PDhw0W6zbgwXi22Q0ND5ePjo7S0NLf2tLQ0RUZGFrpMZGTkGfvn/5uWlqaaNWu69WnVqpWrz759+9zWkZubq4MHD3rcrsPhkMPhcGsLCQk58w5WAMHBwRzY4BH5AU/IDXhCbuBMyA94Qm7Ak9LOjeKc0c7n1Rsf/Pz81Lp1ay1fvtzV5nQ6tXz5csXHxxe6THx8vFt/SUpOTnb1j42NVWRkpFufzMxMfffdd64+8fHxOnTokNas+ed+mxUrVsjpdCouLs6y/QMAAAAAXJi8fhl5UlKSBgwYoDZt2qht27aaNm2ajh49qkGDBkmS+vfvr1q1amnixImSpPvuu08dOnTQ888/r+7du2v+/Pn68ccf9corr0iSbDab7r//fj311FNq2LCh69FfUVFR6tWrlySpSZMm6tKli4YMGaJZs2bpxIkTGjFihPr27VvsSwQAAAAAAMjn9WK7T58+Sk9P1xNPPKHU1FS1atVKS5cudQ1wtmvXLrfR5a644grNmzdPY8aM0aOPPqqGDRtqyZIlrmdsS9K///1vHT16VEOHDtWhQ4fUrl07LV261PWMbUl65513NGLECHXu3Fl2u129e/fWiy++eP52vIxzOBwaO3ZsgUvnAYn8gGfkBjwhN3Am5Ac8ITfgSXnIDa8/ZxsAAAAAgIqGh9UBAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsBjFdgX39NNP64orrlBAQIBCQkIK7WOz2QpM8+fPd+uzatUqXXrppXI4HGrQoIHmzp1bYD0vv/yyYmJi5O/vr7i4OH3//fdu848fP67hw4erRo0aCgwMVO/evZWWlmbVruIcFSU3du3ape7duysgIEDh4eF66KGHlJub69aH3LgwxMTEFDhOTJo0ya3P+vXrddVVV8nf31/R0dF69tlnC6xn0aJFaty4sfz9/dW8eXN9+umnbvONMXriiSdUs2ZNVa5cWQkJCfr9999Ldd9wfpztOIDybdy4cQWOEY0bN3bNL8px3qq/OfC+1atXq0ePHoqKipLNZtOSJUvc5hflWH/w4EHddtttCg4OVkhIiAYPHqwjR4649bHi7w7Or7PlxsCBAwscS7p06eLWp1zlhkGF9sQTT5gpU6aYpKQkU7Vq1UL7SDJz5swxe/fudU3Hjh1zzf/jjz9MQECASUpKMhs3bjQvvfSS8fHxMUuXLnX1mT9/vvHz8zOzZ882GzZsMEOGDDEhISEmLS3N1edf//qXiY6ONsuXLzc//vijufzyy80VV1xRavuOMztbbuTm5ppmzZqZhIQE89NPP5lPP/3UhIaGmtGjR7v6kBsXjrp165oJEya4HSeOHDnimp+RkWEiIiLMbbfdZn799Vfz7rvvmsqVK5v//ve/rj5ff/218fHxMc8++6zZuHGjGTNmjKlUqZL55ZdfXH0mTZpkqlatapYsWWJ+/vlnc/3115vY2Fi3YxLKn6IcB1C+jR071jRt2tTtGJGenu6af7bjvFV/c1A2fPrpp+axxx4z7733npFk3n//fbf5RTnWd+nSxbRs2dJ8++235n//+59p0KCB6devn2u+VX93cH6dLTcGDBhgunTp4nYsOXjwoFuf8pQbFNsXiDlz5pyx2D490U/173//2zRt2tStrU+fPiYxMdH1um3btmb48OGu13l5eSYqKspMnDjRGGPMoUOHTKVKlcyiRYtcfTZt2mQkmZSUlGLsEaziKTc+/fRTY7fbTWpqqqtt5syZJjg42GRnZxtjyI0LSd26dc3UqVM9zp8xY4apVq2aKzeMMebhhx82jRo1cr2+5ZZbTPfu3d2Wi4uLM8OGDTPGGON0Ok1kZKR57rnnXPMPHTpkHA6Heffddy3aE3jD2Y4DKP/Gjh1rWrZsWei8ohznrfqbg7Ln9P9nFuVYv3HjRiPJ/PDDD64+n332mbHZbGb37t3GGGv+7sC7PBXbPXv29LhMecsNLiOHJGn48OEKDQ1V27ZtNXv2bJlTHr+ekpKihIQEt/6JiYlKSUmRJOXk5GjNmjVufex2uxISElx91qxZoxMnTrj1ady4serUqePqg7IlJSVFzZs3V0REhKstMTFRmZmZ2rBhg6sPuXHhmDRpkmrUqKFLLrlEzz33nNvlnSkpKWrfvr38/PxcbYmJidqyZYv+/vtvV58z5cv27duVmprq1qdq1aqKi4sjF8qxohwHUDH8/vvvioqKUr169XTbbbdp165dkop2nLfibw7Kh6Ic61NSUhQSEqI2bdq4+iQkJMhut+u7775z9Snp3x2UTatWrVJ4eLgaNWqku+++WwcOHHDNK2+54Wvp2lAuTZgwQVdffbUCAgL0xRdf6J577tGRI0c0cuRISVJqaqrbHz9JioiIUGZmpo4dO6a///5beXl5hfbZvHmzax1+fn4F7g2OiIhQampq6e0cis3T554/70x9yI2KZ+TIkbr00ktVvXp1ffPNNxo9erT27t2rKVOmSDr5OcbGxrotc2q+VKtWzWO+nJpPpy5XWB+UP/v37z/rcQDlX1xcnObOnatGjRpp7969Gj9+vK666ir9+uuvRTrOW/E3p3LlyqW0d7BSUY71qampCg8Pd5vv6+ur6tWru/Up6d8dlD1dunTRjTfeqNjYWG3btk2PPvqounbtqpSUFPn4+JS73KDYLoceeeQR/ec//zljn02bNrkNTHImjz/+uOvnSy65REePHtVzzz3nKrZRflidG6jYziVfkpKSXG0tWrSQn5+fhg0bpokTJ8rhcJR2qADKuK5du7p+btGiheLi4lS3bl0tXLiQIhhAkfXt29f1c/PmzdWiRQvVr19fq1atUufOnb0YWfFQbJdDDzzwgAYOHHjGPvXq1Sv2+uPi4vTkk08qOztbDodDkZGRBUYMTUtLU3BwsCpXriwfHx/5+PgU2icyMlKSFBkZqZycHB06dMjtm+1T+6DkrMyNyMjIAqMF53/Gp36u5Eb5VZJ8iYuLU25urnbs2KFGjRp5zAXp7Ply6vz8tpo1a7r1adWqVZH3C2VLaGjoWY8DqHhCQkJ00UUXaevWrbrmmmvOepy34m8OyoeiHOsjIyO1b98+t+Vyc3N18ODBs+bDqds4298dlH316tVTaGiotm7dqs6dO5e73OCe7XIoLCxMjRs3PuN06j0K52rdunWqVq2a62xVfHy8li9f7tYnOTlZ8fHxkiQ/Pz+1bt3arY/T6dTy5ctdfVq3bq1KlSq59dmyZYt27drl6oOSszI34uPj9csvv7gd0JKTkxUcHKyLL77Y1YfcKL9Kki/r1q2T3W53XcoVHx+v1atX68SJE64+ycnJatSokapVq+bqc6Z8iY2NVWRkpFufzMxMfffdd+RCOVaU4wAqniNHjmjbtm2qWbNmkY7zVvzNQflQlGN9fHy8Dh06pDVr1rj6rFixQk6nU3Fxca4+Jf27g7Lvr7/+0oEDB1xfzJS73LB0uDWUOTt37jQ//fSTGT9+vAkMDDQ//fST+emnn8zhw4eNMcZ8+OGH5tVXXzW//PKL+f33382MGTNMQECAeeKJJ1zryH/UxkMPPWQ2bdpkXn755UIf7+RwOMzcuXPNxo0bzdChQ01ISIjbqKL/+te/TJ06dcyKFSvMjz/+aOLj4018fPz5ezPg5my5kf8YlmuvvdasW7fOLF261ISFhRX6GBZyo2L75ptvzNSpU826devMtm3bzNtvv23CwsJM//79XX0OHTpkIiIizB133GF+/fVXM3/+fBMQEFDgMRu+vr5m8uTJZtOmTWbs2LGFPvorJCTEfPDBB2b9+vWmZ8+ePPqrAijKcQDl2wMPPGBWrVpltm/fbr7++muTkJBgQkNDzb59+4wxZz/OW/U3B2XD4cOHXf+vkGSmTJlifvrpJ7Nz505jTNGO9V26dDGXXHKJ+e6778xXX31lGjZs6PZ4J6v+7uD8OlNuHD582Dz44IMmJSXFbN++3SxbtsxceumlpmHDhub48eOudZSn3KDYruAGDBhgJBWYVq5caYw5OVR+q1atTGBgoKlSpYpp2bKlmTVrlsnLy3Nbz8qVK02rVq2Mn5+fqVevnpkzZ06Bbb300kumTp06xs/Pz7Rt29Z8++23bvOPHTtm7rnnHlOtWjUTEBBgbrjhBrN3797S2nWcxdlywxhjduzYYbp27WoqV65sQkNDzQMPPGBOnDjhth5yo+Jbs2aNiYuLM1WrVjX+/v6mSZMm5plnnnH7w2eMMT///LNp166dcTgcplatWmbSpEkF1rVw4UJz0UUXGT8/P9O0aVPzySefuM13Op3m8ccfNxEREcbhcJjOnTubLVu2lOr+4fw423EA5VufPn1MzZo1jZ+fn6lVq5bp06eP2bp1q2t+UY7zVv3NgfetXLmy0P9jDBgwwBhTtGP9gQMHTL9+/UxgYKAJDg42gwYNcp0QyGfF3x2cX2fKjaysLHPttdeasLAwU6lSJVO3bl0zZMiQAl/MlqfcsBlzyjOeAAAAAABAiXHPNgAAAAAAFqPYBgAAAADAYhTbAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsBjFNgAAAAAAFqPYBgAAAADAYhTbAAAAAABYjGIbAAAAAACLUWwDAAAAAGAxim0AAAAAACxGsQ0AAAAAgMUotgEAAAAAsBjFNgAAAAAAFqPYBgCgBAYOHKiYmBhL1zl37lzZbDbt2LHD0vWi7ImJidHAgQO9HQYAoBRQbAMAvG7btm0aNmyY6tWrJ39/fwUHB+vKK6/UCy+8oGPHjnk7vFLzzDPPaMmSJd4OwyW/yLfZbPrqq68KzDfGKDo6WjabTdddd50XIvRsx44drthPny6//HKvxvbNN99o3LhxOnTokFfjAACcX77eDgAAcGH75JNPdPPNN8vhcKh///5q1qyZcnJy9NVXX+mhhx7Shg0b9Morr3g7zFLxzDPP6KabblKvXr3c2u+44w717dtXDofDK3H5+/tr3rx5ateunVv7l19+qb/++strcRVFv3791K1bN7e2sLAwL0Vz0jfffKPx48dr4MCBCgkJcZu3ZcsW2e2c+wCAiohiGwDgNdu3b1ffvn1Vt25drVixQjVr1nTNGz58uLZu3apPPvnEixF6h4+Pj3x8fLy2/W7dumnRokV68cUX5ev7z38V5s2bp9atW2v//v1ei+1sLr30Ut1+++3eDqPIyvIXFwCAkuGrVACA1zz77LM6cuSIXn/9dbdCO1+DBg103333SfrnMuG5c+cW6Gez2TRu3DjX63Hjxslms+m3337T7bffrqpVqyosLEyPP/64jDH6888/1bNnTwUHBysyMlLPP/+82/o83TO9atUq2Ww2rVq16oz7NXnyZF1xxRWqUaOGKleurNatW2vx4sUFYj569KjeeOMN1+XO+ffunr796667TvXq1St0W/Hx8WrTpo1b29tvv63WrVurcuXKql69uvr27as///zzjDGfql+/fjpw4ICSk5NdbTk5OVq8eLFuvfXWYu+zJCUnJ6tdu3YKCQlRYGCgGjVqpEcffdStz0svvaSmTZsqICBA1apVU5s2bTRv3rwix+9Jx44d1bFjxwLtp993n59rkydP1iuvvKL69evL4XDosssu0w8//FBg+c2bN+uWW25RWFiYKleurEaNGumxxx6TdDIXH3roIUlSbGys67PO/2wLu2f7jz/+0M0336zq1asrICBAl19+eYEvnfJzceHChXr66adVu3Zt+fv7q3Pnztq6dWvx3yQAgGUotgEAXvPRRx+pXr16uuKKK0pl/X369JHT6dSkSZMUFxenp556StOmTdM111yjWrVq6T//+Y8aNGigBx98UKtXr7Zsuy+88IIuueQSTZgwQc8884x8fX118803uxVMb731lhwOh6666iq99dZbeuuttzRs2DCP+7F9+/YChd7OnTv17bffqm/fvq62p59+Wv3791fDhg01ZcoU3X///Vq+fLnat29f5HuGY2JiFB8fr3fffdfV9tlnnykjI8NtW+e6zxs2bNB1112n7OxsTZgwQc8//7yuv/56ff31164+r776qkaOHKmLL75Y06ZN0/jx49WqVSt99913RYo9KytL+/fvd5tOnDhRpGVPN2/ePD333HMaNmyYnnrqKe3YsUM33nij2/rWr1+vuLg4rVixQkOGDNELL7ygXr166aOPPpIk3XjjjerXr58kaerUqa7P2tOl7Wlpabriiiv0+eef65577tHTTz+t48eP6/rrr9f7779foP+kSZP0/vvv68EHH9To0aP17bff6rbbbivW/gIALGYAAPCCjIwMI8n07NmzSP23b99uJJk5c+YUmCfJjB071vV67NixRpIZOnSoqy03N9fUrl3b2Gw2M2nSJFf733//bSpXrmwGDBjgapszZ46RZLZv3+62nZUrVxpJZuXKla62AQMGmLp167r1y8rKcnudk5NjmjVrZq6++mq39ipVqrht19P2MzIyjMPhMA888IBbv2effdbYbDazc+dOY4wxO3bsMD4+Pubpp5926/fLL78YX1/fAu2etvvDDz+Y6dOnm6CgINe+3HzzzaZTp07GGGPq1q1runfvfs77PHXqVCPJpKene4yhZ8+epmnTpmeMszD5+VHYlP95dejQwXTo0KHAsqd/hvnrqlGjhjl48KCr/YMPPjCSzEcffeRqa9++vQkKCnJ9BvmcTqfr5+eee67QfDLm5Ht5ag7cf//9RpL53//+52o7fPiwiY2NNTExMSYvL88Y808uNmnSxGRnZ7v6vvDCC0aS+eWXX874fgEASh9ntgEAXpGZmSlJCgoKKrVt3HXXXa6ffXx81KZNGxljNHjwYFd7SEiIGjVqpD/++MOy7VauXNn1899//62MjAxdddVVWrt2bbHWFxwcrK5du2rhwoUyxrjaFyxYoMsvv1x16tSRJL333ntyOp265ZZb3M7sRkZGqmHDhlq5cmWRt3nLLbfo2LFj+vjjj3X48GF9/PHHHi8hl4q2z/mDg33wwQdyOp2FrickJER//fVXoZdrF8XQoUOVnJzsNrVs2bJY6+rTp4+qVavmen3VVVdJkitX0tPTtXr1at15552uzyCfzWYr1jY//fRTtW3b1m1wusDAQA0dOlQ7duzQxo0b3foPGjRIfn5+HmMEAHgPA6QBALwiODhYknT48OFS28bpBVDVqlXl7++v0NDQAu0HDhywbLsff/yxnnrqKa1bt07Z2dmu9uIWYNLJwm/JkiVKSUnRFVdcoW3btmnNmjWaNm2aq8/vv/8uY4waNmxY6DoqVapU5O2FhYUpISFB8+bNU1ZWlvLy8nTTTTd57F+Ufe7Tp49ee+013XXXXXrkkUfUuXNn3XjjjbrppptcI3I//PDDWrZsmdq2basGDRro2muv1a233qorr7yySHE3bNhQCQkJRd7PMzk9f/IL77///lvSPwVts2bNLNmedPLWgLi4uALtTZo0cc0/dXtnixEA4D0U2wAArwgODlZUVJR+/fXXIvX3VKjm5eV5XKawEb09jfJ96hnj4mwr3//+9z9df/31at++vWbMmKGaNWuqUqVKmjNnTokG+erRo4cCAgK0cOFCXXHFFVq4cKHsdrtuvvlmVx+n0ymbzabPPvus0P0MDAw8p23eeuutGjJkiFJTU9W1a9cCj63KV9R9rly5slavXq2VK1fqk08+0dKlS7VgwQJdffXV+uKLL+Tj46MmTZpoy5Yt+vjjj7V06VL93//9n2bMmKEnnnhC48ePP6f4T2ez2dw+53yePtei5Iq3lYcYAeBCRbENAPCa6667Tq+88opSUlIUHx9/xr75Z+xOH+Rr586dlsdVkm393//9n/z9/fX555+7PdZpzpw5Bfqey5nuKlWq6LrrrtOiRYs0ZcoULViwQFdddZWioqJcferXry9jjGJjY3XRRRcVed2e3HDDDRo2bJi+/fZbLViwwGO/c9lnu92uzp07q3PnzpoyZYqeeeYZPfbYY1q5cqXrjHSVKlXUp08f9enTRzk5Obrxxhv19NNPa/To0fL39y/2/lSrVq3Qy6uLm0P5I8Sf7Qujc/mc69atqy1bthRo37x5s2s+AKB84J5tAIDX/Pvf/1aVKlV01113KS0trcD8bdu26YUXXpB08kx4aGhogVHDZ8yYYXlc9evXlyS3beXl5emVV14567I+Pj6y2WxuZ0t37NihJUuWFOhbpUqVIo8QLp28DHvPnj167bXX9PPPP6tPnz5u82+88Ub5+Pho/PjxBc5sGmPO+VL5wMBAzZw5U+PGjVOPHj089ivqPh88eLDAsq1atZIk16Xnp8fo5+eniy++WMaYYo8qnq9+/fravHmz0tPTXW0///yz22jo5yIsLEzt27fX7NmztWvXLrd5p77/VapUkVTwy5vCdOvWTd9//71SUlJcbUePHtUrr7yimJgYXXzxxcWKFQBw/nFmGwDgNfXr19e8efPUp08fNWnSRP3791ezZs2Uk5Ojb775RosWLXJ7BvFdd92lSZMm6a677lKbNm20evVq/fbbb5bH1bRpU11++eUaPXq0Dh48qOrVq2v+/PnKzc0967Ldu3fXlClT1KVLF916663at2+fXn75ZTVo0EDr169369u6dWstW7ZMU6ZMUVRUlGJjYwu9Xzdft27dFBQUpAcffFA+Pj7q3bu32/z69evrqaee0ujRo7Vjxw716tVLQUFB2r59u95//30NHTpUDz744Dm9FwMGDLBsnydMmKDVq1ere/fuqlu3rvbt26cZM2aodu3argHBrr32WkVGRurKK69URESENm3apOnTp6t79+4lHkzvzjvv1JQpU5SYmKjBgwdr3759mjVrlpo2beoasO9cvfjii2rXrp0uvfRSDR06VLGxsdqxY4c++eQTrVu3TtLJz1mSHnvsMfXt21eVKlVSjx49XEX4qR555BG9++676tq1q0aOHKnq1avrjTfe0Pbt2/V///d/rnvbAQDlgHcGQQcA4B+//fabGTJkiImJiTF+fn4mKCjIXHnlleall14yx48fd/XLysoygwcPNlWrVjVBQUHmlltuMfv27fP46K/THzE1YMAAU6VKlQLb79ChQ4HHTW3bts0kJCQYh8NhIiIizKOPPmqSk5OL9Oiv119/3TRs2NA4HA7TuHFjM2fOHFdMp9q8ebNp3769qVy5spHkegSUp0ePGWPMbbfdZiSZhIQEj+/n//3f/5l27dqZKlWqmCpVqpjGjRub4cOHmy1btnhc5tTt/vDDD2fsV9ijv4qyz8uXLzc9e/Y0UVFRxs/Pz0RFRZl+/fqZ3377zdXnv//9r2nfvr2pUaOGcTgcpn79+uahhx4yGRkZZ4wp/3Fdzz333Bn7vf3226ZevXrGz8/PtGrVynz++eceH/1V2LpOzzVjjPn111/NDTfcYEJCQoy/v79p1KiRefzxx936PPnkk6ZWrVrGbre7fbanP/rLmJO5d9NNN7nW17ZtW/Pxxx+79cl/9NeiRYsKfR8Ke0QeAOD8shnDCBoAAAAAAFiJa5EAAAAAALAYxTYAAAAAABaj2AYAAAAAwGIU2wAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMV9vB1BeOZ1O7dmzR0FBQbLZbN4OBwAAAABgIWOMDh8+rKioKNnt536emmK7mPbs2aPo6GhvhwEAAAAAKEV//vmnateufc7LUWwXU1BQkKSTb3xwcLCXozl3TqdT6enpCgsLK9a3NLhwkCsoCvIERUGeoCjIExQFeYKiKGmeZGZmKjo62lX7nSuK7WLKv3Q8ODi43Bbbx48fV3BwMAconBG5gqIgT1AU5AmKgjxBUZAnKAqr8qS4tw2TmQAAAAAAWIxiGwAAAAAAi1FsAwAAAABgMe7ZLkXGGOXm5iovL8/boRTgdDp14sQJHT9+vEzd51KpUiX5+Ph4OwwAAAAAKBGK7VKSk5OjvXv3Kisry9uhFMoYI6fTqcOHD5ep54TbbDbVrl1bgYGB3g4FAAAAAIqNYrsUOJ1Obd++XT4+PoqKipKfn1+ZKmilf866+/r6lpnYjDFKT0/XX3/9pYYNG3KGGwAAAEC5RbFdCnJycuR0OhUdHa2AgABvh1OoslhsS1JYWJh27NihEydOUGwDAAAAKLfKzs26JbB69Wr16NFDUVFRstlsWrJkyVmXWbVqlS699FI5HA41aNBAc+fOtTyusnQvdHlRlgp/AAAAACiuClENHj16VC1bttTLL79cpP7bt29X9+7d1alTJ61bt07333+/7rrrLn3++eelHCkAAAAA4EJQIS4j79q1q7p27Vrk/rNmzVJsbKyef/55SVKTJk301VdfaerUqUpMTCytMAEAAFBeGCPl5p6c8vIkp7Po/3pqM8b938LazjavJFP+fp36c2FtZ5p/entx/j19PUV9fdq6Ao4ckc40qO6Zli/ONs/U53wraQxlYR/yPfigVLmyt6MoFRWi2D5XKSkpSkhIcGtLTEzU/fff73GZ7OxsZWdnu15nZmZKOjkYmtPpdOvrdDpljHFNZVV+bGUpxvz3rLD3Fd6Rn898HjgT8gRFQZ7gjJxO6ehROQ8dks+OHXJu3SodOSIdP/7PlJ19cvr/r22n/Hxqu1vfnJx/imZP04kTBdps5GmZZpcU7O0gYAnn3XdLDkfprLuEf3dK+vfqgiy2U1NTFRER4dYWERGhzMxMHTt2TJUL+WZl4sSJGj9+fIH29PR0HT9+3K3txIkTcjqdys3NVW5urrXBW8QY43r+9+n3SaempmrSpEn67LPPtHv3boWHh6tFixYaOXKkrr76ajVs2FA7d+7UW2+9pT59+rgt27JlS23atEmvvfaa+vfvL0mu/qeqVauWtm/fXiCu3NxcOZ1OHThwQJUqVbJyl1FMTqdTGRkZMsYwDgE8Ik9QFOTJBSA3V/b9+2VPTZVPWprsf/8t2+HDsh0+LPv//9f185EjsmVm/vPz4cOyGSO7pDBv70cRGLtdstslHx/JZpPx8Tn5+v9PJn9efpvNdrLNZnNry5/c5uW35y93env+/91OfZ3f7/Tp9H7//7U5dd6pfU5t8zT/9Hap8PUV9u/pTm8/2+v8+I1RzokT8qtU6eT/ZT2s33hYvkixnKlvSVmx3rIw1pEFMRzOzJQppZqppH93Dh8+XKLtX5DFdnGMHj1aSUlJrteZmZmKjo5WWFiYgoPdv1c7fvy4Dh8+LF9fX/n6lu23+PSCdseOHWrXrp1CQkL07LPPqnnz5jpx4oQ+//xz3Xfffdq0aZMkKTo6Wm+99ZZuu+0217Lffvut0tLSVKVKFdntdrd9Hz9+vIYMGeJ67ePjU+h74+vrK7vdrho1asjf39/q3UUxOJ1O2Ww2hYWF8Z9jeESeoCjIk3LM6ZQOHJD27HGbbPk/79178t+0tBKfETY+PjLBwbJVrSoFB5+8vNTf/+SZr1P/Pe1nU1gfh0Py85MqVZJ8fYs+ndrfx+ef6ZTi+mxFRmFzy0BpVGE4nU5lpKcrkONJuVea/+Mv6d+dktYjZbsSLCWRkZFKS0tza0tLS1NwcHChZ7UlyeFwyFHI5Q12u73AB2e322Wz2VyTjJGysqzbgXMREFDoHwNjjOuM9qlntocPHy6bzabvv/9eVapUcbU3a9ZMgwcPdvW97bbbNHXqVP3111+Kjo6WJM2ZM0e33Xab3nzzzX/2/f8LDg5WzZo1zxpu/nKFva/wHj4TFAV5gqIgT8qw3Fxp61Zp/fqT0+bN0u7d/xTTJ04UbT0+PlJEhBQVJYWFSflFc3BwkX42Dof2pacrPDz8nPKEQvbCw/EERVGSPClpbl2QxXZ8fLw+/fRTt7bk5GTFx8eXzgazss48eENpOnJEOqVoPpODBw9q6dKlevrpp90K7XwhISGunyMiIpSYmKg33nhDY8aMUVZWlhYsWKAvv/xSb775plXRAwCA0pCeLv3yyz+F9fr10oYNJ+919sRmk8LDTxbRZ5rCwk4W3MXFvdIAKogKUWwfOXJEW7dudb3evn271q1bp+rVq6tOnToaPXq0du/e7SoC//Wvf2n69On697//rTvvvFMrVqzQwoUL9cknn3hrF8qErVu3yhijxo0bF6n/nXfeqQceeECPPfaYFi9erPr166tVq1aF9n344Yc1ZswY1+tnnnlGI0eOtCJsAADgSXb2yTPUpxbV69dLqamF9w8IkJo3l1q0kJo1k+rU+aeIjog4eXk1AKBIKkSx/eOPP6pTp06u1/n3Vg8YMEBz587V3r17tWvXLtf82NhYffLJJxo1apReeOEF1a5dW6+99lrpPfYrIODkGWZvCAgoctdzHZW8e/fuGjZsmFavXq3Zs2frzjvv9Nj3oYce0sCBA12vQ0NDz2lbAACgCDIzpc8/lz79VPrxx5OFdmEDD9lsUv36J4vq/Kl5c6levZP3JQMASqxCFNsdO3Y8Y6E4d+7cQpf56aefSjGqU9hsRb6U25saNmwom82mzZs3F6m/r6+v7rjjDo0dO1bfffed3n//fY99Q0ND1aBBA6tCBQAA+XbskD766OS0alXBe6tDQtyL6hYtpKZNvXeLGwBcICpEsQ1rVK9eXYmJiXr55Zc1cuTIAvdtHzp0yO2+benkpeSTJ09Wnz59VK1atfMYLQAAFyinU/rhB+nDD08W2L/84j7/ooukHj2kjh2lli2l2rXLxiOCAOACQ7ENNy+//LKuvPJKtW3bVhMmTFCLFi2Um5ur5ORkzZw50/Xor3xNmjTR/v37FXAOl6sDAIBzdPSotGzZyQL7k0+kU5+qYrdL7dqdLLB79JAaNfJenAAAF4ptuKlXr57Wrl2rp59+Wg888ID27t2rsLAwtW7dWjNnzix0mRo1apznKAEAuADs3i19/PHJAnv58pODneULDpa6dDlZXHftKvG3GADKHIptFFCzZk1Nnz5d06dPL3T+jh07zrj8oUOHzqk/AAD4/06ckGbPll59VVqzxn1ebOw/Z6/bt5f8/LwTIwCgSCi2AQAAvM3plBYvlsaMkX7//WSbzSZdfvk/BXbTptx7DQDlCMU2AACANyUnS6NH/3MmOyxMevRR6dZbpfBw78YGACg2im0AAABv+PFH6ZFHTt6PLZ18FNeDD0pJSVJQkHdjAwCUGMU2AADA+fTbbycvF1+06ORrPz/p7rulxx47eVYbAFAhUGyXImOMt0Mod3jPAAAV1p490vjx0uuvS3l5J++/vuOOk20xMd6ODgBgMYrtUlCpUiVJUlZWlipXruzlaMqXnJwcSZKPj4+XIwEAwCKHDkn/+Y/0wgvSsWMn2667TnrmGal5c6+GBgAoPRTbpcDHx0chISHat2+fJCkgIEC2MjZ6qDFGubm58vX1LTOxOZ1OpaenKyAgQL6+pCYAoJw7dkyaPl2aOFH6+++TbVdccbLwbtfOu7EB+H/t3XucjHX/x/H37Nlae9Ce0LJIpETItkpHZStCB5sUSe4o8WupbDmk7lDdyV0pd4RUQt1uKVLaiFjJqZNDcZOb7MFpdy17sHP9/ph2Mu3BsLNz7cy+no/HPPaa7/Wd6/oMH3P57PWd7xeodlQ01SQ2NlaS7AV3TWMYhqxWq3x8fGpMsS1JPj4+aty4cY2KCQCAs3LqlDRnjvTMM9KBA7a2iy+2Fd3du7N8FwDUEhTb1cRisahBgwaKjo5WcXGx2eGUYbVadfjwYZ133nny8fExOxy7gICAGhUPAABnZds26c47pe3bbc8bN5aefVa6916Jr0gBQK1CsV3NfH19a+T3j61Wq/z9/RUUFERxCwCAK3zzjdSjh+072uedZ5txfMgQKSjI7MgAACag2AYAAKiqRYuke+6RCgulxERpyRIpMtLsqAAAJuKWJgAAQFW8/rpt6HhhodSzp5SWRqENAKDYBgAAOCeGIaWmSo8+atseMkT6978llv0EAIhh5AAAAGevqEh68EHp3Xdtz//+d+mpp5hpHABgR7ENAABwNvLybMPGv/jCNsP4zJnS/febHRUAoIah2AYAAHBWRoZ0yy3Sli1S3brShx9KN99sdlQAgBqIYhsAAMAZv/widesm7d0rRUVJS5dKl19udlQAgBqKCdIAAADOZP16qXNnW6HdvLmUnk6hDQCoFMU2AABAZT75RLr+eunwYaljR2ndOlvBDQBAJSi2AQAAKjJjhtSrl3TypO272StXStHRZkcFAPAAFNsAAAB/ZRjSM89If/ubZLVKDzwgffyxFBJidmQAAA/BBGkAAACnO3VKGjJEevtt2/OxY6UJE1hDGwBwVii2AQAASuXnS8nJtpnGfXykN96QHnrI7KgAAB6IYhsAAECyDRfv2VNKS5OCgqT5823PAQA4BxTbAAAAkvSvf9kK7bp1pRUrpMREsyMCAHgwJkgDAADYt0964gnb9uTJFNoAgCqj2AYAALWbYdgmRDt+XLrySunhh82OCADgBSi2AQBA7TZvnvTZZ1JAgDRzpm1iNAAAqoirCQAAqL2ysqQRI2zb48dLrVqZGw8AwGtQbAMAgNprxAjp8GGpbVvp8cfNjgYA4EUotgEAQO20ZIlteS8fH+nttyV/f7MjAgB4EYptAABQ++TkSEOH2rZHjZI6dDA3HgCA16HYBgAAtc8TT0i//y5dcIH0zDNmRwMA8EIU2wAAoHZZuVJ66y3b9syZUp065sYDAPBKFNsAAKD2OHFCGjzYtj1kiHTNNebGAwDwWhTbAACg9njmGWn3bun886UXXjA7GgCAF6PYBgAAtcPGjdLLL9u2p0+XQkPNjQcA4NUotgEAgPcrKpIeeECyWqV77pFuvdXsiAAAXo5iGwAAeL8XX5R+/FGKjJSmTjU7GgBALUCxDQAAvNu2bdJzz9m2X31ViooyNx4AQK1AsQ0AALxXSYn04IO2YeTdu0t33212RACAWsJriu1p06YpPj5eQUFBSkhI0IYNGyrtP3XqVLVs2VJ16tRRXFycHnvsMRUUFLgpWgAA4BbTpknp6VK9etKbb0oWi9kRAQBqCa8othcsWKCUlBSNHz9emzdvVtu2bdWtWzdlZWWV23/evHkaPXq0xo8fr+3bt+vtt9/WggUL9NRTT7k5cgAAUG327pVSU23bL75oW+4LAAA38TM7AFeYMmWKBg8erIEDB0qSpk+frqVLl2rWrFkaPXp0mf7r1q3TlVdeqXvuuUeSFB8fr759++rbb7+t8ByFhYUqLCy0P8/NzZUkWa1WWa1WV74dt7BarTIMwyNjh3uRK3AGeQJnuDVPDEOWwYNlOXFCxtVXy3jwQdtM5Kjx+DyBM8gTOKOqeVLV/PL4YruoqEibNm1SaulvriX5+Pioa9euSk9PL/c1nTt31nvvvacNGzaoU6dO+u9//6tly5bpvvvuq/A8kyZN0oQJE8q0Z2dne+Twc6vVqpycHBmGIR8frxjggGpCrsAZ5Amc4c48qbNggcK+/FJGUJAOTZyokkOHqvV8cB0+T+AM8gTOqGqe5OXlVen8Hl9sHzp0SCUlJYqJiXFoj4mJ0Y4dO8p9zT333KNDhw7pqquukmEYOnXqlIYMGVLpMPLU1FSlpKTYn+fm5iouLk5RUVEKDQ11zZtxI6vVKovFoqioKD6gUClyBc4gT+AMt+VJRoYszzwjSTKeeUbnJSZW37ngcnyewBnkCZxR1TwJCgqq0vk9vtg+F6tWrdLEiRP1xhtvKCEhQbt27dKIESP03HPPaezYseW+JjAwUIGBgWXafXx8PPYfuMVi8ej44T7kCpxBnsAZbsmT4cOlY8ek9u3lM3KkRE56HD5P4AzyBM6oSp5UNbc8vtiOjIyUr6+vMjMzHdozMzMVGxtb7mvGjh2r++67Tw8++KAkqU2bNsrPz9ff/vY3Pf300/yDBQDAUy1aJP3735KfnzRrlu0nAAAm8PiqMiAgQB06dFBaWpq9zWq1Ki0tTYkVDBs7ceJEmYLa19dXkmQYRvUFCwAAqs/Ro9Ijj9i2n3xSatvW3HgAALWaV/y6NyUlRQMGDFDHjh3VqVMnTZ06Vfn5+fbZyfv3769GjRpp0qRJkqQePXpoypQpuuyyy+zDyMeOHasePXrYi24AAOBhXn1VysiQWrWSxowxOxoAQC1nSrGdn5+vunXruux4ycnJys7O1rhx45SRkaF27dpp+fLl9knT9u3b53Ane8yYMbJYLBozZowOHDigqKgo9ejRQ88//7zLYgIAAG5ktUqzZ9u2x4yRqjipDQAAVWUxTBg3HRISoj59+uiBBx7QVVdd5e7Tu0Rubq7CwsKUk5PjsbORZ2VlKTo6mu+oo1LkCpxBnsAZ1ZonaWlS165SWJh08KBUp45rjw+34fMEziBP4Iyq5klVaz5TMvO9997TkSNHdP311+vCCy/U5MmT9fvvv5sRCgAA8Aald7XvvptCGwBQI5hSbPfq1UuLFy/WgQMHNGTIEM2bN09NmjRR9+7dtWjRIp06dcqMsAAAgCc6dsw2A7kkPfCAqaEAAFDK1DEXUVFRSklJ0Q8//KApU6boyy+/1J133qmGDRtq3LhxOnHihJnhAQAAT7BggVRQILVuLV1+udnRAAAgyeTZyDMzM/XOO+9ozpw5+u2333TnnXdq0KBB2r9/v1544QWtX79eX3zxhZkhAgCAmq50CPkDD0gWi7mxAADwB1OK7UWLFmn27Nn6/PPP1bp1az388MO69957FR4ebu/TuXNnXXTRRWaEBwAAPMW2bdK330q+vtK995odDQAAdqYU2wMHDtTdd9+ttWvX6vIKhns1bNhQTz/9tJsjAwAAHqX0rvatt0p/LPkJAEBNYEqxffDgQQUHB1fap06dOho/frybIgIAAB6nuFiaO9e2zcRoAIAaxpQJ0urVq6esrKwy7YcPH5avr68JEQEAAI/z2WdSVpYUHS3dcovZ0QAA4MCUYtswjHLbCwsLFRAQ4OZoAACARyodQn7ffZK/v7mxAADwF24dRv7qq69KkiwWi2bOnKmQkBD7vpKSEq1evVqtWrVyZ0gAAMATZWVJn35q2x440NxYAAAoh1uL7VdeeUWS7c729OnTHYaMBwQEKD4+XtOnT3dnSAAAwBO995506pTUqZN08cVmRwMAQBluLbb37NkjSbruuuu0aNEiRUREuPP0AADAGxiGNGuWbZu72gCAGsqU2chXrlxpxmkBAIA32LhR+vlnKShIuvtus6MBAKBcbiu2U1JS9Nxzz6lu3bpKSUmptO+UKVPcFBUAAPA4pROj3X67FB5uaigAAFTEbcX2li1bVFxcbN+uiMVicVdIAADA05w8Kc2bZ9tmCDkAoAZzW7F9+tBxhpEDAIBzsnixlJMjNW4sXX+92dEAAFAhU9bZBgAAOCelE6Pdf7/kw39jAAA1l9vubN9+++1O9120aFE1RgIAADzSvn1SWppt+/77TQ0FAIAzcVuxHRYW5q5TAQAAb/TOO7Zlv667Tmra1OxoAAColNuK7dmlM4cCAACcLav1z1nImRgNAOAB+LITAACo+VavlvbskerVk+64w+xoAAA4I7fd2W7fvr3S0tIUERGhyy67rNIlvjZv3uyusAAAgCconRjt7rul4GBzYwEAwAluK7Z79uypwMBASVKvXr3cdVoAAODpcnOljz6ybT/wgLmxAADgJLcV2+PHjy93GwAAoFILF0onT0qtWkkJCWZHAwCAU9xWbJdn48aN2r59uySpdevW6tChg5nhAACAmqh0CPnAgVIlX0MDAKAmMaXY3r9/v/r27au1a9cqPDxcknTs2DF17txZ8+fP1/nnn29GWAAAoKbZsUNKT5d8faX77jM7GgAAnGbKbOQPPvigiouLtX37dh05ckRHjhzR9u3bZbVa9eCDD5oREgAAqIlKl/u6+WapQQNzYwEA4CyYcmf766+/1rp169SyZUt7W8uWLfXaa6+pS5cuZoQEAABqmlOnpLlzbdtMjAYA8DCm3NmOi4tTcXFxmfaSkhI1bNjQhIgAAECN8/nnUkaGFBkp3Xqr2dEAAHBWTCm2X3rpJT366KPauHGjvW3jxo0aMWKE/vGPf5gREgAAqGlKJ0a77z4pIMDcWAAAOEtuG0YeEREhy2kziObn5yshIUF+frYQTp06JT8/Pz3wwAOsww0AQG2XnS198olte+BAc2MBAOAcuK3Ynjp1qrtOBQAAPN3770vFxVKHDlKbNmZHAwDAWXNbsT1gwAB3nQoAAHgyw/hzFnImRgMAeChTZiM/XUFBgYqKihzaQkNDTYoGAACYbssW6YcfpMBAqW9fs6MBAOCcmDJBWn5+voYNG6bo6GjVrVtXERERDg8AAFCLlU6M1ru3xP8LAAAeypRi+4knntBXX32lN998U4GBgZo5c6YmTJighg0bam7pepoAAKD2KSiQ5s2zbTMxGgDAg5kyjPyTTz7R3Llzde2112rgwIHq0qWLLrjgAjVp0kTvv/+++vXrZ0ZYAADAbB9/LB09Kp1/vnTDDWZHAwDAOTPlzvaRI0fUrFkzSbbvZx85ckSSdNVVV2n16tVmhAQAAGqC0onR7r9f8vU1NRQAAKrClGK7WbNm2rNnjySpVatWWrhwoSTbHe/w8HAzQgIAAGb73/+kL76wbd9/v6mhAABQVaYU2wMHDtT3338vSRo9erSmTZumoKAgPfbYY3r88cfNCAkAAJht7lzbsl/XXCM1b252NAAAVIkp39l+7LHH7Ntdu3bV9u3btXnzZl1wwQW69NJLzQgJAACYyTCkOXNs20yMBgDwAqavsy1J8fHxio+PNzsMAABglo0bpV27pOBg6c47zY4GAIAqM2UYuSSlpaWpe/fuat68uZo3b67u3bvryy+/NCscAABgpj/mb1GPHlLduubGAgCAC5hSbL/xxhtKSkpSvXr1NGLECI0YMUKhoaG65ZZbNG3aNDNCAgAAZjGMP4vtPn3MjQUAABcxpdieOHGiXnnlFX3wwQcaPny4hg8frnnz5umVV17RxIkTz+mY06ZNU3x8vIKCgpSQkKANGzZU2v/YsWN65JFH1KBBAwUGBurCCy/UsmXLzuncAACgCjZskPbtk0JCpJtvNjsaAABcwpRi+9ixY0pKSirTftNNNyknJ+esj7dgwQKlpKRo/Pjx2rx5s9q2batu3bopKyur3P5FRUW68cYbtXfvXn300UfauXOnZsyYoUaNGp31uQEAQBWV3tW+7TapTh1zYwEAwEVMmSDttttu03/+858yy3x9/PHH6t69+1kfb8qUKRo8eLAG/jF76fTp07V06VLNmjVLo0ePLtN/1qxZOnLkiNatWyd/f39JOuMEbYWFhSosLLQ/z83NlSRZrVZZrdazjtlsVqtVhmF4ZOxwL3IFziBP4Ixy88RqlWXhQlkkWe+8UyKHaj0+T+AM8gTOqGqeVDW/3FZsv/rqq/bt1q1b6/nnn9eqVauUmJgoSVq/fr3Wrl2rkSNHntVxi4qKtGnTJqWmptrbfHx81LVrV6Wnp5f7miVLligxMVGPPPKIPv74Y0VFRemee+7Rk08+KV9f33JfM2nSJE2YMKFMe3Z2tgoKCs4q5prAarUqJydHhmHIx8e0efLgAcgVOIM8gTPKyxP/777Tefv3yxoSoqzLLpMqGJWG2oPPEziDPIEzqponeXl5VTq/24rtV155xeF5RESEtm3bpm3bttnbwsPDNWvWLI0ZM8bp4x46dEglJSWKiYlxaI+JidGOHTvKfc1///tfffXVV+rXr5+WLVumXbt26eGHH1ZxcbHGjx9f7mtSU1OVkpJif56bm6u4uDhFRUUpNDTU6XhrCqvVKovFoqioKD6gUClyBc4gT+CM8vLE8sdKJJaePRXduLGZ4aGG4PMEziBP4Iyq5klQUFCVzu+2YnvPnj3uOtUZWa1WRUdH66233pKvr686dOigAwcO6KWXXqqw2A4MDFRgYGCZdh8fH4/9B26xWDw6frgPuQJnkCdwhkOeWK3SRx/Z2pOTZSF38Ac+T+AM8gTOqEqeVDW3TPnO9ukMw5Bk+0M4F5GRkfL19VVmZqZDe2ZmpmJjY8t9TYMGDeTv7+8wZPyiiy5SRkaGioqKFBAQcE6xAACAs7BunfT771JYmHTTTWZHAwCAS5n2a6C5c+eqTZs2qlOnjurUqaNLL71U77777lkfJyAgQB06dFBaWpq9zWq1Ki0tzf598L+68sortWvXLocvvP/yyy9q0KABhTYAAO6yYIHtZ69eUjmjxwAA8GSmFNtTpkzR0KFDdcstt2jhwoVauHChkpKSNGTIkDLf7XZGSkqKZsyYoXfeeUfbt2/X0KFDlZ+fb5+dvH///g4TqA0dOlRHjhzRiBEj9Msvv2jp0qWaOHGiHnnkEZe9RwAAUImSEvsQcvXpY24sAABUA1OGkb/22mt688031b9/f3vbbbfdposvvljPPPOMHnvssbM6XnJysrKzszVu3DhlZGSoXbt2Wr58uX3StH379jmMt4+Li9Pnn3+uxx57TJdeeqkaNWqkESNG6Mknn3TNGwQAAJX75hspI0MKD5e6djU7GgAAXM6UYvvgwYPq3LlzmfbOnTvr4MGD53TMYcOGadiwYeXuW7VqVZm2xMRErV+//pzOBQAAqmjhQtvP3r0lvsIFAPBCpgwjv+CCC7Sw9CJ7mgULFqhFixYmRAQAANzm9CHkycnmxgIAQDUx5c72hAkTlJycrNWrV+vKK6+UJK1du1ZpaWnlFuEAAMCLfP21lJUl1a8vXX+92dEAAFAtTLmzfccdd2jDhg2KjIzU4sWLtXjxYkVGRmrDhg3q3bu3GSEBAAA3sXz4oW3j9tslf39zgwEAoJq4/c52cXGxHnroIY0dO1bvvfeeu08PAADMdOqUtGiRbZtZyAEAXsztd7b9/f3173//292nBQAANUDAunWyHDoknXeedN11ZocDAEC1MWUYea9evbR48WIzTg0AAEwUtGSJbeOOOyQ/U6aOAQDALUy5yrVo0ULPPvus1q5dqw4dOqhu3boO+4cPH25GWAAAoDoVFyto2TLbNrOQAwC8nCnF9ttvv63w8HBt2rRJmzZtcthnsVgotgEA8EZffSWfo0dlREfLcvXVZkcDAEC1MqXY3rNnjxmnBQAAJnKYhZwh5AAAL+f2K9369ev1ySefqKioSDfccIOSkpLcHQIAAHC3oiLpj/lajLvuksXcaAAAqHZuLbY/+ugjJScnq06dOvL399eUKVP0wgsvaNSoUe4MAwAAuFtamixHj6okOlqWLl3MjgYAgGrn1tnIJ02apMGDBysnJ0dHjx7V3//+d02cONGdIQAAADMsWCBJKujeXfL1NTkYAACqn1uL7Z07d2rUqFHy/eMiO3LkSOXl5SkrK8udYQAAAHcqLLQPIS/o0cPcWAAAcBO3FtsnTpxQaGio/XlAQICCgoJ0/Phxd4YBAADcacUKKSdHRoMGKu7UyexoAABwC7dPkDZz5kyFhITYn586dUpz5sxRZGSkvY2lvwAA8CILF9p+3nmn5OPW3/MDAGAatxbbjRs31owZMxzaYmNj9e6779qfs842AABepKBA+vhjSbZZyAEAqC3cWmzv3bvXnacDAABm+/xzKTdXOv98KTFROnTI7IgAAHALxnIBAIDqUzqE/K67GEIOAKhVuOoBAIDqcfKktGSJbbtPH3NjAQDAzSi2AQBA9Vi+XDp+XGrcWEpIMDsaAADcimIbAABUj9OHkFss5sYCAICbUWwDAADXO3FC+uQT23ZysrmxAABgAtOK7d27d2vMmDHq27evsrKyJEmfffaZfv75Z7NCAgAArrJsmZSfL8XHSx07mh0NAABuZ0qx/fXXX6tNmzb69ttvtWjRIh0/flyS9P3332v8+PFmhAQAAFypdAh5nz4MIQcA1EqmFNujR4/W3//+d61YsUIBAQH29uuvv17r1683IyQAAOAq+fnSp5/atpmFHABQS5lSbP/444/q3bt3mfbo6GgdOnTIhIgAAIDLLF1qW/arWTOpfXuzowEAwBSmFNvh4eE6ePBgmfYtW7aoUaNGJkQEAABcZsEC28/kZIaQAwBqLVOK7bvvvltPPvmkMjIyZLFYZLVatXbtWo0aNUr9+/c3IyQAAOAKeXm2ydEkhpADAGo1U4rtiRMnqlWrVoqLi9Px48fVunVrXX311ercubPGjBljRkgAAMAVPv1UKiiQWrSQ2rY1OxoAAEzjZ8ZJAwICNGPGDI0dO1Y//fSTjh8/rssuu0wtWrQwIxwAAOAqzEIOAIAkk4rtb775RldddZUaN26sxo0bmxECAABwtdxc6bPPbNsMIQcA1HKmDCO//vrr1bRpUz311FPatm2bGSEAAABXW7JEKiyUWrWS2rQxOxoAAExlSrH9+++/a+TIkfr66691ySWXqF27dnrppZe0f/9+M8IBAACuwBByAADsTCm2IyMjNWzYMK1du1a7d+/WXXfdpXfeeUfx8fG6/vrrzQgJAABUxbFj0uef27YZQg4AgDnF9umaNm2q0aNHa/LkyWrTpo2+/vprs0MCAABn6+OPpaIiqXVr6eKLzY4GAADTmVpsr127Vg8//LAaNGige+65R5dccomWLl1qZkgAAOBczJxp+5mcbG4cAADUEKbMRp6amqr58+fr999/14033qh//vOf6tmzp4KDg80IBwAAVMXmzdI330h+ftKDD5odDQAANYIpxfbq1av1+OOPq0+fPoqMjDQjBAAA4CqvvWb7edddUsOG5sYCAEANYUqxvXbtWjNOCwAAXC07W/rgA9v28OHmxgIAQA3itmJ7yZIluvnmm+Xv768lS5ZU2ve2225zU1QAAKBKZsywra3dsaOUkGB2NAAA1BhuK7Z79eqljIwMRUdHq1evXhX2s1gsKikpcVdYAADgXBUXS2+8YdsePpy1tQEAOI3bim2r1VruNgAA8FD/+Y904IAUHc3a2gAA/IUpS3/NnTtXhYWFZdqLioo0d+5cEyICAABn7dVXbT+HDJECA82NBQCAGsaUYnvgwIHKyckp056Xl6eBAweaEBEAADgrmzdLa9falvsaMsTsaAAAqHFMKbYNw5ClnO917d+/X2FhYed0zGnTpik+Pl5BQUFKSEjQhg0bnHrd/PnzZbFYKv0eOQAA+IvTl/tq0MDcWAAAqIHcuvTXZZddJovFIovFohtuuEF+fn+evqSkRHv27FFSUtJZH3fBggVKSUnR9OnTlZCQoKlTp6pbt27auXOnoqOjK3zd3r17NWrUKHXp0uWc3g8AALVSVpY0b55tm+W+AAAol1uL7dK7x1u3blW3bt0UEhJi3xcQEKD4+HjdcccdZ33cKVOmaPDgwfYh6NOnT9fSpUs1a9YsjR49utzXlJSUqF+/fpowYYLWrFmjY8eOnfV5AQColWbMkIqKpMsvZ7kvAAAq4NZie/z48ZKk+Ph4JScnKygoqMrHLCoq0qZNm5Sammpv8/HxUdeuXZWenl7h65599llFR0dr0KBBWrNmzRnPU1hY6DCpW25uriTbzOqeOLu61WqVYRgeGTvci1yBM8iTWqS4WJY33pBFknXYMMkwbA8nkCdwBnkCZ5AncEZV86Sq+eXWYrvUgAEDXHasQ4cOqaSkRDExMQ7tMTEx2rFjR7mv+eabb/T2229r69atTp9n0qRJmjBhQpn27OxsFRQUnFXMNYHValVOTo4Mw5CPjylf3YeHIFfgDPKk9ghaskThv/+ukqgoZV9zjW1IuZPIEziDPIEzyBM4o6p5kpeXV6Xzm1Jsl5SU6JVXXtHChQu1b98+FRUVOew/cuRItZ07Ly9P9913n2bMmKHIyEinX5eamqqUlBT789zcXMXFxSkqKkqhoaHVEWq1slqtslgsioqK4gMKlSJX4AzypPaw/LFEp8+QIYqOizur15IncAZ5AmeQJ3BGVfOkqiOxTSm2J0yYoJkzZ2rkyJEaM2aMnn76ae3du1eLFy/WuHHjzupYkZGR8vX1VWZmpkN7ZmamYmNjy/TfvXu39u7dqx49etjbSocH+Pn5aefOnWrevHmZ1wUGBiqwnDVEfXx8PPYfuMVi8ej44T7kCpxBntQCmzbZl/uyDB0qyzn8XZMncAZ5AmeQJ3BGVfKkqrllSma+//77mjFjhkaOHCk/Pz/17dtXM2fO1Lhx47R+/fqzOlZAQIA6dOigtLQ0e5vValVaWpoSExPL9G/VqpV+/PFHbd261f647bbbdN1112nr1q2KO8vf0gMAUGuULvfVpw/LfQEAcAam3NnOyMhQmzZtJEkhISHKycmRJHXv3l1jx4496+OlpKRowIAB6tixozp16qSpU6cqPz/fPjt5//791ahRI02aNElBQUG65JJLHF4fHh4uSWXaAQDAH7KypA8+sG2z3BcAAGdkSrF9/vnn6+DBg2rcuLGaN2+uL774Qu3bt9d3331X7lDtM0lOTlZ2drbGjRunjIwMtWvXTsuXL7dPmrZv3z6GlwAAUBVvvWVb7qtTJ5b7AgDACaYU271791ZaWpoSEhL06KOP6t5779Xbb7+tffv26bHHHjunYw4bNkzDhg0rd9+qVasqfe2cOXPO6ZwAANQKxcXSm2/atrmrDQCAU0wptidPnmzfTk5OVuPGjZWenq4WLVo4TFwGAABqgEWLpN9/l2JipLvuMjsaAAA8ginF9l8lJiaWO5kZAACoAV591fZzyBApIMDcWAAA8BBuK7aXLFnidN/bbrutGiMBAABO27hRWrdO8veXHnrI7GgAAPAYbiu2e/Xq5VQ/i8WikpKS6g0GAAA4h+W+AAA4J24rtq1Wq7tOBQAAXCErS5o/37bNxGgAAJwV1sMCAADlO325r06dzI4GAACPYsoEac8++2yl+8eNG+emSAAAQLmKi6U33rBtc1cbAICzZkqx/Z///MfheXFxsfbs2SM/Pz81b96cYhsAALP9+9/SwYNSbCzLfQEAcA5MKba3bNlSpi03N1f333+/evfubUJEAADAAct9AQBQJTXmO9uhoaGaMGGCxo4da3YoAADUbhs3SunpLPcFAEAV1JhiW5JycnKUk5NjdhgAANRupy/3FRtrbiwAAHgoU4aRv1o6NO0PhmHo4MGDevfdd3XzzTebERIAAJCkzEyW+wIAwAVMKbZfeeUVh+c+Pj6KiorSgAEDlJqaakZIAABA+nO5r4QElvsCAKAKTCm29+zZY8ZpAQBAZYqKpDfftG1zVxsAgCqpUd/ZBgAAJjp9ua877zQ7GgAAPJopd7YLCgr02muvaeXKlcrKypLVanXYv3nzZjPCAgCgdiudGI3lvgAAqDJTiu1Bgwbpiy++0J133qlOnTrJYrGYEQYAACj13Xcs9wUAgAuZUmx/+umnWrZsma688kozTg8AAP6q9K52cjLLfQEA4AKmfGe7UaNGqlevnhmnBgAAf7V3r7RggW2bidEAAHAJU4rtl19+WU8++aR+++03M04PAABON2KEbSby666TLr/c7GgAAPAKpgwj79ixowoKCtSsWTMFBwfL39/fYf+RI0fMCAsAgNpnyRLbw89Pev11s6MBAMBrmFJs9+3bVwcOHNDEiRMVExPDBGkAAJghP//PYeMjR0qtW5sbDwAAXsSUYnvdunVKT09X27ZtzTg9AACQpL//XfrtN6lxY2nsWLOjAQDAq5jyne1WrVrp5MmTZpwaAABI0rZt0j/+Ydt+9VWpbl1z4wEAwMuYUmxPnjxZI0eO1KpVq3T48GHl5uY6PAAAQDUyDOmRR6RTp6QePaSePc2OCAAAr2PKMPKkpCRJ0g033ODQbhiGLBaLSkpKzAgLAIDa4f33pVWrpDp1bHe1AQCAy5lSbK9cudKM0wIAgKNHbZOhSbbvacfHmxoOAADeypRi+5prrjHjtAAAYMwYKStLatXqz6IbAAC4nCnF9urVqyvdf/XVV7spEgAAapHvvpPefNO2/cYbUkCAufEAAODFTCm2r7322jJtp6+1zXe2AQBwsZISaehQ2+Ro/fpJ111ndkQAAHg1U2YjP3r0qMMjKytLy5cv1+WXX64vvvjCjJAAAPBu06dLmzZJYWF/LvkFAACqjSl3tsPCwsq03XjjjQoICFBKSoo2bdpkQlQAAHipjAzp6adt288/L8XGmhsPAAC1gCl3tisSExOjnTt3mh0GAADeZdQoKSdH6tBBGjLE7GgAAKgVTLmz/cMPPzg8NwxDBw8e1OTJk9WuXTszQgIAwDutXGlbV9tisQ0l9/U1OyIAAGoFU4rtdu3ayWKxyDAMh/YrrrhCs2bNMiMkAAC8T1GR9PDDtu2hQ6WOHc2NBwCAWsSUYnvPnj0Oz318fBQVFaWgoCAzwgEAwDu9/LK0Y4cUHW37rjYAAHAbU4rtJk2amHFaAABqjz17pOees22//LIUHm5qOAAA1DZunSDtq6++UuvWrZWbm1tmX05Oji6++GKtWbPGnSEBAOCdRoyQTp6Urr3Wtq42AABwK7cW21OnTtXgwYMVGhpaZl9YWJgeeughTZkyxZ0hAQDgfT7+WPrkE8nPT3rjDdvkaAAAwK3cWmx///33SkpKqnD/TTfdxBrbAABURX6+NHy4bXvUKOmii8yNBwCAWsqtxXZmZqb8/f0r3O/n56fs7Gw3RgQAgJd57jlp3z6pSRNp7FizowEAoNZya7HdqFEj/fTTTxXu/+GHH9SgQQM3RgQAgBf5+WfbZGiS9OqrUnCwufEAAFCLubXYvuWWWzR27FgVFBSU2Xfy5EmNHz9e3bt3d2dIAAB4B8Owral96pR02222BwAAMI1bl/4aM2aMFi1apAsvvFDDhg1Ty5YtJUk7duzQtGnTVFJSoqefftqdIQEA4B3efVdavVqqU0f65z/NjgYAgFrPrcV2TEyM1q1bp6FDhyo1NVWGYUiSLBaLunXrpmnTpikmJsadIQEA4PmOHrVNhiZJ48ZJ8fGmhgMAANw8jFySmjRpomXLlunQoUP69ttvtX79eh06dEjLli1T06ZNz/m406ZNU3x8vIKCgpSQkKANGzZU2HfGjBnq0qWLIiIiFBERoa5du1baHwCAGsswpGHDpOxs28zjKSlmRwQAAGRCsV0qIiJCl19+uTp16qSIiIgqHWvBggVKSUnR+PHjtXnzZrVt21bdunVTVlZWuf1XrVqlvn37auXKlUpPT1dcXJxuuukmHThwoEpxAADgdmPHSvPmSb6+0vTpUkCA2REBAABJFqN0LLcHS0hI0OWXX67XX39dkmS1WhUXF6dHH31Uo0ePPuPrS0pKFBERoddff139+/cvt09hYaEKCwvtz3NzcxUXF6ejR48qNDTUNW/EjaxWq7KzsxUVFSUfH9N+5wIPQK7AGeSJSf71L/k8/LAkyfrWW9KgQSYHVDnyBM4gT+AM8gTOqGqe5ObmKiIiQjk5OedU87n1O9vVoaioSJs2bVJqaqq9zcfHR127dlV6erpTxzhx4oSKi4tVv379CvtMmjRJEyZMKNOenZ1d7uzqNZ3ValVOTo4Mw+ADCpUiV+AM8sT9Ar/4QuHDhkmSjo8cqeM9ekgVjOiqKcgTOIM8gTPIEzijqnmSl5dXpfN7fLF96NAhlZSUlJlYLSYmRjt27HDqGE8++aQaNmyorl27VtgnNTVVKad9D670znZUVJTH3tm2WCz8NhBnRK7AGeSJm23YIMuQIbJYrTIGDlTwCy8o2GIxO6ozIk/gDPIEziBP4Iyq5klQUFCVzu/xxXZVTZ48WfPnz9eqVasq/cMMDAxUYGBgmXYfHx+P/QdusVg8On64D7kCZ5AnbrJrl9Sjh3TypJSUJMu//iWLr6/ZUTmNPIEzyBM4gzyBM6qSJ1XNLY8vtiMjI+Xr66vMzEyH9szMTMXGxlb62n/84x+aPHmyvvzyS1166aXVGSYAAFWXnS0lJUmHDknt20sffij5+5sdFQAAKIfH/xooICBAHTp0UFpamr3NarUqLS1NiYmJFb7uxRdf1HPPPafly5erY8eO7ggVAIBzl58vde8u7d5tW0d76VIpJMTsqAAAQAU8/s62JKWkpGjAgAHq2LGjOnXqpKlTpyo/P18DBw6UJPXv31+NGjXSpEmTJEkvvPCCxo0bp3nz5ik+Pl4ZGRmSpJCQEIXwHxcAQE1z6pR0993Shg1S/frS8uXSGUZvAQAAc3lFsZ2cnKzs7GyNGzdOGRkZateunZYvX26fNG3fvn0O4+3ffPNNFRUV6c4773Q4zvjx4/XMM8+4M3QAACpnGNKwYdKnn0pBQdInn0gtW5odFQAAOAOvKLYladiwYRr2xxIof7Vq1SqH53v37q3+gAAAcIVJk6R//UuyWKR586TOnc2OCAAAOMHjv7MNAIDXmjtXevpp2/arr0q9e5sbDwAAcBrFNgAANdGKFdKgQbbtJ56wDSUHAAAeg2IbAICaZutW6Y47bBOj9e1rG0oOAAA8CsU2AAA1yW+/SbfcIuXlSdddJ82eLflwuQYAwNNw9QYAoKY4ckS6+Wbp4EHpkkukRYukwECzowIAAOeAYhsAgJqgoEDq1Uvavl1q1Ej67DMpPNzsqAAAwDmi2AYAwGxWq9S/v7RmjRQaaiu0zz/f7KgAAEAVUGwDAGAmw5BGjpQ+/FDy95cWL5batDE7KgAAUEV+ZgcAAECtdeKE9NBD0nvv2Z6/845tUjQAAODxKLYBADDDrl3S7bdLP/4o+fpKr75qW+YLAAB4BYptAADc7eOPbd/Rzs2VoqOlhQula64xOyoAAOBCfGcbAAB3OXVKSk21zTqemytdeaW0ZQuFNgAAXog72wAAuENWlm2Y+Fdf2Z6PGCG99JJtUjQAAOB1KLYBAKhu69dLd90l7d8v1a0rzZwp3X232VEBAIBqxDByAACqi2FI06ZJV19tK7RbtpS+/ZZCGwCAWoBiGwCA6pCfb5sEbdgwqbhYuuMOacMG6eKLzY4MAAC4AcPIAQBwtV9/tRXXpct6vfCClJIiWSxmRwYAANyEYhsAAFdavFgaMMA223hMjLRgAbONAwBQCzGMHAAAVzh1Sho9Wurd+89lvTZvptAGAKCW4s42AABV9ddlvf7v/6QXX2RZLwAAajGKbQAAquLrr6V+/aQDB2zLer39tpScbHZUAADAZAwjBwDgXGzbJvXsKV17ra3QbtXKNts4hTYAABDFNgAAZ2f/fmnQIKlNG2nJEtts4w89ZCu0W7c2OzoAAFBDMIwcAABnHDsmTZ4s/fOfUkGBra13b2niRNtdbQAAgNNQbAMAUJmCAmnaNOn556WjR21tV11lmwAtMdHc2AAAQI1FsQ0AQHlKSqT33pPGjpX+9z9bW+vWtrvb3btLFou58QEAgBqNYhsAgNMZhvTZZ7Y1s3/80dZ2/vnSs89K/fvbvqMNAABwBhTbAACU2rBBeuIJ23JekhQWJj31lPToo1KdOubGBgAAPArFNgAAv/5qK6o/+sj2PDDQVmCnpkr165sbGwAA8EgU2wCA2uvnn6XXX5dmzLB9R9tikQYMkCZMkBo3Njs6AADgwSi2AQC1S3a2NG+eNHeutHnzn+233GKb/KxNG/NiAwAAXoNiGwDg/QoLpU8/ld55xzb52alTtnY/P9vM4iNGSNdea2qIAADAu1BsAwC8k2FI335ru4M9f/6fa2RLUseOtuHid98tRUaaFyMAAPBaFNsAAO/y22+29bHnzpV++eXP9kaNpPvusz1atzYvPgAAUCtQbAMAPF9envTvf9sK7JUr/2wPDpbuuMO2PvZ117FGNgAAcBuKbQCAZzp8WPrmG9tyXYsWSSdO/Lnvuutsw8Rvv12qV8+8GAEAQK1FsQ0A8Az79klr1vz52LbNcf+FF9ruYN97r9SkiTkxAgAA/IFiGwBQ8xiGtH27Y3G9b1/Zfq1aSTfcYPsedqdOtnWyAQAAagCKbQCA+YqLpS1b/iysv/nGNkz8dL6+Uvv2UpcutseVV0pRUebECwAAcAYU2wAA9yoslHbtknbskH74wVZYr1/v+J1rSapTR7riij+L6yuukEJCzIkZAADgLFFsAwCqx9GjtoJ6xw7bkPDS7f/+VyopKds/IkK66qo/i+v27aWAAPfHDQAA4AIU2wCAc2e1Svv3Sz//rOCNG2U5cODPojozs+LX1asnXXSR7TvXpXevW7eWfHzcFzsAAEA1otgGAJSvpETKypJ+/106cKDszwMHbHepT5yQj6TQ8o5x/vm2grpVqz+L61atpAYNmMwMAAB4NYptAKhtiopsQ7wPH668kM7IKH+491/5+clo0UKFTZsqsG1bWS66yFZYt2zJGtcAAKDW8ppie9q0aXrppZeUkZGhtm3b6rXXXlOnTp0q7P/hhx9q7Nix2rt3r1q0aKEXXnhBt9xyixsjBoBzZBi2gjknx1Y0Hzli+1nR46/7/zoRWWV8fKSYGKlRI6lhw7I/4+OlZs1k+PrqWFaWoqOjZWEoOAAAgHcU2wsWLFBKSoqmT5+uhIQETZ06Vd26ddPOnTsVHR1dpv+6devUt29fTZo0Sd27d9e8efPUq1cvbd68WZdccokJ7wCARyspsc2wXVRk+1n6qOx5QYGt6M3P//Nx+vOKtkufO3PH+UzCw8svoE//GRMj+TlxqbBaqx4PAACAF7EYhmGYHURVJSQk6PLLL9frr78uSbJarYqLi9Ojjz6q0aNHl+mfnJys/Px8ffrpp/a2K664Qu3atdP06dOdOmdubq7CwsKUk5Oj0NByv6lorr17bXe9KmC1WnXkyBHVr19fPhXdhaopqVHVOFzxPso7RkXHdabvufQpfe7Kn060WUtKlHPsmMLCwuRjsfzZpyoPq7Xix5n2lz5KSs78s7J9p06d+VFcXPG+0wtnVxS+5yoszDaLd0WP+vXLbw8Ls61b7SJWq1VZf9zZrvAzBbUeeQJnkCdwBnkCZ1Q1T6pa83n8ne2ioiJt2rRJqamp9jYfHx917dpV6enp5b4mPT1dKSkpDm3dunXT4sWLKzxPYWGhCgsL7c9zc3Ml2f4CrTXwjo7lscdkqeT9+EiKdFs08GQ+kiLMDsLDGAEBUmDgn4+/Pi9tq1vX9ggOtm8bpdvBwQ7t5W7Xq1e1gtmFn11Wq1WGYdTIz0PUHOQJnEGewBnkCZxR1Typan55fLF96NAhlZSUKCYmxqE9JiZGO3bsKPc1GRkZ5fbPyMio8DyTJk3ShAkTyrRnZ2eroKDgHCKvXqF16ijwL+/xr6xWq3t+E+iKGYeregx3zHp8pnP8Zb9x+vOKtivad5b9jb++7vSfFe37Y9uQVGK1ytfPT5bSY53+KM2hv7SX6Xf68X19Zfj42F5beow/tstrd2g7/fW+vvbn8vGRUdnz08/p4yPD39/W7udn6+fn57Bt+PlVvN/X1/b6wEAZAQH24trw97cV0e7It6Ii2wRnNYTValVOTo4Mw+AOAypEnsAZ5AmcQZ7AGVXNk7y8vCqd3+OLbXdJTU11uBuem5uruLg4RUVF1cxh5O+9V+luq9WqQ9nZioqK4gPKJJ6y6JHVatVhcgVnYLVaZbFYyBNUijyBM8gTOIM8gTOqmidBQUFVOr/HF9uRkZHy9fVVZmamQ3tmZqZiY2PLfU1sbOxZ9ZekwMBABQYGlmn38fHx2H/gFovFo+OH+5ArcAZ5AmeQJ3AGeQJnkCdwRlXypKq55fGZGRAQoA4dOigtLc3eZrValZaWpsTExHJfk5iY6NBfklasWFFhfwAAAAAAzobH39mWpJSUFA0YMEAdO3ZUp06dNHXqVOXn52vgwIGSpP79+6tRo0aaNGmSJGnEiBG65ppr9PLLL+vWW2/V/PnztXHjRr311ltmvg0AAAAAgJfwimI7OTlZ2dnZGjdunDIyMtSuXTstX77cPgnavn37HIYAdO7cWfPmzdOYMWP01FNPqUWLFlq8eDFrbAMAAAAAXMIrim1JGjZsmIYNG1buvlWrVpVpu+uuu3TXXXdVc1QAAAAAgNrI47+zDQAAAABATUOxDQAAAACAi1FsAwAAAADgYhTbAAAAAAC4GMU2AAAAAAAuRrENAAAAAICLec3SX+5mGIYkKTc31+RIzo3ValVeXp6CgoIc1iAH/opcgTPIEziDPIEzyBM4gzyBM6qaJ6W1Xmntd7Yots9RXl6eJCkuLs7kSAAAAAAA1SUvL09hYWFn/TqLca5lei1ntVr1+++/q169erJYLGaHc9Zyc3MVFxen//3vfwoNDTU7HNRg5AqcQZ7AGeQJnEGewBnkCZxR1TwxDEN5eXlq2LDhOd0Z5872OfLx8dH5559vdhhVFhoaygcUnEKuwBnkCZxBnsAZ5AmcQZ7AGVXJk3O5o12KLzgAAAAAAOBiFNsAAAAAALgYxXYtFRgYqPHjxyswMNDsUFDDkStwBnkCZ5AncAZ5AmeQJ3CG2XnCBGkAAAAAALgYd7YBAAAAAHAxim0AAAAAAFyMYhsAAAAAABej2AYAAAAAwMUotj3c888/r86dOys4OFjh4eHl9rFYLGUe8+fPd+izatUqtW/fXoGBgbrgggs0Z86cMseZNm2a4uPjFRQUpISEBG3YsMFhf0FBgR555BGdd955CgkJ0R133KHMzExXvVVUgTN5sm/fPt16660KDg5WdHS0Hn/8cZ06dcqhD3lS+8THx5f5/Jg8ebJDnx9++EFdunRRUFCQ4uLi9OKLL5Y5zocffqhWrVopKChIbdq00bJlyxz2G4ahcePGqUGDBqpTp466du2qX3/9tVrfG9zrTJ8N8B7PPPNMmc+NVq1a2fc7cx1w1TUJNcvq1avVo0cPNWzYUBaLRYsXL3bY78y14MiRI+rXr59CQ0MVHh6uQYMG6fjx4w59XHFdgnnOlCf3339/mc+YpKQkhz41Jk8MeLRx48YZU6ZMMVJSUoywsLBy+0gyZs+ebRw8eND+OHnypH3/f//7XyM4ONhISUkxtm3bZrz22muGr6+vsXz5cnuf+fPnGwEBAcasWbOMn3/+2Rg8eLARHh5uZGZm2vsMGTLEiIuLM9LS0oyNGzcaV1xxhdG5c+dqe+9w3pny5NSpU8Yll1xidO3a1diyZYuxbNkyIzIy0khNTbX3IU9qpyZNmhjPPvusw+fH8ePH7ftzcnKMmJgYo1+/fsZPP/1kfPDBB0adOnWMf/3rX/Y+a9euNXx9fY0XX3zR2LZtmzFmzBjD39/f+PHHH+19Jk+ebISFhRmLFy82vv/+e+O2224zmjZt6vBZBc/lzGcDvMf48eONiy++2OFzIzs7277/TNcBV12TUPMsW7bMePrpp41FixYZkoz//Oc/DvuduRYkJSUZbdu2NdavX2+sWbPGuOCCC4y+ffva97vqugTznClPBgwYYCQlJTl8xhw5csShT03JE4ptLzF79uxKi+2/JunpnnjiCePiiy92aEtOTja6detmf96pUyfjkUcesT8vKSkxGjZsaEyaNMkwDMM4duyY4e/vb3z44Yf2Ptu3bzckGenp6efwjlAdKsqTZcuWGT4+PkZGRoa97c033zRCQ0ONwsJCwzDIk9qqSZMmxiuvvFLh/jfeeMOIiIiw54lhGMaTTz5ptGzZ0v68T58+xq233urwuoSEBOOhhx4yDMMwrFarERsba7z00kv2/ceOHTMCAwONDz74wEXvBGY602cDvMv48eONtm3blrvPmeuAq65JqNn++v9TZ64F27ZtMyQZ3333nb3PZ599ZlgsFuPAgQOGYbjmuoSao6Jiu2fPnhW+piblCcPIa4lHHnlEkZGR6tSpk2bNmiXjtOXV09PT1bVrV4f+3bp1U3p6uiSpqKhImzZtcujj4+Ojrl272vts2rRJxcXFDn1atWqlxo0b2/ug5kpPT1ebNm0UExNjb+vWrZtyc3P1888/2/uQJ7XT5MmTdd555+myyy7TSy+95DCUMz09XVdffbUCAgLsbd26ddPOnTt19OhRe5/KcmfPnj3KyMhw6BMWFqaEhATywgs489kA7/Prr7+qYcOGatasmfr166d9+/ZJcu464IprEjyPM9eC9PR0hYeHq2PHjvY+Xbt2lY+Pj7799lt7n6pel1DzrVq1StHR0WrZsqWGDh2qw4cP2/fVpDzxO6d3B4/y7LPP6vrrr1dwcLC++OILPfzwwzp+/LiGDx8uScrIyHC4oElSTEyMcnNzdfLkSR09elQlJSXl9tmxY4f9GAEBAWW+DxwTE6OMjIzqe3NwiYpyoHRfZX3IE+82fPhwtW/fXvXr19e6deuUmpqqgwcPasqUKZJsf6dNmzZ1eM3puRMREVFh7pyeW6e/rrw+8FyHDh0642cDvEtCQoLmzJmjli1b6uDBg5owYYK6dOmin376yanrgCuuSXXq1Kmmd4fq4sy1ICMjQ9HR0Q77/fz8VL9+fYc+Vb0uoWZLSkrS7bffrqZNm2r37t166qmndPPNNys9PV2+vr41Kk8otmug0aNH64UXXqi0z/bt2x0mG6nM2LFj7duXXXaZ8vPz9dJLL9mLbXgmV+cJao+zyZ2UlBR726WXXqqAgAA99NBDmjRpkgIDA6s7VAAe6Oabb7ZvX3rppUpISFCTJk20cOFCimAAVXb33Xfbt9u0aaNLL71UzZs316pVq3TDDTeYGFlZFNs10MiRI3X//fdX2qdZs2bnfPyEhAQ999xzKiwsVGBgoGJjY8vMApqZmanQ0FDVqVNHvr6+8vX1LbdPbGysJCk2NlZFRUU6duyYw2+rT+8D13JlnsTGxpaZGbj07/v0v2PyxDtUJXcSEhJ06tQp7d27Vy1btqwwL6Qz587p+0vbGjRo4NCnXbt2Tr8v1EyRkZFn/GyAdwsPD9eFF16oXbt26cYbbzzjdcAV1yR4HmeuBbGxscrKynJ43alTp3TkyJEz5sbp5zjTdQmepVmzZoqMjNSuXbt0ww031Kg84TvbNVBUVJRatWpV6eP07xecra1btyoiIsJ+VyoxMVFpaWkOfVasWKHExERJUkBAgDp06ODQx2q1Ki0tzd6nQ4cO8vf3d+izc+dO7du3z94HruXKPElMTNSPP/7o8MG0YsUKhYaGqnXr1vY+5Il3qErubN26VT4+PvbhWYmJiVq9erWKi4vtfVasWKGWLVsqIiLC3qey3GnatKliY2Md+uTm5urbb78lL7yAM58N8G7Hjx/X7t271aBBA6euA664JsHzOHMtSExM1LFjx7Rp0yZ7n6+++kpWq1UJCQn2PlW9LsGz7N+/X4cPH7b/kqZG5YnTU6mhRvrtt9+MLVu2GBMmTDBCQkKMLVu2GFu2bDHy8vIMwzCMJUuWGDNmzDB+/PFH49dffzXeeOMNIzg42Bg3bpz9GKXLZzz++OPG9u3bjWnTppW7pFNgYKAxZ84cY9u2bcbf/vY3Izw83GGm0CFDhhiNGzc2vvrqK2Pjxo1GYmKikZiY6L4/DFToTHlSuszKTTfdZGzdutVYvny5ERUVVe4yK+RJ7bFu3TrjlVdeMbZu3Wrs3r3beO+994yoqCijf//+9j7Hjh0zYmJijPvuu8/46aefjPnz5xvBwcFlls7w8/Mz/vGPfxjbt283xo8fX+7SX+Hh4cbHH39s/PDDD0bPnj1Z+suLOPPZAO8xcuRIY9WqVcaePXuMtWvXGl27djUiIyONrKwswzDOfB1w1TUJNU9eXp79/yCSjClTphhbtmwxfvvtN8MwnLsWJCUlGZdddpnx7bffGt98843RokULhyWdXHVdgnkqy5O8vDxj1KhRRnp6urFnzx7jyy+/NNq3b2+0aNHCKCgosB+jpuQJxbaHGzBggCGpzGPlypWGYdimuW/Xrp0REhJi1K1b12jbtq0xffp0o6SkxOE4K1euNNq1a2cEBAQYzZo1M2bPnl3mXK+99prRuHFjIyAgwOjUqZOxfv16h/0nT540Hn74YSMiIsIIDg42evfubRw8eLC63jrOwpnyxDAMY+/evcbNN99s1KlTx4iMjDRGjhxpFBcXOxyHPKldNm3aZCQkJBhhYWFGUFCQcdFFFxkTJ050uJgZhmF8//33xlVXXWUEBgYajRo1MiZPnlzmWAsXLjQuvPBCIyAgwLj44ouNpUuXOuy3Wq3G2LFjjZiYGCMwMNC44YYbjJ07d1br+4N7nemzAd4jOTnZaNCggREQEGA0atTISE5ONnbt2mXf78x1wFXXJNQsK1euLPf/IwMGDDAMw7lrweHDh42+ffsaISEhRmhoqDFw4ED7zYNSrrguwTyV5cmJEyeMm266yYiKijL8/f2NJk2aGIMHDy7zy9uakicWwzhtDSgAAAAAAFBlfGcbAAAAAAAXo9gGAAAAAMDFKLYBAAAAAHAxim0AAAAAAFyMYhsAAAAAABej2AYAAAAAwMUotgEAAAAAcDGKbQAAAAAAXIxiGwAAVOraa6/V//3f/5kdBgAAHoViGwAAL9ajRw8lJSWVu2/NmjWyWCz64Ycf3BwVAADej2IbAAAvNmjQIK1YsUL79+8vs2/27Nnq2LGjLr30UhMiAwDAu1FsAwDgxbp3766oqCjNmTPHof348eP68MMP1atXL/Xt21eNGjVScHCw2rRpow8++KDSY1osFi1evNihLTw83OEc//vf/9SnTx+Fh4erfv366tmzp/bu3euaNwUAgAeg2AYAwIv5+fmpf//+mjNnjgzDsLd/+OGHKikp0b333qsOHTpo6dKl+umnn/S3v/1N9913nzZs2HDO5ywuLla3bt1Ur149rVmzRmvXrlVISIiSkpJUVFTkircFAECNR7ENAICXe+CBB7R79259/fXX9rbZs2frjjvuUJMmTTRq1Ci1a9dOzZo106OPPqqkpCQtXLjwnM+3YMECWa1WzZw5U23atNFFF12k2bNna9++fVq1apUL3hEAADUfxTYAAF6uVatW6ty5s2bNmiVJ2rVrl9asWaNBgwappKREzz33nNq0aaP69esrJCREn3/+ufbt23fO5/v++++1a9cu1atXTyEhIQoJCVH9+vVVUFCg3bt3u+ptAQBQo/mZHQAAAKh+gwYN0qOPPqpp06Zp9uzZat68ua655hq98MIL+uc//6mpU6eqTZs2qlu3rv7v//6v0uHeFovFYUi6ZBs6Xur48ePq0KGD3n///TKvjYqKct2bAgCgBqPYBgCgFujTp49GjBihefPmae7cuRo6dKgsFovWrl2rnj176t5775UkWa1W/fLLL2rdunWFx4qKitLBgwftz3/99VedOHHC/rx9+/ZasGCBoqOjFRoaWn1vCgCAGoxh5AAA1AIhISFKTk5WamqqDh48qPvvv1+S1KJFC61YsULr1q3T9u3b9dBDDykzM7PSY11//fV6/fXXtWXLFm3cuFFDhgyRv7+/fX+/fv0UGRmpnj17as2aNdqzZ49WrVql4cOHl7sEGQAA3ohiGwCAWmLQoEE6evSounXrpoYNG0qSxowZo/bt26tbt2669tprFRsbq169elV6nJdffllxcXHq0qWL7rnnHo0aNUrBwcH2/cHBwVq9erUaN26s22+/XRdddJEGDRqkgoIC7nQDAGoNi/HXL10BAAAAAIAq4c42AAAAAAAuRrENAAAAAICLUWwDAAAAAOBiFNsAAAAAALgYxTYAAAAAAC5GsQ0AAAAAgItRbAMAAAAA4GIU2wAAAAAAuBjFNgAAAAAALkaxDQAAAACAi1FsAwAAAADgYv8Pg9HGmqSj9bQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-2098.98, 2099.65]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-2698.78, 2099.65]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-3298.58, 2699.46]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4498.19, 3299.26]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-6297.61, 5098.67]\n", + "\n", + "Analisi per min_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -10.703\n", + "variance: 153119.078\n", + "std: 391.304\n", + "min: -3452.054\n", + "max: 3142.031\n", + "median: 27.116\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-484.72, 438.46]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-616.60, 438.46]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-748.48, 570.34]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-880.36, 702.22]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1407.89, 1097.86]\n", + "\n", + "Analisi per max_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -3.161\n", + "variance: 222900.141\n", + "std: 472.123\n", + "min: -4621.081\n", + "max: 3967.894\n", + "median: 42.622\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-584.26, 446.41]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-756.04, 618.19]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-756.04, 618.19]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-1099.60, 961.75]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1614.94, 1305.31]\n", + "\n", + "Analisi per avg_oil_prod\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: -7.018\n", + "variance: 175727.656\n", + "std: 419.199\n", + "min: -3742.384\n", + "max: 3360.436\n", + "median: 33.824\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-546.11, 448.28]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-546.11, 448.28]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-688.17, 590.34]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-972.28, 732.39]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-1540.51, 1158.56]\n", + "\n", + "Analisi per total_water_need\n", + "--------------------------------------------------\n", + "\n", + "Statistiche degli Errori:\n", + "mean: 65.956\n", + "variance: 3269755.500\n", + "std: 1808.246\n", + "min: -15266.289\n", + "max: 11478.280\n", + "median: 354.974\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Intervallo di Confidenza 80.0%:\n", + "Range: [-2161.45, 2117.68]\n", + "\n", + "Intervallo di Confidenza 85.0%:\n", + "Range: [-2696.34, 2117.68]\n", + "\n", + "Intervallo di Confidenza 90.0%:\n", + "Range: [-3231.23, 2652.57]\n", + "\n", + "Intervallo di Confidenza 95.0%:\n", + "Range: [-4301.02, 3187.46]\n", + "\n", + "Intervallo di Confidenza 99.0%:\n", + "Range: [-6440.58, 4792.14]\n", + "\n", + "2. IMPORTANZA DELLE FEATURE\n", + "--------------------------------------------------\n", + "18750/18750 [==============================] - 187s 10ms/step\n", + "18750/18750 [==============================] - 186s 10ms/step\n", + "18750/18750 [==============================] - 186s 10ms/step\n", + "18750/18750 [==============================] - 189s 10ms/step\n", + "18750/18750 [==============================] - 184s 10ms/step\n", + "\n", + "Importanza relativa delle feature:\n", + "ha: 0.8679\n", + "precip_sum: 0.0541\n", + "solar_energy_sum: 0.0431\n", + "temp_mean: 0.0349\n", + "\n", + "3. ANALISI DISTRIBUZIONALE\n", + "--------------------------------------------------\n", + "18750/18750 [==============================] - 181s 10ms/step\n", + "\n", + "Analisi distribuzionale per olive_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 29887.219\n", + "variance: 257054016.000\n", + "std: 16032.904\n", + "min: 2732.547\n", + "max: 87850.508\n", + "median: 28077.900\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 29870.586\n", + "variance: 270589536.000\n", + "std: 16449.605\n", + "min: 2030.459\n", + "max: 99272.031\n", + "median: 27900.719\n" + ] + }, + { + "data": { + "image/png": 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SoUOHdPz4cft+FEWfPn308MMPa+fOnVqyZIkqVaqkbt262ZfnXSn28/MrVp8ryIXnliT9+uuv+QarK0jdunW1evVqnThxwuFq944dO+zL8/602Wz67bffHK5uO9uHAAAlg59OAcDN1qxZo+eee04REREXTY6OHj2aryzvqlveVEl5SVFBSXBx5I2wnOejjz7SwYMH7SOBS+cSku+++045OTn2suXLl+ebWmz16tWaMGGCnnrqKfXs2dOp7cfGxsrLy0uzZs1yuHr69ttvKz09vVijMPfu3Vv79+/Xm2++mW/ZqVOnlJmZWeQ2i7r95ORkrVq1Kt+y48eP6+zZs5KkLl266OzZs5o7d659eW5url5//fVLbuPgwYPq3bu32rZtq1deecWpuGrWrKmoqCi9++67Dv1n27Zt+uKLL9SlSxen2imOG2+8UbGxsfbXxZLuvDjyRnnPk3fnQnH6RK9eveTh4aEPPvhAS5cu1e233+7wA0Pz5s1Vr149vfrqq/lG4JfO3R5fVMuWLdP+/fvt7zds2KD169c7nFuF6dKli3JzczV79myH8unTp8tisdjbyPtz1qxZDvUuPHYAANfiSjcAXEH//e9/tWPHDp09e1aHDh3SmjVrlJiYqLp16+qzzz6Tj49Poes+++yz+vrrr9W1a1fVrVtXhw8f1htvvKHatWurbdu2ks4lwAEBAZo3b56qVKkiX19fRUdHF/hssTOqVaumtm3basiQITp06JBmzJih+vXrO0xrdv/99+ujjz5Sp06d1Lt3b/322296//338z1H2q9fPwUGBioyMtJhPnJJuvXWWwucviwwMFAJCQmaPHmyOnXqpO7du2vnzp1644031LJlS4dB05x177336sMPP9SDDz6otWvX6sYbb1Rubq527NihDz/80D6vtqs8/vjj+uyzz3T77bdr8ODBat68uTIzM/XTTz/po48+UkpKimrUqKFu3brpxhtv1Lhx45SSkqLrrrtOH3/8sdLT0y+5jVGjRiktLU1PPPGEFi9e7LDs+uuvd7hT4XyvvPKKOnfurJiYGA0dOtQ+ZZi/v/8lb/e+Upo1a6ZBgwZp/vz5On78uNq1a6cNGzbo3XffVc+ePdWhQ4citxkUFKQOHTpo2rRpOnHihPr06eOw3Gq16q233lLnzp3VuHFjDRkyRLVq1dL+/fu1du1a+fn52Z/xd1b9+vXVtm1bPfTQQ8rOztaMGTNUvXr1Qh87OF+3bt3UoUMHPfXUU0pJSVGzZs30xRdf6NNPP9WYMWPs515UVJT69eunN954Q+np6WrTpo2SkpK0e/fuIsUKALhM7hw6HQDKi7ypsPJeXl5eJiQkxNx6661m5syZDtNy5blwGqikpCTTo0cPExoaary8vExoaKjp169fvqmnPv30U3PdddcZT09Ph+mR2rVrZxo3blxgfIVNGfbBBx+YhIQEExQUZCpWrGi6du1q9u7dm2/91157zdSqVct4e3ubG2+80WzatClfm+fv/4WvtWvXOhynvCnD8syePds0bNjQVKhQwQQHB5uHHnrIHDt2LN8+FLR/gwYNMnXr1nUoy8nJMS+99JJp3Lix8fb2NlWrVjXNmzc3kydPNunp6QUeo/Pb8/X1vWgdY85NldW1a9cCl504ccIkJCSY+vXrGy8vL1OjRg3Tpk0b8+qrr5qcnBx7vb/++svce++9xs/Pz/j7+5t7773XfP/995ecMixv+rSCXnnTRBU0fZYxxqxevdrceOONpmLFisbPz89069bN/PLLLw518rZ34fR1BX1+JT1lmDHGnDlzxkyePNlERESYChUqmLCwMJOQkGBOnz7tUM+ZKcPyvPnmm0aSqVKlijl16lSB2/3+++/NnXfeaapXr268vb1N3bp1Te/evU1SUpLTsefF8Morr5jXXnvNhIWFGW9vb3PTTTeZH374waHuxfraiRMnzNixY01oaKipUKGCiYyMNK+88orDlGbGGHPq1CkzatQoU716dePr62u6detm/vjjD6YMA4AryGJMEUc7AQAAQLGkpKQoIiJCr7zyih577DF3hwMAuAJ4phsAAAAAABfhmW4AAIDLlJube8kB1Vw1rRgA4OpG0g0AAHCZ/vjjj0sOWDhx4kQNHjz4ygQEALhq8Ew3AADAZTp9+rS+/fbbi9a55pprLjodGgCgbCLpBgAAAADARRhIDQAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAEkWi0UjRowosfbeeecdWSwWbdq06ZJ127dvr/bt29vfp6SkyGKx6J133rGXTZo0SRaLpcTiw9Xjws8fAFC2kHQDAK5aeYlr3svHx0fXXnutRowYoUOHDrk7PLd78cUXtWzZshJt88svv7Qf7/fff7/AOjfeeKMsFouaNGlSotsuCef3l/NfISEhbo3rl19+0aRJk5SSkuLWOAAAV56nuwMAAOBSnn32WUVEROj06dP69ttvNXfuXK1YsULbtm1TpUqV3B3eZfviiy8uWWfChAkaN26cQ9mLL76ou+66Sz179izxmHx8fLRo0SLdc889DuUpKSlat26dfHx8SnybJeXWW2/VwIEDHcoqVqzopmjO+eWXXzR58mS1b99e4eHhDsuc+fwBAKUXSTcA4KrXuXNntWjRQpJ0//33q3r16po2bZo+/fRT9evXr8B1MjMz5evreyXDLDYvL69L1vH09JSn55X7b7tLly767LPPdOTIEdWoUcNevmjRIgUHBysyMlLHjh27YvEUxbXXXpvvx4KrmTOfPwCg9OL2cgBAqXPLLbdIkvbs2SNJGjx4sCpXrqzffvtNXbp0UZUqVTRgwABJ55LvRx99VGFhYfL29laDBg306quvyhhTYNsLFy5UgwYN5OPjo+bNm+vrr792WL537149/PDDatCggSpWrKjq1avr7rvvLvS24aysLD3wwAOqXr26/Pz8NHDgwHzJqjPP9F74TLfFYlFmZqbeffdd+y3UgwcP1tq1a2WxWPTJJ5/ka2PRokWyWCxKTk6+6LYkqUePHvL29tbSpUvztdG7d295eHjkW2fBggW65ZZbFBQUJG9vb1133XWaO3duvnqbNm1SXFycatSooYoVKyoiIkL33XefQ53FixerefPmqlKlivz8/NS0aVPNnDnzknFfyuDBg/NdaZYKfmY+7zn/ZcuWqUmTJvL29lbjxo21cuXKfOvv379fQ4cOVWhoqLy9vRUREaGHHnpIOTk5euedd3T33XdLkjp06GD/vL788ktJBX/+hw8f1tChQxUcHCwfHx81a9ZM7777rkOdvGf/X331Vc2fP1/16tWTt7e3WrZsqY0bNxb/IAEAShRXugEApc5vv/0mSapevbq97OzZs4qLi1Pbtm316quvqlKlSjLGqHv37lq7dq2GDh2qqKgorVq1So8//rj279+v6dOnO7T71VdfacmSJRo1apS8vb31xhtvqFOnTtqwYYP9+eWNGzdq3bp16tu3r2rXrq2UlBTNnTtX7du31y+//JLvdvcRI0YoICBAkyZN0s6dOzV37lzt3bvX/ux0cb333nu6//771apVKw0fPlySVK9ePbVu3VphYWFauHCh7rjjDod1Fi5cqHr16ikmJuaS7VeqVEk9evTQBx98oIceekiS9MMPP+jnn3/WW2+9pR9//DHfOnPnzlXjxo3VvXt3eXp66vPPP9fDDz8sm82mRx55RNK5ZPK2225TYGCgxo0bp4CAAKWkpOjjjz+2t5OYmKh+/fqpY8eOeumllyRJ27dv1//+9z+NHj36krGfPn1aR44ccSirUqWKvL29L7nuhb799lt9/PHHevjhh1WlShXNmjVLvXr10r59++z978CBA2rVqpWOHz+u4cOHq2HDhtq/f78++ugjZWVl6eabb9aoUaM0a9YsjR8/Xo0aNZIk+58XOnXqlNq3b6/du3drxIgRioiI0NKlSzV48GAdP3483zFYtGiRTpw4oQceeEAWi0Uvv/yy7rzzTv3++++qUKFCkfcZAFDCDAAAV6kFCxYYSWb16tUmLS3N/PHHH2bx4sWmevXqpmLFiubPP/80xhgzaNAgI8mMGzfOYf1ly5YZSeb55593KL/rrruMxWIxu3fvtpdJMpLMpk2b7GV79+41Pj4+5o477rCXZWVl5YszOTnZSDL/+te/8sXevHlzk5OTYy9/+eWXjSTz6aef2svatWtn2rVrZ3+/Z88eI8ksWLDAXjZx4kRz4X/bvr6+ZtCgQfniSUhIMN7e3ub48eP2ssOHDxtPT08zceLEfPXPt3btWiPJLF261CxfvtxYLBazb98+Y4wxjz/+uLnmmmvsMTdu3Nhh3YKOTVxcnH0dY4z55JNPjCSzcePGQmMYPXq08fPzM2fPnr1orAXJ+xwvfOUdy0GDBpm6devmW6+g4yvJeHl5OfSTH374wUgyr7/+ur1s4MCBxmq1FrhPNpvNGGPM0qVLjSSzdu3afHUu/PxnzJhhJJn333/fXpaTk2NiYmJM5cqVTUZGhjHm735SvXp1c/ToUXvdTz/91Egyn3/+eeEHCgBwxXB7OQDgqhcbG6vAwECFhYWpb9++qly5sj755BPVqlXLoV7eFdk8K1askIeHh0aNGuVQ/uijj8oYo//+978O5TExMWrevLn9fZ06ddSjRw+tWrVKubm5khwH5Dpz5oz++usv1a9fXwEBAdqyZUu+2IcPH+5wtfGhhx6Sp6enVqxYUcSj4LyBAwcqOztbH330kb1syZIlOnv2bJGedb7ttttUrVo1LV68WMYYLV68uNBn6CXHY5Oenq4jR46oXbt2+v3335Weni5JCggIkCQtX75cZ86cKbCdgIAAZWZmKjEx0elYz9ejRw8lJiY6vOLi4orVVmxsrOrVq2d/f/3118vPz0+///67JMlms2nZsmXq1q2bfdyB8xXnboYVK1YoJCTE4VhXqFBBo0aN0smTJ/XVV1851O/Tp4+qVq1qf3/TTTdJkj1GAIB7cXs5AOCqN2fOHF177bXy9PRUcHCwGjRoIKvV8XdjT09P1a5d26Fs7969Cg0NVZUqVRzK827r3bt3r0N5ZGRkvm1fe+21ysrKUlpamkJCQnTq1ClNmTJFCxYs0P79+x2eDc9LLC/WZuXKlVWzZk2XTh3VsGFDtWzZUgsXLtTQoUMlnbu1vHXr1qpfv77T7VSoUEF33323Fi1apFatWumPP/5Q//79C63/v//9TxMnTlRycrKysrIclqWnp8vf31/t2rVTr169NHnyZE2fPl3t27dXz5491b9/f/vt3w8//LA+/PBDde7cWbVq1dJtt92m3r17q1OnTk7FXbt2bcXGxjq9nxdTp06dfGVVq1a1P5eflpamjIyMEp0+be/evYqMjMzXxwvrtxfGmJeAX60D3QFAecOVbgDAVa9Vq1aKjY1V+/bt1ahRo3zJiCR5e3sXWF7SRo4cqRdeeEG9e/fWhx9+qC+++EKJiYmqXr26bDaby7fvrIEDB+qrr77Sn3/+qd9++03fffddsUb07t+/v7Zu3apJkyapWbNmuu666wqs99tvv6ljx446cuSIpk2bpv/85z9KTEzU2LFjJcl+bCwWiz766CMlJydrxIgR2r9/v+677z41b95cJ0+elCQFBQVp69at+uyzz+zP5Hfu3FmDBg0q5tH4W2FXnvPuZLhQQQPGSSp0ID53KA0xAkB5RtINACiz6tatqwMHDujEiRMO5Tt27LAvP9+uXbvytfHrr7+qUqVKCgwMlCR99NFHGjRokF577TXddddduvXWW9W2bVsdP368wBgubPPkyZM6ePBggSNoF9XFbl3u27evPDw89MEHH2jhwoWqUKGC+vTpU+RttG3bVnXq1NGXX3550avcn3/+ubKzs/XZZ5/pgQceUJcuXRQbG1vo/NitW7fWCy+8oE2bNmnhwoX6+eeftXjxYvtyLy8vdevWTW+88YZ+++03PfDAA/rXv/6l3bt3F3kfzle1atUCP6sLrx47KzAwUH5+ftq2bdtF6xXlNvO6detq165d+X7EKazfAgCubiTdAIAyq0uXLsrNzdXs2bMdyqdPny6LxaLOnTs7lCcnJzs8l/3HH3/o008/1W233Wa/mujh4ZHvCuLrr79e6JXS+fPnOzy7PHfuXJ09ezbftovD19e30GS/Ro0a6ty5s95//30tXLhQnTp1cphv21kWi0WzZs3SxIkTde+99xZaL+/4XHi7/YIFCxzqHTt2LN/xi4qKkiRlZ2dLkv766y+H5VarVddff71DneKqV6+e0tPTHUZfP3jwYIFTrDnDarWqZ8+e+vzzz7Vp06Z8y/P2NW/O+MI+r/N16dJFqampWrJkib3s7Nmzev3111W5cmW1a9euWLECANyDZ7oBAGVWt27d1KFDBz311FNKSUlRs2bN9MUXX+jTTz/VmDFjHAbIkqQmTZooLi7OYcowSZo8ebK9zu2336733ntP/v7+uu6665ScnKzVq1c7TF92vpycHHXs2FG9e/fWzp079cYbb6ht27bq3r37Ze9f8+bNtXr1ak2bNk2hoaGKiIhQdHS0ffnAgQN11113SZKee+65Ym+nR48e6tGjx0Xr3Hbbbfar0w888IBOnjypN998U0FBQTp48KC93rvvvqs33nhDd9xxh+rVq6cTJ07ozTfflJ+fn7p06SJJuv/++3X06FHdcsstql27tvbu3avXX39dUVFRhU6z5ay+ffvqySef1B133KFRo0YpKytLc+fO1bXXXlvgQHjOePHFF/XFF1+oXbt2Gj58uBo1aqSDBw9q6dKl+vbbbxUQEKCoqCh5eHjopZdeUnp6ury9ve1zml9o+PDh+r//+z8NHjxYmzdvVnh4uD766CP973//04wZM/KNUQAAuLqRdAMAyiyr1arPPvtMzzzzjJYsWaIFCxYoPDxcr7zyih599NF89du1a6eYmBhNnjxZ+/bt03XXXad33nnHfpVVkmbOnCkPDw8tXLhQp0+f1o033qjVq1cXOjr27NmztXDhQj3zzDM6c+aM+vXrp1mzZl3WHN15pk2bpuHDh2vChAk6deqUBg0a5JB0d+vWTVWrVpXNZiuRJP9iGjRooI8++kgTJkzQY489ppCQED300EMKDAzUfffdZ6/Xrl07bdiwQYsXL9ahQ4fk7++vVq1aaeHChYqIiJAk3XPPPZo/f77eeOMNHT9+XCEhIerTp48mTZp02c/tV69eXZ988oni4+P1xBNPKCIiQlOmTNGuXbuKnXTXqlVL69ev19NPP62FCxcqIyNDtWrVUufOne3ztoeEhGjevHmaMmWKhg4dqtzcXK1du7bApLtixYr68ssvNW7cOL377rvKyMhQgwYNtGDBAg0ePPhydh8A4AYWwygbAACUSWfPnlVoaKi6deumt99+293hAABQLvFMNwAAZdSyZcuUlpamgQMHujsUAADKLa50AwBQxqxfv14//vijnnvuOdWoUaPYt00DAIDLx5VuAADKmLlz5+qhhx5SUFCQ/vWvf7k7HAAAyjWudAMAAAAA4CJc6QYAAAAAwEVIugEAAAAAcBHm6S4mm82mAwcOqEqVKiUy1yoAAAAAoPQwxujEiRMKDQ2V1Vr49WyS7mI6cOCAwsLC3B0GAAAAAMCN/vjjD9WuXbvQ5VdF0j1nzhy98sorSk1NVbNmzfT666+rVatWhdZfunSpnn76aaWkpCgyMlIvvfSSunTpUmDdBx98UP/3f/+n6dOna8yYMfbyo0ePauTIkfr8889ltVrVq1cvzZw5U5UrV3Yq5ipVqkg6d4D9/Pyc3lebzaa0tDQFBgZe9NcQoKziHEB5Rv9Hecc5gPKM/l/2ZGRkKCwszJ4bFsbtSfeSJUsUHx+vefPmKTo6WjNmzFBcXJx27typoKCgfPXXrVunfv36acqUKbr99tu1aNEi9ezZU1u2bFGTJk0c6n7yySf67rvvFBoamq+dAQMG6ODBg0pMTNSZM2c0ZMgQDR8+XIsWLXIq7rxbyv38/IqcdJ8+fVp+fn6cbCiXOAdQntH/Ud5xDqA8o/+XXZd63NjtU4ZFR0erZcuWmj17tqRznTEsLEwjR47UuHHj8tXv06ePMjMztXz5cntZ69atFRUVpXnz5tnL9u/fr+joaK1atUpdu3bVmDFj7Fe6t2/fruuuu04bN25UixYtJEkrV65Uly5d9OeffxaYpGdnZys7O9v+Pu9XjWPHjnGlGygCzgGUZ/R/lHecAyjP6P9lT0ZGhqpWrar09PSL5oRuvdKdk5OjzZs3KyEhwV5mtVoVGxur5OTkAtdJTk5WfHy8Q1lcXJyWLVtmf2+z2XTvvffq8ccfV+PGjQtsIyAgwJ5wS1JsbKysVqvWr1+vO+64I986U6ZM0eTJk/OVp6Wl6fTp05fc1/NjS09PlzGGkw3lEucAyjP6P8o7zgGUZ/T/sufEiRNO1XNr0n3kyBHl5uYqODjYoTw4OFg7duwocJ3U1NQC66emptrfv/TSS/L09NSoUaMKbePCW9c9PT1VrVo1h3bOl5CQ4JDs513pDgwMLPKVbovFwi9cKLc4B1Ce0f9R3nEOoDyj/5c9Pj4+TtVz+zPdJW3z5s2aOXOmtmzZUqJTeXl7e8vb2ztfudVqLfJJY7FYirUeUFZwDqA8o/+jvOMcQHl2NfT/3NxcnTlzxm3bL008PDzk6elZaF7p7Ofo1qS7Ro0a8vDw0KFDhxzKDx06pJCQkALXCQkJuWj9b775RocPH1adOnXsy3Nzc/Xoo49qxowZSklJUUhIiA4fPuzQxtmzZ3X06NFCtwsAAAAApdnJkyf1559/ys3DepUqlSpVUs2aNeXl5VXsNtyadHt5eal58+ZKSkpSz549JZ277SIpKUkjRowocJ2YmBglJSU5TP+VmJiomJgYSdK9996r2NhYh3Xi4uJ07733asiQIfY2jh8/rs2bN6t58+aSpDVr1shmsyk6OrqE9xIAAAAA3Cs3N1d//vmnKlWqpMDAwBK9K7gsMsYoJydHaWlp2rNnjyIjI4t9h4Lbby+Pj4/XoEGD1KJFC7Vq1UozZsxQZmamPUEeOHCgatWqpSlTpkiSRo8erXbt2um1115T165dtXjxYm3atEnz58+XJFWvXl3Vq1d32EaFChUUEhKiBg0aSJIaNWqkTp06adiwYZo3b57OnDmjESNGqG/fvgWOXA4AAAAApdmZM2dkjFFgYKAqVqzo7nBKhYoVK6pChQrau3evcnJynH6G+0JuT7r79OmjtLQ0PfPMM0pNTVVUVJRWrlxpHyxt3759Dr8otGnTRosWLdKECRM0fvx4RUZGatmyZfnm6L6UhQsXasSIEerYsaOsVqt69eqlWbNmlei+AQAAAMDVhCvcRVMSz9+7fZ7u0iojI0P+/v6XnJPtQjabTYcPH1ZQUBADiKBc4hxAeUb/R3nHOYDyzN39//Tp09qzZ48iIiKKfcW2PLrYcXM2J+RfOwAAAAAAXMTtt5cDAAAAANxjeuKvV3R7Y2+99opu72pA0o2r3uX+Q1AeT2wAAACgLBg8eLDeffddSecGyK5Tp44GDhyo8ePH69tvv1WHDh0UEBCggwcPOtz+vXHjRrVq1UqS7FOkffnll+rQoUO+bTz11FN6/vnnXbYPJN0AAAAAgKtWp06dtGDBAmVnZ2vFihV65JFHVKFCBfu00VWqVNEnn3yifv362dd5++23VadOHe3bty9fezt37nR4Brty5coujZ+kG2VeSdwyw9VyAAAAwD28vb0VEhIiSXrooYf0ySef6LPPPrMn3YMGDdI///lPe9J96tQpLV68WKNGjdJzzz2Xr72goCAFBARcsfhJugEncIs7AAAAcHWoWLGi/vrrL/v7e++9V6+88or27dunOnXq6N///rfCw8N1ww03uDHKv5F0A6UAV+sBAABQ3hljlJSUpFWrVmnkyJH28qCgIHXu3FnvvPOOnnnmGf3zn//UfffdV2g7tWvXdni/d+9eVa9e3WVxk3QD5cSVHpmyICT+AAAAKKrly5ercuXKOnPmjGw2m/r3769JkyZp48aN9jr33XefRo8erXvuuUfJyclaunSpvvnmmwLb++abb1SlShX7+6pVq7o0fpJuAOUGdwwAAACUPh06dNDcuXPl5eWl0NBQeXrmT2M7d+6s4cOHa+jQoerWrdtFr1xHRETwTDcAAAAAAJLk6+ur+vXrX7SOp6enBg4cqJdffln//e9/r1BkzrG6OwAAAAAAAC7Xc889p7S0NMXFxbk7FAdc6QZwxUxP/FUyRpVyTyrLI12yWIq0Prd2AwAAlKyy9P3Ky8tLNWrUcHcY+ZB0Ayg1robB4AAAAHDlvPPOO4Uua9++vYwxhS7v2bOnw/JL1XcVbi8HAAAAAMBFuNINXAFcoS07LvezLEu3cAEAAODSuNINAAAAAICLkHQDAAAAAOAiJN0AAAAAUE64YyCx0qwkjhdJNwAAAACUcR4eHpKknJwcN0dSumRlZUmSKlSoUOw2GEgNAEoZBnMDAABF5enpqUqVKiktLU0VKlSQ1cr114sxxigrK0uHDx9WQECA/UeL4iDpBoAriJHsAQCAO1gsFtWsWVN79uzR3r173R1OqREQEKCQkJDLaoOkGwDKGa6UAwBQPnl5eSkyMpJbzJ1UoUKFy7rCnYekGwAAAADKCavVKh8fH3eHUa5wIz8AAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CJXRdI9Z84chYeHy8fHR9HR0dqwYcNF6y9dulQNGzaUj4+PmjZtqhUrVjgsnzRpkho2bChfX19VrVpVsbGxWr9+vUOd8PBwWSwWh9fUqVNLfN8AAAAAAOWX25PuJUuWKD4+XhMnTtSWLVvUrFkzxcXF6fDhwwXWX7dunfr166ehQ4fq+++/V8+ePdWzZ09t27bNXufaa6/V7Nmz9dNPP+nbb79VeHi4brvtNqWlpTm09eyzz+rgwYP218iRI126rwAAAACA8sXtSfe0adM0bNgwDRkyRNddd53mzZunSpUq6Z///GeB9WfOnKlOnTrp8ccfV6NGjfTcc8/phhtu0OzZs+11+vfvr9jYWF1zzTVq3Lixpk2bpoyMDP34448ObVWpUkUhISH2l6+vr0v3FQAAAABQvrh1yrCcnBxt3rxZCQkJ9jKr1arY2FglJycXuE5ycrLi4+MdyuLi4rRs2bJCtzF//nz5+/urWbNmDsumTp2q5557TnXq1FH//v01duxYeXoWfEiys7OVnZ1tf5+RkSFJstlsstlsl9zXPDabTcaYIq1T7hnj7ghQkoz5+4VSiX+/io//A1DecQ6gPKP/lz3OfpZuTbqPHDmi3NxcBQcHO5QHBwdrx44dBa6TmppaYP3U1FSHsuXLl6tv377KyspSzZo1lZiYqBo1atiXjxo1SjfccIOqVaumdevWKSEhQQcPHtS0adMK3O6UKVM0efLkfOVpaWk6ffq0U/srnftg0tPTZYyR1er2Gw1KhUq5J90dAkqUkbc5LdkkyeLuYFAMhT3+g0vj/wCUd5wDKM/o/2XPiRMnnKrn1qTblTp06KCtW7fqyJEjevPNN9W7d2+tX79eQUFBkuRwtfz666+Xl5eXHnjgAU2ZMkXe3t752ktISHBYJyMjQ2FhYQoMDJSfn5/TcdlsNlksFgUGBnKyOSnLI93dIaAkGSMZKctaWbKQdJdGef+Oouj4PwDlHecAyjP6f9nj4+PjVD23Jt01atSQh4eHDh065FB+6NAhhYSEFLhOSEiIU/V9fX1Vv3591a9fX61bt1ZkZKTefvtth1vZzxcdHa2zZ88qJSVFDRo0yLfc29u7wGTcarUW+aSxWCzFWq/cIjEreyyWv18odfi36/LwfwDKO84BlGf0/7LF2c/RrZ+2l5eXmjdvrqSkJHuZzWZTUlKSYmJiClwnJibGob4kJSYmFlr//HbPfyb7Qlu3bpXVauUKDgAAAACgxLj99vL4+HgNGjRILVq0UKtWrTRjxgxlZmZqyJAhkqSBAweqVq1amjJliiRp9OjRateunV577TV17dpVixcv1qZNmzR//nxJUmZmpl544QV1795dNWvW1JEjRzRnzhzt379fd999t6Rzg7GtX79eHTp0UJUqVZScnKyxY8fqnnvuUdWqVd1zIACglJie+OtltzH21mtLIBIAAICrn9uT7j59+igtLU3PPPOMUlNTFRUVpZUrV9oHS9u3b5/DZfs2bdpo0aJFmjBhgsaPH6/IyEgtW7ZMTZo0kSR5eHhox44devfdd3XkyBFVr15dLVu21DfffKPGjRtLOner+OLFizVp0iRlZ2crIiJCY8eOzTcqOgAAAAAAl8NiDPP2FEdGRob8/f2Vnp5e5IHUDh8+rKCgIJ7lcFJJXFXDVcQYVco9qSwPBlIrz8rrlW7+D0B5xzmA8oz+X/Y4mxO6/Uo3AKD8udwf08pr0g4AAEoffmIBAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXIR5ugEApQ7zfAMAgNKCK90AAAAAALgISTcAAAAAAC5C0g0AAAAAgIvwTDdc7nKfvQQAAACA0oor3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi/BMNwCg3CmJsSaY6xsAADiDK90AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuclUk3XPmzFF4eLh8fHwUHR2tDRs2XLT+0qVL1bBhQ/n4+Khp06ZasWKFw/JJkyapYcOG8vX1VdWqVRUbG6v169c71Dl69KgGDBggPz8/BQQEaOjQoTp58mSJ7xsAAAAAoPxye9K9ZMkSxcfHa+LEidqyZYuaNWumuLg4HT58uMD669atU79+/TR06FB9//336tmzp3r27Klt27bZ61x77bWaPXu2fvrpJ3377bcKDw/XbbfdprS0NHudAQMG6Oeff1ZiYqKWL1+ur7/+WsOHD3f5/gIAAAAAyg+LMca4M4Do6Gi1bNlSs2fPliTZbDaFhYVp5MiRGjduXL76ffr0UWZmppYvX24va926taKiojRv3rwCt5GRkSF/f3+tXr1aHTt21Pbt23Xddddp48aNatGihSRp5cqV6tKli/7880+FhobmayM7O1vZ2dkObYaFhenYsWPy8/Nzen9tNpvS0tIUGBgoq9Xtv3lcETNX73J3CLiaGKNKuSeV5VFZsljcHQ1QbKNjI4u8Tnn8PwA4H+cAyjP6f9mTkZGhqlWrKj09/aI5oecVjCmfnJwcbd68WQkJCfYyq9Wq2NhYJScnF7hOcnKy4uPjHcri4uK0bNmyQrcxf/58+fv7q1mzZvY2AgIC7Am3JMXGxspqtWr9+vW644478rUzZcoUTZ48OV95WlqaTp8+fcl9zWOz2ZSeni5jTLk52Srlcts+zmfkbU5LNkki6UbpVdgdWRdTHv8PAM7HOYDyjP5f9pw4ccKpem5Nuo8cOaLc3FwFBwc7lAcHB2vHjh0FrpOamlpg/dTUVIey5cuXq2/fvsrKylLNmjWVmJioGjVq2NsICgpyqO/p6alq1arlaydPQkKCQ7Kfd6U7MDCwyFe6LRZLufqFK8sj3d0h4GpijGSkLCtXulG6Xfj/iDPK4/8BwPk4B1Ce0f/LHh8fH6fquTXpdqUOHTpo69atOnLkiN5880317t1b69evL9aXJEny9vaWt7d3vnKr1Vrkk8ZisRRrvVKLxAoXslj+fgGl1Myk3UVfyf54RYbG3tag5IMCSoFy9z0IOA/9v2xx9nN066ddo0YNeXh46NChQw7lhw4dUkhISIHrhISEOFXf19dX9evXV+vWrfX222/L09NTb7/9tr2NC28LPHv2rI4ePVrodgEAAAAAKCq3Jt1eXl5q3ry5kpKS7GU2m01JSUmKiYkpcJ2YmBiH+pKUmJhYaP3z280bCC0mJkbHjx/X5s2b7cvXrFkjm82m6Ojo4u4OAAAAAAAO3H57eXx8vAYNGqQWLVqoVatWmjFjhjIzMzVkyBBJ0sCBA1WrVi1NmTJFkjR69Gi1a9dOr732mrp27arFixdr06ZNmj9/viQpMzNTL7zwgrp3766aNWvqyJEjmjNnjvbv36+7775bktSoUSN16tRJw4YN07x583TmzBmNGDFCffv2LXDkcgAAAAAAisPtSXefPn2UlpamZ555RqmpqYqKitLKlSvtg6Xt27fP4V75Nm3aaNGiRZowYYLGjx+vyMhILVu2TE2aNJEkeXh4aMeOHXr33Xd15MgRVa9eXS1bttQ333yjxo0b29tZuHChRowYoY4dO8pqtapXr16aNWvWld15AAAAAECZ5vZ5ukurvLm/LzUn24VsNpsOHz6soKCgcjOAwvTEX90dAq4mzNON8uy8/s9AaiiPyuP3ICAP/b/scTYn5NMGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFzE090BAABQHk1P/PWy2xh767UlEAkAAHAlrnQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi3i6OwAAAFA80xN/vaz1x956bQlFAgAACsOVbgAAAAAAXOSqSLrnzJmj8PBw+fj4KDo6Whs2bLho/aVLl6phw4by8fFR06ZNtWLFCvuyM2fO6Mknn1TTpk3l6+ur0NBQDRw4UAcOHHBoIzw8XBaLxeE1depUl+wfAAAAAKB8cnvSvWTJEsXHx2vixInasmWLmjVrpri4OB0+fLjA+uvWrVO/fv00dOhQff/99+rZs6d69uypbdu2SZKysrK0ZcsWPf3009qyZYs+/vhj7dy5U927d8/X1rPPPquDBw/aXyNHjnTpvgIAAAAAyheLMca4M4Do6Gi1bNlSs2fPliTZbDaFhYVp5MiRGjduXL76ffr0UWZmppYvX24va926taKiojRv3rwCt7Fx40a1atVKe/fuVZ06dSSdu9I9ZswYjRkzplhxZ2RkyN/fX+np6fLz83N6PZvNpsOHDysoKEhWq9t/87giLveZQ5QxxqhS7klleVSWLBZ3RwNcWVdZ/+eZblxp5fF7EJCH/l/2OJsTunUgtZycHG3evFkJCQn2MqvVqtjYWCUnJxe4TnJysuLj4x3K4uLitGzZskK3k56eLovFooCAAIfyqVOn6rnnnlOdOnXUv39/jR07Vp6eBR+S7OxsZWdn299nZGRIOnfy2Gy2i+2mA5vNJmNMkdYp9dz7uw6uNsb8/QLKm6us/5er/4twVSiX34OA/4/+X/Y4+1m6Nek+cuSIcnNzFRwc7FAeHBysHTt2FLhOampqgfVTU1MLrH/69Gk9+eST6tevn8OvD6NGjdINN9ygatWqad26dUpISNDBgwc1bdq0AtuZMmWKJk+enK88LS1Np0+fvuh+ns9msyk9PV3GmHLzC1el3JPuDgFXFSNvc1qySZL7r/QBV9bV1f8Le5QLcJXy+D0IyEP/L3tOnDjhVL0yPWXYmTNn1Lt3bxljNHfuXIdl518tv/766+Xl5aUHHnhAU6ZMkbe3d762EhISHNbJyMhQWFiYAgMDi3x7ucViUWBgYLk52bI80t0dAq4mxkhGyrJeHbfXAlfUVdb/g4KC3B0Cypny+D0IyEP/L3t8fHycqlespHvt2rXq0KFDcVZ1UKNGDXl4eOjQoUMO5YcOHVJISEiB64SEhDhVPy/h3rt3r9asWXPJxDg6Olpnz55VSkqKGjRokG+5t7d3gcm41Wot8kljsViKtV6pdRV8scRVxmL5+wWUN1dR/y83/w/hqlLuvgcB56H/ly3Ofo7F+rQ7deqkevXq6fnnn9cff/xRnCYkSV5eXmrevLmSkpLsZTabTUlJSYqJiSlwnZiYGIf6kpSYmOhQPy/h3rVrl1avXq3q1atfMpatW7fKarXyqz8AAAAAoMQUK+nev3+/RowYoY8++kjXXHON4uLi9OGHHyonJ6fIbcXHx+vNN9/Uu+++q+3bt+uhhx5SZmamhgwZIkkaOHCgw0Bro0eP1sqVK/Xaa69px44dmjRpkjZt2qQRI0ZIOpdw33XXXdq0aZMWLlyo3NxcpaamKjU11R5fcnKyZsyYoR9++EG///67Fi5cqLFjx+qee+5R1apVi3NIAAAAAADI57KnDNuyZYsWLFigDz74QJLUv39/DR06VM2aNXO6jdmzZ+uVV15RamqqoqKiNGvWLEVHR0uS2rdvr/DwcL3zzjv2+kuXLtWECROUkpKiyMhIvfzyy+rSpYskKSUlRREREQVuZ+3atWrfvr22bNmihx9+WDt27FB2drYiIiJ07733Kj4+vsBbyAvClGHOY8owOLjKpkwCrqgy1v+ZcgxFVR6/BwF56P9lj7M5YYnM033gwAHNnz9fU6dOlaenp06fPq2YmBjNmzdPjRs3vtzmr0ok3c4j6YaDMpZ0AEVSxvo/STeKqjx+DwLy0P/LHmdzwmJ/2mfOnNFHH32kLl26qG7dulq1apVmz56tQ4cOaffu3apbt67uvvvu4jYPAAAAAECpV6zRy0eOHKkPPvhAxhjde++9evnll9WkSRP7cl9fX7366qsKDQ0tsUABAAAAAChtipV0//LLL3r99dd15513FvoMdI0aNbR27drLCg4AAAAAgNKsWLeXT5w4UXfffXe+hPvs2bP6+uuvJUmenp5q167d5UcIAAAAAEApVayku0OHDjp69Gi+8vT0dHXo0OGygwIAAAAAoCwoVtJtjJGlgFFX//rrL/n6+l52UAAAAAAAlAVFeqb7zjvvlCRZLBYNHjzY4fby3Nxc/fjjj2rTpk3JRggAAAAAQClVpKTb399f0rkr3VWqVFHFihXty7y8vNS6dWsNGzasZCMEAAAAAKCUKlLSvWDBAklSeHi4HnvsMW4lBwAAAADgIoo1ZdjEiRNLOg4AAFDKTE/89bLbGHvrtSUQCQAAVy+nk+4bbrhBSUlJqlq1qv7xj38UOJBani1btpRIcAAAAAAAlGZOJ909evSwD5zWs2dPV8UDAAAAAECZ4XTSff4t5dxeDgAAAADApRVrnm4AAAAAAHBpTl/prlq16kWf4z7f0aNHix0QAAAAAABlhdNJ94wZM1wYBgAAAAAAZY/TSfegQYNcGQcAAAAAAGWO00l3RkaG/Pz87H+/mLx6AAAAAACUZ0V6pvvgwYMKCgpSQEBAgc93G2NksViUm5tbokECAAAAAFAaOZ10r1mzRtWqVZMkrV271mUBAQAAAABQVjiddLdr167AvwMAAAAAgII5nXRf6NixY3r77be1fft2SdJ1112nIUOG2K+GAwAAAABQ3hUr6f7666/VrVs3+fv7q0WLFpKkWbNm6dlnn9Xnn3+um2++uUSDBAAAZdP0xF8va/2xt15bQpEAAOAaxUq6H3nkEfXp00dz586Vh4eHJCk3N1cPP/ywHnnkEf30008lGiQAAAAAAKWRtTgr7d69W48++qg94ZYkDw8PxcfHa/fu3SUWHAAAAAAApVmxku4bbrjB/iz3+bZv365mzZpddlAAAAAAAJQFTt9e/uOPP9r/PmrUKI0ePVq7d+9W69atJUnfffed5syZo6lTp5Z8lAAAAAAAlEJOX+mOiorSP/7xD0VFRalfv376448/9MQTT+jmm2/WzTffrCeeeEJ79+5V//79ixzEnDlzFB4eLh8fH0VHR2vDhg0Xrb906VI1bNhQPj4+atq0qVasWGFfdubMGT355JNq2rSpfH19FRoaqoEDB+rAgQMObRw9elQDBgyQn5+fAgICNHToUJ08ebLIsQMAAAAAUBinr3Tv2bPHJQEsWbJE8fHxmjdvnqKjozVjxgzFxcVp586dCgoKyld/3bp16tevn6ZMmaLbb79dixYtUs+ePbVlyxY1adJEWVlZ2rJli55++mk1a9ZMx44d0+jRo9W9e3dt2rTJ3s6AAQN08OBBJSYm6syZMxoyZIiGDx+uRYsWuWQ/AQAAAADlj8UYY9wZQHR0tFq2bKnZs2dLkmw2m8LCwjRy5EiNGzcuX/0+ffooMzNTy5cvt5e1bt1aUVFRmjdvXoHb2Lhxo1q1aqW9e/eqTp062r59u6677jpt3LjRPuXZypUr1aVLF/35558KDQ3N10Z2drays7Pt7zMyMhQWFqZjx47Jz8/P6f212WxKS0tTYGCgrNZiPVJf6sxcvcvdIeBqYowq5Z5UlkdlyWJxdzTAlUX/L3GjYyPdHQKKoDx+DwLy0P/LnoyMDFWtWlXp6ekXzQmLNWVYnl9++UX79u1TTk6OQ3n37t2dWj8nJ0ebN29WQkKCvcxqtSo2NlbJyckFrpOcnKz4+HiHsri4OC1btqzQ7aSnp8tisSggIMDeRkBAgD3hlqTY2FhZrVatX79ed9xxR742pkyZosmTJ+crT0tL0+nTpy+2mw5sNpvS09NljCk3J1ulXG7bx/mMvM1pySZJJB0ob+j/Je3w4cPuDgFFUB6/BwF56P9lz4kTJ5yqV6yk+/fff9cdd9yhn376SRaLRXkXyy3//1f73Nxcp9o5cuSIcnNzFRwc7FAeHBysHTt2FLhOampqgfVTU1MLrH/69Gk9+eST6tevn/3Xh9TU1Hy3rnt6eqpatWqFtpOQkOCQ7Odd6Q4MDCzylW6LxVKufuHK8kh3dwi4mhgjGSnLypU+lEP0/xJX0KNouHqVx+9BQB76f9nj4+PjVL1iJd2jR49WRESEkpKSFBERoQ0bNuivv/7So48+qldffbU4TbrEmTNn1Lt3bxljNHfu3Mtqy9vbW97e3vnKrVZrkU8ai8VSrPVKLb5Y4kIWy98voLyh/5eocvN/aRlS7r4HAeeh/5ctzn6OxUq6k5OTtWbNGtWoUcPeadq2baspU6Zo1KhR+v77751qp0aNGvLw8NChQ4ccyg8dOqSQkJAC1wkJCXGqfl7CvXfvXq1Zs8bhanRISEi+29HOnj2ro0ePFrpdAAAAAACKqlg/seTm5qpKlSqSziXOedNx1a1bVzt37nS6HS8vLzVv3lxJSUn2MpvNpqSkJMXExBS4TkxMjEN9SUpMTHSon5dw79q1S6tXr1b16tXztXH8+HFt3rzZXrZmzRrZbDZFR0c7HT8AAAAAABdTrCvdTZo00Q8//KCIiAhFR0fr5ZdflpeXl+bPn69rrrmmSG3Fx8dr0KBBatGihVq1aqUZM2YoMzNTQ4YMkSQNHDhQtWrV0pQpUySdu7W9Xbt2eu2119S1a1ctXrxYmzZt0vz58yWdS7jvuusubdmyRcuXL1dubq79Oe1q1arJy8tLjRo1UqdOnTRs2DDNmzdPZ86c0YgRI9S3b98CRy4HAAAAAKA4ipV0T5gwQZmZmZKkZ599VrfffrtuuukmVa9eXUuWLClSW3369FFaWpqeeeYZpaamKioqSitXrrQPlrZv3z6He+XbtGmjRYsWacKECRo/frwiIyO1bNkyNWnSRJK0f/9+ffbZZ5KkqKgoh22tXbtW7du3lyQtXLhQI0aMUMeOHWW1WtWrVy/NmjWrOIcDAAAAAIACldg83UePHlXVqlXtI5iXdRkZGfL397/knGwXstlsOnz4sIKCgsrNAArTE391dwi4mjBPMcoz+n+JG3vrte4OAUVQHr8HAXno/2WPsznhZc3TLUl//PGHJCksLOxymwIAAAAAoEwp1k8sZ8+e1dNPPy1/f3+Fh4crPDxc/v7+mjBhgs6cOVPSMQIAAAAAUCoV60r3yJEj9fHHH+vll1+2jxqenJysSZMm6a+//rrsObEBAAAAACgLipV0L1q0SIsXL1bnzp3tZddff73CwsLUr18/km4AAAAAAFTM28u9vb0VHh6erzwiIkJeXl6XGxMAAAAAAGVCsZLuESNG6LnnnlN2dra9LDs7Wy+88IJGjBhRYsEBAAAAAFCaOX17+Z133unwfvXq1apdu7aaNWsmSfrhhx+Uk5Ojjh07lmyEAAAAhSiJaSmZdgwA4EpOJ93+/v4O73v16uXwninDAAAAAABw5HTSvWDBAlfGAQAAAABAmVOs0cvzpKWlaefOnZKkBg0aKDAwsESCAgAAAACgLCjWQGqZmZm67777VLNmTd188826+eabFRoaqqFDhyorK6ukYwQAAAAAoFQqVtIdHx+vr776Sp9//rmOHz+u48eP69NPP9VXX32lRx99tKRjBAAAAACgVCrW7eX//ve/9dFHH6l9+/b2si5duqhixYrq3bu35s6dW1LxAQAAAABQahXrSndWVpaCg4PzlQcFBXF7OQAAAAAA/1+xku6YmBhNnDhRp0+ftpedOnVKkydPVkxMTIkFBwAAAABAaVas28tnzJihTp06qXbt2mrWrJkk6YcffpCPj49WrVpVogECAAAAAFBaFSvpbtq0qXbt2qWFCxdqx44dkqR+/fppwIABqlixYokGCAAAAABAaVXkpPvMmTNq2LChli9frmHDhrkiJgAAAAAAyoQiJ90VKlRweJYbZdv0xF/dHQIAAC51uf/Xjb312hKKBABQFhVrILVHHnlEL730ks6ePVvS8QAAAAAAUGYU65nujRs3KikpSV988YWaNm0qX19fh+Uff/xxiQQHAAAAAEBpVqykOyAgQL169SrpWAAAAAAAKFOKlHTbbDa98sor+vXXX5WTk6NbbrlFkyZNYsRyAAAAAAAKUKRnul944QWNHz9elStXVq1atTRr1iw98sgjrooNAAAAAIBSrUhJ97/+9S+98cYbWrVqlZYtW6bPP/9cCxculM1mc1V8AAAAAACUWkVKuvft26cuXbrY38fGxspisejAgQMlHhgAAAAAAKVdkZLus2fPysfHx6GsQoUKOnPmTLEDmDNnjsLDw+Xj46Po6Ght2LDhovWXLl2qhg0bysfHR02bNtWKFSscln/88ce67bbbVL16dVksFm3dujVfG+3bt5fFYnF4Pfjgg8XeBwAAAAAAClKkgdSMMRo8eLC8vb3tZadPn9aDDz7oMG2Ys1OGLVmyRPHx8Zo3b56io6M1Y8YMxcXFaefOnQoKCspXf926derXr5+mTJmi22+/XYsWLVLPnj21ZcsWNWnSRJKUmZmptm3bqnfv3ho2bFih2x42bJieffZZ+/tKlSo5FTMAAAAAAM6yGGOMs5WHDBniVL0FCxY4VS86OlotW7bU7NmzJZ0bHT0sLEwjR47UuHHj8tXv06ePMjMztXz5cntZ69atFRUVpXnz5jnUTUlJUUREhL7//ntFRUU5LGvfvr2ioqI0Y8YMp+IsSEZGhvz9/ZWeni4/Pz+n17PZbDp8+LCCgoJktRbpRgO3mJ74q7tDQFljjCrlnlSWR2XJYnF3NMCVRf9HAcbeeq27Q7hiStv3IKAk0f/LHmdzwiJd6XY2mXZGTk6ONm/erISEBHuZ1WpVbGyskpOTC1wnOTlZ8fHxDmVxcXFatmxZkbe/cOFCvf/++woJCVG3bt309NNPX/Rqd3Z2trKzs+3vMzIyJJ07eYoykJzNZpMxpvQMPuf8bzKAc4z5+wWUN/R/FKDUfCcoAaXuexBQguj/ZY+zn2WRku6SdOTIEeXm5io4ONihPDg4WDt27ChwndTU1ALrp6amFmnb/fv3V926dRUaGqoff/xRTz75pHbu3HnR2+KnTJmiyZMn5ytPS0vT6dOnnd62zWZTenq6jDGl4heuSrkn3R0Cyhwjb3NaskkSV/pQ3tD/kd/hw4fdHcIVU9q+BwElif5f9pw4ccKpem5Lut1p+PDh9r83bdpUNWvWVMeOHfXbb7+pXr16Ba6TkJDgcJU9IyNDYWFhCgwMLPLt5RaLRYGBgaXiZMvySHd3CChrjJGMlGXl9lqUQ/R/FKCgcWzKqtL2PQgoSfT/sufCQcYL47aku0aNGvLw8NChQ4ccyg8dOqSQkJAC1wkJCSlSfWdFR0dLknbv3l1o0u3t7e0wgFweq9Va5JPGYrEUaz234EshXMFi+fsFlDf0f1ygVHwfKEGl6nsQUMLo/2WLs5+j2z5tLy8vNW/eXElJSfYym82mpKQkxcTEFLhOTEyMQ31JSkxMLLS+s/KmFatZs+ZltQMAAAAAwPncent5fHy8Bg0apBYtWqhVq1aaMWOGMjMz7aOkDxw4ULVq1dKUKVMkSaNHj1a7du302muvqWvXrlq8eLE2bdqk+fPn29s8evSo9u3bpwMHDkiSdu7cKencVfKQkBD99ttvWrRokbp06aLq1avrxx9/1NixY3XzzTfr+uuvv8JHAAAAAABQlrk16e7Tp4/S0tL0zDPPKDU1VVFRUVq5cqV9sLR9+/Y5XLJv06aNFi1apAkTJmj8+PGKjIzUsmXL7HN0S9Jnn33mMLVZ3759JUkTJ07UpEmT5OXlpdWrV9sT/LCwMPXq1UsTJky4QnsNAAAAACgvijRPN/7GPN1AMTFPMcoz+j8KwDzdQPlA/y97XDJPNwAAAEpWSfzAXZ4SdwAobfiJBQAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHAR5ukGAAAo5S53rm/m+QYA1+FKNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC7i6e4AAAAA4F7TE3+9rPXH3nptCUUCAGUPV7oBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARdyedM+ZM0fh4eHy8fFRdHS0NmzYcNH6S5cuVcOGDeXj46OmTZtqxYoVDss//vhj3XbbbapevbosFou2bt2ar43Tp0/rkUceUfXq1VW5cmX16tVLhw4dKsndAgAAAADAvUn3kiVLFB8fr4kTJ2rLli1q1qyZ4uLidPjw4QLrr1u3Tv369dPQoUP1/fffq2fPnurZs6e2bdtmr5OZmam2bdvqpZdeKnS7Y8eO1eeff66lS5fqq6++0oEDB3TnnXeW+P4BAAAAAMo3izHGuGvj0dHRatmypWbPni1JstlsCgsL08iRIzVu3Lh89fv06aPMzEwtX77cXta6dWtFRUVp3rx5DnVTUlIUERGh77//XlFRUfby9PR0BQYGatGiRbrrrrskSTt27FCjRo2UnJys1q1bOxV7RkaG/P39lZ6eLj8/P6f32Waz6fDhwwoKCpLV6vYbDS5peuKv7g4BZY0xqpR7UlkelSWLxd3RAFcW/R9l1Nhbr3WqXmn7HgSUJPp/2eNsTuh5BWNykJOTo82bNyshIcFeZrVaFRsbq+Tk5ALXSU5OVnx8vENZXFycli1b5vR2N2/erDNnzig2NtZe1rBhQ9WpU+eiSXd2drays7Pt7zMyMiSdO3lsNpvT27fZbDLGFGkdt3LfbzIoq4z5+wWUN/R/lFHOfq8pdd+DgBJE/y97nP0s3ZZ0HzlyRLm5uQoODnYoDw4O1o4dOwpcJzU1tcD6qampTm83NTVVXl5eCggIKFI7U6ZM0eTJk/OVp6Wl6fTp005v32azKT09XcaYUvELV6Xck+4OAWWOkbc5LdkkiSt9KG/o/yibCns08EKl7XsQUJLo/2XPiRMnnKrntqS7tElISHC4yp6RkaGwsDAFBgYW+fZyi8WiwMDAUnGyZXmkuzsElDXGSEbKsnJ7Lcoh+j/KqKCgIKfqlbbvQUBJov+XPT4+Pk7Vc1vSXaNGDXl4eOQbNfzQoUMKCQkpcJ2QkJAi1S+sjZycHB0/ftzhavel2vH29pa3t3e+cqvVWuSTxmKxFGs9t+BLIVzBYvn7BZQ39H+UQUX5TlOqvgcBJYz+X7Y4+zm6Len28vJS8+bNlZSUpJ49e0o69+tPUlKSRowYUeA6MTExSkpK0pgxY+xliYmJiomJcXq7zZs3V4UKFZSUlKRevXpJknbu3Kl9+/YVqR0AAACc4/TAq/bBBNPz/fDk7GBsAFDauPX28vj4eA0aNEgtWrRQq1atNGPGDGVmZmrIkCGSpIEDB6pWrVqaMmWKJGn06NFq166dXnvtNXXt2lWLFy/Wpk2bNH/+fHubR48e1b59+3TgwAFJ5xJq6dwV7pCQEPn7+2vo0KGKj49XtWrV5Ofnp5EjRyomJsbpkcsBAAAAAHCGW5PuPn36KC0tTc8884xSU1MVFRWllStX2gdL27dvn8Ml+zZt2mjRokWaMGGCxo8fr8jISC1btkxNmjSx1/nss8/sSbsk9e3bV5I0ceJETZo0SZI0ffp0Wa1W9erVS9nZ2YqLi9Mbb7xxBfYYAAAAAFCeuHWe7tKMebqBYmKeYpRn9H+Udxc5B7i9HGVdacsDcGnO5oR82gAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4iFunDAMAAACky58xhdHPAVytuNINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALuLp7gAAAACAyzU98dfLbmPsrdeWQCQA4Igr3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIiTdAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgISTcAAAAAAC7i6e4AAAAAgKvB9MRfL2v9sbdeW0KRAChLuNINAAAAAICLXBVJ95w5cxQeHi4fHx9FR0drw4YNF62/dOlSNWzYUD4+PmratKlWrFjhsNwYo2eeeUY1a9ZUxYoVFRsbq127djnUCQ8Pl8VicXhNnTq1xPcNAAAAAFB+uT3pXrJkieLj4zVx4kRt2bJFzZo1U1xcnA4fPlxg/XXr1qlfv34aOnSovv/+e/Xs2VM9e/bUtm3b7HVefvllzZo1S/PmzdP69evl6+uruLg4nT592qGtZ599VgcPHrS/Ro4c6dJ9BQAAAACULxZjjHFnANHR0WrZsqVmz54tSbLZbAoLC9PIkSM1bty4fPX79OmjzMxMLV++3F7WunVrRUVFad68eTLGKDQ0VI8++qgee+wxSVJ6erqCg4P1zjvvqG/fvpLOXekeM2aMxowZ41Sc2dnZys7Otr/PyMhQWFiYjh07Jj8/P6f312azKS0tTYGBgbJa3f6bxyXNXL3r0pWAojBGlXJPKsujsmSxuDsa4Mqi/6O8K+PnwOjYSHeHgKtYacsDcGkZGRmqWrWq0tPTL5oTunUgtZycHG3evFkJCQn2MqvVqtjYWCUnJxe4TnJysuLj4x3K4uLitGzZMknSnj17lJqaqtjYWPtyf39/RUdHKzk52Z50S9LUqVP13HPPqU6dOurfv7/Gjh0rT8+CD8mUKVM0efLkfOVpaWn5rqBfjM1mU3p6uowxpeJkq5R70t0hoMwx8janJZsklb0vXMDF0f9R3pXtc6CwOzUBqfTlAbi0EydOOFXPrUn3kSNHlJubq+DgYIfy4OBg7dixo8B1UlNTC6yfmppqX55XVlgdSRo1apRuuOEGVatWTevWrVNCQoIOHjyoadOmFbjdhIQEh2Q/70p3YGBgka90WyyWUvMLV5ZHurtDQFljjGSkLGvZvMoBXBT9H+VdGT8HPvjx8r43caW8bCtteQAuzcfHx6l65XbKsPMT6Ouvv15eXl564IEHNGXKFHl7e+er7+3tXWC51Wot8kljsViKtZ5blMH/EHEVsFj+fgHlDf0f5R3nQKFKxXdDXJZSlQfgkpz9HN36adeoUUMeHh46dOiQQ/mhQ4cUEhJS4DohISEXrZ/3Z1HalM49W3727FmlpKQUdTcAAAAAACiQW5NuLy8vNW/eXElJSfYym82mpKQkxcTEFLhOTEyMQ31JSkxMtNePiIhQSEiIQ52MjAytX7++0DYlaevWrbJarQoKCrqcXQIAAAAAwM7tt5fHx8dr0KBBatGihVq1aqUZM2YoMzNTQ4YMkSQNHDhQtWrV0pQpUyRJo0ePVrt27fTaa6+pa9euWrx4sTZt2qT58+dLOnfLxpgxY/T8888rMjJSERERevrppxUaGqqePXtKOjcY2/r169WhQwdVqVJFycnJGjt2rO655x5VrVrVLccBAAAAAFD2uD3p7tOnj9LS0vTMM88oNTVVUVFRWrlypX0gtH379jncK9+mTRstWrRIEyZM0Pjx4xUZGally5apSZMm9jpPPPGEMjMzNXz4cB0/flxt27bVypUr7Q+6e3t7a/HixZo0aZKys7MVERGhsWPH5hsVHQAAAACAy+H2ebpLq4yMDPn7+19yTrYL2Ww2HT58WEFBQaViAIXpib+6OwSUNWV8jlbgouj/KO84By5q7K3XujsEuFBpywNwac7mhHzaAAAAAAC4CEk3AAAAAAAuQtINAAAAAICLuH0gNQAAAAAlM5YOz4UDVx+udAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSDQAAAACAi5B0AwAAAADgIoxeDgAAAJQRlzsCOqOfAyWPK90AAAAAALgISTcAAAAAAC7C7eVl3OXeYgQAAIDyoyS+O3KLOuCIK90AAAAAALgISTcAAAAAAC5C0g0AAAAAgIuQdAMAAAAA4CIk3QAAAAAAuAijlwMAAAAoMZc7Ajqjn6Os4Uo3AAAAAAAuQtINAAAAAICLkHQDAAAAAOAiJN0AAAAAALgIA6kBAAAAuGowEBvKGpJuAAAAAGXG5SbtEok7ShZJNwAAAACcpyQS93yMUaXck8rySJcslpJv/wL8cHD1uCqS7jlz5uiVV15RamqqmjVrptdff12tWrUqtP7SpUv19NNPKyUlRZGRkXrppZfUpUsX+3JjjCZOnKg333xTx48f14033qi5c+cqMjLSXufo0aMaOXKkPv/8c1mtVvXq1UszZ85U5cqVXbqvAAAAAOBqLvnhoIhI/M9x+0BqS5YsUXx8vCZOnKgtW7aoWbNmiouL0+HDhwusv27dOvXr109Dhw7V999/r549e6pnz57atm2bvc7LL7+sWbNmad68eVq/fr18fX0VFxen06dP2+sMGDBAP//8sxITE7V8+XJ9/fXXGj58uMv3FwAAAABQfliMMcadAURHR6tly5aaPXu2JMlmsyksLEwjR47UuHHj8tXv06ePMjMztXz5cntZ69atFRUVpXnz5skYo9DQUD366KN67LHHJEnp6ekKDg7WO++8o759+2r79u267rrrtHHjRrVo0UKStHLlSnXp0kV//vmnQkNDLxl3RkaG/P39lZ6eLj8/P6f312az6fDhwwoKCpLV6vrfPK6GX7gAB/ZbqypfkVurgKsK/R/lHecAyjP6f5Fd7VfKnc0J3Xp7eU5OjjZv3qyEhAR7mdVqVWxsrJKTkwtcJzk5WfHx8Q5lcXFxWrZsmSRpz549Sk1NVWxsrH25v7+/oqOjlZycrL59+yo5OVkBAQH2hFuSYmNjZbVatX79et1xxx35tpudna3s7Gz7+/T0dEnS8ePHZbPZnN5nm82mjIwMeXl5XZGk+/TJEy7fBlAkxshiO6nTVsN/OCh/6P8o7zgHUJ7R/4vs+PHj7g7hojIyMiSde7z5YtyadB85ckS5ubkKDg52KA8ODtaOHTsKXCc1NbXA+qmpqfbleWUXqxMUFOSw3NPTU9WqVbPXudCUKVM0efLkfOV169YtbPcAAAAAAMU03t0BOOnEiRPy9/cvdPlVMZBaaZCQkOBwhd1ms+no0aOqXr26LEX4pSojI0NhYWH6448/inRbOlBWcA6gPKP/o7zjHEB5Rv8ve4wxOnHixCUfT3Zr0l2jRg15eHjo0KFDDuWHDh1SSEhIgeuEhIRctH7en4cOHVLNmjUd6kRFRdnrXDhQ29mzZ3X06NFCt+vt7S1vb2+HsoCAgIvv4EX4+flxsqFc4xxAeUb/R3nHOYDyjP5ftlzsCncet45e7uXlpebNmyspKcleZrPZlJSUpJiYmALXiYmJcagvSYmJifb6ERERCgkJcaiTkZGh9evX2+vExMTo+PHj2rx5s73OmjVrZLPZFB0dXWL7BwAAAAAo39x+e3l8fLwGDRqkFi1aqFWrVpoxY4YyMzM1ZMgQSdLAgQNVq1YtTZkyRZI0evRotWvXTq+99pq6du2qxYsXa9OmTZo/f74kyWKxaMyYMXr++ecVGRmpiIgIPf300woNDVXPnj0lSY0aNVKnTp00bNgwzZs3T2fOnNGIESPUt29fp0YuBwAAAADAGW5Puvv06aO0tDQ988wzSk1NVVRUlFauXGkfCG3fvn0Oo3y3adNGixYt0oQJEzR+/HhFRkZq2bJlatKkib3OE088oczMTA0fPlzHjx9X27ZttXLlSvn4+NjrLFy4UCNGjFDHjh1ltVrVq1cvzZo1y+X76+3trYkTJ+a7VR0oLzgHUJ7R/1HecQ6gPKP/l19un6cbAAAAAICyyq3PdAMAAAAAUJaRdAMAAAAA4CIk3QAAAAAAuAhJNwAAAAAALkLSfYXNmTNH4eHh8vHxUXR0tDZs2ODukICLmjJlilq2bKkqVaooKChIPXv21M6dOx3qnD59Wo888oiqV6+uypUrq1evXjp06JBDnX379qlr166qVKmSgoKC9Pjjj+vs2bMOdb788kvdcMMN8vb2Vv369fXOO+/ki4dzCO40depU+9SUeej/KOv279+ve+65R9WrV1fFihXVtGlTbdq0yb7cGKNnnnlGNWvWVMWKFRUbG6tdu3Y5tHH06FENGDBAfn5+CggI0NChQ3Xy5EmHOj/++KNuuukm+fj4KCwsTC+//HK+WJYuXaqGDRvKx8dHTZs21YoVK1yz08D/l5ubq6effloRERGqWLGi6tWrp+eee07nj0XNOYBLMrhiFi9ebLy8vMw///lP8/PPP5thw4aZgIAAc+jQIXeHBhQqLi7OLFiwwGzbts1s3brVdOnSxdSpU8ecPHnSXufBBx80YWFhJikpyWzatMm0bt3atGnTxr787NmzpkmTJiY2NtZ8//33ZsWKFaZGjRomISHBXuf33383lSpVMvHx8eaXX34xr7/+uvHw8DArV6601+Ecgjtt2LDBhIeHm+uvv96MHj3aXk7/R1l29OhRU7duXTN48GCzfv168/vvv5tVq1aZ3bt32+tMnTrV+Pv7m2XLlpkffvjBdO/e3URERJhTp07Z63Tq1Mk0a9bMfPfdd+abb74x9evXN/369bMvT09PN8HBwWbAgAFm27Zt5oMPPjAVK1Y0//d//2ev87///c94eHiYl19+2fzyyy9mwoQJpkKFCuann366MgcD5dILL7xgqlevbpYvX2727Nljli5daipXrmxmzpxpr8M5gEsh6b6CWrVqZR555BH7+9zcXBMaGmqmTJnixqiAojl8+LCRZL766itjjDHHjx83FSpUMEuXLrXX2b59u5FkkpOTjTHGrFixwlitVpOammqvM3fuXOPn52eys7ONMcY88cQTpnHjxg7b6tOnj4mLi7O/5xyCu5w4ccJERkaaxMRE065dO3vSTf9HWffkk0+atm3bFrrcZrOZkJAQ88orr9jLjh8/bry9vc0HH3xgjDHml19+MZLMxo0b7XX++9//GovFYvbv32+MMeaNN94wVatWtZ8Tedtu0KCB/X3v3r1N165dHbYfHR1tHnjggcvbSeAiunbtau677z6HsjvvvNMMGDDAGMM5AOdwe/kVkpOTo82bNys2NtZeZrVaFRsbq+TkZDdGBhRNenq6JKlatWqSpM2bN+vMmTMOfbthw4aqU6eOvW8nJyeradOmCg4OtteJi4tTRkaGfv75Z3ud89vIq5PXBucQ3OmRRx5R165d8/VR+j/Kus8++0wtWrTQ3XffraCgIP3jH//Qm2++aV++Z88epaamOvRNf39/RUdHO5wDAQEBatGihb1ObGysrFar1q9fb69z8803y8vLy14nLi5OO3fu1LFjx+x1LnaeAK7Qpk0bJSUl6ddff5Uk/fDDD/r222/VuXNnSZwDcI6nuwMoL44cOaLc3FyHL12SFBwcrB07drgpKqBobDabxowZoxtvvFFNmjSRJKWmpsrLy0sBAQEOdYODg5WammqvU1Dfz1t2sToZGRk6deqUjh07xjkEt1i8eLG2bNmijRs35ltG/0dZ9/vvv2vu3LmKj4/X+PHjtXHjRo0aNUpeXl4aNGiQvQ8X1DfP799BQUEOyz09PVWtWjWHOhEREfnayFtWtWrVQs+TvDYAVxg3bpwyMjLUsGFDeXh4KDc3Vy+88IIGDBggSZwDcApJNwCnPfLII9q2bZu+/fZbd4cCXBF//PGHRo8ercTERPn4+Lg7HOCKs9lsatGihV588UVJ0j/+8Q9t27ZN8+bN06BBg9wcHeB6H374oRYuXKhFixapcePG2rp1q8aMGaPQ0FDOATiN28uvkBo1asjDwyPfiLaHDh1SSEiIm6ICnDdixAgtX75ca9euVe3ate3lISEhysnJ0fHjxx3qn9+3Q0JCCuz7ecsuVsfPz08VK1bkHIJbbN68WYcPH9YNN9wgT09PeXp66quvvtKsWbPk6emp4OBg+j/KtJo1a+q6665zKGvUqJH27dsn6e8+fLG+GRISosOHDzssP3v2rI4ePVoi5wnnAFzp8ccf17hx49S3b181bdpU9957r8aOHaspU6ZI4hyAc0i6rxAvLy81b95cSUlJ9jKbzaakpCTFxMS4MTLg4owxGjFihD755BOtWbMm361PzZs3V4UKFRz69s6dO7Vv3z57346JidFPP/3k8B9OYmKi/Pz87F/mYmJiHNrIq5PXBucQ3KFjx4766aeftHXrVvurRYsWGjBggP3v9H+UZTfeeGO+aSJ//fVX1a1bV5IUERGhkJAQh76ZkZGh9evXO5wDx48f1+bNm+111qxZI5vNpujoaHudr7/+WmfOnLHXSUxMVIMGDVS1alV7nYudJ4ArZGVlyWp1TJk8PDxks9kkcQ7ASe4eya08Wbx4sfH29jbvvPOO+eWXX8zw4cNNQECAw4i2wNXmoYceMv7+/ubLL780Bw8etL+ysrLsdR588EFTp04ds2bNGrNp0yYTExNjYmJi7Mvzpky67bbbzNatW83KlStNYGBggVMmPf7442b79u1mzpw5BU6ZxDkEdzt/9HJj6P8o2zZs2GA8PT3NCy+8YHbt2mUWLlxoKlWqZN5//317nalTp5qAgADz6aefmh9//NH06NGjwOmS/vGPf5j169ebb7/91kRGRjpMl3T8+HETHBxs7r33XrNt2zazePFiU6lSpXzTJXl6eppXX33VbN++3UycOJHpkuBygwYNMrVq1bJPGfbxxx+bGjVqmCeeeMJeh3MAl0LSfYW9/vrrpk6dOsbLy8u0atXKfPfdd+4OCbgoSQW+FixYYK9z6tQp8/DDD5uqVauaSpUqmTvuuMMcPHjQoZ2UlBTTuXNnU7FiRVOjRg3z6KOPmjNnzjjUWbt2rYmKijJeXl7mmmuucdhGHs4huNuFSTf9H2Xd559/bpo0aWK8vb1Nw4YNzfz58x2W22w28/TTT5vg4GDj7e1tOnbsaHbu3OlQ56+//jL9+vUzlStXNn5+fmbIkCHmxIkTDnV++OEH07ZtW+Pt7W1q1aplpk6dmi+WDz/80Fx77bXGy8vLNG7c2PznP/8p+R0GzpORkWFGjx5t6tSpY3x8fMw111xjnnrqKYepvTgHcCkWY4xx55V2AAAAAADKKp7pBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARkm4AAAAAAFyEpBsAAAAAABch6QYAAAAAwEVIugEAAAAAcBGSbgAAAAAAXISkGwAAAAAAFyHpBgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAChhgwcPVnh4eIm2+c4778hisSglJaVE28XVJzw8XIMHD3Z3GACAEkLSDQC4Kv3222964IEHdM0118jHx0d+fn668cYbNXPmTJ06dcrd4bnMiy++qGXLlrk7DLu8ZN9isejbb7/Nt9wYo7CwMFksFt1+++1uiLBwKSkp9tgvfLVu3dqtsa1bt06TJk3S8ePH3RoHAMD1PN0dAAAAF/rPf/6ju+++W97e3ho4cKCaNGminJwcffvtt3r88cf1888/a/78+e4O0yVefPFF3XXXXerZs6dD+b333qu+ffvK29vbLXH5+Pho0aJFatu2rUP5V199pT///NNtcTmjX79+6tKli0NZYGCgm6I5Z926dZo8ebIGDx6sgIAAh2U7d+6U1cp1EQAoK0i6AQBXlT179qhv376qW7eu1qxZo5o1a9qXPfLII9q9e7f+85//uDFC9/Dw8JCHh4fbtt+lSxctXbpUs2bNkqfn318fFi1apObNm+vIkSNui+1SbrjhBt1zzz3uDsNpV/MPGACAouNnVADAVeXll1/WyZMn9fbbbzsk3Hnq16+v0aNHS/r79uF33nknXz2LxaJJkybZ30+aNEkWi0W//vqr7rnnHvn7+yswMFBPP/20jDH6448/1KNHD/n5+SkkJESvvfaaQ3uFPVP95ZdfymKx6Msvv7zofr366qtq06aNqlevrooVK6p58+b66KOP8sWcmZmpd999134bdN6zvRdu//bbb9c111xT4LZiYmLUokULh7L3339fzZs3V8WKFVWtWjX17dtXf/zxx0VjPl+/fv30119/KTEx0V6Wk5Ojjz76SP379y/2PktSYmKi2rZtq4CAAFWuXFkNGjTQ+PHjHeq8/vrraty4sSpVqqSqVauqRYsWWrRokdPxF6Z9+/Zq3759vvILn8vP62uvvvqq5s+fr3r16snb21stW7bUxo0b862/Y8cO9e7dW4GBgapYsaIaNGigp556StK5vvj4449LkiIiIuyfdd5nW9Az3b///rvuvvtuVatWTZUqVVLr1q3z/fiU1xc//PBDvfDCC6pdu7Z8fHzUsWNH7d69u/gHCQBwWUi6AQBXlc8//1zXXHON2rRp45L2+/TpI5vNpqlTpyo6OlrPP/+8ZsyYoVtvvVW1atXSSy+9pPr16+uxxx7T119/XWLbnTlzpv7xj3/o2Wef1YsvvihPT0/dfffdDonTe++9J29vb910001677339N577+mBBx4odD/27NmTL+Hbu3evvvvuO/Xt29de9sILL2jgwIGKjIzUtGnTNGbMGCUlJenmm292+pni8PBwxcTE6IMPPrCX/fe//1V6errDtoq6zz///LNuv/12ZWdn69lnn9Vrr72m7t2763//+5+9zptvvqlRo0bpuuuu04wZMzR58mRFRUVp/fr1TsWelZWlI0eOOLzOnDnj1LoXWrRokV555RU98MADev7555WSkqI777zTob0ff/xR0dHRWrNmjYYNG6aZM2eqZ8+e+vzzzyVJd955p/r16ydJmj59uv2zLuyW90OHDqlNmzZatWqVHn74Yb3wwgs6ffq0unfvrk8++SRf/alTp+qTTz7RY489poSEBH333XcaMGBAsfYXAFACDAAAV4n09HQjyfTo0cOp+nv27DGSzIIFC/Itk2QmTpxofz9x4kQjyQwfPtxedvbsWVO7dm1jsVjM1KlT7eXHjh0zFStWNIMGDbKXLViwwEgye/bscdjO2rVrjSSzdu1ae9mgQYNM3bp1HeplZWU5vM/JyTFNmjQxt9xyi0O5r6+vw3YL2356errx9vY2jz76qEO9l19+2VgsFrN3715jjDEpKSnGw8PDvPDCCw71fvrpJ+Pp6ZmvvLDtbty40cyePdtUqVLFvi9333236dChgzHGmLp165quXbsWeZ+nT59uJJm0tLRCY+jRo4dp3LjxReMsSF7/KOiV93m1a9fOtGvXLt+6F36GeW1Vr17dHD161F7+6aefGknm888/t5fdfPPNpkqVKvbPII/NZrP//ZVXXimwPxlz7lie3wfGjBljJJlvvvnGXnbixAkTERFhwsPDTW5urjHm777YqFEjk52dba87c+ZMI8n89NNPFz1eAADX4Eo3AOCqkZGRIUmqUqWKy7Zx//332//u4eGhFi1ayBijoUOH2ssDAgLUoEED/f777yW23YoVK9r/fuzYMaWnp+umm27Sli1bitWen5+fOnfurA8//FDGGHv5kiVL1Lp1a9WpU0eS9PHHH8tms6l3794OV3pDQkIUGRmptWvXOr3N3r1769SpU1q+fLlOnDih5cuXF3prueTcPucNIvbpp5/KZrMV2E5AQID+/PPPAm/jdsbw4cOVmJjo8GrWrFmx2urTp4+qVq1qf3/TTTdJkr2vpKWl6euvv9Z9991n/wzyWCyWYm1zxYoVatWqlcMgdpUrV9bw4cOVkpKiX375xaH+kCFD5OXlVWiMAIAri4HUAABXDT8/P0nSiRMnXLaNCxMhf39/+fj4qEaNGvnK//rrrxLb7vLly/X8889r69atys7OtpcXNxGTziWAy5YtU3Jystq0aaPffvtNmzdv1owZM+x1du3aJWOMIiMjC2yjQoUKTm8vMDBQsbGxWrRokbKyspSbm6u77rqr0PrO7HOfPn301ltv6f7779e4cePUsWNH3XnnnbrrrrvsI3g/+eSTWr16tVq1aqX69evrtttuU//+/XXjjTc6FXdkZKRiY2Od3s+LubD/5CXgx44dk/R3YtukSZMS2Z507pGB6OjofOWNGjWyLz9/e5eKEQBwZZF0AwCuGn5+fgoNDdW2bducql9Ywpqbm1voOgWNAF7YqODnX0EuzrbyfPPNN+revbtuvvlmvfHGG6pZs6YqVKigBQsWXNZgYN26dVOlSpX04Ycfqk2bNvrwww9ltVp199132+vYbDZZLBb997//LXA/K1euXKRt9u/fX8OGDVNqaqo6d+6cb7qrPM7uc8WKFfX1119r7dq1+s9//qOVK1dqyZIluuWWW/TFF1/Iw8NDjRo10s6dO7V8+XKtXLlS//73v/XGG2/omWee0eTJk4sU/4UsFovD55ynsM/Vmb7ibqUhRgAoT0i6AQBXldtvv13z589XcnKyYmJiLlo37wrehYOB7d27t8Tjupxt/fvf/5aPj49WrVrlMB3UggUL8tUtypVvX19f3X777Vq6dKmmTZumJUuW6KabblJoaKi9Tr169WSMUUREhK699lqn2y7MHXfcoQceeEDfffedlixZUmi9ouyz1WpVx44d1bFjR02bNk0vvviinnrqKa1du9Z+hdrX11d9+vRRnz59lJOTozvvvFMvvPCCEhIS5OPjU+z9qVq1aoG3XRe3D+WNKH+pH46K8jnXrVtXO3fuzFe+Y8cO+3IAwNWLZ7oBAFeVJ554Qr6+vrr//vt16NChfMt/++03zZw5U9K5K+M1atTIN8r4G2+8UeJx1atXT5IctpWbm6v58+dfcl0PDw9ZLBaHq6cpKSlatmxZvrq+vr5Ojygunbs9+8CBA3rrrbf0ww8/qE+fPg7L77zzTnl4eGjy5Mn5rnQaY4p8C33lypU1d+5cTZo0Sd26dSu0nrP7fPTo0XzrRkVFSZL9lvQLY/Ty8tJ1110nY0yxRyHPU69ePe3YsUNpaWn2sh9++MFh9PSiCAwM1M0336x//vOf2rdvn8Oy84+/r6+vpPw/4hSkS5cu2rBhg5KTk+1lmZmZmj9/vsLDw3XdddcVK1YAwJXBlW4AwFWlXr16WrRokfr06aNGjRpp4MCBatKkiXJycrRu3TotXbrUYQ7j+++/X1OnTtX999+vFi1a6Ouvv9avv/5a4nE1btxYrVu3VkJCgo4ePapq1app8eLFOnv27CXX7dq1q6ZNm6ZOnTqpf//+Onz4sObMmaP69evrxx9/dKjbvHlzrV69WtOmTVNoaKgiIiIKfJ43T5cuXVSlShU99thj8vDwUK9evRyW16tXT88//7wSEhKUkpKinj17qkqVKtqzZ48++eQTDR8+XI899liRjsWgQYNKbJ+fffZZff311+ratavq1q2rw4cP64033lDt2rXtA4fddtttCgkJ0Y033qjg4GBt375ds2fPVteuXS970L377rtP06ZNU1xcnIYOHarDhw9r3rx5aty4sX1gv6KaNWuW2rZtqxtuuEHDhw9XRESEUlJS9J///Edbt26VdO5zlqSnnnpKffv2VYUKFdStWzd7Mn6+cePG6YMPPlDnzp01atQoVatWTe+++6727Nmjf//73/Zn3wEAVyn3DJoOAMDF/frrr2bYsGEmPDzceHl5mSpVqpgbb7zRvP766+b06dP2ellZWWbo0KHG39/fVKlSxfTu3dscPny40CnDLpyaatCgQcbX1zff9tu1a5dvmqrffvvNxMbGGm9vbxMcHGzGjx9vEhMTnZoy7O233zaRkZHG29vbNGzY0CxYsMAe0/l27Nhhbr75ZlOxYkUjyT51VGFTlhljzIABA4wkExsbW+jx/Pe//23atm1rfH19ja+vr2nYsKF55JFHzM6dOwtd5/ztbty48aL1CpoyzJl9TkpKMj169DChoaHGy8vLhIaGmn79+plff/3VXuf//u//zM0332yqV69uvL29Tb169czjjz9u0tPTLxpT3jRfr7zyykXrvf/+++aaa64xXl5eJioqyqxatarQKcMKauvCvmaMMdu2bTN33HGHCQgIMD4+PqZBgwbm6aefdqjz3HPPmVq1ahmr1erw2V44ZZgx5/reXXfdZW+vVatWZvny5Q518qYMW7p0aYHHoaCp9QAArmcxhlE1AAAAAABwBe5HAgAAAADARUi6AQAAAABwEZJuAAAAAABchKQbAAAAAAAXIekGAAAAAMBFSLoBAAAAAHART3cHUFrZbDYdOHBAVapUkcVicXc4AAAAAIAryBijEydOKDQ0VFZr4dezSbqL6cCBAwoLC3N3GAAAAAAAN/rjjz9Uu3btQpeTdBdTlSpVJJ07wH5+fvZym82mtLQ0BQYGXvTXDqA84HwA/sb5ADjinAD+xvlQOmVkZCgsLMyeGxaGpLuY8m4p9/Pzy5d0nz59Wn5+fpwwKPc4H4C/cT4AjjgngL9xPpRul3rcmE8UAAAAAAAXIekGAAAAAMBFSLoBAAAAAHARnul2IWOMzp49q9zcXHeHUmpUqFBBHh4e7g4DAAAAAEoESbeL5OTk6NChQ8rKynJ3KKWKxWJR7dq1VblyZXeHAgAAAACXjaTbBYwxSklJkaenp0JDQ+Xl5XXJEe1w7rilpaXpzz//VGRkJFe8AQAAAJR6JN0ukJubK5vNptDQUFWqVMnd4ZQqgYGBSklJ0ZkzZ0i6AQAAAJR6ZWIgta+//lrdunVTaGioLBaLli1bdsl1vvzyS91www3y9vZW/fr19c4775RYPMYYSWKOvWLgjgAAAAAAZUmZyAozMzPVrFkzzZkzx6n6e/bsUdeuXdWhQwdt3bpVY8aM0f33369Vq1a5OFIAAAAAQHlSJm4v79y5szp37ux0/Xnz5ikiIkKvvfaaJKlRo0b69ttvNX36dMXFxbkqTAAAAAAonDGSzSbl5p7783L/XtDrUstL8mXM5b8ee0wq5Y/slomku6iSk5MVGxvrUBYXF6cxY8YUuk52drays7Pt7zMyMiRJNptNNpvNXm6z2ey3lxtj7H+Hc/KO2YXHFaVT3vnAZwlwPgAX4pwoh4yRcnKk7Oxzr/P/fuaMdPZs/ldurnPlee/P/zM3V5bCll1kHXtSeuHfL/zT2b87Uc+Sm6tgm00Wzod8bA8+KPn4uDuMAjn771e5TLpTU1MVHBzsUBYcHKyMjAydOnVKFStWzLfOlClTNHny5HzlaWlpOn36tP29zWbTiRMn7HN0nz17tuR3wMVSU1M1depU/fe//9X+/fsVFBSk66+/XqNGjdItt9yiyMhI7d27V++995769OnjsG6zZs20fft2vfXWWxo4cKAk2eufr1atWtqzZ0++bZ89e1Y2m01//fWXKlSo4LqdxBVhs9mUnp4uYwxjHKDc43wAHHFOXAVycmTJyjr3OnXKqZcKW3b6tHTmjCw5ObL8/4TacubM33///+9RMozFIlmt514eHufee3j8XWa1ypz3d1mtksWSv+zCenntWiwyee2dty1zYb2LlVksji8pf5nFci72i9Q7kZEhc5X+GHHixAmn6pXLpLs4EhISFB8fb3+fkZGhsLAwBQYGys/Pz15us9l09uxZnTx5Up6envL0LF2HOCUlRW3btlVAQIBefvllNW3aVGfOnNGqVas0evRobd++XZIUFham9957TwMGDLCv+9133+nQoUPy9fWV1Wp12PfJkydr2LBh9vceHh4FHhtPT09ZrVZVr15dPlfpL1pwns1mk8ViUWBgIF+oUO5xPgCOOCeKyGaTTp6U0tMdXxkZUmbmudfJk7L8/z/z3uvkSSkrK39ZZqbbk2Dj6fn/2rvzOBvL/4/j7zO7MWZhFktjCdl3mcZSKhoV0re+SYokIVsGMdlSWVosqYlSSCXUr1RfsmRPImtkkCKRsZsZ2wxz7t8fd3PGMUNDM+eeM/N6Ph7n8Z37uq9z7s80c315u+77uiRfX/Pl7W2+vLwyX56ezseXt115ztPTue3yfle+5+82I7t+Ga+/w6zjf69sz8lxdp9xla/tkk6cPq0SYWHyuLyeyz/j8q9zsPhwdj3cccni/JwIcppX3CsR5pKSJUvqyJEjTm1HjhxRYGBgtrPckuTr6ytfX98s7R4eHln+oMhYgdtms5lfG4b5f3ZW8PfP0aDM0KtXL9lsNm3YsEFFixZ1tNesWVNdu3Z1fG8dO3bUxIkTdfDgQUVGRkqSZsyYoY4dO2rWrFmZ3/vfAgMDVapUqX+8fsb7svvvCvfEzxPIxHgAnBW6MZGaKh0/br6OHZNOnJBOn3YO0VceX/7Kq8cWvb3NvzNe76toUfN/ixTJDM8ZLx+ffzy2Wfxzz1cB1G6X4ecnj/DwwjMeCoCc/qwKZeiOjo7WwoULndqWLl2q6OjovLnguXNSQEDefPY/OXPG/D/EHDh58qQWLVqk0aNHOwXuDMHBwY6vIyIiFBMTow8//FDDhg3TuXPnNHfuXK1atUqzZs3KreoBAADyJ8MwZ5kzAvSxY5lfZ9d27JiUw1tRr8nbWwoKynwVK2a+ihY1/74ZEJD5dXZtV54vWtQMxADyTIEI3WfOnNHevXsdx/v27dPWrVtVvHhxlS1bVnFxcTp06JAjDPbo0UNvv/22nn/+eT311FNavny55s2bpwULFlj1LeQLe/fulWEYqlq1ao76P/XUUxowYICGDh2qzz//XBUrVlTdunWz7Tt48GANGzbMcTxmzBj17ds3N8oGAADIPefPS4mJOXulpV3/53t6SqGhma/gYOcQnfG6WnuRItd1FyMA6xWI0L1x40bdeeedjuOMZ687d+6smTNn6vDhwzpw4IDjfIUKFbRgwQL1799fb775pm666Sa9//77ebddmL+/OeNshetYXv96V1q///771b17d61evVrTp0/XU089ddW+gwYN0pNPPuk4Dg0Nva5rAQAA/CtpadLBg9KBA+brr78yw/Phw5lf/71DTY75+0thYWaADgv756+Dg81ncgEUGgUidDdv3vyagXHmzJnZvmfLli15WNVlbLYc3+JtpcqVK8tms2nXrl056u/l5aUnnnhCI0eO1Pr16/Xll19etW9oaKgqVaqUW6UCAABkMgzp1KnMQP3HH5lfZ7wOH875M9F+flLJktd+RURI4eFuv38wgLxXIEI3ckfx4sUVExOj+Ph49e3bN8tz3adPn3Z6rlsybzF/44031L59e4WEhLiwWgAAUKicPCnt3Sv99pv0++9ZA/bZs//8GX5+Utmy5qtMGalUqewDdWAgt3ADyDWEbjiJj49XkyZN1KhRI7300kuqXbu2Ll26pKVLl2rKlCmOLcMyVKtWTcePH5c//8oLAAD+DcOQjhzJDNZ792a+fvvNnMn+J+HhZqAuVy4zXF9+HBpKmAbgcoRuOLn55pu1efNmjR49WgMGDNDhw4cVFhamBg0aaMqUKdm+p0SJEi6uEgAAuCW73Xyueu9e6ddfFbB9u2x//WXOXO/d+8+z1aVLS5UqSTff7Bysy5WTbrrJXGQMAPIZQjeyKFWqlN5++229/fbb2Z7fv3//Nd9/+vTp6+oPAAAKmLQ06ddfpV27pIQE87Vrl/k6d06S5CEpy4aqHh5miK5Y0QzXGa+KFc2g7QZr5ADAlQjdAAAAuDHJyVmDdUKCeTt4enr27/HykipUkFGxos6VKaMitWrJIyNcly8v+fq69FsAgLxG6AYAAMC1nT8v/fyztGWL9MsvmSH7r7+u/p6AAKlaNfNVtWrm1zffLHl7y7DblXL0qIqEh7OFFoACjdANAACATMnJ0tat0ubNma9du64+c12ypHOozgjZZcqwaBkAiNANAABQeJ04Yc5eXx6wf/01+75hYVL9+lLt2pkhu2pViS1DAeCaCN15yDAMq0twO/w3AwAgjxw5Im3c6Byy//gj+76RkWbArl9fqlfP/N/SpZm5BoAbQOjOA56enpKkc+fOqQhbV1yXtLQ0SZn/DQEAwA24cMEM1evXm68ff7x6wK5UKTNYZ4TssDDX1gsABRihOw94eHgoODhYR48elST5+/vLxr8M/yO73a5jx47J399fXl78agIAkCOGYe5xnRGu16+Xtm2TLl507mezmbeDN2iQGbDr1pWCgiwpGwAKC5JNHomIiJDNZnMEb+SMh4eHypYtyz9SAABwNSdPShs2ZIbsDRvMtitFREhRUebrttukhg2lwEDX1wsAhRyhO4/YbDaVKlVK4eHhunjlvzTjqnx8fOTBtiEAAJjsdnNrru+/l9auNYP2nj1Z+/n6mjPXt92WGbTLleMZbADIBwjdeczT05PnkwEAQM6kppqLnX3/fWbQPnUqa7/KlTNnsKOizBXFfXxcXy8A4B8RugEAAKxy6pS0bp20Zo0Zsn/6yQzel/P3l6KjpSZNzJDdqJFUooQ19QIArhuhGwAAwFUOHMicxf7+e2nHDnMhtMuFh0tNm5qvZs2kOnUkb29r6gUA/GuEbgAAgLyQsar4ihXSypVmyP7zz6z9brklM2Q3bWpu4cWz2ABQYBC6AQAAcoNhSPv2mSE7I2gfOuTcx9PTXPCsWTMzYDdubK4yDgAosAjdAAAAN+qPPzID9ooV5u3jl/PxMZ/DvvNO6fbbzUXPiha1pFQAgDUI3QAAADl18KBzyN63z/m8t7e50Nmdd5qv6GipSBFLSgUA5A+EbgAAgKs5flz67jtp+XIzZO/d63ze01O69dbMkN24MTPZAAAnhG4AAIAMaWnmFl6LF0tLlkibNzuvLu7hITVokBmymzSRihWzrl4AQL5H6AYAAIWXYUi//moG7CVLzNnsM2ec+9SpI7VoYYbsZs2kwEBragUAuCVCNwAAKFxOn5aWLcsM2vv3O58PD5fuucd8tWwplSxpRZUAgAKC0A0AAAq2S5ekn37KvGV8/XrJbs887+Njbt8VE2MG7dq1zdvIAQDIBYRuAABQ8Jw4IS1cKH3zjbR0qTm7fblq1cyAHRNjbuXF4mcAgDxC6AYAAAXD7t3S11+bQXvtWufZ7JAQ81bxjFvGy5a1rk4AQKFC6AYAAO7p0iXp++/NkP3NN+aCaJerXVtq21Zq3Vpq2NDc3gsAABcjdAMAAPdx+rS0aJEZsr/9Vjp1KvOct7d0111SmzZm0C5XzrIyAQDIQOgGAAD522+/Zc5mr15tznBnKFHCDNht2pi3jrNnNgAgnyF0AwCA/Gf3bunTT6XPPpN27nQ+V62aedt4mzbSbbdx2zgAIF8jdAMAgPzh4EFp7lxp9mxp8+bMdk9Pc4XxjKBdsaJ1NQIAcJ0I3QAAwDonTkj/939m0F69WjIMs93T07xdvEMHM2gHB1taJgAAN4rQDQAAXOvsWXNrr9mzzUXRLn9Gu1kzM2g//LAUFmZdjQAA5BJCNwAAyHtpadKSJWbQ/uor6dy5zHN165pB+9FH2T8bAFDgELoBAEDesNulNWvMoP3559LJk5nnKlY0g3aHDlL16tbVCABAHiN0AwCA3PXrr9KMGdJHH5mLo2UoWVJq31567DHp1lslm826GgEAcBFCNwAA+PdSUqR588ywvXZtZntQkPTQQ2bQbt6c7b0AAIUOoRsAANwYu91ccXzGDPP28YzntD08pJgYqUsXc+VxPz9r6wQAwEKEbgAAcH3++EP68ENp5kxp377M9ltuMYP2E09IZcpYVh4AAPkJoRsAAPyz8+elL74wZ7WXL8/cT7tYMfM57S5dpOhontMGAOAKhG4AAJA9w5DWrzeD9pw5UnJy5rk77zSD9n/+IxUtal2NAADkc4RuAADg7Ngx8/bx6dOlhITM9nLlpCeflDp3lipUsKw8AADcCaEbAACYs9qrV0vvviv93/9JaWlme5Ei5urjXbqYq497eFhaJgAA7obQDQBAYXbypDRrlhm2d+3KbG/QQHrmGfN57aAg6+oDAMDNFZh/ro6Pj1f58uXl5+enqKgobdiw4Zr9J02apCpVqqhIkSKKjIxU//79deHCBRdVCwCAhQxDWrfOvE28TBmpf38zcBctKnXrJm3caL6eeYbADQDAv1QgZrrnzp2r2NhYTZ06VVFRUZo0aZJiYmK0e/duhYeHZ+k/e/ZsDRkyRNOnT1fjxo21Z88ePfnkk7LZbJowYYIF3wEAAC6QlCR9/LE5q719e2Z7nTpS9+5Sx45SYKB19QEAUAAViNA9YcIEdevWTV26dJEkTZ06VQsWLND06dM1ZMiQLP1/+OEHNWnSRI899pgkqXz58urQoYPWr19/1WukpqYqNTXVcZz89wqudrtddrvd0W6322UYhlMbUFgxHoBMlo6HjRtle/ddac4c2c6dkyQZRYpIjzwi45lnpKiozK2+GK9wEf6MADIxHtxTTn9ebh+609LStGnTJsXFxTnaPDw81KJFC61bty7b9zRu3Fgff/yxNmzYoEaNGun333/XwoUL9cQTT1z1OmPHjtWoUaOytB87dszptnS73a6kpCQZhiEPFptBIcd4ADK5ejzYzp6V3xdfyP+jj+R92az2xVtu0fknntD5hx+WERxsNh47luf1AFfizwggE+PBPaWkpOSon9uH7uPHjys9PV0RERFO7REREdp1+YIwl3nsscd0/PhxNW3aVIZh6NKlS+rRo4deeOGFq14nLi5OsbGxjuPk5GRFRkYqLCxMgZfdime322Wz2RQWFsaAQaHHeAAyuWw87NolW3y89NFHsv39lwHD11d66CEZ3bvLs0kTBdhsCsi7CoAc4c8IIBPjwT35+fnlqJ/bh+4bsXLlSo0ZM0bvvPOOoqKitHfvXvXr108vv/yyhg8fnu17fH195evrm6Xdw8Mjy8Cw2WzZtgOFEeMByJRn48Ful779Vpo8WVqyJLP9lluk7t1l69xZKlFCtty9KvCv8WcEkInx4H5y+rNy+9AdGhoqT09PHTlyxKn9yJEjKlmyZLbvGT58uJ544gk9/fTTkqRatWrp7NmzeuaZZzR06FB+0QEA7iEpSZo5U3r7bWnvXrPNZpPatpX69JHuuivzWW0AAGAJt0+XPj4+atCggZYtW+Zos9vtWrZsmaKjo7N9z7lz57IEa09PT0mSYRh5VywAALlh1y6pd2/pppuk554zA3dQkDRggPn1/PnS3XcTuAEAyAfcfqZbkmJjY9W5c2c1bNhQjRo10qRJk3T27FnHauadOnVSmTJlNHbsWElSmzZtNGHCBNWrV89xe/nw4cPVpk0bR/gGACBfsdulRYvMW8gXL85sr15d6ttXevxxc59tAACQr1gSus+ePauiufgXg/bt2+vYsWMaMWKEEhMTVbduXS1atMixuNqBAwecZraHDRsmm82mYcOG6dChQwoLC1ObNm00evToXKsJAIBckZxs3kL+1lvOt5C3aWOGbW4hBwAgX7MZFtxPHRAQoEceeURPPfWUmjZt6urL54rk5GQFBQUpKSkpy+rlR48eVXh4OM+Go9BjPACZrns87N5tPqs9c6Z05ozZFhQkde0q9eol3XxzntYL5DX+jAAyMR7c09Uy4ZUs+Yl+/PHHOnnypO666y7dcsstGjdunP766y8rSgEAIP8wDHP18XvvlapWNUP3mTPm1++8Ix08KI0fT+AGAMCNWBK627Vrp/nz5+vQoUPq0aOHZs+erXLlyql169b64osvdOnSJSvKAgDAGqmp0owZUu3aUkyM+ex2xi3kS5ZIO3dKPXtKAeyuDQCAu7H03oWwsDDFxsbq559/1oQJE/Tdd9/p4YcfVunSpTVixAidO3fOyvIAAMhbJ05Io0dL5ctLTz0l7dhhLobWt6+0Z4/09ddSy5Y8sw0AgBuzdPXyI0eO6MMPP9TMmTP1xx9/6OGHH1bXrl118OBBvfrqq/rxxx+1ZMkSK0sEACD37d0rTZxoPq+d8Q/MpUubYfuZZ6SQEEvLAwAAuceS0P3FF19oxowZWrx4sapXr65nn31Wjz/+uIKDgx19GjdurGrVqllRHgAAuc8w5L1+vWwzZpgz2BnrmNapY+6v3b695ONjbY0AACDXWRK6u3TpokcffVRr167Vrbfemm2f0qVLa+jQoS6uDACAXHbpkvTFF7KNH68SGzZktt93nxm277yT28cBACjALAndhw8flr+//zX7FClSRCNHjnRRRQAA5LKUFOmDD6Q335T275dNkuHrKz3+uGyxsVL16lZXCAAAXMCS0F2sWDEdPnxY4eHhTu0nTpxQeHi40tPTrSgLAIB/7+BBafJk6b33pKQksy00VEbPnjr23/8qtEYN2diDFQCAQsOS0G1kPMd2hdTUVPnwPBsAwB3t3y+NHWtu/XXxotlWpYrUv7/UqZMMX1/Zjx61tEQAAOB6Lg3dkydPliTZbDa9//77Crhsv9H09HStXr1aVatWdWVJAAD8O7//Lo0ZI334ofn8tiTdcYc0cKD53HbGrLbdbl2NAADAMi4N3RMnTpRkznRPnTpVnp6ejnM+Pj4qX768pk6d6sqSAAC4MXv3mntsf/SRlPFYVMuW0ogRUtOm1tYGAADyDZeG7n379kmS7rzzTn3xxRcKYR9SAIC72bPHDNuffJIZtmNipJEjpehoa2sDAAD5jiXPdK9YscKKywIAcOMSEsyw/emnmbeK33efObMdFWVtbQAAIN9yWeiOjY3Vyy+/rKJFiyo2NvaafSdMmOCiqgAA+Ac7d0ovvyzNnStlLATapo0Zths2tLY2AACQ77ksdG/ZskUX/17NdcuWLVftZ7PZXFUSAABXt327GbY//zwzbLdrJw0fLtWvb2lpAADAfbgsdF9+Szm3lwMA8q1t26SXXpK++CKz7T//McN23bqWlQUAANyTJc90AwCQ7yQkSMOGZYZtm016+GEzbNeqZW1tAADAbbksdP/nP//Jcd8vLp9dAAAgLx04IL34ornPtt1uhu327c0AXqOG1dUBAAA357LQHRQU5KpLAQDwz44dk8aOleLjpbQ0s61dO+mVVwjbAAAg17gsdM+YMcNVlwIA4OpSUqQJE6Tx482vJal5czOA33abpaUBAICCh2e6AQCFQ2qqNHWqOZN9/LjZVr++GbZbtjRvKwcAAMhlLgvd9evX17JlyxQSEqJ69epdc2uwzZs3u6osAEBBl54uffSRNHKk+fy2JFWubIbvhx+WPDysrQ8AABRoLgvdDzzwgHx9fSVJ7dq1c9VlAQCFlWFIX30lDR0q7dxptpUubS6a9uSTkre3ldUBAIBCwmWhe+TIkdl+DQBArlu5UhoyRFq/3jwOCZHi4qTevaUiRSwtDQAAFC6WPtO9ceNGJSQkSJKqV6+uBg0aWFkOAMDdbdokvfCCtGSJeezvLz33nDRokBQcbGVlAACgkLIkdB88eFAdOnTQ2rVrFfz3X4JOnz6txo0ba86cObrpppusKAsA4K5+/928jXzOHPPYy0vq3t3ca7tkSWtrAwAAhZolq8c8/fTTunjxohISEnTy5EmdPHlSCQkJstvtevrpp60oCQDgjk6ckPr3l6pWNQO3zSZ17Cjt2iW9/TaBGwAAWM6Sme5Vq1bphx9+UJUqVRxtVapU0VtvvaVmzZpZURIAwJ2cPy9Nnmxu95WUZLa1bCm99ppUt66lpQEAAFzOktAdGRmpixcvZmlPT09X6dKlLagIAOAW0tOljz82bxs/eNBsq1PHDNv33GNtbQAAANmw5Pby119/XX369NHGjRsdbRs3blS/fv30xhtvWFESACC/W7xYql/f3O7r4EEpMlL68ENz8TQCNwAAyKdcNtMdEhIim83mOD579qyioqLk5WWWcOnSJXl5eempp55iH28AQKYtW6TBg6WlS83joCBzhfI+fdj+CwAA5HsuC92TJk1y1aUAAAXBH39Iw4ebt5MbhuTtbe6zPXSoVKKE1dUBAADkiMtCd+fOnV11KQCAOzt1ylwgbfJkKTXVbOvQQRo9WqpQwdraAAAArpMlC6ld7sKFC0pLS3NqCwwMtKgaAIBlUlOl+HjplVfM4C1JzZtLr78uNWxoaWkAAAA3ypKF1M6ePavevXsrPDxcRYsWVUhIiNMLAFCIGIY0d6651/aAAWbgrlFDWrBAWr6cwA0AANyaJaH7+eef1/LlyzVlyhT5+vrq/fff16hRo1S6dGnNmjXLipIAAFbYsEFq2lR69FFp/36pdGnpgw+kbduk++6TLluAEwAAwB1Zcnv5N998o1mzZql58+bq0qWLmjVrpkqVKqlcuXL65JNP1LFjRyvKAgC4yoEDUlycNHu2eezvb65QPmCAVLSotbUBAADkIktmuk+ePKmbb75Zkvn89smTJyVJTZs21erVq60oCQDgCmfOmCuSV6liBm6bzdx3e88eacQIAjcAAChwLAndN998s/bt2ydJqlq1qubNmyfJnAEPDg62oiQAQF5KT5emT5cqVzYXSrtwQbr9dmnjRmnGDKlMGasrBAAAyBOWhO4uXbpo27ZtkqQhQ4YoPj5efn5+6t+/vwYNGmRFSQCAvLJihbkYWteuUmKiVLGi9MUX0sqVUv36VlcHAACQpyx5prt///6Or1u0aKGEhARt3rxZlSpVUu3ata0oCQCQ2379VRo0SPrqK/M4KMi8tbx3b8nX19raAAAAXMTyfbolqXz58ipfvrzVZQAAcsOpU9JLL0lvvy1duiR5eko9ekgvviiFhlpdHQAAgEtZcnu5JC1btkytW7dWxYoVVbFiRbVu3VrfffedVeUAAP6tixelt96SKlWSJk0yA/d990nbt5sBnMANAAAKIUtC9zvvvKNWrVqpWLFi6tevn/r166fAwEDdd999io+Pt6IkAMCNMgxpwQKpVi2pb1/p5EmpZk1p8WKzvVo1qysEAACwjCWhe8yYMZo4caI+/fRT9e3bV3379tXs2bM1ceJEjRkz5oY+Mz4+XuXLl5efn5+ioqK0YcOGa/Y/ffq0evXqpVKlSsnX11e33HKLFi5ceEPXBoBC65dfpFatpNatpd27pbAwaepUacsW6Z57rK4OAADAcpaE7tOnT6tVq1ZZ2u+55x4lJSVd9+fNnTtXsbGxGjlypDZv3qw6deooJiZGR48ezbZ/WlqaWrZsqf379+vzzz/X7t27NW3aNJVhyxoAyJkTJ8wF0erUkZYskXx8pOefNxdP695d8soXS4YAAABYzpLQ3bZtW3355ZdZ2r/66iu1bt36uj9vwoQJ6tatm7p06aLq1atr6tSp8vf31/Tp07PtP336dJ08eVLz589XkyZNVL58ed1xxx2qU6fOdV8bAAqVixelyZPN/bbj4839tx98UNq5U3r1VXOFcgAAADi4bCpi8uTJjq+rV6+u0aNHa+XKlYqOjpYk/fjjj1q7dq0GDBhwXZ+blpamTZs2KS4uztHm4eGhFi1aaN26ddm+5+uvv1Z0dLR69eqlr776SmFhYXrsscc0ePBgeXp6Zvue1NRUpaamOo6Tk5MlSXa7XXa73dFut9tlGIZTG1BYMR4KmG+/lW3gQNl27ZIkGbVry5gwQbrzTvM8P+drYjwAzhgTQCbGg3vK6c/LZaF74sSJTschISHauXOndu7c6WgLDg7W9OnTNWzYsBx/7vHjx5Wenq6IiAin9oiICO36+y+GV/r999+1fPlydezYUQsXLtTevXv17LPP6uLFixo5cmS27xk7dqxGjRqVpf3YsWO6cOGC49hutyspKUmGYcjDw7LF4YF8gfFQMHj++qsCX3xRvsuXS5LSS5TQmcGDdf6xx8ztwK7yKA+cMR4AZ4wJIBPjwT2lpKTkqJ/LQve+fftcdal/ZLfbFR4ervfee0+enp5q0KCBDh06pNdff/2qoTsuLk6xsbGO4+TkZEVGRiosLEyBgYFOn22z2RQWFsaAQaHHeHBzJ0/K9tJL0pQpsl26JMPbW+rTR7Zhw1QsKEjFrK7PzTAeAGeMCSAT48E9+fn55aif5SvdGIYhSbLZbDf0/tDQUHl6eurIkSNO7UeOHFHJkiWzfU+pUqXk7e3tdCt5tWrVlJiYqLS0NPn4+GR5j6+vr3x9fbO0e3h4ZBkYNpst23agMGI8uKFLl6R335VGjDC3/5Kktm1le+MNqXJl3dj/W0NiPABXYkwAmRgP7ienPyvLfqKzZs1SrVq1VKRIERUpUkS1a9fWRx99dN2f4+PjowYNGmjZsmWONrvdrmXLljmeF79SkyZNtHfvXqd78Pfs2aNSpUplG7gBoNBYssRckbx3bzNw16hhtn31lbl4GgAAAK6LJaF7woQJ6tmzp+677z7NmzdP8+bNU6tWrdSjR48sz37nRGxsrKZNm6YPP/xQCQkJ6tmzp86ePasuXbpIkjp16uS00FrPnj118uRJ9evXT3v27NGCBQs0ZswY9erVK9e+RwBwK3v2SG3aSDEx5krkJUqYq5Nv3Sq1bGl1dQAAAG7LktvL33rrLU2ZMkWdOnVytLVt21Y1atTQiy++qP79+1/X57Vv317Hjh3TiBEjlJiYqLp162rRokWOxdUOHDjgNPUfGRmpxYsXq3///qpdu7bKlCmjfv36afDgwbnzDQKAu0hJkV56SXrzTXM7MC8vc5Z7xAgpJMTq6gAAANyezch4qNqF/Pz8tGPHDlWqVMmp/ddff1WtWrWcVgPPr5KTkxUUFKSkpKQsC6kdPXpU4eHhPI+BQo/xkI8ZhjRvnhQbK/31l9l2333S+PFS1arW1lZAMR4AZ4wJIBPjwT1dLRNeyZKfaKVKlTRv3rws7XPnzlVlnhkEgLyVkCC1aCE9+qgZuCtWlBYsMF8EbgAAgFxlye3lo0aNUvv27bV69Wo1adJEkrR27VotW7Ys2zAOAMgFZ85IL78sTZhgrlDu5ye98II0aJD5NQAAAHKdJaH7oYce0oYNGzRhwgTNnz9fkrll14YNG1SvXj0rSgKAgsswpM8/l/r3lw4dMtvatpUmTZIqVLC0NAAAgILO5aH74sWL6t69u4YPH66PP/7Y1ZcHgMJl1y6pTx/pu+/M4woVpMmTpdatra0LAACgkHD5M93e3t76v//7P1dfFgAKl7NnpSFDpNq1zcDt6yuNHCn98guBGwAAwIUsWUitXbt2jtvKAQC5KONW8mrVpFdfNbcBu/9+M2y/+KJUpIjVFQIAABQqljzTXblyZb300ktau3atGjRooKJFizqd79u3rxVlAYB7273bvJV86VLzuHx581byNm0sLQsAAKAwsyR0f/DBBwoODtamTZu0adMmp3M2m43QDQDX4+xZafRo6Y03zJltX19p8GDz9nJmtgEAACxlSejet2+fFZcFgILFMKT586XnnpMOHDDb7r3XnN2uVMnKygAAAPA3l4fuH3/8Ud98843S0tJ09913q1WrVq4uAQDc3969Ut++0rffmsflyplbgD3wgGSzWVoaAAAAMrk0dH/++edq3769ihQpIm9vb02YMEGvvvqqBg4c6MoyAMB9nT8vjRtnLpKWmip5e0uDBklDh0r+/lZXBwAAgCu4dPXysWPHqlu3bkpKStKpU6f0yiuvaMyYMa4sAQDc14IFUs2a0ksvmYG7ZUtpxw7zeW4CNwAAQL7k0tC9e/duDRw4UJ6enpKkAQMGKCUlRUePHnVlGQDgXv74Q2rXztxf+/ffpTJlpHnzpMWLpVtusbo6AAAAXINLQ/e5c+cUGBjoOPbx8ZGfn5/OnDnjyjIAwD2kpkpjxph7bn/1leTlJQ0cKCUkSP/9L89uAwAAuAGXL6T2/vvvKyAgwHF86dIlzZw5U6GhoY42tgwDUOh9953Uq5e0Z495fMcdUny8VKOGtXUBAADgurg0dJctW1bTpk1zaitZsqQ++ugjxzH7dAMo1A4elGJjpc8+M48jIqTx46XHHmNmGwAAwA25NHTv37/flZcDAPdx8aL05pvSiy9KZ89KHh5S797momlBQVZXBwAAgBvk8tvLAQBXWLXKvJX8l1/M4+ho6Z13pLp1LS0LAAAA/55LF1IDAFwmMVF64gmpeXMzcIeGSh98IH3/PYEbAACggCB0A4CrXbokTZ4sVakiffyx+ax29+7S7t3SU0+Zt5YDAACgQOD2cgBwpR9+kJ59Vtq2zTxu2NC8lfzWW62tCwAAAHmC6RQAcIWjR81Z7CZNzMAdEiJNnSr9+COBGwAAoACzLHT/9ttvGjZsmDp06KCjR49Kkr799lv9krGQEAAUBOnp5kx2lSrSjBlmW9eu5q3k3btLnp7W1gcAAIA8ZUnoXrVqlWrVqqX169friy++0JkzZyRJ27Zt08iRI60oCQBy3/r1UqNG5srkp09L9eqZt5e//74UFmZ1dQAAAHABS0L3kCFD9Morr2jp0qXy8fFxtN9111368ccfrSgJAHLP8ePSM8+YW39t3mzus/3229JPP5ltAAAAKDQsCd3bt2/Xgw8+mKU9PDxcx48ft6AiAMgFdrv03nvmreTTpkmGIXXubN5K3qsXt5IDAAAUQpaE7uDgYB0+fDhL+5YtW1SmTBkLKgKAf2nTJnMWu3t36eRJqXZtac0aaeZMKSLC6uoAAABgEUtC96OPPqrBgwcrMTFRNptNdrtda9eu1cCBA9WpUycrSgKAG3PypLkF2K23Shs2SMWKSZMmmSG8aVOrqwMAAIDFLAndY8aMUdWqVRUZGakzZ86oevXquv3229W4cWMNGzbMipIA4PrY7eZq5FWqSFOmmLeSd+xo3krer5/k5WV1hQAAAMgHLPlboY+Pj6ZNm6bhw4drx44dOnPmjOrVq6fKlStbUQ4AXJ9ffpF69jRvH5ek6tWl+HipeXNLywIAAED+Y0no/v7779W0aVOVLVtWZcuWtaIEALh+Z89KL78sjR8vXbok+ftLL74oPfec5O1tdXUAAADIhyy5vfyuu+5ShQoV9MILL2jnzp1WlAAA1+ebb6QaNaRXXzUDd7t2UkKCNGgQgRsAAABXZUno/uuvvzRgwACtWrVKNWvWVN26dfX666/r4MGDVpQDAFd34ID04INS27bSH39IZctKX30lffml+TUAAABwDZaE7tDQUPXu3Vtr167Vb7/9pv/+97/68MMPVb58ed11111WlAQAzi5elN54w3xee/58c2G0wYOlnTvNAA4AAADkgOXL61aoUEFDhgxRnTp1NHz4cK1atcrqkgAUdj/8IPXoIW3fbh43bWquUF6zprV1AQAAwO1YMtOdYe3atXr22WdVqlQpPfbYY6pZs6YWLFhgZUkACrMTJ6Ru3aQmTczAXaKENH26tGoVgRsAAAA3xJKZ7ri4OM2ZM0d//fWXWrZsqTfffFMPPPCA/P39rSgHQGFnGNKHH5qLoh0/brY99ZS5aFpoqLW1AQAAwK1ZErpXr16tQYMG6ZFHHlEof6EFYKWdO809t1evNo9r1JCmTjVvKQcAAAD+JUtC99q1a624LABkOndOeuUV6fXXM/fcHjlS6t+fLcAAAACQa1wWur/++mvde++98vb21tdff33Nvm1ZGRhAXlq82Jzd3rfPPG7bVpo8WSpXztq6AAAAUOC4LHS3a9dOiYmJCg8PV7t27a7az2azKT093VVlAShMjh41Z7JnzzaPb7pJevtt6YEHrK0LAAAABZbLQrfdbs/2awDIc4ZhrkI+aJB06pTk4SH16SO9/LJUrJjV1QEAAKAAs2TLsFmzZik1NTVLe1pammbNmmVBRQAKrF27pObNpaefNgN33brS+vXSpEkEbgAAAOQ5S0J3ly5dlJSUlKU9JSVFXbp0saAiAAVOaqo0apRUp465Mrm/v/TGG9JPP0kNG1pdHQAAAAoJS1YvNwxDNpstS/vBgwcVFBRkQUUACpTVq6Xu3c1Zbkm6917pnXek8uUtLQsAAACFj0tnuuvVq6f69evLZrPp7rvvVv369R2vOnXqqFmzZmrRosUNfXZ8fLzKly8vPz8/RUVFacOGDTl635w5c2Sz2a65uBsAN3HqlNStm3THHWbgDg+X5syRFiwgcAMAAMASLp3pzgi2W7duVUxMjAICAhznfHx8VL58eT300EPX/blz585VbGyspk6dqqioKE2aNEkxMTHavXu3wsPDr/q+/fv3a+DAgWrWrNl1XxNAPmIY0ty5Ur9+5grlkhm+X31VCgmxtjYAAAAUai4N3SNHjpQklS9fXu3bt5efn1+ufO6ECRPUrVs3x/PgU6dO1YIFCzR9+nQNGTIk2/ekp6erY8eOGjVqlNasWaPTp09f8xqpqalOi78lJydLMldiv3JldsMwWKEdkIvGw759svXqJdvixZIko1o1GVOmSBn/mMZYRD7Bnw+AM8YEkInx4J5y+vOy5Jnuzp0759pnpaWladOmTYqLi3O0eXh4qEWLFlq3bt1V3/fSSy8pPDxcXbt21Zo1a/7xOmPHjtWoUaOytB87dkwXLlxwHNvtdiUlJckwDHl4WLJOHZBv5Ol4uHRJ/u+9p2Kvvy7bhQsyfHx0pl8/ne3VS/L1zZzxBvIJ/nwAnDEmgEyMB/eUkpKSo36WhO709HRNnDhR8+bN04EDB5SWluZ0/uTJkzn+rOPHjys9PV0RERFO7REREdqVsYjSFb7//nt98MEH2rp1a46vExcXp9jYWMdxcnKyIiMjFRYWpsDAQEe73W6XzWZTWFgYAwaFXp6Nhw0bZOvZU7a/x7Bxxx0ypkxR0SpVVDT3rgLkKv58AJwxJoBMjAf3lNM7ty0J3aNGjdL777+vAQMGaNiwYRo6dKj279+v+fPna8SIEXl67ZSUFD3xxBOaNm2aQkNDc/w+X19f+fr6Zmn38PDIMjBsNlu27UBhlKvjISlJeuEFacoU8znukBDpjTdk69Il2x0RgPyGPx8AZ4wJIBPjwf3k9GdlSej+5JNPNG3aNN1///168cUX1aFDB1WsWFG1a9fWjz/+qL59++b4s0JDQ+Xp6akjR444tR85ckQlS5bM0v+3337T/v371aZNG0dbxr34Xl5e2r17typWrHiD3xmAPGEY0rx50nPPSYmJZtvjj0vjx5srlAMAAAD5lCX/jJKYmKhatWpJkgICApSUlCRJat26tRYsWHBdn+Xj46MGDRpo2bJljja73a5ly5YpOjo6S/+qVatq+/bt2rp1q+PVtm1b3Xnnndq6dasiIyP/xXcGINf99pu5z/ajj5qBu3Jl6bvvpI8+InADAAAg37Nkpvumm27S4cOHVbZsWVWsWFFLlixR/fr19dNPP2V7C/c/iY2NVefOndWwYUM1atRIkyZN0tmzZx2rmXfq1EllypTR2LFj5efnp5o1azq9Pzg4WJKytAOwUFqa9Prr0iuvSBcuSD4+5q3lgwdLubTzAQAAAJDXLAndDz74oJYtW6aoqCj16dNHjz/+uD744AMdOHBA/fv3v+7Pa9++vY4dO6YRI0YoMTFRdevW1aJFixyLqx04cIBnIwB3snq11KOHlJBgHt91l/kc9y23WFsXAAAAcJ1shmEYVhexbt06rVu3TpUrV3Z61jo/S05OVlBQkJKSkrKsXn706FGFh4cT9FHoXfd4OH5cev55acYM8zgsTJowQerYUWKhNLg5/nwAnDEmgEyMB/d0tUx4JUtmuq8UHR2d7fPXAAoJw5A+/FAaOFA6ccJse+YZadw4c4VyAAAAwE25LHR//fXXOe7btm3bPKwEQL6SkGDeSr56tXlcs6b07rtS48bW1gUAAADkApeF7nbt2uWon81mU3p6et4WA8B6589Lo0dLr70mXbwoFSkivfii1L+/5O1tdXUAAABArnBZ6M7YCxsAtGSJ9Oyz5nZgknT//dLbb0vly1taFgAAAJDbeEofgOskJkqPPSbFxJiBu0wZ6f/+T/rmGwI3AAAACiRLFlJ76aWXrnl+xIgRLqoEgEvY7dJ770lDhkhJSZKHh9Snj/Tyy1KxYlZXBwAAAOQZS0L3l19+6XR88eJF7du3T15eXqpYsSKhGyhAvBISZBs6VFq3zmxo0MBcKK1BA2sLAwAAAFzAktC9ZcuWLG3Jycl68skn9eCDD1pQEYBcd/asbKNGqcSECbKlp0sBAebCab16SZ6eVlcHAAAAuES+eaY7MDBQo0aN0vDhw60uBcC/tXChVKOGbK+/Llt6uowHHzS3Buvbl8ANAACAQiXfhG5JSkpKUlJSktVlALhRf/0l/fe/5mrkf/who2xZnfrwQxmffy7ddJPV1QEAAAAuZ8nt5ZMnT3Y6NgxDhw8f1kcffaR7773XipIA/Bvp6dLUqVJcnJSSYs5m9+8vY/hwpZ47Z3V1AAAAgGUsCd0TJ050Ovbw8FBYWJg6d+6suLg4K0oCcKO2bpWeeUb66SfzOCrKXCitTh1z1XJCNwAAAAoxS0L3vn37rLgsgNx05ow0cqT05pvmTHdgoDR2rNS9O89tAwAAAH+zJHQDcHNffy317i39+ad5/Mgj0qRJUqlSlpYFAAAA5DeWhO4LFy7orbfe0ooVK3T06FHZ7Xan85s3b7aiLAD/5NAhqU8f6csvzeMKFaR33pFatbK2LgAAACCfsiR0d+3aVUuWLNHDDz+sRo0ayWazWVEGgJyy282F0oYMMRdK8/KSBg6Uhg+X/P2trg4AAADItywJ3f/73/+0cOFCNWnSxIrLA7geO3aYC6WtW2ceR0VJ06ZJtWpZWxcAAADgBizZp7tMmTIqVqyYFZcGkFPnz0tDh0r16pmBu1gx6e23pbVrCdwAAABADlkSusePH6/Bgwfrjz/+sOLyAP7J8uVS7drSmDHSpUtSu3bSzp1Sr16sTA4AAABcB0tuL2/YsKEuXLigm2++Wf7+/vL29nY6f/LkSSvKAnD8uPms9ocfmselS5uz2w8+aG1dAAAAgJuyJHR36NBBhw4d0pgxYxQREcFCaoDVDEP65BOpf38zeNts0rPPSqNHS0FBVlcHAAAAuC1LQvcPP/ygdevWqU6dOlZcHsDlfvtN6tlTWrrUPK5ZU3rvPSk62tq6AAAAgALAkme6q1atqvPnz1txaQAZLl6UXn3VDNlLl0q+vubM9qZNBG4AAAAgl1gSuseNG6cBAwZo5cqVOnHihJKTk51eAPLYhg1Sw4bmvtsXLkh33SVt3y698ILk42N1dQAAAECBYcnt5a1atZIk3X333U7thmHIZrMpPT3dirKAgi8lRRo2THrrLfM57uLFpQkTpE6dzOe4AQAAAOQqS0L3ihUrrLgsULh98425ONrBg+bx44+bgTsszNq6AAAAgALMktB9xx13WHFZoHA6fFjq21f6/HPzuEIFaepU6Z57rK0LAAAAKAQsCd2rV6++5vnbb7/dRZUABZjdbq5CPmSIlJQkeXqae3CPGCH5+1tdHQAAAFAoWBK6mzdvnqXt8r26eaYb+Jd++UV65hnphx/M41tvlaZNk9imDwAAAHApS1YvP3XqlNPr6NGjWrRokW699VYtWbLEipKAguHCBXMmu149M3AHBEhvvimtW0fgBgAAACxgyUx3UFBQlraWLVvKx8dHsbGx2rRpkwVVAW5u5Uqpe3dpzx7zuE0bKT5eioy0tCwAAACgMLNkpvtqIiIitHv3bqvLANzLyZPS009Ld95pBu5SpcxF0776isANAAAAWMySme6ff/7Z6dgwDB0+fFjjxo1T3bp1rSgJcD+GIc2dK/XrJx09arb16CGNHSsFB1taGgAAAACTJaG7bt26stlsMgzDqf22227T9OnTrSgJcC/790s9e0qLFpnH1aubK5U3aWJpWQAAAACcWRK69+3b53Ts4eGhsLAw+fn5WVEO4D4uXTIXRhsxQjp3TvLxkYYPl55/3vwaAAAAQL5iSeguV66cFZcF3NumTVK3btKWLebxHXdI774rValibV0AAAAArsqlC6ktX75c1atXV3JycpZzSUlJqlGjhtasWePKkoD87+xZacAAqVEjM3CHhEgffCCtWEHgBgAAAPI5l4buSZMmqVu3bgoMDMxyLigoSN27d9eECRNcWRKQvy1aJNWoIU2YINntUocOUkKC9NRTks1mdXUAAAAA/oFLQ/e2bdvUqlWrq56/55572KMbkMzVyDt2lO69V/rjD6lcOWnhQmn2bCkiwurqAAAAAOSQS0P3kSNH5O3tfdXzXl5eOnbsmAsrAvIZw5BmzpSqVTMDtoeHFBsr7dhhBnAAAAAAbsWlobtMmTLasWPHVc///PPPKlWqlAsrAvKRvXulFi2kLl2kkyelunWl9eul8eOlgACrqwMAAABwA1wauu+77z4NHz5cFy5cyHLu/PnzGjlypFq3bu3KkgDrXbwojRsn1aolLV8uFSkivfaatGGD1LCh1dUBAAAA+BdcumXYsGHD9MUXX+iWW25R7969VeXvlZd37dql+Ph4paena+jQoa4sCbDW+vXmNmDbt5vHLVtKU6ZIFStaWxcAAACAXOHS0B0REaEffvhBPXv2VFxcnAzDkCTZbDbFxMQoPj5eESwShcIgJUUaNkx66y3zOe4SJaSJE6XHH2dVcgAAAKAAcent5ZJUrlw5LVy4UMePH9f69ev1448/6vjx41q4cKEqVKhww58bHx+v8uXLy8/PT1FRUdqwYcNV+06bNk3NmjVTSEiIQkJC1KJFi2v2B3LV//5nbgM2ebIZuJ94Qtq1y/xfAjcAAABQoLg8dGcICQnRrbfeqkaNGikkJORffdbcuXMVGxurkSNHavPmzapTp45iYmJ09OjRbPuvXLlSHTp00IoVK7Ru3TpFRkbqnnvu0aFDh/5VHcA1JSZKjzwitWkj/fmnVKGCtGSJNGuWFBpqdXUAAAAA8oBloTs3TZgwQd26dVOXLl1UvXp1TZ06Vf7+/po+fXq2/T/55BM9++yzqlu3rqpWrar3339fdrtdy5Ytc3HlKBTS083ntKtWlT77TPL0lAYNMrcBa9nS6uoAAAAA5CGXPtOdF9LS0rRp0ybFxcU52jw8PNSiRQutW7cuR59x7tw5Xbx4UcWLF79qn9TUVKWmpjqOk5OTJUl2u112u93RbrfbZRiGUxsKsS1bZHv2Wdn+fnzBaNBAxrvvSvXqmecL+O8J4wHIxHgAnDEmgEyMB/eU05+X24fu48ePKz09PcsCbBEREdq1a1eOPmPw4MEqXbq0WrRocdU+Y8eO1ahRo7K0Hzt2zGkLNLvdrqSkJBmGIQ+PAnEjAW6ALSVFAa+9Jv/p02Wz22UvVkxn4uJ0rlMnc6b7Ko8+FDSMByAT4wFwxpgAMjEe3FNKSkqO+rl96P63xo0bpzlz5mjlypXy8/O7ar+4uDjFxsY6jpOTkxUZGamwsDAFBgY62u12u2w2m8LCwhgwhZFhSP/3f7L17y/bX3+ZTe3bS+PHK6BUKQVYXJ6rMR6ATIwHwBljAsjEeHBP18qPl3P70B0aGipPT08dOXLEqf3IkSMqWbLkNd/7xhtvaNy4cfruu+9Uu3bta/b19fWVr69vlnYPD48sA8Nms2XbjgLu99+l3r2lb781jytWlN55R7Z77lFhXpOc8QBkYjwAzhgTQCbGg/vJ6c/K7X+iPj4+atCggdMiaBmLokVHR1/1fa+99ppefvllLVq0SA0bNnRFqSio0tKkMWPMbcC+/Vby8ZFGjJC2b5fuucfq6gAAAABYyO1nuiUpNjZWnTt3VsOGDdWoUSNNmjRJZ8+eVZcuXSRJnTp1UpkyZTR27FhJ0quvvqoRI0Zo9uzZKl++vBITEyVJAQEBCggobDcA419ZtUrq2VNKSDCP77pLeucdqUoVa+sCAAAAkC8UiNDdvn17HTt2TCNGjFBiYqLq1q2rRYsWORZXO3DggNPU/5QpU5SWlqaHH37Y6XNGjhypF1980ZWlw10dO2Zu+/Xhh+ZxeLg0YYL02GOSrTDfTA4AAADgcgUidEtS79691bt372zPrVy50ul4//79eV8QCia7XfrgA2nwYOnUKTNgd+9u3l4eEmJ1dQAAAADymQITuoE89/PPUo8eUsb+73XqSFOnSrfdZm1dAAAAAPItt19IDchz585Jzz8v1a9vBu6AAGniRGnjRgI3AAAAgGtiphu4lqVLzdvH9+0zjx96SJo0SbrpJkvLAgAAAOAemOkGsnP8uNS5s7nl1759Zsj++mvp888J3AAAAAByjNANXM4wpE8+kapVk2bNMhdK69NH2rlTatPG6uoAAAAAuBluLwcy7Ntn7rm9eLF5XLOmNG0az20DAAAAuGHMdAOXLknjx5she/FiyddXeuUVadMmAjcAAACAf4WZbhRuW7ZI3bqZAVuS7rhDeu896ZZbrK0LAAAAQIHATDcKp4xtwG691QzcwcHS++9LK1YQuAEAAADkGma6UfhcuQ3YI49Ib74plSxpbV0AAAAAChxmulF4ZLcN2DffSHPnErgBAAAA5AlCNwq+7LYB69vX3AasdWurqwMAAABQgHF7OQq2/fulHj0ytwGrVcvcBiwqytKyAAAAABQOzHSjYEpPlyZOlGrUyNwGbPRoc9E0AjcAAAAAF2GmGwXPtm3S009LGzeax2wDBgAAAMAizHSj4Dh/XoqLkxo0MAN3UJB5K/ny5QRuAAAAAJZgphsFw4oV0jPPSHv3mscPPyxNniyVKmVtXQAAAAAKNWa64d5OnZK6dpXuussM3GXKSPPnS599RuAGAAAAYDlCN9yTYUjz5pnbgE2fbrY9+6y5DdgDD1hbGwAAAAD8jdvL4X7+/FPq1Uv65hvzuGpV89ntpk2trQsAAAAArsBMN9yH3S7Fx0vVq5uB29tbGjlS2rqVwA0AAAAgX2KmG+7hl1+kbt2kdevM4+hoc3a7Rg1r6wIAAACAa2CmG/lbaqo5m12vnhm4ixUzZ7u//57ADQAAACDfY6Yb+dcPP0hPPy0lJJjHbdpI77wj3XSTtXUBAAAAQA4x0438JyVF6tPHfE47IUEKD5fmzpW++orADQAAAMCtMNON/GXhQqlHD3OFcknq0kV64w2peHFr6wIAAACAG0DoRv5w7JjUr5/06afmcYUK0nvvSS1aWFsXAAAAAPwL3F4OaxmG9NFHUrVqZuD28JAGDJC2bydwAwAAAHB7zHTDOn/8IXXvLi1ebB7Xri198IHUsKG1dQEAAABALmGmG66Xni69+aa55dfixZKvrzRmjLRxI4EbAAAAQIHCTDdc65dfpK5dpfXrzeNmzaRp06QqVaytCwAAAADyADPdcI3UVGnkSKlePTNwBwZKU6dKK1cSuAEAAAAUWMx0I+/98IP09NPmntuS1Lat9M47Upky1tYFAAAAAHmMmW7kncOHzYXSmjY1A3d4uDRvnjR/PoEbAAAAQKHATDdyX3Ky9Prr0oQJ0rlzZtuTT0rjx0vFi1taGgAAAAC4EqEbuSc1VZoyRXrlFenECbPtttukV1+Vbr/d2toAAAAAwAKEbvx7drs0e7Y0fLi0f7/ZVqWKNHas1K6dZLNZWR0AAAAAWIbQjRtnGNKiRVJcnLRtm9lWurT04otSly6SF79eAAAAAAo3UhFuzIYN0uDB5pZfkhQUJA0ZIvXtK/n7W1oaAAAAAOQXhG5cnz17pKFDpc8/N499fKQ+fczZ7hIlrK0NAAAAAPIZQjdy5vBh6aWXpGnTpPR08zntTp2kUaOkcuWsrg4AAAAA8iVCN64tKcnc/mvixMztv1q3lsaMkWrVsrY2AAAAAMjnCN3IKjlZWr5cWrxY+uwztv8CAAAAgBtE6Ia55demTdKSJWbQXrdOunQp8zzbfwEAAADADSF0F1Z//ZUZspcuzZzNzlCpknTPPVKrVtK997L9FwAAAADcAA+rC8gt8fHxKl++vPz8/BQVFaUNGzZcs/9nn32mqlWrys/PT7Vq1dLChQtdVKlFLlwww/XAgVLt2lKZMuZe2nPmmIG7WDHpgQekd96RfvtN+vVXKT5eatOGwA0AAAAAN6hApKm5c+cqNjZWU6dOVVRUlCZNmqSYmBjt3r1b4eHhWfr/8MMP6tChg8aOHavWrVtr9uzZateunTZv3qyaNWta8B3kAcOQEhIyZ7NXrZLOn888b7NJDRpIMTHmjHZ0tOTtbV29AAAAAFAA2QzDMKwu4t+KiorSrbfeqrfffluSZLfbFRkZqT59+mjIkCFZ+rdv315nz57V//73P0fbbbfdprp162rq1KnZXiM1NVWpqamO4+TkZEVGRurUqVMKDAx0tNvtdh07dkxhYWHy8LDwRoKpU+XRq5dTk1GqlHTPPTJatpRatpRCQy0qDoVFvhkPQD7AeACcMSaATIwH95ScnKyQkBAlJSU5ZcIruf1Md1pamjZt2qS4uDhHm4eHh1q0aKF169Zl+55169YpNjbWqS0mJkbz58+/6nXGjh2rUaNGZWk/duyYLly44Di22+1KSkqSYRiWDhjPGjUU6uurtKgopTZvrrTmzXWpatXMhdDsdunoUcvqQ+GQX8YDkB8wHgBnjAkgE+PBPaWkpOSon9uH7uPHjys9PV0RERFO7REREdq1a1e270lMTMy2f2Ji4lWvExcX5xTUM2a6w8LCssx022w26/+VKixMxvHj8vb3FzeNwyr5ZjwA+QDjAXDGmAAyMR7ck5+fX476uX3odhVfX1/5+vpmaffw8MgyMGw2W7btLhcQYO31AeWj8QDkA4wHwBljAsjEeHA/Of1Zuf1PNDQ0VJ6enjpy5IhT+5EjR1SyZMls31OyZMnr6g8AAAAAwI1w+9Dt4+OjBg0aaNmyZY42u92uZcuWKTo6Otv3REdHO/WXpKVLl161PwAAAAAAN6JA3F4eGxurzp07q2HDhmrUqJEmTZqks2fPqkuXLpKkTp06qUyZMho7dqwkqV+/frrjjjs0fvx43X///ZozZ442btyo9957z8pvAwAAAABQwBSI0N2+fXsdO3ZMI0aMUGJiourWratFixY5Fks7cOCA0/32jRs31uzZszVs2DC98MILqly5subPn19w9ugGAAAAAOQLBWKfbiskJycrKCgoy55sdrtdR48eVXh4OIsgoNBjPACZGA+AM8YEkInx4J6ulgmvxE8UAAAAAIA8QugGAAAAACCPELoBAAAAAMgjhG4AAAAAAPIIoRsAAAAAgDxSILYMs0LGou/JyclO7Xa7XSkpKfLz82PlQRR6jAcgE+MBcMaYADIxHtxTRhb8pw3BCN03KCUlRZIUGRlpcSUAAAAAAKukpKQoKCjoqufZp/sG2e12/fXXXypWrJhsNpujPTk5WZGRkfrzzz+vuVcbUBgwHoBMjAfAGWMCyMR4cE+GYSglJUWlS5e+5h0KzHTfIA8PD910001XPR8YGMiAAf7GeAAyMR4AZ4wJIBPjwf1ca4Y7Aw8MAAAAAACQRwjdAAAAAADkEUJ3LvP19dXIkSPl6+trdSmA5RgPQCbGA+CMMQFkYjwUbCykBgAAAABAHmGmGwAAAACAPELoBgAAAAAgjxC6AQAAAADII4RuAAAAAADyCKE7F8XHx6t8+fLy8/NTVFSUNmzYYHVJwHUZO3asbr31VhUrVkzh4eFq166ddu/e7dTnwoUL6tWrl0qUKKGAgAA99NBDOnLkiFOfAwcO6P7775e/v7/Cw8M1aNAgXbp0yanPypUrVb9+ffn6+qpSpUqaOXNmlnoYU8hPxo0bJ5vNpueee87RxnhAYXPo0CE9/vjjKlGihIoUKaJatWpp48aNjvOGYWjEiBEqVaqUihQpohYtWujXX391+oyTJ0+qY8eOCgwMVHBwsLp27aozZ8449fn555/VrFkz+fn5KTIyUq+99lqWWj777DNVrVpVfn5+qlWrlhYuXJg33zSQjfT0dA0fPlwVKlRQkSJFVLFiRb388su6fI1qxgMcDOSKOXPmGD4+Psb06dONX375xejWrZsRHBxsHDlyxOrSgByLiYkxZsyYYezYscPYunWrcd999xlly5Y1zpw54+jTo0cPIzIy0li2bJmxceNG47bbbjMaN27sOH/p0iWjZs2aRosWLYwtW7YYCxcuNEJDQ424uDhHn99//93w9/c3YmNjjZ07dxpvvfWW4enpaSxatMjRhzGF/GTDhg1G+fLljdq1axv9+vVztDMeUJicPHnSKFeunPHkk08a69evN37//Xdj8eLFxt69ex19xo0bZwQFBRnz5883tm3bZrRt29aoUKGCcf78eUefVq1aGXXq1DF+/PFHY82aNUalSpWMDh06OM4nJSUZERERRseOHY0dO3YYn376qVGkSBHj3XffdfRZu3at4enpabz22mvGzp07jWHDhhne3t7G9u3bXfMfA4Xe6NGjjRIlShj/+9//jH379hmfffaZERAQYLz55puOPowHZCB055JGjRoZvXr1chynp6cbpUuXNsaOHWthVcC/c/ToUUOSsWrVKsMwDOP06dOGt7e38dlnnzn6JCQkGJKMdevWGYZhGAsXLjQ8PDyMxMRER58pU6YYgYGBRmpqqmEYhvH8888bNWrUcLpW+/btjZiYGMcxYwr5RUpKilG5cmVj6dKlxh133OEI3YwHFDaDBw82mjZtetXzdrvdKFmypPH666872k6fPm34+voan376qWEYhrFz505DkvHTTz85+nz77beGzWYzDh06ZBiGYbzzzjtGSEiIY4xkXLtKlSqO40ceecS4//77na4fFRVldO/e/d99k0AO3X///cZTTz3l1Paf//zH6Nixo2EYjAc44/byXJCWlqZNmzapRYsWjjYPDw+1aNFC69ats7Ay4N9JSkqSJBUvXlyStGnTJl28eNHpd71q1aoqW7as43d93bp1qlWrliIiIhx9YmJilJycrF9++cXR5/LPyOiT8RmMKeQnvXr10v3335/ld5bxgMLm66+/VsOGDfXf//5X4eHhqlevnqZNm+Y4v2/fPiUmJjr9rgYFBSkqKsppTAQHB6thw4aOPi1atJCHh4fWr1/v6HP77bfLx8fH0ScmJka7d+/WqVOnHH2uNW6AvNa4cWMtW7ZMe/bskSRt27ZN33//ve69915JjAc487K6gILg+PHjSk9Pd/pLlSRFRERo165dFlUF/Dt2u13PPfecmjRpopo1a0qSEhMT5ePjo+DgYKe+ERERSkxMdPTJbixknLtWn+TkZJ0/f16nTp1iTCFfmDNnjjZv3qyffvopyznGAwqb33//XVOmTFFsbKxeeOEF/fTTT+rbt698fHzUuXNnx+90dr+rl/++h4eHO5338vJS8eLFnfpUqFAhy2dknAsJCbnquMn4DCCvDRkyRMnJyapatao8PT2Vnp6u0aNHq2PHjpLEeIATQjeAbPXq1Us7duzQ999/b3UpgCX+/PNP9evXT0uXLpWfn5/V5QCWs9vtatiwocaMGSNJqlevnnbs2KGpU6eqc+fOFlcHuNa8efP0ySefaPbs2apRo4a2bt2q5557TqVLl2Y8IAtuL88FoaGh8vT0zLJi7ZEjR1SyZEmLqgJuXO/evfW///1PK1as0E033eRoL1mypNLS0nT69Gmn/pf/rpcsWTLbsZBx7lp9AgMDVaRIEcYU8oVNmzbp6NGjql+/vry8vOTl5aVVq1Zp8uTJ8vLyUkREBOMBhUqpUqVUvXp1p7Zq1arpwIEDkjJ/p6/1u1qyZEkdPXrU6fylS5d08uTJXBk3jAm4yqBBgzRkyBA9+uijqlWrlp544gn1799fY8eOlcR4gDNCdy7w8fFRgwYNtGzZMkeb3W7XsmXLFB0dbWFlwPUxDEO9e/fWl19+qeXLl2e5nalBgwby9vZ2+l3fvXu3Dhw44Phdj46O1vbt253+EFm6dKkCAwMdf1mLjo52+oyMPhmfwZhCfnD33Xdr+/bt2rp1q+PVsGFDdezY0fE14wGFSZMmTbJsI7lnzx6VK1dOklShQgWVLFnS6Xc1OTlZ69evdxoTp0+f1qZNmxx9li9fLrvdrqioKEef1atX6+LFi44+S5cuVZUqVRQSEuLoc61xA+S1c+fOycPDOUp5enrKbrdLYjzgClav5FZQzJkzx/D19TVmzpxp7Ny503jmmWeM4OBgpxVrgfyuZ8+eRlBQkLFy5Urj8OHDjte5c+ccfXr06GGULVvWWL58ubFx40YjOjraiI6OdpzP2CLpnnvuMbZu3WosWrTICAsLy3aLpEGDBhkJCQlGfHx8tlskMaaQ31y+erlhMB5QuGzYsMHw8vIyRo8ebfz666/GJ598Yvj7+xsff/yxo8+4ceOM4OBg46uvvjJ+/vln44EHHsh2i6R69eoZ69evN77//nujcuXKTlsknT592oiIiDCeeOIJY8eOHcacOXMMf3//LFskeXl5GW+88YaRkJBgjBw5ki2S4FKdO3c2ypQp49gy7IsvvjBCQ0ON559/3tGH8YAMhO5c9NZbbxlly5Y1fHx8jEaNGhk//vij1SUB10VStq8ZM2Y4+pw/f9549tlnjZCQEMPf39948MEHjcOHDzt9zv79+417773XKFKkiBEaGmoMGDDAuHjxolOfFStWGHXr1jV8fHyMm2++2ekaGRhTyG+uDN2MBxQ233zzjVGzZk3D19fXqFq1qvHee+85nbfb7cbw4cONiIgIw9fX17j77ruN3bt3O/U5ceKE0aFDByMgIMAIDAw0unTpYqSkpDj12bZtm9G0aVPD19fXKFOmjDFu3LgstcybN8+45ZZbDB8fH6NGjRrGggULcv8bBq4iOTnZ6Nevn1G2bFnDz8/PuPnmm42hQ4c6be3FeEAGm2EYhpUz7QAAAAAAFFQ80w0AAAAAQB4hdAMAAAAAkEcI3QAAAAAA5BFCNwAAAAAAeYTQDQAAAABAHiF0AwAAAACQRwjdAAAAAADkEUI3AAAAAAB5hNANAACuS/PmzfXcc89ZXQYAAG6B0A0AQCHSpk0btWrVKttza9askc1m088//+ziqgAAKLgI3QAAFCJdu3bV0qVLdfDgwSznZsyYoYYNG6p27doWVAYAQMFE6AYAoBBp3bq1wsLCNHPmTKf2M2fO6LPPPlO7du3UoUMHlSlTRv7+/qpVq5Y+/fTTa36mzWbT/PnzndqCg4OdrvHnn3/qkUceUXBwsIoXL64HHnhA+/fvz51vCgCAfIzQDQBAIeLl5aVOnTpp5syZMgzD0f7ZZ58pPT1djz/+uBo0aKAFCxZox44deuaZZ/TEE09ow4YNN3zNixcvKiYmRsWKFdOaNWu0du1aBQQEqFWrVkpLS8uNbwsAgHyL0A0AQCHz1FNP6bffftOqVascbTNmzNBDDz2kcuXKaeDAgapbt65uvvlm9enTR61atdK8efNu+Hpz586V3W7X+++/r1q1aqlatWqaMWOGDhw4oJUrV+bCdwQAQP5F6AYAoJCpWrWqGjdurOnTp0uS9u7dqzVr1qhr165KT0/Xyy+/rFq1aql48eIKCAjQ4sWLdeDAgRu+3rZt27R3714VK1ZMAQEBCggIUPHixXXhwgX99ttvufVtAQCQL3lZXQAAAHC9rl27qk+fPoqPj9eMGTNUsWJF3XHHHXr11Vf15ptvatKkSapVq5aKFi2q55577pq3gdtsNqdb1SXzlvIMZ86cUYMGDfTJJ59keW9YWFjufVMAAORDhG4AAAqhRx55RP369dPs2bM1a9Ys9ezZUzabTWvXrtUDDzygxx9/XJJkt9u1Z88eVa9e/aqfFRYWpsOHDzuOf/31V507d85xXL9+fc2dO1fh4eEKDAzMu28KAIB8iNvLAQAohAICAtS+fXvFxcXp8OHDevLJJyVJlStX1tKlS/XDDz8oISFB3bt315EjR675WXfddZfefvttbdmyRRs3blSPHj3k7e3tON+xY0eFhobqgQce0Jo1a7Rv3z6tXLlSffv2zXbrMgAAChJCNwAAhVTXrl116tQpxcTEqHTp0pKkYcOGqX79+oqJiVHz5s1VsmRJtWvX7pqfM378eEVGRqpZs2Z67LHHNHDgQPn7+zvO+/v7a/Xq1Spbtqz+85//qFq1auratasuXLjAzDcAoMCzGVc+hAUAAAAAAHIFM90AAAAAAOQRQjcAAAAAAHmE0A0AAAAAQB4hdAMAAAAAkEcI3QAAAAAA5BFCNwAAAAAAeYTQDQAAAABAHiF0AwAAAACQRwjdAAAAAADkEUI3AAAAAAB5hNANAAAAAEAe+X9kqpLiJxskqgAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per min_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 5911.217\n", + "variance: 11063664.000\n", + "std: 3326.208\n", + "min: 552.748\n", + "max: 19390.480\n", + "median: 5451.560\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 5921.921\n", + "variance: 11676302.000\n", + "std: 3417.060\n", + "min: 374.026\n", + "max: 22359.766\n", + "median: 5426.787\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per max_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 7153.816\n", + "variance: 16302536.000\n", + "std: 4037.640\n", + "min: 664.406\n", + "max: 24333.047\n", + "median: 6596.512\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 7156.977\n", + "variance: 17168200.000\n", + "std: 4143.453\n", + "min: 458.719\n", + "max: 27636.207\n", + "median: 6558.154\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per avg_oil_prod\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 6532.427\n", + "variance: 13545224.000\n", + "std: 3680.384\n", + "min: 608.335\n", + "max: 21836.357\n", + "median: 6024.207\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 6539.446\n", + "variance: 14259229.000\n", + "std: 3776.139\n", + "min: 415.672\n", + "max: 24818.859\n", + "median: 5996.325\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Analisi distribuzionale per total_water_need\n", + "\n", + "Statistiche Predizioni:\n", + "mean: 60131.293\n", + "variance: 836238848.000\n", + "std: 28917.795\n", + "min: 10634.454\n", + "max: 142663.500\n", + "median: 59327.203\n", + "\n", + "Statistiche Target Reali:\n", + "mean: 60065.336\n", + "variance: 879319232.000\n", + "std: 29653.316\n", + "min: 8061.355\n", + "max: 151159.875\n", + "median: 59136.551\n" + ] + }, + { + "data": { + "image/png": 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Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from 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/usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n", + "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n", + "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.4)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.1)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.3.7)\n", + "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.1)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n", + "Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (2.0.5)\n", + "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.7.22)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 13:56:39.957016: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-27 13:56:39.957067: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-27 13:56:39.957117: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-27 13:56:39.966205: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import (\n", + " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", + " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", + " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", + " Conv1D, Multiply\n", + ")\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "from tensorflow.keras.metrics import AUC\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# Data processing and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.metrics import (\n", + " mean_absolute_error, mean_squared_error, r2_score, \n", + " confusion_matrix, classification_report, roc_auc_score\n", + ")\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Additional utilities\n", + "import tensorflow_addons as tfa\n", + "from scipy import stats\n", + "import json\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Features based only on radiation and other available variables\n", + " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Energy-specific features\n", + " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Temperature impact on theoretical efficiency\n", + " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", + "\n", + " # Combined features\n", + " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", + " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_effect'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = np.abs(12 - df['hour'])\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "def add_atmospheric_features(df):\n", + " # Indice di Massa d'Aria (Air Mass Index)\n", + " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", + " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", + " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", + "\n", + " # Indice di Stabilità Atmosferica\n", + " # Combina temperatura, umidità e pressione\n", + " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", + "\n", + " # Vapor Pressure Deficit (VPD)\n", + " # Importante per la radiazione diffusa\n", + " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", + " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", + " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", + "\n", + " return df\n", + "\n", + "def add_diffusion_features(df):\n", + " # Indice di Diffusione\n", + " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", + "\n", + " # Radiazione Diretta vs Diffusa\n", + " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", + " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", + "\n", + " # Fattore di Trasparenza Atmosferica\n", + " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", + "\n", + " return df\n", + "\n", + "def calculate_trend(x):\n", + " try:\n", + " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", + " except:\n", + " return np.nan\n", + "\n", + "def add_persistence_features(df):\n", + " # Create a copy to avoid modifying the original dataframe\n", + " df = df.copy()\n", + "\n", + " # Calculate trends more efficiently\n", + " windows = [3, 6, 12, 24]\n", + " for w in windows:\n", + " # Use numba or vectorized operations if possible\n", + " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", + " window=w,\n", + " min_periods=w\n", + " ).apply(calculate_trend, raw=True)\n", + "\n", + " # Optimize volatility calculation by doing it in one pass\n", + " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", + " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", + "\n", + " return df\n", + "\n", + "def add_weather_pattern_features(df):\n", + " # Pattern giornalieri\n", + " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", + " lambda x: (x < 30).sum()\n", + " )\n", + "\n", + " # Stabilità delle condizioni\n", + " for col in ['temp', 'humidity', 'cloudcover']:\n", + " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", + "\n", + " # Indice di Variabilità Meteorologica\n", + " df['weather_variability_index'] = (df['temp_stability'] +\n", + " df['humidity_stability'] +\n", + " df['cloudcover_stability']) / 3\n", + "\n", + " return df\n", + "\n", + "def add_efficiency_features(df):\n", + " # Perdite per temperatura\n", + " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", + "\n", + " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", + " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", + "\n", + " # Efficienza complessiva stimata\n", + " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", + " df['atmospheric_transmittance']\n", + "\n", + " # Potenziale di produzione\n", + " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", + "\n", + " return df\n", + "\n", + "def add_advanced_seasonal_features(df):\n", + " # Differenza dalla durata media del giorno\n", + " avg_day_length = 12\n", + " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", + "\n", + " # Intensità stagionale\n", + " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", + "\n", + " # Indice di Stagionalità\n", + " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", + "\n", + " # Correzione per alba/tramonto\n", + " df['daylight_correction'] = np.where(\n", + " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", + " 0,\n", + " 1\n", + " )\n", + "\n", + " return df\n", + "\n", + "def add_basic_interactions(df):\n", + " \"\"\"\n", + " Aggiunge le interazioni base tra variabili meteorologiche\n", + " \"\"\"\n", + " # Feature esistenti originali\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # Clear sky e trasparenza atmosferica\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " return df\n", + "\n", + "def add_rolling_and_lag_features(df):\n", + " \"\"\"\n", + " Aggiunge feature rolling e lag\n", + " \"\"\"\n", + " # Rolling means esistenti\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features esistenti\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " return df\n", + "\n", + "def add_condition_indicators(df):\n", + " \"\"\"\n", + " Aggiunge indicatori di condizioni particolari\n", + " \"\"\"\n", + " # Extreme conditions indicator esistente\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " return df\n", + "\n", + "def add_physics_based_conversion_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", + " \"\"\"\n", + " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", + " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", + "\n", + " # Efficienza di conversione reale vs teorica\n", + " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " # Energia accumulata nel tempo (integrazione)\n", + " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", + "\n", + " # Differenza tra energia teorica e reale\n", + " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", + "\n", + " # Indice di performance del sistema\n", + " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " return df\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " \"\"\"\n", + " # Feature esistenti di base\n", + " # 1. Feature temporali di base\n", + " df = add_time_features(df)\n", + "\n", + " # 2. Feature solari e meteorologiche\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # 3. Feature atmosferiche e di diffusione\n", + " df = add_atmospheric_features(df)\n", + " df = add_diffusion_features(df)\n", + "\n", + " # 4. Feature di persistenza e pattern\n", + " df = add_persistence_features(df)\n", + " df = add_weather_pattern_features(df)\n", + "\n", + " # 5. Feature di efficienza e stagionalità\n", + " df = add_efficiency_features(df)\n", + " df = add_advanced_seasonal_features(df)\n", + "\n", + " # 6. Interazioni e feature derivate\n", + " df = add_basic_interactions(df)\n", + " df = add_rolling_and_lag_features(df)\n", + " df = add_condition_indicators(df)\n", + "\n", + " # 7. Nuove feature di conversione fisica\n", + " df = add_physics_based_conversion_features(df)\n", + "\n", + " # 8. One-hot encoding delle feature categoriche\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex',\n", + " 'cloudcover',\n", + " 'visibility',\n", + " 'temp',\n", + " 'pressure',\n", + " 'humidity',\n", + " 'solarradiation'\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation',\n", + " 'solar_angle',\n", + " 'day_length',\n", + " 'hour_sin',\n", + " 'hour_cos',\n", + " 'day_of_year_sin',\n", + " 'day_of_year_cos',\n", + " 'month_sin',\n", + " 'month_cos',\n", + " 'solar_noon',\n", + " 'daylight_correction'\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index',\n", + " 'atmospheric_attenuation',\n", + " 'theoretical_radiation',\n", + " 'expected_radiation',\n", + " 'cloud_elevation',\n", + " 'visibility_elevation',\n", + " 'uv_cloud_interaction',\n", + " 'temp_radiation_potential',\n", + " 'air_mass_index',\n", + " 'atmospheric_stability',\n", + " 'vapor_pressure_deficit',\n", + " 'diffusion_index',\n", + " 'atmospheric_transmittance',\n", + " 'temp_humidity_interaction',\n", + " 'clear_sky_factor'\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_12h',\n", + " 'uv_rolling_12h',\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " 'energy_rolling_mean_6h',\n", + " 'uv_rolling_mean_6h',\n", + " 'energy_volatility',\n", + " 'uv_volatility'\n", + " ],\n", + "\n", + " # Lag Features\n", + " 'lag_features': [\n", + " 'temp_1h_lag',\n", + " 'cloudcover_1h_lag',\n", + " 'humidity_1h_lag',\n", + " 'energy_lag_1h',\n", + " 'uv_lag_1h'\n", + " ],\n", + "\n", + " # Efficiency and Performance Features\n", + " 'efficiency_features': [\n", + " 'temp_losses',\n", + " 'soiling_loss_factor',\n", + " 'estimated_efficiency',\n", + " 'production_potential',\n", + " 'system_performance_ratio',\n", + " 'conversion_efficiency_ratio'\n", + " ],\n", + "\n", + " # Weather Pattern Features\n", + " 'weather_pattern_features': [\n", + " 'clear_sky_duration',\n", + " 'weather_variability_index',\n", + " 'temp_stability',\n", + " 'humidity_stability',\n", + " 'cloudcover_stability'\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring',\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning',\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night'\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " df[column] = df[column].interpolate(method='time')\n", + " else:\n", + " df[column] = df[column].interpolate(method='linear')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarenergy']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", + " from tensorflow import keras\n", + " from keras.models import Model\n", + " from keras.layers import (\n", + " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", + " MultiHeadAttention, LayerNormalization, Lambda,\n", + " Concatenate, Activation, Bidirectional, LSTM, Add\n", + " )\n", + " from keras.regularizers import l2\n", + " from keras.optimizers import AdamW\n", + " import tensorflow as tf\n", + " import numpy as np\n", + " import tensorflow_addons as tfa\n", + " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", + " \n", + " # Input layer\n", + " inputs = Input(shape=input_shape)\n", + " \n", + " # Feature groups definition\n", + " feature_dims = {\n", + " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", + " 'weather': [0, 1, 2, 3, 4, 5],\n", + " 'temporal': [10, 11, 12, 13, 14, 15],\n", + " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", + " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", + " 'lag': [40, 41, 42, 43, 44],\n", + " 'performance': [45, 46, 47, 48, 49, 50]\n", + " }\n", + " \n", + " # Feature extraction\n", + " feature_tensors = {}\n", + " for name, indices in feature_dims.items():\n", + " valid_indices = [i for i in indices if i < input_shape[-1]]\n", + " if valid_indices:\n", + " feature_tensors[name] = Lambda(\n", + " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", + " )(inputs)\n", + " \n", + " # Feature processing with residual connections\n", + " def process_feature_group(tensor, units, name):\n", + " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", + " kernel_regularizer=l2(l2_lambda))(tensor)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", + " x = Add()([x, residual])\n", + " x = LayerNormalization()(x)\n", + " \n", + " return x\n", + " \n", + " # Process each feature group\n", + " processed_features = {}\n", + " for name, tensor in feature_tensors.items():\n", + " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", + " processed_features[name] = process_feature_group(tensor, units, name)\n", + " \n", + " # Enhanced attention mechanism\n", + " def attention_block(x, num_heads=4):\n", + " attention_output = MultiHeadAttention(\n", + " num_heads=num_heads, \n", + " key_dim=x.shape[-1] // num_heads\n", + " )(x, x)\n", + " x = LayerNormalization()(x + attention_output)\n", + " \n", + " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", + " ffn = Dropout(0.1)(ffn)\n", + " ffn = Dense(x.shape[-1])(ffn)\n", + " \n", + " return LayerNormalization()(x + ffn)\n", + " \n", + " # Merge primary features with attention\n", + " primary_features = [\n", + " processed_features['solar'],\n", + " processed_features['weather'],\n", + " processed_features['performance']\n", + " ]\n", + " primary_context = Concatenate(axis=-1)(primary_features)\n", + " primary_context = attention_block(primary_context)\n", + " \n", + " # Merge secondary features\n", + " secondary_features = [\n", + " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", + " if name in processed_features\n", + " ]\n", + " if secondary_features:\n", + " secondary_context = Concatenate(axis=-1)(secondary_features)\n", + " secondary_context = attention_block(secondary_context)\n", + " else:\n", + " secondary_context = primary_context\n", + " \n", + " # Final feature merge\n", + " combined = Concatenate(axis=-1)([\n", + " primary_context, \n", + " secondary_context,\n", + " processed_features['derived']\n", + " ])\n", + " \n", + " # Sequential processing with residual LSTM\n", + " def residual_lstm_block(x, units):\n", + " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", + " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", + " x = Add()([lstm_out, residual])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + " \n", + " x = residual_lstm_block(combined, 128)\n", + " x = residual_lstm_block(x, 64)\n", + " x = Bidirectional(LSTM(64))(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " # Classification branch\n", + " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " class_x = BatchNormalization()(class_x)\n", + " class_x = Dropout(0.2)(class_x)\n", + " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", + " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", + " \n", + " # Enhanced regression branch with multiple pathways\n", + " def create_regression_pathway(x, name):\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = x\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = Add()([x, residual])\n", + " \n", + " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " return Dense(1, name=f'{name}_output')(x)\n", + " \n", + " # Create specialized regression pathways\n", + " low_range = create_regression_pathway(x, 'low_range')\n", + " mid_range = create_regression_pathway(x, 'mid_range')\n", + " high_range = create_regression_pathway(x, 'high_range')\n", + " \n", + " # Create feature representation for attention\n", + " feature_vector = Dense(32, activation='swish')(x)\n", + " \n", + " # Stack the range predictions\n", + " range_stack = tf.stack([low_range, mid_range, high_range], axis=1)\n", + " \n", + " # Create attention mechanism\n", + " attention_context = Dense(32, activation='swish')(feature_vector)\n", + " \n", + " # Calculate attention weights using the context\n", + " attention_weights = Dense(3, activation='softmax')(attention_context)\n", + " \n", + " # Apply attention weights to combine predictions\n", + " reg_output = Lambda(\n", + " lambda inputs: tf.reduce_sum(inputs[0] * inputs[1], axis=1),\n", + " name='regression_output'\n", + " )([attention_weights, range_stack])\n", + " \n", + " # Final output with enhanced processing\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dropout(0.2)(final_x)\n", + " \n", + " residual = final_x\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Add()([final_x, residual])\n", + " \n", + " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Dense(1)(final_x)\n", + " final_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='final_output'\n", + " )(final_x)\n", + " \n", + " # Build model\n", + " model = Model(inputs=inputs, outputs=[class_output, reg_output, final_output])\n", + " \n", + " # Enhanced loss functions\n", + " def enhanced_regression_loss(y_true, y_pred):\n", + " mae = tf.abs(y_true - y_pred)\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 1.5 + \\\n", + " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.2 + \\\n", + " tf.cast(y_true <= 1.0, tf.float32)\n", + " \n", + " weighted_loss = (0.5 * mae + 0.5 * mse) * value_ranges\n", + " return tf.reduce_mean(weighted_loss)\n", + " \n", + " def final_loss(y_true, y_pred):\n", + " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", + " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", + " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", + " return 0.5 * mae + 0.5 * mse\n", + " \n", + " # Learning rate schedule\n", + " clr = CosineDecayRestarts(\n", + " initial_learning_rate=2e-4,\n", + " first_decay_steps=1000,\n", + " t_mul=2.0,\n", + " m_mul=0.9,\n", + " alpha=1e-7\n", + " )\n", + " \n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=clr,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0\n", + " )\n", + " \n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': enhanced_regression_loss,\n", + " 'final_output': final_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.4,\n", + " 'final_output': 0.4\n", + " }\n", + " )\n", + "\n", + " # Plot model architecture\n", + " try:\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + " except Exception as e:\n", + " print(f\"Warning: Could not plot model architecture: {e}\")\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar energy values (kWh)\n", + " y_pred : array-like\n", + " Predicted solar energy values (kWh)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 kWh\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", + "\n", + " # Energy level classification\n", + " def get_energy_level(value):\n", + " if value <= 0.5:\n", + " return 'Very Low'\n", + " elif value <= 2.0:\n", + " return 'Low'\n", + " elif value <= 4.0:\n", + " return 'Moderate'\n", + " elif value <= 6.0:\n", + " return 'High'\n", + " elif value <= 8.0:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate energy levels\n", + " y_true_levels = [get_energy_level(v) for v in y_true]\n", + " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Energy Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} kWh\")\n", + " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for energy levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", + " print(\"\\nConfusion Matrix for Energy Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=unique_levels,\n", + " index=unique_levels\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Predicted Energy (kWh)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (kWh)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 0.5\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by Energy level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Energy Level')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_energy_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Calculates comprehensive metrics for the solar energy prediction model.\n", + " \n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Ground truth values\n", + " y_class : array-like\n", + " Classification predictions (probability of non-zero values)\n", + " y_reg : array-like\n", + " Regression predictions (unrestricted values)\n", + " y_final : array-like\n", + " Final clipped predictions\n", + " min_output : float\n", + " Minimum allowed output value\n", + " max_output : float\n", + " Maximum allowed output value\n", + " \n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " # Ensure proper array formatting and dimensionality\n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Validate input dimensions\n", + " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", + " \"All input arrays must have the same length\"\n", + " \n", + " # Classification metrics with error handling\n", + " print(\"\\nClassification Metrics:\")\n", + " try:\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", + " print(conf_matrix)\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " class_report = classification_report(\n", + " y_true_binary, \n", + " y_pred_binary,\n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4\n", + " )\n", + " print(class_report)\n", + " except Exception as e:\n", + " print(f\"Error in classification metrics calculation: {str(e)}\")\n", + " \n", + " # Regression metrics with error handling\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " try:\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " # Range validation\n", + " out_of_range = np.sum(\n", + " (y_reg_nonzero < min_output) | \n", + " (y_reg_nonzero > max_output)\n", + " )\n", + " \n", + " # Error metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", + " (y_true_nonzero + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " # Calculate metrics\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in regression metrics calculation: {str(e)}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final output metrics with error handling\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " try:\n", + " # Ensure outputs are within bounds\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " \n", + " # Calculate metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in final output metrics calculation: {str(e)}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar energy model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " def evaluate_epoch(epoch, logs):\n", + " if epoch % 10 == 0:\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=35,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-5\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", + " tf.keras.callbacks.TerminateOnNaN()\n", + " ]\n", + "\n", + " '''\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.8,\n", + " patience=10,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " '''\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar energy predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarenergy_predicted': final_pred.flatten(),\n", + " 'solarenergy_classification': classification_pred.flatten(),\n", + " 'solarenergy_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar energy column where missing\n", + " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", + " 'solarenergy_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar energy model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 66\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (112882, 24, 66)\n", + "Test data shape: (16849, 24, 66)\n", + "Saving scaler X to: 2024-11-27_13-56_scale_X.joblib\n", + "Saving scaler X to: 2024-11-27_13-56_scale_y.joblib\n", + "Saving features to: 2024-11-27_13-56_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "print(\"Initializing solar energy model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", + "\n", + "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", + "Max dataset solar energy increased by 15% : 4.6 - Scaled Version : 3.833333333333333\n", + "\n", + "Class distribution in training set:\n", + "Zeros: 56899 (50.41%)\n", + "Non-zeros: 55983 (49.59%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 8576 (50.90%)\n", + "Non-zeros: 8273 (49.10%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 14:02:24.816496: W tensorflow/core/framework/op_kernel.cc:1827] INVALID_ARGUMENT: required broadcastable shapes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error during training: Graph execution error:\n", + "\n", + "Detected at node model/regression_output/mul defined at (most recent call last):\n", + " File \"\", line 198, in _run_module_as_main\n", + "\n", + " File \"\", line 88, in _run_code\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel_launcher.py\", line 17, in \n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 1046, in launch_instance\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 736, in start\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n", + "\n", + " File \"/usr/lib/python3.11/asyncio/base_events.py\", line 604, in run_forever\n", + "\n", + " File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1909, in _run_once\n", + "\n", + " File \"/usr/lib/python3.11/asyncio/events.py\", line 80, in _run\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 505, in process_one\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n", + "\n", + " File \"/tmp/ipykernel_341907/1713792660.py\", line 47, in \n", + "\n", + " File \"/tmp/ipykernel_341907/594795021.py\", line 730, in train_hybrid_model\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1783, in fit\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1377, in train_function\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1360, in step_function\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1349, in run_step\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1126, in train_step\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 589, in __call__\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 515, in call\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 672, in _run_internal_graph\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n", + "\n", + " File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/lambda_layer.py\", line 212, in call\n", + "\n", + " File \"/tmp/ipykernel_341907/594795021.py\", line 153, in \n", + "\n", + "required broadcastable shapes\n", + "\t [[{{node model/regression_output/mul}}]] [Op:__inference_train_function_106117]\n", + "\n", + "Model output names: ['classification_output/Sigmoid:0', 'regression_output/Sum:0', 'final_output/clip_by_value:0']\n", + "Training targets keys: dict_keys(['classification_output', 'regression_output', 'final_output'])\n" + ] + }, + { + "ename": "InvalidArgumentError", + "evalue": "Graph execution error:\n\nDetected at node model/regression_output/mul defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel_launcher.py\", line 17, in \n\n File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 1046, in launch_instance\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 736, in start\n\n File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 604, in run_forever\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1909, in _run_once\n\n File \"/usr/lib/python3.11/asyncio/events.py\", line 80, in _run\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 505, in process_one\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n\n File \"/tmp/ipykernel_341907/1713792660.py\", line 47, in \n\n File \"/tmp/ipykernel_341907/594795021.py\", line 730, in train_hybrid_model\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1783, in fit\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1377, in train_function\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1360, in step_function\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1349, in run_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1126, in train_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 589, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 515, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 672, in _run_internal_graph\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/lambda_layer.py\", line 212, in call\n\n File \"/tmp/ipykernel_341907/594795021.py\", line 153, in \n\nrequired broadcastable shapes\n\t [[{{node model/regression_output/mul}}]] [Op:__inference_train_function_106117]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 47\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mModel output names:\u001b[39m\u001b[38;5;124m\"\u001b[39m, output_names)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m4. Starting training...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 47\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_hybrid_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_train_seq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_test_seq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my_test\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m150\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m512\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43mfolder_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfolder_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mmin_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmin_val_scaled\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_val_scaled\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[8], line 730\u001b[0m, in \u001b[0;36mtrain_hybrid_model\u001b[0;34m(model, X_train, y_train, X_test, y_test, epochs, batch_size, folder_name, min_output, max_output)\u001b[0m\n\u001b[1;32m 717\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;124;03mtf.keras.callbacks.ReduceLROnPlateau(\u001b[39;00m\n\u001b[1;32m 719\u001b[0m \u001b[38;5;124;03m monitor='val_final_output_loss',\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[38;5;124;03m ),\u001b[39;00m\n\u001b[1;32m 728\u001b[0m \u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 729\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 730\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 731\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 732\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_targets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 733\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_targets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 734\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 735\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 736\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 737\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 738\u001b[0m \u001b[43m \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\n\u001b[1;32m 739\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 741\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mTraining completed successfully!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 743\u001b[0m \u001b[38;5;66;03m# Final evaluation\u001b[39;00m\n", + "File \u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", + "File \u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow/python/eager/execute.py:60\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;66;03m# Convert any objects of type core_types.Tensor to Tensor.\u001b[39;00m\n\u001b[1;32m 54\u001b[0m inputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 55\u001b[0m tensor_conversion_registry\u001b[38;5;241m.\u001b[39mconvert(t)\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, core_types\u001b[38;5;241m.\u001b[39mTensor)\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m t\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m inputs\n\u001b[1;32m 59\u001b[0m ]\n\u001b[0;32m---> 60\u001b[0m tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m 61\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node model/regression_output/mul defined at (most recent call last):\n File \"\", line 198, in _run_module_as_main\n\n File \"\", line 88, in _run_code\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel_launcher.py\", line 17, in \n\n File \"/usr/local/lib/python3.11/dist-packages/traitlets/config/application.py\", line 1046, in launch_instance\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelapp.py\", line 736, in start\n\n File \"/usr/local/lib/python3.11/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 604, in run_forever\n\n File \"/usr/lib/python3.11/asyncio/base_events.py\", line 1909, in _run_once\n\n File \"/usr/lib/python3.11/asyncio/events.py\", line 80, in _run\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 505, in process_one\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n\n File \"/usr/local/lib/python3.11/dist-packages/ipykernel/zmqshell.py\", line 546, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n\n File \"/usr/local/lib/python3.11/dist-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n\n File \"/tmp/ipykernel_341907/1713792660.py\", line 47, in \n\n File \"/tmp/ipykernel_341907/594795021.py\", line 730, in train_hybrid_model\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1783, in fit\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1377, in train_function\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1360, in step_function\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1349, in run_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 1126, in train_step\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py\", line 589, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 515, in call\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/functional.py\", line 672, in _run_internal_graph\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/engine/base_layer.py\", line 1149, in __call__\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\", line 96, in error_handler\n\n File \"/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/lambda_layer.py\", line 212, in call\n\n File \"/tmp/ipykernel_341907/594795021.py\", line 153, in \n\nrequired broadcastable shapes\n\t [[{{node model/regression_output/mul}}]] [Op:__inference_train_function_106117]" + ] + } + ], + "source": [ + "#Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "min_val = df['solarenergy'].min()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "\n", + "max_val = df['solarenergy'].max()\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", + "\n", + "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 15\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarenergy_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=150,\n", + " batch_size=512,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, min_val_scaled, max_val_scaled)\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'classification': {\n", + " 'final_loss': float(history.history['val_classification_output_loss'][-1]),\n", + " 'final_auc': float(history.history['val_classification_output_auc'][-1])\n", + " },\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_regression_output_mae'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_final_output_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", + " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", + " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", + "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", + "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [], + "source": [ + "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1-checkpoint.ipynb b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1-checkpoint.ipynb new file mode 100644 index 0000000..b4179ae --- /dev/null +++ b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1-checkpoint.ipynb @@ -0,0 +1,2986 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:17:43.475455: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-27 23:17:43.475499: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-27 23:17:43.475533: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-27 23:17:43.483362: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import (\n", + " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", + " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", + " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", + " Conv1D, Multiply\n", + ")\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "from tensorflow.keras.metrics import AUC\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# Data processing and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.metrics import (\n", + " mean_absolute_error, mean_squared_error, r2_score, \n", + " confusion_matrix, classification_report, roc_auc_score\n", + ")\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Additional utilities\n", + "import tensorflow_addons as tfa\n", + "from scipy import stats\n", + "import json\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Features based only on radiation and other available variables\n", + " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Energy-specific features\n", + " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Temperature impact on theoretical efficiency\n", + " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", + "\n", + " # Combined features\n", + " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", + " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_effect'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = np.abs(12 - df['hour'])\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "def add_atmospheric_features(df):\n", + " # Indice di Massa d'Aria (Air Mass Index)\n", + " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", + " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", + " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", + "\n", + " # Indice di Stabilità Atmosferica\n", + " # Combina temperatura, umidità e pressione\n", + " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", + "\n", + " # Vapor Pressure Deficit (VPD)\n", + " # Importante per la radiazione diffusa\n", + " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", + " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", + " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", + "\n", + " return df\n", + "\n", + "def add_diffusion_features(df):\n", + " # Indice di Diffusione\n", + " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", + "\n", + " # Radiazione Diretta vs Diffusa\n", + " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", + " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", + "\n", + " # Fattore di Trasparenza Atmosferica\n", + " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", + "\n", + " return df\n", + "\n", + "def calculate_trend(x):\n", + " try:\n", + " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", + " except:\n", + " return np.nan\n", + "\n", + "def add_persistence_features(df):\n", + " # Create a copy to avoid modifying the original dataframe\n", + " df = df.copy()\n", + "\n", + " # Calculate trends more efficiently\n", + " windows = [3, 6, 12, 24]\n", + " for w in windows:\n", + " # Use numba or vectorized operations if possible\n", + " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", + " window=w,\n", + " min_periods=w\n", + " ).apply(calculate_trend, raw=True)\n", + "\n", + " # Optimize volatility calculation by doing it in one pass\n", + " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", + " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", + "\n", + " return df\n", + "\n", + "def add_weather_pattern_features(df):\n", + " # Pattern giornalieri\n", + " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", + " lambda x: (x < 30).sum()\n", + " )\n", + "\n", + " # Stabilità delle condizioni\n", + " for col in ['temp', 'humidity', 'cloudcover']:\n", + " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", + "\n", + " # Indice di Variabilità Meteorologica\n", + " df['weather_variability_index'] = (df['temp_stability'] +\n", + " df['humidity_stability'] +\n", + " df['cloudcover_stability']) / 3\n", + "\n", + " return df\n", + "\n", + "def add_efficiency_features(df):\n", + " # Perdite per temperatura\n", + " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", + "\n", + " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", + " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", + "\n", + " # Efficienza complessiva stimata\n", + " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", + " df['atmospheric_transmittance']\n", + "\n", + " # Potenziale di produzione\n", + " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", + "\n", + " return df\n", + "\n", + "def add_advanced_seasonal_features(df):\n", + " # Differenza dalla durata media del giorno\n", + " avg_day_length = 12\n", + " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", + "\n", + " # Intensità stagionale\n", + " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", + "\n", + " # Indice di Stagionalità\n", + " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", + "\n", + " # Correzione per alba/tramonto\n", + " df['daylight_correction'] = np.where(\n", + " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", + " 0,\n", + " 1\n", + " )\n", + "\n", + " return df\n", + "\n", + "def add_basic_interactions(df):\n", + " \"\"\"\n", + " Aggiunge le interazioni base tra variabili meteorologiche\n", + " \"\"\"\n", + " # Feature esistenti originali\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # Clear sky e trasparenza atmosferica\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " return df\n", + "\n", + "def add_rolling_and_lag_features(df):\n", + " \"\"\"\n", + " Aggiunge feature rolling e lag\n", + " \"\"\"\n", + " # Rolling means esistenti\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features esistenti\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " return df\n", + "\n", + "def add_condition_indicators(df):\n", + " \"\"\"\n", + " Aggiunge indicatori di condizioni particolari\n", + " \"\"\"\n", + " # Extreme conditions indicator esistente\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " return df\n", + "\n", + "def add_physics_based_conversion_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", + " \"\"\"\n", + " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", + " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", + "\n", + " # Efficienza di conversione reale vs teorica\n", + " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " # Energia accumulata nel tempo (integrazione)\n", + " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", + "\n", + " # Differenza tra energia teorica e reale\n", + " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", + "\n", + " # Indice di performance del sistema\n", + " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " return df\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " \"\"\"\n", + " # Feature esistenti di base\n", + " # 1. Feature temporali di base\n", + " df = add_time_features(df)\n", + "\n", + " # 2. Feature solari e meteorologiche\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # 3. Feature atmosferiche e di diffusione\n", + " df = add_atmospheric_features(df)\n", + " df = add_diffusion_features(df)\n", + "\n", + " # 4. Feature di persistenza e pattern\n", + " df = add_persistence_features(df)\n", + " df = add_weather_pattern_features(df)\n", + "\n", + " # 5. Feature di efficienza e stagionalità\n", + " df = add_efficiency_features(df)\n", + " df = add_advanced_seasonal_features(df)\n", + "\n", + " # 6. Interazioni e feature derivate\n", + " df = add_basic_interactions(df)\n", + " df = add_rolling_and_lag_features(df)\n", + " df = add_condition_indicators(df)\n", + "\n", + " # 7. Nuove feature di conversione fisica\n", + " df = add_physics_based_conversion_features(df)\n", + "\n", + " # 8. One-hot encoding delle feature categoriche\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex',\n", + " 'cloudcover',\n", + " 'visibility',\n", + " 'temp',\n", + " 'pressure',\n", + " 'humidity',\n", + " 'solarradiation'\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation',\n", + " 'solar_angle',\n", + " 'day_length',\n", + " 'hour_sin',\n", + " 'hour_cos',\n", + " 'day_of_year_sin',\n", + " 'day_of_year_cos',\n", + " 'month_sin',\n", + " 'month_cos',\n", + " 'solar_noon',\n", + " 'daylight_correction'\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index',\n", + " 'atmospheric_attenuation',\n", + " 'theoretical_radiation',\n", + " 'expected_radiation',\n", + " 'cloud_elevation',\n", + " 'visibility_elevation',\n", + " 'uv_cloud_interaction',\n", + " 'temp_radiation_potential',\n", + " 'air_mass_index',\n", + " 'atmospheric_stability',\n", + " 'vapor_pressure_deficit',\n", + " 'diffusion_index',\n", + " 'atmospheric_transmittance',\n", + " 'temp_humidity_interaction',\n", + " 'clear_sky_factor'\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_12h',\n", + " 'uv_rolling_12h',\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " 'energy_rolling_mean_6h',\n", + " 'uv_rolling_mean_6h',\n", + " 'energy_volatility',\n", + " 'uv_volatility'\n", + " ],\n", + "\n", + " # Lag Features\n", + " 'lag_features': [\n", + " 'temp_1h_lag',\n", + " 'cloudcover_1h_lag',\n", + " 'humidity_1h_lag',\n", + " 'energy_lag_1h',\n", + " 'uv_lag_1h'\n", + " ],\n", + "\n", + " # Efficiency and Performance Features\n", + " 'efficiency_features': [\n", + " 'temp_losses',\n", + " 'soiling_loss_factor',\n", + " 'estimated_efficiency',\n", + " 'production_potential',\n", + " 'system_performance_ratio',\n", + " 'conversion_efficiency_ratio'\n", + " ],\n", + "\n", + " # Weather Pattern Features\n", + " 'weather_pattern_features': [\n", + " 'clear_sky_duration',\n", + " 'weather_variability_index',\n", + " 'temp_stability',\n", + " 'humidity_stability',\n", + " 'cloudcover_stability'\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring',\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning',\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night'\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " df[column] = df[column].interpolate(method='time')\n", + " else:\n", + " df[column] = df[column].interpolate(method='linear')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarenergy']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", + " from tensorflow import keras\n", + " from keras.models import Model\n", + " from keras.layers import (\n", + " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", + " MultiHeadAttention, LayerNormalization, Lambda,\n", + " Concatenate, Activation, Bidirectional, LSTM, Add\n", + " )\n", + " from keras.regularizers import l2\n", + " from keras.optimizers import AdamW\n", + " import tensorflow as tf\n", + " import numpy as np\n", + " import tensorflow_addons as tfa\n", + " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", + " \n", + " # Input layer\n", + " inputs = Input(shape=input_shape)\n", + " \n", + " # Feature groups definition\n", + " feature_dims = {\n", + " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", + " 'weather': [0, 1, 2, 3, 4, 5],\n", + " 'temporal': [10, 11, 12, 13, 14, 15],\n", + " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", + " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", + " 'lag': [40, 41, 42, 43, 44],\n", + " 'performance': [45, 46, 47, 48, 49, 50]\n", + " }\n", + " \n", + " # Feature extraction\n", + " feature_tensors = {}\n", + " for name, indices in feature_dims.items():\n", + " valid_indices = [i for i in indices if i < input_shape[-1]]\n", + " if valid_indices:\n", + " feature_tensors[name] = Lambda(\n", + " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", + " )(inputs)\n", + " \n", + " # Feature processing with residual connections\n", + " def process_feature_group(tensor, units, name):\n", + " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", + " kernel_regularizer=l2(l2_lambda))(tensor)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", + " x = Add()([x, residual])\n", + " x = LayerNormalization()(x)\n", + " \n", + " return x\n", + " \n", + " # Process each feature group\n", + " processed_features = {}\n", + " for name, tensor in feature_tensors.items():\n", + " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", + " processed_features[name] = process_feature_group(tensor, units, name)\n", + " \n", + " # Enhanced attention mechanism\n", + " def attention_block(x, num_heads=4):\n", + " attention_output = MultiHeadAttention(\n", + " num_heads=num_heads, \n", + " key_dim=x.shape[-1] // num_heads\n", + " )(x, x)\n", + " x = LayerNormalization()(x + attention_output)\n", + " \n", + " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", + " ffn = Dropout(0.1)(ffn)\n", + " ffn = Dense(x.shape[-1])(ffn)\n", + " \n", + " return LayerNormalization()(x + ffn)\n", + " \n", + " # Merge primary features with attention\n", + " primary_features = [\n", + " processed_features['solar'],\n", + " processed_features['weather'],\n", + " processed_features['performance']\n", + " ]\n", + " primary_context = Concatenate(axis=-1)(primary_features)\n", + " primary_context = attention_block(primary_context)\n", + " \n", + " # Merge secondary features\n", + " secondary_features = [\n", + " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", + " if name in processed_features\n", + " ]\n", + " if secondary_features:\n", + " secondary_context = Concatenate(axis=-1)(secondary_features)\n", + " secondary_context = attention_block(secondary_context)\n", + " else:\n", + " secondary_context = primary_context\n", + " \n", + " # Final feature merge\n", + " combined = Concatenate(axis=-1)([\n", + " primary_context, \n", + " secondary_context,\n", + " processed_features['derived']\n", + " ])\n", + " \n", + " # Sequential processing with residual LSTM\n", + " def residual_lstm_block(x, units):\n", + " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", + " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", + " x = Add()([lstm_out, residual])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + " \n", + " x = residual_lstm_block(combined, 128)\n", + " x = residual_lstm_block(x, 64)\n", + " x = Bidirectional(LSTM(64))(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " # Classification branch\n", + " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " class_x = BatchNormalization()(class_x)\n", + " class_x = Dropout(0.2)(class_x)\n", + " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", + " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", + " \n", + " # Enhanced regression branch with multiple pathways\n", + " def create_regression_pathway(x, name):\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = x\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = Add()([x, residual])\n", + " \n", + " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " return Dense(1, name=f'{name}_output')(x)\n", + " \n", + " # Create specialized regression pathways\n", + " low_range = create_regression_pathway(x, 'low_range')\n", + " mid_range = create_regression_pathway(x, 'mid_range')\n", + " high_range = create_regression_pathway(x, 'high_range')\n", + " \n", + " # Create context vector for attention\n", + " context = Dense(64, activation='swish')(x)\n", + " \n", + " # Calculate attention scores\n", + " attention_scores = Dense(3, activation='softmax')(context)\n", + " \n", + " # Combine predictions using attention weights\n", + " reg_output = Lambda(\n", + " lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n", + " name='regression_output'\n", + " )([attention_scores, low_range, mid_range, high_range])\n", + "\n", + " # Final output processing remains the same...\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dropout(0.2)(final_x)\n", + " \n", + " residual = final_x\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Add()([final_x, residual])\n", + " \n", + " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Dense(1)(final_x)\n", + " final_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='final_output'\n", + " )(final_x)\n", + " \n", + " # Build model with all outputs\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[class_output, reg_output, final_output]\n", + " )\n", + " \n", + " # Enhanced loss functions\n", + " def enhanced_regression_loss(y_true, y_pred):\n", + " mae = tf.abs(y_true - y_pred)\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 1.5 + \\\n", + " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.2 + \\\n", + " tf.cast(y_true <= 1.0, tf.float32)\n", + " \n", + " weighted_loss = (0.5 * mae + 0.5 * mse) * value_ranges\n", + " return tf.reduce_mean(weighted_loss)\n", + " \n", + " def final_loss(y_true, y_pred):\n", + " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", + " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", + " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", + " return 0.5 * mae + 0.5 * mse\n", + " \n", + " # Learning rate schedule\n", + " clr = CosineDecayRestarts(\n", + " initial_learning_rate=2e-4,\n", + " first_decay_steps=1000,\n", + " t_mul=2.0,\n", + " m_mul=0.9,\n", + " alpha=1e-7\n", + " )\n", + " \n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=clr,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0\n", + " )\n", + " \n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': enhanced_regression_loss,\n", + " 'final_output': final_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.4,\n", + " 'final_output': 0.4\n", + " }\n", + " )\n", + "\n", + " # Plot model architecture\n", + " try:\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + " except Exception as e:\n", + " print(f\"Warning: Could not plot model architecture: {e}\")\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar energy values (kWh)\n", + " y_pred : array-like\n", + " Predicted solar energy values (kWh)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 kWh\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", + "\n", + " # Energy level classification\n", + " def get_energy_level(value):\n", + " if value <= 0.5:\n", + " return 'Very Low'\n", + " elif value <= 2.0:\n", + " return 'Low'\n", + " elif value <= 4.0:\n", + " return 'Moderate'\n", + " elif value <= 6.0:\n", + " return 'High'\n", + " elif value <= 8.0:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate energy levels\n", + " y_true_levels = [get_energy_level(v) for v in y_true]\n", + " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Energy Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} kWh\")\n", + " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for energy levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", + " print(\"\\nConfusion Matrix for Energy Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=unique_levels,\n", + " index=unique_levels\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Predicted Energy (kWh)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (kWh)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 0.5\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by Energy level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Energy Level')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_energy_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Calculates comprehensive metrics for the solar energy prediction model.\n", + " \n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Ground truth values\n", + " y_class : array-like\n", + " Classification predictions (probability of non-zero values)\n", + " y_reg : array-like\n", + " Regression predictions (unrestricted values)\n", + " y_final : array-like\n", + " Final clipped predictions\n", + " min_output : float\n", + " Minimum allowed output value\n", + " max_output : float\n", + " Maximum allowed output value\n", + " \n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " # Ensure proper array formatting and dimensionality\n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Validate input dimensions\n", + " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", + " \"All input arrays must have the same length\"\n", + " \n", + " # Classification metrics with error handling\n", + " print(\"\\nClassification Metrics:\")\n", + " try:\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", + " print(conf_matrix)\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " class_report = classification_report(\n", + " y_true_binary, \n", + " y_pred_binary,\n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4\n", + " )\n", + " print(class_report)\n", + " except Exception as e:\n", + " print(f\"Error in classification metrics calculation: {str(e)}\")\n", + " \n", + " # Regression metrics with error handling\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " try:\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " # Range validation\n", + " out_of_range = np.sum(\n", + " (y_reg_nonzero < min_output) | \n", + " (y_reg_nonzero > max_output)\n", + " )\n", + " \n", + " # Error metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", + " (y_true_nonzero + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " # Calculate metrics\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in regression metrics calculation: {str(e)}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final output metrics with error handling\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " try:\n", + " # Ensure outputs are within bounds\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " \n", + " # Calculate metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in final output metrics calculation: {str(e)}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar energy model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " def evaluate_epoch(epoch, logs):\n", + " if epoch % 20 == 0:\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=35,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-5\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", + " tf.keras.callbacks.TerminateOnNaN()\n", + " ]\n", + "\n", + " '''\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.8,\n", + " patience=10,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " '''\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar energy predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarenergy_predicted': final_pred.flatten(),\n", + " 'solarenergy_classification': classification_pred.flatten(),\n", + " 'solarenergy_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar energy column where missing\n", + " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", + " 'solarenergy_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar energy model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 66\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (112882, 24, 66)\n", + "Test data shape: (16849, 24, 66)\n", + "Saving scaler X to: 2024-11-27_23-17_scale_X.joblib\n", + "Saving scaler X to: 2024-11-27_23-17_scale_y.joblib\n", + "Saving features to: 2024-11-27_23-17_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "print(\"Initializing solar energy model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", + "\n", + "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", + "Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:18:54.766545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:c1:00.0, compute capability: 8.9\n", + "2024-11-27 23:18:55.999926: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Class distribution in training set:\n", + "Zeros: 56899 (50.41%)\n", + "Non-zeros: 55983 (49.59%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 8576 (50.90%)\n", + "Non-zeros: 8273 (49.10%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:19:24.436497: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-27 23:19:24.593649: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-27 23:19:26.676664: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x237e6dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-27 23:19:26.676699: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-27 23:19:26.682750: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-27 23:19:26.852932: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "221/221 [==============================] - ETA: 0s - loss: 10.1498 - classification_output_loss: 0.2192 - regression_output_loss: 0.3883 - final_output_loss: 0.2518\n", + "Epoch 1 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 95.36%\n", + "AUC-ROC: 0.9917\n", + "\n", + "Confusion Matrix:\n", + "[[8285 291]\n", + " [ 491 7782]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9441 0.9661 0.9549 8576\n", + " Non-Zero 0.9640 0.9407 0.9522 8273\n", + "\n", + " accuracy 0.9536 16849\n", + " macro avg 0.9540 0.9534 0.9535 16849\n", + "weighted avg 0.9538 0.9536 0.9536 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 246 predictions\n", + "MAPE: 56.03%\n", + "Within ±10%: 4.04%\n", + "MAE: 0.66\n", + "RMSE: 0.87\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 25.95%\n", + "Within ±2%: 48.48%\n", + "Within ±5%: 49.50%\n", + "Within ±10%: 51.42%\n", + "Within ±20%: 55.81%\n", + "MAE: 0.24\n", + "RMSE: 0.45\n", + "221/221 [==============================] - 66s 124ms/step - loss: 10.1498 - classification_output_loss: 0.2192 - regression_output_loss: 0.3883 - final_output_loss: 0.2518 - val_loss: 7.6804 - val_classification_output_loss: 0.2792 - val_regression_output_loss: 0.4849 - val_final_output_loss: 0.2209\n", + "Epoch 2/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 5.9091 - classification_output_loss: 0.1070 - regression_output_loss: 0.1877 - final_output_loss: 0.1142 - val_loss: 4.7197 - val_classification_output_loss: 0.1352 - val_regression_output_loss: 0.2361 - val_final_output_loss: 0.1195\n", + "Epoch 3/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 3.9752 - classification_output_loss: 0.0814 - regression_output_loss: 0.1177 - final_output_loss: 0.0640 - val_loss: 3.4943 - val_classification_output_loss: 0.0998 - val_regression_output_loss: 0.1060 - val_final_output_loss: 0.0623\n", + "Epoch 4/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 3.2835 - classification_output_loss: 0.0751 - regression_output_loss: 0.1008 - final_output_loss: 0.0540 - val_loss: 3.1666 - val_classification_output_loss: 0.0896 - val_regression_output_loss: 0.0793 - val_final_output_loss: 0.0562\n", + "Epoch 5/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 2.9948 - classification_output_loss: 0.0926 - regression_output_loss: 0.1700 - final_output_loss: 0.1103 - val_loss: 2.3640 - val_classification_output_loss: 0.1197 - val_regression_output_loss: 0.1617 - val_final_output_loss: 0.1375\n", + "Epoch 6/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 1.7550 - classification_output_loss: 0.0797 - regression_output_loss: 0.1151 - final_output_loss: 0.0827 - val_loss: 1.2843 - val_classification_output_loss: 0.0880 - val_regression_output_loss: 0.0697 - val_final_output_loss: 0.0442\n", + "Epoch 7/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 1.0277 - classification_output_loss: 0.0647 - regression_output_loss: 0.0847 - final_output_loss: 0.0549 - val_loss: 0.8079 - val_classification_output_loss: 0.0836 - val_regression_output_loss: 0.0610 - val_final_output_loss: 0.0438\n", + "Epoch 8/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.6795 - classification_output_loss: 0.0600 - regression_output_loss: 0.0716 - final_output_loss: 0.0498 - val_loss: 0.5649 - val_classification_output_loss: 0.0770 - val_regression_output_loss: 0.0542 - val_final_output_loss: 0.0392\n", + "Epoch 9/150\n", + "221/221 [==============================] - 15s 67ms/step - loss: 0.4970 - classification_output_loss: 0.0545 - regression_output_loss: 0.0634 - final_output_loss: 0.0434 - val_loss: 0.4335 - val_classification_output_loss: 0.0751 - val_regression_output_loss: 0.0452 - val_final_output_loss: 0.0354\n", + "Epoch 10/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.3957 - classification_output_loss: 0.0517 - regression_output_loss: 0.0524 - final_output_loss: 0.0386 - val_loss: 0.3625 - val_classification_output_loss: 0.0749 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0325\n", + "Epoch 11/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.3395 - classification_output_loss: 0.0503 - regression_output_loss: 0.0451 - final_output_loss: 0.0335 - val_loss: 0.3256 - val_classification_output_loss: 0.0750 - val_regression_output_loss: 0.0407 - val_final_output_loss: 0.0317\n", + "Epoch 12/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.3114 - classification_output_loss: 0.0509 - regression_output_loss: 0.0411 - final_output_loss: 0.0309 - val_loss: 0.3090 - val_classification_output_loss: 0.0738 - val_regression_output_loss: 0.0406 - val_final_output_loss: 0.0322\n", + "Epoch 13/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.3011 - classification_output_loss: 0.0523 - regression_output_loss: 0.0406 - final_output_loss: 0.0305 - val_loss: 0.2999 - val_classification_output_loss: 0.0677 - val_regression_output_loss: 0.0358 - val_final_output_loss: 0.0293\n", + "Epoch 14/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.3141 - classification_output_loss: 0.0616 - regression_output_loss: 0.0705 - final_output_loss: 0.0576 - val_loss: 0.3864 - val_classification_output_loss: 0.0790 - val_regression_output_loss: 0.2013 - val_final_output_loss: 0.1696\n", + "Epoch 15/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.2690 - classification_output_loss: 0.0643 - regression_output_loss: 0.1000 - final_output_loss: 0.0724 - val_loss: 0.2078 - val_classification_output_loss: 0.0773 - val_regression_output_loss: 0.0603 - val_final_output_loss: 0.0349\n", + "Epoch 16/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.1958 - classification_output_loss: 0.0566 - regression_output_loss: 0.0729 - final_output_loss: 0.0548 - val_loss: 0.1644 - val_classification_output_loss: 0.0686 - val_regression_output_loss: 0.0517 - val_final_output_loss: 0.0378\n", + "Epoch 17/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.1549 - classification_output_loss: 0.0523 - regression_output_loss: 0.0585 - final_output_loss: 0.0489 - val_loss: 0.1353 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0478 - val_final_output_loss: 0.0354\n", + "Epoch 18/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.1323 - classification_output_loss: 0.0503 - regression_output_loss: 0.0551 - final_output_loss: 0.0493 - val_loss: 0.1225 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0496 - val_final_output_loss: 0.0421\n", + "Epoch 19/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.1139 - classification_output_loss: 0.0501 - regression_output_loss: 0.0497 - final_output_loss: 0.0457 - val_loss: 0.1095 - val_classification_output_loss: 0.0744 - val_regression_output_loss: 0.0481 - val_final_output_loss: 0.0386\n", + "Epoch 20/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0980 - classification_output_loss: 0.0462 - regression_output_loss: 0.0436 - final_output_loss: 0.0403 - val_loss: 0.0943 - val_classification_output_loss: 0.0679 - val_regression_output_loss: 0.0407 - val_final_output_loss: 0.0344\n", + "Epoch 21/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0874 - classification_output_loss: 0.0439 - regression_output_loss: 0.0402 - final_output_loss: 0.0375\n", + "Epoch 21 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.16%\n", + "AUC-ROC: 0.9962\n", + "\n", + "Confusion Matrix:\n", + "[[8389 187]\n", + " [ 291 7982]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9665 0.9782 0.9723 8576\n", + " Non-Zero 0.9771 0.9648 0.9709 8273\n", + "\n", + " accuracy 0.9716 16849\n", + " macro avg 0.9718 0.9715 0.9716 16849\n", + "weighted avg 0.9717 0.9716 0.9716 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 26 predictions\n", + "MAPE: 19.29%\n", + "Within ±10%: 44.86%\n", + "MAE: 0.11\n", + "RMSE: 0.14\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 13.12%\n", + "Within ±2%: 55.12%\n", + "Within ±5%: 62.25%\n", + "Within ±10%: 74.22%\n", + "Within ±20%: 84.48%\n", + "MAE: 0.06\n", + "RMSE: 0.10\n", + "221/221 [==============================] - 20s 91ms/step - loss: 0.0874 - classification_output_loss: 0.0439 - regression_output_loss: 0.0402 - final_output_loss: 0.0375 - val_loss: 0.0881 - val_classification_output_loss: 0.0742 - val_regression_output_loss: 0.0395 - val_final_output_loss: 0.0330\n", + "Epoch 22/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0800 - classification_output_loss: 0.0425 - regression_output_loss: 0.0390 - final_output_loss: 0.0352 - val_loss: 0.0900 - val_classification_output_loss: 0.0677 - val_regression_output_loss: 0.0532 - val_final_output_loss: 0.0388\n", + "Epoch 23/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0748 - classification_output_loss: 0.0402 - regression_output_loss: 0.0385 - final_output_loss: 0.0340 - val_loss: 0.0783 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0371 - val_final_output_loss: 0.0365\n", + "Epoch 24/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0670 - classification_output_loss: 0.0385 - regression_output_loss: 0.0327 - final_output_loss: 0.0290 - val_loss: 0.0738 - val_classification_output_loss: 0.0631 - val_regression_output_loss: 0.0350 - val_final_output_loss: 0.0350\n", + "Epoch 25/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0620 - classification_output_loss: 0.0378 - regression_output_loss: 0.0294 - final_output_loss: 0.0260 - val_loss: 0.0657 - val_classification_output_loss: 0.0624 - val_regression_output_loss: 0.0286 - val_final_output_loss: 0.0271\n", + "Epoch 26/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0591 - classification_output_loss: 0.0374 - regression_output_loss: 0.0284 - final_output_loss: 0.0248 - val_loss: 0.0618 - val_classification_output_loss: 0.0628 - val_regression_output_loss: 0.0258 - val_final_output_loss: 0.0240\n", + "Epoch 27/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0570 - classification_output_loss: 0.0361 - regression_output_loss: 0.0277 - final_output_loss: 0.0243 - val_loss: 0.0591 - val_classification_output_loss: 0.0622 - val_regression_output_loss: 0.0257 - val_final_output_loss: 0.0203\n", + "Epoch 28/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0555 - classification_output_loss: 0.0362 - regression_output_loss: 0.0272 - final_output_loss: 0.0233 - val_loss: 0.0584 - val_classification_output_loss: 0.0615 - val_regression_output_loss: 0.0266 - val_final_output_loss: 0.0198\n", + "Epoch 29/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0550 - classification_output_loss: 0.0364 - regression_output_loss: 0.0273 - final_output_loss: 0.0231 - val_loss: 0.0588 - val_classification_output_loss: 0.0611 - val_regression_output_loss: 0.0273 - val_final_output_loss: 0.0214\n", + "Epoch 30/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0548 - classification_output_loss: 0.0375 - regression_output_loss: 0.0272 - final_output_loss: 0.0231 - val_loss: 0.0565 - val_classification_output_loss: 0.0579 - val_regression_output_loss: 0.0247 - val_final_output_loss: 0.0201\n", + "Epoch 31/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0553 - classification_output_loss: 0.0371 - regression_output_loss: 0.0285 - final_output_loss: 0.0236 - val_loss: 0.0548 - val_classification_output_loss: 0.0564 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0191\n", + "Epoch 32/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0793 - classification_output_loss: 0.0410 - regression_output_loss: 0.0607 - final_output_loss: 0.0465 - val_loss: 0.2093 - val_classification_output_loss: 0.1111 - val_regression_output_loss: 0.1922 - val_final_output_loss: 0.1775\n", + "Epoch 33/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.1067 - classification_output_loss: 0.0635 - regression_output_loss: 0.0839 - final_output_loss: 0.0643 - val_loss: 0.0728 - val_classification_output_loss: 0.0623 - val_regression_output_loss: 0.0473 - val_final_output_loss: 0.0327\n", + "Epoch 34/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0784 - classification_output_loss: 0.0467 - regression_output_loss: 0.0531 - final_output_loss: 0.0493 - val_loss: 0.0785 - val_classification_output_loss: 0.0949 - val_regression_output_loss: 0.0493 - val_final_output_loss: 0.0359\n", + "Epoch 35/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0675 - classification_output_loss: 0.0457 - regression_output_loss: 0.0424 - final_output_loss: 0.0420 - val_loss: 0.0692 - val_classification_output_loss: 0.0691 - val_regression_output_loss: 0.0519 - val_final_output_loss: 0.0288\n", + "Epoch 36/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0676 - classification_output_loss: 0.0418 - regression_output_loss: 0.0452 - final_output_loss: 0.0455 - val_loss: 0.0689 - val_classification_output_loss: 0.0829 - val_regression_output_loss: 0.0430 - val_final_output_loss: 0.0324\n", + "Epoch 37/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0595 - classification_output_loss: 0.0396 - regression_output_loss: 0.0376 - final_output_loss: 0.0386 - val_loss: 0.0798 - val_classification_output_loss: 0.0626 - val_regression_output_loss: 0.0699 - val_final_output_loss: 0.0473\n", + "Epoch 38/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0606 - classification_output_loss: 0.0404 - regression_output_loss: 0.0414 - final_output_loss: 0.0402 - val_loss: 0.0661 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0558 - val_final_output_loss: 0.0315\n", + "Epoch 39/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0570 - classification_output_loss: 0.0375 - regression_output_loss: 0.0370 - final_output_loss: 0.0393 - val_loss: 0.0550 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0365 - val_final_output_loss: 0.0288\n", + "Epoch 40/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0544 - classification_output_loss: 0.0390 - regression_output_loss: 0.0361 - final_output_loss: 0.0359 - val_loss: 0.0600 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0424 - val_final_output_loss: 0.0381\n", + "Epoch 41/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0505 - classification_output_loss: 0.0366 - regression_output_loss: 0.0326 - final_output_loss: 0.0335\n", + "Epoch 41 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.79%\n", + "AUC-ROC: 0.9980\n", + "\n", + "Confusion Matrix:\n", + "[[8337 239]\n", + " [ 133 8140]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9843 0.9721 0.9782 8576\n", + " Non-Zero 0.9715 0.9839 0.9777 8273\n", + "\n", + " accuracy 0.9779 16849\n", + " macro avg 0.9779 0.9780 0.9779 16849\n", + "weighted avg 0.9780 0.9779 0.9779 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 66 predictions\n", + "MAPE: 16.65%\n", + "Within ±10%: 48.35%\n", + "MAE: 0.13\n", + "RMSE: 0.19\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 10.82%\n", + "Within ±2%: 56.88%\n", + "Within ±5%: 64.73%\n", + "Within ±10%: 74.46%\n", + "Within ±20%: 86.63%\n", + "MAE: 0.06\n", + "RMSE: 0.11\n", + "221/221 [==============================] - 20s 89ms/step - loss: 0.0505 - classification_output_loss: 0.0366 - regression_output_loss: 0.0326 - final_output_loss: 0.0335 - val_loss: 0.0626 - val_classification_output_loss: 0.0581 - val_regression_output_loss: 0.0524 - val_final_output_loss: 0.0347\n", + "Epoch 42/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0519 - classification_output_loss: 0.0342 - regression_output_loss: 0.0354 - final_output_loss: 0.0366 - val_loss: 0.0468 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0282 - val_final_output_loss: 0.0241\n", + "Epoch 43/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0489 - classification_output_loss: 0.0327 - regression_output_loss: 0.0326 - final_output_loss: 0.0343 - val_loss: 0.0487 - val_classification_output_loss: 0.0563 - val_regression_output_loss: 0.0302 - val_final_output_loss: 0.0271\n", + "Epoch 44/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0477 - classification_output_loss: 0.0337 - regression_output_loss: 0.0313 - final_output_loss: 0.0340 - val_loss: 0.0483 - val_classification_output_loss: 0.0535 - val_regression_output_loss: 0.0292 - val_final_output_loss: 0.0297\n", + "Epoch 45/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0455 - classification_output_loss: 0.0308 - regression_output_loss: 0.0296 - final_output_loss: 0.0330 - val_loss: 0.0433 - val_classification_output_loss: 0.0494 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0220\n", + "Epoch 46/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0433 - classification_output_loss: 0.0298 - regression_output_loss: 0.0286 - final_output_loss: 0.0304 - val_loss: 0.0455 - val_classification_output_loss: 0.0634 - val_regression_output_loss: 0.0265 - val_final_output_loss: 0.0224\n", + "Epoch 47/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0413 - classification_output_loss: 0.0300 - regression_output_loss: 0.0274 - final_output_loss: 0.0281 - val_loss: 0.0418 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0273 - val_final_output_loss: 0.0227\n", + "Epoch 48/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0418 - classification_output_loss: 0.0295 - regression_output_loss: 0.0282 - final_output_loss: 0.0301 - val_loss: 0.0518 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0372 - val_final_output_loss: 0.0337\n", + "Epoch 49/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0404 - classification_output_loss: 0.0272 - regression_output_loss: 0.0272 - final_output_loss: 0.0293 - val_loss: 0.0580 - val_classification_output_loss: 0.0484 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0473\n", + "Epoch 50/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0399 - classification_output_loss: 0.0275 - regression_output_loss: 0.0270 - final_output_loss: 0.0284 - val_loss: 0.0492 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0317 - val_final_output_loss: 0.0357\n", + "Epoch 51/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0362 - classification_output_loss: 0.0262 - regression_output_loss: 0.0236 - final_output_loss: 0.0246 - val_loss: 0.0476 - val_classification_output_loss: 0.0431 - val_regression_output_loss: 0.0343 - val_final_output_loss: 0.0346\n", + "Epoch 52/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0351 - classification_output_loss: 0.0258 - regression_output_loss: 0.0231 - final_output_loss: 0.0238 - val_loss: 0.0457 - val_classification_output_loss: 0.0419 - val_regression_output_loss: 0.0328 - val_final_output_loss: 0.0331\n", + "Epoch 53/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0329 - classification_output_loss: 0.0245 - regression_output_loss: 0.0213 - final_output_loss: 0.0216 - val_loss: 0.0407 - val_classification_output_loss: 0.0418 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0273\n", + "Epoch 54/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0315 - classification_output_loss: 0.0237 - regression_output_loss: 0.0206 - final_output_loss: 0.0203 - val_loss: 0.0371 - val_classification_output_loss: 0.0387 - val_regression_output_loss: 0.0254 - val_final_output_loss: 0.0229\n", + "Epoch 55/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0311 - classification_output_loss: 0.0225 - regression_output_loss: 0.0206 - final_output_loss: 0.0206 - val_loss: 0.0356 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0235 - val_final_output_loss: 0.0219\n", + "Epoch 56/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0302 - classification_output_loss: 0.0223 - regression_output_loss: 0.0201 - final_output_loss: 0.0198 - val_loss: 0.0351 - val_classification_output_loss: 0.0411 - val_regression_output_loss: 0.0224 - val_final_output_loss: 0.0207\n", + "Epoch 57/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0301 - classification_output_loss: 0.0221 - regression_output_loss: 0.0199 - final_output_loss: 0.0201 - val_loss: 0.0340 - val_classification_output_loss: 0.0393 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0205\n", + "Epoch 58/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0296 - classification_output_loss: 0.0213 - regression_output_loss: 0.0199 - final_output_loss: 0.0197 - val_loss: 0.0326 - val_classification_output_loss: 0.0389 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0186\n", + "Epoch 59/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0296 - classification_output_loss: 0.0210 - regression_output_loss: 0.0200 - final_output_loss: 0.0200 - val_loss: 0.0311 - val_classification_output_loss: 0.0367 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0161\n", + "Epoch 60/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0295 - classification_output_loss: 0.0211 - regression_output_loss: 0.0202 - final_output_loss: 0.0198 - val_loss: 0.0315 - val_classification_output_loss: 0.0365 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0165\n", + "Epoch 61/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0290 - classification_output_loss: 0.0201 - regression_output_loss: 0.0199 - final_output_loss: 0.0195\n", + "Epoch 61 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.60%\n", + "AUC-ROC: 0.9993\n", + "\n", + "Confusion Matrix:\n", + "[[8473 103]\n", + " [ 133 8140]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9845 0.9880 0.9863 8576\n", + " Non-Zero 0.9875 0.9839 0.9857 8273\n", + "\n", + " accuracy 0.9860 16849\n", + " macro avg 0.9860 0.9860 0.9860 16849\n", + "weighted avg 0.9860 0.9860 0.9860 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 11.30%\n", + "Within ±10%: 73.14%\n", + "MAE: 0.06\n", + "RMSE: 0.09\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.72%\n", + "Within ±2%: 60.84%\n", + "Within ±5%: 74.53%\n", + "Within ±10%: 86.72%\n", + "Within ±20%: 91.58%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 20s 90ms/step - loss: 0.0290 - classification_output_loss: 0.0201 - regression_output_loss: 0.0199 - final_output_loss: 0.0195 - val_loss: 0.0315 - val_classification_output_loss: 0.0356 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0171\n", + "Epoch 62/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0290 - classification_output_loss: 0.0207 - regression_output_loss: 0.0199 - final_output_loss: 0.0194 - val_loss: 0.0311 - val_classification_output_loss: 0.0355 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0172\n", + "Epoch 63/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0288 - classification_output_loss: 0.0205 - regression_output_loss: 0.0199 - final_output_loss: 0.0192 - val_loss: 0.0308 - val_classification_output_loss: 0.0349 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0175\n", + "Epoch 64/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0289 - classification_output_loss: 0.0207 - regression_output_loss: 0.0200 - final_output_loss: 0.0194 - val_loss: 0.0302 - val_classification_output_loss: 0.0348 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0168\n", + "Epoch 65/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0289 - classification_output_loss: 0.0204 - regression_output_loss: 0.0202 - final_output_loss: 0.0194 - val_loss: 0.0297 - val_classification_output_loss: 0.0349 - val_regression_output_loss: 0.0185 - val_final_output_loss: 0.0160\n", + "Epoch 66/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0295 - classification_output_loss: 0.0209 - regression_output_loss: 0.0211 - final_output_loss: 0.0198 - val_loss: 0.0294 - val_classification_output_loss: 0.0350 - val_regression_output_loss: 0.0180 - val_final_output_loss: 0.0157\n", + "Epoch 67/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0302 - classification_output_loss: 0.0208 - regression_output_loss: 0.0215 - final_output_loss: 0.0210 - val_loss: 0.0303 - val_classification_output_loss: 0.0348 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0170\n", + "Epoch 68/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0304 - classification_output_loss: 0.0223 - regression_output_loss: 0.0210 - final_output_loss: 0.0212 - val_loss: 0.0636 - val_classification_output_loss: 0.0548 - val_regression_output_loss: 0.0283 - val_final_output_loss: 0.0759\n", + "Epoch 69/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0798 - classification_output_loss: 0.0495 - regression_output_loss: 0.0662 - final_output_loss: 0.0655 - val_loss: 0.0591 - val_classification_output_loss: 0.0509 - val_regression_output_loss: 0.0539 - val_final_output_loss: 0.0388\n", + "Epoch 70/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0506 - classification_output_loss: 0.0340 - regression_output_loss: 0.0369 - final_output_loss: 0.0415 - val_loss: 0.0465 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0398 - val_final_output_loss: 0.0249\n", + "Epoch 71/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0450 - classification_output_loss: 0.0282 - regression_output_loss: 0.0332 - final_output_loss: 0.0362 - val_loss: 0.0431 - val_classification_output_loss: 0.0442 - val_regression_output_loss: 0.0316 - val_final_output_loss: 0.0284\n", + "Epoch 72/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0425 - classification_output_loss: 0.0302 - regression_output_loss: 0.0303 - final_output_loss: 0.0330 - val_loss: 0.0478 - val_classification_output_loss: 0.0484 - val_regression_output_loss: 0.0391 - val_final_output_loss: 0.0306\n", + "Epoch 73/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0413 - classification_output_loss: 0.0268 - regression_output_loss: 0.0300 - final_output_loss: 0.0335 - val_loss: 0.0437 - val_classification_output_loss: 0.0455 - val_regression_output_loss: 0.0275 - val_final_output_loss: 0.0344\n", + "Epoch 74/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0429 - classification_output_loss: 0.0309 - regression_output_loss: 0.0297 - final_output_loss: 0.0353 - val_loss: 0.0438 - val_classification_output_loss: 0.0651 - val_regression_output_loss: 0.0286 - val_final_output_loss: 0.0228\n", + "Epoch 75/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0391 - classification_output_loss: 0.0249 - regression_output_loss: 0.0278 - final_output_loss: 0.0318 - val_loss: 0.0420 - val_classification_output_loss: 0.0521 - val_regression_output_loss: 0.0279 - val_final_output_loss: 0.0266\n", + "Epoch 76/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0378 - classification_output_loss: 0.0254 - regression_output_loss: 0.0252 - final_output_loss: 0.0311 - val_loss: 0.0443 - val_classification_output_loss: 0.0531 - val_regression_output_loss: 0.0255 - val_final_output_loss: 0.0357\n", + "Epoch 77/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0387 - classification_output_loss: 0.0283 - regression_output_loss: 0.0267 - final_output_loss: 0.0322 - val_loss: 0.0744 - val_classification_output_loss: 0.0440 - val_regression_output_loss: 0.0526 - val_final_output_loss: 0.0837\n", + "Epoch 78/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0428 - classification_output_loss: 0.0288 - regression_output_loss: 0.0317 - final_output_loss: 0.0347 - val_loss: 0.0552 - val_classification_output_loss: 0.0460 - val_regression_output_loss: 0.0467 - val_final_output_loss: 0.0405\n", + "Epoch 79/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0370 - classification_output_loss: 0.0250 - regression_output_loss: 0.0260 - final_output_loss: 0.0290 - val_loss: 0.0362 - val_classification_output_loss: 0.0526 - val_regression_output_loss: 0.0227 - val_final_output_loss: 0.0187\n", + "Epoch 80/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0367 - classification_output_loss: 0.0248 - regression_output_loss: 0.0252 - final_output_loss: 0.0299 - val_loss: 0.0427 - val_classification_output_loss: 0.0726 - val_regression_output_loss: 0.0270 - val_final_output_loss: 0.0209\n", + "Epoch 81/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0363 - classification_output_loss: 0.0254 - regression_output_loss: 0.0261 - final_output_loss: 0.0294\n", + "Epoch 81 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.52%\n", + "AUC-ROC: 0.9992\n", + "\n", + "Confusion Matrix:\n", + "[[8431 145]\n", + " [ 104 8169]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9878 0.9831 0.9854 8576\n", + " Non-Zero 0.9826 0.9874 0.9850 8273\n", + "\n", + " accuracy 0.9852 16849\n", + " macro avg 0.9852 0.9853 0.9852 16849\n", + "weighted avg 0.9852 0.9852 0.9852 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 18 predictions\n", + "MAPE: 17.42%\n", + "Within ±10%: 42.09%\n", + "MAE: 0.15\n", + "RMSE: 0.21\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 13.33%\n", + "Within ±2%: 53.80%\n", + "Within ±5%: 59.62%\n", + "Within ±10%: 68.52%\n", + "Within ±20%: 80.93%\n", + "MAE: 0.08\n", + "RMSE: 0.14\n", + "221/221 [==============================] - 20s 90ms/step - loss: 0.0363 - classification_output_loss: 0.0254 - regression_output_loss: 0.0261 - final_output_loss: 0.0294 - val_loss: 0.0601 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0604 - val_final_output_loss: 0.0479\n", + "Epoch 82/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0396 - classification_output_loss: 0.0282 - regression_output_loss: 0.0283 - final_output_loss: 0.0328 - val_loss: 0.0370 - val_classification_output_loss: 0.0409 - val_regression_output_loss: 0.0238 - val_final_output_loss: 0.0237\n", + "Epoch 83/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0357 - classification_output_loss: 0.0229 - regression_output_loss: 0.0256 - final_output_loss: 0.0287 - val_loss: 0.0380 - val_classification_output_loss: 0.0534 - val_regression_output_loss: 0.0252 - val_final_output_loss: 0.0216\n", + "Epoch 84/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0337 - classification_output_loss: 0.0232 - regression_output_loss: 0.0235 - final_output_loss: 0.0272 - val_loss: 0.0497 - val_classification_output_loss: 0.0303 - val_regression_output_loss: 0.0465 - val_final_output_loss: 0.0407\n", + "Epoch 85/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0380 - classification_output_loss: 0.0252 - regression_output_loss: 0.0267 - final_output_loss: 0.0329 - val_loss: 0.0559 - val_classification_output_loss: 0.0405 - val_regression_output_loss: 0.0447 - val_final_output_loss: 0.0485\n", + "Epoch 86/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0339 - classification_output_loss: 0.0219 - regression_output_loss: 0.0249 - final_output_loss: 0.0265 - val_loss: 0.0419 - val_classification_output_loss: 0.0481 - val_regression_output_loss: 0.0285 - val_final_output_loss: 0.0306\n", + "Epoch 87/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0327 - classification_output_loss: 0.0218 - regression_output_loss: 0.0230 - final_output_loss: 0.0265 - val_loss: 0.0339 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0204\n", + "Epoch 88/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0328 - classification_output_loss: 0.0223 - regression_output_loss: 0.0236 - final_output_loss: 0.0267 - val_loss: 0.0476 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0346 - val_final_output_loss: 0.0431\n", + "Epoch 89/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0349 - classification_output_loss: 0.0226 - regression_output_loss: 0.0249 - final_output_loss: 0.0295 - val_loss: 0.0416 - val_classification_output_loss: 0.0428 - val_regression_output_loss: 0.0297 - val_final_output_loss: 0.0298\n", + "Epoch 90/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0321 - classification_output_loss: 0.0202 - regression_output_loss: 0.0225 - final_output_loss: 0.0262 - val_loss: 0.0324 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0226 - val_final_output_loss: 0.0197\n", + "Epoch 91/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0307 - classification_output_loss: 0.0208 - regression_output_loss: 0.0223 - final_output_loss: 0.0245 - val_loss: 0.0384 - val_classification_output_loss: 0.0717 - val_regression_output_loss: 0.0236 - val_final_output_loss: 0.0179\n", + "Epoch 92/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0302 - classification_output_loss: 0.0204 - regression_output_loss: 0.0212 - final_output_loss: 0.0250 - val_loss: 0.0435 - val_classification_output_loss: 0.0330 - val_regression_output_loss: 0.0379 - val_final_output_loss: 0.0356\n", + "Epoch 93/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0327 - classification_output_loss: 0.0197 - regression_output_loss: 0.0238 - final_output_loss: 0.0283 - val_loss: 0.0357 - val_classification_output_loss: 0.0459 - val_regression_output_loss: 0.0234 - val_final_output_loss: 0.0223\n", + "Epoch 94/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0300 - classification_output_loss: 0.0179 - regression_output_loss: 0.0221 - final_output_loss: 0.0241 - val_loss: 0.0309 - val_classification_output_loss: 0.0322 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0210\n", + "Epoch 95/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0293 - classification_output_loss: 0.0181 - regression_output_loss: 0.0207 - final_output_loss: 0.0246 - val_loss: 0.0310 - val_classification_output_loss: 0.0385 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0183\n", + "Epoch 96/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0278 - classification_output_loss: 0.0172 - regression_output_loss: 0.0199 - final_output_loss: 0.0227 - val_loss: 0.0361 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0203\n", + "Epoch 97/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0295 - classification_output_loss: 0.0197 - regression_output_loss: 0.0209 - final_output_loss: 0.0247 - val_loss: 0.0316 - val_classification_output_loss: 0.0417 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0181\n", + "Epoch 98/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0289 - classification_output_loss: 0.0174 - regression_output_loss: 0.0211 - final_output_loss: 0.0240 - val_loss: 0.0450 - val_classification_output_loss: 0.0319 - val_regression_output_loss: 0.0309 - val_final_output_loss: 0.0451\n", + "Epoch 99/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0302 - classification_output_loss: 0.0194 - regression_output_loss: 0.0216 - final_output_loss: 0.0255 - val_loss: 0.0351 - val_classification_output_loss: 0.0486 - val_regression_output_loss: 0.0228 - val_final_output_loss: 0.0221\n", + "Epoch 100/150\n", + "221/221 [==============================] - 15s 68ms/step - loss: 0.0268 - classification_output_loss: 0.0169 - regression_output_loss: 0.0194 - final_output_loss: 0.0214 - val_loss: 0.0330 - val_classification_output_loss: 0.0376 - val_regression_output_loss: 0.0208 - val_final_output_loss: 0.0257\n", + "Epoch 101/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0261 - classification_output_loss: 0.0137 - regression_output_loss: 0.0188 - final_output_loss: 0.0227Restoring model weights from the end of the best epoch: 66.\n", + "\n", + "Epoch 101 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.65%\n", + "AUC-ROC: 0.9994\n", + "\n", + "Confusion Matrix:\n", + "[[8497 79]\n", + " [ 148 8125]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9829 0.9908 0.9868 8576\n", + " Non-Zero 0.9904 0.9821 0.9862 8273\n", + "\n", + " accuracy 0.9865 16849\n", + " macro avg 0.9866 0.9864 0.9865 16849\n", + "weighted avg 0.9866 0.9865 0.9865 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 10.76%\n", + "Within ±10%: 75.03%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.87%\n", + "Within ±2%: 61.66%\n", + "Within ±5%: 75.67%\n", + "Within ±10%: 86.32%\n", + "Within ±20%: 91.11%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 20s 92ms/step - loss: 0.0261 - classification_output_loss: 0.0137 - regression_output_loss: 0.0188 - final_output_loss: 0.0227 - val_loss: 0.0359 - val_classification_output_loss: 0.0278 - val_regression_output_loss: 0.0242 - val_final_output_loss: 0.0340\n", + "Epoch 101: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.65%\n", + "AUC-ROC: 0.9994\n", + "\n", + "Confusion Matrix:\n", + "[[8497 79]\n", + " [ 148 8125]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9829 0.9908 0.9868 8576\n", + " Non-Zero 0.9904 0.9821 0.9862 8273\n", + "\n", + " accuracy 0.9865 16849\n", + " macro avg 0.9866 0.9864 0.9865 16849\n", + "weighted avg 0.9866 0.9865 0.9865 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 10.76%\n", + "Within ±10%: 75.03%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.87%\n", + "Within ±2%: 61.66%\n", + "Within ±5%: 75.67%\n", + "Within ±10%: 86.32%\n", + "Within ±20%: 91.11%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n" + ] + } + ], + "source": [ + "#Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "min_val = df['solarenergy'].min()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "\n", + "max_val = df['solarenergy'].max()\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", + "\n", + "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 8\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarenergy_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=150,\n", + " batch_size=512,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "527/527 [==============================] - 6s 10ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Energy Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 0.03 kWh\n", + "RMSE: 0.07 kWh\n", + "R² Score: 0.995\n", + "MAPE: N/A (insufficient data)\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 kWh: 100.0%\n", + "Within ±10 kWh: 100.0%\n", + "Within ±20 kWh: 100.0%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 97.6%\n", + "\n", + "Confusion Matrix for Energy Levels:\n", + " Low Moderate Very Low\n", + "Low 3539 133 1\n", + "Moderate 26 2082 0\n", + "Very Low 247 0 10821\n", + "\n", + "Plot saved as: 2024-11-27_23-17_energy_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: -0.000\n", + "Error standard deviation: 0.068\n", + "Median error: 0.000\n", + "95th percentile absolute error: 0.137\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 73s 10ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar energy after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 117206\n", + "Non-zero predictions (classification >= 0.5): 110673\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 1.10\n", + "Median: 0.93\n", + "Std: 0.95\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 117206 (51.43%)\n", + "Predicted non-zeros: 110673 (48.57%)\n", + "Mean classification confidence: 0.4896\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar energy: 0.64\n", + "Min solar energy: 0.00\n", + "Max solar energy: 3.30\n", + "Zero predictions: 95673 (41.98%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "#final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", + " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", + " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", + "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", + "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Energy:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 161,156.0000\n", + "mean : 0.6529\n", + "median : 0.0736\n", + "std : 0.9288\n", + "min : 0.0000\n", + "max : 4.0000\n", + "skewness : 1.2834\n", + "kurtosis : 0.3742\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 0.0000\n", + "percentile_50 : 0.0736\n", + "percentile_75 : 1.1913\n", + "percentile_90 : 2.2530\n", + "percentile_95 : 2.7314\n", + "percentile_99 : 3.1348\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (45.06%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 161156,\n", + " 'mean': 0.6529324282684227,\n", + " 'median': 0.07359524816274643,\n", + " 'std': 0.928826011992019,\n", + " 'min': 0.0,\n", + " 'max': 4.0,\n", + " 'skewness': 1.2833967112068252,\n", + " 'kurtosis': 0.37419692300276486,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 0.0,\n", + " 'percentile_50': 0.07359524816274643,\n", + " 'percentile_75': 1.191302478313446,\n", + " 'percentile_90': 2.2529743671417237,\n", + " 'percentile_95': 2.7313732862472535,\n", + " 'percentile_99': 3.134775576591491}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "too many values to unpack (expected 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 157\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredictions within ±\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mthreshold\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwithin_threshold\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;66;03m# Example usage\u001b[39;00m\n\u001b[0;32m--> 157\u001b[0m \u001b[43mplot_error_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_test_original\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfinal_pred_original\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfolder_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfolder_name\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[24], line 23\u001b[0m, in \u001b[0;36mplot_error_analysis\u001b[0;34m(y_true, predictions, folder_name)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m roc_curve\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Unpack predictions\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m classification_pred, regression_pred, final_pred \u001b[38;5;241m=\u001b[39m predictions\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Convert to 1D numpy arrays if needed\u001b[39;00m\n\u001b[1;32m 26\u001b[0m y_true \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mravel(y_true)\n", + "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c41d23-65bf-4a38-9241-ea9b17effbd5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1.1-checkpoint.ipynb b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1.1-checkpoint.ipynb new file mode 100644 index 0000000..83d30bd --- /dev/null +++ b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1.1-checkpoint.ipynb @@ -0,0 +1,2990 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", + "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1513 kB]\n", + "Fetched 4508 kB in 2s (2961 kB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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(0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 13:56:39.957016: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-27 13:56:39.957067: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-27 13:56:39.957117: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-27 13:56:39.966205: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import (\n", + " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", + " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", + " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", + " Conv1D, Multiply\n", + ")\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "from tensorflow.keras.metrics import AUC\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# Data processing and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.metrics import (\n", + " mean_absolute_error, mean_squared_error, r2_score, \n", + " confusion_matrix, classification_report, roc_auc_score\n", + ")\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Additional utilities\n", + "import tensorflow_addons as tfa\n", + "from scipy import stats\n", + "import json\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Features based only on radiation and other available variables\n", + " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Energy-specific features\n", + " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Temperature impact on theoretical efficiency\n", + " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", + "\n", + " # Combined features\n", + " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", + " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_effect'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = np.abs(12 - df['hour'])\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "def add_atmospheric_features(df):\n", + " # Indice di Massa d'Aria (Air Mass Index)\n", + " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", + " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", + " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", + "\n", + " # Indice di Stabilità Atmosferica\n", + " # Combina temperatura, umidità e pressione\n", + " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", + "\n", + " # Vapor Pressure Deficit (VPD)\n", + " # Importante per la radiazione diffusa\n", + " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", + " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", + " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", + "\n", + " return df\n", + "\n", + "def add_diffusion_features(df):\n", + " # Indice di Diffusione\n", + " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", + "\n", + " # Radiazione Diretta vs Diffusa\n", + " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", + " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", + "\n", + " # Fattore di Trasparenza Atmosferica\n", + " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", + "\n", + " return df\n", + "\n", + "def calculate_trend(x):\n", + " try:\n", + " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", + " except:\n", + " return np.nan\n", + "\n", + "def add_persistence_features(df):\n", + " # Create a copy to avoid modifying the original dataframe\n", + " df = df.copy()\n", + "\n", + " # Calculate trends more efficiently\n", + " windows = [3, 6, 12, 24]\n", + " for w in windows:\n", + " # Use numba or vectorized operations if possible\n", + " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", + " window=w,\n", + " min_periods=w\n", + " ).apply(calculate_trend, raw=True)\n", + "\n", + " # Optimize volatility calculation by doing it in one pass\n", + " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", + " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", + "\n", + " return df\n", + "\n", + "def add_weather_pattern_features(df):\n", + " # Pattern giornalieri\n", + " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", + " lambda x: (x < 30).sum()\n", + " )\n", + "\n", + " # Stabilità delle condizioni\n", + " for col in ['temp', 'humidity', 'cloudcover']:\n", + " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", + "\n", + " # Indice di Variabilità Meteorologica\n", + " df['weather_variability_index'] = (df['temp_stability'] +\n", + " df['humidity_stability'] +\n", + " df['cloudcover_stability']) / 3\n", + "\n", + " return df\n", + "\n", + "def add_efficiency_features(df):\n", + " # Perdite per temperatura\n", + " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", + "\n", + " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", + " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", + "\n", + " # Efficienza complessiva stimata\n", + " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", + " df['atmospheric_transmittance']\n", + "\n", + " # Potenziale di produzione\n", + " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", + "\n", + " return df\n", + "\n", + "def add_advanced_seasonal_features(df):\n", + " # Differenza dalla durata media del giorno\n", + " avg_day_length = 12\n", + " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", + "\n", + " # Intensità stagionale\n", + " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", + "\n", + " # Indice di Stagionalità\n", + " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", + "\n", + " # Correzione per alba/tramonto\n", + " df['daylight_correction'] = np.where(\n", + " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", + " 0,\n", + " 1\n", + " )\n", + "\n", + " return df\n", + "\n", + "def add_basic_interactions(df):\n", + " \"\"\"\n", + " Aggiunge le interazioni base tra variabili meteorologiche\n", + " \"\"\"\n", + " # Feature esistenti originali\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # Clear sky e trasparenza atmosferica\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " return df\n", + "\n", + "def add_rolling_and_lag_features(df):\n", + " \"\"\"\n", + " Aggiunge feature rolling e lag\n", + " \"\"\"\n", + " # Rolling means esistenti\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features esistenti\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " return df\n", + "\n", + "def add_condition_indicators(df):\n", + " \"\"\"\n", + " Aggiunge indicatori di condizioni particolari\n", + " \"\"\"\n", + " # Extreme conditions indicator esistente\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " return df\n", + "\n", + "def add_physics_based_conversion_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", + " \"\"\"\n", + " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", + " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", + "\n", + " # Efficienza di conversione reale vs teorica\n", + " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " # Energia accumulata nel tempo (integrazione)\n", + " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", + "\n", + " # Differenza tra energia teorica e reale\n", + " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", + "\n", + " # Indice di performance del sistema\n", + " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " return df\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " \"\"\"\n", + " # Feature esistenti di base\n", + " # 1. Feature temporali di base\n", + " df = add_time_features(df)\n", + "\n", + " # 2. Feature solari e meteorologiche\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # 3. Feature atmosferiche e di diffusione\n", + " df = add_atmospheric_features(df)\n", + " df = add_diffusion_features(df)\n", + "\n", + " # 4. Feature di persistenza e pattern\n", + " df = add_persistence_features(df)\n", + " df = add_weather_pattern_features(df)\n", + "\n", + " # 5. Feature di efficienza e stagionalità\n", + " df = add_efficiency_features(df)\n", + " df = add_advanced_seasonal_features(df)\n", + "\n", + " # 6. Interazioni e feature derivate\n", + " df = add_basic_interactions(df)\n", + " df = add_rolling_and_lag_features(df)\n", + " df = add_condition_indicators(df)\n", + "\n", + " # 7. Nuove feature di conversione fisica\n", + " df = add_physics_based_conversion_features(df)\n", + "\n", + " # 8. One-hot encoding delle feature categoriche\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex',\n", + " 'cloudcover',\n", + " 'visibility',\n", + " 'temp',\n", + " 'pressure',\n", + " 'humidity',\n", + " 'solarradiation'\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation',\n", + " 'solar_angle',\n", + " 'day_length',\n", + " 'hour_sin',\n", + " 'hour_cos',\n", + " 'day_of_year_sin',\n", + " 'day_of_year_cos',\n", + " 'month_sin',\n", + " 'month_cos',\n", + " 'solar_noon',\n", + " 'daylight_correction'\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index',\n", + " 'atmospheric_attenuation',\n", + " 'theoretical_radiation',\n", + " 'expected_radiation',\n", + " 'cloud_elevation',\n", + " 'visibility_elevation',\n", + " 'uv_cloud_interaction',\n", + " 'temp_radiation_potential',\n", + " 'air_mass_index',\n", + " 'atmospheric_stability',\n", + " 'vapor_pressure_deficit',\n", + " 'diffusion_index',\n", + " 'atmospheric_transmittance',\n", + " 'temp_humidity_interaction',\n", + " 'clear_sky_factor'\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_12h',\n", + " 'uv_rolling_12h',\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " 'energy_rolling_mean_6h',\n", + " 'uv_rolling_mean_6h',\n", + " 'energy_volatility',\n", + " 'uv_volatility'\n", + " ],\n", + "\n", + " # Lag Features\n", + " 'lag_features': [\n", + " 'temp_1h_lag',\n", + " 'cloudcover_1h_lag',\n", + " 'humidity_1h_lag',\n", + " 'energy_lag_1h',\n", + " 'uv_lag_1h'\n", + " ],\n", + "\n", + " # Efficiency and Performance Features\n", + " 'efficiency_features': [\n", + " 'temp_losses',\n", + " 'soiling_loss_factor',\n", + " 'estimated_efficiency',\n", + " 'production_potential',\n", + " 'system_performance_ratio',\n", + " 'conversion_efficiency_ratio'\n", + " ],\n", + "\n", + " # Weather Pattern Features\n", + " 'weather_pattern_features': [\n", + " 'clear_sky_duration',\n", + " 'weather_variability_index',\n", + " 'temp_stability',\n", + " 'humidity_stability',\n", + " 'cloudcover_stability'\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring',\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning',\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night'\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " df[column] = df[column].interpolate(method='time')\n", + " else:\n", + " df[column] = df[column].interpolate(method='linear')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarenergy']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", + " from tensorflow import keras\n", + " from keras.models import Model\n", + " from keras.layers import (\n", + " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", + " MultiHeadAttention, LayerNormalization, Lambda,\n", + " Concatenate, Activation, Bidirectional, LSTM, Add\n", + " )\n", + " from keras.regularizers import l2\n", + " from keras.optimizers import AdamW\n", + " import tensorflow as tf\n", + " import numpy as np\n", + " import tensorflow_addons as tfa\n", + " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", + " \n", + " # Input layer\n", + " inputs = Input(shape=input_shape)\n", + " \n", + " # Feature groups definition\n", + " feature_dims = {\n", + " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", + " 'weather': [0, 1, 2, 3, 4, 5],\n", + " 'temporal': [10, 11, 12, 13, 14, 15],\n", + " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", + " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", + " 'lag': [40, 41, 42, 43, 44],\n", + " 'performance': [45, 46, 47, 48, 49, 50]\n", + " }\n", + " \n", + " # Feature extraction\n", + " feature_tensors = {}\n", + " for name, indices in feature_dims.items():\n", + " valid_indices = [i for i in indices if i < input_shape[-1]]\n", + " if valid_indices:\n", + " feature_tensors[name] = Lambda(\n", + " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", + " )(inputs)\n", + " \n", + " # Feature processing with residual connections\n", + " def process_feature_group(tensor, units, name):\n", + " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", + " kernel_regularizer=l2(l2_lambda))(tensor)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", + " x = Add()([x, residual])\n", + " x = LayerNormalization()(x)\n", + " \n", + " return x\n", + " \n", + " # Process each feature group\n", + " processed_features = {}\n", + " for name, tensor in feature_tensors.items():\n", + " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", + " processed_features[name] = process_feature_group(tensor, units, name)\n", + " \n", + " # Enhanced attention mechanism\n", + " def attention_block(x, num_heads=4):\n", + " attention_output = MultiHeadAttention(\n", + " num_heads=num_heads, \n", + " key_dim=x.shape[-1] // num_heads\n", + " )(x, x)\n", + " x = LayerNormalization()(x + attention_output)\n", + " \n", + " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", + " ffn = Dropout(0.1)(ffn)\n", + " ffn = Dense(x.shape[-1])(ffn)\n", + " \n", + " return LayerNormalization()(x + ffn)\n", + " \n", + " # Merge primary features with attention\n", + " primary_features = [\n", + " processed_features['solar'],\n", + " processed_features['weather'],\n", + " processed_features['performance']\n", + " ]\n", + " primary_context = Concatenate(axis=-1)(primary_features)\n", + " primary_context = attention_block(primary_context)\n", + " \n", + " # Merge secondary features\n", + " secondary_features = [\n", + " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", + " if name in processed_features\n", + " ]\n", + " if secondary_features:\n", + " secondary_context = Concatenate(axis=-1)(secondary_features)\n", + " secondary_context = attention_block(secondary_context)\n", + " else:\n", + " secondary_context = primary_context\n", + " \n", + " # Final feature merge\n", + " combined = Concatenate(axis=-1)([\n", + " primary_context, \n", + " secondary_context,\n", + " processed_features['derived']\n", + " ])\n", + " \n", + " # Sequential processing with residual LSTM\n", + " def residual_lstm_block(x, units):\n", + " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", + " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", + " x = Add()([lstm_out, residual])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + " \n", + " x = residual_lstm_block(combined, 128)\n", + " x = residual_lstm_block(x, 64)\n", + " x = Bidirectional(LSTM(64))(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " # Classification branch\n", + " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " class_x = BatchNormalization()(class_x)\n", + " class_x = Dropout(0.2)(class_x)\n", + " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", + " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", + " \n", + " # Enhanced regression branch with multiple pathways\n", + " def create_regression_pathway(x, name):\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = x\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = Add()([x, residual])\n", + " \n", + " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " return Dense(1, name=f'{name}_output')(x)\n", + " \n", + " # Create specialized regression pathways\n", + " low_range = create_regression_pathway(x, 'low_range')\n", + " mid_range = create_regression_pathway(x, 'mid_range')\n", + " high_range = create_regression_pathway(x, 'high_range')\n", + " \n", + " # Create context vector for attention\n", + " context = Dense(64, activation='swish')(x)\n", + " \n", + " # Calculate attention scores\n", + " attention_scores = Dense(3, activation='softmax')(context)\n", + " \n", + " # Combine predictions using attention weights\n", + " reg_output = Lambda(\n", + " lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n", + " name='regression_output'\n", + " )([attention_scores, low_range, mid_range, high_range])\n", + "\n", + " # Final output processing remains the same...\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dropout(0.2)(final_x)\n", + " \n", + " residual = final_x\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Add()([final_x, residual])\n", + " \n", + " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Dense(1)(final_x)\n", + " final_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='final_output'\n", + " )(final_x)\n", + " \n", + " # Build model with all outputs\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[class_output, reg_output, final_output]\n", + " )\n", + " \n", + " # Enhanced loss functions\n", + " def enhanced_regression_loss(y_true, y_pred):\n", + " mae = tf.abs(y_true - y_pred)\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 1.5 + \\\n", + " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.2 + \\\n", + " tf.cast(y_true <= 1.0, tf.float32)\n", + " \n", + " weighted_loss = (0.5 * mae + 0.5 * mse) * value_ranges\n", + " return tf.reduce_mean(weighted_loss)\n", + " \n", + " def final_loss(y_true, y_pred):\n", + " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", + " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", + " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", + " return 0.5 * mae + 0.5 * mse\n", + " \n", + " # Learning rate schedule\n", + " clr = CosineDecayRestarts(\n", + " initial_learning_rate=2e-4,\n", + " first_decay_steps=1000,\n", + " t_mul=2.0,\n", + " m_mul=0.9,\n", + " alpha=1e-7\n", + " )\n", + " \n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=clr,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0\n", + " )\n", + " \n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': enhanced_regression_loss,\n", + " 'final_output': final_loss\n", + " }\n", + " )\n", + "\n", + " # Plot model architecture\n", + " try:\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + " except Exception as e:\n", + " print(f\"Warning: Could not plot model architecture: {e}\")\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar energy values (kWh)\n", + " y_pred : array-like\n", + " Predicted solar energy values (kWh)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 kWh\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", + "\n", + " # Energy level classification\n", + " def get_energy_level(value):\n", + " if value <= 0.5:\n", + " return 'Very Low'\n", + " elif value <= 2.0:\n", + " return 'Low'\n", + " elif value <= 4.0:\n", + " return 'Moderate'\n", + " elif value <= 6.0:\n", + " return 'High'\n", + " elif value <= 8.0:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate energy levels\n", + " y_true_levels = [get_energy_level(v) for v in y_true]\n", + " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Energy Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} kWh\")\n", + " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for energy levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", + " print(\"\\nConfusion Matrix for Energy Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=unique_levels,\n", + " index=unique_levels\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Predicted Energy (kWh)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (kWh)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 0.5\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by Energy level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Energy Level')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_energy_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Calculates comprehensive metrics for the solar energy prediction model.\n", + " \n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Ground truth values\n", + " y_class : array-like\n", + " Classification predictions (probability of non-zero values)\n", + " y_reg : array-like\n", + " Regression predictions (unrestricted values)\n", + " y_final : array-like\n", + " Final clipped predictions\n", + " min_output : float\n", + " Minimum allowed output value\n", + " max_output : float\n", + " Maximum allowed output value\n", + " \n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " # Ensure proper array formatting and dimensionality\n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Validate input dimensions\n", + " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", + " \"All input arrays must have the same length\"\n", + " \n", + " # Classification metrics with error handling\n", + " print(\"\\nClassification Metrics:\")\n", + " try:\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", + " print(conf_matrix)\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " class_report = classification_report(\n", + " y_true_binary, \n", + " y_pred_binary,\n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4\n", + " )\n", + " print(class_report)\n", + " except Exception as e:\n", + " print(f\"Error in classification metrics calculation: {str(e)}\")\n", + " \n", + " # Regression metrics with error handling\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " try:\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " # Range validation\n", + " out_of_range = np.sum(\n", + " (y_reg_nonzero < min_output) | \n", + " (y_reg_nonzero > max_output)\n", + " )\n", + " \n", + " # Error metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", + " (y_true_nonzero + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " # Calculate metrics\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in regression metrics calculation: {str(e)}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final output metrics with error handling\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " try:\n", + " # Ensure outputs are within bounds\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " \n", + " # Calculate metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in final output metrics calculation: {str(e)}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar energy model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " def evaluate_epoch(epoch, logs):\n", + " if epoch % 20 == 0:\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=35,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-5\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", + " tf.keras.callbacks.TerminateOnNaN()\n", + " ]\n", + "\n", + " '''\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.8,\n", + " patience=10,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " '''\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar energy predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarenergy_predicted': final_pred.flatten(),\n", + " 'solarenergy_classification': classification_pred.flatten(),\n", + " 'solarenergy_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar energy column where missing\n", + " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", + " 'solarenergy_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar energy model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 66\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (112882, 24, 66)\n", + "Test data shape: (16849, 24, 66)\n", + "Saving scaler X to: 2024-11-27_13-56_scale_X.joblib\n", + "Saving scaler X to: 2024-11-27_13-56_scale_y.joblib\n", + "Saving features to: 2024-11-27_13-56_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "print(\"Initializing solar energy model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", + "\n", + "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", + "Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n", + "\n", + "Class distribution in training set:\n", + "Zeros: 56899 (50.41%)\n", + "Non-zeros: 55983 (49.59%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 8576 (50.90%)\n", + "Non-zeros: 8273 (49.10%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/150\n", + "221/221 [==============================] - ETA: 0s - loss: 10.1910 - classification_output_loss: 0.2183 - regression_output_loss: 0.3452 - final_output_loss: 0.2500\n", + "Epoch 1 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 95.14%\n", + "AUC-ROC: 0.9914\n", + "\n", + "Confusion Matrix:\n", + "[[8046 530]\n", + " [ 289 7984]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9653 0.9382 0.9516 8576\n", + " Non-Zero 0.9377 0.9651 0.9512 8273\n", + "\n", + " accuracy 0.9514 16849\n", + " macro avg 0.9515 0.9516 0.9514 16849\n", + "weighted avg 0.9518 0.9514 0.9514 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 148 predictions\n", + "MAPE: 51.77%\n", + "Within ±10%: 4.40%\n", + "MAE: 0.63\n", + "RMSE: 0.84\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 26.69%\n", + "Within ±2%: 51.07%\n", + "Within ±5%: 51.72%\n", + "Within ±10%: 52.69%\n", + "Within ±20%: 55.31%\n", + "MAE: 0.28\n", + "RMSE: 0.52\n", + "221/221 [==============================] - 58s 112ms/step - loss: 10.1910 - classification_output_loss: 0.2183 - regression_output_loss: 0.3452 - final_output_loss: 0.2500 - val_loss: 7.7224 - val_classification_output_loss: 0.2687 - val_regression_output_loss: 0.4593 - val_final_output_loss: 0.2756\n", + "Epoch 2/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 5.8851 - classification_output_loss: 0.1001 - regression_output_loss: 0.1639 - final_output_loss: 0.0979 - val_loss: 4.6694 - val_classification_output_loss: 0.1285 - val_regression_output_loss: 0.2137 - val_final_output_loss: 0.1128\n", + "Epoch 3/150\n", + "221/221 [==============================] - 16s 71ms/step - loss: 3.9307 - classification_output_loss: 0.0793 - regression_output_loss: 0.1165 - final_output_loss: 0.0672 - val_loss: 3.4524 - val_classification_output_loss: 0.0937 - val_regression_output_loss: 0.1159 - val_final_output_loss: 0.0695\n", + "Epoch 4/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 3.2319 - classification_output_loss: 0.0735 - regression_output_loss: 0.0949 - final_output_loss: 0.0527 - val_loss: 3.1207 - val_classification_output_loss: 0.0850 - val_regression_output_loss: 0.0849 - val_final_output_loss: 0.0601\n", + "Epoch 5/150\n", + "221/221 [==============================] - 15s 69ms/step - loss: 2.9473 - classification_output_loss: 0.0913 - regression_output_loss: 0.1650 - final_output_loss: 0.1204 - val_loss: 2.3847 - val_classification_output_loss: 0.1023 - val_regression_output_loss: 0.2639 - val_final_output_loss: 0.2111\n", + "Epoch 6/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 1.7403 - classification_output_loss: 0.0797 - regression_output_loss: 0.1275 - final_output_loss: 0.1103 - val_loss: 1.2609 - val_classification_output_loss: 0.0809 - val_regression_output_loss: 0.0645 - val_final_output_loss: 0.0449\n", + "Epoch 7/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 1.0165 - classification_output_loss: 0.0666 - regression_output_loss: 0.0859 - final_output_loss: 0.0577 - val_loss: 0.7915 - val_classification_output_loss: 0.0723 - val_regression_output_loss: 0.0517 - val_final_output_loss: 0.0379\n", + "Epoch 8/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.6764 - classification_output_loss: 0.0585 - regression_output_loss: 0.0728 - final_output_loss: 0.0537 - val_loss: 0.5565 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0461 - val_final_output_loss: 0.0349\n", + "Epoch 9/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.4936 - classification_output_loss: 0.0550 - regression_output_loss: 0.0576 - final_output_loss: 0.0426 - val_loss: 0.4275 - val_classification_output_loss: 0.0706 - val_regression_output_loss: 0.0355 - val_final_output_loss: 0.0321\n", + "Epoch 10/150\n", + "221/221 [==============================] - 15s 68ms/step - loss: 0.3914 - classification_output_loss: 0.0525 - regression_output_loss: 0.0459 - final_output_loss: 0.0336 - val_loss: 0.3597 - val_classification_output_loss: 0.0706 - val_regression_output_loss: 0.0372 - val_final_output_loss: 0.0319\n", + "Epoch 11/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.3393 - classification_output_loss: 0.0518 - regression_output_loss: 0.0439 - final_output_loss: 0.0333 - val_loss: 0.3203 - val_classification_output_loss: 0.0724 - val_regression_output_loss: 0.0320 - val_final_output_loss: 0.0283\n", + "Epoch 12/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.3109 - classification_output_loss: 0.0509 - regression_output_loss: 0.0403 - final_output_loss: 0.0305 - val_loss: 0.3037 - val_classification_output_loss: 0.0705 - val_regression_output_loss: 0.0329 - val_final_output_loss: 0.0285\n", + "Epoch 13/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.3006 - classification_output_loss: 0.0529 - regression_output_loss: 0.0396 - final_output_loss: 0.0300 - val_loss: 0.2963 - val_classification_output_loss: 0.0673 - val_regression_output_loss: 0.0301 - val_final_output_loss: 0.0262\n", + "Epoch 14/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.3137 - classification_output_loss: 0.0644 - regression_output_loss: 0.0694 - final_output_loss: 0.0570 - val_loss: 0.4100 - val_classification_output_loss: 0.0884 - val_regression_output_loss: 0.2605 - val_final_output_loss: 0.1666\n", + "Epoch 15/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.2755 - classification_output_loss: 0.0625 - regression_output_loss: 0.1108 - final_output_loss: 0.0794 - val_loss: 0.2491 - val_classification_output_loss: 0.0744 - val_regression_output_loss: 0.1431 - val_final_output_loss: 0.0542\n", + "Epoch 16/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.1950 - classification_output_loss: 0.0579 - regression_output_loss: 0.0713 - final_output_loss: 0.0523 - val_loss: 0.1741 - val_classification_output_loss: 0.0664 - val_regression_output_loss: 0.0509 - val_final_output_loss: 0.0638\n", + "Epoch 17/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.1556 - classification_output_loss: 0.0523 - regression_output_loss: 0.0559 - final_output_loss: 0.0525 - val_loss: 0.1413 - val_classification_output_loss: 0.0684 - val_regression_output_loss: 0.0566 - val_final_output_loss: 0.0392\n", + "Epoch 18/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.1328 - classification_output_loss: 0.0533 - regression_output_loss: 0.0550 - final_output_loss: 0.0497 - val_loss: 0.1157 - val_classification_output_loss: 0.0687 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0333\n", + "Epoch 19/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.1164 - classification_output_loss: 0.0504 - regression_output_loss: 0.0539 - final_output_loss: 0.0463 - val_loss: 0.1044 - val_classification_output_loss: 0.0741 - val_regression_output_loss: 0.0402 - val_final_output_loss: 0.0340\n", + "Epoch 20/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.1014 - classification_output_loss: 0.0468 - regression_output_loss: 0.0480 - final_output_loss: 0.0427 - val_loss: 0.0948 - val_classification_output_loss: 0.0719 - val_regression_output_loss: 0.0393 - val_final_output_loss: 0.0335\n", + "Epoch 21/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0903 - classification_output_loss: 0.0442 - regression_output_loss: 0.0435 - final_output_loss: 0.0394\n", + "Epoch 21 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.44%\n", + "AUC-ROC: 0.9967\n", + "\n", + "Confusion Matrix:\n", + "[[8334 242]\n", + " [ 189 8084]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9778 0.9718 0.9748 8576\n", + " Non-Zero 0.9709 0.9772 0.9740 8273\n", + "\n", + " accuracy 0.9744 16849\n", + " macro avg 0.9744 0.9745 0.9744 16849\n", + "weighted avg 0.9744 0.9744 0.9744 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 15.37%\n", + "Within ±10%: 52.36%\n", + "MAE: 0.09\n", + "RMSE: 0.12\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 12.08%\n", + "Within ±2%: 56.62%\n", + "Within ±5%: 65.80%\n", + "Within ±10%: 77.45%\n", + "Within ±20%: 86.37%\n", + "MAE: 0.05\n", + "RMSE: 0.09\n", + "221/221 [==============================] - 21s 93ms/step - loss: 0.0903 - classification_output_loss: 0.0442 - regression_output_loss: 0.0435 - final_output_loss: 0.0394 - val_loss: 0.0834 - val_classification_output_loss: 0.0671 - val_regression_output_loss: 0.0350 - val_final_output_loss: 0.0276\n", + "Epoch 22/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0806 - classification_output_loss: 0.0424 - regression_output_loss: 0.0390 - final_output_loss: 0.0346 - val_loss: 0.0752 - val_classification_output_loss: 0.0653 - val_regression_output_loss: 0.0304 - val_final_output_loss: 0.0250\n", + "Epoch 23/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0738 - classification_output_loss: 0.0397 - regression_output_loss: 0.0367 - final_output_loss: 0.0320 - val_loss: 0.0805 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0418 - val_final_output_loss: 0.0347\n", + "Epoch 24/150\n", + "221/221 [==============================] - 12s 55ms/step - loss: 0.0691 - classification_output_loss: 0.0393 - regression_output_loss: 0.0349 - final_output_loss: 0.0304 - val_loss: 0.0790 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0393 - val_final_output_loss: 0.0401\n", + "Epoch 25/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0635 - classification_output_loss: 0.0381 - regression_output_loss: 0.0313 - final_output_loss: 0.0264 - val_loss: 0.0660 - val_classification_output_loss: 0.0640 - val_regression_output_loss: 0.0269 - val_final_output_loss: 0.0273\n", + "Epoch 26/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0606 - classification_output_loss: 0.0377 - regression_output_loss: 0.0300 - final_output_loss: 0.0254 - val_loss: 0.0620 - val_classification_output_loss: 0.0636 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0247\n", + "Epoch 27/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0586 - classification_output_loss: 0.0375 - regression_output_loss: 0.0292 - final_output_loss: 0.0247 - val_loss: 0.0586 - val_classification_output_loss: 0.0626 - val_regression_output_loss: 0.0229 - val_final_output_loss: 0.0202\n", + "Epoch 28/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0568 - classification_output_loss: 0.0368 - regression_output_loss: 0.0286 - final_output_loss: 0.0235 - val_loss: 0.0576 - val_classification_output_loss: 0.0613 - val_regression_output_loss: 0.0241 - val_final_output_loss: 0.0192\n", + "Epoch 29/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0561 - classification_output_loss: 0.0376 - regression_output_loss: 0.0283 - final_output_loss: 0.0231 - val_loss: 0.0575 - val_classification_output_loss: 0.0607 - val_regression_output_loss: 0.0244 - val_final_output_loss: 0.0198\n", + "Epoch 30/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0559 - classification_output_loss: 0.0386 - regression_output_loss: 0.0283 - final_output_loss: 0.0230 - val_loss: 0.0560 - val_classification_output_loss: 0.0580 - val_regression_output_loss: 0.0228 - val_final_output_loss: 0.0196\n", + "Epoch 31/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0564 - classification_output_loss: 0.0383 - regression_output_loss: 0.0294 - final_output_loss: 0.0235 - val_loss: 0.0549 - val_classification_output_loss: 0.0565 - val_regression_output_loss: 0.0209 - val_final_output_loss: 0.0194\n", + "Epoch 32/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0808 - classification_output_loss: 0.0454 - regression_output_loss: 0.0574 - final_output_loss: 0.0500 - val_loss: 0.2867 - val_classification_output_loss: 0.1518 - val_regression_output_loss: 0.2633 - val_final_output_loss: 0.2595\n", + "Epoch 33/150\n", + "221/221 [==============================] - 16s 73ms/step - loss: 0.1274 - classification_output_loss: 0.0714 - regression_output_loss: 0.0997 - final_output_loss: 0.0752 - val_loss: 0.0907 - val_classification_output_loss: 0.0657 - val_regression_output_loss: 0.0706 - val_final_output_loss: 0.0496\n", + "Epoch 34/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0842 - classification_output_loss: 0.0474 - regression_output_loss: 0.0614 - final_output_loss: 0.0538 - val_loss: 0.0677 - val_classification_output_loss: 0.0667 - val_regression_output_loss: 0.0386 - val_final_output_loss: 0.0308\n", + "Epoch 35/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0717 - classification_output_loss: 0.0459 - regression_output_loss: 0.0460 - final_output_loss: 0.0472 - val_loss: 0.0621 - val_classification_output_loss: 0.0637 - val_regression_output_loss: 0.0334 - val_final_output_loss: 0.0299\n", + "Epoch 36/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0646 - classification_output_loss: 0.0419 - regression_output_loss: 0.0418 - final_output_loss: 0.0416 - val_loss: 0.0593 - val_classification_output_loss: 0.0620 - val_regression_output_loss: 0.0338 - val_final_output_loss: 0.0294\n", + "Epoch 37/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0596 - classification_output_loss: 0.0426 - regression_output_loss: 0.0384 - final_output_loss: 0.0366 - val_loss: 0.0512 - val_classification_output_loss: 0.0627 - val_regression_output_loss: 0.0245 - val_final_output_loss: 0.0231\n", + "Epoch 38/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0604 - classification_output_loss: 0.0406 - regression_output_loss: 0.0406 - final_output_loss: 0.0407 - val_loss: 0.0608 - val_classification_output_loss: 0.0703 - val_regression_output_loss: 0.0375 - val_final_output_loss: 0.0331\n", + "Epoch 39/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0584 - classification_output_loss: 0.0401 - regression_output_loss: 0.0394 - final_output_loss: 0.0397 - val_loss: 0.0669 - val_classification_output_loss: 0.0657 - val_regression_output_loss: 0.0483 - val_final_output_loss: 0.0424\n", + "Epoch 40/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0594 - classification_output_loss: 0.0389 - regression_output_loss: 0.0415 - final_output_loss: 0.0420 - val_loss: 0.0562 - val_classification_output_loss: 0.0665 - val_regression_output_loss: 0.0356 - val_final_output_loss: 0.0276\n", + "Epoch 41/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0554 - classification_output_loss: 0.0376 - regression_output_loss: 0.0377 - final_output_loss: 0.0388\n", + "Epoch 41 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.17%\n", + "AUC-ROC: 0.9972\n", + "\n", + "Confusion Matrix:\n", + "[[8195 381]\n", + " [ 96 8177]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9884 0.9556 0.9717 8576\n", + " Non-Zero 0.9555 0.9884 0.9717 8273\n", + "\n", + " accuracy 0.9717 16849\n", + " macro avg 0.9720 0.9720 0.9717 16849\n", + "weighted avg 0.9722 0.9717 0.9717 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 36 predictions\n", + "MAPE: 13.32%\n", + "Within ±10%: 65.24%\n", + "MAE: 0.07\n", + "RMSE: 0.10\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 9.74%\n", + "Within ±2%: 57.72%\n", + "Within ±5%: 67.29%\n", + "Within ±10%: 80.49%\n", + "Within ±20%: 89.54%\n", + "MAE: 0.04\n", + "RMSE: 0.09\n", + "221/221 [==============================] - 19s 85ms/step - loss: 0.0554 - classification_output_loss: 0.0376 - regression_output_loss: 0.0377 - final_output_loss: 0.0388 - val_loss: 0.0519 - val_classification_output_loss: 0.0735 - val_regression_output_loss: 0.0259 - val_final_output_loss: 0.0259\n", + "Epoch 42/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0509 - classification_output_loss: 0.0371 - regression_output_loss: 0.0339 - final_output_loss: 0.0344 - val_loss: 0.0480 - val_classification_output_loss: 0.0602 - val_regression_output_loss: 0.0278 - val_final_output_loss: 0.0235\n", + "Epoch 43/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0487 - classification_output_loss: 0.0352 - regression_output_loss: 0.0327 - final_output_loss: 0.0329 - val_loss: 0.0547 - val_classification_output_loss: 0.0679 - val_regression_output_loss: 0.0422 - val_final_output_loss: 0.0236\n", + "Epoch 44/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0519 - classification_output_loss: 0.0353 - regression_output_loss: 0.0379 - final_output_loss: 0.0365 - val_loss: 0.0542 - val_classification_output_loss: 0.0592 - val_regression_output_loss: 0.0421 - val_final_output_loss: 0.0267\n", + "Epoch 45/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0480 - classification_output_loss: 0.0316 - regression_output_loss: 0.0347 - final_output_loss: 0.0335 - val_loss: 0.0737 - val_classification_output_loss: 0.0704 - val_regression_output_loss: 0.0508 - val_final_output_loss: 0.0603\n", + "Epoch 46/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0424 - classification_output_loss: 0.0313 - regression_output_loss: 0.0283 - final_output_loss: 0.0270 - val_loss: 0.0604 - val_classification_output_loss: 0.0554 - val_regression_output_loss: 0.0507 - val_final_output_loss: 0.0393\n", + "Epoch 47/150\n", + "221/221 [==============================] - 12s 54ms/step - loss: 0.0466 - classification_output_loss: 0.0329 - regression_output_loss: 0.0325 - final_output_loss: 0.0346 - val_loss: 0.0596 - val_classification_output_loss: 0.0603 - val_regression_output_loss: 0.0387 - val_final_output_loss: 0.0460\n", + "Epoch 48/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0439 - classification_output_loss: 0.0302 - regression_output_loss: 0.0305 - final_output_loss: 0.0312 - val_loss: 0.0587 - val_classification_output_loss: 0.0572 - val_regression_output_loss: 0.0415 - val_final_output_loss: 0.0438\n", + "Epoch 49/150\n", + "221/221 [==============================] - 12s 54ms/step - loss: 0.0405 - classification_output_loss: 0.0296 - regression_output_loss: 0.0272 - final_output_loss: 0.0277 - val_loss: 0.0537 - val_classification_output_loss: 0.0566 - val_regression_output_loss: 0.0406 - val_final_output_loss: 0.0345\n", + "Epoch 50/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0379 - classification_output_loss: 0.0294 - regression_output_loss: 0.0256 - final_output_loss: 0.0246 - val_loss: 0.0520 - val_classification_output_loss: 0.0551 - val_regression_output_loss: 0.0376 - val_final_output_loss: 0.0356\n", + "Epoch 51/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0376 - classification_output_loss: 0.0280 - regression_output_loss: 0.0258 - final_output_loss: 0.0256 - val_loss: 0.0502 - val_classification_output_loss: 0.0509 - val_regression_output_loss: 0.0355 - val_final_output_loss: 0.0359\n", + "Epoch 52/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0353 - classification_output_loss: 0.0265 - regression_output_loss: 0.0240 - final_output_loss: 0.0231 - val_loss: 0.0491 - val_classification_output_loss: 0.0519 - val_regression_output_loss: 0.0348 - val_final_output_loss: 0.0345\n", + "Epoch 53/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0343 - classification_output_loss: 0.0259 - regression_output_loss: 0.0232 - final_output_loss: 0.0226 - val_loss: 0.0422 - val_classification_output_loss: 0.0486 - val_regression_output_loss: 0.0270 - val_final_output_loss: 0.0280\n", + "Epoch 54/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0336 - classification_output_loss: 0.0255 - regression_output_loss: 0.0231 - final_output_loss: 0.0220 - val_loss: 0.0381 - val_classification_output_loss: 0.0474 - val_regression_output_loss: 0.0225 - val_final_output_loss: 0.0235\n", + "Epoch 55/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0331 - classification_output_loss: 0.0244 - regression_output_loss: 0.0228 - final_output_loss: 0.0222 - val_loss: 0.0339 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0192 - val_final_output_loss: 0.0175\n", + "Epoch 56/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0322 - classification_output_loss: 0.0240 - regression_output_loss: 0.0224 - final_output_loss: 0.0211 - val_loss: 0.0334 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0190 - val_final_output_loss: 0.0175\n", + "Epoch 57/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0311 - classification_output_loss: 0.0228 - regression_output_loss: 0.0217 - final_output_loss: 0.0202 - val_loss: 0.0330 - val_classification_output_loss: 0.0446 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0180\n", + "Epoch 58/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0307 - classification_output_loss: 0.0227 - regression_output_loss: 0.0216 - final_output_loss: 0.0198 - val_loss: 0.0320 - val_classification_output_loss: 0.0437 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0164\n", + "Epoch 59/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0306 - classification_output_loss: 0.0229 - regression_output_loss: 0.0216 - final_output_loss: 0.0198 - val_loss: 0.0312 - val_classification_output_loss: 0.0420 - val_regression_output_loss: 0.0188 - val_final_output_loss: 0.0153\n", + "Epoch 60/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0303 - classification_output_loss: 0.0226 - regression_output_loss: 0.0215 - final_output_loss: 0.0196 - val_loss: 0.0315 - val_classification_output_loss: 0.0412 - val_regression_output_loss: 0.0203 - val_final_output_loss: 0.0151\n", + "Epoch 61/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0302 - classification_output_loss: 0.0224 - regression_output_loss: 0.0215 - final_output_loss: 0.0195\n", + "Epoch 61 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.47%\n", + "AUC-ROC: 0.9989\n", + "\n", + "Confusion Matrix:\n", + "[[8426 150]\n", + " [ 108 8165]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9873 0.9825 0.9849 8576\n", + " Non-Zero 0.9820 0.9869 0.9844 8273\n", + "\n", + " accuracy 0.9847 16849\n", + " macro avg 0.9847 0.9847 0.9847 16849\n", + "weighted avg 0.9847 0.9847 0.9847 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 3 predictions\n", + "MAPE: 11.46%\n", + "Within ±10%: 73.71%\n", + "MAE: 0.06\n", + "RMSE: 0.09\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.33%\n", + "Within ±2%: 61.98%\n", + "Within ±5%: 76.04%\n", + "Within ±10%: 87.28%\n", + "Within ±20%: 91.97%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 18s 80ms/step - loss: 0.0302 - classification_output_loss: 0.0224 - regression_output_loss: 0.0215 - final_output_loss: 0.0195 - val_loss: 0.0322 - val_classification_output_loss: 0.0401 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0160\n", + "Epoch 62/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0300 - classification_output_loss: 0.0223 - regression_output_loss: 0.0214 - final_output_loss: 0.0194 - val_loss: 0.0326 - val_classification_output_loss: 0.0397 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0172\n", + "Epoch 63/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0300 - classification_output_loss: 0.0224 - regression_output_loss: 0.0216 - final_output_loss: 0.0193 - val_loss: 0.0316 - val_classification_output_loss: 0.0394 - val_regression_output_loss: 0.0202 - val_final_output_loss: 0.0167\n", + "Epoch 64/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0300 - classification_output_loss: 0.0223 - regression_output_loss: 0.0216 - final_output_loss: 0.0193 - val_loss: 0.0307 - val_classification_output_loss: 0.0389 - val_regression_output_loss: 0.0188 - val_final_output_loss: 0.0160\n", + "Epoch 65/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0301 - classification_output_loss: 0.0228 - regression_output_loss: 0.0216 - final_output_loss: 0.0194 - val_loss: 0.0297 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0173 - val_final_output_loss: 0.0153\n", + "Epoch 66/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0304 - classification_output_loss: 0.0229 - regression_output_loss: 0.0223 - final_output_loss: 0.0196 - val_loss: 0.0290 - val_classification_output_loss: 0.0379 - val_regression_output_loss: 0.0161 - val_final_output_loss: 0.0149\n", + "Epoch 67/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0310 - classification_output_loss: 0.0223 - regression_output_loss: 0.0230 - final_output_loss: 0.0206 - val_loss: 0.0295 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0163 - val_final_output_loss: 0.0159\n", + "Epoch 68/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0309 - classification_output_loss: 0.0224 - regression_output_loss: 0.0226 - final_output_loss: 0.0207 - val_loss: 0.0684 - val_classification_output_loss: 0.0568 - val_regression_output_loss: 0.0484 - val_final_output_loss: 0.0650\n", + "Epoch 69/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0856 - classification_output_loss: 0.0495 - regression_output_loss: 0.0722 - final_output_loss: 0.0719 - val_loss: 0.0708 - val_classification_output_loss: 0.0585 - val_regression_output_loss: 0.0718 - val_final_output_loss: 0.0365\n", + "Epoch 70/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0494 - classification_output_loss: 0.0324 - regression_output_loss: 0.0353 - final_output_loss: 0.0392 - val_loss: 0.0511 - val_classification_output_loss: 0.0511 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0326\n", + "Epoch 71/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0468 - classification_output_loss: 0.0323 - regression_output_loss: 0.0350 - final_output_loss: 0.0360 - val_loss: 0.0500 - val_classification_output_loss: 0.0791 - val_regression_output_loss: 0.0313 - val_final_output_loss: 0.0274\n", + "Epoch 72/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0464 - classification_output_loss: 0.0292 - regression_output_loss: 0.0353 - final_output_loss: 0.0372 - val_loss: 0.0456 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0265 - val_final_output_loss: 0.0248\n", + "Epoch 73/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0434 - classification_output_loss: 0.0299 - regression_output_loss: 0.0321 - final_output_loss: 0.0346 - val_loss: 0.0395 - val_classification_output_loss: 0.0458 - val_regression_output_loss: 0.0251 - val_final_output_loss: 0.0250\n", + "Epoch 74/150\n", + "221/221 [==============================] - 12s 55ms/step - loss: 0.0417 - classification_output_loss: 0.0296 - regression_output_loss: 0.0302 - final_output_loss: 0.0321 - val_loss: 0.0424 - val_classification_output_loss: 0.0670 - val_regression_output_loss: 0.0280 - val_final_output_loss: 0.0196\n", + "Epoch 75/150\n", + "221/221 [==============================] - 15s 67ms/step - loss: 0.0413 - classification_output_loss: 0.0321 - regression_output_loss: 0.0302 - final_output_loss: 0.0313 - val_loss: 0.0469 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0394 - val_final_output_loss: 0.0278\n", + "Epoch 76/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0441 - classification_output_loss: 0.0293 - regression_output_loss: 0.0320 - final_output_loss: 0.0370 - val_loss: 0.0386 - val_classification_output_loss: 0.0517 - val_regression_output_loss: 0.0234 - val_final_output_loss: 0.0223\n", + "Epoch 77/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0398 - classification_output_loss: 0.0254 - regression_output_loss: 0.0291 - final_output_loss: 0.0324 - val_loss: 0.0391 - val_classification_output_loss: 0.0423 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0239\n", + "Epoch 78/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0394 - classification_output_loss: 0.0269 - regression_output_loss: 0.0292 - final_output_loss: 0.0311 - val_loss: 0.0409 - val_classification_output_loss: 0.0598 - val_regression_output_loss: 0.0259 - val_final_output_loss: 0.0228\n", + "Epoch 79/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0391 - classification_output_loss: 0.0290 - regression_output_loss: 0.0278 - final_output_loss: 0.0307 - val_loss: 0.0429 - val_classification_output_loss: 0.0595 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0275\n", + "Epoch 80/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0377 - classification_output_loss: 0.0264 - regression_output_loss: 0.0277 - final_output_loss: 0.0304 - val_loss: 0.0384 - val_classification_output_loss: 0.0522 - val_regression_output_loss: 0.0217 - val_final_output_loss: 0.0247\n", + "Epoch 81/150\n", + "220/221 [============================>.] - ETA: 0s - loss: 0.0388 - classification_output_loss: 0.0237 - regression_output_loss: 0.0278 - final_output_loss: 0.0333\n", + "Epoch 81 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.00%\n", + "AUC-ROC: 0.9985\n", + "\n", + "Confusion Matrix:\n", + "[[8307 269]\n", + " [ 68 8205]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9919 0.9686 0.9801 8576\n", + " Non-Zero 0.9683 0.9918 0.9799 8273\n", + "\n", + " accuracy 0.9800 16849\n", + " macro avg 0.9801 0.9802 0.9800 16849\n", + "weighted avg 0.9803 0.9800 0.9800 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 12.72%\n", + "Within ±10%: 71.45%\n", + "MAE: 0.07\n", + "RMSE: 0.09\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 8.60%\n", + "Within ±2%: 60.42%\n", + "Within ±5%: 72.54%\n", + "Within ±10%: 85.01%\n", + "Within ±20%: 90.31%\n", + "MAE: 0.03\n", + "RMSE: 0.07\n", + "221/221 [==============================] - 18s 81ms/step - loss: 0.0388 - classification_output_loss: 0.0238 - regression_output_loss: 0.0278 - final_output_loss: 0.0333 - val_loss: 0.0374 - val_classification_output_loss: 0.0522 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0189\n", + "Epoch 82/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0362 - classification_output_loss: 0.0243 - regression_output_loss: 0.0261 - final_output_loss: 0.0289 - val_loss: 0.0404 - val_classification_output_loss: 0.0759 - val_regression_output_loss: 0.0210 - val_final_output_loss: 0.0202\n", + "Epoch 83/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0381 - classification_output_loss: 0.0257 - regression_output_loss: 0.0282 - final_output_loss: 0.0311 - val_loss: 0.0443 - val_classification_output_loss: 0.0467 - val_regression_output_loss: 0.0348 - val_final_output_loss: 0.0287\n", + "Epoch 84/150\n", + "221/221 [==============================] - 12s 55ms/step - loss: 0.0395 - classification_output_loss: 0.0270 - regression_output_loss: 0.0296 - final_output_loss: 0.0321 - val_loss: 0.0554 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0469 - val_final_output_loss: 0.0469\n", + "Epoch 85/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0371 - classification_output_loss: 0.0265 - regression_output_loss: 0.0273 - final_output_loss: 0.0296 - val_loss: 0.0588 - val_classification_output_loss: 0.0505 - val_regression_output_loss: 0.0473 - val_final_output_loss: 0.0508\n", + "Epoch 86/150\n", + "221/221 [==============================] - 12s 54ms/step - loss: 0.0354 - classification_output_loss: 0.0232 - regression_output_loss: 0.0255 - final_output_loss: 0.0294 - val_loss: 0.0574 - val_classification_output_loss: 0.0465 - val_regression_output_loss: 0.0445 - val_final_output_loss: 0.0525\n", + "Epoch 87/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0342 - classification_output_loss: 0.0247 - regression_output_loss: 0.0241 - final_output_loss: 0.0274 - val_loss: 0.0581 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0468 - val_final_output_loss: 0.0525\n", + "Epoch 88/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0332 - classification_output_loss: 0.0225 - regression_output_loss: 0.0243 - final_output_loss: 0.0265 - val_loss: 0.0604 - val_classification_output_loss: 0.0435 - val_regression_output_loss: 0.0500 - val_final_output_loss: 0.0577\n", + "Epoch 89/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0340 - classification_output_loss: 0.0237 - regression_output_loss: 0.0252 - final_output_loss: 0.0271 - val_loss: 0.0487 - val_classification_output_loss: 0.0424 - val_regression_output_loss: 0.0385 - val_final_output_loss: 0.0406\n", + "Epoch 90/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0324 - classification_output_loss: 0.0201 - regression_output_loss: 0.0233 - final_output_loss: 0.0268 - val_loss: 0.0375 - val_classification_output_loss: 0.0365 - val_regression_output_loss: 0.0240 - val_final_output_loss: 0.0314\n", + "Epoch 91/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0308 - classification_output_loss: 0.0210 - regression_output_loss: 0.0222 - final_output_loss: 0.0248 - val_loss: 0.0620 - val_classification_output_loss: 0.0410 - val_regression_output_loss: 0.0594 - val_final_output_loss: 0.0555\n", + "Epoch 92/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0320 - classification_output_loss: 0.0218 - regression_output_loss: 0.0241 - final_output_loss: 0.0256 - val_loss: 0.0486 - val_classification_output_loss: 0.0387 - val_regression_output_loss: 0.0335 - val_final_output_loss: 0.0487\n", + "Epoch 93/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0302 - classification_output_loss: 0.0189 - regression_output_loss: 0.0222 - final_output_loss: 0.0246 - val_loss: 0.0351 - val_classification_output_loss: 0.0392 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0264\n", + "Epoch 94/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0318 - classification_output_loss: 0.0213 - regression_output_loss: 0.0241 - final_output_loss: 0.0261 - val_loss: 0.0389 - val_classification_output_loss: 0.0408 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0318\n", + "Epoch 95/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0289 - classification_output_loss: 0.0183 - regression_output_loss: 0.0209 - final_output_loss: 0.0234 - val_loss: 0.0330 - val_classification_output_loss: 0.0442 - val_regression_output_loss: 0.0201 - val_final_output_loss: 0.0224\n", + "Epoch 96/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0309 - classification_output_loss: 0.0187 - regression_output_loss: 0.0233 - final_output_loss: 0.0260 - val_loss: 0.0332 - val_classification_output_loss: 0.0377 - val_regression_output_loss: 0.0280 - val_final_output_loss: 0.0180\n", + "Epoch 97/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0315 - classification_output_loss: 0.0188 - regression_output_loss: 0.0240 - final_output_loss: 0.0263 - val_loss: 0.0290 - val_classification_output_loss: 0.0359 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0160\n", + "Epoch 98/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0284 - classification_output_loss: 0.0178 - regression_output_loss: 0.0207 - final_output_loss: 0.0234 - val_loss: 0.0283 - val_classification_output_loss: 0.0347 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0180\n", + "Epoch 99/150\n", + "221/221 [==============================] - 12s 54ms/step - loss: 0.0282 - classification_output_loss: 0.0188 - regression_output_loss: 0.0204 - final_output_loss: 0.0232 - val_loss: 0.0331 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0196 - val_final_output_loss: 0.0194\n", + "Epoch 100/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0271 - classification_output_loss: 0.0170 - regression_output_loss: 0.0202 - final_output_loss: 0.0216 - val_loss: 0.0318 - val_classification_output_loss: 0.0343 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0241\n", + "Epoch 101/150\n", + "220/221 [============================>.] - ETA: 0s - loss: 0.0269 - classification_output_loss: 0.0168 - regression_output_loss: 0.0206 - final_output_loss: 0.0216Restoring model weights from the end of the best epoch: 66.\n", + "\n", + "Epoch 101 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.55%\n", + "AUC-ROC: 0.9991\n", + "\n", + "Confusion Matrix:\n", + "[[8442 134]\n", + " [ 110 8163]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9871 0.9844 0.9858 8576\n", + " Non-Zero 0.9838 0.9867 0.9853 8273\n", + "\n", + " accuracy 0.9855 16849\n", + " macro avg 0.9855 0.9855 0.9855 16849\n", + "weighted avg 0.9855 0.9855 0.9855 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 4 predictions\n", + "MAPE: 10.69%\n", + "Within ±10%: 75.69%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.46%\n", + "Within ±2%: 62.80%\n", + "Within ±5%: 77.28%\n", + "Within ±10%: 86.92%\n", + "Within ±20%: 91.37%\n", + "MAE: 0.03\n", + "RMSE: 0.05\n", + "221/221 [==============================] - 19s 87ms/step - loss: 0.0269 - classification_output_loss: 0.0169 - regression_output_loss: 0.0206 - final_output_loss: 0.0216 - val_loss: 0.0341 - val_classification_output_loss: 0.0528 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0203\n", + "Epoch 101: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.55%\n", + "AUC-ROC: 0.9991\n", + "\n", + "Confusion Matrix:\n", + "[[8442 134]\n", + " [ 110 8163]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9871 0.9844 0.9858 8576\n", + " Non-Zero 0.9838 0.9867 0.9853 8273\n", + "\n", + " accuracy 0.9855 16849\n", + " macro avg 0.9855 0.9855 0.9855 16849\n", + "weighted avg 0.9855 0.9855 0.9855 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 4 predictions\n", + "MAPE: 10.69%\n", + "Within ±10%: 75.69%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.46%\n", + "Within ±2%: 62.80%\n", + "Within ±5%: 77.28%\n", + "Within ±10%: 86.92%\n", + "Within ±20%: 91.37%\n", + "MAE: 0.03\n", + "RMSE: 0.05\n" + ] + } + ], + "source": [ + "#Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "min_val = df['solarenergy'].min()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "\n", + "max_val = df['solarenergy'].max()\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", + "\n", + "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 8\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarenergy_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=150,\n", + " batch_size=512,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "527/527 [==============================] - 6s 11ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Energy Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 0.03 kWh\n", + "RMSE: 0.06 kWh\n", + "R² Score: 0.995\n", + "MAPE: N/A (insufficient data)\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 kWh: 100.0%\n", + "Within ±10 kWh: 100.0%\n", + "Within ±20 kWh: 100.0%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 97.7%\n", + "\n", + "Confusion Matrix for Energy Levels:\n", + " Low Moderate Very Low\n", + "Low 3537 135 1\n", + "Moderate 8 2100 0\n", + "Very Low 250 0 10818\n", + "\n", + "Plot saved as: 2024-11-27_13-56_energy_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: 0.000\n", + "Error standard deviation: 0.065\n", + "Median error: 0.000\n", + "95th percentile absolute error: 0.126\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, min_val_scaled, max_val_scaled)\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 81s 11ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar energy after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 121515\n", + "Non-zero predictions (classification >= 0.5): 106364\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 1.32\n", + "Median: 1.21\n", + "Std: 0.96\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 121515 (53.32%)\n", + "Predicted non-zeros: 106364 (46.68%)\n", + "Mean classification confidence: 0.4731\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar energy: 0.65\n", + "Min solar energy: 0.00\n", + "Max solar energy: 3.38\n", + "Zero predictions: 115218 (50.56%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", + " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", + " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", + "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", + "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Energy:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 180,701.0000\n", + "mean : 0.6626\n", + "median : 0.0000\n", + "std : 0.9546\n", + "min : 0.0000\n", + "max : 4.0000\n", + "skewness : 1.2789\n", + "kurtosis : 0.3378\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 0.0000\n", + "percentile_50 : 0.0000\n", + "percentile_75 : 1.2000\n", + "percentile_90 : 2.3082\n", + "percentile_95 : 2.8033\n", + "percentile_99 : 3.2000\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (50.52%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 180701,\n", + " 'mean': 0.6626335375253618,\n", + " 'median': 0.0,\n", + " 'std': 0.9546401546018566,\n", + " 'min': 0.0,\n", + " 'max': 4.0,\n", + " 'skewness': 1.2788578488075855,\n", + " 'kurtosis': 0.33780217102281096,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 0.0,\n", + " 'percentile_50': 0.0,\n", + " 'percentile_75': 1.2,\n", + " 'percentile_90': 2.3082294940948502,\n", + " 'percentile_95': 2.8033479690551752,\n", + " 'percentile_99': 3.2}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-27_13-56_error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Statistics:\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.99 0.98 0.99 8576\n", + " 1.0 0.98 0.99 0.99 8273\n", + "\n", + " accuracy 0.99 16849\n", + " macro avg 0.99 0.99 0.99 16849\n", + "weighted avg 0.99 0.99 0.99 16849\n", + "\n", + "AUC-ROC: 0.9991\n", + "\n", + "Regression Statistics (Non-zero values):\n", + "MAE: 0.0480\n", + "RMSE: 0.0668\n", + "Mean error: -0.0089\n", + "Error std: 0.0662\n", + "\n", + "Final Prediction Statistics:\n", + "MAE: 0.0268\n", + "RMSE: 0.0540\n", + "Mean error: 0.0004\n", + "Error std: 0.0540\n", + "\n", + "Error Thresholds (Final Predictions):\n", + "Predictions within ±0.5: 99.9%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v2-checkpoint.ipynb b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v2-checkpoint.ipynb new file mode 100644 index 0000000..48b9f81 --- /dev/null +++ b/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v2-checkpoint.ipynb @@ -0,0 +1,2991 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", + "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", + "Get:5 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1224 kB]\n", + "Get:6 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2454 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1513 kB]\n", + "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", + "Fetched 8313 kB in 2s (5391 kB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 21:08:46.612732: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-27 21:08:46.612772: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-27 21:08:46.612813: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-27 21:08:46.620849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import (\n", + " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", + " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", + " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", + " Conv1D, Multiply\n", + ")\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "from tensorflow.keras.metrics import AUC\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# Data processing and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.metrics import (\n", + " mean_absolute_error, mean_squared_error, r2_score, \n", + " confusion_matrix, classification_report, roc_auc_score\n", + ")\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Additional utilities\n", + "import tensorflow_addons as tfa\n", + "from scipy import stats\n", + "import json\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Features based only on radiation and other available variables\n", + " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Energy-specific features\n", + " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Temperature impact on theoretical efficiency\n", + " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", + "\n", + " # Combined features\n", + " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", + " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_effect'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = np.abs(12 - df['hour'])\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "def add_atmospheric_features(df):\n", + " # Indice di Massa d'Aria (Air Mass Index)\n", + " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", + " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", + " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", + "\n", + " # Indice di Stabilità Atmosferica\n", + " # Combina temperatura, umidità e pressione\n", + " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", + "\n", + " # Vapor Pressure Deficit (VPD)\n", + " # Importante per la radiazione diffusa\n", + " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", + " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", + " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", + "\n", + " return df\n", + "\n", + "def add_diffusion_features(df):\n", + " # Indice di Diffusione\n", + " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", + "\n", + " # Radiazione Diretta vs Diffusa\n", + " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", + " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", + "\n", + " # Fattore di Trasparenza Atmosferica\n", + " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", + "\n", + " return df\n", + "\n", + "def calculate_trend(x):\n", + " try:\n", + " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", + " except:\n", + " return np.nan\n", + "\n", + "def add_persistence_features(df):\n", + " # Create a copy to avoid modifying the original dataframe\n", + " df = df.copy()\n", + "\n", + " # Calculate trends more efficiently\n", + " windows = [3, 6, 12, 24]\n", + " for w in windows:\n", + " # Use numba or vectorized operations if possible\n", + " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", + " window=w,\n", + " min_periods=w\n", + " ).apply(calculate_trend, raw=True)\n", + "\n", + " # Optimize volatility calculation by doing it in one pass\n", + " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", + " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", + "\n", + " return df\n", + "\n", + "def add_weather_pattern_features(df):\n", + " # Pattern giornalieri\n", + " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", + " lambda x: (x < 30).sum()\n", + " )\n", + "\n", + " # Stabilità delle condizioni\n", + " for col in ['temp', 'humidity', 'cloudcover']:\n", + " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", + "\n", + " # Indice di Variabilità Meteorologica\n", + " df['weather_variability_index'] = (df['temp_stability'] +\n", + " df['humidity_stability'] +\n", + " df['cloudcover_stability']) / 3\n", + "\n", + " return df\n", + "\n", + "def add_efficiency_features(df):\n", + " # Perdite per temperatura\n", + " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", + "\n", + " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", + " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", + "\n", + " # Efficienza complessiva stimata\n", + " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", + " df['atmospheric_transmittance']\n", + "\n", + " # Potenziale di produzione\n", + " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", + "\n", + " return df\n", + "\n", + "def add_advanced_seasonal_features(df):\n", + " # Differenza dalla durata media del giorno\n", + " avg_day_length = 12\n", + " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", + "\n", + " # Intensità stagionale\n", + " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", + "\n", + " # Indice di Stagionalità\n", + " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", + "\n", + " # Correzione per alba/tramonto\n", + " df['daylight_correction'] = np.where(\n", + " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", + " 0,\n", + " 1\n", + " )\n", + "\n", + " return df\n", + "\n", + "def add_basic_interactions(df):\n", + " \"\"\"\n", + " Aggiunge le interazioni base tra variabili meteorologiche\n", + " \"\"\"\n", + " # Feature esistenti originali\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # Clear sky e trasparenza atmosferica\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " return df\n", + "\n", + "def add_rolling_and_lag_features(df):\n", + " \"\"\"\n", + " Aggiunge feature rolling e lag\n", + " \"\"\"\n", + " # Rolling means esistenti\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features esistenti\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " return df\n", + "\n", + "def add_condition_indicators(df):\n", + " \"\"\"\n", + " Aggiunge indicatori di condizioni particolari\n", + " \"\"\"\n", + " # Extreme conditions indicator esistente\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " return df\n", + "\n", + "def add_physics_based_conversion_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", + " \"\"\"\n", + " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", + " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", + "\n", + " # Efficienza di conversione reale vs teorica\n", + " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " # Energia accumulata nel tempo (integrazione)\n", + " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", + "\n", + " # Differenza tra energia teorica e reale\n", + " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", + "\n", + " # Indice di performance del sistema\n", + " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " return df\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " \"\"\"\n", + " # Feature esistenti di base\n", + " # 1. Feature temporali di base\n", + " df = add_time_features(df)\n", + "\n", + " # 2. Feature solari e meteorologiche\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # 3. Feature atmosferiche e di diffusione\n", + " df = add_atmospheric_features(df)\n", + " df = add_diffusion_features(df)\n", + "\n", + " # 4. Feature di persistenza e pattern\n", + " df = add_persistence_features(df)\n", + " df = add_weather_pattern_features(df)\n", + "\n", + " # 5. Feature di efficienza e stagionalità\n", + " df = add_efficiency_features(df)\n", + " df = add_advanced_seasonal_features(df)\n", + "\n", + " # 6. Interazioni e feature derivate\n", + " df = add_basic_interactions(df)\n", + " df = add_rolling_and_lag_features(df)\n", + " df = add_condition_indicators(df)\n", + "\n", + " # 7. Nuove feature di conversione fisica\n", + " df = add_physics_based_conversion_features(df)\n", + "\n", + " # 8. One-hot encoding delle feature categoriche\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex',\n", + " 'cloudcover',\n", + " 'visibility',\n", + " 'temp',\n", + " 'pressure',\n", + " 'humidity',\n", + " 'solarradiation'\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation',\n", + " 'solar_angle',\n", + " 'day_length',\n", + " 'hour_sin',\n", + " 'hour_cos',\n", + " 'day_of_year_sin',\n", + " 'day_of_year_cos',\n", + " 'month_sin',\n", + " 'month_cos',\n", + " 'solar_noon',\n", + " 'daylight_correction'\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index',\n", + " 'atmospheric_attenuation',\n", + " 'theoretical_radiation',\n", + " 'expected_radiation',\n", + " 'cloud_elevation',\n", + " 'visibility_elevation',\n", + " 'uv_cloud_interaction',\n", + " 'temp_radiation_potential',\n", + " 'air_mass_index',\n", + " 'atmospheric_stability',\n", + " 'vapor_pressure_deficit',\n", + " 'diffusion_index',\n", + " 'atmospheric_transmittance',\n", + " 'temp_humidity_interaction',\n", + " 'clear_sky_factor'\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_12h',\n", + " 'uv_rolling_12h',\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " 'energy_rolling_mean_6h',\n", + " 'uv_rolling_mean_6h',\n", + " 'energy_volatility',\n", + " 'uv_volatility'\n", + " ],\n", + "\n", + " # Lag Features\n", + " 'lag_features': [\n", + " 'temp_1h_lag',\n", + " 'cloudcover_1h_lag',\n", + " 'humidity_1h_lag',\n", + " 'energy_lag_1h',\n", + " 'uv_lag_1h'\n", + " ],\n", + "\n", + " # Efficiency and Performance Features\n", + " 'efficiency_features': [\n", + " 'temp_losses',\n", + " 'soiling_loss_factor',\n", + " 'estimated_efficiency',\n", + " 'production_potential',\n", + " 'system_performance_ratio',\n", + " 'conversion_efficiency_ratio'\n", + " ],\n", + "\n", + " # Weather Pattern Features\n", + " 'weather_pattern_features': [\n", + " 'clear_sky_duration',\n", + " 'weather_variability_index',\n", + " 'temp_stability',\n", + " 'humidity_stability',\n", + " 'cloudcover_stability'\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring',\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning',\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night'\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " df[column] = df[column].interpolate(method='time')\n", + " else:\n", + " df[column] = df[column].interpolate(method='linear')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarenergy']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", + " from tensorflow import keras\n", + " from keras.models import Model\n", + " from keras.layers import (\n", + " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", + " MultiHeadAttention, LayerNormalization, Lambda,\n", + " Concatenate, Activation, Bidirectional, LSTM, Add\n", + " )\n", + " from keras.regularizers import l2\n", + " from keras.optimizers import AdamW\n", + " import tensorflow as tf\n", + " import numpy as np\n", + " import tensorflow_addons as tfa\n", + " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", + " \n", + " # Input layer\n", + " inputs = Input(shape=input_shape)\n", + " \n", + " # Feature groups definition\n", + " feature_dims = {\n", + " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", + " 'weather': [0, 1, 2, 3, 4, 5],\n", + " 'temporal': [10, 11, 12, 13, 14, 15],\n", + " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", + " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", + " 'lag': [40, 41, 42, 43, 44],\n", + " 'performance': [45, 46, 47, 48, 49, 50]\n", + " }\n", + " \n", + " # Feature extraction\n", + " feature_tensors = {}\n", + " for name, indices in feature_dims.items():\n", + " valid_indices = [i for i in indices if i < input_shape[-1]]\n", + " if valid_indices:\n", + " feature_tensors[name] = Lambda(\n", + " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", + " )(inputs)\n", + " \n", + " # Feature processing with residual connections\n", + " def process_feature_group(tensor, units, name):\n", + " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", + " kernel_regularizer=l2(l2_lambda))(tensor)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", + " x = Add()([x, residual])\n", + " x = LayerNormalization()(x)\n", + " \n", + " return x\n", + " \n", + " # Process each feature group\n", + " processed_features = {}\n", + " for name, tensor in feature_tensors.items():\n", + " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", + " processed_features[name] = process_feature_group(tensor, units, name)\n", + " \n", + " # Enhanced attention mechanism\n", + " def attention_block(x, num_heads=4):\n", + " attention_output = MultiHeadAttention(\n", + " num_heads=num_heads, \n", + " key_dim=x.shape[-1] // num_heads\n", + " )(x, x)\n", + " x = LayerNormalization()(x + attention_output)\n", + " \n", + " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", + " ffn = Dropout(0.1)(ffn)\n", + " ffn = Dense(x.shape[-1])(ffn)\n", + " \n", + " return LayerNormalization()(x + ffn)\n", + " \n", + " # Merge primary features with attention\n", + " primary_features = [\n", + " processed_features['solar'],\n", + " processed_features['weather'],\n", + " processed_features['performance']\n", + " ]\n", + " primary_context = Concatenate(axis=-1)(primary_features)\n", + " primary_context = attention_block(primary_context)\n", + " \n", + " # Merge secondary features\n", + " secondary_features = [\n", + " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", + " if name in processed_features\n", + " ]\n", + " if secondary_features:\n", + " secondary_context = Concatenate(axis=-1)(secondary_features)\n", + " secondary_context = attention_block(secondary_context)\n", + " else:\n", + " secondary_context = primary_context\n", + " \n", + " # Final feature merge\n", + " combined = Concatenate(axis=-1)([\n", + " primary_context, \n", + " secondary_context,\n", + " processed_features['derived']\n", + " ])\n", + " \n", + " # Sequential processing with residual LSTM\n", + " def residual_lstm_block(x, units):\n", + " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", + " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", + " x = Add()([lstm_out, residual])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + " \n", + " x = residual_lstm_block(combined, 128)\n", + " x = residual_lstm_block(x, 64)\n", + " x = Bidirectional(LSTM(64))(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " # Classification branch\n", + " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " class_x = BatchNormalization()(class_x)\n", + " class_x = Dropout(0.2)(class_x)\n", + " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", + " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", + " \n", + " # Enhanced regression branch with multiple pathways\n", + " def create_regression_pathway(x, name):\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + "\n", + " high_value_attention = Dense(128, activation='sigmoid')(x)\n", + " x = x * high_value_attention\n", + " \n", + " residual = x\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = Add()([x, residual])\n", + " \n", + " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " return Dense(1, name=f'{name}_output')(x)\n", + " \n", + " # Create specialized regression pathways\n", + " low_range = create_regression_pathway(x, 'low_range')\n", + " mid_range = create_regression_pathway(x, 'mid_range')\n", + " high_range = create_regression_pathway(x, 'high_range')\n", + " \n", + " # Create context vector for attention\n", + " context = Dense(64, activation='swish')(x)\n", + " \n", + " # Calculate attention scores\n", + " attention_scores = Dense(3, activation='softmax', \n", + " bias_initializer=tf.keras.initializers.Constant([0.2, 0.3, 0.5]))(context)\n", + " \n", + " # Combine predictions using attention weights\n", + " reg_output = Lambda(\n", + " lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n", + " name='regression_output'\n", + " )([attention_scores, low_range, mid_range, high_range])\n", + "\n", + " # Final output processing remains the same...\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dropout(0.2)(final_x)\n", + " \n", + " residual = final_x\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Add()([final_x, residual])\n", + " \n", + " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Dense(1)(final_x)\n", + " final_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='final_output'\n", + " )(final_x)\n", + " \n", + " # Build model with all outputs\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[class_output, reg_output, final_output]\n", + " )\n", + " \n", + " # Enhanced loss functions\n", + " def enhanced_regression_loss(y_true, y_pred):\n", + " mae = tf.abs(y_true - y_pred)\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " # Aumentiamo i pesi per i valori più alti\n", + " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 2.0 + \\\n", + " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.5 + \\\n", + " tf.cast(y_true <= 1.0, tf.float32)\n", + " \n", + " # Aggiungiamo un termine per penalizzare specificamente la sottostima\n", + " underestimation_penalty = tf.maximum(0.0, y_true - y_pred) * 0.3\n", + " \n", + " weighted_loss = (0.4 * mae + 0.4 * mse + 0.2 * underestimation_penalty) * value_ranges\n", + " return tf.reduce_mean(weighted_loss)\n", + " \n", + " def final_loss(y_true, y_pred):\n", + " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", + " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", + " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", + " return 0.5 * mae + 0.5 * mse\n", + " \n", + " # Learning rate schedule\n", + " clr = CosineDecayRestarts(\n", + " initial_learning_rate=2e-4,\n", + " first_decay_steps=1000,\n", + " t_mul=2.0,\n", + " m_mul=0.9,\n", + " alpha=1e-7\n", + " )\n", + " \n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=clr,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0\n", + " )\n", + " \n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': enhanced_regression_loss,\n", + " 'final_output': final_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.4,\n", + " 'final_output': 0.4\n", + " }\n", + " )\n", + "\n", + " # Plot model architecture\n", + " try:\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + " except Exception as e:\n", + " print(f\"Warning: Could not plot model architecture: {e}\")\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar energy values (kWh)\n", + " y_pred : array-like\n", + " Predicted solar energy values (kWh)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 kWh\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", + "\n", + " # Energy level classification\n", + " def get_energy_level(value):\n", + " if value <= 0.5:\n", + " return 'Very Low'\n", + " elif value <= 2.0:\n", + " return 'Low'\n", + " elif value <= 4.0:\n", + " return 'Moderate'\n", + " elif value <= 6.0:\n", + " return 'High'\n", + " elif value <= 8.0:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate energy levels\n", + " y_true_levels = [get_energy_level(v) for v in y_true]\n", + " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Energy Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} kWh\")\n", + " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for energy levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", + " print(\"\\nConfusion Matrix for Energy Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=unique_levels,\n", + " index=unique_levels\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Predicted Energy (kWh)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (kWh)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 0.5\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by Energy level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Energy Level')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_energy_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Calculates comprehensive metrics for the solar energy prediction model.\n", + " \n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Ground truth values\n", + " y_class : array-like\n", + " Classification predictions (probability of non-zero values)\n", + " y_reg : array-like\n", + " Regression predictions (unrestricted values)\n", + " y_final : array-like\n", + " Final clipped predictions\n", + " min_output : float\n", + " Minimum allowed output value\n", + " max_output : float\n", + " Maximum allowed output value\n", + " \n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " # Ensure proper array formatting and dimensionality\n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Validate input dimensions\n", + " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", + " \"All input arrays must have the same length\"\n", + " \n", + " # Classification metrics with error handling\n", + " print(\"\\nClassification Metrics:\")\n", + " try:\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", + " print(conf_matrix)\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " class_report = classification_report(\n", + " y_true_binary, \n", + " y_pred_binary,\n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4\n", + " )\n", + " print(class_report)\n", + " except Exception as e:\n", + " print(f\"Error in classification metrics calculation: {str(e)}\")\n", + " \n", + " # Regression metrics with error handling\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " try:\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " # Range validation\n", + " out_of_range = np.sum(\n", + " (y_reg_nonzero < min_output) | \n", + " (y_reg_nonzero > max_output)\n", + " )\n", + " \n", + " # Error metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", + " (y_true_nonzero + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " # Calculate metrics\n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in regression metrics calculation: {str(e)}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final output metrics with error handling\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " try:\n", + " # Ensure outputs are within bounds\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " \n", + " # Calculate metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in final output metrics calculation: {str(e)}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar energy model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " def evaluate_epoch(epoch, logs):\n", + " if epoch % 20 == 0:\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=35,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-5\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", + " tf.keras.callbacks.TerminateOnNaN()\n", + " ]\n", + "\n", + " '''\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.8,\n", + " patience=10,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " '''\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar energy predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarenergy_predicted': final_pred.flatten(),\n", + " 'solarenergy_classification': classification_pred.flatten(),\n", + " 'solarenergy_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar energy column where missing\n", + " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", + " 'solarenergy_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar energy model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 66\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (112882, 24, 66)\n", + "Test data shape: (16849, 24, 66)\n", + "Saving scaler X to: 2024-11-27_21-08_scale_X.joblib\n", + "Saving scaler X to: 2024-11-27_21-08_scale_y.joblib\n", + "Saving features to: 2024-11-27_21-08_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "print(\"Initializing solar energy model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", + "\n", + "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", + "Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 21:10:01.813937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:c1:00.0, compute capability: 8.9\n", + "2024-11-27 21:10:03.248774: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Class distribution in training set:\n", + "Zeros: 56899 (50.41%)\n", + "Non-zeros: 55983 (49.59%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 8576 (50.90%)\n", + "Non-zeros: 8273 (49.10%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 21:10:32.191900: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-27 21:10:32.298794: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-27 21:10:34.451815: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x788398f56dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-27 21:10:34.451844: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-27 21:10:34.457783: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-27 21:10:34.617898: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "221/221 [==============================] - ETA: 0s - loss: 9.8910 - classification_output_loss: 0.2126 - regression_output_loss: 0.2540 - final_output_loss: 0.2462\n", + "Epoch 1 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 95.32%\n", + "AUC-ROC: 0.9922\n", + "\n", + "Confusion Matrix:\n", + "[[8143 433]\n", + " [ 356 7917]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9581 0.9495 0.9538 8576\n", + " Non-Zero 0.9481 0.9570 0.9525 8273\n", + "\n", + " accuracy 0.9532 16849\n", + " macro avg 0.9531 0.9532 0.9532 16849\n", + "weighted avg 0.9532 0.9532 0.9532 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 254 predictions\n", + "MAPE: 56.51%\n", + "Within ±10%: 2.84%\n", + "MAE: 0.66\n", + "RMSE: 0.87\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 26.05%\n", + "Within ±2%: 50.80%\n", + "Within ±5%: 51.52%\n", + "Within ±10%: 52.70%\n", + "Within ±20%: 55.44%\n", + "MAE: 0.26\n", + "RMSE: 0.50\n", + "221/221 [==============================] - 66s 118ms/step - loss: 9.8910 - classification_output_loss: 0.2126 - regression_output_loss: 0.2540 - final_output_loss: 0.2462 - val_loss: 7.4260 - val_classification_output_loss: 0.2758 - val_regression_output_loss: 0.5297 - val_final_output_loss: 0.2561\n", + "Epoch 2/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 5.5438 - classification_output_loss: 0.1039 - regression_output_loss: 0.1222 - final_output_loss: 0.1019 - val_loss: 4.4098 - val_classification_output_loss: 0.1265 - val_regression_output_loss: 0.2653 - val_final_output_loss: 0.1421\n", + "Epoch 3/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 3.6318 - classification_output_loss: 0.0805 - regression_output_loss: 0.0784 - final_output_loss: 0.0640 - val_loss: 3.1895 - val_classification_output_loss: 0.0942 - val_regression_output_loss: 0.1152 - val_final_output_loss: 0.0740\n", + "Epoch 4/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 2.9613 - classification_output_loss: 0.0749 - regression_output_loss: 0.0657 - final_output_loss: 0.0525 - val_loss: 2.8679 - val_classification_output_loss: 0.0888 - val_regression_output_loss: 0.0849 - val_final_output_loss: 0.0594\n", + "Epoch 5/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 2.6830 - classification_output_loss: 0.0980 - regression_output_loss: 0.1210 - final_output_loss: 0.1113 - val_loss: 2.0443 - val_classification_output_loss: 0.1070 - val_regression_output_loss: 0.0857 - val_final_output_loss: 0.0795\n", + "Epoch 6/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 1.5181 - classification_output_loss: 0.0801 - regression_output_loss: 0.0955 - final_output_loss: 0.0794 - val_loss: 1.0910 - val_classification_output_loss: 0.0828 - val_regression_output_loss: 0.0707 - val_final_output_loss: 0.0415\n", + "Epoch 7/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.8616 - classification_output_loss: 0.0660 - regression_output_loss: 0.0747 - final_output_loss: 0.0623 - val_loss: 0.6750 - val_classification_output_loss: 0.0770 - val_regression_output_loss: 0.0747 - val_final_output_loss: 0.0501\n", + "Epoch 8/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.5565 - classification_output_loss: 0.0590 - regression_output_loss: 0.0660 - final_output_loss: 0.0551 - val_loss: 0.4601 - val_classification_output_loss: 0.0789 - val_regression_output_loss: 0.0524 - val_final_output_loss: 0.0450\n", + "Epoch 9/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.3984 - classification_output_loss: 0.0550 - regression_output_loss: 0.0527 - final_output_loss: 0.0459 - val_loss: 0.3471 - val_classification_output_loss: 0.0811 - val_regression_output_loss: 0.0377 - val_final_output_loss: 0.0388\n", + "Epoch 10/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.3124 - classification_output_loss: 0.0515 - regression_output_loss: 0.0426 - final_output_loss: 0.0362 - val_loss: 0.2867 - val_classification_output_loss: 0.0779 - val_regression_output_loss: 0.0344 - val_final_output_loss: 0.0309\n", + "Epoch 11/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.2684 - classification_output_loss: 0.0505 - regression_output_loss: 0.0387 - final_output_loss: 0.0341 - val_loss: 0.2611 - val_classification_output_loss: 0.0774 - val_regression_output_loss: 0.0425 - val_final_output_loss: 0.0295\n", + "Epoch 12/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.2453 - classification_output_loss: 0.0511 - regression_output_loss: 0.0351 - final_output_loss: 0.0309 - val_loss: 0.2517 - val_classification_output_loss: 0.0762 - val_regression_output_loss: 0.0492 - val_final_output_loss: 0.0317\n", + "Epoch 13/150\n", + "221/221 [==============================] - 16s 72ms/step - loss: 0.2371 - classification_output_loss: 0.0527 - regression_output_loss: 0.0345 - final_output_loss: 0.0305 - val_loss: 0.2434 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0442 - val_final_output_loss: 0.0288\n", + "Epoch 14/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.2522 - classification_output_loss: 0.0646 - regression_output_loss: 0.0616 - final_output_loss: 0.0581 - val_loss: 0.2371 - val_classification_output_loss: 0.0925 - val_regression_output_loss: 0.0673 - val_final_output_loss: 0.0559\n", + "Epoch 15/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.2149 - classification_output_loss: 0.0641 - regression_output_loss: 0.0808 - final_output_loss: 0.0715 - val_loss: 0.1643 - val_classification_output_loss: 0.0859 - val_regression_output_loss: 0.0367 - val_final_output_loss: 0.0422\n", + "Epoch 16/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.1577 - classification_output_loss: 0.0550 - regression_output_loss: 0.0564 - final_output_loss: 0.0577 - val_loss: 0.1408 - val_classification_output_loss: 0.0782 - val_regression_output_loss: 0.0429 - val_final_output_loss: 0.0535\n", + "Epoch 17/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.1291 - classification_output_loss: 0.0543 - regression_output_loss: 0.0494 - final_output_loss: 0.0513 - val_loss: 0.1191 - val_classification_output_loss: 0.0814 - val_regression_output_loss: 0.0446 - val_final_output_loss: 0.0376\n", + "Epoch 18/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.1125 - classification_output_loss: 0.0515 - regression_output_loss: 0.0482 - final_output_loss: 0.0502 - val_loss: 0.0997 - val_classification_output_loss: 0.0746 - val_regression_output_loss: 0.0386 - val_final_output_loss: 0.0277\n", + "Epoch 19/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.1009 - classification_output_loss: 0.0477 - regression_output_loss: 0.0482 - final_output_loss: 0.0463 - val_loss: 0.0908 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0392 - val_final_output_loss: 0.0290\n", + "Epoch 20/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0917 - classification_output_loss: 0.0479 - regression_output_loss: 0.0449 - final_output_loss: 0.0450 - val_loss: 0.0862 - val_classification_output_loss: 0.0749 - val_regression_output_loss: 0.0395 - val_final_output_loss: 0.0288\n", + "Epoch 21/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0817 - classification_output_loss: 0.0455 - regression_output_loss: 0.0404 - final_output_loss: 0.0391\n", + "Epoch 21 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.19%\n", + "AUC-ROC: 0.9972\n", + "\n", + "Confusion Matrix:\n", + "[[8267 309]\n", + " [ 165 8108]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9804 0.9640 0.9721 8576\n", + " Non-Zero 0.9633 0.9801 0.9716 8273\n", + "\n", + " accuracy 0.9719 16849\n", + " macro avg 0.9719 0.9720 0.9719 16849\n", + "weighted avg 0.9720 0.9719 0.9719 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 15.37%\n", + "Within ±10%: 54.25%\n", + "MAE: 0.09\n", + "RMSE: 0.12\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 12.10%\n", + "Within ±2%: 56.40%\n", + "Within ±5%: 64.44%\n", + "Within ±10%: 74.69%\n", + "Within ±20%: 86.26%\n", + "MAE: 0.06\n", + "RMSE: 0.10\n", + "221/221 [==============================] - 18s 82ms/step - loss: 0.0817 - classification_output_loss: 0.0455 - regression_output_loss: 0.0404 - final_output_loss: 0.0391 - val_loss: 0.0788 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0328 - val_final_output_loss: 0.0330\n", + "Epoch 22/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0735 - classification_output_loss: 0.0416 - regression_output_loss: 0.0358 - final_output_loss: 0.0353 - val_loss: 0.0816 - val_classification_output_loss: 0.0700 - val_regression_output_loss: 0.0360 - val_final_output_loss: 0.0482\n", + "Epoch 23/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0671 - classification_output_loss: 0.0405 - regression_output_loss: 0.0324 - final_output_loss: 0.0304 - val_loss: 0.0769 - val_classification_output_loss: 0.0669 - val_regression_output_loss: 0.0366 - val_final_output_loss: 0.0434\n", + "Epoch 24/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0625 - classification_output_loss: 0.0384 - regression_output_loss: 0.0299 - final_output_loss: 0.0290 - val_loss: 0.0634 - val_classification_output_loss: 0.0635 - val_regression_output_loss: 0.0243 - val_final_output_loss: 0.0282\n", + "Epoch 25/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0598 - classification_output_loss: 0.0376 - regression_output_loss: 0.0286 - final_output_loss: 0.0286 - val_loss: 0.0611 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0279\n", + "Epoch 26/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0575 - classification_output_loss: 0.0372 - regression_output_loss: 0.0278 - final_output_loss: 0.0272 - val_loss: 0.0580 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0231 - val_final_output_loss: 0.0214\n", + "Epoch 27/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0550 - classification_output_loss: 0.0372 - regression_output_loss: 0.0259 - final_output_loss: 0.0253 - val_loss: 0.0571 - val_classification_output_loss: 0.0634 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0183\n", + "Epoch 28/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0537 - classification_output_loss: 0.0362 - regression_output_loss: 0.0255 - final_output_loss: 0.0243 - val_loss: 0.0586 - val_classification_output_loss: 0.0624 - val_regression_output_loss: 0.0291 - val_final_output_loss: 0.0193\n", + "Epoch 29/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0531 - classification_output_loss: 0.0366 - regression_output_loss: 0.0253 - final_output_loss: 0.0240 - val_loss: 0.0584 - val_classification_output_loss: 0.0612 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0207\n", + "Epoch 30/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0531 - classification_output_loss: 0.0372 - regression_output_loss: 0.0255 - final_output_loss: 0.0242 - val_loss: 0.0560 - val_classification_output_loss: 0.0585 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0197\n", + "Epoch 31/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0540 - classification_output_loss: 0.0383 - regression_output_loss: 0.0268 - final_output_loss: 0.0246 - val_loss: 0.0532 - val_classification_output_loss: 0.0567 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0191\n", + "Epoch 32/150\n", + "221/221 [==============================] - 15s 67ms/step - loss: 0.0749 - classification_output_loss: 0.0499 - regression_output_loss: 0.0496 - final_output_loss: 0.0445 - val_loss: 0.1607 - val_classification_output_loss: 0.1001 - val_regression_output_loss: 0.0736 - val_final_output_loss: 0.1997\n", + "Epoch 33/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0973 - classification_output_loss: 0.0590 - regression_output_loss: 0.0656 - final_output_loss: 0.0644 - val_loss: 0.0712 - val_classification_output_loss: 0.0623 - val_regression_output_loss: 0.0377 - val_final_output_loss: 0.0417\n", + "Epoch 34/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0738 - classification_output_loss: 0.0466 - regression_output_loss: 0.0489 - final_output_loss: 0.0476 - val_loss: 0.0797 - val_classification_output_loss: 0.0901 - val_regression_output_loss: 0.0561 - val_final_output_loss: 0.0314\n", + "Epoch 35/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0667 - classification_output_loss: 0.0457 - regression_output_loss: 0.0417 - final_output_loss: 0.0450 - val_loss: 0.0609 - val_classification_output_loss: 0.0789 - val_regression_output_loss: 0.0319 - val_final_output_loss: 0.0248\n", + "Epoch 36/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0641 - classification_output_loss: 0.0440 - regression_output_loss: 0.0410 - final_output_loss: 0.0432 - val_loss: 0.0593 - val_classification_output_loss: 0.0600 - val_regression_output_loss: 0.0364 - val_final_output_loss: 0.0304\n", + "Epoch 37/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0605 - classification_output_loss: 0.0402 - regression_output_loss: 0.0382 - final_output_loss: 0.0434 - val_loss: 0.0544 - val_classification_output_loss: 0.0662 - val_regression_output_loss: 0.0297 - val_final_output_loss: 0.0239\n", + "Epoch 38/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0568 - classification_output_loss: 0.0400 - regression_output_loss: 0.0357 - final_output_loss: 0.0397 - val_loss: 0.0514 - val_classification_output_loss: 0.0625 - val_regression_output_loss: 0.0275 - val_final_output_loss: 0.0244\n", + "Epoch 39/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0553 - classification_output_loss: 0.0391 - regression_output_loss: 0.0359 - final_output_loss: 0.0379 - val_loss: 0.0512 - val_classification_output_loss: 0.0554 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0281\n", + "Epoch 40/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0545 - classification_output_loss: 0.0368 - regression_output_loss: 0.0363 - final_output_loss: 0.0394 - val_loss: 0.0502 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0272 - val_final_output_loss: 0.0280\n", + "Epoch 41/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0531 - classification_output_loss: 0.0383 - regression_output_loss: 0.0354 - final_output_loss: 0.0374\n", + "Epoch 41 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.16%\n", + "AUC-ROC: 0.9981\n", + "\n", + "Confusion Matrix:\n", + "[[8170 406]\n", + " [ 72 8201]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9913 0.9527 0.9716 8576\n", + " Non-Zero 0.9528 0.9913 0.9717 8273\n", + "\n", + " accuracy 0.9716 16849\n", + " macro avg 0.9720 0.9720 0.9716 16849\n", + "weighted avg 0.9724 0.9716 0.9716 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 15.53%\n", + "Within ±10%: 52.10%\n", + "MAE: 0.11\n", + "RMSE: 0.15\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 11.09%\n", + "Within ±2%: 54.76%\n", + "Within ±5%: 61.83%\n", + "Within ±10%: 73.46%\n", + "Within ±20%: 86.85%\n", + "MAE: 0.06\n", + "RMSE: 0.12\n", + "221/221 [==============================] - 20s 93ms/step - loss: 0.0531 - classification_output_loss: 0.0383 - regression_output_loss: 0.0354 - final_output_loss: 0.0374 - val_loss: 0.0628 - val_classification_output_loss: 0.0729 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0382\n", + "Epoch 42/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0510 - classification_output_loss: 0.0360 - regression_output_loss: 0.0341 - final_output_loss: 0.0371 - val_loss: 0.0813 - val_classification_output_loss: 0.0869 - val_regression_output_loss: 0.0547 - val_final_output_loss: 0.0684\n", + "Epoch 43/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0471 - classification_output_loss: 0.0342 - regression_output_loss: 0.0303 - final_output_loss: 0.0331 - val_loss: 0.0712 - val_classification_output_loss: 0.0698 - val_regression_output_loss: 0.0475 - val_final_output_loss: 0.0620\n", + "Epoch 44/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0450 - classification_output_loss: 0.0357 - regression_output_loss: 0.0286 - final_output_loss: 0.0312 - val_loss: 0.0759 - val_classification_output_loss: 0.0638 - val_regression_output_loss: 0.0598 - val_final_output_loss: 0.0638\n", + "Epoch 45/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0428 - classification_output_loss: 0.0342 - regression_output_loss: 0.0271 - final_output_loss: 0.0297 - val_loss: 0.0562 - val_classification_output_loss: 0.0535 - val_regression_output_loss: 0.0389 - val_final_output_loss: 0.0422\n", + "Epoch 46/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0433 - classification_output_loss: 0.0321 - regression_output_loss: 0.0281 - final_output_loss: 0.0324 - val_loss: 0.0522 - val_classification_output_loss: 0.0545 - val_regression_output_loss: 0.0308 - val_final_output_loss: 0.0420\n", + "Epoch 47/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0379 - classification_output_loss: 0.0302 - regression_output_loss: 0.0238 - final_output_loss: 0.0248 - val_loss: 0.0420 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0272\n", + "Epoch 48/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0416 - classification_output_loss: 0.0301 - regression_output_loss: 0.0277 - final_output_loss: 0.0318 - val_loss: 0.0371 - val_classification_output_loss: 0.0525 - val_regression_output_loss: 0.0195 - val_final_output_loss: 0.0173\n", + "Epoch 49/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0393 - classification_output_loss: 0.0289 - regression_output_loss: 0.0266 - final_output_loss: 0.0279 - val_loss: 0.0393 - val_classification_output_loss: 0.0540 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0210\n", + "Epoch 50/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0358 - classification_output_loss: 0.0273 - regression_output_loss: 0.0230 - final_output_loss: 0.0251 - val_loss: 0.0379 - val_classification_output_loss: 0.0504 - val_regression_output_loss: 0.0200 - val_final_output_loss: 0.0230\n", + "Epoch 51/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0336 - classification_output_loss: 0.0270 - regression_output_loss: 0.0211 - final_output_loss: 0.0225 - val_loss: 0.0357 - val_classification_output_loss: 0.0516 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0177\n", + "Epoch 52/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0346 - classification_output_loss: 0.0268 - regression_output_loss: 0.0224 - final_output_loss: 0.0249 - val_loss: 0.0362 - val_classification_output_loss: 0.0489 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0191\n", + "Epoch 53/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0323 - classification_output_loss: 0.0252 - regression_output_loss: 0.0205 - final_output_loss: 0.0224 - val_loss: 0.0373 - val_classification_output_loss: 0.0466 - val_regression_output_loss: 0.0209 - val_final_output_loss: 0.0245\n", + "Epoch 54/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0319 - classification_output_loss: 0.0243 - regression_output_loss: 0.0204 - final_output_loss: 0.0226 - val_loss: 0.0378 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0288\n", + "Epoch 55/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0303 - classification_output_loss: 0.0241 - regression_output_loss: 0.0189 - final_output_loss: 0.0206 - val_loss: 0.0362 - val_classification_output_loss: 0.0470 - val_regression_output_loss: 0.0193 - val_final_output_loss: 0.0250\n", + "Epoch 56/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0299 - classification_output_loss: 0.0231 - regression_output_loss: 0.0191 - final_output_loss: 0.0203 - val_loss: 0.0341 - val_classification_output_loss: 0.0454 - val_regression_output_loss: 0.0180 - val_final_output_loss: 0.0220\n", + "Epoch 57/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0295 - classification_output_loss: 0.0230 - regression_output_loss: 0.0187 - final_output_loss: 0.0204 - val_loss: 0.0323 - val_classification_output_loss: 0.0449 - val_regression_output_loss: 0.0175 - val_final_output_loss: 0.0178\n", + "Epoch 58/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0294 - classification_output_loss: 0.0228 - regression_output_loss: 0.0190 - final_output_loss: 0.0201 - val_loss: 0.0315 - val_classification_output_loss: 0.0443 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0148\n", + "Epoch 59/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0291 - classification_output_loss: 0.0223 - regression_output_loss: 0.0190 - final_output_loss: 0.0199 - val_loss: 0.0314 - val_classification_output_loss: 0.0421 - val_regression_output_loss: 0.0194 - val_final_output_loss: 0.0145\n", + "Epoch 60/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0289 - classification_output_loss: 0.0215 - regression_output_loss: 0.0190 - final_output_loss: 0.0198 - val_loss: 0.0314 - val_classification_output_loss: 0.0406 - val_regression_output_loss: 0.0198 - val_final_output_loss: 0.0150\n", + "Epoch 61/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0286 - classification_output_loss: 0.0215 - regression_output_loss: 0.0188 - final_output_loss: 0.0195\n", + "Epoch 61 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.47%\n", + "AUC-ROC: 0.9991\n", + "\n", + "Confusion Matrix:\n", + "[[8422 154]\n", + " [ 104 8169]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9878 0.9820 0.9849 8576\n", + " Non-Zero 0.9815 0.9874 0.9845 8273\n", + "\n", + " accuracy 0.9847 16849\n", + " macro avg 0.9846 0.9847 0.9847 16849\n", + "weighted avg 0.9847 0.9847 0.9847 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 10.90%\n", + "Within ±10%: 75.56%\n", + "MAE: 0.06\n", + "RMSE: 0.08\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.64%\n", + "Within ±2%: 61.83%\n", + "Within ±5%: 75.90%\n", + "Within ±10%: 86.14%\n", + "Within ±20%: 91.10%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 20s 89ms/step - loss: 0.0286 - classification_output_loss: 0.0215 - regression_output_loss: 0.0188 - final_output_loss: 0.0195 - val_loss: 0.0319 - val_classification_output_loss: 0.0408 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0160\n", + "Epoch 62/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0285 - classification_output_loss: 0.0218 - regression_output_loss: 0.0187 - final_output_loss: 0.0194 - val_loss: 0.0318 - val_classification_output_loss: 0.0398 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0168\n", + "Epoch 63/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0283 - classification_output_loss: 0.0212 - regression_output_loss: 0.0187 - final_output_loss: 0.0193 - val_loss: 0.0311 - val_classification_output_loss: 0.0394 - val_regression_output_loss: 0.0189 - val_final_output_loss: 0.0166\n", + "Epoch 64/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0284 - classification_output_loss: 0.0216 - regression_output_loss: 0.0187 - final_output_loss: 0.0193 - val_loss: 0.0301 - val_classification_output_loss: 0.0386 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0156\n", + "Epoch 65/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0285 - classification_output_loss: 0.0215 - regression_output_loss: 0.0190 - final_output_loss: 0.0194 - val_loss: 0.0294 - val_classification_output_loss: 0.0384 - val_regression_output_loss: 0.0167 - val_final_output_loss: 0.0152\n", + "Epoch 66/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0291 - classification_output_loss: 0.0220 - regression_output_loss: 0.0198 - final_output_loss: 0.0198 - val_loss: 0.0287 - val_classification_output_loss: 0.0382 - val_regression_output_loss: 0.0157 - val_final_output_loss: 0.0147\n", + "Epoch 67/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0294 - classification_output_loss: 0.0210 - regression_output_loss: 0.0203 - final_output_loss: 0.0206 - val_loss: 0.0298 - val_classification_output_loss: 0.0383 - val_regression_output_loss: 0.0166 - val_final_output_loss: 0.0165\n", + "Epoch 68/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0292 - classification_output_loss: 0.0215 - regression_output_loss: 0.0197 - final_output_loss: 0.0205 - val_loss: 0.0533 - val_classification_output_loss: 0.0485 - val_regression_output_loss: 0.0295 - val_final_output_loss: 0.0615\n", + "Epoch 69/150\n", + "221/221 [==============================] - 15s 69ms/step - loss: 0.0710 - classification_output_loss: 0.0471 - regression_output_loss: 0.0555 - final_output_loss: 0.0583 - val_loss: 0.0680 - val_classification_output_loss: 0.0545 - val_regression_output_loss: 0.0634 - val_final_output_loss: 0.0480\n", + "Epoch 70/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0486 - classification_output_loss: 0.0344 - regression_output_loss: 0.0357 - final_output_loss: 0.0410 - val_loss: 0.0567 - val_classification_output_loss: 0.0625 - val_regression_output_loss: 0.0441 - val_final_output_loss: 0.0364\n", + "Epoch 71/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0421 - classification_output_loss: 0.0306 - regression_output_loss: 0.0299 - final_output_loss: 0.0348 - val_loss: 0.0354 - val_classification_output_loss: 0.0437 - val_regression_output_loss: 0.0220 - val_final_output_loss: 0.0191\n", + "Epoch 72/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0435 - classification_output_loss: 0.0292 - regression_output_loss: 0.0323 - final_output_loss: 0.0362 - val_loss: 0.0408 - val_classification_output_loss: 0.0599 - val_regression_output_loss: 0.0255 - val_final_output_loss: 0.0216\n", + "Epoch 73/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0400 - classification_output_loss: 0.0282 - regression_output_loss: 0.0288 - final_output_loss: 0.0327 - val_loss: 0.0370 - val_classification_output_loss: 0.0472 - val_regression_output_loss: 0.0226 - val_final_output_loss: 0.0226\n", + "Epoch 74/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0380 - classification_output_loss: 0.0259 - regression_output_loss: 0.0276 - final_output_loss: 0.0313 - val_loss: 0.0367 - val_classification_output_loss: 0.0419 - val_regression_output_loss: 0.0256 - val_final_output_loss: 0.0209\n", + "Epoch 75/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0402 - classification_output_loss: 0.0304 - regression_output_loss: 0.0280 - final_output_loss: 0.0354 - val_loss: 0.0743 - val_classification_output_loss: 0.0526 - val_regression_output_loss: 0.0704 - val_final_output_loss: 0.0586\n", + "Epoch 76/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0383 - classification_output_loss: 0.0291 - regression_output_loss: 0.0271 - final_output_loss: 0.0307 - val_loss: 0.0345 - val_classification_output_loss: 0.0395 - val_regression_output_loss: 0.0229 - val_final_output_loss: 0.0203\n", + "Epoch 77/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0376 - classification_output_loss: 0.0269 - regression_output_loss: 0.0269 - final_output_loss: 0.0311 - val_loss: 0.0397 - val_classification_output_loss: 0.0391 - val_regression_output_loss: 0.0284 - val_final_output_loss: 0.0288\n", + "Epoch 78/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0406 - classification_output_loss: 0.0314 - regression_output_loss: 0.0305 - final_output_loss: 0.0321 - val_loss: 0.0363 - val_classification_output_loss: 0.0440 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0245\n", + "Epoch 79/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0345 - classification_output_loss: 0.0234 - regression_output_loss: 0.0241 - final_output_loss: 0.0291 - val_loss: 0.0360 - val_classification_output_loss: 0.0415 - val_regression_output_loss: 0.0232 - val_final_output_loss: 0.0255\n", + "Epoch 80/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0384 - classification_output_loss: 0.0242 - regression_output_loss: 0.0280 - final_output_loss: 0.0336 - val_loss: 0.0311 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0170\n", + "Epoch 81/150\n", + "220/221 [============================>.] - ETA: 0s - loss: 0.0370 - classification_output_loss: 0.0254 - regression_output_loss: 0.0275 - final_output_loss: 0.0306\n", + "Epoch 81 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.21%\n", + "AUC-ROC: 0.9992\n", + "\n", + "Confusion Matrix:\n", + "[[8336 240]\n", + " [ 62 8211]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9926 0.9720 0.9822 8576\n", + " Non-Zero 0.9716 0.9925 0.9819 8273\n", + "\n", + " accuracy 0.9821 16849\n", + " macro avg 0.9821 0.9823 0.9821 16849\n", + "weighted avg 0.9823 0.9821 0.9821 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 13.51%\n", + "Within ±10%: 63.69%\n", + "MAE: 0.08\n", + "RMSE: 0.11\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 9.85%\n", + "Within ±2%: 55.76%\n", + "Within ±5%: 64.52%\n", + "Within ±10%: 78.28%\n", + "Within ±20%: 89.15%\n", + "MAE: 0.05\n", + "RMSE: 0.09\n", + "221/221 [==============================] - 19s 88ms/step - loss: 0.0370 - classification_output_loss: 0.0255 - regression_output_loss: 0.0275 - final_output_loss: 0.0306 - val_loss: 0.0402 - val_classification_output_loss: 0.0444 - val_regression_output_loss: 0.0279 - val_final_output_loss: 0.0278\n", + "Epoch 82/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0354 - classification_output_loss: 0.0252 - regression_output_loss: 0.0253 - final_output_loss: 0.0298 - val_loss: 0.0540 - val_classification_output_loss: 0.0589 - val_regression_output_loss: 0.0413 - val_final_output_loss: 0.0413\n", + "Epoch 83/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0334 - classification_output_loss: 0.0233 - regression_output_loss: 0.0236 - final_output_loss: 0.0282 - val_loss: 0.0580 - val_classification_output_loss: 0.0433 - val_regression_output_loss: 0.0434 - val_final_output_loss: 0.0611\n", + "Epoch 84/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0347 - classification_output_loss: 0.0248 - regression_output_loss: 0.0246 - final_output_loss: 0.0291 - val_loss: 0.0545 - val_classification_output_loss: 0.0436 - val_regression_output_loss: 0.0441 - val_final_output_loss: 0.0502\n", + "Epoch 85/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0343 - classification_output_loss: 0.0215 - regression_output_loss: 0.0257 - final_output_loss: 0.0286 - val_loss: 0.0355 - val_classification_output_loss: 0.0364 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0287\n", + "Epoch 86/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0329 - classification_output_loss: 0.0255 - regression_output_loss: 0.0238 - final_output_loss: 0.0262 - val_loss: 0.0344 - val_classification_output_loss: 0.0436 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0222\n", + "Epoch 87/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0337 - classification_output_loss: 0.0200 - regression_output_loss: 0.0247 - final_output_loss: 0.0293 - val_loss: 0.0319 - val_classification_output_loss: 0.0355 - val_regression_output_loss: 0.0233 - val_final_output_loss: 0.0180\n", + "Epoch 88/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0324 - classification_output_loss: 0.0207 - regression_output_loss: 0.0244 - final_output_loss: 0.0264 - val_loss: 0.0315 - val_classification_output_loss: 0.0424 - val_regression_output_loss: 0.0198 - val_final_output_loss: 0.0185\n", + "Epoch 89/150\n", + "221/221 [==============================] - 12s 55ms/step - loss: 0.0318 - classification_output_loss: 0.0229 - regression_output_loss: 0.0221 - final_output_loss: 0.0272 - val_loss: 0.0302 - val_classification_output_loss: 0.0393 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0170\n", + "Epoch 90/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0317 - classification_output_loss: 0.0227 - regression_output_loss: 0.0217 - final_output_loss: 0.0280 - val_loss: 0.0327 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0184\n", + "Epoch 91/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0311 - classification_output_loss: 0.0193 - regression_output_loss: 0.0234 - final_output_loss: 0.0260 - val_loss: 0.0487 - val_classification_output_loss: 0.0357 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0414\n", + "Epoch 92/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0302 - classification_output_loss: 0.0207 - regression_output_loss: 0.0215 - final_output_loss: 0.0256 - val_loss: 0.0466 - val_classification_output_loss: 0.0446 - val_regression_output_loss: 0.0367 - val_final_output_loss: 0.0396\n", + "Epoch 93/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0300 - classification_output_loss: 0.0211 - regression_output_loss: 0.0223 - final_output_loss: 0.0239 - val_loss: 0.0289 - val_classification_output_loss: 0.0364 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0174\n", + "Epoch 94/150\n", + "220/221 [============================>.] - ETA: 0s - loss: 0.0281 - classification_output_loss: 0.0154 - regression_output_loss: 0.0205 - final_output_loss: 0.0246Restoring model weights from the end of the best epoch: 59.\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0281 - classification_output_loss: 0.0154 - regression_output_loss: 0.0205 - final_output_loss: 0.0246 - val_loss: 0.0277 - val_classification_output_loss: 0.0366 - val_regression_output_loss: 0.0174 - val_final_output_loss: 0.0154\n", + "Epoch 94: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.39%\n", + "AUC-ROC: 0.9990\n", + "\n", + "Confusion Matrix:\n", + "[[8434 142]\n", + " [ 129 8144]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9849 0.9834 0.9842 8576\n", + " Non-Zero 0.9829 0.9844 0.9836 8273\n", + "\n", + " accuracy 0.9839 16849\n", + " macro avg 0.9839 0.9839 0.9839 16849\n", + "weighted avg 0.9839 0.9839 0.9839 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 1 predictions\n", + "MAPE: 11.01%\n", + "Within ±10%: 74.37%\n", + "MAE: 0.05\n", + "RMSE: 0.08\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.37%\n", + "Within ±2%: 63.64%\n", + "Within ±5%: 77.48%\n", + "Within ±10%: 86.83%\n", + "Within ±20%: 91.82%\n", + "MAE: 0.03\n", + "RMSE: 0.05\n" + ] + } + ], + "source": [ + "#Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "min_val = df['solarenergy'].min()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "\n", + "max_val = df['solarenergy'].max()\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", + "\n", + "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 8\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarenergy_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=150,\n", + " batch_size=512,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "527/527 [==============================] - 6s 11ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Energy Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 0.03 kWh\n", + "RMSE: 0.06 kWh\n", + "R² Score: 0.995\n", + "MAPE: N/A (insufficient data)\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 kWh: 100.0%\n", + "Within ±10 kWh: 100.0%\n", + "Within ±20 kWh: 100.0%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 98.1%\n", + "\n", + "Confusion Matrix for Energy Levels:\n", + " Low Moderate Very Low\n", + "Low 3540 132 1\n", + "Moderate 13 2095 0\n", + "Very Low 169 0 10899\n", + "\n", + "Plot saved as: 2024-11-27_21-08_energy_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: -0.006\n", + "Error standard deviation: 0.064\n", + "Median error: 0.000\n", + "95th percentile absolute error: 0.121\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, min_val_scaled, max_val_scaled)\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 77s 11ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar energy after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 119217\n", + "Non-zero predictions (classification >= 0.5): 108662\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 1.25\n", + "Median: 1.12\n", + "Std: 0.88\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 119217 (52.32%)\n", + "Predicted non-zeros: 108662 (47.68%)\n", + "Mean classification confidence: 0.4824\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar energy: 0.61\n", + "Min solar energy: 0.00\n", + "Max solar energy: 3.20\n", + "Zero predictions: 116719 (51.22%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", + " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", + " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", + "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", + "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3332np556Sm3atNHatWvVvXt3p1g6d+6s0NBQzZkzRw888IDTtjlz5qhmzZpq27btdc8dAAAAKEmeeuopvfnmm1q+fLkGDRqUZZ29e/fqH//4h5o0aaJ33nlHnp6eOnTokDZu3CjJfs/1zjvvaOTIkXr22Wcd947pZ9G6cOGC7r33Xj3++OPq06ePgoKCcozr3//+t0wmk15//XVFRUVp0qRJ6tq1q8LDw3OcASsrkyZNUlxcnFPZxx9/rPDw8BzvQ0ePHq0xY8aoa9euev7553Xw4EFNmTJFW7du1caNG+Xu7u6oe+nSJd1zzz168MEH9eijj2rhwoV6/fXX1bhxY917772S7COSe/TooQ0bNujZZ59V/fr1tXv3bn388cf666+/8mWt6dzeZ/7888967LHH1LhxY40bN06XLl3SwIEDValSpese42b6e+bMmfLz89Pw4cPl5+enVatWaeTIkYqNjXU8bJ+cnKywsDAlJSXpxRdfVHBwsE6fPq2ffvpJ0dHRKl++vGbNmqV///vfiouL07hx4xxxGYahHj16aPXq1Ro4cKCaNWumZcuW6dVXX9Xp06czTbu+atUqfffddxo6dKjKlSunatWqOdZNTzv2mDFjtGXLFn3++ecqVaqUNm3apCpVqui9997TL7/8ogkTJqhRo0bq27evo93JkyerR48eevLJJ5WcnKx58+bpkUce0U8//eS4l501a5aeeeYZ3X777Xr22WclSTVr1pQkbd26VZs2bdLjjz+uypUr69ixY5oyZYo6d+6sffv25Tj63jAM9ezZUxs2bNDgwYNVv359ff/999f9jiI7FSpUkLe3t3788Ue9+OKLKlOmTLZ1ExMT1blzZx06dEhDhw5V9erVtWDBAvXv31/R0dF6+eWXs903r+f8wgsvqHz58ho5cqTi4+Ovex4JCQlZftdRqlQpp1Hzhw4d0sMPP6yBAweqX79+mj59uvr376+WLVuqYcOGjrY6deqk06dP67nnnlOVKlW0adMmjRgxQmfPntWkSZOcjjFjxgxduXJFzz77rDw9PVWmTBnt3LlT99xzj0JCQjRmzBhZrVa98847mQakPPXUUxo0aJD27NmjRo0aOX1ef/31l95+++3rnjsAOBgAABQz0dHRhiSjZ8+eOdbr0aOHIcmIjY0ttPaOHj1qSDLGjBljnDt3zoiIiDDWrFljNG/e3JBk/O9//3PUlWSMGjXK8b5Xr16Gh4eHcfjwYUfZmTNnDH9/f6Njx46OsgULFhiSjNWrV+cYb5pmzZoZISEhRnR0tKNs+fLlhiSjatWqTnUzxpSViIgIo3z58oYko169esbgwYONuXPnOrVvGIaRnJxsVKhQwWjUqJGRmJjoKP/pp58MScbIkSMdZaNGjTIy/rMlISEh07HDwsKMGjVqOJVVrVrVkGQsXbo0x7jT9OvXz5CU5SssLMxRb8aMGYYko2vXrobNZnOUv/LKK4bFYnGc7+XLl41SpUoZgwYNyvQ5BQYGOpWnHfuNN95wqrt9+3ZDkjFs2DCn8v79+2fqkxEjRhienp5On3dUVJTh5uZ23b4DAAAAiqO0f7tv3bo12zqBgYFG8+bNHe8z3oN8/PHHhiTj3Llz2baxdetWQ5IxY8aMTNs6depkSDKmTp2a5bZOnTo53q9evdqQZFSqVMnp/vK7774zJBmTJ092lFWtWtXo16/fddvMKK2td955x1GW9jkdPXrUMAz7fYSHh4dx9913G1ar1VHvk08+MSQZ06dPz3R+33zzjaMsKSnJCA4ONh566CFH2axZswyz2WysX7/eKZ6pU6cakoyNGzdmG7Nh2O+ZfH19s92el/vMxo0bG5UrVzYuX77sKFuzZk2W98JZudH+zupe9rnnnjN8fHyMK1euGIZhGDt37jQkGQsWLMgxhk6dOhkNGzZ0Klu8eLEhyXj33Xedyh9++GHDZDIZhw4dcpRJMsxms7F3716numk/C2FhYU73u23btjVMJpMxePBgR1lqaqpRuXLlTD9vGc8zOTnZaNSokXHnnXc6lfv6+mb5M5zV57R58+ZMP2dZSfsMPvjgA6c4O3TokKnPsvq+ISsjR440JBm+vr7Gvffea/z73/82tm/fnqnepEmTDEnG7NmzHWXJyclG27ZtDT8/P6drOuP9fG7POa1/7rjjDiM1NfW6sad9F5Xda/PmzY66ad+hrFu3zlEWFRVleHp6Gv/85z8dZWPHjjV8fX2Nv/76y+lYb7zxhmGxWIwTJ044HTsgIMCIiopyqnv//fcbPj4+xunTpx1lf//9t+Hm5ubUJ9HR0YaXl5fx+uuvO+3/0ksvGb6+vkZcXNx1PwMASMNU6gCAYufy5cuSJH9//xzrpW1Pq19Y7UnSqFGjVL58eQUHB6tz5846fPiw3n//fT344INZ1rdarVq+fLl69eqlGjVqOMpDQkL0xBNPaMOGDYqNjb3ucTM6e/aswsPD1a9fPwUGBjrKu3XrpgYNGuS5PUkKCgrSn3/+qcGDB+vSpUuaOnWqnnjiCVWoUEFjx451TK+2bds2RUVF6YUXXnCaXr179+6qV6+efv755xyPk36ERExMjM6fP69OnTrpyJEjiomJcapbvXr1PE2t5eXlpRUrVmR6jR8/PlPdZ5991mmKrw4dOshqter48eOS7NMtRkdHq3fv3jp//rzjZbFY1Lp1a61evTpTm88//7zT+6VLl0qyPw2e3osvvphp3759+yopKclpirr58+crNTW1SKwlBgAAABRFfn5+Od7LpY0yXrJkidM0wXnh6empAQMG5Lp+3759ne5DH374YYWEhOiXX365oeOn2bdvn55++mn17Nkzx5GWv/32m5KTkzVs2DCZzde+Rh40aJACAgIy3bP5+fk53XN4eHjo9ttv15EjRxxlCxYsUP369VWvXj2n+6O05cWyuj/Ki9zeZ545c0a7d+9W37595efn56jXqVMnNW7c+KZiSJNdf6e/l718+bLOnz+vDh06KCEhQQcOHJAkx/35smXLlJCQkKfj/vLLL7JYLHrppZecyv/5z3/KMAz9+uuvTuWdOnXK9v5/4MCBTve7rVu3lmEYGjhwoKPMYrGoVatWTv2c8TwvXbqkmJgYdejQQTt27MjVeaTfPyUlRRcuXFCtWrVUqlSp67bxyy+/yM3Nzene2mKxZHkPnVtjxozR3Llz1bx5cy1btkxvvfWWWrZsqRYtWmj//v1Oxw4ODlbv3r0dZe7u7nrppZcUFxentWvXZnuMvJ7zoEGDZLFYcn0Ozz77bJbfdWTs/wYNGjjNmFi+fHnVrVs307XcoUMHlS5d2ula7tq1q6xWq9atW+fU5kMPPeQ0Etxqteq3335Tr169VLFiRUd5rVq1HDNMpAkMDFTPnj317bffOr5Tslqtmj9/vnr16nXdtdUBID2mUgcAFDt5SXibTCaVK1dOknTx4kUlJyc7tnt7eyswMPCG28vJs88+q0ceeURms1mlSpVyrOWVnXPnzikhISHLKcDr168vm82mkydPOqa0yq205G3t2rUzbatbt26ub1gzCgkJ0ZQpU/TZZ5/p77//1rJly/T+++9r5MiRCgkJ0TPPPOM4dlbnVK9ePW3YsCHHY2zcuFGjRo3S5s2bM31REBMT45Tor169ep7it1gs6tq1a67qVqlSxel96dKlJdlv/CX7unDStXXkM8o4Nbubm1umaeyPHz8us9mc6Txq1aqVqb169erptttu05w5cxxfVsyZM0dt2rTJsj4AAAAAKS4uLtM6wek99thj+vLLL/XMM8/ojTfe0F133aUHH3xQDz/8sFPSOCeVKlWSh4dHrmPKeJ9mMplUq1atXK+xnJXY2Fg9+OCDqlSpkr755psc11bO7p7Nw8NDNWrUcGxPU7ly5UztlS5dWrt27XK8//vvv7V///5MUyWniYqKytP55DZmyfk+M61eVvdItWrVcroXPnfunNP6335+fk7J9Oxk19979+7V22+/rVWrVmV6wD3tIe/q1atr+PDhmjhxoubMmaMOHTqoR48e6tOnj9O9blaOHz+uihUrZnq4P22JtYz9ltP9csb73bRjh4aGZipPuwdO89NPP+ndd99VeHi40/rx11vPO01iYqLGjRunGTNm6PTp005rmGd8GD6j48ePKyQkJFM/5WZZtZz07t1bvXv3VmxsrH7//XfNnDlTc+fO1f333689e/bIy8tLx48fV+3atTP9fyG7zz+9vJ5zXr/rqF27dq6+68jY75L9Wk7fx3///bd27dqV62s5Y6xRUVFKTEzM9hrMqG/fvpo/f77Wr1+vjh076rffflNkZKSeeuqp654PAKRHYhwAUOwEBgaqYsWKTjffWdm1a5cqV67suFF98MEHnZ7c7devn2bOnHnD7eUktzcjtzqTyaQ6deqoTp066t69u2rXrq05c+bomWeeual2Dx8+rLvuukv16tXTxIkTFRoaKg8PD/3yyy/6+OOPM43gyOv6e3mR3dPZaTewabHMmjVLwcHBmeqlX8dLso8qyO0Xa9np27evXn75ZZ06dUpJSUnasmWLPvnkk5tqEwAAACiuTp06pZiYmBwfJPX29ta6deu0evVq/fzzz1q6dKnmz5+vO++8U8uXL8/VqM2CuC/JLslotVqzjKl///46c+aM/vjjj0wP6d6s690bSfb7o8aNG2vixIlZ1s2YcC0KbrvtNqdk5qhRozR69Ojr7pdVf0dHR6tTp04KCAjQO++8o5o1a8rLy0s7duzQ66+/7nQv+9FHH6l///5asmSJli9frpdeeknjxo3Tli1bMj1MfTNy+rnMrk+zKk/fz+vXr1ePHj3UsWNHffbZZwoJCZG7u7tmzJihuXPn5iquF198UTNmzNCwYcPUtm1bBQYGymQy6fHHH7/hWRvyS0BAgLp166Zu3brJ3d1dX3/9tX7//Xd16tTpptrN6zkX1Hcdub2Wu3Xrptdeey3LunXq1HF6f7OxhoWFKSgoSLNnz1bHjh01e/ZsBQcHl4jv1gDkLxLjAIBi6f7779e0adO0YcMG3XHHHZm2r1+/XseOHdPw4cMdZR999JHT06/pp3K6kfbyU/ny5eXj46ODBw9m2nbgwAGZzWbHFwi5ffpakqpWrSrp2qjm9LI61s2oUaOGSpcurbNnzzod++DBg5lGUx88eNCxPSs//vijkpKS9MMPPzg9yXyz0+4VhJo1a0qSKlSocMM3bFWrVpXNZtPRo0edRo0cOnQoy/qPP/64hg8frm+//VaJiYlyd3fXY489dkPHBgAAAIq7WbNmSdJ1l18ym8266667dNddd2nixIl677339NZbb2n16tXq2rVrnu7FciPjfZphGDp06JCaNGniKCtdurSio6Mz7Xv8+HGnZbgkafz48Vq8eLEWLVqkevXqXff46e/Z0reVnJyso0eP3tD9Tc2aNfXnn3/qrrvuyvfPS8r9fWban1ndU2UsmzNnjhITEx3v0z6LG4l/zZo1unDhghYtWqSOHTs6yo8ePZpl/caNG6tx48Z6++23tWnTJrVv315Tp07Vu+++m+0xqlatqt9++02XL192GjWeNk17Tvfa+eV///ufvLy8tGzZMqfZ8WbMmJGpbnaf48KFC9WvXz999NFHjrIrV65k+fOeUdWqVbVy5UrFxcU5jRrP7+85JKlVq1b6+uuvnb7r2LVrl2w2m9ND77n5/G/mnAtbzZo1FRcXd8Pfc1SoUEFeXl65ugYle7L+iSee0MyZM/X+++9r8eLFeZ5KHgAkiTXGAQDF0v/93//Jx8dHzz33nC5cuOC07eLFixo8eLACAgI0dOhQR3nLli3VtWtXxyv9Gks30l5+slgsuvvuu7VkyRKnafMiIyM1d+5c3XHHHY6n/dPWVsrNjVNISIiaNWumr7/+2mlarhUrVmjfvn03FOvvv/+u+Pj4TOV//PGHLly44Ji6rFWrVqpQoYKmTp3qNK3ar7/+qv3796t79+7ZHiPtxifjtGJZ3WS7WlhYmAICAvTee+8pJSUl0/Zz587lqg1J+uyzz5zK//vf/2ZZv1y5crr33ns1e/ZszZkzR/fcc0+upvgHAAAASppVq1Zp7Nixql69up588sls6128eDFTWbNmzSTJcT+Tl3ux3Pjmm2+clvRauHChzp4967T+bs2aNbVlyxanZcF++uknnTx50qmt3377TW+//bbeeust9erVK1fH79q1qzw8PPSf//zH6d7rq6++UkxMTI73bNl59NFHdfr0aX3xxReZtiUmJmZ5L5kXub3PrFixoho1aqRvvvlGcXFxjnpr167V7t27ndps376903cFaYnxG+nvrO5lk5OTM93rxcbGKjU11amscePGMpvNTueVlfvuu09WqzXTrGEff/yxTCZTpvWbC4LFYpHJZHKagv7YsWNavHhxprq+vr5ZfoYWi8Xpc5Ls98Dp28zOfffdp9TUVE2ZMsVRZrVas72Hvp6EhARt3rw5y21pa7anfddx3333KSIiQvPnz3fUSU1N1X//+1/5+fnlOKr8Zs65sD366KPavHmzli1blmlbdHR0pp/fjNKWsFu8eLHOnDnjKD906JDjM83oqaee0qVLl/Tcc88pLi5Offr0ubmTAFAiMWIcAFAs1apVS99884169+6txo0ba+DAgapevbqOHTumr776SpcuXdK8efNyvR5Tfrd3I959912tWLFCd9xxh1544QW5ublp2rRpSkpK0gcffOCo16xZM1ksFr3//vuKiYmRp6en7rzzzmzXyxs3bpy6d++uO+64Q08//bQuXryo//73v2rYsKHTFwS5NWvWLM2ZM0cPPPCAWrZsKQ8PD+3fv1/Tp0+Xl5eX3nzzTUmSu7u73n//fQ0YMECdOnVS7969FRkZqcmTJ6tatWp65ZVXsj3G3XffLQ8PD91///2OG6IvvvhCFSpUcDylfTNSU1M1e/bsLLc98MADji9AciMgIEBTpkzRU089pRYtWujxxx9X+fLldeLECf38889q3779dac5b9mypR566CFNmjRJFy5cUJs2bbR27Vr99ddfkrJ+wr5v3756+OGHJUljx47NdbwAAABAcfXrr7/qwIEDSk1NVWRkpFatWqUVK1aoatWq+uGHH+Tl5ZXtvu+8847WrVun7t27q2rVqoqKitJnn32mypUrO2YVq1mzpkqVKqWpU6fK399fvr6+at269Q3fJ5YpU0Z33HGHBgwYoMjISE2aNEm1atXSoEGDHHWeeeYZLVy4UPfcc48effRRHT58WLNnz3bMXJWmd+/eKl++vGrXrp3pXqdbt24KCgrKdPzy5ctrxIgRGjNmjO655x716NFDBw8e1GeffabbbrvthpJSTz31lL777jsNHjxYq1evVvv27WW1WnXgwAF99913WrZsmVq1apVjGykpKVmOmC5TpoxeeOGFXN9nvvfee+rZs6fat2+vAQMG6NKlS/rkk0/UqFGjXN0L30h/t2vXTqVLl1a/fv300ksvyWQyadasWZmSoatWrdLQoUP1yCOPqE6dOkpNTdWsWbNksVj00EMP5RjX/fffry5duuitt97SsWPH1LRpUy1fvlxLlizRsGHDMv1sFITu3btr4sSJuueee/TEE08oKipKn376qWrVqpVpmbqWLVvqt99+08SJE1WxYkVVr15drVu31j/+8Q/NmjVLgYGBatCggTZv3qzffvtNZcuWve7x77//frVv315vvPGGjh07pgYNGmjRokXXXZs8OwkJCWrXrp3atGmje+65R6GhoYqOjtbixYu1fv169erVS82bN5ckPfvss5o2bZr69++v7du3q1q1alq4cKE2btyoSZMmZVr7Pb2bOefc2LFjR5bfddSsWVNt27bNU1uvvvqqfvjhB/3jH/9Q//791bJlS8XHx2v37t1auHChjh07dt0H9EePHq3ly5erffv2ev755x0PdDRq1Ejh4eGZ6jdv3lyNGjXSggULVL9+fbVo0SJPMQOAJMkAAKAY2717t/HEE08YwcHBhtlsNiQZXl5ext69e13S3tGjRw1JxoQJE65bV5IxatQop7IdO3YYYWFhhp+fn+Hj42N06dLF2LRpU6Z9v/jiC6NGjRqGxWIxJBmrV6/O8Vj/+9//jPr16xuenp5GgwYNjEWLFhn9+vUzqlatet2YMtq1a5fx6quvGi1atDDKlCljuLm5GSEhIcYjjzxi7NixI1P9+fPnG82bNzc8PT2NMmXKGE8++aRx6tQppzqjRo0yMv6z5YcffjCaNGlieHl5GdWqVTPef/99Y/r06YYk4+jRo456VatWNbp3755jzOn169fPkJTtK63tGTNmGJKMrVu3Ou2/evXqLD/z1atXG2FhYUZgYKDh5eVl1KxZ0+jfv7+xbds2p2P7+vpmGVd8fLwxZMgQo0yZMoafn5/Rq1cv4+DBg4YkY/z48ZnqJyUlGaVLlzYCAwONxMTEXJ8/AAAAUNyk/ds97eXh4WEEBwcb3bp1MyZPnmzExsZm2ifjPcjKlSuNnj17GhUrVjQ8PDyMihUrGr179zb++usvp/2WLFliNGjQwHBzczMkGTNmzDAMwzA6depkNGzYMMv4OnXqZHTq1MnxPu2e4ttvvzVGjBhhVKhQwfD29ja6d+9uHD9+PNP+H330kVGpUiXD09PTaN++vbFt27ZMbeZ0j5N275L2OaW/nzIMw/jkk0+MevXqGe7u7kZQUJDx/PPPG5cuXcp0DlmdX1b3lcnJycb7779vNGzY0PD09DRKly5ttGzZ0hgzZowRExOT5WeUvr3szqNmzZqOerm5zzQMw5g3b55Rr149w9PT02jUqJHxww8/GA899JBRr169HONIcyP9vXHjRqNNmzaGt7e3UbFiReO1114zli1b5tQXR44cMZ5++mmjZs2ahpeXl1GmTBmjS5cuxm+//ebUVnbHuXz5svHKK68YFStWNNzd3Y3atWsbEyZMMGw2m1M9ScaQIUMy7Z/d/W7adXHu3Dmn8qzuZb/66iujdu3ahqenp1GvXj1jxowZWd7bHzhwwOjYsaPh7e1tSDL69etnGIZhXLp0yRgwYIBRrlw5w8/PzwgLCzMOHDhgVK1a1VEnJxcuXDCeeuopIyAgwAgMDDSeeuopY+fOnU79lP6ccpKSkmJ88cUXRq9evYyqVasanp6eho+Pj9G8eXNjwoQJRlJSklP9yMhIR+weHh5G48aNnY6ZJuN3LLk95+z6Jztp30Vl90rfdnbfoWT8f4ph2H/ORowYYdSqVcvw8PAwypUrZ7Rr18748MMPjeTkZKdjZ/c92MqVK43mzZsbHh4eRs2aNY0vv/zS+Oc//2l4eXllWf+DDz4wJBnvvfders4dADIyGUaGx9EAACjGvvnmG/Xv3199+vTRN998U+TaA/IiPDxczZs31+zZszNN+5iamqqKFSvq/vvv11dffeWiCAEAAADg1tKsWTOVL19eK1ascHUoQInUq1cv7d27V3///XembZMnT9Yrr7yiY8eOqUqVKi6IDsCtjjXGAQAlSt++fTVu3DjNmjXLMaV3UWoPyE5iYmKmskmTJslsNqtjx46Zti1evFjnzp1T3759CyM8AAAAALilpKSkZFoHec2aNfrzzz/VuXNn1wQFlDAZv+v4+++/9csvv2R5DRqGoa+++kqdOnUiKQ7ghjFiHAAA4BYwZswYbd++XV26dJGbm5t+/fVX/frrr471y9L8/vvv2rVrl8aOHaty5cppx44dLowaAAAAAIqmY8eOqWvXrurTp48qVqyoAwcOaOrUqQoMDNSePXvybV1nANkLCQlR//79VaNGDR0/flxTpkxRUlKSdu7cqdq1a0uS4uPj9cMPP2j16tX64osvtGTJEvXo0cPFkQO4Vbm5OgAAAABcX7t27bRixQqNHTtWcXFxqlKlikaPHq233nrLqd6UKVM0e/ZsNWvWTDNnznRNsAAAAABQxJUuXVotW7bUl19+qXPnzsnX11fdu3fX+PHjSYoDheSee+7Rt99+q4i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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Energy:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 182,202.0000\n", + "mean : 0.6344\n", + "median : 0.0000\n", + "std : 0.9133\n", + "min : 0.0000\n", + "max : 4.0000\n", + "skewness : 1.3007\n", + "kurtosis : 0.4372\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 0.0000\n", + "percentile_50 : 0.0000\n", + "percentile_75 : 1.1064\n", + "percentile_90 : 2.2000\n", + "percentile_95 : 2.6993\n", + "percentile_99 : 3.1000\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (50.94%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 182202,\n", + " 'mean': 0.6344184307798103,\n", + " 'median': 0.0,\n", + " 'std': 0.9132957616282624,\n", + " 'min': 0.0,\n", + " 'max': 4.0,\n", + " 'skewness': 1.3006834240564749,\n", + " 'kurtosis': 0.4371730534542304,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 0.0,\n", + " 'percentile_50': 0.0,\n", + " 'percentile_75': 1.106435239315033,\n", + " 'percentile_90': 2.2,\n", + " 'percentile_95': 2.699264335632324,\n", + " 'percentile_99': 3.1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-27_21-08_error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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tJUllypTRH3/8obFjx6pdu3YKCAiQZK5qGBgYaFkvJiZG5cuXl2Sugnju3Dmr7d6+fVsXL160rJ9WpcTkZfaMh9ICAAAAqdi1a5cqVKigChUqSJL69u2rChUqWB7lc/bsWUVF/Zs0Dg4O1tq1axUREaFy5cppwoQJ+vzzzxUWFmaT+B/W9YTbqjgywiohXjaft46MepmEOAAA9iouTnr3XenaP9VdHBykefNIiAMAAGRhrq6u8vLysprSSopfv35dDg7WaV9HR0clJSVJMl+3CggI0KZNmyzL4+LitH37dkuVw5CQEMXGxmr37t2WPps3b1ZSUpKqVKli6bNlyxbdunXL0iciIkLPPPOMXZdOlxgpDgAAAKSqdu3aMgwjzeXz5s1LdZ29e/dmYlSZ792Vv+rS9X9/2GzsW1NF/XLaMCIAAHBPu3ZJLVtKx49LFy9Kn39u64gAAACeTA4mm+26YcOGGj16tAoUKKBSpUpp7969mjhxojp27CjJ/Djq3r17a9SoUXr66acVHByswYMHKygoSK+88ookqUSJEqpXr566dOmimTNn6tatW+rZs6datmypoKAgSVLr1q01fPhwderUSQMGDND+/fs1ZcoUTZo0yVaHnm4kxQEAAABIks5duak1v56VJNUs5qsFHZ+zcUQAACBNhiFNmWIeIX7rllSwoNSpk62jAgAAeHJlcPn0B/Hxxx9r8ODBevPNN3Xu3DkFBQXpjTfesFQ8lKR3331X165dU9euXRUbG6vq1atr3bp1cnNzs/RZvHixevbsqRdffFEODg5q2rSppk6dalnu7e2tDRs2qEePHqpUqZLy5s2rIUOGqGvXro/1eB+GybjX8BekW1xcnLy9vXX58mV5eXll6r46zduZrn6z21fO1DgAPEbXrkk5cphfX70qeXraNh4A2dLjPJ9B6mz5GWw5cl5t5+ywzP8+sp7cnB0fawwAACCdLlwwPzd8zRrzfJMm5hHidlCyknNK++BeoaetQwCeeJd2fmLrEIAnmpsNhgW7vzA6Q7d3Y/N/MnR7TzpGigNAVuDsLPXr9+9rAAAy0M1biVYJ8TdqFSYhDgCAvdq9W/q//5POnJFcXaVJk6Ru3SST7cp1AgAAQJyP2TmS4gCQFbi4SB9+aOsoAGQCKsDAHnRduNvyekW3EFUulNuG0QAAgHsKCjKXSy9eXFq2TCpb1tYRAQAAQLJp+XTcH0lxAAAA4AmWlGRoy5HzkqTiATlJiAMAYI8uX5a8vc2vAwOlDRukp5/m0VoAAABAOnHLAgBkBUlJ0qlT5ikpydbRAACykdgbtyyvl3UNsWEkAAAgVWvWSEWKSCtX/ttWvjwJcQAAAHtjMmXshAxFUhwAsoIbN6TgYPN044atowEAZCPf7j9ree3lTiEpAADsRny81KeP1LCh9Pff0owZkmHYOioAAAAgS+KqFwAAAPAE+89X+yVJPh7OMnEXMgAA9uHYMallS2n3bvN8797SBx8wYggAAMCe8Uxxu0ZSHAAAAHhCGXeMNqtb0t+GkQAAAIslS6Q33pCuXpXy5JHmzjWPFgcAAIB94wZGu0ZSHAAAAHhCrf7ljOX18P8rbcNIAACAJGnvXqlNG/PrmjWlxYulfPlsGxMAAACQDZAUBwAAAJ5Qb3+xz/La3cXRdoEAAACzChXMpdJ9fKRBgyRH/j4DAABkGZRPt2skxQEAAIAnUFLSv6XTO1cPtmEkAAA8wQxD+uwzqX79f0eET5xI6U0AAAAgg3HLAgAAAPAEOh17w/K690vFbBgJAABPqEuXpGbNzM8Pb91aun3b3E5CHAAAIGsymTJ2QoZipDgAZAVOTtKbb/77GgCAR3TmjqR4Dlf+tgAA8Fht3Sq1aiVFRUnOzlKTJpRKBwAAyOoon27XuPoFAFmBq6s0bZqtowAAZCMrd/8lSSqX38e2gQAA8CRJSpLGjZMGD5YSE6UiRaQvvpCefdbWkQEAAADZGklxAAAA4AmU/Ejx6/G3bRsIAABPir//llq2lDZuNM+3aiXNnCl5edk2LgAAAGQMSp7bNZLiAJAVGIZ04YL5dd68/HEFADyyG7fMyfA6xf1sHAkAAE8IDw8pOlpyd5c++UTq0IHfdgAAANkJ5dPtGklxAMgKrl+X/P5JWly9Knl62jYeAECW93v0FUlS3hwuNo4EAIBs7NYtycHB/Lxwd3dp+XJze4kSto0LAAAAeMJwywIAAADwBDpx/pokycmBnwQAAGSKkyelGjWk8eP/bStRgoQ4AABAdmVyyNgJGYp3FAAAAHiCPZXL3dYhAACQ/axcKVWoIG3fLk2cKMXF2ToiAAAA4IlGUhwAAAB4ggV5kxQHACDD3Lghde8uNW8uXb4shYRIO3dKXl62jgwAAACZzWTK2AkZimeKAwAAAE8YwzAsr/29XG0YCQAA2cjBg1KLFtL+/eaLmO+9Jw0fLjk72zoyAAAAPA6UPLdrJMUBAACAJ8zlG7csr73cuVAPAMAju3JFql5dunRJ8vOTFi2SXnrJ1lEBAAAA+AdJcQAAAOAJcyvx35Hirk7cxQwAwCPLmVMaMUJavVpasEAKCLB1RAAAAHjcKHlu10iKA0BW4OQktWv372sAAB5BYpI5Ke7kYJKJH2wAADycXbskBwepYkXzfI8e0ptvmtsAAAAA2BUyKwCQFbi6SvPm2ToKAEA2EX87UZLk6EBCHACAB2YY0uTJ0oABUv780t69kpeXeWQQN5sBAAA8uXimuF0jKQ4AAAA8YWKvm58pHn87ycaRAACQxVy4ILVvL61da54vX15K4u8pAAAAxA2Sdo5bFgAgKzAM6do182QY9+8PAMA9JP7ztySXh7ONIwEAIAv5/nupXDlzQtzVVZo+XVq5UvLxsXVkAAAAAO6DkeIAkBVcvy7lyGF+ffWq5Olp23gAAFnaL3/GSpJyebrYNhAAALKCxERpxAhp5EjzTcrFi0vLlklly9o6MgAAANgREyPF7RpJcQAAAOAJc/FagiTpxPlrNo4EAIAswGSSdu40J8Q7dpSmTuVGZQAAAKRAUty+kRQHAAAAnjD7/hkpHl4m0LaBAABgz5KSJAcH8zR/vvTdd9Krr9o6KgAAAAAPgWeKAwAAAE+YAC83SZKjA3cwAwCQQny81Lu31Lnzv22+viTEAQAAcG+mDJ6QoRgpDgAAADxhEg1DklT6KS8bRwIAgJ05elRq0ULau9c836OHVKmSbWMCAAAA8MgYKQ4AAAA8YRKTzElxB551BQDAvxYtkipWNCfE8+SRvv6ahDgAAADSzWQyZeiEjMVIcQAAAOAJsyfqkiTJifLpAABIV69KPXuanxsuSbVqSYsXS089Zdu4AAAAkKWQyLZvJMUBICtwdJSaNfv3NQAAjyB/Lg/9efGGLl5LsHUoAADYlmFI4eHSli2Sg4M0ZIg0aBC/uwAAAIBsxu7LpxcqVCjVkgE9evSQJN28eVM9evRQnjx5lCNHDjVt2lQxMTFW24iKilJ4eLg8PDzk5+en/v376/bt21Z9vv/+e1WsWFGurq4qWrSo5s2b97gOEQDuz81NWrHCPLm52ToaAEAWl3zjcmHfHLYNBAAAWzOZpIEDpXz5pM2bpaFDSYgDAADgoVA+3b7ZfVJ8586dOnv2rGWKiIiQJDVv3lyS1KdPH3399ddasWKFfvjhB505c0ZNmjSxrJ+YmKjw8HAlJCRo69atmj9/vubNm6chQ4ZY+pw8eVLh4eGqU6eO9u3bp969e6tz585av3794z1YAAAA4DEwzI8UF7+vAABPpIsXpZ9++ne+Xj3p6FFz2XQAAADgIZEUt292Xz7d19fXav6DDz5QkSJFVKtWLV2+fFmzZ8/WkiVL9MILL0iS5s6dqxIlSmjbtm2qWrWqNmzYoIMHD2rjxo3y9/dX+fLlNXLkSA0YMEDDhg2Ti4uLZs6cqeDgYE2YMEGSVKJECf3000+aNGmSwsLCHvsxAwAAAJkp6Z+sOD+wAABPnJ9/llq3li5flvbulYKDze1U5AIAAACyNbsfKX6nhIQELVq0SB07dpTJZNLu3bt169YthYaGWvoUL15cBQoUUGRkpCQpMjJSZcqUkb+/v6VPWFiY4uLidODAAUufO7eR3Cd5G6mJj49XXFyc1QQAmebaNfNwPpPJ/BoAgEdgGSlu2zAAAHh8EhOlMWPMo8GjoqS8eaUrV2wdFQAAALITUwZPyFBZKim+atUqxcbGqn379pKk6Ohoubi4yMfHx6qfv7+/oqOjLX3uTIgnL09edq8+cXFxunHjRqqxjB07Vt7e3pYpf/78j3p4AAAAwGPxT06c8ukAgCdDdLQUFib95z/m5Hjr1tKePVLZsraODAAAAMBjkqWS4rNnz9bLL7+soKAgW4eigQMH6vLly5bpzz//tHVIAAAAQPpYRoqTFQcAZHPr10vlykmbNkkeHtLcudKiRZKXl60jAwAAQDZjy2eKFypUKNVt9OjRQ5J08+ZN9ejRQ3ny5FGOHDnUtGlTxcTEWG0jKipK4eHh8vDwkJ+fn/r376/bt29b9fn+++9VsWJFubq6qmjRopo3b94jvWePU5ZJiv/xxx/auHGjOnfubGkLCAhQQkKCYmNjrfrGxMQoICDA0ufuDzV5/n59vLy85O7unmo8rq6u8vLyspoAAACArMBQ8jPFbRwIAACZ7dtvpXPnzKPCd++W2rfnDyAAAAAyhS2T4jt37tTZs2ctU0REhCSpefPmkqQ+ffro66+/1ooVK/TDDz/ozJkzatKkiWX9xMREhYeHKyEhQVu3btX8+fM1b948DRkyxNLn5MmTCg8PV506dbRv3z717t1bnTt31vr16zPg3ct8WSYpPnfuXPn5+Sk8PNzSVqlSJTk7O2vTpk2WtsOHDysqKkohISGSpJCQEP322286d+6cpU9ERIS8vLxUsmRJS587t5HcJ3kbAAAAQHbCM8UBAE+McePMzxLftk0qXtzW0QAAAACZwtfXVwEBAZZpzZo1KlKkiGrVqqXLly9r9uzZmjhxol544QVVqlRJc+fO1datW7Vt2zZJ0oYNG3Tw4EEtWrRI5cuX18svv6yRI0dq2rRpSkhIkCTNnDlTwcHBmjBhgkqUKKGePXuqWbNmmjRpki0PPd2yRFI8KSlJc+fOVbt27eTk5GRp9/b2VqdOndS3b19999132r17tzp06KCQkBBVrVpVklS3bl2VLFlSr7/+un755RetX79egwYNUo8ePeTq6ipJ6tatm06cOKF3331Xv//+u6ZPn67ly5erT58+NjleAAAAIDPxTHEAQLa1YoX0f/8nJZd5dHWVBg6U0qgECAAAAGQUW44Uv1NCQoIWLVqkjh07ymQyaffu3bp165ZCQ0MtfYoXL64CBQooMjJSkhQZGakyZcrI39/f0icsLExxcXE6cOCApc+d20juk7wNe+d0/y62t3HjRkVFRaljx44plk2aNEkODg5q2rSp4uPjFRYWpunTp1uWOzo6as2aNerevbtCQkLk6empdu3aacSIEZY+wcHBWrt2rfr06aMpU6YoX758+vzzzxUWFvZYjg8AAAB4nAzDkha3aRwAAGSYGzekPn2kTz81z8+eLb3xhm1jAgAAAB5BfHy84uPjrdpcXV0tg37TsmrVKsXGxqp9+/aSpOjoaLm4uMjHx8eqn7+/v6Kjoy197kyIJy9PXnavPnFxcbpx40aaj6S2F1kiKV63bt07LtxZc3Nz07Rp0zRt2rQ01y9YsKC++eabe+6jdu3a2rt37yPFCQCZxtFRql//39cAADwCRooDALKVgwelFi2k/fvNf9wGDpQ6dbJ1VAAAAHjCPMro7tSMHTtWw4cPt2obOnSohg0bds/1Zs+erZdffllBQUEZGk9WlyWS4gDwxHNzk9autXUUAIBs4uatJEmSA1lxAEBWZhjmEeG9eplHivv7S4sWSXeVdAQAAAAeiwy+zDJw4ED17dvXqu1+o8T/+OMPbdy4UV9++aWlLSAgQAkJCYqNjbUaLR4TE6OAgABLnx07dlhtKyYmxrIs+b/JbXf28fLysvtR4lIWeaY4AAAAgIxz6GycJCkpjWpMAABkCYMHS126mBPidetKv/xCQhwAAADZhqurq7y8vKym+yXF586dKz8/P4WHh1vaKlWqJGdnZ23atMnSdvjwYUVFRSkkJESSFBISot9++03nzp2z9ImIiJCXl5dKlixp6XPnNpL7JG/D3pEUBwAAAJ4wwXk9JUnOjowUBwBkYa1aSV5e0rhx0rffmkeKAwAAADZiMpkydHpQSUlJmjt3rtq1aycnp3+LhXt7e6tTp07q27evvvvuO+3evVsdOnRQSEiIqlatKsn8KOuSJUvq9ddf1y+//KL169dr0KBB6tGjhyUR361bN504cULvvvuufv/9d02fPl3Lly9Xnz59MuYNzGSUTweArODaNcnPz/z63DnJ09O28QAAsjTjnxHiXm7ONo4EAIAHkJQk7dwpValini9VSjp1SsqVy6ZhAQAAAFLGP1P8QW3cuFFRUVHq2LFjimWTJk2Sg4ODmjZtqvj4eIWFhWn69OmW5Y6OjlqzZo26d++ukJAQeXp6ql27dhoxYoSlT3BwsNauXas+ffpoypQpypcvnz7//HOFhYU9luN7VCTFASCruH7d1hEAALKJpH+qptv6xxoAAOl2/rzUvr20fr30ww9StWrmdhLiAAAAgCTzaG8jjUflubm5adq0aZo2bVqa6xcsWFDffPPNPfdRu3Zt7d2795HitBXKpwMAAABPGEPmH0jkxAEAWcJ330nlyknffCM5OZlHhwMAAAB2xtbl03FvJMUBAACAJ0zyTcMO/MACANiz27elIUOkF1+Uzp6VSpQwl09v08bWkQEAAADIYiifDgAAADxhkpPipMQBAHbrzz/Nye8ffzTPd+okTZkieXraNi4AAAAgLVxosWskxQEAAIAnTPLzpRgoDgCwW2vXmhPiOXNKn34qtWpl64gAAACAe6LkuX0jKQ4AAAA8Yf4ZKE75dACA/XrjDfNo8Q4dpKJFbR0NAAAAgCyOZ4oDQFbg4CDVqmWeHPi/bgDAo0lKrp8OAIC9OHJEatpUioszz5tM0ujRJMQBAACQZZhMpgydkLEYKQ4AWYG7u/T997aOAgCQTVy6dksS5dMBAHZi4UKpe3fp2jXJ11eaOdPWEQEAAADIZkiKAwAAAE+YhMQkSRIDxgEANnX1qtSjh7RggXm+dm1p8GCbhgQAAAA8LEZ32zeS4gAAAMATxsXJQQm3k+Tt7mzrUAAAT6p9+6QWLcxl0x0cpGHDpPfflxwdbR0ZAAAA8FBIits3kuIAkBVcuyYVKmR+feqU5Olpy2gAAFndPyPEHRz4sQYAsIG1a83PD4+Pl/LlkxYvlmrWtHVUAAAAALIxkuIAkFVcuGDrCAAA2QwpcQCATTz3nJQ7t1S5sjRnjpQnj60jAgAAAB4dF1rsGklxAAAA4Alj/DNUnKpeAIDH5uhR6emnza99faXt282jxPljBAAAgGyC8un2zcHWAQAAAAD2atq0aSpUqJDc3NxUpUoV7dix4579J0+erGeeeUbu7u7Knz+/+vTpo5s3bz6maNPPMGwdAQDgiZGYKI0eLZUoIS1a9G97/vwkxAEAAAA8NiTFAQAAgFQsW7ZMffv21dChQ7Vnzx6VK1dOYWFhOnfuXKr9lyxZovfee09Dhw7VoUOHNHv2bC1btkzvv//+Y448/UzU9QIAZKazZ6WwMGnQIHNy/OefbR0RAAAAkGlMJlOGTshYJMUBAACAVEycOFFdunRRhw4dVLJkSc2cOVMeHh6aM2dOqv23bt2qatWqqXXr1ipUqJDq1q2rVq1a3Xd0uS0wUBwAkOnWrZPKlZM2bZI8PKR586QZM2wdFQAAAIAnFElxAAAA4C4JCQnavXu3QkNDLW0ODg4KDQ1VZGRkqus8//zz2r17tyUJfuLECX3zzTeqX7/+Y4n5YXDTMQAgw926JQ0YIL38snT+vDkxvmeP1K6drSMDAAAAMhUjxe2bk60DAACkg4OD9Oyz/74GAGSqCxcuKDExUf7+/lbt/v7++v3331Ndp3Xr1rpw4YKqV68uwzB0+/ZtdevW7Z7l0+Pj4xUfH2+Zj4uLy5gDuA/jn4eK8/MKAJDhtm2Txo83v+7RQ/roI8nNzbYxAQAAAI8DF1rsGpkVAMgK3N2lnTvNk7u7raMBAKTi+++/15gxYzR9+nTt2bNHX375pdauXauRI0emuc7YsWPl7e1tmfLnz/9YYqV8OgAg09SoIY0cKX35pfTJJyTEAQAAANgFkuIAAADAXfLmzStHR0fFxMRYtcfExCggICDVdQYPHqzXX39dnTt3VpkyZdS4cWONGTNGY8eOVVJSUqrrDBw4UJcvX7ZMf/75Z4Yfyz1xBzMA4FFdvy69/bZ08uS/bYMGSY0b2y4mAAAAwAYon27fSIoDAAAAd3FxcVGlSpW0adMmS1tSUpI2bdqkkJCQVNe5fv26HO56xIWjo6Okf8uV383V1VVeXl5W0+OQHI6JrDgA4FEcOCA995w0darUuvW/f2AAAACAJxBJcftGUhwAsoLr16VChczT9eu2jgYAngh9+/bVZ599pvnz5+vQoUPq3r27rl27pg4dOkiS2rZtq4EDB1r6N2zYUDNmzNAXX3yhkydPKiIiQoMHD1bDhg0tyXEAALIFw5A+/1yqXNmcGPf3N5dM58IdAFg4OJg05M1wHVozTBcjJ+rA6qF6r0u9NPtP/U9L3dj7iXq2rm3VvmLyGzryzQhd2jZJJzaM1uyRbRXo653qNgrnz6tzP32ks1vGZ+ShANnG7l079dab3RRau7rKlXpGmzdtTLPvyOFDVK7UM1q0YJ6l7fTpvzR08Pt6ue4Leq5iWYXXC9X0T6bqVkLCY4gewKNysnUAAIB0MAzpjz/+fQ0AyHQtWrTQ+fPnNWTIEEVHR6t8+fJat26d/P39JUlRUVFWI8MHDRokk8mkQYMG6fTp0/L19VXDhg01evRoWx3CfZG7AAA8sMuXpTfekJYtM8/XrSstWGBOjAMALN5p/5K6NKuhLkMW6uDxs6pUqoA+Hfaa4q7e0PSlP1j1/b86ZfVcmUI6cy42xXa27DyiD2evV/SFywry89HYPo215MNOqtN+olU/JycHLRjbQT/vPa6q5YIz89CALOvGjet65pln9EqTpur7ds80+23aGKHffvlFvn5+Vu2nTpxQUpKhwUNHqECBgjp29IiGDxusGzdu6J3+AzI7fGQBjO62byTFAQAAgDT07NlTPXum/kP5+++/t5p3cnLS0KFDNXTo0McQ2cNLq5Q7AAD3deKE9NJL5v86OUmjR0v9+kkOFCIEgLtVLVdYa374Vet+OiBJijp7Ua/We1bPlipo1S/I11sTBzRXwzen6auPu6fYzseLv7O8jjp7SR/NjdDyiV3k5OSg27eTLMuGvdlQh0/G6Lsdh0mKA2moXqOWqteodc8+MTEx+mDMSM2YNVtvdX/Dalm1GjVVrUZNy3y+/Pl16tRJLV+2lKQ4kAWQFAcAAACeUNy/DAB4IE89Jfn4mB/rtHSpVLWqrSMCsoULFy5ozpw5ioyMVHR0tCQpICBAzz//vNq3by9fX18bR4iHse2XE+rUtJqKFvDTsahzKlPsKYWUL6z3Jnxp6WMymTR7VFtNmr9Jh05E33ebubw81PLlZ7Xtl5NWCfFalYupyUsVVKXlB2r0QrlMOR7gSZCUlKT/vNdf7Tt0UtGiT6drnatXrsjbO/VHGuDJw0hx+0ZSHAAAAHiC3DlQnB9rAID7unDBnAh3cpJcXaUvv5S8vc1tAB7Zzp07FRYWJg8PD4WGhqpYsWKSzCMVp06dqg8++EDr16/Xs88+a+NI8aA+mhshrxxu+uWrQUpMNOToaNLQaWv0xbe7LH3e6fCSbicmadrS7++5rVG9Gqlby5rydHfV9l9PqkmvmZZlub099dnw19Rh0HxduXYzsw4HeCLMnf2ZHJ2c1Pq1tunqH/XHH1q6ZJH69mOUOP7BZRa7RlIcAAAAAAAAKW3aJL32mtSlizRihLmtYMF7rwPggbz11ltq3ry5Zs6cmeKGRcMw1K1bN7311luKjIxMcxvx8fGKj4+3XjcpUSYHx0yJGenTrG5FtXy5stq/P18Hj59V2Wee0of9muns+cta/PV2VSiRXz1a1dbzrcfdd1uTFmzUvFWRKhCYW/9542V9PvJ1S2J8+uBWWrZul37eczyzDwnI1g4e2K/FCxfoi5VfpusG8piYGL35Rme9FFZPTZu/+hgiBPCoSIoDAAAAT5A7nyjODcwAgFTdvi0NGyaNGWMuMfLVV9J//mMeKQ4gQ/3yyy+aN29eqgkYk8mkPn36qEKFCvfcxtixYzV8+HCrNkf/ynIOfC5DY8WDGdP7FX00N0Ir1u+WJB04dkYFAnOrf4eXtPjr7apWoYj8cufQkW9GWNZxcnLUB32bqGebOioePtTS/nfsNf0de03Hos7p8MloHVs/SlXKBmv7rydV67liCq9VRr1ff1GS+Xvj6OigKzunqMeopVrwv22P98CBLGrP7l26ePFv1QutY2lLTEzUhA/HafHCBfo2YrOl/dy5GHXu0FblKlTQkGEjbREu7BQV+ewbSXEAyApMJqlkyX9fAwCQAfiTAgBIISpKat1a+vln83znztKUKSTEgUwSEBCgHTt2qHjx4qku37Fjh/z9/e+5jYEDB6pv375WbX41KOVra+5uLkoykqzaEpMMOTg4SJKWrN2pzdsPWy3/enoPLVm7456JbAcH80m8i7P50n7tdhPk+M82JalB7bJ6p32o6rSfqDPnYjPiUIAnQoP/a6QqIc9btXXv2kkNGjbSK42bWNpiYswJ8ZIlS2nEqLGWf9MA7B9JcQDICjw8pAMHbB0FACAbMO58qDgAAHdatUrq2FG6dEnKmVOaNUtq2dLWUQHZWr9+/dS1a1ft3r1bL774oiUBHhMTo02bNumzzz7TRx99dM9tuLq6yvWuG1conW5732z5TQM6henPs5d08PhZlS+eT71eq6MFq8wJ74uXr+ni5WtW69y6naiYC3E6+sc5SVLl0gVVqVRBbd17XLFXris4n6+Gvhmu41Hntf3Xk5KkwydjrLZRsWQBJRmGDh4/+xiOEsharl+7pqioKMv86b/+0u+HDsnb21uBQUHy8cll1d/ZyVl58+ZVoeDCkv5JiLd/XYFBQerbf4AuXbxo6ZvX1/fxHATsGiPF7RtJcQAAAOAJYl0+nR9rAIB/nDsntWkjXb8uVa4sLV0qFSli66iAbK9Hjx7KmzevJk2apOnTpysxMVGS5OjoqEqVKmnevHl69VWeVZsV9R23QkPfbKAp77eQb64cOnv+smav/FljZn2b7m1cv3lLjV4op0HdwuXp7qLoC5e1YeshjftsjhJu3c7E6IHs6cCB/ercoa1l/qPxYyVJ/9eosUaO+eC+62/b+rOiov5QVNQfqvtCTatlvxw4nMZaeJKQE7dvJoOhIhkiLi5O3t7eunz5sry8vDJ1X53m7UxXv9ntK2dqHAAA4NHZ09/1x3k+g9Q9js/gVmKSnv6P+ULcL0PrytvdOVP2AwDIgubMkQ4eND9L3MXF1tEgi+Kc8uHdunVLFy5ckCTlzZtXzs4Pf57mXqFnRoUF4CFd2vmJrUMAnmhuNhgWXLRf+m98So9jH72codt70jFSHACyguTRGpK0c6e5nDoAAA+BW2IBABYLF0qFC0vVqpnnO3a0bTzAE87Z2VmBgYG2DgMAADwkyqfbN5LiAJAVGIZ5xEbyawAAHpJxRwF1fqsBwBPq6lWpRw9pwQIpf37pl1+kXLnuvx4AAACANHGdxb452DqA+zl9+rRee+015cmTR+7u7ipTpox27dplWW4YhoYMGaLAwEC5u7srNDRUR48etdrGxYsX1aZNG3l5ecnHx0edOnXS1atXrfr8+uuvqlGjhtzc3JQ/f36NHz/+sRwfAAAA8Dhdufnvswc9nB1tGAkAwCb27ZMqVTInxB0cpK5dJUpcAwAAAMjm7DopfunSJVWrVk3Ozs769ttvdfDgQU2YMEG57rh7efz48Zo6dapmzpyp7du3y9PTU2FhYbp586alT5s2bXTgwAFFRERozZo12rJli7p27WpZHhcXp7p166pgwYLavXu3PvzwQw0bNkyzZs16rMcLAAAAPE5Ojnb9cwAAkJEMQ/rkE6lKFenIESlfPun776VBgyRHbpICAAAAHpXJZMrQCRnLrsunjxs3Tvnz59fcuXMtbcHBwZbXhmFo8uTJGjRokBo1aiRJWrBggfz9/bVq1Sq1bNlShw4d0rp167Rz5049++yzkqSPP/5Y9evX10cffaSgoCAtXrxYCQkJmjNnjlxcXFSqVCnt27dPEydOtEqeAwAAAAAAZDk3bkitW0urVpnn/+//pDlzpDx5bBoWAAAAADwudj00ZPXq1Xr22WfVvHlz+fn5qUKFCvrss88sy0+ePKno6GiFhoZa2ry9vVWlShVFRkZKkiIjI+Xj42NJiEtSaGioHBwctH37dkufmjVrysXFxdInLCxMhw8f1qVLlzL7MAEAAAAAADKPm5v5AYcuLtLUqebkOAlxAAAAIEOZTBk7IWPZ9UjxEydOaMaMGerbt6/ef/997dy5U7169ZKLi4vatWun6OhoSZK/v7/Vev7+/pZl0dHR8vPzs1ru5OSk3LlzW/W5cwT6nduMjo62KteeLD4+XvHx8Zb5uLi4RzxaAAAAAACADJKYKMXHSx4e5itqs2dLp05JFSrYOjIAAAAgW3JwIJNtz+w6KZ6UlKRnn31WY8aMkSRVqFBB+/fv18yZM9WuXTubxjZ27FgNHz7cpjEAeIKYTFLBgv++BgAAAIC0nDkjvfaa5OcnLV1q/g2RK5d5AgAAAIAnkF2XTw8MDFTJkiWt2kqUKKGoqChJUkBAgCQpJibGqk9MTIxlWUBAgM6dO2e1/Pbt27p48aJVn9S2cec+7jZw4EBdvnzZMv35558Pc4gAkD4eHuZRHadOmV8DAAAAQGq+/VYqX1767jvp66+l48dtHREAAADwRLB1+fTTp0/rtddeU548eeTu7q4yZcpo165dluWGYWjIkCEKDAyUu7u7QkNDdfToUattXLx4UW3atJGXl5d8fHzUqVMnXb161arPr7/+qho1asjNzU358+fX+PHjH+r9etzsOilerVo1HT582KrtyJEjKvjPaMng4GAFBARo06ZNluVxcXHavn27QkJCJEkhISGKjY3V7t27LX02b96spKQkValSxdJny5YtunXrlqVPRESEnnnmmVRLp0uSq6urvLy8rCYAAAAAAACbSEiQ+veX6teXzp+XypWT9uyRiha1dWQAAADAE8FkMmXo9CAuXbqkatWqydnZWd9++60OHjyoCRMmWOU5x48fr6lTp2rmzJnavn27PD09FRYWpps3b1r6tGnTRgcOHFBERITWrFmjLVu2qGvXrpblcXFxqlu3rgoWLKjdu3frww8/1LBhwzRr1qxHfwMzmV2XT+/Tp4+ef/55jRkzRq+++qp27NihWbNmWd5Yk8mk3r17a9SoUXr66acVHByswYMHKygoSK+88ook88jyevXqqUuXLpo5c6Zu3bqlnj17qmXLlgoKCpIktW7dWsOHD1enTp00YMAA7d+/X1OmTNGkSZNsdegAAAAAAADpc+KE1LKltHOneb5nT+nDDyU3N9vGBQAAAOCxGDdunPLnz6+5c+da2oKDgy2vDcPQ5MmTNWjQIDVq1EiStGDBAvn7+2vVqlVq2bKlDh06pHXr1mnnzp169tlnJUkff/yx6tevr48++khBQUFavHixEhISNGfOHLm4uKhUqVLat2+fJk6caJU8t0d2PVK8cuXK+uqrr7R06VKVLl1aI0eO1OTJk9WmTRtLn3fffVdvvfWWunbtqsqVK+vq1atat26d3O744bd48WIVL15cL774ourXr6/q1atb3bHg7e2tDRs26OTJk6pUqZLeeecdDRkyxO4/PABPkBs3pMqVzdONG7aOBgAAAIC9SEqSGjQwJ8Rz5ZK++kr6+GMS4gAAAMBjZsvy6atXr9azzz6r5s2by8/PTxUqVNBnn31mWX7y5ElFR0crNDTU0ubt7a0qVaooMjJSkhQZGSkfHx9LQlySQkND5eDgoO3bt1v61KxZUy4uLpY+YWFhOnz4sC5duvQwb9tjY9cjxSWpQYMGatCgQZrLTSaTRowYoREjRqTZJ3fu3FqyZMk991O2bFn9+OOPDx0nAGSqpCQp+dkfSUm2jQUAAACA/XBwkKZPl4YOlRYulAoUsHVEAAAAADJAfHy84uPjrdpcXV3l6uqaou+JEyc0Y8YM9e3bV++//7527typXr16ycXFRe3atVN0dLQkyd/f32o9f39/y7Lo6Gj5+flZLXdyclLu3Lmt+tw5Av3ObUZHR6f5WGp7YNcjxQEAAAAAAHCX/ful//3v3/nataXvvychDgAAANhQRj9TfOzYsfL29raaxo4dm+q+k5KSVLFiRY0ZM0YVKlRQ165dLY+WhhlJcQAAAAAAgKzAMKRZs8yPVWrTRjpy5N9lD1pfEQAAAECGyuik+MCBA3X58mWraeDAganuOzAwUCVLlrRqK1GihKKioiRJAQEBkqSYmBirPjExMZZlAQEBOnfunNXy27dv6+LFi1Z9UtvGnfuwVyTFAQAAAAAA7F1srNSihfTGG9LNm1L16pKPj62jAgAAAJBJXF1d5eXlZTWlVjpdkqpVq6bDhw9btR05ckQFCxaUJAUHBysgIECbNm2yLI+Li9P27dsVEhIiSQoJCVFsbKx2795t6bN582YlJSWpSpUqlj5btmzRrVu3LH0iIiL0zDPP2HXpdImkOAAAAAAAgH3bvl2qUEFasUJycpLGj5e++Ua663l/AAAAAGzHZMrY6UH06dNH27Zt05gxY3Ts2DEtWbJEs2bNUo8ePf6JzaTevXtr1KhRWr16tX777Te1bdtWQUFBeuWVVySZR5bXq1dPXbp00Y4dO/Tzzz+rZ8+eatmypYKCgiRJrVu3louLizp16qQDBw5o2bJlmjJlivr27ZuRb2WmcLJ1AAAAAAAAAEjDhAnSe+9Jt29LhQpJX3wh/TNKAwAAAAAkqXLlyvrqq680cOBAjRgxQsHBwZo8ebLatGlj6fPuu+/q2rVr6tq1q2JjY1W9enWtW7dObm5ulj6LFy9Wz5499eKLL8rBwUFNmzbV1KlTLcu9vb21YcMG9ejRQ5UqVVLevHk1ZMgQde3a9bEe78MgKQ4AWUXevLaOAAAAAMDjdv68OSHevLn5eeKUTAcAAADskulBh3dnsAYNGqhBgwZpLjeZTBoxYoRGjBiRZp/cuXNryZIl99xP2bJl9eOPPz50nLZCUhwAsgJPT/PFMAAAAADZ361bkrOz+fXIkdKzz0pNmz54DUUAAAAAjw2n6/aNZ4oDAAAATxDDsHUEAIA03b4tDRok1aplToxL5uR4s2ZcYQMAAACAR8BIcQAAAAAAAFuLipJat5Z+/tk8v3q1eXQ4AAAAgCzB1uXTcW+MFAeArODGDal2bfN044atowEAZAP8TgMAO7JqlVS+vDkh7uUlffEFCXEAAAAgizGZMnZCxmKkOABkBUlJ0g8//PsaAAAAQNZ386bUv7/0ySfm+eeek5YulQoXtm1cAAAAAJDNkBQHAAAAAACwhW7dpPnzza/795dGjZJcXGwbEwAAAICHQvl0+0b5dAAAAAAAAFsYNMg8Kvzbb6Xx40mIAwAAAEAmYaQ4AAAAAADA43DlihQRITVpYp4vWlQ6fFhy4vIMAAAAkNUxUNy+MVIcAAAAAAAgs+3dK1WqJDVrJm3e/G87CXEAAAAgWzCZTBk6IWORFAcAAAAAAMgshiFNnSpVrSodPSo99ZTk6mrrqAAAAADgicLtyACQVXh42DoCAAAAAA/i77+ljh2l1avN840aSXPmSLlz2zYuAAAAABmOwd32jaQ4AGQFnp7StWu2jgIAAABAev34o9S6tfTXX5KLizRhgtSjB1fKAAAAAMAGSIoDAAAAAABktKNHzQnxYsWkZcuk8uVtHREAAACATMRzwO0bSXEAAAAAAICMYBj/jgTv0EG6dUtq00bKkcO2cQEAAADIdOTE7ZuDrQMAAKTDzZtSeLh5unnT1tEAAAAAuNu330rPPitdvGieN5mkN94gIQ4AAAAAdoCkOABkBYmJ0jffmKfERFtHAwAAACBZQoLUr59Uv760Z480dqytIwIAAABgAyaTKUMnZCzKpwMAAAAAADyM48elli2lXbvM8716SaNG2TYmAAAAADZBHtu+kRQHAAAAAAB4UMuWSV26SFeuSLlySXPnSo0a2ToqAAAAAEAqSIoDAAAgW7p586bc3NxsHYbdMWTYOgQAyPpmzTI/L1ySqleXliyR8ue3bUwAAAAAbIqS5/aNZ4oDAAAg20hKStLIkSP11FNPKUeOHDpx4oQkafDgwZo9e7aNowMAZBvNm0vBwdKgQdJ335EQBwAAAAA7R1IcAAAA2caoUaM0b948jR8/Xi4uLpb20qVL6/PPP7dhZACALM0wpA0bzP+VzOXSf/tNGjlScqIIHwAAAADzSPGMnJCxSIoDAAAg21iwYIFmzZqlNm3ayNHR0dJerlw5/f777zaMDACQZcXGSi1aSGFh0p03WHl62iwkAAAAAPbHZMrYCRmL25kBICvw9Px3VAoAIE2nT59W0aJFU7QnJSXp1q1bNojIfvHbCgDSYft2qWVL6dQp84jwGzdsHREAAAAA4CEwUhwAAADZRsmSJfXjjz+maF+5cqUqVKhgg4gAAFlSUpI0frxUvbo5IR4cLP38s9Srl60jAwAAAGCnKJ9u3xgpDgAAgGxjyJAhateunU6fPq2kpCR9+eWXOnz4sBYsWKA1a9bYOjwAQFYQEyO1bWt+hrhkLp3+6aeSt7dt4wIAAABg18hj2zdGigNAVnDzptS8uXm6edPW0QCA3WrUqJG+/vprbdy4UZ6enhoyZIgOHTqkr7/+Wi+99JKtwwMAZAWHD0sbN0ru7tJnn0lLl5IQBwAAAIAsjpHiAJAVJCZKK1eaX8+bZ9NQAMDe1ahRQxEREbYOAwCQVdWsKc2YIVWrJpUqZetoAAAAAGQRlDy3b4wUBwAAQLZRuHBh/f333ynaY2NjVbhwYRtEBACwe1FRUliYeYR4sq5dSYgDAAAAQDZCUhwAAADZxqlTp5SYmJiiPT4+XqdPn7ZBRAAAu/bVV1K5cubnh7/xhq2jAQAAAJCFmUwZOyFjUT4dAAAAWd7q1astr9evXy/vO579mpiYqE2bNqlQoUI2iAwAYJdu3pT69ZOmTTPPP/ecNGeObWMCAAAAkKU5kMm2ayTFAQAAkOW98sorkszPbmrXrp3VMmdnZxUqVEgTJkywQWQAALtz+LDUooX0yy/m+f79pVGjJBcX28YFAAAAAMg0JMUBAACQ5SUlJUmSgoODtXPnTuXNm9fGEQEA7NKuXVLt2tK1a5Kvr7RggVSvnq2jAgAAAJANMFDcvpEUBwAAQLZx8uRJW4cAALBn5cpJZctK7u7SokVSYKCtIwIAAAAAPAYOtg7gXoYNGyaTyWQ1FS9e3LL85s2b6tGjh/LkyaMcOXKoadOmiomJsdpGVFSUwsPD5eHhIT8/P/Xv31+3b9+26vP999+rYsWKcnV1VdGiRTVv3rzHcXgAkH4eHtLVq+bJw8PW0QCAXbt27Zq++eYbzZw5U1OnTrWaAABPoN9+kxISzK+dnaU1a6QNG0iIAwAAAMhQd+c0H3VCxrL7keKlSpXSxo0bLfNOTv+G3KdPH61du1YrVqyQt7e3evbsqSZNmujnn3+WJCUmJio8PFwBAQHaunWrzp49q7Zt28rZ2VljxoyRZB5NFB4erm7dumnx4sXatGmTOnfurMDAQIWFhT3egwWAtJhMkqenraMAALu3d+9e1a9fX9evX9e1a9eUO3duXbhwwXKDZK9evWwdIgDgcTEMaepU8zPDe/eWxo83t+fObdOwAAAAAGRPDuSx7ZrdJ8WdnJwUEBCQov3y5cuaPXu2lixZohdeeEGSNHfuXJUoUULbtm1T1apVtWHDBh08eFAbN26Uv7+/ypcvr5EjR2rAgAEaNmyYXFxcNHPmTAUHB2vChAmSpBIlSuinn37SpEmTSIoDAABkMX369FHDhg01c+ZMeXt7a9u2bXJ2dtZrr72mt99+29bhAQAelwsXpI4dpa+/Ns8fOyYlJkqOjraNCwAAAABgE3ZdPl2Sjh49qqCgIBUuXFht2rRRVFSUJGn37t26deuWQkNDLX2LFy+uAgUKKDIyUpIUGRmpMmXKyN/f39InLCxMcXFxOnDggKXPndtI7pO8DQCwC/HxUvv25ik+3tbRAIDd2rdvn9555x05ODjI0dFR8fHxyp8/v8aPH6/333/f1uHZB8PWAQBAJtuyRSpf3pwQd3GRPvlE+u9/SYgDAAAAyFSUT7dvdp0Ur1KliubNm6d169ZpxowZOnnypGrUqKErV64oOjpaLi4u8vHxsVrH399f0dHRkqTo6GirhHjy8uRl9+oTFxenGzdupBlbfHy84uLirCYAyDS3b0vz55un27dtHQ0A2C1nZ2c5OJhPcf38/Cw3VHp7e+vPP/984O1NmzZNhQoVkpubm6pUqaIdO3bcs39sbKx69OihwMBAubq6qlixYvrmm28e/EAAAA8uMVEaMUKqU0c6fVoqVkzavl3q0cP8OCIAAAAAyEQmU8ZOyFh2XT795ZdftrwuW7asqlSpooIFC2r58uVyd3e3YWTS2LFjNXz4cJvGAAAAAGsVKlTQzp079fTTT6tWrVoaMmSILly4oIULF6p06dIPtK1ly5apb9++mjlzpqpUqaLJkycrLCxMhw8flp+fX4r+CQkJeumll+Tn56eVK1fqqaee0h9//JHiJk4AQCb56y/pww+lpCSpXTvzCPEcOWwdFQAAAADADtj1SPG7+fj4qFixYjp27JgCAgKUkJCg2NhYqz4xMTGWZ5AHBAQoJiYmxfLkZffq4+Xldc/E+8CBA3X58mXL9DAjjwAAAJCxxowZo8DAQEnS6NGjlStXLnXv3l3nz5/Xp59++kDbmjhxorp06aIOHTqoZMmSmjlzpjw8PDRnzpxU+8+ZM0cXL17UqlWrVK1aNRUqVEi1atVSuXLlHvm4AADpULCg9Pnn0oIF0rx5JMQBAAAAPFamDP7fgxg2bFiK8uvFixe3LL9586Z69OihPHnyKEeOHGratGmK/GhUVJTCw8Pl4eEhPz8/9e/fX7fvqlz7/fffq2LFinJ1dVXRokU1b968h36/HrcslRS/evWqjh8/rsDAQFWqVEnOzs7atGmTZfnhw4cVFRWlkJAQSVJISIh+++03nTt3ztInIiJCXl5eKlmypKXPndtI7pO8jbS4urrKy8vLagIAAIBtPfvss6pTp44kc/n0devWKS4uTrt371b58uXTvZ2EhATt3r1boaGhljYHBweFhoYqMjIy1XVWr16tkJAQ9ejRQ/7+/ipdurTGjBmjxMTERzomAEAaEhKkd96R7vxN36KF9PrrtosJAAAAAGykVKlSOnv2rGX66aefLMv69Omjr7/+WitWrNAPP/ygM2fOqEmTJpbliYmJCg8PV0JCgrZu3ar58+dr3rx5GjJkiKXPyZMnFR4erjp16mjfvn3q3bu3OnfurPXr1z/W43xYdp0U79evn3744QedOnVKW7duVePGjeXo6KhWrVrJ29tbnTp1Ut++ffXdd99p9+7d6tChg0JCQlS1alVJUt26dVWyZEm9/vrr+uWXX7R+/XoNGjRIPXr0kKurqySpW7duOnHihN599139/vvvmj59upYvX64+ffrY8tABAACQgfbs2aMGDRqku/+FCxeUmJgof39/q3Z/f39FR0enus6JEye0cuVKJSYm6ptvvtHgwYM1YcIEjRo1Ks39xMfHKy4uzmp6XEw8nApAVnbsmPT889LEieYk+LVrto4IAAAAwBPOwZSx04NycnJSQECAZcqbN68k6fLly5o9e7YmTpyoF154QZUqVdLcuXO1detWbdu2TZK0YcMGHTx4UIsWLVL58uX18ssva+TIkZo2bZoSEhIkSTNnzlRwcLAmTJigEiVKqGfPnmrWrJkmTZqUYe9hZrLrpPhff/2lVq1a6ZlnntGrr76qPHnyaNu2bfL19ZUkTZo0SQ0aNFDTpk1Vs2ZNBQQE6Msvv7Ss7+joqDVr1sjR0VEhISF67bXX1LZtW40YMcLSJzg4WGvXrlVERITKlSunCRMm6PPPP1dYWNhjP14AAAA8vPXr16tfv356//33deLECUnS77//rldeeUWVK1dWUlJSpu4/KSlJfn5+mjVrlipVqqQWLVroP//5j2bOnJnmOmPHjpW3t7dlyp8/f6bGCADZwtKlUsWK0u7dUu7c0syZkqenraMCAAAA8IS7u3z5o04P6ujRowoKClLhwoXVpk0bRUVFSZJ2796tW7duWVVELF68uAoUKGCpiBgZGakyZcpYDRAJCwtTXFycDhw4YOlz5zaS+6RVVdHeONk6gHv54osv7rnczc1N06ZN07Rp09LsU7BgQX3zzTf33E7t2rW1d+/eh4oRAAAAtjd79mx16dJFuXPn1qVLl/T5559r4sSJeuutt9SiRQvt379fJUqUSPf28ubNK0dHxxTPVoqJiVFAQECq6wQGBsrZ2VmOjo6WthIlSig6OloJCQlycXFJsc7AgQPVt29fy3xcXByJcQBIy7VrUq9e0pw55vnq1aUlSyT+fxMAAABANhQfH6/4+HirNldXV0s17DtVqVJF8+bN0zPPPKOzZ89q+PDhqlGjhvbv36/o6Gi5uLjIx8fHap07KyJGR0enWjExedm9+sTFxenGjRtyd3d/pOPNbHY9UhwA8A8PD+ncOfPk4WHraADA7kyZMkXjxo3ThQsXtHz5cl24cEHTp0/Xb7/9ppkzZz5QQlySXFxcVKlSJW264zm1SUlJ2rRpk0JCQlJdp1q1ajp27JjViPQjR44oMDAw1YS4ZP4h4+XlZTUBAFJx6ZJUubI5IW4ySUOGSN99R0IcAAAAgN0wmTJ2urvCoLe3t8aOHZvqvl9++WU1b95cZcuWVVhYmL755hvFxsZq+fLlj/ldsF8kxQEgKzCZJF9f88QzYAEghePHj6t58+aSpCZNmsjJyUkffvih8uXL99Db7Nu3rz777DPNnz9fhw4dUvfu3XXt2jV16NBBktS2bVsNHDjQ0r979+66ePGi3n77bR05ckRr167VmDFj1KNHj0c7OACA5OMjVaggBQZKmzZJw4dLTnZd/A4AAAAAHsnAgQN1+fJlq+nOa1H34uPjo2LFiunYsWMKCAhQQkKCYmNjrfrcWRExICAg1YqJycvu1cfLy8vuR4lLmVg+/cSJEypcuHBmbR4AAACwuHHjhjz+qaRhMpnk6uqqwMDAR9pmixYtdP78eQ0ZMkTR0dEqX7681q1bZykTFRUVJQeHf+8xzZ8/v9avX68+ffqobNmyeuqpp/T2229rwIABjxQHADyxYmMlw5By5TLfGDpzpnTzpvlGUQAAAACwMw4ZPKAtrVLp6XH16lUdP35cr7/+uipVqiRnZ2dt2rRJTZs2lSQdPnxYUVFRloqIISEhGj16tM6dOyc/Pz9JUkREhLy8vFSyZElLn7sfWR0REZFmVUV7k2lJ8aJFi6pWrVrq1KmTmjVrJjc3t8zaFQBkf/HxUvIzZydOlB7yDyEAZGeff/65cuTIIUm6ffu25s2bp7x581r16dWr1wNts2fPnurZs2eqy77//vsUbSEhIdq2bdsD7QMAkIpt26SWLaVy5aRVq8xJ8Zw5zRMAAAAA2CFbFnnt16+fGjZsqIIFC+rMmTMaOnSoHB0d1apVK3l7e6tTp07q27evcufOLS8vL7311lsKCQlR1apVJUl169ZVyZIl9frrr2v8+PGKjo7WoEGD1KNHD0tivlu3bvrkk0/07rvvqmPHjtq8ebOWL1+utWvX2u7AH0CmJcX37NmjuXPnqm/fvurZs6datGihTp066bnnnsusXQJA9nX7tjR9uvn1+PEkxQHgLgUKFNBnn31mmQ8ICNDChQut+phMpgdOigMAHrOkJOnDD6X//EdKTJQcHaWYGOmfcn0AAAAAgJT++usvtWrVSn///bd8fX1VvXp1bdu2Tb7/VNqaNGmSHBwc1LRpU8XHxyssLEzTk3MOkhwdHbVmzRp1795dISEh8vT0VLt27TRixAhLn+DgYK1du1Z9+vTRlClTlC9fPn3++ecKCwt77Mf7MDItKV6+fHlNmTJFEyZM0OrVqzVv3jxVr15dxYoVU8eOHfX6669bPggAAADgUZw6dcrWIQAAHlVMjNS2rbRhg3m+ZUvp008lLy/bxgUAAAAA6WCy4VDxL7744p7L3dzcNG3aNE2bNi3NPgULFkxRHv1utWvX1t69ex8qRltzuH+XR+Pk5KQmTZpoxYoVGjdunI4dO6Z+/fopf/78atu2rc6ePZvZIQAAAAAAAHu2caO5VPqGDZK7uzR7trRkCQlxAAAAAFmGyZSxEzJWpifFd+3apTfffFOBgYGaOHGi+vXrp+PHjysiIkJnzpxRo0aNMjsEAAAAAABgrxISpK5dzSPFS5eWdu2SOnbkKhAAAAAAIMNkWvn0iRMnau7cuTp8+LDq16+vBQsWqH79+nJwMOfhg4ODNW/ePBUqVCizQgAAAAAAAPbOxUVavFhauFCaMME8UhwAAAAAshgHbuy1a5mWFJ8xY4Y6duyo9u3bKzAwMNU+fn5+mj17dmaFAAAAAAAA7NGXX0pXrkjt2pnnQ0LMEwAAAAAAmSDTkuJHjx69bx8XFxe1S/4BDAAAACDTGbYOAMCT7eZN6Z13pOnTJTc3qUoVqXhxW0cFAAAAAI+MceL2LdOeKT537lytWLEiRfuKFSs0f/78zNotAGRP7u7SyZPmiXKSAHBPx48f16BBg9SqVSudO3dOkvTtt9/qwIEDNo4MAJ5wv/9uToJPn26e79VLKlLEtjEBAAAAQAYxmUwZOiFjZVpSfOzYscqbN2+Kdj8/P40ZMyazdgsA2ZODg1SokHlyyLT/6waALO+HH35QmTJltH37dn355Ze6evWqJOmXX37R0KFDbRwdADyhDEOaO1eqVEn69VfJ11dat04aN05ydrZ1dAAAAACAJ0CmZVaioqIUHBycor1gwYKKiorKrN0CAADgCfbee+9p1KhRioiIkIuLi6X9hRde0LZt22wYGQA8oQxDat9e6thRun5devFF6ZdfpLAwW0cGAAAAABnKwZSxEzJWpiXF/fz89Ouvv6Zo/+WXX5QnT57M2i0AZE8JCVL//uYpIcHW0QCA3frtt9/UuHHjFO1+fn66cOGCDSICgCecySQVLiw5OkqjR0vr10uBgbaOCgAAAAAy3P+zd+dxVVT/H8ffF2RzAbcATVzKNMkttZTKnSQ10/RbZu5LfTVwIzUtc0/MXEvUMhUrDbPM/KppiqKpmCtmLrSoYSWYmSKogHB/f9wfV2/ignGZC7yePubRzJlzZz7DBBzmM+cchk93bHZLinfp0kWDBg3Sli1blJGRoYyMDG3evFmDBw/WCy+8YK/TAkDBlJ4uTZtmWdLTjY4GABxWyZIldfr06RvKDxw4oHvvvdeAiACgEDKbpb/+urY9erS0Z4/0+uuW5DgAAAAAAHmsiL0OPHHiRJ08eVItW7ZUkSKW02RmZqpHjx7MKQ4AAAC7eOGFF/Taa69pxYoVMplMyszM1I4dOzRs2DD16NHD6PAcCu8bA7CLs2el3r2lU6ekXbskd3dLIvzhh42ODAD+ldWrV99x3WeeecaOkQAAAEdF527HZrekuKurq5YvX66JEyfq4MGD8vDwUK1atVSpUiV7nRIAAACF3OTJkxUcHCw/Pz9lZGTI399fGRkZevHFFzV69GijwwOAgm3rVunFF6U//pDc3KTdu6UmTYyOCgByRYcOHe6onslkUkZGhn2DAQAAQI7ZLSmepVq1aqpWrZq9TwMAAADI1dVVCxYs0JtvvqkffvhBycnJevjhh/XAAw8YHRoAFFwZGdKkSdKECVJmplS9urR8uVSnjtGRAUCuyczMNDoEAADg4JgH3LHZLSmekZGhiIgIRUVF6cyZMzc0HDdv3myvUwMAAKCQ2r59u5544glVrFhRFStWNDocACj4fv9d6trV0ktcknr1kubMkYoVMzQsAAAAAMhrTuTEHZrdkuKDBw9WRESE2rZtq5o1a/J2BAAAAOyuRYsWuvfee9WlSxd169ZN/v7+RocEAAXbgAGWhHjx4tK8eVK3bkZHBAB5IiUlRVu3blV8fLzS0tJs9g0aNMigqAAAAHAzdkuKR0ZG6rPPPlObNm3sdQoAAADAxh9//KHIyEh9+umnmjJlimrXrq2uXbuqS5cuqlChgtHhAUDB89570qVLloQ4U1UAKCQOHDigNm3a6NKlS0pJSVHp0qV19uxZFS1aVN7e3iTFAQAopOgg7Nic7HVgV1dXVa1a1V6HB4DCxcND+uEHy+LhYXQ0AOCwypYtq5CQEO3YsUO//PKLnnvuOS1ZskSVK1dWixYtjA4PAPK/n3+WwsOvbVeqJG3aREIcQKEydOhQtWvXTn///bc8PDy0a9cu/frrr6pfv76mTZtmdHgAAADIht2S4q+++qpmz54ts9lsr1MAQOHh5CQ99JBlcbLbj24AKFCqVKmikSNHasqUKapVq5a2Zs13CwC4O59+KtWrJ4WESOvWGR0NABgmNjZWr776qpycnOTs7KzU1FT5+flp6tSpev31140ODwAAGMSUywtyl92GT9++fbu2bNmir7/+Wg899JBcXFxs9q9cudJepwYAAEAht2PHDi1dulSff/65rly5ovbt2yssLMzosAAgf0pJkQYNkhYtsmw3bizVrm1sTABgIBcXFzn9/wvr3t7eio+PV40aNeTl5aVTp04ZHB0AADCKE8OnOzS7JcVLliypZ5991l6HB4DCJS1NmjzZsv7665Krq7HxAICDGjVqlCIjI/XHH3/oySef1OzZs9W+fXsVLVrU6NAAIH/6/nupc2fp2DHLiEVvvimNHi0VsdvjBABweA8//LD27NmjBx54QE2bNtWYMWN09uxZffzxx6pZs6bR4QEAACAbdvsrdvHixfY6NAAUPunp0vjxlvXhw0mKA8BNbNu2TcOHD9fzzz+vsmXLGh0OAORvixZJr7wipaZK5ctLS5dKzZoZHRUAGG7y5Mm6ePGiJOmtt95Sjx49NGDAAD3wwANalDWqBgAAKHToKO7Y7Ppq99WrVxUdHa1ffvlFL774okqUKKE//vhDnp6eKl68uD1PDQAAgEJox44dRocAAAWHh4clId62rRQRIfGyEQBIkho0aGBd9/b21vr16w2MBgAAOAoTWXGHZrek+K+//qqnnnpK8fHxSk1N1ZNPPqkSJUro7bffVmpqqubPn2+vUwMAAKAQWb16tVq3bi0XFxetXr36lnWfeeaZPIrKcZnNRkcAwKFduiRlTTnRpYtUurTUqhVdHgAAAAAA+ZrdkuKDBw9WgwYNdPDgQZUpU8Za/uyzz+qll16y12kBAABQyHTo0EEJCQny9vZWhw4dblrPZDIpIyMj7wIDgPwkM1OaOlUKD5f27pV8fCzlQUHGxgUADqhKlSq37Al2/PjxPIwGAAA4Ct4ldmx2S4p/++232rlzp1z/Me9t5cqV9fvvv9vrtAAAAChkMjMzs10HANyhxESpe3dp40bL9kcfScOHGxsTADiwIUOG2Gynp6frwIEDWr9+vYbz8xMAAMAh2S0pnpmZmW1PnN9++00lSpSw12kBAABQiH300Ufq3Lmz3NzcbMrT0tIUGRmpHj16GBQZADiojRstCfHERMsc4u+9J/XpY3RUAODQBg8enG15eHi49u7dm8fRAAAAR+FEV3GH5mSvA7dq1UqzZs2ybptMJiUnJ2vs2LFq06aNvU4LAACAQqx37966cOHCDeUXL15U7969DYgIABxUero0apRlePTERKlmTcuw6X37MuYfANyl1q1b64svvjA6DAAAYBCTKXcX5C679RSfPn26goKC5O/vrytXrujFF1/UTz/9pLJly+rTTz+112kBoGByd5d27762DgDIltlsznZ+x99++01eXl4GRAQADiosTJoyxbLev780Y4alpzgA4K59/vnnKl26tNFhAAAAIBt2S4pXqFBBBw8eVGRkpL7//nslJyerb9++6tq1qzz4QxsAcsbZWXrkEaOjAACH9fDDD8tkMslkMqlly5YqUuRaMzcjI0MnTpzQU089ZWCEAOBghg6V/vc/acQI6bnnjI4GAPKVrLZnFrPZrISEBP3555+aO3eugZEBAAAjZddRA47DbklxSSpSpIi6detmz1MAAAAA6tChgyQpNjZWQUFBKl68uHWfq6urKleurE6dOhkUnWPi7zSgkLl8WVqyRPrvfy0/AEqUsIxExA8DAMix9u3b2zz0dnJy0j333KNmzZrpwQcfNDAyAAAA3IzdkuIfffTRLff36NHDXqcGgIInLU2aPduyPniw5OpqbDwA4GDGjh0rSapcubI6d+4sd6aaAIBrjh6VOneWDh2ytCsHDbKUkxAHgLsybtw4o0O4rb/3zDE6BKDQK9V4pNEhAIXa5ZgpeX5Opzw/I3LCbknxwYMH22ynp6fr0qVLcnV1VdGiRUmKA0BOpKdbhraUpFdeISkOADfRs2dPo0MAAMdhNkuLF0sDB0qXLkne3hI9GAHgX3N2dtbp06fl7e1tU/7XX3/J29tbGRkZBkUGAACMxPDpjs1uSfG///77hrKffvpJAwYM0PDhw+11WgAAABQypUuX1o8//qiyZcuqVKlSt/wD5Ny5c3kYGQAYKClJ6t9f+vRTy3ZgoPTxx5Kvr7FxAUABYDabsy1PTU2VKy+xAwAAOCS7zin+Tw888ICmTJmibt266dixYzn+/JQpUzRq1CgNHjxYs2bNkiRduXJFr776qiIjI5WamqqgoCDNnTtXPj4+1s/Fx8drwIAB2rJli4oXL66ePXsqLCxMRYpcu/zo6GiFhobq8OHD8vPz0+jRo9WrV69/e8kAAACws5kzZ6pEiRLWdd7KBVDo7d8vPf+89MsvkrOzNGmSZdQhJwbzA4B/491335Vk6QX24Ycfqnjx4tZ9GRkZ2rZtG3OKAwBQiDnxSMqh5WlSXJKKFCmiP/74I8ef27Nnj95//33Vrl3bpnzo0KFau3atVqxYIS8vL4WEhKhjx47asWOHJEuDtG3btvL19dXOnTt1+vRp9ejRQy4uLpo8ebIk6cSJE2rbtq369++vpUuXKioqSv369VO5cuUUFBT07y8aAAAAdnP9kOm81AgAsswbfvKkVLGipaf4Y48ZHREAFAgzZ86UZOkpPn/+fDk7O1v3ubq6qnLlypo/f75R4QEAAIORFHdsdkuKr1692mbbbDbr9OnTmjNnjh5//PEcHSs5OVldu3bVggULNGnSJGv5hQsXtHDhQi1btkwtWrSQJC1evFg1atTQrl271KhRI33zzTc6cuSINm3aJB8fH9WtW1cTJ07Ua6+9pnHjxsnV1VXz589XlSpVNH36dElSjRo1tH37ds2cOZOkOAAAQD6yf/9+ubi4qFatWpKkr776SosXL5a/v7+17QcABVJGhqVXuCQ1aiR9/rnUtKlUqpSxcQFAAXLixAlJUvPmzbVy5UqV4mcsAABAvmG3sdM6dOhgs3Ts2FHjxo1T7dq1tWjRohwdKzg4WG3btlVgYKBN+b59+5Senm5T/uCDD6pixYqKiYmRJMXExKhWrVo2w6kHBQUpKSlJhw8fttb557GDgoKsxwAAAED+8N///lc//vijJOn48ePq3LmzihYtqhUrVmjEiBEGRwcAdrJ1q/Tgg9KhQ9fKOnQgIQ4AdrJlyxYS4gAA4AYmkylXF+QuuyXFMzMzbZaMjAwlJCRo2bJlKleu3B0fJzIyUvv371dYWNgN+xISEuTq6qqSJUvalPv4+CghIcFa5/qEeNb+rH23qpOUlKTLly9nG1dqaqqSkpJsFgAAABjrxx9/VN26dSVJK1asUNOmTbVs2TJFREToiy++MDY4AMhtGRnSuHFSixbSzz9LY8caHREAFAqdOnXS22+/fUP51KlT9dxzzxkQEQAAAG7Hbknx3HDq1CkNHjxYS5culbu7u9Hh2AgLC5OXl5d18fPzMzokAAWZu7u0ZYtlcbCfhwDgSMxmszIzMyVJmzZtUps2bSRJfn5+Onv2rJGhAUDu+u03SzJ8/HgpM1Pq3Vv6+GOjowKAQmHbtm3Wdub1WrdurW3bthkQEQAAcAROptxdkLvsNqd4aGjoHdedMWNGtuX79u3TmTNnVK9ePWtZRkaGtm3bpjlz5mjDhg1KS0vT+fPnbXqLJyYmytfXV5Lk6+ur3bt32xw3MTHRui/rv1ll19fx9PSUh4dHtrGNGjXK5hqTkpJIjAOwH2dnqVkzo6MAAIfXoEEDTZo0SYGBgdq6davmzZsnyTL/4z9HBgKAfGvNGqlXL+mvv6TixaX586WuXY2OCgAKjeTkZLm6ut5Q7uLiwmiSAAAUYo404vmUKVM0atQoDR48WLNmzZIkXblyRa+++qoiIyOVmpqqoKAgzZ071+aZWXx8vAYMGKAtW7aoePHi6tmzp8LCwlSkyLWUcnR0tEJDQ3X48GH5+flp9OjR6tWrVx5fYc7ZLSl+4MABHThwQOnp6apevboky3CWzs7ONknuW42J37JlSx26fk40Sb1799aDDz6o1157TX5+fnJxcVFUVJQ6deokSYqLi1N8fLwCAgIkSQEBAXrrrbd05swZeXt7S5I2btwoT09P+fv7W+usW7fO5jwbN260HiM7bm5ucnNzu9MvBwAAAPLArFmz1LVrV61atUpvvPGGqlatKkn6/PPP9dhjjxkcnWMwy2x0CAD+jQ0bpHbtLOv16knLl0v//7MOAJA3atWqpeXLl2vMmDE25ZGRkdbnjQAAAEbZs2eP3n//fdWuXdumfOjQoVq7dq1WrFghLy8vhYSEqGPHjtqxY4ckS8fktm3bytfXVzt37tTp06fVo0cPubi4aPLkyZIsHU/atm2r/v37a+nSpYqKilK/fv1Urlw5BQUF5fm15oTdkuLt2rVTiRIltGTJEpUqVUqS9Pfff6t3795q3LixXn311dseo0SJEqpZs6ZNWbFixVSmTBlred++fRUaGqrSpUvL09NTAwcOVEBAgBo1aiRJatWqlfz9/dW9e3dNnTpVCQkJGj16tIKDg61J7f79+2vOnDkaMWKE+vTpo82bN+uzzz7T2rVrc/NLAgB3Lz1d+uADy/rLL0suLsbGAwAOqnbt2je8VClJ77zzjpydnQ2ICAByWWCg1Ly5VLeuFBYm8bI2AOS5N998Ux07dtQvv/yiFi1aSJKioqK0bNkyff755wZHBwAAjOLkAF3Fk5OT1bVrVy1YsECTJk2yll+4cEELFy7UsmXLrO2XxYsXq0aNGtq1a5caNWqkb775RkeOHNGmTZvk4+OjunXrauLEiXrttdc0btw4ubq6av78+apSpYqmT58uSapRo4a2b9+umTNnOnxS3G5zik+fPl1hYWHWhLgklSpVSpMmTbJ+oXLDzJkz9fTTT6tTp05q0qSJfH19tXLlSut+Z2dnrVmzRs7OzgoICFC3bt3Uo0cPTZgwwVqnSpUqWrt2rTZu3Kg6depo+vTp+vDDDx3+5gEoRNLSpJAQy5KWZnQ0AODw9u3bp08++USffPKJ9u/fL3d3d7nwQhGA/Oqrr6QrVyzrzs6W3uIzZpAQBwCDtGvXTqtWrdLPP/+sV155Ra+++qp+//13bd682TpSEQAAKHyccnm5G8HBwWrbtq0CAwNtyvft26f09HSb8gcffFAVK1ZUTEyMJCkmJka1atWyGU49KChISUlJOnz4sLXOP48dFBRkPYYjs1tP8aSkJP355583lP/555+6ePHiXR83OjraZtvd3V3h4eEKDw+/6WcqVap0w/Do/9SsWTMdOHDgruMCAACA8c6cOaPOnTtr69atKlmypCTp/Pnzat68uSIjI3XPPfcYGyAA5ERKiuWlyIgIy3/fe89Szks+AGC4tm3bqm3btpIsz0E//fRTDRs2TPv27VNGRobB0QEAgIIgNTVVqampNmW3mt45MjJS+/fv1549e27Yl5CQIFdXV+vzsiw+Pj5KSEiw1rk+IZ61P2vfreokJSXp8uXL8vDwuPMLzGN26yn+7LPPqnfv3lq5cqV+++03/fbbb/riiy/Ut29fdezY0V6nBQAAQCE2cOBAJScn6/Dhwzp37pzOnTunH374QUlJSRo0aJDR4QHAnTt4UKpf35IQd3KSypSRzGajowIAXGfbtm3q2bOnypcvr+nTp6tFixbatWuX0WEBAACDmEy5u4SFhcnLy8tmCQsLy/bcp06d0uDBg7V06VK5u7vn8ZXnD3brKT5//nwNGzZML774otLT0y0nK1JEffv21TvvvGOv0wIAAKAQW79+vTZt2qQaNWpYy/z9/RUeHq5WrVoZGBkA3CGzWZo3TwoNlVJTpfLlpaVLpWbNjI4MACBL76iIiAgtXLhQSUlJev7555WamqpVq1bJ39/f6PAAAEABMmrUKIWGhtqU3ayX+L59+3TmzBnVq1fPWpaRkaFt27Zpzpw52rBhg9LS0nT+/Hmb3uKJiYny9fWVJPn6+mr37t02x01MTLTuy/pvVtn1dTw9PR26l7hkx57iRYsW1dy5c/XXX3/pwIEDOnDggM6dO6e5c+eqWLFi9jotAAAACrHMzMxs5w53cXFRZmamAREBQA78/bf0n/9IwcGWhHjbtpYe4yTEAcAhtGvXTtWrV9f333+vWbNm6Y8//tB7WVNbAACAQs/JZMrVxc3NTZ6enjbLzZLiLVu21KFDhxQbG2tdGjRooK5du1rXXVxcFBUVZf1MXFyc4uPjFRAQIEkKCAjQoUOHdObMGWudjRs3ytPT0/ryX0BAgM0xsupkHcOR2a2neJbTp0/r9OnTatKkiTw8PGQ2m2Uymex9WgAAABRCLVq00ODBg/Xpp5+qfPnykqTff/9dQ4cOVcuWLQ2ODgBuIylJ2rzZMmf41KnS4MGWMfMAAA7h66+/1qBBgzRgwAA98MADRocDAAAcjJF/vpUoUUI1a9a0KStWrJjKlCljLe/bt69CQ0NVunRpeXp6auDAgQoICFCjRo0kSa1atZK/v7+6d++uqVOnKiEhQaNHj1ZwcLA1Gd+/f3/NmTNHI0aMUJ8+fbR582Z99tlnWrt2bd5e8F2wW0/xv/76Sy1btlS1atXUpk0bnT59WpLlC/7qq6/a67QAAAAoxObMmaOkpCRVrlxZ999/v+6//35VqVJFSUlJ9OL5B5NItAEO4fp5witVkpYtk3bulIYMISEOAA5m+/btunjxourXr6+GDRtqzpw5Onv2rNFhAQAA3JGZM2fq6aefVqdOndSkSRP5+vpq5cqV1v3Ozs5as2aNnJ2dFRAQoG7duqlHjx6aMGGCtU6VKlW0du1abdy4UXXq1NH06dP14YcfKigoyIhLyhG79RQfOnSoXFxcFB8fbzOnY+fOnRUaGqrp06fb69QAUPC4uUlr1lxbBwBky8/PT/v371dUVJSOHj0qSapRo4YCAwMNjgwAspGQIPXoYekR3ratpax1a2NjAgDcVKNGjdSoUSPNmjVLy5cv16JFixQaGqrMzExt3LhRfn5+KlGihNFhAgAAgzg52HvN0dHRNtvu7u4KDw9XeHj4TT9TqVIlrVu37pbHbdasmQ4cOJAbIeYpuyXFv/nmG23YsEEVKlSwKX/ggQf066+/2uu0AFAwFSly7UEpACBby5cv1+rVq5WWlqaWLVtq4MCBRocEADf3zTdS9+7SmTPSjz9KrVpZhk0HADi8YsWKqU+fPurTp4/i4uK0cOFCTZkyRSNHjtSTTz6p1atXGx0iAAAA/sFuw6enpKSoaNGiN5SfO3fuppPAAwAAAHdj3rx56tKli/bu3auffvpJwcHBGj58uNFhAcCN0tOlkSOloCBLQrxWLenrr0mIA0A+Vb16dU2dOlW//fabPv30U6PDAQAABnIymXJ1Qe6yW1K8cePG+uijj6zbJpNJmZmZmjp1qpo3b26v0wJAwZSeLkVEWJb0dKOjAQCHM2fOHI0dO1ZxcXGKjY3VkiVLNHfuXKPDAgBbJ05IjRtLb79t2R4wQPruO+m6KccAAPmTs7OzOnToQC9xAAAKMZMpdxfkLrsNnz516lS1bNlSe/fuVVpamkaMGKHDhw/r3Llz2rFjh71OCwAFU1qa1Lu3Zf255+hJBAD/cPz4cfXs2dO6/eKLL6pv3746ffq0ypUrZ2BkAPD/fv9devhh6cIFyctLWrhQ6tTJ6KgAAAAAACgU7JYUr1mzpn788UfNmTNHJUqUUHJysjp27Kjg4GAeTAIAACBXpaamqlixYtZtJycnubq66vLlywZGBQDXufde6T//kQ4flj79VKpc2eiIAAAAAAC5yIne3Q7NLknx9PR0PfXUU5o/f77eeOMNe5wCAAAAsPHmm2+qaNGi1u20tDS99dZb8vLyspbNmDHDiNAAFFZHj0qlS0s+Ppbt996TihRh1B8AAAAAKIBMIivuyOySFHdxcdH3339vj0MDAAAAN2jSpIni4uJsyh577DEdP37cum1iMiYAecVslhYvlkJCpCZNpHXrJCcnycPD6MgAAAAAACiU7DZ8erdu3bRw4UJNmTLFXqcAAAAAJEnR0dFGhwAAFklJUv/+liHSJSkzU0pOljw9jY0LAAAAAGBXDJ/u2OyWFL969aoWLVqkTZs2qX79+jZzPEoMXQkAAAAYwWw2OgKgANu7V+rcWTp+XHJ2lt56Sxo+3NJLHAAAAAAAGCbXk+LHjx9X5cqV9cMPP6hevXqSpB9//NGmDkNXAgAAAAAKjMxMadYsaeRIKT1dqlTJ0lM8IMDoyAAAAAAAeYSe4o4t15PiDzzwgE6fPq0tW7ZIkjp37qx3331XPj4+uX0qACg83Nykzz67tg4AAADHcfmyNH++JSHeqZP04YdSyZJGRwUAAAAAyEN0CnZsuZ4UN/9jPMavv/5aKSkpuX0aAChcihSRnnvO6CgAAACQnWLFpMhIafdu6b//lXgQAgAAAACAQ7HbnOJZ/pkkBwAAAAAgX7t6VZo4USpbVho40FJWr55lAQAAAAAUSgyf7ticcvuAJpPphuEBGC4AAP6lq1elFSssy9WrRkcDAA7t22+/Vbdu3RQQEKDff/9dkvTxxx9r+/btd3W88PBwVa5cWe7u7mrYsKF27959R5+LjIyUyWRShw4d7uq8ABzUb79JLVpIEyZIw4ZJv/5qdEQAAAAAAOA27DJ8eq9eveT2/3PeXrlyRf3791exYsVs6q1cuTK3Tw0ABVdqqvT885b15GTLcOoAgBt88cUX6t69u7p27aoDBw4oNTVVknThwgVNnjxZ69aty9Hxli9frtDQUM2fP18NGzbUrFmzFBQUpLi4OHl7e9/0cydPntSwYcPUuHHjf3U9ABzM//4n9eolnTsnFS8uvf++VKmS0VEBAAAAABwAfYQdW673FO/Zs6e8vb3l5eUlLy8vdevWTeXLl7duZy0AAABAbps0aZLmz5+vBQsWyMXFxVr++OOPa//+/Tk+3owZM/TSSy+pd+/e8vf31/z581W0aFEtWrTopp/JyMhQ165dNX78eN133313dR0AHExqqjRkiPTMM5aEeL160oED0osvGh0ZAAAAAMBBOJlMubogd+V6V8PFixfn9iEBAACAOxIXF6cmTZrcUO7l5aXz58/n6FhpaWnat2+fRo0aZS1zcnJSYGCgYmJibvq5CRMmyNvbW3379tW33357y3OkpqZae7NLUlJSUo5iBJAHMjKkpk2l776zbA8dKoWFSf8/OhoAAAAAAHB8ud5THAAAADCKr6+vfv755xvKt2/fnuNe22fPnlVGRoZ8fHxsyn18fJSQkJDtZ7Zv366FCxdqwYIFd3SOsLAwm9GU/Pz8chTjv8ILx8CdcXaWOnaUypSxDJ8+YwYJcQAAAADADZxMubsgd5EUBwAAQIHx0ksvafDgwfruu+9kMpn0xx9/aOnSpRo2bJgGDBhg13NfvHhR3bt314IFC1S2bNk7+syoUaN04cIF63Lq1Cm7xgjgDiUnS8ePX9seNkw6fFh6+mnjYgIAAAAAODSTKXcX5K5cHz4dAAAAMMrIkSOVmZmpli1b6tKlS2rSpInc3Nw0bNgwDRw4MEfHKlu2rJydnZWYmGhTnpiYKF9f3xvq//LLLzp58qTatWtnLcvMzJQkFSlSRHFxcbr//vttPuPm5iY3epwCjuXgQalzZ8nJSdqzRypWzLL+j1EjAAAAAABA/kFPcQAAABQYJpNJb7zxhs6dO6cffvhBu3bt0p9//qmJEyfm+Fiurq6qX7++oqKirGWZmZmKiopSQEDADfUffPBBHTp0SLGxsdblmWeeUfPmzRUbG5u3Q6MDyDmzWQoPlxo2lOLipAsXpJMnjY4KAAAAAJBPOMmUqwtyFz3FASA/cHWVFi++tg4AuCVXV1f5+/v/6+OEhoaqZ8+eatCggR599FHNmjVLKSkp6t27tySpR48euvfeexUWFiZ3d3fVrFnT5vMlS5aUpBvKATiYc+ekfv2kL7+0bD/9tKXtdYdTIQAAAAAAAMdGUhwA8gMXF6lXL6OjAACH17x5c5luMenS5s2bc3S8zp07688//9SYMWOUkJCgunXrav369fL5/2GU4+Pj5eTE4EtAvrZjh/Tii1J8vKXN9c470qBBTOAGAAAAAMgR/ox0bCTFAQAAUGDUrVvXZjs9PV2xsbH64Ycf1LNnz7s6ZkhIiEJCQrLdFx0dfcvPRkRE3NU5AeSh8eMtCfH775eWL5fq1zc6IgAAAABAPuREUtyhkRQHgPzg6lVpwwbLelCQVIQf3wCQnZkzZ2ZbPm7cOCUnJ+dxNADyhYgIadIkacoUydPT6GgAAAAAAIAdMNYjAOQHqamWuS2fftqyDgDIkW7dumnRokVGhwHAEWzYII0Zc227fHlp7lwS4gAAAACAf8XJZMrVBbmLroYAAAAo8GJiYuTu7m50GA7BbHQAgFHS06XRo6WpUy3bAQFS69bGxgQAAAAAKDDIYzs2kuIAAAAoMDp27GizbTabdfr0ae3du1dvvvmmQVEBMNyJE1KXLtJ331m2g4Ol5s2NjQkAAAAAAOQZkuIAAAAoMLy8vGy2nZycVL16dU2YMEGtWrUyKCoAhlqxQurXT0pKkkqWlBYulP7xAg0AAAAAAP8WQ547NpLiAAAAKBAyMjLUu3dv1apVS6VKlTI6HACOYMQI6Z13LOsBAdKnn0qVKhkbEwAAAAAAyHNORgcAAAAA5AZnZ2e1atVK58+fNzoUAI4iIMAyqdvrr0tbt5IQBwAAAADYjcmUuwtyFz3FAQAAUGDUrFlTx48fV5UqVYwOBYARzGbpt98kPz/L9rPPSkePStWrGxsXAAAAAKDAoyeyY+P+AEB+4OoqzZljWVxdjY4GABzWpEmTNGzYMK1Zs0anT59WUlKSzQKgALtwQXrxRenhh6Xff79WTkIcAAAAAIBCz6GT4vPmzVPt2rXl6ekpT09PBQQE6Ouvv7buv3LlioKDg1WmTBkVL15cnTp1UmJios0x4uPj1bZtWxUtWlTe3t4aPny4rl69alMnOjpa9erVk5ubm6pWraqIiIi8uDwAuHMuLlJwsGVxcTE6GgBwOBMmTFBKSoratGmjgwcP6plnnlGFChVUqlQplSpVSiVLlmSecaAg27NHqldPioyUzp+Xvv3W6IgAAAAAAIWMyWTK1QW5y6GHT69QoYKmTJmiBx54QGazWUuWLFH79u114MABPfTQQxo6dKjWrl2rFStWyMvLSyEhIerYsaN27NghScrIyFDbtm3l6+urnTt36vTp0+rRo4dcXFw0efJkSdKJEyfUtm1b9e/fX0uXLlVUVJT69euncuXKKSgoyMjLBwAAwB0aP368+vfvry1bthgdCoC8lJkpzZwpjRwpXb1qmTP8008tc4kDAAAAAAD8P4dOirdr185m+6233tK8efO0a9cuVahQQQsXLtSyZcvUokULSdLixYtVo0YN7dq1S40aNdI333yjI0eOaNOmTfLx8VHdunU1ceJEvfbaaxo3bpxcXV01f/58ValSRdOnT5ck1ahRQ9u3b9fMmTNJigNwHBkZ13o8NW4sOTsbGw8AOBiz2SxJatq0qcGRAMgzf/4p9eolrVtn2e7USfrwQ6lkSSOjAgAAAAAUUvTtdmwOPXz69TIyMhQZGamUlBQFBARo3759Sk9PV2BgoLXOgw8+qIoVKyomJkaSFBMTo1q1asnHx8daJygoSElJSTp8+LC1zvXHyKqTdYybSU1NZY5KAHnnyhWpeXPLcuWK0dEAgENiWKmc4auFfO/tty0JcXd3af58acUKEuIAAAAAAMM4mUy5uiB3OXRPcUk6dOiQAgICdOXKFRUvXlxffvml/P39FRsbK1dXV5X8x0MPHx8fJSQkSJISEhJsEuJZ+7P23apOUlKSLl++LA8Pj2zjCgsL0/jx43PjEgEAAJALqlWrdtvE+Llz5/IoGgB2N2GCdPKkNHasVKuW0dEAAAAAAAAH5vBJ8erVqys2NlYXLlzQ559/rp49e2rr1q1Gh6VRo0YpNDTUup2UlCQ/Pz8DIwIAACjcxo8fLy8vL6PDAGAvp05J4eHS5MmSk5NUtKj0+edGRwUAAAAAgCRG5XN0Dp8Ud3V1VdWqVSVJ9evX1549ezR79mx17txZaWlpOn/+vE1v8cTERPn6+kqSfH19tXv3bpvjJSYmWvdl/Ter7Po6np6eN+0lLklubm5yc3P719cHAACA3PHCCy/I29vb6DAA2MNXX0m9e0t//y2VLSsNG2Z0RAAAAAAA2DByxPN58+Zp3rx5OnnypCTpoYce0pgxY9S6dWtJ0pUrV/Tqq68qMjJSqampCgoK0ty5c21G046Pj9eAAQO0ZcsWFS9eXD179lRYWJiKFLmWTo6OjlZoaKgOHz4sPz8/jR49Wr169crLS71r+WZO8SyZmZlKTU1V/fr15eLioqioKOu+uLg4xcfHKyAgQJIUEBCgQ4cO6cyZM9Y6GzdulKenp/z9/a11rj9GVp2sYwAAAMDxMZ84UEClpkqDBkkdOlgS4g0aSM8+a3RUAAAAAAA4lAoVKmjKlCnat2+f9u7dqxYtWqh9+/Y6fPiwJGno0KH63//+pxUrVmjr1q36448/1LFjR+vnMzIy1LZtW6WlpWnnzp1asmSJIiIiNGbMGGudEydOqG3btmrevLliY2M1ZMgQ9evXTxs2bMjz670bDt1TfNSoUWrdurUqVqyoixcvatmyZYqOjtaGDRvk5eWlvn37KjQ0VKVLl5anp6cGDhyogIAANWrUSJLUqlUr+fv7q3v37po6daoSEhI0evRoBQcHW3t59+/fX3PmzNGIESPUp08fbd68WZ999pnWrl1r5KUDAAAgB8xms9EhAMhtP/4ovfCCdOCAZfvVVy1Dp7u6GhsXAAAAAADZMLLTRrt27Wy233rrLc2bN0+7du1ShQoVtHDhQi1btkwtWrSQJC1evFg1atTQrl271KhRI33zzTc6cuSINm3aJB8fH9WtW1cTJ07Ua6+9pnHjxsnV1VXz589XlSpVNH36dElSjRo1tH37ds2cOVNBQUF5fs055dA9xc+cOaMePXqoevXqatmypfbs2aMNGzboySeflCTNnDlTTz/9tDp16qQmTZrI19dXK1eutH7e2dlZa9askbOzswICAtStWzf16NFDEyZMsNapUqWK1q5dq40bN6pOnTqaPn26Pvzww3xx8wAAAGCRmZnJ0OlAQbJqlVSvniUhXrastHatNG0aCXEAAAAAQKGRmpqqpKQkmyU1NfW2n8vIyFBkZKRSUlIUEBCgffv2KT09XYGBgdY6Dz74oCpWrKiYmBhJUkxMjGrVqmUznHpQUJCSkpKsvc1jYmJsjpFVJ+sYjs6he4ovXLjwlvvd3d0VHh6u8PDwm9apVKmS1q1bd8vjNGvWTAeyeh8AgCNycZGmTr22DgDAXaJXPfKFihWl9HSpWTPpk0+ke+81OiIAAAAAAG4pt3sih4WFafz48TZlY8eO1bhx47Ktf+jQIQUEBOjKlSsqXry4vvzyS/n7+ys2Nlaurq4qWbKkTX0fHx8lJCRIkhISEmwS4ln7s/bdqk5SUpIuX74sDw+Pu73UPOHQSXEAwP9zdZWGDzc6CgAAAPs5f17K+gO9Xj1p2zbLHOLOzkZGBQAAAADAHcnt4dNHjRql0NBQm7Ks6aGzU716dcXGxurChQv6/PPP1bNnT23dujVXY8rPHHr4dAAAAABAAWc2S+Hhlt7h+/ZdK2/YkIQ4AAAAAKDQcnNzk6enp81yq6S4q6urqlatqvr16yssLEx16tTR7Nmz5evrq7S0NJ0/f96mfmJionx9fSVJvr6+SkxMvGF/1r5b1fH09HT4XuISSXEAyB8yMqQ9eyxLRobR0QAAAOSOc+ekjh2lkBDp4kUpIsLoiAAAAAAAuCumXF7+rczMTKWmpqp+/fpycXFRVFSUdV9cXJzi4+MVEBAgSQoICNChQ4d05swZa52NGzfK09NT/v7+1jrXHyOrTtYxHB3DpwNAfnDlivToo5b15GSpWDFj4wEAAPi3duyQunSRTp2yTBXzzjvSwIFGRwUAAAAAQL4zatQotW7dWhUrVtTFixe1bNkyRUdHa8OGDfLy8lLfvn0VGhqq0qVLy9PTUwMHDlRAQIAaNWokSWrVqpX8/f3VvXt3TZ06VQkJCRo9erSCg4OtvdP79++vOXPmaMSIEerTp482b96szz77TGvXrjXy0u8YSXEAAAAAQN7JyJCmTJHGjrWsV60qLV9umUccAAAAAIB8KrfnFM+JM2fOqEePHjp9+rS8vLxUu3ZtbdiwQU8++aQkaebMmXJyclKnTp2UmpqqoKAgzZ071/p5Z2dnrVmzRgMGDFBAQICKFSumnj17asKECdY6VapU0dq1azV06FDNnj1bFSpU0IcffqigoKA8v967QVIcAAAAAJB3li+XRo+2rHftKs2bJ5UoYWxMAAAAAAD8S0bOWb1w4cJb7nd3d1d4eLjCw8NvWqdSpUpat27dLY/TrFkzHThw4K5iNBpJcQAAAABA3nnhBWnlSqldO6lHD8nAN+kBAAAAAEDhQFIcAAAAAGA/aWnSrFlScLBUrJjk5CR9/rnRUQEAAAAAkKuMHD4dt0dSHAAAAABgH8ePS126SLt3S3Fx0m2GcwMAAAAAIL8iJe7YjBzeHgAAAABQUK1YIT38sCUhXrKk9PTTRkcEAAAAAAAKKXqKA0B+4OIijR17bR0AAMBRXbokDR0qffCBZfuxx6Rly6RKlYyNCwAAAAAAO2L0dMdGUhwA8gNXV2ncOKOjAAAUIPyhBrv46Sfp2Welw4ct/5O9/rqlDVOEPz0BAAAAAIBxeDIBAAAAAMgdxYpJiYmSr6/0ySdSy5ZGRwQAAAAAQJ5wYlZxh0ZSHADyg8xM6ehRy3qNGpKTk7HxAAAAZLlyRXJ3t6yXLy/973/SffdJ3t7GxgUAAAAAQB5iVD7HRlYFAPKDy5elmjUty+XLRkcDAABgsXu35O8vrVx5raxRIxLiAAAAAADAoZAUBwAAAADkTGamNG2a9Pjj0okT0qRJljIAAAAAAAopUy7/Q+5i+HQAAAAAwJ07c0bq2VNav96y/Z//SAsWML0LAAAAAKBQY/h0x8ZTCwAAAKAQMZuNjgD52ubNUt26loS4u7v0/vvSZ59JJUsaHRkAAAAAAMBN0VMcAAAAAHB7cXHSk09ahkn395eWL5dq1jQ6KgAAAAAAHIITQ547NJLiAAAAAIDbq15deuUV6coVafZsqWhRoyMCAAAAAAC4IyTFAQAAAADZW71aqldPqlDBsj17NnOHAwAAAACQDeYUd2wkxQEgP3BxkYYNu7YOAABgT1euSCNGSO+9JzVtKkVFSc7OJMQBAAAAALgJkuKOjaQ4AOQHrq7SO+8YHQUAACgM4uKkF16QYmMt2488YplH3NnZ0LAAAAAAAADuFklxAAAAAIDFRx9Z5g1PSZHKlpWWLJHatDE6KgAAAAAAHJ5JdBV3ZCTFASA/yMyU4uMt6xUrMnQpAADIXSkp0oAB0scfW7abN5c++UQqX97YuAAAAAAAAHIBSXEAyA8uX5aqVLGsJydLxYoZGw8AAChYTCZp/37Li3fjxkmvv85w6QAAAAAA5IATHcUdGklxAAAAACiMzGbL4uQkFS0qLV8unTsnNW5sdGQAAAAAAOQ7DJ/u2Bh/FwAAAAAKm3PnpGeflaZOvVb20EMkxAEAAAAAQIFEUhwAAAAACpPt26W6daWvvpImTZLOnjU6IgAAAAAA8j2TKXcX5C6S4gAAAABQGGRkWJLgTZtKp05JDzwgffutVLas0ZEBAAAAAJDvmXL5H3IXc4oDAAAAhRB/XBUyf/whdesmbdli2e7eXQoPl0qUMDYuAAAAAACAPEBSHAAAAAAKsitXpIYNpd9+k4oVk+bOlXr0MDoqAAAAAAAKFCf6Hzg0kuIAkB8UKSK98sq1dQAAgDvl7i6NGCEtWiQtXy5Vq2Z0RAAAAAAAAHmKOcUBID9wc7MMcRoeblkHAAC4lePHpe+/v7YdEiLt2kVCHAAAwCD79u7RwFf6K7DZE6rzUHVtjtpk3Zeenq6Z099Rpw7t1LBBXQU2e0JvjBqhM2cSbY5x9Mhh/bdfbz3RqIGaPNZQE8a+qUspKXl9KUC+4ORk0piXn9TRL0boXPREHV4xXCN7t7Cp077pQ/rfrD76bf2buhwzRbUfKHfLY66a0VuXY6aoXRP/bPeX9iyqn78apcsxU+RV3D3XrgX5B3OKOzaS4gAAAABQkCxfLj38sNSxo5SUZCkzmXixDgAAwECXL19S9erVNWr02Bv2XblyRceOHtHL/Qdo+YqVmjF7jk6eOKHBIQOsdc6cSdTLfXvLr2JFffLpZ5r7/gL98vNPevONUXl5GUC+8Wr3pnrp2UYaOv0r1X1hhkbP/VqhXZvqleces9Yp6uGqnd//qtHh6297vIEvPCGz2XzLOvNf76RDP5/+17Ej/zKZcndB7mIMXgDID8xm6exZy3rZsvxGBAAAN7p0SRoyRFqwwLJds6aUkiJ5ehoaFgAAAKQnGjfVE42bZruvRIkSev/DxTZlo954U11feE6n//hD5cqX17boaBVxKaLXR4+Vk5Olr9voseP1n2efUfyvv6pipUp2vwYgP2lUq5LWfHtE63fGSZLiE/7W80/WVQN/P2udT9cfkCRV9C11y2PVfqCcBndprMd7v6eTa0dnW+elZxvKq4SHJi+K0lOPPZhLVwEgN9FTHADyg0uXJG9vy3LpktHRAAAAR/PDD9Ijj1gS4iaT9MYb0tatUrlbD/8HAAAAx5ScnCyTyaQS//+CY1p6mlxcXKwJcUlyc7MMz3xg/z5DYgQc2a5Dv6p5g6qq6ldWklSrajkF1Kmkb2LicnQcDzcXRYx/QUOmfaXEc8nZ1nmwsrdG9WmpfhOWKzPz1r3JUbCZcnlB7iIpDgAAAAD5ldlsSYQ/8oh05Ijk6ytt3ChNmiQVYWAwAACA/Cg1NVWzZkxT6zZtVbx4cUnSow0b6a+zZxWx6EOlp6Up6cIFzZ45XZJ09uyfRoYLOKRpH23Vio0HdTAyVEnfvqVdSwZqzvIdivwmNkfHmTrkae06FK813x7Jdr+ri7OWTOii1+es06nEC7kQOQB7ISkOAAAAAPmV2SytXClduSIFBUkHD0otWxodFQAAyCWnTp1Snz59blknNTVVSUlJNktqamoeRYjclp6eruGhg2U2m/XGmPHW8qpVH9DEt6boo4jFatigrlo0fVz3VrhXZcqUlYlp9oAb/KdlLb0QVFe9xkYqoNe76jdxhYa82Fhd29S742O0faKGmtW/X8Nn/e+mdSYOeEpxJ88ockNsLkSN/M7JZMrVBbnLoZPiYWFheuSRR1SiRAl5e3urQ4cOiouzHdriypUrCg4OVpkyZVS8eHF16tRJiYmJNnXi4+PVtm1bFS1aVN7e3ho+fLiuXr1qUyc6Olr16tWTm5ubqlatqoiICHtfHgAAAADcHfP/D8nn5CQtWSLNni2tW2eZagUAABQY586d05IlS25ZJywsTF5eXjbLO2+H5VGEyE3p6eka/uoQnf7jD73/4SJrL/EsbZ5up83bdmjj5m3atuM79X9loP7++5wq+Pnd5IhA4TU5pI2mfRytFZu+1+FfEvXp+gN6L3KHhvdodsfHaNbgft13b2klfDNWF799Sxe/fUuS9OnkbtoQ/rIkqWn9+9WxRS3r/q/f6ydJ+u3rNzW6X2BuXxYcHMOnOzaHHk9v69atCg4O1iOPPKKrV6/q9ddfV6tWrXTkyBEVK1ZMkjR06FCtXbtWK1askJeXl0JCQtSxY0ft2LFDkpSRkaG2bdvK19dXO3fu1OnTp9WjRw+5uLho8uTJkqQTJ06obdu26t+/v5YuXaqoqCj169dP5cqVU1BQkGHXDwAAAAA2MjOladOk48el+fMtZd7e0qBBxsYFAADuyurVq2+5//jx47c9xqhRoxQaGmpTZnZ2+1dxIe9lJcTjf/1VHy7+SCVLlrpp3TJlLXMkf7nyc7m6ualRwON5FSaQb3i4u9wwv3dGZmaOet9O+yhai1fvsSnbt3SoRsxeo7Xbj0qSurz+iTzcXKz769eooA9GP6fAAe/r+O9//YsrAJDbHDopvn79epvtiIgIeXt7a9++fWrSpIkuXLighQsXatmyZWrRooUkafHixapRo4Z27dqlRo0a6ZtvvtGRI0e0adMm+fj4qG7dupo4caJee+01jRs3Tq6urpo/f76qVKmi6dMtc7DUqFFD27dv18yZM0mKAwAAAHAMZ85IPXpIGzZYtl98UWrSxNiYAADAv9KhQweZTCaZzeab1rnd0Nhubm5yc7NNgl+5epPKMMyllBTFx8dbt3//7TcdO3pUXl5eKnvPPRo2dJCOHj2i98LfV2ZGhs7+aZkn3MvLSy6urpKkT5d+oroPPyyPokW1a+dOzZw+VYOGvipPT09DrglwZOu2H9NrvVroVOJ5HTl+RnWrl9egF57QR2v2WuuU8vSQn09JlStr+R6qVvEeSVLiXxeVeC7ZuvzTqcTz+vX035KkE7+fs9lXxsvSofPYyTO6kHzFLtcGB0b3bofm0Enxf7pw4YIkqXTp0pKkffv2KT09XYGB14agePDBB1WxYkXFxMSoUaNGiomJUa1ateTj42OtExQUpAEDBujw4cN6+OGHFRMTY3OMrDpDhgy5aSypqak2c/MkJSXlxiUCAAAAwI2ioqRu3aSEBMndXXr3XalxY6OjAgAA/1K5cuU0d+5ctW/fPtv9sbGxql+/fh5HBXs4fPgH9evdw7o9bapliPtn2j+r/sEhit6yWZL0fCfb/xc+XPyRHnm0oSTphx++17zw93TpUoqqVLlPo8eOV7tnOuTNBQD5TOiMrzT25VaaPayD7ildXKf/TNLCVbs1eVGUtU7bJ/y14M3nrNsfT3pRkjTpw016a+GmPI8Z+Z+JrLhDyzdJ8czMTA0ZMkSPP/64atasKUlKSEiQq6urSpYsaVPXx8dHCQkJ1jrXJ8Sz9mftu1WdpKQkXb58WR4eHjfEExYWpvHjx+fKtQHAbRUpIvXseW0dAAAUDlevSmPHSmFhlnnEH3pIWr7c8l8AAJDv1a9fX/v27btpUvx2vciRfzzyaEMdPBx30/232pflrbCpuRkSUKAlX0rT8FlrNHzWmpvW+WTdPn2ybl+OjusRMPKW+789cPy2dQAYw8noAO5UcHCwfvjhB0VGRhodiiTLXD0XLlywLqdOnTI6JAAFmZubFBFhWdyYFwwA8lJ4eLgqV64sd3d3NWzYULt3775p3QULFqhx48YqVaqUSpUqpcDAwFvWB27rueekyZMtCfGXX5Z27yYhDgBAATJ8+HA99thjN91ftWpVbdmyJQ8jAgAAd8tkyt0FuStfJMVDQkK0Zs0abdmyRRUqVLCW+/r6Ki0tTefPn7epn5iYKF9fX2udxMTEG/Zn7btVHU9Pz2x7iUuWuXo8PT1tFgAAABQsy5cvV2hoqMaOHav9+/erTp06CgoK0pkzZ7KtHx0drS5dumjLli2KiYmRn5+fWrVqpd9//z2PI0eB8fLLkpeXFBkpvf++VLSo0REBAIBc1LhxYz311FM33V+sWDE1bdo0DyMCAAD5UVhYmB555BGVKFFC3t7e6tChg+LibEchuXLlioKDg1WmTBkVL15cnTp1uiE/Gh8fr7Zt26po0aLy9vbW8OHDdfXqVZs60dHRqlevntzc3FS1alVFRETY+/JyhUMnxc1ms0JCQvTll19q8+bNqlKlis3++vXry8XFRVFR1+aAiIuLU3x8vAICAiRJAQEBOnTokM2Dy40bN8rT01P+/v7WOtcfI6tO1jEAwHBms5SSYlkYNg0A8syMGTP00ksvqXfv3vL399f8+fNVtGhRLVq0KNv6S5cu1SuvvKK6devqwQcf1IcffqjMzMwb2prATV25Iu3ff227dWvp5Empc2fDQgIAAAAAALdnyuUlJ7Zu3arg4GDt2rVLGzduVHp6ulq1aqWUlBRrnaFDh+p///ufVqxYoa1bt+qPP/5Qx44drfszMjLUtm1bpaWlaefOnVqyZIkiIiI0ZswYa50TJ06obdu2at68uWJjYzVkyBD169dPGzZsyGHEec+hJ6YNDg7WsmXL9NVXX6lEiRLWOcC9vLzk4eEhLy8v9e3bV6GhoSpdurQ8PT01cOBABQQEqFGjRpKkVq1ayd/fX927d9fUqVOVkJCg0aNHKzg4WG7/PwRx//79NWfOHI0YMUJ9+vTR5s2b9dlnn2nt2rWGXTsA2Lh0SSpe3LKenCwVK2ZsPABQCKSlpWnfvn0aNWqUtczJyUmBgYGKiYm5o2NcunRJ6enpKl26tL3CREESFye98IJ04oQUGytVrmwpL1nSwKAAAAAAAMAdMXDI8/Xr19tsR0REyNvbW/v27VOTJk104cIFLVy4UMuWLVOLFi0kSYsXL1aNGjW0a9cuNWrUSN98842OHDmiTZs2ycfHR3Xr1tXEiRP12muvady4cXJ1ddX8+fNVpUoVTZ8+XZJUo0YNbd++XTNnzlRQUFCeX3dOOHRP8Xnz5unChQtq1qyZypUrZ12WL19urTNz5kw9/fTT6tSpk5o0aSJfX1+tXLnSut/Z2Vlr1qyRs7OzAgIC1K1bN/Xo0UMTJkyw1qlSpYrWrl2rjRs3qk6dOpo+fbo+/PBDh795AAAAsJ+zZ88qIyNDPj4+NuU+Pj7WlzVv57XXXlP58uUVGBiY7f7U1FQlJSXZLCikPvpIql/fkgx3dZVOnTI6IgAAAAAAYKDsnhulpqbe0WcvXLggSdaOGvv27VN6errNM6oHH3xQFStWtHb+iImJUa1atWyehQUFBSkpKUmHDx+21vnnc66goKA77kBiJIfuKW6+gyGC3d3dFR4ervDw8JvWqVSpktatW3fL4zRr1kwHDhzIcYwAAABAdqZMmaLIyEhFR0fL3d092zphYWEaP358HkdmYTLw7WVc5+JFKThY+vhjy3aLFpb18uWNjQsAAAAAAOSIKZe7imf33Gjs2LEaN27cLT+XmZmpIUOG6PHHH1fNmjUlSQkJCXJ1dVXJf4xGd33nj4SEhGw7h2Ttu1WdpKQkXb58WR4eHjm6xrzk0ElxAAAAwChly5aVs7OzEhMTbcoTExPl6+t7y89OmzZNU6ZM0aZNm1S7du2b1hs1apRCQ0Ot20lJSfLz8/t3gSP/OHDAMlf4Tz9JTk7ShAnSyJGSs7PRkQEAAAAAgBzK7Q4I/3xuJMk6NfStBAcH64cfftD27dtzN6B8zqGHTwcAAACM4urqqvr16ysqKspalpmZqaioKAUEBNz0c1OnTtXEiRO1fv16NWjQ4JbncHNzk6enp82CQuSjjywJ8QoVpK1bpTfeICEOAAAAAAAkZf/c6HZJ8ZCQEK1Zs0ZbtmxRhQoVrOW+vr5KS0vT+fPnbepf3/nD19c3284hWftuVcfT09Ohe4lLJMUBAACAmwoNDdWCBQu0ZMkSHT16VAMGDFBKSop69+4tSerRo4dGjRplrf/222/rzTff1KJFi1S5cmUlJCQoISFBycnJRl0CHFlYmDRsmHTwoPTEE0ZHAwAAAAAA/gVTLi85YTabFRISoi+//FKbN29WlSpVbPbXr19fLi4uNp0/4uLiFB8fb+38ERAQoEOHDunMmTPWOhs3bpSnp6f8/f2tda4/RladW3UgcRQMnw4AAADcROfOnfXnn39qzJgxSkhIUN26dbV+/Xrr3Enx8fFycrr2num8efOUlpam//znPzbHuZP5nvKK2Wx0BIXYt99K8+dbeog7O0vu7tI77xgdFQAAAAAAyOeCg4O1bNkyffXVVypRooR1DnAvLy95eHjIy8tLffv2VWhoqEqXLi1PT08NHDhQAQEBatSokSSpVatW8vf3V/fu3TV16lQlJCRo9OjRCg4OtvZQ79+/v+bMmaMRI0aoT58+2rx5sz777DOtXbvWsGu/UyTFASA/cHaWshIsDKsKAHkqJCREISEh2e6Ljo622T558qT9A0L+k5EhTZ4sjRsnZWZKjzwiDRlidFQAAAAAACA35fKc4jkxb948SVKzZs1syhcvXqxevXpJkmbOnCknJyd16tRJqampCgoK0ty5c611nZ2dtWbNGg0YMEABAQEqVqyYevbsqQkTJljrVKlSRWvXrtXQoUM1e/ZsVahQQR9++KGCgoLsfo3/FklxAMgP3N2lFSuMjgIAAOTUH39IXbtKWS9Q9Ogh9etnaEgAAAAAACD3mQzMipvvYGhAd3d3hYeHKzw8/KZ1KlWqpHXr1t3yOM2aNdOBAwdyHKPRmFMcAAAAAOxh3TqpTh1LQrxYMWnJEstSvLjRkQEAAAAAABQq9BQHAAAAgNw2a5Y0dKhlvW5daflyqVo1IyMCAAAAAAB2ZDJw+HTcHj3FASA/SEmx/EY1mSzrAADAsQUGSh4e0sCBUkwMCXEAAAAAAAAD0VMcAAAAAHLDsWPSgw9a1mvWlOLiJD8/Y2MCAAAAAAB5go7ijo2e4gAAAADwb6SkSP36WRLhO3deKychDgAAAABA4WHK5QW5iqQ4AAAAANytQ4ekRx6RFi6UMjOl3buNjggAAAAAAAD/wPDpAAAAAJBTZrP0wQfSkCHSlStSuXLSJ59ILVoYHRkAAAAAADCAie7dDo2kOAAAAADkxPnz0ksvSZ9/btlu3VqKiJC8vY2MCgAAAAAAGMhETtyhMXw6AAAAAOTEF19YEuJFikjTpklr1pAQBwAAAAAAcGD0FAeA/MDZWWrT5to6AAAwTp8+0sGDUrdu0qOPGh0NAAAAAABwAHQUd2z0FAeA/MDdXVq71rK4uxsdDQAAhUtiomW49KQky7bJJL37LglxAAAAAACAfIKe4gAAAABwM5s2Sd27SwkJ0tWr0uLFRkcEAAAAAAAcEV3FHRo9xQEAAIBCiL/TbuPqVemNN6RWrSwJ8Zo1peHDjY4KAAAAAAA4KFMu/0PuIikOAPlBSopUrJhlSUkxOhoAAAq2+HipaVNp8mTJbJb++19p927J39/oyAAAAAAAAHAXGD4dAPKLS5eMjgAAgIJv+3apXTvp/HnJ01P68EPpueeMjgoAAAAAADg4E527HRpJcQAAAKAQMctsdAiOrXp1ycNDqlZNioyUqlQxOiIAAAAAAJAPkBN3bCTFAQAAABRup09L5cpZ1u+5R9qyxZIMd3U1Ni4AAAAAAADkCuYUBwAAAFA4mc1SRIRUtaq0dOm18urVSYgDAAAAAICcMeXyglxFUhwAAABA4XPxotSjh9S7t3TpkvT550ZHBAAAAAAAADshKQ4AAACgcNm/X6pXT/rkE8nZWZo0iaQ4AAAAAAD4V0y5/A+5iznFASA/cHKSmja9tg4AAHLObJbefVcaPlxKT5f8/KRly6QnnjA6MgAAAAAAkM+ZyGM7NJLiAJAfeHhI0dFGRwEAQP62d680ZIhlvUMHaeFCqXRpIyMCAAAAAABAHiApDgAAAKBweOQRafRoycdHCg7mFW4AAAAAAJBreMrg2EiKAwAAACiYMjKkt9+WunSRqlSxlE2caGxMAAAAAAAAyHNMTAsA+UFKinTPPZYlJcXoaAAAcHy//y61bCm98YYlKZ6RYXREAAAAAACgIDPl8oJcRU9xAMgvzp41OgIAAPKHtWulXr0svzuLFbMMle7sbHRUAAAAAACgADORyXZo9BQHAAAAUDCkpUmvvio9/bQlIV63rrR/v9S9u9GRAQAAAAAAwED0FAcAAACQ/50+LT3zjLR3r2V70CBp6lTJzc3YuAAAAAAAQKFgoqO4QyMpDgAAACD/K13aMm946dLS4sWWBDkAAAAAAEAeISfu2EiKAwAAAMifLl2SXF2lIkUsPcJXrLBs+/kZHRkAAAAAAAAcCHOKAwAAAMh/Dh2SGjSQJk26Vnb//STEAQAAAACAMUy5vCBXkRQHgPzAycny4L9BA8s6AAD/kim/TnRlNkvz50uPPiodPSotWiQlJxsdFQAAAAAAAByYw2dWtm3bpnbt2ql8+fIymUxatWqVzX6z2awxY8aoXLly8vDwUGBgoH766SebOufOnVPXrl3l6empkiVLqm/fvkr+x4Oz77//Xo0bN5a7u7v8/Pw0depUe18aANw5Dw9pzx7L4uFhdDQAABjj/HnpueekAQOkK1ekNm2kffuk4sWNjgwAAAAAABRyplz+h9zl8EnxlJQU1alTR+Hh4dnunzp1qt59913Nnz9f3333nYoVK6agoCBduXLFWqdr1646fPiwNm7cqDVr1mjbtm16+eWXrfuTkpLUqlUrVapUSfv27dM777yjcePG6YMPPrD79QEAAAB5yWw2OoK7FBMj1a0rffGF5OIiTZ8u/e9/0j33GB0ZAAAAAACATKbcXZC7ihgdwO20bt1arVu3znaf2WzWrFmzNHr0aLVv316S9NFHH8nHx0erVq3SCy+8oKNHj2r9+vXas2ePGjRoIEl677331KZNG02bNk3ly5fX0qVLlZaWpkWLFsnV1VUPPfSQYmNjNWPGDJvkOQAAAAADnD8vPfWUlJQk3XefFBkpPfKI0VEBAAAAAAAgn3D4nuK3cuLECSUkJCgwMNBa5uXlpYYNGyomJkaSFBMTo5IlS1oT4pIUGBgoJycnfffdd9Y6TZo0kaurq7VOUFCQ4uLi9Pfff+fR1QDALVy6JFWubFkuXTI6GgAA8lbJkpae4S+8IB04QEIcAAAAAAA4HFMuL8hdDt9T/FYSEhIkST4+PjblPj4+1n0JCQny9va22V+kSBGVLl3apk6VKlVuOEbWvlKlSt1w7tTUVKWmplq3k5KS/uXVAMAtmM3Sr79eWwcAoKDbtEkqVkwKCLBs9+1rWRg/DAAAAAAAADmUr3uKGyksLExeXl7Wxc/Pz+iQAAAAgPwvPV16/XWpVStLz/Bz5yzlTKgFAAAAAAAcGV3FHVq+Tor7+vpKkhITE23KExMTrft8fX115swZm/1Xr17VuXPnbOpkd4zrz/FPo0aN0oULF6zLqVOn/v0FAQAAAIXZr79KTZtKYWGWkVFat5bc3Y2OCgAAAAAA4LZMufwPuStfJ8WrVKkiX19fRUVFWcuSkpL03XffKeD/h1kMCAjQ+fPntW/fPmudzZs3KzMzUw0bNrTW2bZtm9LT0611Nm7cqOrVq2c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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Statistics:\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.98 0.98 0.98 8576\n", + " 1.0 0.98 0.98 0.98 8273\n", + "\n", + " accuracy 0.98 16849\n", + " macro avg 0.98 0.98 0.98 16849\n", + "weighted avg 0.98 0.98 0.98 16849\n", + "\n", + "AUC-ROC: 0.9990\n", + "\n", + "Regression Statistics (Non-zero values):\n", + "MAE: 0.0539\n", + "RMSE: 0.0752\n", + "Mean error: -0.0245\n", + "Error std: 0.0711\n", + "\n", + "Final Prediction Statistics:\n", + "MAE: 0.0261\n", + "RMSE: 0.0540\n", + "Mean error: -0.0052\n", + "Error std: 0.0537\n", + "\n", + "Error Thresholds (Final Predictions):\n", + "Predictions within ±0.5: 99.9%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarenergy/2024-11-27_23-17_energy_analysis.png b/models/solarenergy/2024-11-27_23-17_energy_analysis.png new file mode 100644 index 0000000..40af274 Binary files /dev/null and b/models/solarenergy/2024-11-27_23-17_energy_analysis.png differ diff --git a/models/solarenergy/2024-11-27_23-17_error_analysis.png b/models/solarenergy/2024-11-27_23-17_error_analysis.png new file mode 100644 index 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(0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:17:43.475455: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-27 23:17:43.475499: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-27 23:17:43.475533: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-27 23:17:43.483362: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import (\n", + " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", + " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", + " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", + " Conv1D, Multiply\n", + ")\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "from tensorflow.keras.metrics import AUC\n", + "from tensorflow.keras.utils import plot_model\n", + "\n", + "# Data processing and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.metrics import (\n", + " mean_absolute_error, mean_squared_error, r2_score, \n", + " confusion_matrix, classification_report, roc_auc_score\n", + ")\n", + "\n", + "# Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Additional utilities\n", + "import tensorflow_addons as tfa\n", + "from scipy import stats\n", + "import json\n", + "from datetime import datetime\n", + "import os\n", + "import joblib\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Features based only on radiation and other available variables\n", + " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Energy-specific features\n", + " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Temperature impact on theoretical efficiency\n", + " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", + "\n", + " # Combined features\n", + " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", + " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_effect'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = np.abs(12 - df['hour'])\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "def add_atmospheric_features(df):\n", + " # Indice di Massa d'Aria (Air Mass Index)\n", + " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", + " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", + " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", + "\n", + " # Indice di Stabilità Atmosferica\n", + " # Combina temperatura, umidità e pressione\n", + " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", + "\n", + " # Vapor Pressure Deficit (VPD)\n", + " # Importante per la radiazione diffusa\n", + " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", + " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", + " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", + "\n", + " return df\n", + "\n", + "def add_diffusion_features(df):\n", + " # Indice di Diffusione\n", + " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", + "\n", + " # Radiazione Diretta vs Diffusa\n", + " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", + " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", + "\n", + " # Fattore di Trasparenza Atmosferica\n", + " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", + "\n", + " return df\n", + "\n", + "def calculate_trend(x):\n", + " try:\n", + " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", + " except:\n", + " return np.nan\n", + "\n", + "def add_persistence_features(df):\n", + " # Create a copy to avoid modifying the original dataframe\n", + " df = df.copy()\n", + "\n", + " # Calculate trends more efficiently\n", + " windows = [3, 6, 12, 24]\n", + " for w in windows:\n", + " # Use numba or vectorized operations if possible\n", + " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", + " window=w,\n", + " min_periods=w\n", + " ).apply(calculate_trend, raw=True)\n", + "\n", + " # Optimize volatility calculation by doing it in one pass\n", + " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", + " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", + "\n", + " return df\n", + "\n", + "def add_weather_pattern_features(df):\n", + " # Pattern giornalieri\n", + " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", + " lambda x: (x < 30).sum()\n", + " )\n", + "\n", + " # Stabilità delle condizioni\n", + " for col in ['temp', 'humidity', 'cloudcover']:\n", + " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", + "\n", + " # Indice di Variabilità Meteorologica\n", + " df['weather_variability_index'] = (df['temp_stability'] +\n", + " df['humidity_stability'] +\n", + " df['cloudcover_stability']) / 3\n", + "\n", + " return df\n", + "\n", + "def add_efficiency_features(df):\n", + " # Perdite per temperatura\n", + " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", + "\n", + " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", + " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", + "\n", + " # Efficienza complessiva stimata\n", + " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", + " df['atmospheric_transmittance']\n", + "\n", + " # Potenziale di produzione\n", + " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", + "\n", + " return df\n", + "\n", + "def add_advanced_seasonal_features(df):\n", + " # Differenza dalla durata media del giorno\n", + " avg_day_length = 12\n", + " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", + "\n", + " # Intensità stagionale\n", + " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", + "\n", + " # Indice di Stagionalità\n", + " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", + "\n", + " # Correzione per alba/tramonto\n", + " df['daylight_correction'] = np.where(\n", + " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", + " 0,\n", + " 1\n", + " )\n", + "\n", + " return df\n", + "\n", + "def add_basic_interactions(df):\n", + " \"\"\"\n", + " Aggiunge le interazioni base tra variabili meteorologiche\n", + " \"\"\"\n", + " # Feature esistenti originali\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # Clear sky e trasparenza atmosferica\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " return df\n", + "\n", + "def add_rolling_and_lag_features(df):\n", + " \"\"\"\n", + " Aggiunge feature rolling e lag\n", + " \"\"\"\n", + " # Rolling means esistenti\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features esistenti\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " return df\n", + "\n", + "def add_condition_indicators(df):\n", + " \"\"\"\n", + " Aggiunge indicatori di condizioni particolari\n", + " \"\"\"\n", + " # Extreme conditions indicator esistente\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " return df\n", + "\n", + "def add_physics_based_conversion_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", + " \"\"\"\n", + " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", + " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", + "\n", + " # Efficienza di conversione reale vs teorica\n", + " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " # Energia accumulata nel tempo (integrazione)\n", + " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", + "\n", + " # Differenza tra energia teorica e reale\n", + " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", + "\n", + " # Indice di performance del sistema\n", + " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", + "\n", + " return df\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " \"\"\"\n", + " # Feature esistenti di base\n", + " # 1. Feature temporali di base\n", + " df = add_time_features(df)\n", + "\n", + " # 2. Feature solari e meteorologiche\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # 3. Feature atmosferiche e di diffusione\n", + " df = add_atmospheric_features(df)\n", + " df = add_diffusion_features(df)\n", + "\n", + " # 4. Feature di persistenza e pattern\n", + " df = add_persistence_features(df)\n", + " df = add_weather_pattern_features(df)\n", + "\n", + " # 5. Feature di efficienza e stagionalità\n", + " df = add_efficiency_features(df)\n", + " df = add_advanced_seasonal_features(df)\n", + "\n", + " # 6. Interazioni e feature derivate\n", + " df = add_basic_interactions(df)\n", + " df = add_rolling_and_lag_features(df)\n", + " df = add_condition_indicators(df)\n", + "\n", + " # 7. Nuove feature di conversione fisica\n", + " df = add_physics_based_conversion_features(df)\n", + "\n", + " # 8. One-hot encoding delle feature categoriche\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex',\n", + " 'cloudcover',\n", + " 'visibility',\n", + " 'temp',\n", + " 'pressure',\n", + " 'humidity',\n", + " 'solarradiation'\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation',\n", + " 'solar_angle',\n", + " 'day_length',\n", + " 'hour_sin',\n", + " 'hour_cos',\n", + " 'day_of_year_sin',\n", + " 'day_of_year_cos',\n", + " 'month_sin',\n", + " 'month_cos',\n", + " 'solar_noon',\n", + " 'daylight_correction'\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index',\n", + " 'atmospheric_attenuation',\n", + " 'theoretical_radiation',\n", + " 'expected_radiation',\n", + " 'cloud_elevation',\n", + " 'visibility_elevation',\n", + " 'uv_cloud_interaction',\n", + " 'temp_radiation_potential',\n", + " 'air_mass_index',\n", + " 'atmospheric_stability',\n", + " 'vapor_pressure_deficit',\n", + " 'diffusion_index',\n", + " 'atmospheric_transmittance',\n", + " 'temp_humidity_interaction',\n", + " 'clear_sky_factor'\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_12h',\n", + " 'uv_rolling_12h',\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " 'energy_rolling_mean_6h',\n", + " 'uv_rolling_mean_6h',\n", + " 'energy_volatility',\n", + " 'uv_volatility'\n", + " ],\n", + "\n", + " # Lag Features\n", + " 'lag_features': [\n", + " 'temp_1h_lag',\n", + " 'cloudcover_1h_lag',\n", + " 'humidity_1h_lag',\n", + " 'energy_lag_1h',\n", + " 'uv_lag_1h'\n", + " ],\n", + "\n", + " # Efficiency and Performance Features\n", + " 'efficiency_features': [\n", + " 'temp_losses',\n", + " 'soiling_loss_factor',\n", + " 'estimated_efficiency',\n", + " 'production_potential',\n", + " 'system_performance_ratio',\n", + " 'conversion_efficiency_ratio'\n", + " ],\n", + "\n", + " # Weather Pattern Features\n", + " 'weather_pattern_features': [\n", + " 'clear_sky_duration',\n", + " 'weather_variability_index',\n", + " 'temp_stability',\n", + " 'humidity_stability',\n", + " 'cloudcover_stability'\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring',\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning',\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night'\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " df[column] = df[column].interpolate(method='time')\n", + " else:\n", + " df[column] = df[column].interpolate(method='linear')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarenergy']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", + " from tensorflow import keras\n", + " from keras.models import Model\n", + " from keras.layers import (\n", + " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", + " MultiHeadAttention, LayerNormalization, Lambda,\n", + " Concatenate, Activation, Bidirectional, LSTM, Add\n", + " )\n", + " from keras.regularizers import l2\n", + " from keras.optimizers import AdamW\n", + " import tensorflow as tf\n", + " import numpy as np\n", + " import tensorflow_addons as tfa\n", + " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", + " \n", + " # Input layer\n", + " inputs = Input(shape=input_shape)\n", + " \n", + " # Feature groups definition\n", + " feature_dims = {\n", + " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", + " 'weather': [0, 1, 2, 3, 4, 5],\n", + " 'temporal': [10, 11, 12, 13, 14, 15],\n", + " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", + " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", + " 'lag': [40, 41, 42, 43, 44],\n", + " 'performance': [45, 46, 47, 48, 49, 50]\n", + " }\n", + " \n", + " # Feature extraction\n", + " feature_tensors = {}\n", + " for name, indices in feature_dims.items():\n", + " valid_indices = [i for i in indices if i < input_shape[-1]]\n", + " if valid_indices:\n", + " feature_tensors[name] = Lambda(\n", + " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", + " )(inputs)\n", + " \n", + " # Feature processing with residual connections\n", + " def process_feature_group(tensor, units, name):\n", + " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", + " kernel_regularizer=l2(l2_lambda))(tensor)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", + " x = Add()([x, residual])\n", + " x = LayerNormalization()(x)\n", + " \n", + " return x\n", + " \n", + " # Process each feature group\n", + " processed_features = {}\n", + " for name, tensor in feature_tensors.items():\n", + " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", + " processed_features[name] = process_feature_group(tensor, units, name)\n", + " \n", + " # Enhanced attention mechanism\n", + " def attention_block(x, num_heads=4):\n", + " attention_output = MultiHeadAttention(\n", + " num_heads=num_heads, \n", + " key_dim=x.shape[-1] // num_heads\n", + " )(x, x)\n", + " x = LayerNormalization()(x + attention_output)\n", + " \n", + " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", + " ffn = Dropout(0.1)(ffn)\n", + " ffn = Dense(x.shape[-1])(ffn)\n", + " \n", + " return LayerNormalization()(x + ffn)\n", + " \n", + " # Merge primary features with attention\n", + " primary_features = [\n", + " processed_features['solar'],\n", + " processed_features['weather'],\n", + " processed_features['performance']\n", + " ]\n", + " primary_context = Concatenate(axis=-1)(primary_features)\n", + " primary_context = attention_block(primary_context)\n", + " \n", + " # Merge secondary features\n", + " secondary_features = [\n", + " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", + " if name in processed_features\n", + " ]\n", + " if secondary_features:\n", + " secondary_context = Concatenate(axis=-1)(secondary_features)\n", + " secondary_context = attention_block(secondary_context)\n", + " else:\n", + " secondary_context = primary_context\n", + " \n", + " # Final feature merge\n", + " combined = Concatenate(axis=-1)([\n", + " primary_context, \n", + " secondary_context,\n", + " processed_features['derived']\n", + " ])\n", + " \n", + " # Sequential processing with residual LSTM\n", + " def residual_lstm_block(x, units):\n", + " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", + " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", + " x = Add()([lstm_out, residual])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + " \n", + " x = residual_lstm_block(combined, 128)\n", + " x = residual_lstm_block(x, 64)\n", + " x = Bidirectional(LSTM(64))(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " # Classification branch\n", + " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " class_x = BatchNormalization()(class_x)\n", + " class_x = Dropout(0.2)(class_x)\n", + " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", + " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", + " \n", + " # Enhanced regression branch with multiple pathways\n", + " def create_regression_pathway(x, name):\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.2)(x)\n", + " \n", + " residual = x\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " x = Add()([x, residual])\n", + " \n", + " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " return Dense(1, name=f'{name}_output')(x)\n", + " \n", + " # Create specialized regression pathways\n", + " low_range = create_regression_pathway(x, 'low_range')\n", + " mid_range = create_regression_pathway(x, 'mid_range')\n", + " high_range = create_regression_pathway(x, 'high_range')\n", + " \n", + " # Create context vector for attention\n", + " context = Dense(64, activation='swish')(x)\n", + " \n", + " # Calculate attention scores\n", + " attention_scores = Dense(3, activation='softmax')(context)\n", + " \n", + " # Combine predictions using attention weights\n", + " reg_output = Lambda(\n", + " lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n", + " name='regression_output'\n", + " )([attention_scores, low_range, mid_range, high_range])\n", + "\n", + " # Final output processing remains the same...\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dropout(0.2)(final_x)\n", + " \n", + " residual = final_x\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = BatchNormalization()(final_x)\n", + " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Add()([final_x, residual])\n", + " \n", + " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", + " final_x = Dense(1)(final_x)\n", + " final_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='final_output'\n", + " )(final_x)\n", + " \n", + " # Build model with all outputs\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[class_output, reg_output, final_output]\n", + " )\n", + " \n", + " # Enhanced loss functions\n", + " def enhanced_regression_loss(y_true, y_pred):\n", + " mae = tf.abs(y_true - y_pred)\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 1.5 + \\\n", + " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.2 + \\\n", + " tf.cast(y_true <= 1.0, tf.float32)\n", + " \n", + " weighted_loss = (0.5 * mae + 0.5 * mse) * value_ranges\n", + " return tf.reduce_mean(weighted_loss)\n", + " \n", + " def final_loss(y_true, y_pred):\n", + " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", + " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", + " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", + " return 0.5 * mae + 0.5 * mse\n", + " \n", + " # Learning rate schedule\n", + " clr = CosineDecayRestarts(\n", + " initial_learning_rate=2e-4,\n", + " first_decay_steps=1000,\n", + " t_mul=2.0,\n", + " m_mul=0.9,\n", + " alpha=1e-7\n", + " )\n", + " \n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=clr,\n", + " weight_decay=0.01,\n", + " clipnorm=1.0\n", + " )\n", + " \n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': enhanced_regression_loss,\n", + " 'final_output': final_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.4,\n", + " 'final_output': 0.4\n", + " }\n", + " )\n", + "\n", + " # Plot model architecture\n", + " try:\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + " except Exception as e:\n", + " print(f\"Warning: Could not plot model architecture: {e}\")\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar energy values (kWh)\n", + " y_pred : array-like\n", + " Predicted solar energy values (kWh)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 kWh\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", + "\n", + " # Energy level classification\n", + " def get_energy_level(value):\n", + " if value <= 0.5:\n", + " return 'Very Low'\n", + " elif value <= 2.0:\n", + " return 'Low'\n", + " elif value <= 4.0:\n", + " return 'Moderate'\n", + " elif value <= 6.0:\n", + " return 'High'\n", + " elif value <= 8.0:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate energy levels\n", + " y_true_levels = [get_energy_level(v) for v in y_true]\n", + " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Energy Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} kWh\")\n", + " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for energy levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", + " print(\"\\nConfusion Matrix for Energy Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=unique_levels,\n", + " index=unique_levels\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} kWh\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Predicted Energy (kWh)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (kWh)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 0.5\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Energy (kWh)')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by Energy level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Energy Level')\n", + " plt.ylabel('Error (kWh)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_energy_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Calculates comprehensive metrics for the solar energy prediction model.\n", + " \n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Ground truth values\n", + " y_class : array-like\n", + " Classification predictions (probability of non-zero values)\n", + " y_reg : array-like\n", + " Regression predictions (unrestricted values)\n", + " y_final : array-like\n", + " Final clipped predictions\n", + " min_output : float\n", + " Minimum allowed output value\n", + " max_output : float\n", + " Maximum allowed output value\n", + " \n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " # Ensure proper array formatting and dimensionality\n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Validate input dimensions\n", + " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", + " \"All input arrays must have the same length\"\n", + " \n", + " # Classification metrics with error handling\n", + " print(\"\\nClassification Metrics:\")\n", + " try:\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", + " print(conf_matrix)\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " class_report = classification_report(\n", + " y_true_binary, \n", + " y_pred_binary,\n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4\n", + " )\n", + " print(class_report)\n", + " except Exception as e:\n", + " print(f\"Error in classification metrics calculation: {str(e)}\")\n", + " \n", + " # Regression metrics with error handling\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " try:\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " # Range validation\n", + " out_of_range = np.sum(\n", + " (y_reg_nonzero < min_output) | \n", + " (y_reg_nonzero > max_output)\n", + " )\n", + " \n", + " # Error metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", + " (y_true_nonzero + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " # Calculate metrics\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in regression metrics calculation: {str(e)}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final output metrics with error handling\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " try:\n", + " # Ensure outputs are within bounds\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " \n", + " # Calculate metrics with numerical stability\n", + " epsilon = 1e-7\n", + " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", + " diff = np.clip(diff, 0, 1)\n", + " \n", + " mape = np.mean(diff) * 100\n", + " within_2_percent = np.mean(diff <= 0.02) * 100\n", + " within_5_percent = np.mean(diff <= 0.05) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " within_20_percent = np.mean(diff <= 0.20) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", + " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " except Exception as e:\n", + " print(f\"Error in final output metrics calculation: {str(e)}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar energy model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " def evaluate_epoch(epoch, logs):\n", + " if epoch % 20 == 0:\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " callbacks = [\n", + " tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=35,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-5\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", + " tf.keras.callbacks.TerminateOnNaN()\n", + " ]\n", + "\n", + " '''\n", + " tf.keras.callbacks.ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.8,\n", + " patience=10,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " '''\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar energy predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarenergy_predicted': final_pred.flatten(),\n", + " 'solarenergy_classification': classification_pred.flatten(),\n", + " 'solarenergy_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar energy column where missing\n", + " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", + " 'solarenergy_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar energy model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 66\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (112882, 24, 66)\n", + "Test data shape: (16849, 24, 66)\n", + "Saving scaler X to: 2024-11-27_23-17_scale_X.joblib\n", + "Saving scaler X to: 2024-11-27_23-17_scale_y.joblib\n", + "Saving features to: 2024-11-27_23-17_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "print(\"Initializing solar energy model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", + "\n", + "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", + "Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:18:54.766545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:c1:00.0, compute capability: 8.9\n", + "2024-11-27 23:18:55.999926: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Class distribution in training set:\n", + "Zeros: 56899 (50.41%)\n", + "Non-zeros: 55983 (49.59%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 8576 (50.90%)\n", + "Non-zeros: 8273 (49.10%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/150\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-27 23:19:24.436497: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-27 23:19:24.593649: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-27 23:19:26.676664: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x237e6dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-27 23:19:26.676699: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-27 23:19:26.682750: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-27 23:19:26.852932: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "221/221 [==============================] - ETA: 0s - loss: 10.1498 - classification_output_loss: 0.2192 - regression_output_loss: 0.3883 - final_output_loss: 0.2518\n", + "Epoch 1 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 95.36%\n", + "AUC-ROC: 0.9917\n", + "\n", + "Confusion Matrix:\n", + "[[8285 291]\n", + " [ 491 7782]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9441 0.9661 0.9549 8576\n", + " Non-Zero 0.9640 0.9407 0.9522 8273\n", + "\n", + " accuracy 0.9536 16849\n", + " macro avg 0.9540 0.9534 0.9535 16849\n", + "weighted avg 0.9538 0.9536 0.9536 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 246 predictions\n", + "MAPE: 56.03%\n", + "Within ±10%: 4.04%\n", + "MAE: 0.66\n", + "RMSE: 0.87\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 25.95%\n", + "Within ±2%: 48.48%\n", + "Within ±5%: 49.50%\n", + "Within ±10%: 51.42%\n", + "Within ±20%: 55.81%\n", + "MAE: 0.24\n", + "RMSE: 0.45\n", + "221/221 [==============================] - 66s 124ms/step - loss: 10.1498 - classification_output_loss: 0.2192 - regression_output_loss: 0.3883 - final_output_loss: 0.2518 - val_loss: 7.6804 - val_classification_output_loss: 0.2792 - val_regression_output_loss: 0.4849 - val_final_output_loss: 0.2209\n", + "Epoch 2/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 5.9091 - classification_output_loss: 0.1070 - regression_output_loss: 0.1877 - final_output_loss: 0.1142 - val_loss: 4.7197 - val_classification_output_loss: 0.1352 - val_regression_output_loss: 0.2361 - val_final_output_loss: 0.1195\n", + "Epoch 3/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 3.9752 - classification_output_loss: 0.0814 - regression_output_loss: 0.1177 - final_output_loss: 0.0640 - val_loss: 3.4943 - val_classification_output_loss: 0.0998 - val_regression_output_loss: 0.1060 - val_final_output_loss: 0.0623\n", + "Epoch 4/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 3.2835 - classification_output_loss: 0.0751 - regression_output_loss: 0.1008 - final_output_loss: 0.0540 - val_loss: 3.1666 - val_classification_output_loss: 0.0896 - val_regression_output_loss: 0.0793 - val_final_output_loss: 0.0562\n", + "Epoch 5/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 2.9948 - classification_output_loss: 0.0926 - regression_output_loss: 0.1700 - final_output_loss: 0.1103 - val_loss: 2.3640 - val_classification_output_loss: 0.1197 - val_regression_output_loss: 0.1617 - val_final_output_loss: 0.1375\n", + "Epoch 6/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 1.7550 - classification_output_loss: 0.0797 - regression_output_loss: 0.1151 - final_output_loss: 0.0827 - val_loss: 1.2843 - val_classification_output_loss: 0.0880 - val_regression_output_loss: 0.0697 - val_final_output_loss: 0.0442\n", + "Epoch 7/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 1.0277 - classification_output_loss: 0.0647 - regression_output_loss: 0.0847 - final_output_loss: 0.0549 - val_loss: 0.8079 - val_classification_output_loss: 0.0836 - val_regression_output_loss: 0.0610 - val_final_output_loss: 0.0438\n", + "Epoch 8/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.6795 - classification_output_loss: 0.0600 - regression_output_loss: 0.0716 - final_output_loss: 0.0498 - val_loss: 0.5649 - val_classification_output_loss: 0.0770 - val_regression_output_loss: 0.0542 - val_final_output_loss: 0.0392\n", + "Epoch 9/150\n", + "221/221 [==============================] - 15s 67ms/step - loss: 0.4970 - classification_output_loss: 0.0545 - regression_output_loss: 0.0634 - final_output_loss: 0.0434 - val_loss: 0.4335 - val_classification_output_loss: 0.0751 - val_regression_output_loss: 0.0452 - val_final_output_loss: 0.0354\n", + "Epoch 10/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.3957 - classification_output_loss: 0.0517 - regression_output_loss: 0.0524 - final_output_loss: 0.0386 - val_loss: 0.3625 - val_classification_output_loss: 0.0749 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0325\n", + "Epoch 11/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.3395 - classification_output_loss: 0.0503 - regression_output_loss: 0.0451 - final_output_loss: 0.0335 - val_loss: 0.3256 - val_classification_output_loss: 0.0750 - val_regression_output_loss: 0.0407 - val_final_output_loss: 0.0317\n", + "Epoch 12/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.3114 - classification_output_loss: 0.0509 - regression_output_loss: 0.0411 - final_output_loss: 0.0309 - val_loss: 0.3090 - val_classification_output_loss: 0.0738 - val_regression_output_loss: 0.0406 - val_final_output_loss: 0.0322\n", + "Epoch 13/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.3011 - classification_output_loss: 0.0523 - regression_output_loss: 0.0406 - final_output_loss: 0.0305 - val_loss: 0.2999 - val_classification_output_loss: 0.0677 - val_regression_output_loss: 0.0358 - val_final_output_loss: 0.0293\n", + "Epoch 14/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.3141 - classification_output_loss: 0.0616 - regression_output_loss: 0.0705 - final_output_loss: 0.0576 - val_loss: 0.3864 - val_classification_output_loss: 0.0790 - val_regression_output_loss: 0.2013 - val_final_output_loss: 0.1696\n", + "Epoch 15/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.2690 - classification_output_loss: 0.0643 - regression_output_loss: 0.1000 - final_output_loss: 0.0724 - val_loss: 0.2078 - val_classification_output_loss: 0.0773 - val_regression_output_loss: 0.0603 - val_final_output_loss: 0.0349\n", + "Epoch 16/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.1958 - classification_output_loss: 0.0566 - regression_output_loss: 0.0729 - final_output_loss: 0.0548 - val_loss: 0.1644 - val_classification_output_loss: 0.0686 - val_regression_output_loss: 0.0517 - val_final_output_loss: 0.0378\n", + "Epoch 17/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.1549 - classification_output_loss: 0.0523 - regression_output_loss: 0.0585 - final_output_loss: 0.0489 - val_loss: 0.1353 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0478 - val_final_output_loss: 0.0354\n", + "Epoch 18/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.1323 - classification_output_loss: 0.0503 - regression_output_loss: 0.0551 - final_output_loss: 0.0493 - val_loss: 0.1225 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0496 - val_final_output_loss: 0.0421\n", + "Epoch 19/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.1139 - classification_output_loss: 0.0501 - regression_output_loss: 0.0497 - final_output_loss: 0.0457 - val_loss: 0.1095 - val_classification_output_loss: 0.0744 - val_regression_output_loss: 0.0481 - val_final_output_loss: 0.0386\n", + "Epoch 20/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0980 - classification_output_loss: 0.0462 - regression_output_loss: 0.0436 - final_output_loss: 0.0403 - val_loss: 0.0943 - val_classification_output_loss: 0.0679 - val_regression_output_loss: 0.0407 - val_final_output_loss: 0.0344\n", + "Epoch 21/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0874 - classification_output_loss: 0.0439 - regression_output_loss: 0.0402 - final_output_loss: 0.0375\n", + "Epoch 21 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.16%\n", + "AUC-ROC: 0.9962\n", + "\n", + "Confusion Matrix:\n", + "[[8389 187]\n", + " [ 291 7982]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9665 0.9782 0.9723 8576\n", + " Non-Zero 0.9771 0.9648 0.9709 8273\n", + "\n", + " accuracy 0.9716 16849\n", + " macro avg 0.9718 0.9715 0.9716 16849\n", + "weighted avg 0.9717 0.9716 0.9716 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 26 predictions\n", + "MAPE: 19.29%\n", + "Within ±10%: 44.86%\n", + "MAE: 0.11\n", + "RMSE: 0.14\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 13.12%\n", + "Within ±2%: 55.12%\n", + "Within ±5%: 62.25%\n", + "Within ±10%: 74.22%\n", + "Within ±20%: 84.48%\n", + "MAE: 0.06\n", + "RMSE: 0.10\n", + "221/221 [==============================] - 20s 91ms/step - loss: 0.0874 - classification_output_loss: 0.0439 - regression_output_loss: 0.0402 - final_output_loss: 0.0375 - val_loss: 0.0881 - val_classification_output_loss: 0.0742 - val_regression_output_loss: 0.0395 - val_final_output_loss: 0.0330\n", + "Epoch 22/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0800 - classification_output_loss: 0.0425 - regression_output_loss: 0.0390 - final_output_loss: 0.0352 - val_loss: 0.0900 - val_classification_output_loss: 0.0677 - val_regression_output_loss: 0.0532 - val_final_output_loss: 0.0388\n", + "Epoch 23/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0748 - classification_output_loss: 0.0402 - regression_output_loss: 0.0385 - final_output_loss: 0.0340 - val_loss: 0.0783 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0371 - val_final_output_loss: 0.0365\n", + "Epoch 24/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0670 - classification_output_loss: 0.0385 - regression_output_loss: 0.0327 - final_output_loss: 0.0290 - val_loss: 0.0738 - val_classification_output_loss: 0.0631 - val_regression_output_loss: 0.0350 - val_final_output_loss: 0.0350\n", + "Epoch 25/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0620 - classification_output_loss: 0.0378 - regression_output_loss: 0.0294 - final_output_loss: 0.0260 - val_loss: 0.0657 - val_classification_output_loss: 0.0624 - val_regression_output_loss: 0.0286 - val_final_output_loss: 0.0271\n", + "Epoch 26/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0591 - classification_output_loss: 0.0374 - regression_output_loss: 0.0284 - final_output_loss: 0.0248 - val_loss: 0.0618 - val_classification_output_loss: 0.0628 - val_regression_output_loss: 0.0258 - val_final_output_loss: 0.0240\n", + "Epoch 27/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0570 - classification_output_loss: 0.0361 - regression_output_loss: 0.0277 - final_output_loss: 0.0243 - val_loss: 0.0591 - val_classification_output_loss: 0.0622 - val_regression_output_loss: 0.0257 - val_final_output_loss: 0.0203\n", + "Epoch 28/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0555 - classification_output_loss: 0.0362 - regression_output_loss: 0.0272 - final_output_loss: 0.0233 - val_loss: 0.0584 - val_classification_output_loss: 0.0615 - val_regression_output_loss: 0.0266 - val_final_output_loss: 0.0198\n", + "Epoch 29/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0550 - classification_output_loss: 0.0364 - regression_output_loss: 0.0273 - final_output_loss: 0.0231 - val_loss: 0.0588 - val_classification_output_loss: 0.0611 - val_regression_output_loss: 0.0273 - val_final_output_loss: 0.0214\n", + "Epoch 30/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0548 - classification_output_loss: 0.0375 - regression_output_loss: 0.0272 - final_output_loss: 0.0231 - val_loss: 0.0565 - val_classification_output_loss: 0.0579 - val_regression_output_loss: 0.0247 - val_final_output_loss: 0.0201\n", + "Epoch 31/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0553 - classification_output_loss: 0.0371 - regression_output_loss: 0.0285 - final_output_loss: 0.0236 - val_loss: 0.0548 - val_classification_output_loss: 0.0564 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0191\n", + "Epoch 32/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0793 - classification_output_loss: 0.0410 - regression_output_loss: 0.0607 - final_output_loss: 0.0465 - val_loss: 0.2093 - val_classification_output_loss: 0.1111 - val_regression_output_loss: 0.1922 - val_final_output_loss: 0.1775\n", + "Epoch 33/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.1067 - classification_output_loss: 0.0635 - regression_output_loss: 0.0839 - final_output_loss: 0.0643 - val_loss: 0.0728 - val_classification_output_loss: 0.0623 - val_regression_output_loss: 0.0473 - val_final_output_loss: 0.0327\n", + "Epoch 34/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0784 - classification_output_loss: 0.0467 - regression_output_loss: 0.0531 - final_output_loss: 0.0493 - val_loss: 0.0785 - val_classification_output_loss: 0.0949 - val_regression_output_loss: 0.0493 - val_final_output_loss: 0.0359\n", + "Epoch 35/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0675 - classification_output_loss: 0.0457 - regression_output_loss: 0.0424 - final_output_loss: 0.0420 - val_loss: 0.0692 - val_classification_output_loss: 0.0691 - val_regression_output_loss: 0.0519 - val_final_output_loss: 0.0288\n", + "Epoch 36/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0676 - classification_output_loss: 0.0418 - regression_output_loss: 0.0452 - final_output_loss: 0.0455 - val_loss: 0.0689 - val_classification_output_loss: 0.0829 - val_regression_output_loss: 0.0430 - val_final_output_loss: 0.0324\n", + "Epoch 37/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0595 - classification_output_loss: 0.0396 - regression_output_loss: 0.0376 - final_output_loss: 0.0386 - val_loss: 0.0798 - val_classification_output_loss: 0.0626 - val_regression_output_loss: 0.0699 - val_final_output_loss: 0.0473\n", + "Epoch 38/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0606 - classification_output_loss: 0.0404 - regression_output_loss: 0.0414 - final_output_loss: 0.0402 - val_loss: 0.0661 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0558 - val_final_output_loss: 0.0315\n", + "Epoch 39/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0570 - classification_output_loss: 0.0375 - regression_output_loss: 0.0370 - final_output_loss: 0.0393 - val_loss: 0.0550 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0365 - val_final_output_loss: 0.0288\n", + "Epoch 40/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0544 - classification_output_loss: 0.0390 - regression_output_loss: 0.0361 - final_output_loss: 0.0359 - val_loss: 0.0600 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0424 - val_final_output_loss: 0.0381\n", + "Epoch 41/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0505 - classification_output_loss: 0.0366 - regression_output_loss: 0.0326 - final_output_loss: 0.0335\n", + "Epoch 41 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.79%\n", + "AUC-ROC: 0.9980\n", + "\n", + "Confusion Matrix:\n", + "[[8337 239]\n", + " [ 133 8140]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9843 0.9721 0.9782 8576\n", + " Non-Zero 0.9715 0.9839 0.9777 8273\n", + "\n", + " accuracy 0.9779 16849\n", + " macro avg 0.9779 0.9780 0.9779 16849\n", + "weighted avg 0.9780 0.9779 0.9779 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 66 predictions\n", + "MAPE: 16.65%\n", + "Within ±10%: 48.35%\n", + "MAE: 0.13\n", + "RMSE: 0.19\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 10.82%\n", + "Within ±2%: 56.88%\n", + "Within ±5%: 64.73%\n", + "Within ±10%: 74.46%\n", + "Within ±20%: 86.63%\n", + "MAE: 0.06\n", + "RMSE: 0.11\n", + "221/221 [==============================] - 20s 89ms/step - loss: 0.0505 - classification_output_loss: 0.0366 - regression_output_loss: 0.0326 - final_output_loss: 0.0335 - val_loss: 0.0626 - val_classification_output_loss: 0.0581 - val_regression_output_loss: 0.0524 - val_final_output_loss: 0.0347\n", + "Epoch 42/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0519 - classification_output_loss: 0.0342 - regression_output_loss: 0.0354 - final_output_loss: 0.0366 - val_loss: 0.0468 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0282 - val_final_output_loss: 0.0241\n", + "Epoch 43/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0489 - classification_output_loss: 0.0327 - regression_output_loss: 0.0326 - final_output_loss: 0.0343 - val_loss: 0.0487 - val_classification_output_loss: 0.0563 - val_regression_output_loss: 0.0302 - val_final_output_loss: 0.0271\n", + "Epoch 44/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0477 - classification_output_loss: 0.0337 - regression_output_loss: 0.0313 - final_output_loss: 0.0340 - val_loss: 0.0483 - val_classification_output_loss: 0.0535 - val_regression_output_loss: 0.0292 - val_final_output_loss: 0.0297\n", + "Epoch 45/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0455 - classification_output_loss: 0.0308 - regression_output_loss: 0.0296 - final_output_loss: 0.0330 - val_loss: 0.0433 - val_classification_output_loss: 0.0494 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0220\n", + "Epoch 46/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0433 - classification_output_loss: 0.0298 - regression_output_loss: 0.0286 - final_output_loss: 0.0304 - val_loss: 0.0455 - val_classification_output_loss: 0.0634 - val_regression_output_loss: 0.0265 - val_final_output_loss: 0.0224\n", + "Epoch 47/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0413 - classification_output_loss: 0.0300 - regression_output_loss: 0.0274 - final_output_loss: 0.0281 - val_loss: 0.0418 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0273 - val_final_output_loss: 0.0227\n", + "Epoch 48/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0418 - classification_output_loss: 0.0295 - regression_output_loss: 0.0282 - final_output_loss: 0.0301 - val_loss: 0.0518 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0372 - val_final_output_loss: 0.0337\n", + "Epoch 49/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0404 - classification_output_loss: 0.0272 - regression_output_loss: 0.0272 - final_output_loss: 0.0293 - val_loss: 0.0580 - val_classification_output_loss: 0.0484 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0473\n", + "Epoch 50/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0399 - classification_output_loss: 0.0275 - regression_output_loss: 0.0270 - final_output_loss: 0.0284 - val_loss: 0.0492 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0317 - val_final_output_loss: 0.0357\n", + "Epoch 51/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0362 - classification_output_loss: 0.0262 - regression_output_loss: 0.0236 - final_output_loss: 0.0246 - val_loss: 0.0476 - val_classification_output_loss: 0.0431 - val_regression_output_loss: 0.0343 - val_final_output_loss: 0.0346\n", + "Epoch 52/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0351 - classification_output_loss: 0.0258 - regression_output_loss: 0.0231 - final_output_loss: 0.0238 - val_loss: 0.0457 - val_classification_output_loss: 0.0419 - val_regression_output_loss: 0.0328 - val_final_output_loss: 0.0331\n", + "Epoch 53/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0329 - classification_output_loss: 0.0245 - regression_output_loss: 0.0213 - final_output_loss: 0.0216 - val_loss: 0.0407 - val_classification_output_loss: 0.0418 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0273\n", + "Epoch 54/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0315 - classification_output_loss: 0.0237 - regression_output_loss: 0.0206 - final_output_loss: 0.0203 - val_loss: 0.0371 - val_classification_output_loss: 0.0387 - val_regression_output_loss: 0.0254 - val_final_output_loss: 0.0229\n", + "Epoch 55/150\n", + "221/221 [==============================] - 14s 61ms/step - loss: 0.0311 - classification_output_loss: 0.0225 - regression_output_loss: 0.0206 - final_output_loss: 0.0206 - val_loss: 0.0356 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0235 - val_final_output_loss: 0.0219\n", + "Epoch 56/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0302 - classification_output_loss: 0.0223 - regression_output_loss: 0.0201 - final_output_loss: 0.0198 - val_loss: 0.0351 - val_classification_output_loss: 0.0411 - val_regression_output_loss: 0.0224 - val_final_output_loss: 0.0207\n", + "Epoch 57/150\n", + "221/221 [==============================] - 13s 57ms/step - loss: 0.0301 - classification_output_loss: 0.0221 - regression_output_loss: 0.0199 - final_output_loss: 0.0201 - val_loss: 0.0340 - val_classification_output_loss: 0.0393 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0205\n", + "Epoch 58/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0296 - classification_output_loss: 0.0213 - regression_output_loss: 0.0199 - final_output_loss: 0.0197 - val_loss: 0.0326 - val_classification_output_loss: 0.0389 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0186\n", + "Epoch 59/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0296 - classification_output_loss: 0.0210 - regression_output_loss: 0.0200 - final_output_loss: 0.0200 - val_loss: 0.0311 - val_classification_output_loss: 0.0367 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0161\n", + "Epoch 60/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0295 - classification_output_loss: 0.0211 - regression_output_loss: 0.0202 - final_output_loss: 0.0198 - val_loss: 0.0315 - val_classification_output_loss: 0.0365 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0165\n", + "Epoch 61/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0290 - classification_output_loss: 0.0201 - regression_output_loss: 0.0199 - final_output_loss: 0.0195\n", + "Epoch 61 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.60%\n", + "AUC-ROC: 0.9993\n", + "\n", + "Confusion Matrix:\n", + "[[8473 103]\n", + " [ 133 8140]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9845 0.9880 0.9863 8576\n", + " Non-Zero 0.9875 0.9839 0.9857 8273\n", + "\n", + " accuracy 0.9860 16849\n", + " macro avg 0.9860 0.9860 0.9860 16849\n", + "weighted avg 0.9860 0.9860 0.9860 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 11.30%\n", + "Within ±10%: 73.14%\n", + "MAE: 0.06\n", + "RMSE: 0.09\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.72%\n", + "Within ±2%: 60.84%\n", + "Within ±5%: 74.53%\n", + "Within ±10%: 86.72%\n", + "Within ±20%: 91.58%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 20s 90ms/step - loss: 0.0290 - classification_output_loss: 0.0201 - regression_output_loss: 0.0199 - final_output_loss: 0.0195 - val_loss: 0.0315 - val_classification_output_loss: 0.0356 - val_regression_output_loss: 0.0215 - val_final_output_loss: 0.0171\n", + "Epoch 62/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0290 - classification_output_loss: 0.0207 - regression_output_loss: 0.0199 - final_output_loss: 0.0194 - val_loss: 0.0311 - val_classification_output_loss: 0.0355 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0172\n", + "Epoch 63/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0288 - classification_output_loss: 0.0205 - regression_output_loss: 0.0199 - final_output_loss: 0.0192 - val_loss: 0.0308 - val_classification_output_loss: 0.0349 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0175\n", + "Epoch 64/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0289 - classification_output_loss: 0.0207 - regression_output_loss: 0.0200 - final_output_loss: 0.0194 - val_loss: 0.0302 - val_classification_output_loss: 0.0348 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0168\n", + "Epoch 65/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0289 - classification_output_loss: 0.0204 - regression_output_loss: 0.0202 - final_output_loss: 0.0194 - val_loss: 0.0297 - val_classification_output_loss: 0.0349 - val_regression_output_loss: 0.0185 - val_final_output_loss: 0.0160\n", + "Epoch 66/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0295 - classification_output_loss: 0.0209 - regression_output_loss: 0.0211 - final_output_loss: 0.0198 - val_loss: 0.0294 - val_classification_output_loss: 0.0350 - val_regression_output_loss: 0.0180 - val_final_output_loss: 0.0157\n", + "Epoch 67/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0302 - classification_output_loss: 0.0208 - regression_output_loss: 0.0215 - final_output_loss: 0.0210 - val_loss: 0.0303 - val_classification_output_loss: 0.0348 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0170\n", + "Epoch 68/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0304 - classification_output_loss: 0.0223 - regression_output_loss: 0.0210 - final_output_loss: 0.0212 - val_loss: 0.0636 - val_classification_output_loss: 0.0548 - val_regression_output_loss: 0.0283 - val_final_output_loss: 0.0759\n", + "Epoch 69/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0798 - classification_output_loss: 0.0495 - regression_output_loss: 0.0662 - final_output_loss: 0.0655 - val_loss: 0.0591 - val_classification_output_loss: 0.0509 - val_regression_output_loss: 0.0539 - val_final_output_loss: 0.0388\n", + "Epoch 70/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0506 - classification_output_loss: 0.0340 - regression_output_loss: 0.0369 - final_output_loss: 0.0415 - val_loss: 0.0465 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0398 - val_final_output_loss: 0.0249\n", + "Epoch 71/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0450 - classification_output_loss: 0.0282 - regression_output_loss: 0.0332 - final_output_loss: 0.0362 - val_loss: 0.0431 - val_classification_output_loss: 0.0442 - val_regression_output_loss: 0.0316 - val_final_output_loss: 0.0284\n", + "Epoch 72/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0425 - classification_output_loss: 0.0302 - regression_output_loss: 0.0303 - final_output_loss: 0.0330 - val_loss: 0.0478 - val_classification_output_loss: 0.0484 - val_regression_output_loss: 0.0391 - val_final_output_loss: 0.0306\n", + "Epoch 73/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0413 - classification_output_loss: 0.0268 - regression_output_loss: 0.0300 - final_output_loss: 0.0335 - val_loss: 0.0437 - val_classification_output_loss: 0.0455 - val_regression_output_loss: 0.0275 - val_final_output_loss: 0.0344\n", + "Epoch 74/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0429 - classification_output_loss: 0.0309 - regression_output_loss: 0.0297 - final_output_loss: 0.0353 - val_loss: 0.0438 - val_classification_output_loss: 0.0651 - val_regression_output_loss: 0.0286 - val_final_output_loss: 0.0228\n", + "Epoch 75/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0391 - classification_output_loss: 0.0249 - regression_output_loss: 0.0278 - final_output_loss: 0.0318 - val_loss: 0.0420 - val_classification_output_loss: 0.0521 - val_regression_output_loss: 0.0279 - val_final_output_loss: 0.0266\n", + "Epoch 76/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0378 - classification_output_loss: 0.0254 - regression_output_loss: 0.0252 - final_output_loss: 0.0311 - val_loss: 0.0443 - val_classification_output_loss: 0.0531 - val_regression_output_loss: 0.0255 - val_final_output_loss: 0.0357\n", + "Epoch 77/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0387 - classification_output_loss: 0.0283 - regression_output_loss: 0.0267 - final_output_loss: 0.0322 - val_loss: 0.0744 - val_classification_output_loss: 0.0440 - val_regression_output_loss: 0.0526 - val_final_output_loss: 0.0837\n", + "Epoch 78/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0428 - classification_output_loss: 0.0288 - regression_output_loss: 0.0317 - final_output_loss: 0.0347 - val_loss: 0.0552 - val_classification_output_loss: 0.0460 - val_regression_output_loss: 0.0467 - val_final_output_loss: 0.0405\n", + "Epoch 79/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0370 - classification_output_loss: 0.0250 - regression_output_loss: 0.0260 - final_output_loss: 0.0290 - val_loss: 0.0362 - val_classification_output_loss: 0.0526 - val_regression_output_loss: 0.0227 - val_final_output_loss: 0.0187\n", + "Epoch 80/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0367 - classification_output_loss: 0.0248 - regression_output_loss: 0.0252 - final_output_loss: 0.0299 - val_loss: 0.0427 - val_classification_output_loss: 0.0726 - val_regression_output_loss: 0.0270 - val_final_output_loss: 0.0209\n", + "Epoch 81/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0363 - classification_output_loss: 0.0254 - regression_output_loss: 0.0261 - final_output_loss: 0.0294\n", + "Epoch 81 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.52%\n", + "AUC-ROC: 0.9992\n", + "\n", + "Confusion Matrix:\n", + "[[8431 145]\n", + " [ 104 8169]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9878 0.9831 0.9854 8576\n", + " Non-Zero 0.9826 0.9874 0.9850 8273\n", + "\n", + " accuracy 0.9852 16849\n", + " macro avg 0.9852 0.9853 0.9852 16849\n", + "weighted avg 0.9852 0.9852 0.9852 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 18 predictions\n", + "MAPE: 17.42%\n", + "Within ±10%: 42.09%\n", + "MAE: 0.15\n", + "RMSE: 0.21\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 13.33%\n", + "Within ±2%: 53.80%\n", + "Within ±5%: 59.62%\n", + "Within ±10%: 68.52%\n", + "Within ±20%: 80.93%\n", + "MAE: 0.08\n", + "RMSE: 0.14\n", + "221/221 [==============================] - 20s 90ms/step - loss: 0.0363 - classification_output_loss: 0.0254 - regression_output_loss: 0.0261 - final_output_loss: 0.0294 - val_loss: 0.0601 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0604 - val_final_output_loss: 0.0479\n", + "Epoch 82/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0396 - classification_output_loss: 0.0282 - regression_output_loss: 0.0283 - final_output_loss: 0.0328 - val_loss: 0.0370 - val_classification_output_loss: 0.0409 - val_regression_output_loss: 0.0238 - val_final_output_loss: 0.0237\n", + "Epoch 83/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0357 - classification_output_loss: 0.0229 - regression_output_loss: 0.0256 - final_output_loss: 0.0287 - val_loss: 0.0380 - val_classification_output_loss: 0.0534 - val_regression_output_loss: 0.0252 - val_final_output_loss: 0.0216\n", + "Epoch 84/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0337 - classification_output_loss: 0.0232 - regression_output_loss: 0.0235 - final_output_loss: 0.0272 - val_loss: 0.0497 - val_classification_output_loss: 0.0303 - val_regression_output_loss: 0.0465 - val_final_output_loss: 0.0407\n", + "Epoch 85/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0380 - classification_output_loss: 0.0252 - regression_output_loss: 0.0267 - final_output_loss: 0.0329 - val_loss: 0.0559 - val_classification_output_loss: 0.0405 - val_regression_output_loss: 0.0447 - val_final_output_loss: 0.0485\n", + "Epoch 86/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0339 - classification_output_loss: 0.0219 - regression_output_loss: 0.0249 - final_output_loss: 0.0265 - val_loss: 0.0419 - val_classification_output_loss: 0.0481 - val_regression_output_loss: 0.0285 - val_final_output_loss: 0.0306\n", + "Epoch 87/150\n", + "221/221 [==============================] - 14s 65ms/step - loss: 0.0327 - classification_output_loss: 0.0218 - regression_output_loss: 0.0230 - final_output_loss: 0.0265 - val_loss: 0.0339 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0204\n", + "Epoch 88/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0328 - classification_output_loss: 0.0223 - regression_output_loss: 0.0236 - final_output_loss: 0.0267 - val_loss: 0.0476 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0346 - val_final_output_loss: 0.0431\n", + "Epoch 89/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0349 - classification_output_loss: 0.0226 - regression_output_loss: 0.0249 - final_output_loss: 0.0295 - val_loss: 0.0416 - val_classification_output_loss: 0.0428 - val_regression_output_loss: 0.0297 - val_final_output_loss: 0.0298\n", + "Epoch 90/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0321 - classification_output_loss: 0.0202 - regression_output_loss: 0.0225 - final_output_loss: 0.0262 - val_loss: 0.0324 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0226 - val_final_output_loss: 0.0197\n", + "Epoch 91/150\n", + "221/221 [==============================] - 13s 60ms/step - loss: 0.0307 - classification_output_loss: 0.0208 - regression_output_loss: 0.0223 - final_output_loss: 0.0245 - val_loss: 0.0384 - val_classification_output_loss: 0.0717 - val_regression_output_loss: 0.0236 - val_final_output_loss: 0.0179\n", + "Epoch 92/150\n", + "221/221 [==============================] - 12s 56ms/step - loss: 0.0302 - classification_output_loss: 0.0204 - regression_output_loss: 0.0212 - final_output_loss: 0.0250 - val_loss: 0.0435 - val_classification_output_loss: 0.0330 - val_regression_output_loss: 0.0379 - val_final_output_loss: 0.0356\n", + "Epoch 93/150\n", + "221/221 [==============================] - 13s 59ms/step - loss: 0.0327 - classification_output_loss: 0.0197 - regression_output_loss: 0.0238 - final_output_loss: 0.0283 - val_loss: 0.0357 - val_classification_output_loss: 0.0459 - val_regression_output_loss: 0.0234 - val_final_output_loss: 0.0223\n", + "Epoch 94/150\n", + "221/221 [==============================] - 14s 64ms/step - loss: 0.0300 - classification_output_loss: 0.0179 - regression_output_loss: 0.0221 - final_output_loss: 0.0241 - val_loss: 0.0309 - val_classification_output_loss: 0.0322 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0210\n", + "Epoch 95/150\n", + "221/221 [==============================] - 14s 63ms/step - loss: 0.0293 - classification_output_loss: 0.0181 - regression_output_loss: 0.0207 - final_output_loss: 0.0246 - val_loss: 0.0310 - val_classification_output_loss: 0.0385 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0183\n", + "Epoch 96/150\n", + "221/221 [==============================] - 13s 58ms/step - loss: 0.0278 - classification_output_loss: 0.0172 - regression_output_loss: 0.0199 - final_output_loss: 0.0227 - val_loss: 0.0361 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0203\n", + "Epoch 97/150\n", + "221/221 [==============================] - 15s 66ms/step - loss: 0.0295 - classification_output_loss: 0.0197 - regression_output_loss: 0.0209 - final_output_loss: 0.0247 - val_loss: 0.0316 - val_classification_output_loss: 0.0417 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0181\n", + "Epoch 98/150\n", + "221/221 [==============================] - 14s 62ms/step - loss: 0.0289 - classification_output_loss: 0.0174 - regression_output_loss: 0.0211 - final_output_loss: 0.0240 - val_loss: 0.0450 - val_classification_output_loss: 0.0319 - val_regression_output_loss: 0.0309 - val_final_output_loss: 0.0451\n", + "Epoch 99/150\n", + "221/221 [==============================] - 13s 61ms/step - loss: 0.0302 - classification_output_loss: 0.0194 - regression_output_loss: 0.0216 - final_output_loss: 0.0255 - val_loss: 0.0351 - val_classification_output_loss: 0.0486 - val_regression_output_loss: 0.0228 - val_final_output_loss: 0.0221\n", + "Epoch 100/150\n", + "221/221 [==============================] - 15s 68ms/step - loss: 0.0268 - classification_output_loss: 0.0169 - regression_output_loss: 0.0194 - final_output_loss: 0.0214 - val_loss: 0.0330 - val_classification_output_loss: 0.0376 - val_regression_output_loss: 0.0208 - val_final_output_loss: 0.0257\n", + "Epoch 101/150\n", + "221/221 [==============================] - ETA: 0s - loss: 0.0261 - classification_output_loss: 0.0137 - regression_output_loss: 0.0188 - final_output_loss: 0.0227Restoring model weights from the end of the best epoch: 66.\n", + "\n", + "Epoch 101 Detailed Metrics:\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.65%\n", + "AUC-ROC: 0.9994\n", + "\n", + "Confusion Matrix:\n", + "[[8497 79]\n", + " [ 148 8125]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9829 0.9908 0.9868 8576\n", + " Non-Zero 0.9904 0.9821 0.9862 8273\n", + "\n", + " accuracy 0.9865 16849\n", + " macro avg 0.9866 0.9864 0.9865 16849\n", + "weighted avg 0.9866 0.9865 0.9865 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 10.76%\n", + "Within ±10%: 75.03%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.87%\n", + "Within ±2%: 61.66%\n", + "Within ±5%: 75.67%\n", + "Within ±10%: 86.32%\n", + "Within ±20%: 91.11%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n", + "221/221 [==============================] - 20s 92ms/step - loss: 0.0261 - classification_output_loss: 0.0137 - regression_output_loss: 0.0188 - final_output_loss: 0.0227 - val_loss: 0.0359 - val_classification_output_loss: 0.0278 - val_regression_output_loss: 0.0242 - val_final_output_loss: 0.0340\n", + "Epoch 101: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 98.65%\n", + "AUC-ROC: 0.9994\n", + "\n", + "Confusion Matrix:\n", + "[[8497 79]\n", + " [ 148 8125]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9829 0.9908 0.9868 8576\n", + " Non-Zero 0.9904 0.9821 0.9862 8273\n", + "\n", + " accuracy 0.9865 16849\n", + " macro avg 0.9866 0.9864 0.9865 16849\n", + "weighted avg 0.9866 0.9865 0.9865 16849\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 10.76%\n", + "Within ±10%: 75.03%\n", + "MAE: 0.05\n", + "RMSE: 0.07\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 7.87%\n", + "Within ±2%: 61.66%\n", + "Within ±5%: 75.67%\n", + "Within ±10%: 86.32%\n", + "Within ±20%: 91.11%\n", + "MAE: 0.03\n", + "RMSE: 0.06\n" + ] + } + ], + "source": [ + "#Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "min_val = df['solarenergy'].min()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "\n", + "max_val = df['solarenergy'].max()\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", + "\n", + "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 8\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarenergy_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=150,\n", + " batch_size=512,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "527/527 [==============================] - 6s 10ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Energy Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 0.03 kWh\n", + "RMSE: 0.07 kWh\n", + "R² Score: 0.995\n", + "MAPE: N/A (insufficient data)\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 kWh: 100.0%\n", + "Within ±10 kWh: 100.0%\n", + "Within ±20 kWh: 100.0%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 97.6%\n", + "\n", + "Confusion Matrix for Energy Levels:\n", + " Low Moderate Very Low\n", + "Low 3539 133 1\n", + "Moderate 26 2082 0\n", + "Very Low 247 0 10821\n", + "\n", + "Plot saved as: 2024-11-27_23-17_energy_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: -0.000\n", + "Error standard deviation: 0.068\n", + "Median error: 0.000\n", + "95th percentile absolute error: 0.137\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 73s 10ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar energy after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 117206\n", + "Non-zero predictions (classification >= 0.5): 110673\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 1.10\n", + "Median: 0.93\n", + "Std: 0.95\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 117206 (51.43%)\n", + "Predicted non-zeros: 110673 (48.57%)\n", + "Mean classification confidence: 0.4896\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar energy: 0.64\n", + "Min solar energy: 0.00\n", + "Max solar energy: 3.30\n", + "Zero predictions: 95673 (41.98%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "#final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", + " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", + " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", + "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", + "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Energy:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 161,156.0000\n", + "mean : 0.6529\n", + "median : 0.0736\n", + "std : 0.9288\n", + "min : 0.0000\n", + "max : 4.0000\n", + "skewness : 1.2834\n", + "kurtosis : 0.3742\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 0.0000\n", + "percentile_50 : 0.0736\n", + "percentile_75 : 1.1913\n", + "percentile_90 : 2.2530\n", + "percentile_95 : 2.7314\n", + "percentile_99 : 3.1348\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (45.06%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 161156,\n", + " 'mean': 0.6529324282684227,\n", + " 'median': 0.07359524816274643,\n", + " 'std': 0.928826011992019,\n", + " 'min': 0.0,\n", + " 'max': 4.0,\n", + " 'skewness': 1.2833967112068252,\n", + " 'kurtosis': 0.37419692300276486,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 0.0,\n", + " 'percentile_50': 0.07359524816274643,\n", + " 'percentile_75': 1.191302478313446,\n", + " 'percentile_90': 2.2529743671417237,\n", + " 'percentile_95': 2.7313732862472535,\n", + " 'percentile_99': 3.134775576591491}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-27_23-17_error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Statistics:\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.98 0.99 0.99 8576\n", + " 1.0 0.99 0.98 0.99 8273\n", + "\n", + " accuracy 0.99 16849\n", + " macro avg 0.99 0.99 0.99 16849\n", + "weighted avg 0.99 0.99 0.99 16849\n", + "\n", + "AUC-ROC: 0.9994\n", + "\n", + "Regression Statistics (Non-zero values):\n", + "MAE: 0.0533\n", + "RMSE: 0.0728\n", + "Mean error: -0.0042\n", + "Error std: 0.0727\n", + "\n", + "Final Prediction Statistics:\n", + "MAE: 0.0282\n", + "RMSE: 0.0563\n", + "Mean error: -0.0004\n", + "Error std: 0.0563\n", + "\n", + "Error Thresholds (Final Predictions):\n", + "Predictions within ±0.5: 99.9%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26c41d23-65bf-4a38-9241-ea9b17effbd5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarradiation/.ipynb_checkpoints/2024-11-20_14-25_model_architecture-checkpoint.png b/models/solarradiation/.ipynb_checkpoints/2024-11-20_14-25_model_architecture-checkpoint.png new file mode 100644 index 0000000..1dab72e Binary files /dev/null and b/models/solarradiation/.ipynb_checkpoints/2024-11-20_14-25_model_architecture-checkpoint.png differ diff --git a/models/solarradiation/.ipynb_checkpoints/2024-11-20_16-50_model_architecture-checkpoint.png b/models/solarradiation/.ipynb_checkpoints/2024-11-20_16-50_model_architecture-checkpoint.png new file mode 100644 index 0000000..167c3b8 Binary files /dev/null and b/models/solarradiation/.ipynb_checkpoints/2024-11-20_16-50_model_architecture-checkpoint.png differ diff --git a/models/solarradiation/.ipynb_checkpoints/2024-11-20_17-41_model_architecture-checkpoint.png b/models/solarradiation/.ipynb_checkpoints/2024-11-20_17-41_model_architecture-checkpoint.png new file mode 100644 index 0000000..c6c54de Binary files /dev/null and b/models/solarradiation/.ipynb_checkpoints/2024-11-20_17-41_model_architecture-checkpoint.png differ diff --git a/models/solarradiation/.ipynb_checkpoints/2024-11-25_13-52_features-checkpoint.json b/models/solarradiation/.ipynb_checkpoints/2024-11-25_13-52_features-checkpoint.json new file mode 100644 index 0000000..9c56af1 --- /dev/null +++ b/models/solarradiation/.ipynb_checkpoints/2024-11-25_13-52_features-checkpoint.json @@ -0,0 +1 @@ +["uvindex", "cloudcover", "visibility", "temp", "pressure", "humidity", "solar_elevation", "solar_angle", "day_length", "hour_sin", "hour_cos", "day_of_year_sin", "day_of_year_cos", "month_sin", "month_cos", "clear_sky_index", "atmospheric_attenuation", "theoretical_radiation", "expected_radiation", "cloud_elevation", "visibility_elevation", "uv_cloud_interaction", "temp_radiation_potential", "cloud_rolling_12h", "temp_rolling_12h", "uv_rolling_12h", "cloudcover_rolling_mean_6h", "temp_rolling_mean_6h", "temp_1h_lag", "cloudcover_1h_lag", "humidity_1h_lag", "uv_lag_1h", "season_Spring", "season_Summer", "season_Autumn", "season_Winter", "time_period_Morning", "time_period_Afternoon", "time_period_Evening", "time_period_Night"] \ No newline at end of file diff --git a/models/solarradiation/.ipynb_checkpoints/2024-11-25_20-39_model_architecture-checkpoint.png b/models/solarradiation/.ipynb_checkpoints/2024-11-25_20-39_model_architecture-checkpoint.png new file mode 100644 index 0000000..ac242d2 Binary files /dev/null and b/models/solarradiation/.ipynb_checkpoints/2024-11-25_20-39_model_architecture-checkpoint.png differ diff --git a/models/solarradiation/.ipynb_checkpoints/solarradiation_model-checkpoint.ipynb b/models/solarradiation/.ipynb_checkpoints/solarradiation_model-checkpoint.ipynb new file mode 100644 index 0000000..d6da0e0 --- /dev/null +++ b/models/solarradiation/.ipynb_checkpoints/solarradiation_model-checkpoint.ipynb @@ -0,0 +1,3023 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Hit:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease \n", + "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease \n", + "Hit:5 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:41:43.497052: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-26 05:41:43.497104: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-26 05:41:43.497156: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-26 05:41:43.506575: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix, classification_report, roc_auc_score\n", + "from tensorflow.keras.metrics import AUC\n", + "from scipy import stats\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " \"\"\"\n", + " Add time-based features to the DataFrame.\n", + " Works with both 'datetime' as column or index.\n", + " \"\"\"\n", + " # Se datetime è l'indice, lo usiamo direttamente\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " datetime_col = df.index\n", + " else:\n", + " # Se datetime è una colonna, la convertiamo\n", + " if 'datetime' in df.columns:\n", + " datetime_col = pd.to_datetime(df['datetime'])\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Creazione delle feature temporali\n", + " df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n", + " df['year'] = datetime_col.year\n", + " df['month'] = datetime_col.month\n", + " df['day'] = datetime_col.day\n", + " df['hour'] = datetime_col.hour\n", + " df['minute'] = datetime_col.minute\n", + " df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n", + " df['day_of_week'] = datetime_col.dayofweek\n", + " df['day_of_year'] = datetime_col.dayofyear\n", + " df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n", + " df['quarter'] = datetime_col.quarter\n", + " df['is_month_end'] = datetime_col.is_month_end.astype(int)\n", + " df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n", + " df['is_year_end'] = datetime_col.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['season'] = datetime_col.map(get_season)\n", + " df['time_period'] = datetime_col.hour.map(get_time_period)\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Solar angle calculation\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Assicuriamoci che l'indice sia di tipo datetime\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df['datetime'])\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " Assumes df has a DatetimeIndex\n", + " \"\"\"\n", + " # Verifichiamo che abbiamo un DatetimeIndex\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " raise ValueError(\"DataFrame must have a DatetimeIndex\")\n", + "\n", + " # Existing features\n", + " df = add_time_features(df)\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # Weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # Derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # Rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # Extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Verifichiamo se datetime è già l'indice\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex', # Direct radiation indicator\n", + " 'cloudcover', # Cloud coverage\n", + " 'visibility', # Atmospheric transparency\n", + " 'temp', # Temperature\n", + " 'pressure', # Atmospheric pressure\n", + " 'humidity', # Humidity\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation', # Solar elevation\n", + " 'solar_angle', # Solar angle\n", + " 'day_length', # Day length\n", + " 'hour_sin', # Daily cycle\n", + " 'hour_cos',\n", + " 'day_of_year_sin', # Annual cycle\n", + " 'day_of_year_cos',\n", + " 'month_sin', # Monthly cycle\n", + " 'month_cos',\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index', # Clear sky index\n", + " 'atmospheric_attenuation', # Atmospheric attenuation\n", + " 'theoretical_radiation', # Theoretical radiation\n", + " 'expected_radiation', # Expected radiation\n", + " 'cloud_elevation', # Cloud-elevation interaction\n", + " 'visibility_elevation', # Visibility-elevation interaction\n", + " 'uv_cloud_interaction', # UV-cloud interaction\n", + " 'temp_radiation_potential', # Temperature-radiation potential\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h', # Cloud coverage moving average\n", + " 'temp_rolling_12h', # Temperature moving average\n", + " 'uv_rolling_12h', # UV moving average\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " ],\n", + "\n", + " # Lag Features (most recent)\n", + " 'lag_features': [\n", + " 'temp_1h_lag', # 1-hour temperature lag\n", + " 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n", + " 'humidity_1h_lag', # 1-hour humidity lag\n", + " 'uv_lag_1h', # 1-hour UV lag\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring', # Seasons\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning', # Time periods\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night',\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " df[column] = df[column].interpolate(method='time')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarradiation']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a bidirectional LSTM layer with residual connections and regularization.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Number of LSTM units\n", + " dropout_rate: Dropout rate for regularization\n", + " l2_reg: L2 regularization factor\n", + " return_sequences: Whether to return sequences or just the last output\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " residual = x\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " if return_sequences:\n", + " if int(residual.shape[-1]) != 2 * units:\n", + " residual = Dense(2 * units, activation='linear')(residual)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a multi-head attention block with residual connections.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Dimension of the key space\n", + " num_heads: Number of attention heads\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + "\n", + "\n", + "def asymmetric_loss(y_true, y_pred):\n", + " \"\"\"\n", + " Loss function che penalizza maggiormente la sottostima dei valori alti\n", + " \"\"\"\n", + " diff = y_true - y_pred\n", + " abs_diff = tf.abs(diff)\n", + "\n", + " # Calcola il peso basato sul valore reale\n", + " value_weight = tf.exp(y_true / tf.reduce_max(y_true)) - 1\n", + "\n", + " # Penalizza maggiormente la sottostima (quando y_pred < y_true)\n", + " underestimation_penalty = tf.where(diff > 0, 2.0, 1.0)\n", + "\n", + " # Combina i pesi\n", + " total_weight = value_weight * underestimation_penalty\n", + "\n", + " # Calcola la loss pesata\n", + " weighted_loss = total_weight * abs_diff\n", + "\n", + " return tf.reduce_mean(weighted_loss)\n", + "\n", + "def add_peak_features(x):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per identificare potenziali picchi\n", + " \"\"\"\n", + " # Moving average delle ultime n osservazioni\n", + " ma = tf.keras.layers.Conv1D(1, kernel_size=5, padding='same')(x)\n", + "\n", + " # Differenza dal moving average (identifica anomalie)\n", + " diff_ma = Lambda(lambda x: x[0] - x[1])([x, ma])\n", + "\n", + " # Rate of change\n", + " roc = Lambda(lambda x: x[:, 1:] - x[:, :-1])(x)\n", + " roc = tf.pad(roc, [[0, 0], [1, 0], [0, 0]])\n", + "\n", + " # Concatena tutte le feature\n", + " enhanced_x = Concatenate()([x, diff_ma, roc])\n", + "\n", + " return enhanced_x\n", + "\n", + "def create_regression_branch(shared_features, l2_lambda=0.005, name_suffix=''):\n", + " \"\"\"\n", + " Branch di regressione migliorato per valori alti\n", + " \"\"\"\n", + " # Branch principale\n", + " main_branch = shared_features\n", + " dense_units = [512, 256, 128, 64] # Unità aumentate\n", + "\n", + " for units in dense_units:\n", + " main_branch = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda)\n", + " )(main_branch)\n", + " main_branch = BatchNormalization()(main_branch)\n", + " main_branch = Activation('swish')(main_branch)\n", + "\n", + " # Branch specializzato per valori alti\n", + " high_values_branch = shared_features\n", + " for units in [256, 128, 64]:\n", + " high_values_branch = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " activation='relu' # Usa ReLU per preservare valori alti\n", + " )(high_values_branch)\n", + "\n", + " # Gate per decidere quanto pesare il branch dei valori alti\n", + " gate = Dense(1, activation='sigmoid')(shared_features)\n", + "\n", + " # Combina i branch\n", + " main_output = Dense(1)(main_branch)\n", + " high_values_output = Dense(1)(high_values_branch)\n", + "\n", + " # Output finale pesato\n", + " final_output = Lambda(lambda x: x[0] * (1 - x[2]) + x[1] * x[2])(\n", + " [main_output, high_values_output, gate]\n", + " )\n", + "\n", + " return final_output\n", + "\n", + "def create_peak_specialized_ensemble(shared_features, l2_lambda=0.005):\n", + " \"\"\"\n", + " Ensemble di modelli specializzati per diverse fasce di valori\n", + " \"\"\"\n", + " # Modello generale\n", + " general_model = create_regression_branch(shared_features, name_suffix='general')\n", + "\n", + " # Modello specializzato per valori alti\n", + " high_values_features = Dense(256, activation='relu')(shared_features)\n", + " high_values_model = create_regression_branch(high_values_features, name_suffix='high')\n", + "\n", + " # Modello specializzato per picchi estremi\n", + " peak_features = Dense(512, activation='relu')(shared_features)\n", + " peak_model = create_regression_branch(peak_features, name_suffix='peak')\n", + "\n", + " # Gate network per pesare i modelli\n", + " gate_features = Concatenate()([shared_features,\n", + " Dense(32)(shared_features),\n", + " Dense(32)(high_values_features),\n", + " Dense(32)(peak_features)])\n", + "\n", + " gates = Dense(3, activation='softmax')(gate_features)\n", + "\n", + " # Combina le predizioni\n", + " final_output = Lambda(lambda x: (x[0] * x[3][:, 0:1] +\n", + " x[1] * x[3][:, 1:2] +\n", + " x[2] * x[3][:, 2:3]))([general_model,\n", + " high_values_model,\n", + " peak_model,\n", + " gates])\n", + "\n", + " return final_output\n", + "\n", + "def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n", + " \"\"\"\n", + " Creates a hybrid model with enhanced peak prediction capabilities\n", + " \"\"\"\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Backbone comune\n", + " survival_probs = [0.9, 0.8, 0.7, 0.6]\n", + " attention_survival_probs = [0.85, 0.75, 0.65, 0.55]\n", + " lstm_units = [256, 128, 64, 32]\n", + " dropout_rates = [0.4, 0.3, 0.2, 0.2]\n", + " attention_heads = [32, 24, 16, 8]\n", + "\n", + " x = inputs\n", + " lstm_blocks = 4\n", + " for i in range(lstm_blocks):\n", + " x = create_residual_lstm_layer(\n", + " x,\n", + " units=lstm_units[i],\n", + " dropout_rate=dropout_rates[i],\n", + " l2_reg=l2_lambda,\n", + " return_sequences=True,\n", + " survival_probability=survival_probs[i]\n", + " )\n", + " x = attention_block(\n", + " x,\n", + " units=lstm_units[i],\n", + " num_heads=attention_heads[i],\n", + " survival_probability=attention_survival_probs[i]\n", + " )\n", + " if i < lstm_blocks - 1:\n", + " x = MaxPooling1D()(x)\n", + "\n", + " # Final shared LSTM layer\n", + " shared_features = create_residual_lstm_layer(\n", + " x,\n", + " units=32,\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Enhance features for peak detection\n", + " enhanced_features = add_peak_features(x)\n", + " enhanced_shared_features = create_residual_lstm_layer(\n", + " enhanced_features,\n", + " units=64, # Increased units for enhanced features\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Classification branch\n", + " classification_x = Dense(64, kernel_regularizer=regularizers.l2(l2_lambda))(shared_features)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_x = Dropout(0.2)(classification_x)\n", + " classification_x = Dense(32, kernel_regularizer=regularizers.l2(l2_lambda))(classification_x)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_output = Dense(1, activation='sigmoid', name='classification_output')(classification_x)\n", + "\n", + " # Combined features for regression\n", + " regression_features = Concatenate()([shared_features, enhanced_shared_features])\n", + "\n", + " # Create specialized ensemble for regression\n", + " regression_output = create_peak_specialized_ensemble(regression_features, l2_lambda)\n", + "\n", + " # Clip regression values\n", + " regression_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='regression_output'\n", + " )(regression_output)\n", + "\n", + " # Combine outputs using threshold activation\n", + " thresholded_classification = ThresholdedReLU(theta=0.5)(classification_output)\n", + " normalized_classification = Lambda(lambda x: tf.cast(x > 0, tf.float32))(thresholded_classification)\n", + " final_output = Lambda(\n", + " lambda inputs: inputs[0] * inputs[1],\n", + " name='final_output'\n", + " )([regression_output, normalized_classification])\n", + "\n", + " # Create model\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[\n", + " classification_output,\n", + " regression_output,\n", + " final_output\n", + " ],\n", + " name=\"SolarRadiationModel\"\n", + " )\n", + "\n", + " # Custom loss functions\n", + " def hybrid_focal_loss(y_true, y_pred):\n", + " mse = tf.square(y_true - y_pred)\n", + " error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n", + " focal_weight = tf.pow(error_ratio, 2)\n", + " weighted_mse = focal_weight * mse\n", + " mae = tf.abs(y_true - y_pred)\n", + " return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n", + "\n", + " def masked_regression_loss(y_true, y_pred):\n", + " mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n", + " return asymmetric_loss(y_true * mask, y_pred * mask)\n", + "\n", + " # Metrics\n", + " def rmse(y_true, y_pred):\n", + " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n", + "\n", + " def custom_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n", + " diff = tf.clip_by_value(diff, 0, 1)\n", + " return tf.reduce_mean(diff) * 100\n", + "\n", + " # Optimizer with reduced initial learning rate\n", + " optimizer = AdamW(\n", + " learning_rate=0.0002, # Reduced from 0.0003\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7,\n", + " weight_decay=0.001,\n", + " amsgrad=True\n", + " )\n", + "\n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': masked_regression_loss,\n", + " 'final_output': hybrid_focal_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.5,\n", + " 'final_output': 0.3\n", + " },\n", + " metrics={\n", + " 'classification_output': ['accuracy', AUC()],\n", + " 'regression_output': ['mse', 'mae', rmse, custom_mape],\n", + " 'final_output': ['mse', 'mae', rmse, custom_mape]\n", + " }\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " # Save model architecture visualization\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar radiation values (W/m²)\n", + " y_pred : array-like\n", + " Predicted solar radiation values (W/m²)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 W/m²\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n", + "\n", + " # Radiation level classification\n", + " def get_radiation_level(value):\n", + " if value <= 200:\n", + " return 'Very Low'\n", + " elif value <= 400:\n", + " return 'Low'\n", + " elif value <= 600:\n", + " return 'Moderate'\n", + " elif value <= 800:\n", + " return 'High'\n", + " elif value <= 1000:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate radiation levels\n", + " y_true_levels = [get_radiation_level(v) for v in y_true]\n", + " y_pred_levels = [get_radiation_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Radiation Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} W/m²\")\n", + " print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for radiation levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels)\n", + " print(\"\\nConfusion Matrix for Radiation Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n", + " index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Predicted Radiation (W/m²)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (W/m²)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 10\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by radiation level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Radiation Level')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_radiation_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Helper function to calculate and print metrics for all outputs\n", + " \n", + " Parameters:\n", + " - y_true: true values\n", + " - y_class: classification predictions\n", + " - y_reg: regression predictions\n", + " - y_final: final combined predictions\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Classification metrics\n", + " print(\"\\nClassification Metrics:\")\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " print(confusion_matrix(y_true_binary, y_pred_binary))\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_true_binary, y_pred_binary, \n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4))\n", + " \n", + " # Regression metrics (non-zero values)\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero): # verifichiamo che ci siano valori non-zero\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " out_of_range = np.sum((y_reg_nonzero < min_output) | (y_reg_nonzero > max_output))\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / (y_true_nonzero + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final combined output metrics\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " diff = np.abs((y_true - y_final) / (y_true + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar radiation model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " callbacks = [\n", + " EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-4\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.5,\n", + " patience=7,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=False\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: (\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n", + " calculate_metrics(y_test, *model.predict(X_test, verbose=0), min_output, max_output)\n", + " ) if epoch % 10 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar radiation predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar radiation predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarradiation_predicted': final_pred.flatten(),\n", + " 'solarradiation_classification': classification_pred.flatten(),\n", + " 'solarradiation_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar radiation column where missing\n", + " df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarradiation'] == df['solarradiation_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarradiation_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarradiation_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarradiation_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarradiation_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarradiation_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarradiation_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarradiation_predicted', 'solarradiation_classification',\n", + " 'solarradiation_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar radiation model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 40\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (103798, 24, 40)\n", + "Test data shape: (25933, 24, 40)\n", + "Saving scaler X to: 2024-11-26_05-41_scale_X.joblib\n", + "Saving scaler X to: 2024-11-26_05-41_scale_y.joblib\n", + "Saving features to: 2024-11-26_05-41_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n", + "\n", + "print(\"Initializing solar radiation model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "76deb4deb84dc4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\n", + "Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n", + "Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:41:50.507143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n", + "2024-11-26 05:41:51.386109: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"SolarRadiationModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 40)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 4 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n", + " sticDepth) 'multi_head_attention[0][0]']\n", + " \n", + " layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n", + " D) ] \n", + " \n", + " bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_1[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n", + " sticDepth) 'multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_2[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n", + " sticDepth) 'multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n", + " sticDepth) 'multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " conv1d (Conv1D) (None, 3, 1) 321 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " lambda_1 (Lambda) (None, 2, 64) 0 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " lambda (Lambda) (None, 3, 64) 0 ['layer_normalization_7[0][0]'\n", + " , 'conv1d[0][0]'] \n", + " \n", + " tf.compat.v1.pad (TFOpLamb (None, 3, 64) 0 ['lambda_1[0][0]'] \n", + " da) \n", + " \n", + " concatenate (Concatenate) (None, 3, 192) 0 ['layer_normalization_7[0][0]'\n", + " , 'lambda[0][0]', \n", + " 'tf.compat.v1.pad[0][0]'] \n", + " \n", + " bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n", + " onal) ] \n", + " \n", + " bidirectional_5 (Bidirecti (None, 128) 131584 ['concatenate[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " layer_normalization_9 (Lay (None, 128) 256 ['bidirectional_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " dropout_5 (Dropout) (None, 128) 0 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " concatenate_1 (Concatenate (None, 192) 0 ['dropout_4[0][0]', \n", + " ) 'dropout_5[0][0]'] \n", + " \n", + " dense_16 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n", + " \n", + " dense_27 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n", + " \n", + " dense_6 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n", + " \n", + " dense_17 (Dense) (None, 512) 131584 ['dense_16[0][0]'] \n", + " \n", + " dense_28 (Dense) (None, 512) 262656 ['dense_27[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 512) 2048 ['dense_6[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_6 (Bat (None, 512) 2048 ['dense_17[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_10 (Ba (None, 512) 2048 ['dense_28[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_2 (Activation) (None, 512) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " activation_6 (Activation) (None, 512) 0 ['batch_normalization_6[0][0]'\n", + " ] \n", + " \n", + " activation_10 (Activation) (None, 512) 0 ['batch_normalization_10[0][0]\n", + " '] \n", + " \n", + " dense_7 (Dense) (None, 256) 131328 ['activation_2[0][0]'] \n", + " \n", + " dense_18 (Dense) (None, 256) 131328 ['activation_6[0][0]'] \n", + " \n", + " dense_29 (Dense) (None, 256) 131328 ['activation_10[0][0]'] \n", + " \n", + " batch_normalization_3 (Bat (None, 256) 1024 ['dense_7[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_7 (Bat (None, 256) 1024 ['dense_18[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_11 (Ba (None, 256) 1024 ['dense_29[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_3 (Activation) (None, 256) 0 ['batch_normalization_3[0][0]'\n", + " ] \n", + " \n", + " activation_7 (Activation) (None, 256) 0 ['batch_normalization_7[0][0]'\n", + " ] \n", + " \n", + " activation_11 (Activation) (None, 256) 0 ['batch_normalization_11[0][0]\n", + " '] \n", + " \n", + " dense_4 (Dense) (None, 64) 4160 ['dropout_4[0][0]'] \n", + " \n", + " dense_8 (Dense) (None, 128) 32896 ['activation_3[0][0]'] \n", + " \n", + " dense_19 (Dense) (None, 128) 32896 ['activation_7[0][0]'] \n", + " \n", + " dense_30 (Dense) (None, 128) 32896 ['activation_11[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 64) 256 ['dense_4[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_4 (Bat (None, 128) 512 ['dense_8[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_8 (Bat (None, 128) 512 ['dense_19[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_12 (Ba (None, 128) 512 ['dense_30[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation (Activation) (None, 64) 0 ['batch_normalization[0][0]'] \n", + " \n", + " activation_4 (Activation) (None, 128) 0 ['batch_normalization_4[0][0]'\n", + " ] \n", + " \n", + " activation_8 (Activation) (None, 128) 0 ['batch_normalization_8[0][0]'\n", + " ] \n", + " \n", + " activation_12 (Activation) (None, 128) 0 ['batch_normalization_12[0][0]\n", + " '] \n", + " \n", + " dropout_6 (Dropout) (None, 64) 0 ['activation[0][0]'] \n", + " \n", + " dense_9 (Dense) (None, 64) 8256 ['activation_4[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n", + " \n", + " dense_20 (Dense) (None, 64) 8256 ['activation_8[0][0]'] \n", + " \n", + " dense_21 (Dense) (None, 256) 65792 ['dense_16[0][0]'] \n", + " \n", + " dense_31 (Dense) (None, 64) 8256 ['activation_12[0][0]'] \n", + " \n", + " dense_32 (Dense) (None, 256) 131328 ['dense_27[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 32) 2080 ['dropout_6[0][0]'] \n", + " \n", + " batch_normalization_5 (Bat (None, 64) 256 ['dense_9[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_11 (Dense) (None, 128) 32896 ['dense_10[0][0]'] \n", + " \n", + " batch_normalization_9 (Bat (None, 64) 256 ['dense_20[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_22 (Dense) (None, 128) 32896 ['dense_21[0][0]'] \n", + " \n", + " batch_normalization_13 (Ba (None, 64) 256 ['dense_31[0][0]'] \n", + " tchNormalization) \n", + " \n", + " dense_33 (Dense) (None, 128) 32896 ['dense_32[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 32) 128 ['dense_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_5 (Activation) (None, 64) 0 ['batch_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dense_12 (Dense) (None, 64) 8256 ['dense_11[0][0]'] \n", + " \n", + " activation_9 (Activation) (None, 64) 0 ['batch_normalization_9[0][0]'\n", + " ] \n", + " \n", + " dense_23 (Dense) (None, 64) 8256 ['dense_22[0][0]'] \n", + " \n", + " activation_13 (Activation) (None, 64) 0 ['batch_normalization_13[0][0]\n", + " '] \n", + " \n", + " dense_34 (Dense) (None, 64) 8256 ['dense_33[0][0]'] \n", + " \n", + " dense_38 (Dense) (None, 32) 6176 ['concatenate_1[0][0]'] \n", + " \n", + " dense_39 (Dense) (None, 32) 8224 ['dense_16[0][0]'] \n", + " \n", + " dense_40 (Dense) (None, 32) 16416 ['dense_27[0][0]'] \n", + " \n", + " activation_1 (Activation) (None, 32) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_14 (Dense) (None, 1) 65 ['activation_5[0][0]'] \n", + " \n", + " dense_15 (Dense) (None, 1) 65 ['dense_12[0][0]'] \n", + " \n", + " dense_13 (Dense) (None, 1) 193 ['concatenate_1[0][0]'] \n", + " \n", + " dense_25 (Dense) (None, 1) 65 ['activation_9[0][0]'] \n", + " \n", + " dense_26 (Dense) (None, 1) 65 ['dense_23[0][0]'] \n", + " \n", + " dense_24 (Dense) (None, 1) 257 ['dense_16[0][0]'] \n", + " \n", + " dense_36 (Dense) (None, 1) 65 ['activation_13[0][0]'] \n", + " \n", + " dense_37 (Dense) (None, 1) 65 ['dense_34[0][0]'] \n", + " \n", + " dense_35 (Dense) (None, 1) 513 ['dense_27[0][0]'] \n", + " \n", + " concatenate_2 (Concatenate (None, 288) 0 ['concatenate_1[0][0]', \n", + " ) 'dense_38[0][0]', \n", + " 'dense_39[0][0]', \n", + " 'dense_40[0][0]'] \n", + " \n", + " classification_output (Den (None, 1) 33 ['activation_1[0][0]'] \n", + " se) \n", + " \n", + " lambda_2 (Lambda) (None, 1) 0 ['dense_14[0][0]', \n", + " 'dense_15[0][0]', \n", + " 'dense_13[0][0]'] \n", + " \n", + " lambda_3 (Lambda) (None, 1) 0 ['dense_25[0][0]', \n", + " 'dense_26[0][0]', \n", + " 'dense_24[0][0]'] \n", + " \n", + " lambda_4 (Lambda) (None, 1) 0 ['dense_36[0][0]', \n", + " 'dense_37[0][0]', \n", + " 'dense_35[0][0]'] \n", + " \n", + " dense_41 (Dense) (None, 3) 867 ['concatenate_2[0][0]'] \n", + " \n", + " lambda_5 (Lambda) (None, 1) 0 ['lambda_2[0][0]', \n", + " 'lambda_3[0][0]', \n", + " 'lambda_4[0][0]', \n", + " 'dense_41[0][0]'] \n", + " \n", + " thresholded_re_lu (Thresho (None, 1) 0 ['classification_output[0][0]'\n", + " ldedReLU) ] \n", + " \n", + " regression_output (Lambda) (None, 1) 0 ['lambda_5[0][0]'] \n", + " \n", + " lambda_6 (Lambda) (None, 1) 0 ['thresholded_re_lu[0][0]'] \n", + " \n", + " final_output (Lambda) (None, 1) 0 ['regression_output[0][0]', \n", + " 'lambda_6[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 23955918 (91.38 MB)\n", + "Trainable params: 23949966 (91.36 MB)\n", + "Non-trainable params: 5952 (23.25 KB)\n", + "__________________________________________________________________________________________________\n", + "\n", + "Class distribution in training set:\n", + "Zeros: 52022 (50.12%)\n", + "Non-zeros: 51776 (49.88%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 13007 (50.16%)\n", + "Non-zeros: 12926 (49.84%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:42:25.841427: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-26 05:42:26.758143: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-26 05:42:28.319667: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x74802ce90ad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-26 05:42:28.319705: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-26 05:42:28.325479: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-26 05:42:28.469866: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "541/541 [==============================] - ETA: 0s - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 1 Detailed Metrics:\n", + "541/541 [==============================] - 106s 111ms/step - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850 - val_loss: 4.8619 - val_classification_output_loss: 0.2702 - val_regression_output_loss: 0.1998 - val_final_output_loss: 0.1203 - val_classification_output_accuracy: 0.8850 - val_classification_output_auc: 0.9648 - val_regression_output_mse: 2.6679 - val_regression_output_mae: 1.1926 - val_regression_output_rmse: 1.4470 - val_regression_output_custom_mape: 76.3626 - val_final_output_mse: 0.1619 - val_final_output_mae: 0.2603 - val_final_output_rmse: 0.3866 - val_final_output_custom_mape: 77.3426 - lr: 2.0000e-04\n", + "Epoch 2/100\n", + "541/541 [==============================] - 53s 97ms/step - loss: 2.5852 - classification_output_loss: 0.1672 - regression_output_loss: 0.1392 - final_output_loss: 0.0880 - classification_output_accuracy: 0.9350 - classification_output_auc: 0.9830 - regression_output_mse: 5.1786 - regression_output_mae: 1.6349 - regression_output_rmse: 2.2598 - regression_output_custom_mape: 71.9229 - final_output_mse: 0.1133 - final_output_mae: 0.2027 - final_output_rmse: 0.3209 - final_output_custom_mape: 72.9816 - val_loss: 1.4136 - val_classification_output_loss: 0.2138 - val_regression_output_loss: 0.1684 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9236 - val_classification_output_auc: 0.9842 - val_regression_output_mse: 1.8603 - val_regression_output_mae: 1.0648 - val_regression_output_rmse: 1.3566 - val_regression_output_custom_mape: 75.8244 - val_final_output_mse: 0.1512 - val_final_output_mae: 0.2497 - val_final_output_rmse: 0.3643 - val_final_output_custom_mape: 75.8084 - lr: 2.0000e-04\n", + "Epoch 3/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.9558 - classification_output_loss: 0.1719 - regression_output_loss: 0.1366 - final_output_loss: 0.0895 - classification_output_accuracy: 0.9322 - classification_output_auc: 0.9815 - regression_output_mse: 5.6874 - regression_output_mae: 1.7310 - regression_output_rmse: 2.3695 - regression_output_custom_mape: 71.9037 - final_output_mse: 0.1126 - final_output_mae: 0.2038 - final_output_rmse: 0.3184 - final_output_custom_mape: 72.9206 - val_loss: 0.9508 - val_classification_output_loss: 0.4633 - val_regression_output_loss: 0.3961 - val_final_output_loss: 0.3536 - val_classification_output_accuracy: 0.8129 - val_classification_output_auc: 0.9060 - val_regression_output_mse: 1.6205 - val_regression_output_mae: 0.8607 - val_regression_output_rmse: 1.2559 - val_regression_output_custom_mape: 66.2296 - val_final_output_mse: 0.4082 - val_final_output_mae: 0.4125 - val_final_output_rmse: 0.6197 - val_final_output_custom_mape: 78.8389 - lr: 2.0000e-04\n", + "Epoch 4/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.5850 - classification_output_loss: 0.1792 - regression_output_loss: 0.2007 - final_output_loss: 0.1027 - classification_output_accuracy: 0.9293 - classification_output_auc: 0.9798 - regression_output_mse: 2.2127 - regression_output_mae: 0.9460 - regression_output_rmse: 1.2398 - regression_output_custom_mape: 73.1762 - final_output_mse: 0.1435 - final_output_mae: 0.2295 - final_output_rmse: 0.3588 - final_output_custom_mape: 74.5266 - val_loss: 0.4332 - val_classification_output_loss: 0.1561 - val_regression_output_loss: 0.1114 - val_final_output_loss: 0.1238 - val_classification_output_accuracy: 0.9345 - val_classification_output_auc: 0.9856 - val_regression_output_mse: 4.7324 - val_regression_output_mae: 1.5163 - val_regression_output_rmse: 2.1370 - val_regression_output_custom_mape: 76.3284 - val_final_output_mse: 0.1509 - val_final_output_mae: 0.2353 - val_final_output_rmse: 0.3733 - val_final_output_custom_mape: 76.9648 - lr: 2.0000e-04\n", + "Epoch 5/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.3949 - classification_output_loss: 0.1662 - regression_output_loss: 0.1463 - final_output_loss: 0.0885 - classification_output_accuracy: 0.9320 - classification_output_auc: 0.9831 - regression_output_mse: 2.7376 - regression_output_mae: 0.9965 - regression_output_rmse: 1.2987 - regression_output_custom_mape: 71.5179 - final_output_mse: 0.1125 - final_output_mae: 0.2059 - final_output_rmse: 0.3178 - final_output_custom_mape: 73.1178 - val_loss: 0.3969 - val_classification_output_loss: 0.2723 - val_regression_output_loss: 0.2098 - val_final_output_loss: 0.0787 - val_classification_output_accuracy: 0.8948 - val_classification_output_auc: 0.9729 - val_regression_output_mse: 0.1621 - val_regression_output_mae: 0.3438 - val_regression_output_rmse: 0.3980 - val_regression_output_custom_mape: 72.4170 - val_final_output_mse: 0.1050 - val_final_output_mae: 0.1904 - val_final_output_rmse: 0.3098 - val_final_output_custom_mape: 74.6832 - lr: 2.0000e-04\n", + "Epoch 6/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.2983 - classification_output_loss: 0.1354 - regression_output_loss: 0.1470 - final_output_loss: 0.0704 - classification_output_accuracy: 0.9464 - classification_output_auc: 0.9883 - regression_output_mse: 0.2598 - regression_output_mae: 0.4423 - regression_output_rmse: 0.5024 - regression_output_custom_mape: 71.7011 - final_output_mse: 0.0909 - final_output_mae: 0.1841 - final_output_rmse: 0.2871 - final_output_custom_mape: 71.9341 - val_loss: 0.2936 - val_classification_output_loss: 0.1702 - val_regression_output_loss: 0.1509 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9348 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 0.2456 - val_regression_output_mae: 0.4147 - val_regression_output_rmse: 0.4831 - val_regression_output_custom_mape: 74.9922 - val_final_output_mse: 0.1488 - val_final_output_mae: 0.2237 - val_final_output_rmse: 0.3632 - val_final_output_custom_mape: 74.9167 - lr: 2.0000e-04\n", + "Epoch 7/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.2505 - classification_output_loss: 0.1442 - regression_output_loss: 0.1384 - final_output_loss: 0.0775 - classification_output_accuracy: 0.9443 - classification_output_auc: 0.9866 - regression_output_mse: 1.4617 - regression_output_mae: 0.7991 - regression_output_rmse: 1.0508 - regression_output_custom_mape: 71.1954 - final_output_mse: 0.0988 - final_output_mae: 0.1902 - final_output_rmse: 0.2984 - final_output_custom_mape: 72.2324 - val_loss: 0.3214 - val_classification_output_loss: 0.3465 - val_regression_output_loss: 0.1456 - val_final_output_loss: 0.2147 - val_classification_output_accuracy: 0.8648 - val_classification_output_auc: 0.9798 - val_regression_output_mse: 0.8371 - val_regression_output_mae: 0.7323 - val_regression_output_rmse: 0.9107 - val_regression_output_custom_mape: 73.4417 - val_final_output_mse: 0.2713 - val_final_output_mae: 0.3340 - val_final_output_rmse: 0.4815 - val_final_output_custom_mape: 76.5578 - lr: 2.0000e-04\n", + "Epoch 8/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.2237 - classification_output_loss: 0.1397 - regression_output_loss: 0.1364 - final_output_loss: 0.0796 - classification_output_accuracy: 0.9446 - classification_output_auc: 0.9878 - regression_output_mse: 5.1974 - regression_output_mae: 1.6560 - regression_output_rmse: 2.2692 - regression_output_custom_mape: 71.2507 - final_output_mse: 0.1033 - final_output_mae: 0.1957 - final_output_rmse: 0.3040 - final_output_custom_mape: 72.5075 - val_loss: 0.2685 - val_classification_output_loss: 0.3914 - val_regression_output_loss: 0.1217 - val_final_output_loss: 0.1222 - val_classification_output_accuracy: 0.8613 - val_classification_output_auc: 0.9606 - val_regression_output_mse: 3.7820 - val_regression_output_mae: 1.2866 - val_regression_output_rmse: 1.8121 - val_regression_output_custom_mape: 68.2919 - val_final_output_mse: 0.1599 - val_final_output_mae: 0.2535 - val_final_output_rmse: 0.3599 - val_final_output_custom_mape: 71.1078 - lr: 2.0000e-04\n", + "Epoch 9/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.1754 - classification_output_loss: 0.1188 - regression_output_loss: 0.0985 - final_output_loss: 0.0632 - classification_output_accuracy: 0.9523 - classification_output_auc: 0.9911 - regression_output_mse: 5.4764 - regression_output_mae: 1.6783 - regression_output_rmse: 2.3144 - regression_output_custom_mape: 69.8070 - final_output_mse: 0.0766 - final_output_mae: 0.1685 - final_output_rmse: 0.2609 - final_output_custom_mape: 70.8609 - val_loss: 0.1706 - val_classification_output_loss: 0.1096 - val_regression_output_loss: 0.1077 - val_final_output_loss: 0.0658 - val_classification_output_accuracy: 0.9538 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 5.1203 - val_regression_output_mae: 1.6260 - val_regression_output_rmse: 2.2470 - val_regression_output_custom_mape: 70.2256 - val_final_output_mse: 0.0762 - val_final_output_mae: 0.1588 - val_final_output_rmse: 0.2636 - val_final_output_custom_mape: 72.1157 - lr: 2.0000e-04\n", + "Epoch 10/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.1727 - classification_output_loss: 0.1290 - regression_output_loss: 0.1097 - final_output_loss: 0.0692 - classification_output_accuracy: 0.9479 - classification_output_auc: 0.9894 - regression_output_mse: 5.5291 - regression_output_mae: 1.6998 - regression_output_rmse: 2.3152 - regression_output_custom_mape: 70.2356 - final_output_mse: 0.0878 - final_output_mae: 0.1771 - final_output_rmse: 0.2731 - final_output_custom_mape: 71.4182 - val_loss: 0.1912 - val_classification_output_loss: 0.1751 - val_regression_output_loss: 0.0882 - val_final_output_loss: 0.0951 - val_classification_output_accuracy: 0.9463 - val_classification_output_auc: 0.9833 - val_regression_output_mse: 6.5049 - val_regression_output_mae: 1.9127 - val_regression_output_rmse: 2.5318 - val_regression_output_custom_mape: 75.4162 - val_final_output_mse: 0.1245 - val_final_output_mae: 0.2154 - val_final_output_rmse: 0.3377 - val_final_output_custom_mape: 75.3060 - lr: 2.0000e-04\n", + "Epoch 11/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452\n", + "Epoch 11 Detailed Metrics:\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452 - val_loss: 0.2093 - val_classification_output_loss: 0.2277 - val_regression_output_loss: 0.1437 - val_final_output_loss: 0.1177 - val_classification_output_accuracy: 0.9283 - val_classification_output_auc: 0.9779 - val_regression_output_mse: 6.0570 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.4544 - val_regression_output_custom_mape: 72.7221 - val_final_output_mse: 0.1364 - val_final_output_mae: 0.2321 - val_final_output_rmse: 0.3485 - val_final_output_custom_mape: 73.0335 - lr: 2.0000e-04\n", + "Epoch 12/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.1464 - classification_output_loss: 0.1072 - regression_output_loss: 0.1053 - final_output_loss: 0.0601 - classification_output_accuracy: 0.9561 - classification_output_auc: 0.9927 - regression_output_mse: 6.1092 - regression_output_mae: 1.8112 - regression_output_rmse: 2.4620 - regression_output_custom_mape: 69.8113 - final_output_mse: 0.0740 - final_output_mae: 0.1632 - final_output_rmse: 0.2523 - final_output_custom_mape: 70.3597 - val_loss: 0.1542 - val_classification_output_loss: 0.1503 - val_regression_output_loss: 0.0985 - val_final_output_loss: 0.0841 - val_classification_output_accuracy: 0.9489 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 5.9678 - val_regression_output_mae: 1.7956 - val_regression_output_rmse: 2.4203 - val_regression_output_custom_mape: 72.0743 - val_final_output_mse: 0.1016 - val_final_output_mae: 0.1921 - val_final_output_rmse: 0.2866 - val_final_output_custom_mape: 72.4481 - lr: 2.0000e-04\n", + "Epoch 13/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.1351 - classification_output_loss: 0.0971 - regression_output_loss: 0.0999 - final_output_loss: 0.0592 - classification_output_accuracy: 0.9601 - classification_output_auc: 0.9939 - regression_output_mse: 6.1488 - regression_output_mae: 1.8152 - regression_output_rmse: 2.4696 - regression_output_custom_mape: 69.9159 - final_output_mse: 0.0739 - final_output_mae: 0.1643 - final_output_rmse: 0.2517 - final_output_custom_mape: 70.7436 - val_loss: 0.2396 - val_classification_output_loss: 0.3289 - val_regression_output_loss: 0.2191 - val_final_output_loss: 0.0616 - val_classification_output_accuracy: 0.8966 - val_classification_output_auc: 0.9705 - val_regression_output_mse: 0.7583 - val_regression_output_mae: 0.6739 - val_regression_output_rmse: 0.8690 - val_regression_output_custom_mape: 73.5880 - val_final_output_mse: 0.0818 - val_final_output_mae: 0.1722 - val_final_output_rmse: 0.2787 - val_final_output_custom_mape: 73.8794 - lr: 2.0000e-04\n", + "Epoch 14/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.1342 - classification_output_loss: 0.1155 - regression_output_loss: 0.0989 - final_output_loss: 0.0567 - classification_output_accuracy: 0.9515 - classification_output_auc: 0.9914 - regression_output_mse: 6.1207 - regression_output_mae: 1.8088 - regression_output_rmse: 2.4631 - regression_output_custom_mape: 69.6780 - final_output_mse: 0.0681 - final_output_mae: 0.1613 - final_output_rmse: 0.2447 - final_output_custom_mape: 70.4782 - val_loss: 0.1483 - val_classification_output_loss: 0.1125 - val_regression_output_loss: 0.1075 - val_final_output_loss: 0.0949 - val_classification_output_accuracy: 0.9545 - val_classification_output_auc: 0.9921 - val_regression_output_mse: 3.9937 - val_regression_output_mae: 1.5182 - val_regression_output_rmse: 1.9883 - val_regression_output_custom_mape: 74.0972 - val_final_output_mse: 0.1191 - val_final_output_mae: 0.2071 - val_final_output_rmse: 0.3226 - val_final_output_custom_mape: 73.9669 - lr: 2.0000e-04\n", + "Epoch 15/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.1104 - classification_output_loss: 0.0837 - regression_output_loss: 0.0778 - final_output_loss: 0.0495 - classification_output_accuracy: 0.9664 - classification_output_auc: 0.9953 - regression_output_mse: 6.4510 - regression_output_mae: 1.8607 - regression_output_rmse: 2.5321 - regression_output_custom_mape: 68.8861 - final_output_mse: 0.0558 - final_output_mae: 0.1437 - final_output_rmse: 0.2201 - final_output_custom_mape: 69.3373 - val_loss: 0.1422 - val_classification_output_loss: 0.1152 - val_regression_output_loss: 0.1280 - val_final_output_loss: 0.0574 - val_classification_output_accuracy: 0.9537 - val_classification_output_auc: 0.9933 - val_regression_output_mse: 5.8958 - val_regression_output_mae: 1.7291 - val_regression_output_rmse: 2.4158 - val_regression_output_custom_mape: 68.3024 - val_final_output_mse: 0.0661 - val_final_output_mae: 0.1556 - val_final_output_rmse: 0.2529 - val_final_output_custom_mape: 69.4068 - lr: 2.0000e-04\n", + "Epoch 16/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.1238 - classification_output_loss: 0.1192 - regression_output_loss: 0.0907 - final_output_loss: 0.0547 - classification_output_accuracy: 0.9500 - classification_output_auc: 0.9910 - regression_output_mse: 6.1427 - regression_output_mae: 1.7993 - regression_output_rmse: 2.4683 - regression_output_custom_mape: 68.9479 - final_output_mse: 0.0637 - final_output_mae: 0.1549 - final_output_rmse: 0.2359 - final_output_custom_mape: 69.7157 - val_loss: 0.1268 - val_classification_output_loss: 0.1301 - val_regression_output_loss: 0.0858 - val_final_output_loss: 0.0656 - val_classification_output_accuracy: 0.9430 - val_classification_output_auc: 0.9895 - val_regression_output_mse: 6.4156 - val_regression_output_mae: 1.8944 - val_regression_output_rmse: 2.5145 - val_regression_output_custom_mape: 71.3515 - val_final_output_mse: 0.0826 - val_final_output_mae: 0.1624 - val_final_output_rmse: 0.2670 - val_final_output_custom_mape: 71.2686 - lr: 2.0000e-04\n", + "Epoch 17/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0942 - classification_output_loss: 0.0838 - regression_output_loss: 0.0617 - final_output_loss: 0.0405 - classification_output_accuracy: 0.9650 - classification_output_auc: 0.9955 - regression_output_mse: 6.3983 - regression_output_mae: 1.8268 - regression_output_rmse: 2.5217 - regression_output_custom_mape: 67.0560 - final_output_mse: 0.0399 - final_output_mae: 0.1244 - final_output_rmse: 0.1906 - final_output_custom_mape: 67.8066 - val_loss: 0.0979 - val_classification_output_loss: 0.1114 - val_regression_output_loss: 0.0596 - val_final_output_loss: 0.0473 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9919 - val_regression_output_mse: 5.9015 - val_regression_output_mae: 1.7669 - val_regression_output_rmse: 2.4102 - val_regression_output_custom_mape: 67.6186 - val_final_output_mse: 0.0482 - val_final_output_mae: 0.1296 - val_final_output_rmse: 0.2055 - val_final_output_custom_mape: 67.8666 - lr: 2.0000e-04\n", + "Epoch 18/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0781 - classification_output_loss: 0.0719 - regression_output_loss: 0.0469 - final_output_loss: 0.0356 - classification_output_accuracy: 0.9714 - classification_output_auc: 0.9967 - regression_output_mse: 6.5024 - regression_output_mae: 1.8395 - regression_output_rmse: 2.5405 - regression_output_custom_mape: 65.9929 - final_output_mse: 0.0320 - final_output_mae: 0.1099 - final_output_rmse: 0.1657 - final_output_custom_mape: 66.5092 - val_loss: 0.1399 - val_classification_output_loss: 0.2071 - val_regression_output_loss: 0.1092 - val_final_output_loss: 0.0458 - val_classification_output_accuracy: 0.9400 - val_classification_output_auc: 0.9852 - val_regression_output_mse: 6.7711 - val_regression_output_mae: 1.9289 - val_regression_output_rmse: 2.5859 - val_regression_output_custom_mape: 69.2916 - val_final_output_mse: 0.0527 - val_final_output_mae: 0.1335 - val_final_output_rmse: 0.2186 - val_final_output_custom_mape: 69.7183 - lr: 2.0000e-04\n", + "Epoch 19/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.1056 - classification_output_loss: 0.1087 - regression_output_loss: 0.0770 - final_output_loss: 0.0488 - classification_output_accuracy: 0.9540 - classification_output_auc: 0.9925 - regression_output_mse: 6.1650 - regression_output_mae: 1.7928 - regression_output_rmse: 2.4682 - regression_output_custom_mape: 68.5078 - final_output_mse: 0.0533 - final_output_mae: 0.1445 - final_output_rmse: 0.2165 - final_output_custom_mape: 69.2497 - val_loss: 0.1351 - val_classification_output_loss: 0.2118 - val_regression_output_loss: 0.0784 - val_final_output_loss: 0.0744 - val_classification_output_accuracy: 0.9309 - val_classification_output_auc: 0.9809 - val_regression_output_mse: 6.2044 - val_regression_output_mae: 1.8057 - val_regression_output_rmse: 2.4610 - val_regression_output_custom_mape: 71.1880 - val_final_output_mse: 0.0989 - val_final_output_mae: 0.1831 - val_final_output_rmse: 0.2888 - val_final_output_custom_mape: 71.7069 - lr: 2.0000e-04\n", + "Epoch 20/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0826 - classification_output_loss: 0.0899 - regression_output_loss: 0.0503 - final_output_loss: 0.0377 - classification_output_accuracy: 0.9641 - classification_output_auc: 0.9946 - regression_output_mse: 6.4599 - regression_output_mae: 1.8346 - regression_output_rmse: 2.5319 - regression_output_custom_mape: 66.5618 - final_output_mse: 0.0347 - final_output_mae: 0.1170 - final_output_rmse: 0.1767 - final_output_custom_mape: 67.1488 - val_loss: 0.1313 - val_classification_output_loss: 0.1914 - val_regression_output_loss: 0.0979 - val_final_output_loss: 0.0590 - val_classification_output_accuracy: 0.9393 - val_classification_output_auc: 0.9835 - val_regression_output_mse: 7.0486 - val_regression_output_mae: 2.0071 - val_regression_output_rmse: 2.6313 - val_regression_output_custom_mape: 72.4229 - val_final_output_mse: 0.0695 - val_final_output_mae: 0.1603 - val_final_output_rmse: 0.2545 - val_final_output_custom_mape: 72.8319 - lr: 2.0000e-04\n", + "Epoch 21/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971\n", + "Epoch 21 Detailed Metrics:\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971 - val_loss: 0.1567 - val_classification_output_loss: 0.1925 - val_regression_output_loss: 0.1182 - val_final_output_loss: 0.1052 - val_classification_output_accuracy: 0.9335 - val_classification_output_auc: 0.9888 - val_regression_output_mse: 6.3370 - val_regression_output_mae: 1.8864 - val_regression_output_rmse: 2.4981 - val_regression_output_custom_mape: 76.4414 - val_final_output_mse: 0.1555 - val_final_output_mae: 0.2558 - val_final_output_rmse: 0.3667 - val_final_output_custom_mape: 76.3954 - lr: 2.0000e-04\n", + "Epoch 22/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0895 - classification_output_loss: 0.0784 - regression_output_loss: 0.0675 - final_output_loss: 0.0448 - classification_output_accuracy: 0.9675 - classification_output_auc: 0.9960 - regression_output_mse: 6.3547 - regression_output_mae: 1.8204 - regression_output_rmse: 2.5032 - regression_output_custom_mape: 67.3088 - final_output_mse: 0.0469 - final_output_mae: 0.1335 - final_output_rmse: 0.2007 - final_output_custom_mape: 68.4431 - val_loss: 0.1160 - val_classification_output_loss: 0.1100 - val_regression_output_loss: 0.1048 - val_final_output_loss: 0.0471 - val_classification_output_accuracy: 0.9584 - val_classification_output_auc: 0.9936 - val_regression_output_mse: 6.0390 - val_regression_output_mae: 1.7293 - val_regression_output_rmse: 2.4455 - val_regression_output_custom_mape: 66.8834 - val_final_output_mse: 0.0490 - val_final_output_mae: 0.1419 - val_final_output_rmse: 0.2174 - val_final_output_custom_mape: 69.0459 - lr: 2.0000e-04\n", + "Epoch 23/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0919 - classification_output_loss: 0.0994 - regression_output_loss: 0.0648 - final_output_loss: 0.0429 - classification_output_accuracy: 0.9578 - classification_output_auc: 0.9935 - regression_output_mse: 6.2936 - regression_output_mae: 1.8038 - regression_output_rmse: 2.4971 - regression_output_custom_mape: 67.2289 - final_output_mse: 0.0428 - final_output_mae: 0.1306 - final_output_rmse: 0.1954 - final_output_custom_mape: 68.1846 - val_loss: 0.1030 - val_classification_output_loss: 0.1001 - val_regression_output_loss: 0.0829 - val_final_output_loss: 0.0521 - val_classification_output_accuracy: 0.9620 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 4.9840 - val_regression_output_mae: 1.6330 - val_regression_output_rmse: 2.2237 - val_regression_output_custom_mape: 67.5453 - val_final_output_mse: 0.0686 - val_final_output_mae: 0.1495 - val_final_output_rmse: 0.2307 - val_final_output_custom_mape: 68.2594 - lr: 2.0000e-04\n", + "Epoch 24/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0805 - classification_output_loss: 0.0740 - regression_output_loss: 0.0572 - final_output_loss: 0.0406 - classification_output_accuracy: 0.9696 - classification_output_auc: 0.9964 - regression_output_mse: 6.5178 - regression_output_mae: 1.8487 - regression_output_rmse: 2.5431 - regression_output_custom_mape: 66.7225 - final_output_mse: 0.0413 - final_output_mae: 0.1227 - final_output_rmse: 0.1848 - final_output_custom_mape: 67.3500 - val_loss: 0.1503 - val_classification_output_loss: 0.1089 - val_regression_output_loss: 0.1781 - val_final_output_loss: 0.0504 - val_classification_output_accuracy: 0.9567 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 7.3781 - val_regression_output_mae: 2.0860 - val_regression_output_rmse: 2.7043 - val_regression_output_custom_mape: 71.4390 - val_final_output_mse: 0.0652 - val_final_output_mae: 0.1535 - val_final_output_rmse: 0.2412 - val_final_output_custom_mape: 71.1141 - lr: 2.0000e-04\n", + "Epoch 25/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826\n", + "Epoch 25: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-05.\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826 - val_loss: 0.0920 - val_classification_output_loss: 0.1283 - val_regression_output_loss: 0.0552 - val_final_output_loss: 0.0492 - val_classification_output_accuracy: 0.9526 - val_classification_output_auc: 0.9923 - val_regression_output_mse: 6.4349 - val_regression_output_mae: 1.8355 - val_regression_output_rmse: 2.5185 - val_regression_output_custom_mape: 69.3148 - val_final_output_mse: 0.0507 - val_final_output_mae: 0.1446 - val_final_output_rmse: 0.2100 - val_final_output_custom_mape: 69.2727 - lr: 2.0000e-04\n", + "Epoch 26/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0650 - classification_output_loss: 0.0610 - regression_output_loss: 0.0419 - final_output_loss: 0.0313 - classification_output_accuracy: 0.9754 - classification_output_auc: 0.9975 - regression_output_mse: 6.6869 - regression_output_mae: 1.8694 - regression_output_rmse: 2.5765 - regression_output_custom_mape: 65.2689 - final_output_mse: 0.0247 - final_output_mae: 0.0999 - final_output_rmse: 0.1486 - final_output_custom_mape: 65.4804 - val_loss: 0.0663 - val_classification_output_loss: 0.0825 - val_regression_output_loss: 0.0378 - val_final_output_loss: 0.0327 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9952 - val_regression_output_mse: 6.7468 - val_regression_output_mae: 1.8843 - val_regression_output_rmse: 2.5794 - val_regression_output_custom_mape: 64.9190 - val_final_output_mse: 0.0274 - val_final_output_mae: 0.1009 - val_final_output_rmse: 0.1557 - val_final_output_custom_mape: 64.8372 - lr: 1.0000e-04\n", + "Epoch 27/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0520 - classification_output_loss: 0.0582 - regression_output_loss: 0.0251 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9761 - classification_output_auc: 0.9978 - regression_output_mse: 6.7296 - regression_output_mae: 1.8683 - regression_output_rmse: 2.5843 - regression_output_custom_mape: 64.1190 - final_output_mse: 0.0169 - final_output_mae: 0.0854 - final_output_rmse: 0.1247 - final_output_custom_mape: 64.2327 - val_loss: 0.0596 - val_classification_output_loss: 0.0822 - val_regression_output_loss: 0.0315 - val_final_output_loss: 0.0279 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9952 - val_regression_output_mse: 6.7299 - val_regression_output_mae: 1.8644 - val_regression_output_rmse: 2.5772 - val_regression_output_custom_mape: 64.0526 - val_final_output_mse: 0.0205 - val_final_output_mae: 0.0887 - val_final_output_rmse: 0.1379 - val_final_output_custom_mape: 64.1891 - lr: 1.0000e-04\n", + "Epoch 28/100\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.0545 - classification_output_loss: 0.0621 - regression_output_loss: 0.0298 - final_output_loss: 0.0277 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9975 - regression_output_mse: 6.5792 - regression_output_mae: 1.8345 - regression_output_rmse: 2.5551 - regression_output_custom_mape: 63.7945 - final_output_mse: 0.0191 - final_output_mae: 0.0891 - final_output_rmse: 0.1301 - final_output_custom_mape: 64.3093 - val_loss: 0.0707 - val_classification_output_loss: 0.0879 - val_regression_output_loss: 0.0470 - val_final_output_loss: 0.0366 - val_classification_output_accuracy: 0.9646 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6182 - val_regression_output_mae: 1.8513 - val_regression_output_rmse: 2.5551 - val_regression_output_custom_mape: 65.1533 - val_final_output_mse: 0.0342 - val_final_output_mae: 0.1136 - val_final_output_rmse: 0.1726 - val_final_output_custom_mape: 65.5781 - lr: 1.0000e-04\n", + "Epoch 29/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0504 - classification_output_loss: 0.0586 - regression_output_loss: 0.0257 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9756 - classification_output_auc: 0.9978 - regression_output_mse: 6.7302 - regression_output_mae: 1.8661 - regression_output_rmse: 2.5837 - regression_output_custom_mape: 63.8101 - final_output_mse: 0.0171 - final_output_mae: 0.0858 - final_output_rmse: 0.1252 - final_output_custom_mape: 64.0432 - val_loss: 0.0755 - val_classification_output_loss: 0.0734 - val_regression_output_loss: 0.0692 - val_final_output_loss: 0.0298 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5720 - val_regression_output_mae: 1.8334 - val_regression_output_rmse: 2.5485 - val_regression_output_custom_mape: 65.7695 - val_final_output_mse: 0.0219 - val_final_output_mae: 0.0957 - val_final_output_rmse: 0.1415 - val_final_output_custom_mape: 65.9068 - lr: 1.0000e-04\n", + "Epoch 30/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0618 - classification_output_loss: 0.0646 - regression_output_loss: 0.0430 - final_output_loss: 0.0325 - classification_output_accuracy: 0.9726 - classification_output_auc: 0.9974 - regression_output_mse: 6.6295 - regression_output_mae: 1.8588 - regression_output_rmse: 2.5643 - regression_output_custom_mape: 65.4595 - final_output_mse: 0.0267 - final_output_mae: 0.1036 - final_output_rmse: 0.1522 - final_output_custom_mape: 65.8207 - val_loss: 0.0563 - val_classification_output_loss: 0.0878 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0269 - val_classification_output_accuracy: 0.9639 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6181 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5541 - val_regression_output_custom_mape: 63.1844 - val_final_output_mse: 0.0184 - val_final_output_mae: 0.0849 - val_final_output_rmse: 0.1292 - val_final_output_custom_mape: 63.5460 - lr: 1.0000e-04\n", + "Epoch 31/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0539 - classification_output_loss: 0.0608 - regression_output_loss: 0.0321 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9746 - classification_output_auc: 0.9975 - regression_output_mse: 6.6719 - 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val_classification_output_auc: 0.9946 - val_regression_output_mse: 6.6598 - val_regression_output_mae: 1.8470 - val_regression_output_rmse: 2.5624 - val_regression_output_custom_mape: 63.3716 - val_final_output_mse: 0.0182 - val_final_output_mae: 0.0846 - val_final_output_rmse: 0.1293 - val_final_output_custom_mape: 63.4470 - lr: 1.0000e-04\n", + "Epoch 34/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0589 - regression_output_loss: 0.0381 - final_output_loss: 0.0284 - classification_output_accuracy: 0.9753 - classification_output_auc: 0.9977 - regression_output_mse: 6.7001 - regression_output_mae: 1.8630 - regression_output_rmse: 2.5779 - regression_output_custom_mape: 63.9678 - final_output_mse: 0.0203 - final_output_mae: 0.0921 - final_output_rmse: 0.1348 - final_output_custom_mape: 64.2577 - val_loss: 0.0620 - val_classification_output_loss: 0.0928 - val_regression_output_loss: 0.0371 - val_final_output_loss: 0.0294 - val_classification_output_accuracy: 0.9603 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 6.7330 - val_regression_output_mae: 1.8643 - val_regression_output_rmse: 2.5765 - val_regression_output_custom_mape: 64.8509 - val_final_output_mse: 0.0217 - val_final_output_mae: 0.0925 - val_final_output_rmse: 0.1422 - val_final_output_custom_mape: 65.1600 - lr: 1.0000e-04\n", + "Epoch 35/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0614 - regression_output_loss: 0.0367 - final_output_loss: 0.0297 - classification_output_accuracy: 0.9745 - classification_output_auc: 0.9976 - regression_output_mse: 6.6579 - regression_output_mae: 1.8582 - regression_output_rmse: 2.5696 - regression_output_custom_mape: 64.6971 - final_output_mse: 0.0221 - final_output_mae: 0.0956 - final_output_rmse: 0.1398 - final_output_custom_mape: 65.0271 - val_loss: 0.0594 - val_classification_output_loss: 0.0939 - val_regression_output_loss: 0.0300 - 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val_regression_output_loss: 0.0418 - val_final_output_loss: 0.0242 - val_classification_output_accuracy: 0.9594 - val_classification_output_auc: 0.9931 - val_regression_output_mse: 6.5540 - val_regression_output_mae: 1.8143 - val_regression_output_rmse: 2.5381 - val_regression_output_custom_mape: 62.4616 - val_final_output_mse: 0.0149 - val_final_output_mae: 0.0784 - val_final_output_rmse: 0.1183 - val_final_output_custom_mape: 62.9478 - lr: 1.0000e-04\n", + "Epoch 37/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0585 - classification_output_loss: 0.0619 - regression_output_loss: 0.0427 - final_output_loss: 0.0304 - classification_output_accuracy: 0.9744 - classification_output_auc: 0.9975 - regression_output_mse: 6.6088 - regression_output_mae: 1.8483 - regression_output_rmse: 2.5604 - regression_output_custom_mape: 64.4680 - final_output_mse: 0.0237 - final_output_mae: 0.0974 - final_output_rmse: 0.1426 - final_output_custom_mape: 64.9015 - val_loss: 0.0691 - 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val_loss: 0.0563 - val_classification_output_loss: 0.0809 - val_regression_output_loss: 0.0317 - val_final_output_loss: 0.0292 - val_classification_output_accuracy: 0.9680 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 6.5566 - val_regression_output_mae: 1.8266 - val_regression_output_rmse: 2.5428 - val_regression_output_custom_mape: 63.8044 - val_final_output_mse: 0.0222 - val_final_output_mae: 0.0928 - val_final_output_rmse: 0.1405 - val_final_output_custom_mape: 64.0602 - lr: 1.0000e-04\n", + "Epoch 39/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0515 - classification_output_loss: 0.0622 - regression_output_loss: 0.0313 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6419 - regression_output_mae: 1.8481 - regression_output_rmse: 2.5667 - regression_output_custom_mape: 63.7719 - final_output_mse: 0.0181 - final_output_mae: 0.0884 - final_output_rmse: 0.1283 - final_output_custom_mape: 64.1751 - val_loss: 0.0704 - val_classification_output_loss: 0.1092 - val_regression_output_loss: 0.0453 - val_final_output_loss: 0.0350 - val_classification_output_accuracy: 0.9609 - val_classification_output_auc: 0.9938 - val_regression_output_mse: 6.1988 - val_regression_output_mae: 1.7405 - val_regression_output_rmse: 2.4718 - val_regression_output_custom_mape: 63.3963 - val_final_output_mse: 0.0315 - val_final_output_mae: 0.1112 - val_final_output_rmse: 0.1671 - val_final_output_custom_mape: 65.2804 - lr: 1.0000e-04\n", + "Epoch 40/100\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.0517 - classification_output_loss: 0.0625 - regression_output_loss: 0.0314 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9742 - classification_output_auc: 0.9975 - regression_output_mse: 6.6585 - regression_output_mae: 1.8533 - regression_output_rmse: 2.5700 - regression_output_custom_mape: 64.0952 - final_output_mse: 0.0199 - final_output_mae: 0.0913 - final_output_rmse: 0.1320 - final_output_custom_mape: 64.4904 - val_loss: 0.0712 - val_classification_output_loss: 0.0779 - val_regression_output_loss: 0.0654 - val_final_output_loss: 0.0267 - val_classification_output_accuracy: 0.9666 - val_classification_output_auc: 0.9957 - val_regression_output_mse: 6.7348 - val_regression_output_mae: 1.8600 - val_regression_output_rmse: 2.5770 - val_regression_output_custom_mape: 63.5495 - val_final_output_mse: 0.0176 - val_final_output_mae: 0.0874 - val_final_output_rmse: 0.1274 - val_final_output_custom_mape: 63.5438 - lr: 1.0000e-04\n", + "Epoch 41/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0537 - classification_output_loss: 0.0611 - regression_output_loss: 0.0359 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9976 - regression_output_mse: 6.7033 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5791 - regression_output_custom_mape: 64.1775 - final_output_mse: 0.0202 - final_output_mae: 0.0923 - final_output_rmse: 0.1340 - final_output_custom_mape: 64.4763\n", + "Epoch 41 Detailed Metrics:\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0537 - classification_output_loss: 0.0611 - regression_output_loss: 0.0359 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9976 - regression_output_mse: 6.7033 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5791 - regression_output_custom_mape: 64.1775 - final_output_mse: 0.0202 - final_output_mae: 0.0923 - final_output_rmse: 0.1340 - final_output_custom_mape: 64.4763 - val_loss: 0.0600 - val_classification_output_loss: 0.0930 - val_regression_output_loss: 0.0397 - val_final_output_loss: 0.0232 - val_classification_output_accuracy: 0.9621 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 6.4476 - val_regression_output_mae: 1.7785 - val_regression_output_rmse: 2.5232 - val_regression_output_custom_mape: 62.2171 - 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val_regression_output_custom_mape: 62.9115 - val_final_output_mse: 0.0159 - val_final_output_mae: 0.0809 - val_final_output_rmse: 0.1218 - val_final_output_custom_mape: 62.8272 - lr: 1.0000e-04\n", + "Epoch 43/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0545 - classification_output_loss: 0.0614 - regression_output_loss: 0.0368 - final_output_loss: 0.0291 - classification_output_accuracy: 0.9748 - classification_output_auc: 0.9975 - regression_output_mse: 6.7100 - regression_output_mae: 1.8688 - regression_output_rmse: 2.5810 - regression_output_custom_mape: 64.6285 - final_output_mse: 0.0208 - final_output_mae: 0.0943 - final_output_rmse: 0.1371 - final_output_custom_mape: 64.8706 - val_loss: 0.0639 - val_classification_output_loss: 0.1024 - val_regression_output_loss: 0.0432 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9622 - val_classification_output_auc: 0.9938 - val_regression_output_mse: 6.9339 - val_regression_output_mae: 1.9011 - val_regression_output_rmse: 2.6127 - val_regression_output_custom_mape: 62.8598 - val_final_output_mse: 0.0143 - val_final_output_mae: 0.0768 - val_final_output_rmse: 0.1158 - val_final_output_custom_mape: 62.6952 - lr: 1.0000e-04\n", + "Epoch 44/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0503 - classification_output_loss: 0.0579 - regression_output_loss: 0.0320 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9979 - regression_output_mse: 6.7157 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5809 - regression_output_custom_mape: 63.8468 - final_output_mse: 0.0185 - final_output_mae: 0.0884 - final_output_rmse: 0.1284 - final_output_custom_mape: 64.1197 - val_loss: 0.0535 - val_classification_output_loss: 0.0933 - val_regression_output_loss: 0.0278 - val_final_output_loss: 0.0222 - val_classification_output_accuracy: 0.9624 - val_classification_output_auc: 0.9953 - val_regression_output_mse: 6.5462 - val_regression_output_mae: 1.7951 - val_regression_output_rmse: 2.5414 - val_regression_output_custom_mape: 61.7450 - val_final_output_mse: 0.0123 - val_final_output_mae: 0.0720 - val_final_output_rmse: 0.1075 - val_final_output_custom_mape: 61.9004 - lr: 1.0000e-04\n", + "Epoch 45/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0461 - classification_output_loss: 0.0569 - regression_output_loss: 0.0258 - final_output_loss: 0.0260 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9980 - regression_output_mse: 6.7333 - regression_output_mae: 1.8649 - regression_output_rmse: 2.5850 - regression_output_custom_mape: 63.6520 - final_output_mse: 0.0163 - final_output_mae: 0.0845 - final_output_rmse: 0.1212 - final_output_custom_mape: 63.8765 - val_loss: 0.0649 - val_classification_output_loss: 0.1188 - val_regression_output_loss: 0.0354 - val_final_output_loss: 0.0316 - val_classification_output_accuracy: 0.9471 - val_classification_output_auc: 0.9966 - val_regression_output_mse: 6.0672 - val_regression_output_mae: 1.7044 - val_regression_output_rmse: 2.4462 - val_regression_output_custom_mape: 62.7387 - val_final_output_mse: 0.0251 - val_final_output_mae: 0.1018 - val_final_output_rmse: 0.1497 - val_final_output_custom_mape: 63.6189 - lr: 1.0000e-04\n", + "Epoch 46/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0510 - classification_output_loss: 0.0628 - regression_output_loss: 0.0325 - final_output_loss: 0.0277 - classification_output_accuracy: 0.9741 - classification_output_auc: 0.9974 - regression_output_mse: 6.6063 - regression_output_mae: 1.8383 - regression_output_rmse: 2.5597 - regression_output_custom_mape: 63.7547 - final_output_mse: 0.0190 - final_output_mae: 0.0901 - final_output_rmse: 0.1300 - final_output_custom_mape: 64.2525 - val_loss: 0.0523 - val_classification_output_loss: 0.0814 - val_regression_output_loss: 0.0291 - val_final_output_loss: 0.0254 - val_classification_output_accuracy: 0.9658 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 6.7808 - val_regression_output_mae: 1.8736 - val_regression_output_rmse: 2.5860 - val_regression_output_custom_mape: 65.4947 - val_final_output_mse: 0.0155 - val_final_output_mae: 0.0831 - val_final_output_rmse: 0.1224 - val_final_output_custom_mape: 65.5416 - lr: 1.0000e-04\n", + "Epoch 47/100\n", + "541/541 [==============================] - 51s 94ms/step - loss: 0.0509 - classification_output_loss: 0.0589 - regression_output_loss: 0.0343 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9977 - regression_output_mse: 6.7076 - regression_output_mae: 1.8620 - regression_output_rmse: 2.5796 - regression_output_custom_mape: 63.6718 - final_output_mse: 0.0182 - final_output_mae: 0.0885 - final_output_rmse: 0.1278 - final_output_custom_mape: 63.9957 - val_loss: 0.0655 - val_classification_output_loss: 0.1041 - val_regression_output_loss: 0.0332 - val_final_output_loss: 0.0474 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 5.7525 - val_regression_output_mae: 1.6878 - val_regression_output_rmse: 2.3943 - val_regression_output_custom_mape: 62.4874 - val_final_output_mse: 0.0233 - val_final_output_mae: 0.0817 - val_final_output_rmse: 0.1449 - val_final_output_custom_mape: 62.4108 - lr: 1.0000e-04\n", + "Epoch 48/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0480 - classification_output_loss: 0.0625 - regression_output_loss: 0.0275 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6081 - regression_output_mae: 1.8378 - regression_output_rmse: 2.5612 - regression_output_custom_mape: 63.5768 - final_output_mse: 0.0170 - final_output_mae: 0.0853 - final_output_rmse: 0.1224 - final_output_custom_mape: 63.9522 - val_loss: 0.0500 - val_classification_output_loss: 0.0769 - val_regression_output_loss: 0.0292 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9676 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5384 - val_regression_output_mae: 1.7942 - val_regression_output_rmse: 2.5409 - val_regression_output_custom_mape: 61.9763 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0710 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 62.2404 - lr: 1.0000e-04\n", + "Epoch 49/100\n", + "541/541 [==============================] - 51s 95ms/step - loss: 0.0474 - classification_output_loss: 0.0564 - regression_output_loss: 0.0296 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9760 - classification_output_auc: 0.9980 - regression_output_mse: 6.7164 - regression_output_mae: 1.8615 - regression_output_rmse: 2.5813 - regression_output_custom_mape: 63.5453 - final_output_mse: 0.0171 - final_output_mae: 0.0857 - final_output_rmse: 0.1235 - final_output_custom_mape: 63.7933 - val_loss: 0.0498 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0283 - val_final_output_loss: 0.0260 - val_classification_output_accuracy: 0.9693 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.5693 - val_regression_output_mae: 1.8158 - val_regression_output_rmse: 2.5473 - val_regression_output_custom_mape: 64.0865 - val_final_output_mse: 0.0165 - val_final_output_mae: 0.0844 - val_final_output_rmse: 0.1243 - val_final_output_custom_mape: 64.5164 - lr: 1.0000e-04\n", + "Epoch 50/100\n", + "541/541 [==============================] - 49s 91ms/step - loss: 0.0395 - classification_output_loss: 0.0549 - regression_output_loss: 0.0169 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9766 - classification_output_auc: 0.9981 - regression_output_mse: 6.7595 - regression_output_mae: 1.8651 - regression_output_rmse: 2.5900 - regression_output_custom_mape: 63.1470 - final_output_mse: 0.0132 - final_output_mae: 0.0776 - final_output_rmse: 0.1109 - final_output_custom_mape: 63.3583 - val_loss: 0.0451 - val_classification_output_loss: 0.0768 - val_regression_output_loss: 0.0201 - val_final_output_loss: 0.0230 - val_classification_output_accuracy: 0.9683 - val_classification_output_auc: 0.9956 - val_regression_output_mse: 6.4914 - val_regression_output_mae: 1.8122 - val_regression_output_rmse: 2.5338 - val_regression_output_custom_mape: 62.2374 - val_final_output_mse: 0.0132 - val_final_output_mae: 0.0753 - val_final_output_rmse: 0.1099 - val_final_output_custom_mape: 62.2972 - lr: 1.0000e-04\n", + "Epoch 51/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354\n", + "Epoch 51 Detailed Metrics:\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354 - val_loss: 0.0727 - val_classification_output_loss: 0.0944 - val_regression_output_loss: 0.0562 - val_final_output_loss: 0.0432 - val_classification_output_accuracy: 0.9653 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 4.2029 - val_regression_output_mae: 1.5086 - val_regression_output_rmse: 2.0452 - val_regression_output_custom_mape: 67.4825 - val_final_output_mse: 0.0431 - val_final_output_mae: 0.1288 - val_final_output_rmse: 0.1947 - val_final_output_custom_mape: 67.4020 - lr: 1.0000e-04\n", + "Epoch 52/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0518 - classification_output_loss: 0.0650 - regression_output_loss: 0.0344 - final_output_loss: 0.0286 - classification_output_accuracy: 0.9733 - classification_output_auc: 0.9972 - regression_output_mse: 6.6534 - regression_output_mae: 1.8507 - regression_output_rmse: 2.5694 - regression_output_custom_mape: 64.0731 - final_output_mse: 0.0204 - final_output_mae: 0.0922 - final_output_rmse: 0.1333 - final_output_custom_mape: 64.4775 - val_loss: 0.0449 - val_classification_output_loss: 0.0869 - val_regression_output_loss: 0.0155 - val_final_output_loss: 0.0226 - val_classification_output_accuracy: 0.9648 - val_classification_output_auc: 0.9948 - val_regression_output_mse: 6.4651 - val_regression_output_mae: 1.8048 - val_regression_output_rmse: 2.5262 - val_regression_output_custom_mape: 62.5471 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0736 - val_final_output_rmse: 0.1087 - val_final_output_custom_mape: 62.6503 - lr: 1.0000e-04\n", + "Epoch 53/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0411 - classification_output_loss: 0.0548 - regression_output_loss: 0.0207 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9980 - regression_output_mse: 6.7604 - regression_output_mae: 1.8647 - regression_output_rmse: 2.5895 - regression_output_custom_mape: 62.7709 - final_output_mse: 0.0133 - final_output_mae: 0.0777 - final_output_rmse: 0.1099 - final_output_custom_mape: 62.9869 - val_loss: 0.0771 - val_classification_output_loss: 0.0905 - val_regression_output_loss: 0.0766 - val_final_output_loss: 0.0281 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9944 - val_regression_output_mse: 6.3203 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5019 - val_regression_output_custom_mape: 64.9538 - val_final_output_mse: 0.0197 - val_final_output_mae: 0.0919 - val_final_output_rmse: 0.1340 - val_final_output_custom_mape: 64.7748 - lr: 1.0000e-04\n", + "Epoch 54/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0483 - classification_output_loss: 0.0616 - regression_output_loss: 0.0307 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9738 - classification_output_auc: 0.9975 - regression_output_mse: 6.6227 - regression_output_mae: 1.8377 - regression_output_rmse: 2.5629 - regression_output_custom_mape: 63.1048 - final_output_mse: 0.0169 - final_output_mae: 0.0860 - final_output_rmse: 0.1239 - final_output_custom_mape: 63.6682 - val_loss: 0.0536 - val_classification_output_loss: 0.0971 - val_regression_output_loss: 0.0289 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9942 - val_regression_output_mse: 6.6649 - val_regression_output_mae: 1.8270 - val_regression_output_rmse: 2.5626 - val_regression_output_custom_mape: 62.0142 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0733 - val_final_output_rmse: 0.1081 - val_final_output_custom_mape: 62.4347 - lr: 1.0000e-04\n", + "Epoch 55/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - final_output_custom_mape: 63.4037\n", + "Epoch 55: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - final_output_custom_mape: 63.4037 - val_loss: 0.0547 - val_classification_output_loss: 0.1083 - val_regression_output_loss: 0.0260 - val_final_output_loss: 0.0251 - val_classification_output_accuracy: 0.9587 - val_classification_output_auc: 0.9929 - val_regression_output_mse: 6.7626 - val_regression_output_mae: 1.8696 - val_regression_output_rmse: 2.5790 - val_regression_output_custom_mape: 64.1653 - val_final_output_mse: 0.0161 - val_final_output_mae: 0.0809 - val_final_output_rmse: 0.1229 - val_final_output_custom_mape: 64.2964 - lr: 1.0000e-04\n", + "Epoch 56/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0357 - classification_output_loss: 0.0520 - regression_output_loss: 0.0131 - final_output_loss: 0.0217 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9983 - regression_output_mse: 6.7730 - regression_output_mae: 1.8622 - regression_output_rmse: 2.5912 - regression_output_custom_mape: 62.1226 - final_output_mse: 0.0110 - final_output_mae: 0.0715 - final_output_rmse: 0.1009 - final_output_custom_mape: 62.2862 - val_loss: 0.0406 - val_classification_output_loss: 0.0722 - val_regression_output_loss: 0.0164 - val_final_output_loss: 0.0202 - val_classification_output_accuracy: 0.9707 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5865 - val_regression_output_mae: 1.8079 - val_regression_output_rmse: 2.5506 - val_regression_output_custom_mape: 62.1180 - val_final_output_mse: 0.0098 - val_final_output_mae: 0.0662 - val_final_output_rmse: 0.0959 - val_final_output_custom_mape: 62.3044 - lr: 5.0000e-05\n", + "Epoch 57/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0331 - classification_output_loss: 0.0515 - regression_output_loss: 0.0100 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9983 - regression_output_mse: 6.7417 - regression_output_mae: 1.8522 - regression_output_rmse: 2.5856 - regression_output_custom_mape: 61.5541 - final_output_mse: 0.0094 - final_output_mae: 0.0674 - final_output_rmse: 0.0938 - final_output_custom_mape: 61.7115 - val_loss: 0.0406 - val_classification_output_loss: 0.0763 - val_regression_output_loss: 0.0160 - val_final_output_loss: 0.0196 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5545 - val_regression_output_mae: 1.7924 - val_regression_output_rmse: 2.5441 - val_regression_output_custom_mape: 61.5195 - val_final_output_mse: 0.0089 - val_final_output_mae: 0.0644 - val_final_output_rmse: 0.0920 - val_final_output_custom_mape: 61.8914 - lr: 5.0000e-05\n", + "Epoch 58/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0325 - classification_output_loss: 0.0506 - regression_output_loss: 0.0099 - final_output_loss: 0.0203 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9983 - regression_output_mse: 6.7571 - regression_output_mae: 1.8542 - regression_output_rmse: 2.5887 - regression_output_custom_mape: 61.4581 - final_output_mse: 0.0094 - final_output_mae: 0.0670 - final_output_rmse: 0.0932 - final_output_custom_mape: 61.6506 - val_loss: 0.0405 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0185 - val_final_output_loss: 0.0197 - val_classification_output_accuracy: 0.9710 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6865 - val_regression_output_mae: 1.8298 - val_regression_output_rmse: 2.5699 - val_regression_output_custom_mape: 62.0339 - val_final_output_mse: 0.0091 - val_final_output_mae: 0.0648 - val_final_output_rmse: 0.0928 - val_final_output_custom_mape: 62.0572 - lr: 5.0000e-05\n", + "Epoch 59/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0322 - classification_output_loss: 0.0514 - regression_output_loss: 0.0097 - final_output_loss: 0.0201 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7708 - regression_output_mae: 1.8569 - regression_output_rmse: 2.5913 - regression_output_custom_mape: 61.4620 - final_output_mse: 0.0090 - final_output_mae: 0.0665 - final_output_rmse: 0.0918 - final_output_custom_mape: 61.6222 - val_loss: 0.0479 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0320 - val_final_output_loss: 0.0218 - val_classification_output_accuracy: 0.9702 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6692 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5661 - val_regression_output_custom_mape: 63.2262 - val_final_output_mse: 0.0109 - val_final_output_mae: 0.0716 - val_final_output_rmse: 0.1014 - val_final_output_custom_mape: 63.3540 - lr: 5.0000e-05\n", + "Epoch 60/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0336 - classification_output_loss: 0.0512 - regression_output_loss: 0.0125 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7842 - regression_output_mae: 1.8627 - regression_output_rmse: 2.5937 - regression_output_custom_mape: 61.7020 - final_output_mse: 0.0100 - final_output_mae: 0.0691 - final_output_rmse: 0.0961 - final_output_custom_mape: 61.8323 - val_loss: 0.0496 - val_classification_output_loss: 0.0797 - val_regression_output_loss: 0.0308 - val_final_output_loss: 0.0249 - val_classification_output_accuracy: 0.9691 - val_classification_output_auc: 0.9953 - val_regression_output_mse: 6.8019 - val_regression_output_mae: 1.8752 - val_regression_output_rmse: 2.5909 - val_regression_output_custom_mape: 66.9446 - val_final_output_mse: 0.0148 - val_final_output_mae: 0.0816 - val_final_output_rmse: 0.1192 - val_final_output_custom_mape: 67.0260 - lr: 5.0000e-05\n", + "Epoch 61/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795\n", + "Epoch 61 Detailed Metrics:\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795 - val_loss: 0.0505 - val_classification_output_loss: 0.0742 - val_regression_output_loss: 0.0356 - val_final_output_loss: 0.0244 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9958 - val_regression_output_mse: 6.6354 - val_regression_output_mae: 1.8309 - val_regression_output_rmse: 2.5603 - val_regression_output_custom_mape: 65.7531 - val_final_output_mse: 0.0137 - val_final_output_mae: 0.0801 - val_final_output_rmse: 0.1142 - val_final_output_custom_mape: 65.9202 - lr: 5.0000e-05\n", + "Epoch 62/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0332 - classification_output_loss: 0.0518 - regression_output_loss: 0.0119 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7770 - regression_output_mae: 1.8601 - regression_output_rmse: 2.5926 - regression_output_custom_mape: 61.7351 - final_output_mse: 0.0097 - final_output_mae: 0.0689 - final_output_rmse: 0.0954 - final_output_custom_mape: 61.8624 - val_loss: 0.0481 - val_classification_output_loss: 0.0746 - val_regression_output_loss: 0.0310 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7120 - val_regression_output_mae: 1.8451 - val_regression_output_rmse: 2.5737 - val_regression_output_custom_mape: 65.7528 - val_final_output_mse: 0.0131 - val_final_output_mae: 0.0782 - val_final_output_rmse: 0.1123 - val_final_output_custom_mape: 65.8633 - lr: 5.0000e-05\n", + "Epoch 63/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0304 - classification_output_loss: 0.0504 - regression_output_loss: 0.0079 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7759 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5922 - regression_output_custom_mape: 61.3674 - final_output_mse: 0.0087 - final_output_mae: 0.0655 - final_output_rmse: 0.0902 - final_output_custom_mape: 61.5143 - val_loss: 0.0364 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0121 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6819 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5687 - val_regression_output_custom_mape: 62.2443 - val_final_output_mse: 0.0086 - val_final_output_mae: 0.0635 - val_final_output_rmse: 0.0902 - val_final_output_custom_mape: 62.3224 - lr: 5.0000e-05\n", + "Epoch 64/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0309 - classification_output_loss: 0.0507 - regression_output_loss: 0.0090 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.7723 - regression_output_mae: 1.8552 - regression_output_rmse: 2.5911 - regression_output_custom_mape: 61.1519 - final_output_mse: 0.0086 - final_output_mae: 0.0653 - final_output_rmse: 0.0898 - final_output_custom_mape: 61.3432 - val_loss: 0.0393 - val_classification_output_loss: 0.0725 - val_regression_output_loss: 0.0162 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7377 - val_regression_output_mae: 1.8477 - val_regression_output_rmse: 2.5799 - val_regression_output_custom_mape: 64.4454 - val_final_output_mse: 0.0108 - val_final_output_mae: 0.0712 - val_final_output_rmse: 0.1021 - val_final_output_custom_mape: 64.4477 - lr: 5.0000e-05\n", + "Epoch 65/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0312 - classification_output_loss: 0.0508 - regression_output_loss: 0.0096 - final_output_loss: 0.0202 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9983 - regression_output_mse: 6.7588 - regression_output_mae: 1.8526 - regression_output_rmse: 2.5891 - regression_output_custom_mape: 61.3250 - final_output_mse: 0.0091 - final_output_mae: 0.0665 - final_output_rmse: 0.0922 - final_output_custom_mape: 61.5369 - val_loss: 0.0398 - val_classification_output_loss: 0.0764 - val_regression_output_loss: 0.0174 - val_final_output_loss: 0.0194 - val_classification_output_accuracy: 0.9673 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6505 - val_regression_output_mae: 1.8149 - val_regression_output_rmse: 2.5623 - val_regression_output_custom_mape: 61.7415 - val_final_output_mse: 0.0087 - val_final_output_mae: 0.0638 - val_final_output_rmse: 0.0908 - val_final_output_custom_mape: 61.9581 - lr: 5.0000e-05\n", + "Epoch 66/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0352 - classification_output_loss: 0.0543 - regression_output_loss: 0.0094 - final_output_loss: 0.0300 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9981 - regression_output_mse: 6.6501 - regression_output_mae: 1.8334 - regression_output_rmse: 2.5658 - regression_output_custom_mape: 61.4441 - final_output_mse: 0.0123 - final_output_mae: 0.0678 - final_output_rmse: 0.0945 - final_output_custom_mape: 61.6992 - val_loss: 0.0378 - val_classification_output_loss: 0.0773 - val_regression_output_loss: 0.0096 - val_final_output_loss: 0.0197 - val_classification_output_accuracy: 0.9685 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5211 - val_regression_output_mae: 1.7843 - val_regression_output_rmse: 2.5370 - val_regression_output_custom_mape: 61.5626 - val_final_output_mse: 0.0090 - val_final_output_mae: 0.0649 - val_final_output_rmse: 0.0924 - val_final_output_custom_mape: 62.0307 - lr: 5.0000e-05\n", + "Epoch 67/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0315 - classification_output_loss: 0.0499 - regression_output_loss: 0.0088 - final_output_loss: 0.0201 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.7018 - regression_output_mae: 1.8423 - regression_output_rmse: 2.5784 - regression_output_custom_mape: 61.3784 - final_output_mse: 0.0090 - final_output_mae: 0.0662 - final_output_rmse: 0.0914 - final_output_custom_mape: 61.5973 - val_loss: 0.0422 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0203 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6187 - val_regression_output_mae: 1.8151 - val_regression_output_rmse: 2.5569 - val_regression_output_custom_mape: 62.3132 - val_final_output_mse: 0.0093 - val_final_output_mae: 0.0670 - val_final_output_rmse: 0.0939 - val_final_output_custom_mape: 62.4799 - lr: 5.0000e-05\n", + "Epoch 68/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0337 - classification_output_loss: 0.0514 - regression_output_loss: 0.0130 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9983 - regression_output_mse: 6.7455 - regression_output_mae: 1.8556 - regression_output_rmse: 2.5868 - regression_output_custom_mape: 61.6488 - final_output_mse: 0.0101 - final_output_mae: 0.0693 - final_output_rmse: 0.0959 - final_output_custom_mape: 61.7737 - val_loss: 0.0471 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0315 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9718 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6541 - val_regression_output_mae: 1.8264 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 63.9875 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0740 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 64.1717 - lr: 5.0000e-05\n", + "Epoch 69/100\n", + "462/541 [========================>.....] - ETA: 7s - loss: 0.0330 - classification_output_loss: 0.0495 - regression_output_loss: 0.0129 - final_output_loss: 0.0208 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.8607 - regression_output_mae: 1.8819 - regression_output_rmse: 2.6091 - regression_output_custom_mape: 61.7354 - final_output_mse: 0.0096 - final_output_mae: 0.0686 - final_output_rmse: 0.0946 - final_output_custom_mape: 61.9095" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "541/541 [==============================] - 56s 104ms/step - loss: 0.0270 - classification_output_loss: 0.0498 - regression_output_loss: 0.0037 - final_output_loss: 0.0185 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.8101 - regression_output_mae: 1.8609 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.8799 - final_output_mse: 0.0074 - final_output_mae: 0.0611 - final_output_rmse: 0.0833 - final_output_custom_mape: 60.9907 - val_loss: 0.0326 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7669 - val_regression_output_mae: 1.8484 - val_regression_output_rmse: 2.5858 - val_regression_output_custom_mape: 62.3941 - val_final_output_mse: 0.0084 - val_final_output_mae: 0.0634 - val_final_output_rmse: 0.0894 - val_final_output_custom_mape: 62.3456 - lr: 2.5000e-05\n", + "Epoch 74/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0266 - classification_output_loss: 0.0496 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9984 - regression_output_mse: 6.8160 - regression_output_mae: 1.8619 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.7902 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0824 - final_output_custom_mape: 60.9197 - val_loss: 0.0329 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7283 - val_regression_output_mae: 1.8351 - val_regression_output_rmse: 2.5782 - val_regression_output_custom_mape: 61.8653 - val_final_output_mse: 0.0081 - val_final_output_mae: 0.0621 - val_final_output_rmse: 0.0877 - val_final_output_custom_mape: 61.8723 - lr: 2.5000e-05\n", + "Epoch 75/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0266 - classification_output_loss: 0.0498 - regression_output_loss: 0.0035 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9984 - regression_output_mse: 6.8235 - regression_output_mae: 1.8647 - regression_output_rmse: 2.6014 - regression_output_custom_mape: 60.8557 - final_output_mse: 0.0072 - final_output_mae: 0.0609 - final_output_rmse: 0.0826 - final_output_custom_mape: 60.9257 - val_loss: 0.0314 - val_classification_output_loss: 0.0692 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7276 - val_regression_output_mae: 1.8331 - val_regression_output_rmse: 2.5784 - val_regression_output_custom_mape: 61.4127 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0603 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.4123 - lr: 2.5000e-05\n", + "Epoch 76/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0259 - classification_output_loss: 0.0485 - regression_output_loss: 0.0029 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8060 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6619 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7422 - val_loss: 0.0325 - val_classification_output_loss: 0.0705 - val_regression_output_loss: 0.0072 - val_final_output_loss: 0.0185 - val_classification_output_accuracy: 0.9713 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6720 - val_regression_output_mae: 1.8182 - val_regression_output_rmse: 2.5674 - val_regression_output_custom_mape: 61.5169 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0608 - val_final_output_rmse: 0.0849 - val_final_output_custom_mape: 61.6409 - lr: 2.5000e-05\n", + "Epoch 77/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0261 - classification_output_loss: 0.0495 - regression_output_loss: 0.0031 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7979 - regression_output_mae: 1.8569 - regression_output_rmse: 2.5965 - regression_output_custom_mape: 60.6774 - final_output_mse: 0.0070 - final_output_mae: 0.0603 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7930 - val_loss: 0.0312 - val_classification_output_loss: 0.0681 - val_regression_output_loss: 0.0057 - val_final_output_loss: 0.0188 - val_classification_output_accuracy: 0.9739 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6716 - val_regression_output_mae: 1.8195 - val_regression_output_rmse: 2.5681 - val_regression_output_custom_mape: 61.4893 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0619 - val_final_output_rmse: 0.0865 - val_final_output_custom_mape: 61.6521 - lr: 2.5000e-05\n", + "Epoch 78/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0258 - classification_output_loss: 0.0489 - regression_output_loss: 0.0030 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8116 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5993 - regression_output_custom_mape: 60.6759 - final_output_mse: 0.0071 - final_output_mae: 0.0601 - final_output_rmse: 0.0814 - final_output_custom_mape: 60.7954 - val_loss: 0.0317 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0069 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9725 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7023 - val_regression_output_mae: 1.8251 - val_regression_output_rmse: 2.5736 - val_regression_output_custom_mape: 61.2798 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0596 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.3668 - lr: 2.5000e-05\n", + "Epoch 79/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0256 - classification_output_loss: 0.0491 - regression_output_loss: 0.0027 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8148 - regression_output_mae: 1.8608 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.6601 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0806 - final_output_custom_mape: 60.7576 - val_loss: 0.0317 - val_classification_output_loss: 0.0696 - val_regression_output_loss: 0.0068 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7091 - val_regression_output_mae: 1.8271 - val_regression_output_rmse: 2.5748 - val_regression_output_custom_mape: 61.2307 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0830 - val_final_output_custom_mape: 61.2448 - lr: 2.5000e-05\n", + "Epoch 80/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0259 - classification_output_loss: 0.0489 - regression_output_loss: 0.0035 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9795 - classification_output_auc: 0.9985 - regression_output_mse: 6.7880 - regression_output_mae: 1.8545 - regression_output_rmse: 2.5947 - regression_output_custom_mape: 60.6354 - final_output_mse: 0.0069 - final_output_mae: 0.0603 - final_output_rmse: 0.0812 - final_output_custom_mape: 60.7862 - val_loss: 0.0316 - val_classification_output_loss: 0.0719 - val_regression_output_loss: 0.0061 - val_final_output_loss: 0.0177 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6953 - val_regression_output_mae: 1.8237 - val_regression_output_rmse: 2.5712 - val_regression_output_custom_mape: 60.8627 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0583 - val_final_output_rmse: 0.0812 - val_final_output_custom_mape: 60.9105 - lr: 2.5000e-05\n", + "Epoch 81/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657\n", + "Epoch 81 Detailed Metrics:\n", + "541/541 [==============================] - 52s 95ms/step - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657 - val_loss: 0.0309 - val_classification_output_loss: 0.0678 - val_regression_output_loss: 0.0059 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9965 - val_regression_output_mse: 6.7524 - val_regression_output_mae: 1.8408 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.7875 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0607 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.7851 - lr: 2.5000e-05\n", + "Epoch 82/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0256 - classification_output_loss: 0.0487 - regression_output_loss: 0.0031 - final_output_loss: 0.0183 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9985 - regression_output_mse: 6.8378 - regression_output_mae: 1.8668 - regression_output_rmse: 2.6042 - regression_output_custom_mape: 60.8146 - final_output_mse: 0.0070 - final_output_mae: 0.0605 - final_output_rmse: 0.0816 - final_output_custom_mape: 60.8914 - val_loss: 0.0354 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0140 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7485 - val_regression_output_mae: 1.8401 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.0076 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0622 - val_final_output_rmse: 0.0868 - val_final_output_custom_mape: 61.9773 - lr: 2.5000e-05\n", + "Epoch 83/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0263 - classification_output_loss: 0.0488 - regression_output_loss: 0.0043 - final_output_loss: 0.0187 - classification_output_accuracy: 0.9794 - classification_output_auc: 0.9985 - regression_output_mse: 6.8124 - regression_output_mae: 1.8617 - regression_output_rmse: 2.5990 - regression_output_custom_mape: 60.8479 - final_output_mse: 0.0075 - final_output_mae: 0.0616 - final_output_rmse: 0.0838 - final_output_custom_mape: 60.9637 - val_loss: 0.0335 - val_classification_output_loss: 0.0685 - val_regression_output_loss: 0.0107 - val_final_output_loss: 0.0191 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.6785 - val_regression_output_mae: 1.8221 - val_regression_output_rmse: 2.5690 - val_regression_output_custom_mape: 62.2821 - val_final_output_mse: 0.0082 - val_final_output_mae: 0.0628 - val_final_output_rmse: 0.0883 - val_final_output_custom_mape: 62.4273 - lr: 2.5000e-05\n", + "Epoch 84/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0253 - classification_output_loss: 0.0486 - regression_output_loss: 0.0028 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9786 - classification_output_auc: 0.9985 - regression_output_mse: 6.7787 - regression_output_mae: 1.8528 - regression_output_rmse: 2.5927 - regression_output_custom_mape: 60.6893 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0811 - final_output_custom_mape: 60.8322 - val_loss: 0.0320 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0076 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6612 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 61.7606 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0606 - val_final_output_rmse: 0.0845 - val_final_output_custom_mape: 61.8305 - lr: 2.5000e-05\n", + "Epoch 85/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0481 - regression_output_loss: 0.0023 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8113 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.7074 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0808 - final_output_custom_mape: 60.7955 - val_loss: 0.0304 - val_classification_output_loss: 0.0693 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7028 - val_regression_output_mae: 1.8257 - val_regression_output_rmse: 2.5734 - val_regression_output_custom_mape: 60.9782 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0592 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.0249 - lr: 2.5000e-05\n", + "Epoch 86/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8052 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6683 - final_output_mse: 0.0067 - final_output_mae: 0.0597 - final_output_rmse: 0.0800 - final_output_custom_mape: 60.7617 - val_loss: 0.0318 - val_classification_output_loss: 0.0694 - val_regression_output_loss: 0.0074 - val_final_output_loss: 0.0187 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7475 - val_regression_output_mae: 1.8414 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.4116 - val_final_output_mse: 0.0077 - val_final_output_mae: 0.0615 - val_final_output_rmse: 0.0857 - val_final_output_custom_mape: 62.3664 - lr: 2.5000e-05\n", + "Epoch 87/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929\n", + "Epoch 87: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929 - val_loss: 0.0304 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7331 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5791 - val_regression_output_custom_mape: 61.3001 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0831 - val_final_output_custom_mape: 61.3393 - lr: 2.5000e-05\n", + "Epoch 88/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0246 - classification_output_loss: 0.0491 - regression_output_loss: 0.0018 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9984 - regression_output_mse: 6.8066 - regression_output_mae: 1.8585 - regression_output_rmse: 2.5979 - regression_output_custom_mape: 60.5242 - final_output_mse: 0.0065 - final_output_mae: 0.0587 - final_output_rmse: 0.0789 - final_output_custom_mape: 60.5975 - val_loss: 0.0309 - val_classification_output_loss: 0.0703 - val_regression_output_loss: 0.0056 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7678 - val_regression_output_mae: 1.8449 - val_regression_output_rmse: 2.5861 - val_regression_output_custom_mape: 61.8515 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0604 - val_final_output_rmse: 0.0843 - val_final_output_custom_mape: 61.8132 - lr: 1.2500e-05\n", + "Epoch 89/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0243 - classification_output_loss: 0.0484 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9985 - regression_output_mse: 6.8169 - regression_output_mae: 1.8608 - regression_output_rmse: 2.5999 - regression_output_custom_mape: 60.5160 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5783 - val_loss: 0.0308 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9712 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7522 - val_regression_output_mae: 1.8404 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.3068 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0594 - val_final_output_rmse: 0.0827 - val_final_output_custom_mape: 61.2601 - lr: 1.2500e-05\n", + "Epoch 90/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0244 - classification_output_loss: 0.0490 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9985 - regression_output_mse: 6.8088 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5983 - regression_output_custom_mape: 60.4771 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5605 - val_loss: 0.0306 - val_classification_output_loss: 0.0704 - val_regression_output_loss: 0.0052 - val_final_output_loss: 0.0182 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7138 - val_regression_output_mae: 1.8292 - val_regression_output_rmse: 2.5759 - val_regression_output_custom_mape: 61.5655 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0599 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.6074 - lr: 1.2500e-05\n", + "Epoch 91/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501\n", + "Epoch 91 Detailed Metrics:\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501 - val_loss: 0.0300 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0046 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7305 - val_regression_output_mae: 1.8337 - val_regression_output_rmse: 2.5790 - val_regression_output_custom_mape: 61.3197 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0590 - val_final_output_rmse: 0.0820 - val_final_output_custom_mape: 61.3129 - lr: 1.2500e-05\n", + "Epoch 92/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0241 - classification_output_loss: 0.0482 - regression_output_loss: 0.0015 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9986 - regression_output_mse: 6.8190 - regression_output_mae: 1.8612 - regression_output_rmse: 2.6005 - regression_output_custom_mape: 60.5316 - final_output_mse: 0.0064 - final_output_mae: 0.0585 - final_output_rmse: 0.0783 - final_output_custom_mape: 60.5853 - val_loss: 0.0303 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7579 - val_regression_output_mae: 1.8427 - val_regression_output_rmse: 2.5845 - val_regression_output_custom_mape: 61.9738 - val_final_output_mse: 0.0074 - val_final_output_mae: 0.0605 - val_final_output_rmse: 0.0841 - val_final_output_custom_mape: 61.9213 - lr: 1.2500e-05\n", + "Epoch 93/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.0240 - classification_output_loss: 0.0480 - regression_output_loss: 0.0015 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8070 - regression_output_mae: 1.8578 - regression_output_rmse: 2.5980 - regression_output_custom_mape: 60.4706 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0784 - final_output_custom_mape: 60.5622 - val_loss: 0.0301 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0050 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7989 - val_regression_output_mae: 1.8531 - val_regression_output_rmse: 2.5925 - val_regression_output_custom_mape: 61.7481 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.6569 - lr: 1.2500e-05\n", + "Epoch 94/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0239 - classification_output_loss: 0.0480 - regression_output_loss: 0.0013 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8109 - regression_output_mae: 1.8589 - regression_output_rmse: 2.5988 - regression_output_custom_mape: 60.4838 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0785 - final_output_custom_mape: 60.5632 - val_loss: 0.0301 - val_classification_output_loss: 0.0701 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7354 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5800 - val_regression_output_custom_mape: 61.3804 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0593 - val_final_output_rmse: 0.0824 - val_final_output_custom_mape: 61.3907 - lr: 1.2500e-05\n", + "Epoch 95/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216Restoring model weights from the end of the best epoch: 80.\n", + "\n", + "Epoch 95: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216 - val_loss: 0.0304 - val_classification_output_loss: 0.0712 - val_regression_output_loss: 0.0051 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6979 - val_regression_output_mae: 1.8236 - val_regression_output_rmse: 2.5727 - val_regression_output_custom_mape: 61.3934 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0591 - val_final_output_rmse: 0.0818 - val_final_output_custom_mape: 61.4749 - lr: 1.2500e-05\n", + "Epoch 95: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.00%\n", + "AUC-ROC: 0.9968\n", + "\n", + "Confusion Matrix:\n", + "[[12503 504]\n", + " [ 275 12651]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9785 0.9613 0.9698 13007\n", + " Non-Zero 0.9617 0.9787 0.9701 12926\n", + "\n", + " accuracy 0.9700 25933\n", + " macro avg 0.9701 0.9700 0.9700 25933\n", + "weighted avg 0.9701 0.9700 0.9700 25933\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 23.39%\n", + "Within ±10%: 54.66%\n", + "MAE: 0.11\n", + "RMSE: 0.29\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 60.97%\n", + "Within ±10%: 27.97%\n", + "MAE: 0.06\n", + "RMSE: 0.08\n" + ] + } + ], + "source": [ + "# Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "max_val = df['solarradiation'].max()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 15\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarradiation_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=192,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "811/811 [==============================] - 13s 15ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Radiation Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 19.32 W/m²\n", + "RMSE: 27.95 W/m²\n", + "R² Score: 0.989\n", + "MAPE: 16.92%\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 W/m²: 8.8%\n", + "Within ±10 W/m²: 16.3%\n", + "Within ±20 W/m²: 74.5%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 92.6%\n", + "\n", + "Confusion Matrix for Radiation Levels:\n", + " Very Low Low Moderate High Very High Extreme\n", + "Very Low 0 0 0 0 10 0\n", + "Low 0 1494 0 174 153 0\n", + "Moderate 0 0 2041 413 0 407\n", + "High 0 215 156 1925 0 0\n", + "Very High 0 99 0 0 1038 0\n", + "Extreme 0 0 298 0 0 17510\n", + "\n", + "Plot saved as: 2024-11-26_05-41_radiation_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: 4.431\n", + "Error standard deviation: 27.596\n", + "Median error: 12.000\n", + "95th percentile absolute error: 57.806\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, 0, 11)\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarradiation_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'classification': {\n", + " 'final_loss': float(history.history['val_classification_output_loss'][-1]),\n", + " 'final_accuracy': float(history.history['val_classification_output_accuracy'][-1]),\n", + " 'final_auc': float(history.history['val_classification_output_auc'][-1])\n", + " },\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_regression_output_mae'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > 11)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_final_output_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > 11)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 112s 16ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar radiation after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 113630\n", + "Non-zero predictions (classification >= 0.5): 114249\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 181.31\n", + "Median: 12.00\n", + "Std: 254.32\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 113630 (49.86%)\n", + "Predicted non-zeros: 114249 (50.14%)\n", + "Mean classification confidence: 0.4989\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar radiation: 181.31\n", + "Min solar radiation: 12.00\n", + "Max solar radiation: 966.98\n", + "Zero predictions: 0 (0.00%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarradiation': float(final_pred_original.mean()),\n", + " 'min_predicted_solarradiation': float(final_pred_original.min()),\n", + " 'max_predicted_solarradiation': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar radiation: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarradiation']:.2f}\")\n", + "print(f\"Min solar radiation: {training_results['prediction_stats']['final_predictions']['min_predicted_solarradiation']:.2f}\")\n", + "print(f\"Max solar radiation: {training_results['prediction_stats']['final_predictions']['max_predicted_solarradiation']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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369YNVqsVCQkJuOWWW6pvv/DvqCruC+MhIiIi8iRMohMRERGZRJMmTXDPPffgnnvuwY033oh3330Xq1evxpgxY2C1WuHl5YXt27fXukr3wsTqhaun7bFarbjtttswderUWu+/MIHtDKvViscffxzffPNNrfdXJbir2Ft9bO/285Oxaqntefz+++8xatQovPfeexg/fjzatWuHBg0aYNiwYTUapl6o6r6vvvoKffv2rfWY66+/XpnAXdC6dWs8/vjjePzxx9G4cWMsWbIE/v7+6NWrl1uPO3ToUCxatAjDhg1Dz5490aZNG3h5eeHVV1+t9fly9LVb5YYbbkCfPn0AAE8//TQaNmyI4cOH49FHH0WPHj0AnGsy2rt3b9x8882YOnUqOnfujCZNmmDbtm2YNm1anb83R1ksFjz++OPIzs7Gf//7X9x8881o2bIlkpKS8M477ygyhiMk/16IiIiIpDCJTkRERGRCVcm/lJQUAMB1110Hm82Grl27XpR8dsd1112H4OBg9O7d+6JV3Eo8dmFhYXWCU21VK73P/zkiIiIAAF26dAFwrpFreHj4Rd8bFhZWfX991qxZg0cffRQLFiyocXtubi7+8Y9/2P2+qnIcjRs3duk5ueaaa+Dt7Y3CwsIakya1/Tzu6tGjB5YsWVL9+rvmmmtw8uRJWK3WGqvRHXne1qxZg0GDBuHnn3+uvq20tBS5ubmKxw0A//d//4f58+dj5MiR2LFjBwBg8+bNKCsrw6ZNm2qs1L6wXFHVzxEZGVmjfEpGRka9K7lDQkIQERGBJUuW4O23366+fffu3Rcd6+jf2uWXX44WLVrYfc02aNDArYkuIiIiIk/BmuhEREREHmzfvn21rhDdtm0bgL9LdTz//PNo2LAhxo4de9HxNpsNWVlZLo3/8ssvIykpCfPnz7/ovpKSEhQVFbn0uFWP7efnh507d150X25uLiorK11+7NokJydj/fr11f/Oz8/H0qVL8c9//hMdO3YEAPTv3x9Hjx6Fn59f9XFFRUWYN28eunTpgu7du9c7TsOGDS/6HaxevbrOeuYA0L59ezzyyCP47bffqpPT58vIyKjz+/v374/KykrMmTOn+jaLxYKZM2fWG3NtiouLazwP59u+fTuAv19//fv3R2pqKlauXFl9TGVlJWbOnIlWrVrVuVq9tudr5syZtZZcUULbtm3x4YcfYufOnQgKCqqOAcBFpWQWLVpU43v79OmDxo0bY+bMmTWOnT59er3j1jaGzWbDL7/8ctGxLVu2BIB6JxIaNmyIJ554Ahs3bkRcXFz17WlpaVi+fDkefPBBtG7dut7YiIiIiDwdV6ITERERebChQ4eiuLgYzz33HG6++WaUl5fD19cXK1euRJcuXaqbg1533XWYMGECRowYgbi4OAwcOBCXXHIJYmNjsX79egwePBhfffWV0+O/9dZbWLVqFf7zn/9g3759eOCBB2CxWBAWFoZVq1Zh586d1avinfX1119j06ZNePrpp/HOO+/g7rvvRlFREUJCQrBmzRrExcXVuXLbWTfeeCPef/99BAQEoEOHDli4cCHS0tJqJEqHDx+Ov/76C/369cOnn36Kdu3aYcmSJYiNjcXatWsvqvldm6effhrjxo3Du+++i/vvvx8hISFYtmxZjZXL9syaNQsPPvggbrvtNnzwwQe49tprkZaWBj8/PyQmJiI4ONju9z7zzDN44IEHMHz4cMTFxaF79+5Yt26dS7W2gXNJ9Pvvvx/33XcfnnzySXTu3Bm5ubnYsGEDDh06hIEDB+LOO+8EcK7u+G+//YZ33nkHgYGB6NKlC9asWYPDhw9j+vTpuOSSS+yO8/TTT+OPP/5AmzZt0L17d/j5+WHPnj111o9312effYbp06fjhx9+wIoVK/DEE0+gSZMmeOaZZ/Dhhx+isLAQ8+fPR/v27WtMaFx++eX46quvMGnSJDz99NPo378/Tpw4ge3bt9f7Wr355ptx3XXX4auvvkJSUhJat26NtWvX1rqC/e677wYAfPrpp+jbty8aNmyIV199tdbHnTBhAnbv3o0HH3wQH3/8MRo1aoTffvsNZWVlmDx5shvPEhEREZHnYBKdiIiIyINNmTIFq1evxrZt2zBv3jyUl5fj6quvxscff4yRI0eibdu21ccOHz4cN954I6ZNm4axY8cCOFez/IknnsCAAQNcGr9BgwbYsGEDpk2bhqVLl2L9+vVo0aIFrr32Wnz22WdulY5p0aIFDhw4gO+//x6rV6/G0qVL0bp1a9x4440YO3ZsdYNQpdxwww2YOXMmvv76a4SHh6Nr165YuXJljfrjHTp0gK+vL/773/9i5syZKC0txe23347Nmzfjqaeecmicb7/9FkVFRVi+fDlWrlyJu+66C1u3bsXw4cPr/d7u3bvj2LFjGDt2LBYvXoysrCy0b98ed955J0aPHl3n9zZo0ACbNm3CsGHD8Oeff8LLywsDBgzAzz//XJ3sdkbbtm0xf/58bN26FYsWLUJqaioaNmyIm266CT/99BM+/fTT6mObN2+O/fv3Y/jw4ViyZAny8/Nx0003YdGiRXjnnXfqHOeXX35Bw4YNsWzZMpSWluKBBx7Anj177NaFV0KnTp3w+uuv448//kB0dDRuuukmrFmzBiNHjsRXX32Fjh074qOPPsLll1+O9957r8b3TpgwAc2aNcPcuXOxb98+3Hvvvdi1a1e9r4/GjRtj8+bN+PTTTzFp0iQ0a9YMzz33HIYMGYI77rijxrHPP/88hg4dihUrVuDPP/+EzWazm0S/5ZZbcOjQIYwYMQKTJk2C1WrFvffeiz///LPWJrlEREREZuRlYwcYIiIiIqI6denSBbfeeiu2bNkiHQoREREREWmMNdGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GBNdCIiIiIiIiIiIiIiO7gSnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIiIjsaCQdgBFYrVYkJyfjkksugZeXl3Q4REREREREREREROQmm82GgoICdOrUCQ0a2F9vziS6A5KTk9G5c2fpMIiIiIiIiIiIiIhIYQkJCbjqqqvs3s8kugMuueQSAOeezNatWwtHQ0RERERERERERETuys/PR+fOnavzv/Ywie6AqhIurVu3ZhKdiIiIiIiIiIiIyIPUV8KbjUWJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIiIjsYBKdiIiIiIiIiIiIiMgOJtGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIiIjsYBKdiIiIiIiIiIiIiMgOJtGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIiDzAiqPxmOkdKR2Gx2kkHQARERERERERERERuSenqBzD14UAAJ68tSNu6HCJcESegyvRiYiIiIiIiIiIiAzMZrPhzvG7q/9dUFYpGI3nYRKdiIiIiIiIiIiIiMgOJtGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIFBZ4NgchiXnSYRCRAphEJyIiIiIiIiIiUlBecQVemOOLZ371gdVqkw6HTCinqBwj1p3E8fgc6VA8ApPoRERERERERERECsooLKv+f6uNSXTS3nebT+Ovowl4fravdCgegUl0IiIiIiIiIiIiIg+SkF0iHYJHYRKdiIiIiIiIiIiIiMgOJtGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIiIjsYBKdiIiIiIiIiIiIiMgOJtGJiIiIiIiIiIiIiOxgEp2IiIiIiIiIiIiIyA4m0YmIiIiIiIiIiIiI7GASnYiIiIiIiIiIiIjIDibRiYiIiIiIiIiIFJSYU1z9/15eXoKREJESmEQnIiIiIiIiIiKPY7Ha8OveSByLy9Z0XKvVhncWBWg6JhGpq5F0AEREREREREREREpbdSwBU3ZFAADifnhKs3HzSio0G4uItMGV6ERERERERERE5HGi0wtFxo3JLBIZl8wtt5iTN2piEp2IiIiIiIiIiEglPlGZ0iGQCbyz6Kh0CB6NSXQiIiIiIiIiIiKV/HfNSekQyASCE/OkQ/BoTKITERERERERERERGUxSbglm7YtCbnG5dCgej41FiYiIiIiIiIiIiAzmxTm+SMkrxYn4HOlQPB5XohMREREREREREREZTEpeKQDgcFSWcCSej0l0IiIiIiIiIiIiIiI7mEQnIiIiIiIiIiKPtvpYAtYGJkqHQUQGxZroRERERERERETk0b5ecxIA0O+2jmjRhOkwInIOV6ITEREREREREZEpVFTapEMgElNWaYHNxr8BVzCJTkREREREREREROTBsovK0W3UDry14Kh0KIYkmkQ/ePAgnnnmGXTq1AleXl7YsGFDjfttNhtGjx6NK664As2bN0efPn0QGRlZ45js7Gy88cYbaN26Ndq2bYv3338fhYWFNY45efIkHnroITRr1gydO3fG5MmT1f7RiIiIiIiIiIiI4OUlHQERsP1UCqw2wCcqUzoUQxJNohcVFeGOO+7ArFmzar1/8uTJmDFjBubOnQt/f3+0bNkSffv2RWlpafUxb7zxBk6fPo3du3djy5YtOHjwIAYPHlx9f35+Pp544glcc801CAwMxE8//YTvvvsO8+bNU/3nIyIiIiIiIiIiInNa4BOLJ6YdQEZBmXQo5CbRTgr9+vVDv379ar3PZrNh+vTpGDlyJJ599lkAwNKlS9GhQwds2LABr776KkJDQ7Fjxw4EBASgR48eAICZM2eif//+mDJlCjp16oRly5ahvLwcCxcuRJMmTXDLLbcgKCgIU6dOrZFsJyIiIiIiIjKT+Kxi+ERl4sW7r0KTRqz2SqQc1pzWg1NJeYhML8Bzd14lFsP4LWcAAL94R2DCwNvE4iD36fZTMjY2FqmpqejTp0/1bW3atMG9994LPz8/AICfnx/atm1bnUAHgD59+qBBgwbw9/evPubhhx9GkyZNqo/p27cvwsPDkZOTo9FPQ0RERERERKQvD/+0D9+uD8H8QzHSoRB5lAv7NqbklSKnqFwmGBN7eqYPPl8ZDJ9I+fIl5ZVW6RDITbpNoqempgIAOnToUOP2Dh06VN+XmpqK9u3b17i/UaNGaNeuXY1januM88e4UFlZGfLz82t8EREREREREXmCskoLbOdl+Y7GZgtGQ2QOr80/Ih2CaUWkFUiHoLqSCot0CB5Pt0l0SZMmTUKbNm2qvzp37iwdEhEREREREXkgi9WG9ILS+g9USFZhGbqP3on3FgdU33YgIkOz8YnMKixV+0RuUVklYjOLNB+XaldeaUVEWkGNSUwyDt0m0Tt27AgASEtLq3F7Wlpa9X0dO3ZEenp6jfsrKyuRnZ1d45jaHuP8MS40YsQI5OXlVX8lJCS4/wMRERERERERXeDthf7410RvHIvTZjX45uBkWKw27AuvmTjPK67QZPwqFRYrTibmwmo1XzIpp6gcqwISUFhWKR2KKWUUmqfBY6+f9uPRKftxKilPOhTTi84owo0jt+OJaQexMShZOhxygW6T6F27dkXHjh3h7e1dfVt+fj78/f3Rs2dPAEDPnj2Rm5uLwMDA6mP27t0Lq9WKe++9t/qYgwcPoqLi7xOC3bt346abbsKll15a69hNmzZF69ata3wRERERERGRZwlJzMMny44jPqtYLIbDUVkAgOX+8WIxAIBFo5WRm4OT8dYCf7y3OAADfj2MGXsjNRlXL0rKLXhncQC+WXsSI9aFSIdjSu8vCaj/IBXtPJ2K08naJLUz/zdhsONUKiosrMktKfDs330Z/zhyVjAScpVoEr2wsBBBQUEICgoCcK6ZaFBQEOLj4+Hl5YVhw4ZhwoQJ2LRpE0JCQvD222+jU6dOGDhwIACgW7duePLJJ/HBBx/g6NGjOHz4MIYMGYJXX30VnTp1AgC8/vrraNKkCd5//32cPn0aK1euxC+//IIvvvhC6KcmIiIiIiIiPXjmVx9sDUnB4D+OSYdiGkP/OoFDkZk49L9Gf9P3RJpmNfrR2Gx0G70DwQm5AICtJ7kaVcJZwUmzkMQ8fPhHIJ6a4aPpuL/ui8J933vDYpK/tQt5eUlHQJ6gkeTgx44dw6OPPlr976rE9qBBg7B48WJ88803KCoqwuDBg5Gbm4sHH3wQO3bsQLNmzaq/Z9myZRgyZAh69+6NBg0a4IUXXsCMGTOq72/Tpg127dqFTz75BHfffTf+8Y9/YPTo0Rg8eLB2PygRERERERHpVlyW9jWDKyxWhLDEAgBgkW8c3n+wq2bjnUnOx5Vtm6NNi8aajQkAk7aH1vi3SfOZmtLbcxyZLtfgMquoHFmFZWjfuln9BxPRRUST6I888kidxfS9vLwwbtw4jBs3zu4x7dq1w/Lly+sc5/bbb8ehQ4dcjpOIiIiIiIhISd+uC8HqwETpMHRh/JYzmiXRA89m44U5fmjeuCEWDOqBP46cxdgBtzCx6KEWHo7VdLz1JxLxy55IfNjrOk3HJX2w2WyYdzAGd3Rui/uuvUw6HFKYaBKdiIiIiIiIyIz0lkB/ff4RXHVpC/w+qId0KKopKK3AC3P8AAAlFRa8/rs/AKDSasP8t2V+7tIKC5o1bigytpnZbDZ4qVDj4/OVwQDAevcmFJVegO82nYFP1LlSVXE/PCUcESlNt41FiYiIiIiIiLSSkF2MwrJKsfETc0rExgaAsNQC7AlNQ2mFRbUxouooZaFFXfSqBOeFUvNKVR/bnh2nUsXGNjPv0HTpEExBL81MtSiJ3mfqweoEOnkmJtGJiIiIiIjI1EorrHho8j70mLBbLIajcdmqj2Gz2XAmJV/1cez5eNlxu/ftCU1TfXx7Y0jWprfWUeKW1BOeJlebXJSGDTY3BiXhhv/bjo1BSdoNSqQiJtGJiIiIiIiIcC6ZroXvNp22M756q8ABYPnReKw6VncZmW0hKaqNH5FWaPe+3OIK1cbVixPxudIhkMkl52qz6+J0ch4+WxEEANX/JVk2mw2cs3MPk+hE5BFsNhvS8uW2YRIREREROaK0woLFvnG13nfzqB2IzSxSbeylvmfrPWaNUK320kp1JxD0Kk7F3zfRhaLS7U9kKempGT6ajEOOS8qVLRnmCZhEJ9XkFpdrntQsrbAgKbcEFqsN/jFZKC6Xq2lI2pq0PQz3fu+NOfujNR97uX883l54lK83IiIiconVaoONy8M0J5W8PJ1cd+mQR6fs1yYQO4ISckXG1aImuh7N2BslHQJ5GJvNhk3ByWJ/y3px/ueqGk1kjSYus1g6BMNjEp0UZ7PZ8H/rQ/DPcbtx7/feyC/Vblve49MO4IEf9uLzlUF4Zd4RvLMoQLOxq+QVV2DVsQQUaPhzn0/yAiwuswi+Qo005h2MAQD8uCNM87G/XR+CgxEZWHQ4TvOxyZxCEvMQnmrSOo5E5LGqEslV5zJS5zShKfkYsS4EeRqVlqi0WNF/xiH8e8kxlJl0Ja6UjMKyWm9PzFE30bAqQGalt6OKy2Veh9I1qtVO4nMVKGnlUGQmPv3rBAbOOnzRfWZKJQ9fG1L9/+w9QEpgEp0UF51RiGX+8dX/PqvhbFdC9rkTk03ByQCAo7HqN+e50OA/juGbNSfx5araO7+roaTcgvDUAvy4IwxdR2zTPLlWabFi3fFEPDJlP17/3R+nBBvzSPppZziOadAQ6ny/7InEF6uCMGT5cfjHZGk6NsnIKSrHM7/6oO/0g3h9/hHpcIiIFDF7fxTuGLsLt4zZiQ+WHsP0PRG4b5I3UvO029XoH5OFlLwS9PvlEP46Go+3F/prMm5wYh7CUgvgHZaOm0buwPC1J5FbXI7RG0+JrCKssGhTE1zPHvxxHw5GZKj2+JHp9Z+rF5WZb4fjX0cTRMdfZKfEjlK+WBlk9755B7XfTUsytCincqqe3S5q08uukpXH/n5PKSg133sqKY9JdFLc2wuOSodQg5YXXwDg/7/E/a4z6neXrzLgfwm1qlImfacf1DSh+uu+KHxx3qTBmwv8NdmBkJpXih4TdqPL8K2qj3WhskoLbv9u50VjvzjXT7MYItMKMG1PBNYdT8KWkyl4ZR4Tqp4uIbsYj5y3xds32vMnTmbti8Jzsw+juLwSlRaryMpUm82G9PxSlFVasPhwLKIztKklWTW2RfBCxCcyEztPp4qNT+YxeUc4CsoqUVxuwZ7QdEzfE4m0/DLM2Bupyfi+0Zl4Zd4R9Jy0t/q24ESZJMSKgAT8c9xuLPU7W+sqQjXtC0/HDf+3HX/4xWk6rh69vVC9a5rsonJFjnFFhZWTJPaM33JG1cf3r2OB1/fbwpDO/k6mMPu88qOP/bwfOSr9rUta4BMrNnaFxYoZ3pE4EZ9T43YzrcAn9TCJTopLviBp/cyvPlh3XG7L4n2TvBGiwUXQTzvDNL/QqRJZy2y2lgnV6XtqXuDmFlfg+62hqo/7w/ZQZBZefNLx3uIA1RNt3647hXzh2eynZ8o1a4nPKsbGoCTdrDLQks1mQ3xWsUgy96HJ+5BXIlMqSspPO8NxIj4XS/3O4vr/246uI7Zp/tx/vy0U//reG3eN243vNp9B758PaDb2y7/54aEf96K8Uibp8eYCf3z4RyAv7EnM3tB0TcZ5fb42q85rc0RwJ1lZpaVGE8shy44DAEZtPK1ZDDlF5Vh1LAFdhm9Fl+FbNX2Pn74nQrOxzheXJVeXNiaDTSz1qqSC5ZzMJiajCHM9cBfCMv/6GxirZYlvHKbujsBzs33FYiDPxSQ6KWqPndXXX2hY2qQ2608kqT7GrH3Rpm/ccb5QDUrKhKfVvhp0b1g6Dkepe0G6VnBiqEqZUFINAB7+aR8+WxEk+jzsPpOGB37Yi5UB8VgVkKBZQn/cljN4+Kd91XX4zSQxpxjvLw7AC3N8NU/mn7+aZI9GSbUq8w+dW01TJFAjNiAuB8l5pfU2oVNbSl4ppu2O0GRSmuh8qf/bBSJFi5199TW3LFUxsfbCHF88OmW/qqVL6jJx6xncOX43vllzsvq2x6cd1Gx8tc8XyXl+JthlZ09ti4PI82m9KEmL/pqlFRdfp55KytOkXFiknRyB3nBlvDExiU6K+vfSY9IhkImEpuTbve+DpcfgHapdSR2zCjivBrzWJ4AfLD2GpNwS/HdtCL5Ze1KTyTIA1Q1kJ20Pwx9H5FZZSHjwx33wDktH4NkczD2g7aqZ1Py/m79FCDceM6N/Lz2GX7wj8cyvcjtgyLyWqFynuC4x9SS4taDm59uppHPnUmuPJ6KwrFLzycKqScrzaVEvGAACz+bUe0x6gXl34UgtDnrNQ/u9VDqQPHxhjq9m59Prjici2OQLwFYGyNbg92SptexgfHqmD3pM2KPqxDCR2phEJ1MoLpcpuyHdEMg3OlN0fEklFRa8v4STOlrwjc7EzaO249pvt2GhBvXv1p9IxNJaarWeTMxVddz80goMuqA+6qgNpzSp/1+XO8ftEhk3MC4HM7wjNTsRlrrQ07L+uZ5lFJTVf5CHCTybg/3h2u560AOL1aa7+qzfbwsTGztEg2bptSUbzjd64ynVYwCAO8bW/DzJK1b3862gjs/PuMwi1T9fX5hT/1b/f030VjUGPfPEBOOsfVFiYyfmlDh0nEWDckZ+0Vn4YlUwnhUqRbo3LA3zdbCjMz5brqySWeWVVOCkirsaKyxWu5/bWqzAJ8/HJDopRnKVUH1WBCSoWl/R3kXGPRP3qDamI75efbL+g9xQXmlF/18O1XpfcEKuKetlAxCrXQwAt323U9PnfdWxRLw+3796y944lRsyAcDnK4MxWsNarVVm7YvCgVq2u1cI/r4BIKe4os5dGUr5anXNslxH47IxdXeE5ivStVZcxtUyZvXCHF+8syhA8wblVTILy/DavCOI1HjnxUtzfXHn+N2ITCswzSrcuibLtKjPfSiy7kUPFRb1Y9gYlHxRE2Oryj/7nP32Pz8embIfPSbInkdLG73x1EWN8bT019F4sbF3qdTQ+qed4ao8riMORcqUTKpNlPACgfcWH8PEbaF4b3EASgRK5Z0vXIMSpHqTXlCG5FzHJnXUkFtcXuckqjv+u/Ykzti5Ljoal4MpO8NVm6DdWcv71sGIDPT+eb8q47mCEwnuYxKdFDNmk/ZJrSqFZZX1drBPy1dvFV1xRe0rzouFTwpyi9VdSeYTlWH3QwoAYrPkt0BLGLL8uOpjZBXW/nouKK3E0fNKrJBy7E2WBcTlOLRFV02Ho9TddRKVXog1gbXXvz+TrH4CvzaSk1UAkKZxo8194Rmw2Ww4k5yv2oUH1e6+Sd4iDbJ6TNgDv5gsTetDA8Dx+FwA5+pS/2uit27KVv1+SL1VixM1aIZuRHeO361q+ayUeiaopN/npe0LzzBtY7zBfwRKh0Aa2BuWjjd+ly3fM/Qv9a/b9OaH7WG4/4e9YqVPB/8RiNu+26XKJPW64/bLnx2MyMCv+6Iw5n+LsVYdS8Dr848gr7gCJeUWt6/nPqzlfevthUcRzWbOHoVJdFKE9Hb3W8fsxF3jd9d5zK4z6qxo0DO161pazX1tY9cuOw12lVRYR6kgLRq2SKmrdIjUCcp//gzEZA1WNtXXeE5NdU3IxQlMlk3bHYEbR24XbeZ87/fabvGf4R2Jb9efQv8Zh/DYzwc0HVsvtFgRbM//rdemnIYejdqgj599ggcmuo/GZuu+9N4TKk7iSCbJw1JlJoCJzKa0woKnZx7CxK1nYLPZ8POumufMVRO3UqQXvUlaoEEZTj06Gntuwdk3a07CNzoLP+4MQ7fRO/CojlaMk34xiU6K6G2AC/qzWTI1z/aFp6OsUp0P57JKC/xjZDrYF5ZVwjtM/7VipS7QotI9d2tgfavS1FyVvdzf/tZiH5VXY9dlngZ1HR+Zsl/1MWqTXVSOpX72V6JGpKk3ifqdnR1Olf8rOfC9Bkm1mEz91ESv2lpvxvrkHy8LxJPTD3n0JKHNZsPkHWHYGKRNk2T6W11lS6outpVWXmnFy7/54fX5/qo8vqe4sMSMUoYuP6HK4+pdAmtA12u3SothclXuMaBXm4KTcSopH/MPxWJvWDpm7pWrS28mVaU26WJJF5Sy8flfSbWEbLkSN2QcTKKTZiRXUUp6d1EAbhq5A7Eq/PzfrDmJV+bJbIEbvPSYaK1ER0ltU5uyM0LVx98WIrOzosJirXdVWqmKExe5JXIXIGbtJP/vJQHYFJys+biZhWVYrINeG5+tCLJ7n+RrQus62dK2haQiPK0AASolNC9UW7Je7fOYw1FZmL0/us7XnKfSujzS+corrdgfbr9WsVq7nMo9eEJISTEq7XbNsFMWz9OZNZHrjA+WHoOvwgsz1h1PxM+71b020KvzJ8J+3lX7c6DWgjMzU/o1TETnMIlOmnlkyn6PXkFWn4UqbJfaGKR9YquKb3T9K+DThJqxna9SqLnpycRcVR//xx1hqj6+Pa/Pr3/S5jcVG01KJi0LddxgUs2SWlLbbCs1aKTnLsnmPGr3PrDZbAg8q4/+CjV2FGn0nD/4496Lbus9Vd1dd1lFfyf1isvtl+zyREV1lChTmyNloTx194dkiSRHvbckQJXHZTKZ6hKcmKfo443USVms2iTnlmDd8URNrtPt9dL659i6y7KS8/IcWHhkgI8AUgF/7e5hEp00VVcpBvI8Pwglej1ZdlE59oXLldEJiMup9xg1t2km5XCbXW3+OppgiGQIKUftGt2rAxPxwhw/VcdwlJa7nn4/FINnf/WptRm5WmUlahPPkgu6cs/EPTjmgU2719bRgE0v9LC9XnKnhLS6evCQOlJytX29PTJlP75YFSxaH7tEcJFMoo6uLQrLKhU714hMr3+BjZ9QWVg90EPpvIScYu7CMCAm0UlTF9afMhMzloI4mZjnkYk9ycmgvtMP4t1Fda/KemvBUY+teZlaz4VsfqmaK8vsv5bVXpHsyAn1tD2R6gahQzGZRdgcnKza+0yJQLOpCosVvx+KQaid1VpaWV9Hcm3RYW0vtMfYqY2vhglbQxVfgeiKJ6cfEhlXy4kCV6ixYr2ueujne82BnVhG89XqYNHx9xqgtw4AnIivfwGBp/qjjp4onuzHHWGKnls407wyu45m7u4qrbBgb1gaSs+Lp2q31w/bufhJUmJOMW4dsxPXfbtNOhS35ar4GlaKHkrnpeWX4eZRO5BdpP/ni/7GJDqZhpo5LkfqnW8UqCmsBysCEqRDUFRZpQXfrg+p97jkvFJVVi45uqX8S+ELY7UEnq37Qvb273apNvaeULmLfUeSPDO85ZLo4zafERk3s7AMQ/86gV0qNQF7v54yAmrk7pf4xmHC1lD0+0UmieqIsRr+vqMcWEllBuWVVjw5/SBGrDup6jjLdd7r5LnZhxV/TEd7PlQIl5jKEqzhvdQvTpXHdXT1qfSCjN80aB6uV2YuxRmaItN7RM3zudu+24n3Fh/DxG3qN2Ynx3l5eWHiVs/5nXBCxnE2G7DztHa9zuYeiMYoHZeXMgIm0cltzmxBkZxlK1Ox2eHr8/3rPcasJ6Ej1tWfcHaFo7saHF1h5ihnVumN3ij3AZXjgTPa6QX63U5ts8lf5EtaqPHK5Asdr2dyxVX19X544/f63/uddTr57xXo9b2mKgU/V37eFa7JOGrW+zeSG0duR1hqAf46qu7EtF907Y3I/r3kGEZuUOfz3BkRacq/HtR6/1Ba76kHYBXaKTB6o3a7QWojXXLghFBvEFKWs+dpWu+6qqLmDg3pycC6SJ5Hl6uYJzAjPZXI0YpRrgMPRbLhrLuYRCe3FZY6vrV2TWCiipHU7Y8j6mxHPJtV/yp0Ut6ghUcdOm7LyRSVI7GPW7OUNWe/eg1LlbD+hHxtPU9iM0Dbm/p2RrirvpUpk1Ra6WOz2epNWqnZ+2BNYCIemrwXEWkFF72PTvfgskVxmfotw7UnNA1/HtH3KnVPl1tcIdoTRVKehzUBXSG446PCymSho47EmrdetNlINrcmz2CEHiOkDCbRidzU66f90iGYkqNb/D2pFID0DLf0Kg1HuswDQESazPbb1cfkJgk90TEHmth6up921r3aW60mYI6Wx1HrPemr1cFIyC7BE9MOwv+CZP7RWM9r7lillM2lRGQWGmfC26xNHit0XqvfGWn5pRiu0i7N+uwPT8fzs31FxnZFcEKudAgeY29YGiZulSm9R+aj9/4qavhTpQWbagvRQR8go2ESnYjICZIXsMedaGwVp8IOiV+8IxR/TGesc3CG/4lpBxUdNyG7GL1/3l/vcX4xWao1EC4uM19yzZnn0lO3jUrtZonPcmxF9Pca1FTdEHRxvWrJ8miz90chXYV+F2ZWabHioz+PO3y8kpM3u8+kIVOw1jg5ZmWA5+yCKHChAXpxuTLnnkOXn1DkcdwR50AfqSrPzlKuB4LNZsPgPwIVezyjeW/xMcw/JFt6rz6S11hqln01I+kSXOS4Z371kQ7BcJhEJ805U0PdEc6UiHG0jrYnYT1Z5eSXVuBfE70dPl7pSfgsJ1bLVVhsiv+tbQ6WK40jaezmM4jOcOyib75KzcfeXexY+SKzynchKaEUMyfgpC7ID0ZkiIwLAJN3hONf33ur0jjaCNTYfbD7TBrCndhBpOSk2bTdspPD5Bhnzn880ZnzemUY3S9CTdBDkvKwW6Um5KQMNWvB10ftpt1mItW7g0grTKKT5hb6xCn2WEEJufhqdbDDx3tSvTNHmyz2/vmAypHom5IX/M5uK1W7XnJ9fjugTkLXbJyZjEjOUz6xdjw+B8fZ1KxO9TUAVZN0HyHJ5qJS9FDSQo1mlCcTcxV/TGfVV9pEjcUIReXOTfhW8gJdhHRJOSUZrZ9RjmBN+NAUZRP4Uv1jJHcwmV2ug6/fU0nKlpXILip3uBTlvvAM9P/lEDawv5Hbtp+qu58PkdExiU6aU/LE1WgnwUp6e4HjK1M9rS5ZpBMr1uKyisVqZEuTTuKTMpS+qPBESr/HxWcVY6lfnEPH+kYr2+Xe67z/dyRxsqOe5qNGEpaaj4EObN//UaWGqtIOR8luf7ZYbfXWnPegPKo46aS0s6sFT3hQfeqMAmPtIJKcYNt+KhVZJt5x5Qmk32t+3OHYZ/b8Q7GK7aLdHJyMu8bvxo0jtzv8PWdS8jFsZZAi4ztLcocdcK505Ru/H1EkjsQc/TZJtyfVpLsLyTVMohNpROnzl7BUxxPDy496Ti3J3w/F4HEna16P3nhKpWjMxQZmT8g+T0r2P/bzfozeeNqhYz9bEaRuMPVY6mvMRka1+WDpMQQ5kKhTY8eHkahVvkjpEmCkb8ecnGiXbi5uZjP3RomO//p8f0UeJyrdnItaXLE/XLnSJjtPG6eMjVK1yYf+JV//3xnOXNer4YtVQTgclYW3F7pfPtKIV4v8fCNnMIlO5AajbA084kHNPSZsVb+RHZGrpFf7SHp6prKNaSSfSSOVizgaV/fKYSPJNnHdY2feO0Zt8IyJ4RPxORi+9iSyi7jKVYKz57BKf7wlm7BPkVE507OgLlHpxurTlJAt9xp9Z1GAYo/lyOQ0yZO6hLBabYruzjkRL7cTOizVc/pHkH4xiU6GJp2vWnbEGCsAt55MMXVyz1N4eXnVf5BO5BSZMxm2IiABhyJlt2QqaVNwsnQIplPsZH1oszJaOYb6FDhR5/1QpLIlhJzx0OR9il2kPjfbFysCEvD9NuOU5zFrQ1kAijdlvP+HvQ4fq/QprCu7ORKyjVeiQG/msFcP1SPN5DvNlJJX4vh73IqABEXHluxb4x8jt7CEmRbzYBKd3CaZ2It0clvgiqPKfkjEZRnnhHpbiFzdXHbplnFAsL7eqmPK/q0ZyVtO9CvQu08Nth3WEyhdY91TvTjXVzoEjxOb6VifmSenH1I5krpZBRcFjNt8RmxsQPace+HhWBSXK5Mc2RfmXKkKpVZCV5m73/lk7kOT9ymywrKk3Bi7WNUQ7MKKaE/r66R37y0OEK3B3+8X2c8XT/HdJsdKEgLAt+tDVIxEW2Oc+LkV58a5ydaTKQoGQmpjEp0Mzdn3qoWHY1FaIbfKT3IF08mkXLGxp+6OEBsbkN+xQEREyjqbVYwkhctBVDpR3kLpsfXgqRnKlmRSS++fDyDPgaa7ajBiwzQlDVmuzMTqu4uVK1XhCldXSm4Mcn931g87WJbQGX8aZNevp9gblo4Bv9bf4FstRiqnp7QihSYpASDQyZ4XJMsnigtojIRJdDKdHafkVmSHJMo13tsWIjfD+es+2YZI/rGeUzNYEicjzMVovRQk6+vGZBirzquSJEuFRStcX/f5OY6vbpfsz5Ft0nJZ5xvy13GRcYMT8zxyAsVRe51cQU4XOxzl2merWcsymrmMjie813MngeMkF/qR+8w8AWQ2TKKT6QxbGSQdggjJ5jhmFppizgYn0/You/vAEy4kjMbR0g56IVl65rGfDyj2WHrofWBzorKjEisz/x7XOZmFytZFPyk40U3OkawLv02hbddWF6p6KJlkyeLnqqFsUeB1ZzFpIh6QXQgS5+L5VIELtfv1ZstJ9tYxAmdqqNenotKc7zOnk417ze/MTkxiEp0EHFao3mtRWSVm749W5LG0sjowwbQrScyq3y+HFFului/cOCvASiusiq4+eWLaQcUey5N50iqWY3HO7SCJEl4NXmHSE1AlEjuu+sODtvnz1MB8/vR3/vX7h59yr/mZ3pGKPRapb0+o+41dzdy4WnIXxXOzXSuP4gnnFak6aBTK1fD1y1GwRNpRJ87fPa1JvFGtCUyUDsFQmEQnzSm1InreQeN1eN95Ok20wSfJUGp14xkXZril6sYCwB1jdyk2aaT0ilNPJdlwT2mrjhnrhM6TnntnKNVo0BVKl3Mh85H8q9152vnzwch0ZRpsWqw2RJr47+eMSXcJSnKlqSigXP8JyYVXriYov15zUpHx5x4w1qIzpbk6iUHq+9f33i6/N9C5Ml9frw7G99vcKzGYYPJ+L85iEp0MSw/lHVYExDv9PWdSuF3cqLwgX2bBWaeSlXu9OZsjLCyrRJGJVz0pZZMT5TJGrAtRMRLydKuPJaDvtINOTXb7Rhurdj6RkZVWKLMydf2JJEUex4i4bV2Gq5M2MRnGKi2npBPxudIheASjlGoLiDNnM1CugnZdTGYRVgcmGnJxqZExiU7kol2nU126mDHpYkVSQBBn6k3p2FnHT6qVrE/tCqvgltmCUrkV0UqSLPn19ZqTCE8rMEyZlHzh37nk653IHYkmXnX2V0CCy9/rCeU1jEa6ka8nlckj84g3cUNed0nu4nZWeSU/kyQwiU7kopEbTrn0fUpccwc4WSuYSIoSjVVdachk5vqHkhecmwUbSJn5d04y+kxTpqFscXkl1h/nSiyz4aIKGSfiXV/tuczf+R2o5yvh7jynFZbJTpaWc+KEyFQqXen6TabCJDq5zXgFLpRJ7El6aa6fS99XVil38n7cjYsWJSjR4T5KoVqkZqJEnbuTSc5vw7zu223YGKTMdvVDkco0Q9bKAz/sFRs7Ik32b+RorPEnGL28jPepKrl6XqnPtSIXEjVKlRkYueEUvtt8xqnvcSVeupgr54OVwhN2eSXGWSVHNYWlun79wUkXGXyv1Z6Pwc671fDyXD/RnjNEZB+T6GRY7tREf3dRgIKROGefYHf4DYI1MPeHZ4iNDbhf6iG/tMLpJIcezPCOVOyxJFbj7A1Lw6d/nXDpez9bEaRsMCbjygW79GJwV18rRpeWXyo6/vxDytRidOU1d9/33oqMvdAnVpHHcYUzfQ+q3D52lwqRkCN+3BHm9mNkF5W7vA3bOzTN7fHNzKilv04kmLNesrSwFC6gcYcrp4VvLvBXPA6jORqXjT8NUlqPyGyYRCfDinRjVXBqfqnY9sDwtAJkFpaJjM1tpK5LF05SucpfwZW5GQXOv25T8tx73t5bfMyt7ydtzdkfLTp+an6pKcu69PvlkOj4cw/INTTKUah2pXTdXWeZ8XXuKdILSnHX+N0Id3HnzherghWOyBi+Xa9M4+zdZ4w5CeFMw2dSTgFXoptOrAtlJNXgaiNpo9XxN0ofHqIqTKKTaSULXjC/8ptr5VjIddtCUsTGlm5ElSU0aQMAKXm86JNgpKY45D53dmbROSvcaDboLunyIKQtv+gs6RCQX2K8xOBy/3icSTZ2OUaSkePGZ+RvB9xbHJBbzM9no1ngI7cwQAmrA9ljxR1GLKtI2mISnUzLncZCgGvb06pEK1RHVcrU3RFOf0++cA1PyRWy0qtz8wW3LucUMZnrDldLMPnFyNWTlOy9IEmpRLYR6x1LJ/GN3K/C6kYCXXqC1ujyFeiVYlQLD8uVL3KH0XsakWvc3SlU4sbKXHfL/8w7aOyErBmdSjL2+8yoDaekQyCNsFeGDCbRybDcTTQk5RhzhWylDi6aXamzvdg3TvlADCIms0gXvzcJfjHyK+6MbNjKIOkQnDZyvTlP3r1D5fpdmJ2rW571wJ3rH+kyPkaX6EZ5DOmGb+kFciXmJBsJkzm9PFduB687CXgALvc+IDlBCbnSIRCRjjGJTiJ+3BHm1uorAEjLlytRIem52b4uf29moXm3FEpf8hmt3i65R7qMjWSOw6zbSI1Wg5KMLyq9UDoE0/rDT7aGa5ngxNHO06liYxudkSf83OXO5IvkObQr/YD0hEl8MoLBS48h0sU+IURaYxKdRMzZH421x42daJGqORySlOfy9/66L0rBSMgZs/jcm8qyI/Gi43+07Ljo+GY0YWuodAiGZxOf7iRyTJEbjdr1UBPdHbtOG7Mxpx5kFRk7IUvGUyjcGJUbV8zF1Z3Xu86k4fXf/RWOhkgdTKKT21ztveATJVezF3DvAggAyk1anoNcU1TGVapmciLBvZ4LgPzK5kzBhrRkPBuDXKvfb3Tu7qoj85FsYquEdS726iBgyPIT0iEQOUSpxT8/7ghT5HHIGGIyXe/7ppddH2wrSvVhEp3EpOTK1XMEgAU+xmyqRK7JLipHhEm3iRm1fmmBgZu+HY5yf6Xh9lMpCkTiutn7Xb+AKnFzktIdPPmVMfeA+83TLAZMSH++Kkg6BBFm7fNBsvaEchU8Oe+XPc73clKKu41Jpfy0M1x0/Nxi85YgJXMa8KuPaL8TchyT6EQm8/sh83aJf5PbxAzFqBceVdwt+eRujsrdxnfuJDSPx7u3Ej+7iBdPRhOaku/2Y1RYjJdE3xiULB2CiH997y3eXNNd7pQPWu4fz1rDArafYj12o5J8d195TG73h7tjn3KjjCcg24TYHVzsRmZzMjEPk3c4N3nFMogymEQnMpnNwea84AeAdJ1sEyPjcCdJ5BcjW7Jq/Ba5Gt1vuDlhJb0CylVGXElN5IrsonL4KrDjRpI7O2YyC8vw24FoBaNxjkE3mJGbJMtH5Rt4d6CRPT3Tx63v/3xlsEKRaCsmw/WyIErYelJ2N6hRbWC5L7dI7uQlxzGJTkTk4Yy+oltSoRvP3X/+lG3uuTfMeNveU/JK8NYCf6w/YczG01xBL8PI73FGLj/kTsknPVjkG+fW9/vHZisTiMkYtcScHlQKJtHfWnDUre/fG5quUCTkDCV2iUmQ7gv06QrX+xcYfZeWO2bvl5tclsTkt7kwiU5usVht2GTilc1mZNYade5e8+06I7cFedyWM2JjG92JhFzpEEzl23UhOBSZidIKY5ZJyCvhSj0Jr80/Ih2CKR2PzxUd/4SbZaPyDfz3Gp9dLDq+O42EPxKeYDYydyaYk3JL3Bo72M3zoR2nWYqHzOGvo8ZuHm12Xi6sbpC8ziftMYlOblkZkIDRG0+79L2s4WRMRl7xJ0my1m+QiRPBX65ybxvrasE6mmaUWWjsSTqeRLuOpXDIWWaeIH5zgWyPl1EbTrn8vUymum5zsOslJh74Ya+CkRDVz6y7TuYfVKb/WIYLZUilV/CTtkz6JyaOSXRyi1+MsethGo0SSQbmKchM1h6XLQviTv1S6YsPIzZ5BGR3D0zeEY5Fh9kMyxXL/M9Kh0BEHq7A4HW9faJke60QUf3KKpVJZB93YbfVX0fjFRlbyr+XBCAmo1A6DKI6MYlOZCBKNOsIScpDpUWmVAJXGpLRuJvH3hLi+qqx7afcW62Xlm/ORrovzPF16fui0gsQlV7g9vhjN5t3daw7jphwUl56oozcwx2VMrxDXe/3sehwnHKBkKYkm6qSa9w5jy1VKBEtIadYbrKu2OC1ufeEpuPfS45Jh0FUJybRyWWnkvKwmfXQNZWQo0wNzFPJMk1m2LHbnCLT3E9MGlV8VpHL33syMU/BSMzF2eRkaYUFfaYeRJ+pB7kVljRhs9nwyrwjGLTIvWZ9JKewjO8VEt53I8FSwvd3w1oneA3BCU/XfLzM9f4Hh6PMN7GuFw/+uNelUjJKScxxr38DkdqYRCeXvfKbn3QI5CIX+mUo4svV7tWnJjnZRa7Xqk7OK1UwEm25e9lU5NaKEF60ucrZhtdFZZW1/r+RmP0iv7jcWL+3xJwSHI3NxqFI+fIMNpsNZ92Y8DOrkMRc6RCITOOM0AIgAPh6zUmxsYmcEZXufimUxJwSzN4fpUA0xuQllilxjtVqwzuLAqTDMCUm0cll7iWHjPMGRUTAUzMOSYdgSFmF5iypIu1obLZ0CJqLzjB3EnRtoGz/AyMbu/kMev2036XvNfNOI3NPWxE5Tqka0VLW8POFDGK9Qjs2TL4uwxBOJOQik9eZInSdRLdYLBg1ahS6du2K5s2b47rrrsP48eNrrLay2WwYPXo0rrjiCjRv3hx9+vRBZGRkjcfJzs7GG2+8gdatW6Nt27Z4//33UVjIhgXSWEuSSBtKrFBNEVhNrod3CLOv7iXj2OZG/X1PMGOveVdNuWuxb5zL33tacHWoNH48EDlmwMzD0iEQEXkU9pqTo+sk+o8//og5c+bg119/RWhoKH788UdMnjwZM2fOrD5m8uTJmDFjBubOnQt/f3+0bNkSffv2RWnp3wmfN954A6dPn8bu3buxZcsWHDx4EIMHD5b4kUhnWPuWiOri7umJO0mWzELXS+iQ+UzdHWHqzzTJ+p2S0vKNWy5LmpcXd0S6qqxSpkG9kRWUyjUblBZu4h0rpD0lkov8dDCe08ke0EuKLzxD0HUS3dfXF88++yyeeuopdOnSBS+++CKeeOIJHD16rgmTzWbD9OnTMXLkSDz77LO4/fbbsXTpUiQnJ2PDhg0AgNDQUOzYsQO///477r33Xjz44IOYOXMmVqxYgeRkNsU0u9n7o6VDcIpSq57cuW7kyivj4US16/h6JyPJKebEi9n0mXpAOgTD4k4j1xk1ia5EfuJgRIZL33c8PleB0c1r4eFY6RBMxz/G9eaekpP6sZlyFQc+W3FCbGyze2qGjzIPxEQ21UPXSfT7778f3t7eiIiIAAAEBwfDx8cH/fr1AwDExsYiNTUVffr0qf6eNm3a4N5774Wf37mml35+fmjbti169OhRfUyfPn3QoEED+Pv7a/jTkB4dizNf3Vx3fbZS7uTAzKt4jGzjiSQmK8ghLPPlnnfZYMh0CkqN1VCVPMeJ+BzpEET8tDNcOgQiTbwy74jL33sqyQNWBbtgYxAXaRJ5ukbSAdRl+PDhyM/Px80334yGDRvCYrFg4sSJeOONNwAAqampAIAOHTrU+L4OHTpU35eamor27dvXuL9Ro0Zo165d9TEXKisrQ1nZ39uC8/PNW++R6EInBFfSSKbXmNpz3boTSXjk5vYYcEcn6VA0Z9RqASVuNo6WZtTn3V1hqa5vmVdi+3N0RiGuu7yV249DxpGcVyI2NudmHReckIv//BmI4f1uxrP/vFKRxzRrCSVXeUITXpvNxjJIBhKVbvzXnCv2hbm2W4SIyBG6Xom+atUqLFu2DMuXL8fx48exZMkSTJkyBUuWLFF13EmTJqFNmzbVX507d1Z1PCIzyXdjNfnxs3KrnhKyi8XG9gQzvCPrP0iH4rKK3Pr+qHRjNrE+Euv6Fl5yTUZBGTIL5ZJSh6My3X6MCosxSzyQ6ybvkFuVG5SQKza20Xy87DhS8krx2Yog6VAML8vF9+kDLpaB0ZNNwVxlayRfrj4pHYLmghJyMXFbqHQYZDI7T9e+ONcoErLlFkQYka6T6F9//TWGDx+OV199FbfddhveeustfP7555g0aRIAoGPHjgCAtLS0Gt+XlpZWfV/Hjh2Rnp5e4/7KykpkZ2dXH3OhESNGIC8vr/orISFB6R+NSJSXYLGvsgrXkyzvCJUqOB6fg/eXHBMZWylH3KhraNYtmQCQlOPeSQVroJIjSissuGfiHvSYsEcshkqrbAKcq4pJa2ZaUVvOCS7FJOeZt5kvJ2GMJc+EfVJCU1hBQEq5QXtlKGFbiDJJ9DShz5dNwcksveoEXSfRi4uL0aBBzRAbNmwI6/8u9Lp27YqOHTvC29u7+v78/Hz4+/ujZ8+eAICePXsiNzcXgYGB1cfs3bsXVqsV9957b63jNm3aFK1bt67xRcrTw9+pb7Tcasu8Etb3NopNHlDf7lU36hq+s+iogpEYiw7epsgFxU6Wo5H+PafnsywCkdZ4wWg+Jpo3IUJcFnfRuqpSgRJ3ZlNaaexSkHpwTHDXPev5O07XSfRnnnkGEydOxNatWxEXF4f169dj6tSpeO655wCcW0EybNgwTJgwAZs2bUJISAjefvttdOrUCQMHDgQAdOvWDU8++SQ++OADHD16FIcPH8aQIUPw6quvolMn89Xn1ROryS9eVhyNd/p7POEZW3XMvDs7ytw4uZAsk5BbbN4JHyXqRJP21h1Pkg6BiIiIyG2lFUxOOopl5eSw0bmxrQhwPjdlVrpOos+cORMvvvgiPv74Y3Tr1g1fffUVPvzwQ4wfP776mG+++QZDhw7F4MGDcc8996CwsBA7duxAs2bNqo9ZtmwZbr75ZvTu3Rv9+/fHgw8+iHnz5kn8SHQes5c52H0mrf6DPNBPO8ORatKtsD3G73F55ZvRJ524+IuMQqLEg80jpkiJyEz4riXDlcl9fzdK+rmrsIyJNXd99Gdg/QcRAGDewRjpEEwrPJWldMgcGkkHUJdLLrkE06dPx/Tp0+0e4+XlhXHjxmHcuHF2j2nXrh2WL1+uQoRE5IrCsgoAzeo97nwpecZveFFQVolKqw2NG5ovpcyLbXKEHuaK1gSad7cMkZmYqSY6eY6tISkYcIdzu6k/WX5CpWjqt+zIWbGxpRWUKrOTc1+48ZvSaiXRzV5GemLWjyhXenhw5zBpSdcr0cnz/eePQG5RE2DED+U35vtLh0AaY71akvD9tjDNx/SUl7pk02pJhyKZ4DAic75azctms2HWvmjB8ZV5HFd6KkmWuDBzeQ3J1xuRmcRlFUmHQCbCJDqJ2nE61aXa4GQ+MZme8eEYkpQnHYLpVJr4As5VTC4ROe6tBeZtvgwAp5P5uaZ3fE8HwtMKpENQRCHrDjvlWFy22NjZReZtGu4hawMMZctJ2caQHrMghB+YVA8m0UlcPk8GyUSi0gqlQxAheT6yNSRFcHSi+nnIdQcGzjpsyN1lJxNzpUMwNFdWxpL7OHnhnIRszyjzYPQeOVp7ca6fdAhEmhgiWLaJyEyYRCcyEp44kwCjz8izqZVx8C3O2EoqLNhwIkk6DKcN+PWwdAhETntqhg8yC+VWuvL92jlKNZBOyC5W5HGMyGg7C89mmfd3RUTkqZhEJzKhpX5x0iEQEZEHqmBzJ4cYfXJSL6IzzLm7q0pMhmeUuiPHrQhwvvl1SbnxdgjVZvqeSOkQnOIfK1dKhsgdPEchso9JdCITWnUsUToEItIxdrnXlpJNdF19LK4qNaeDEcZuitr75wOGW51KpLVyD/kb+XVflHQIRJqJNGkJUCK9YxKdyAXF5e6Xhzh2NkeBSEgLSia4yHFeCi2D4K/PeRuDZJsTac2TXiOjN56WDsElSvwOPGW1pVYC4rLx9kLjN0WtdHHSr9LqGYlFIiJPZPYFHWWVcp9RXi50s/KUc2kuwqf6MIlO4lLzS6VDcNrEraHSIRCRg+bsj2ZyzUnZReXSIWhqZUC86PhKXnf8ceSsgo9mLJN3hkmHYCgn4s09mX84KsvtxygySM8Nbs0ncs8Bg+/aMaLxW85Ih0BUL0/ZDXckhuWnHMUkOok7EZ8rOn5KXonT33M4KlOFSIjs85TZfQlJuSUYs+mUdBgiXF3FY7aEy5RdEYo+XlmliSdtBN+sfBVIippJer4yTSnN/LwXlBojia6G0goTv8+Rw5TaVShtkAfs2tFSUo7z19cXWuwb534gbvj9UIzo+Ebj6q4wI4vJKMQtY3ZKh0EaYxKdTG/abmWTJ+QIzzihNgtXtvTpzbrjSdIhiFgT6HwDMnIfJ73ICJR6mW47laLQI5GzJMvNpRlwJ6kUi9WG8FTWNybzGLYySDoEt03YGor80grpMAzDjLsBp+2JFC27QzKYRCfTM9KkqYFC9SiFZVxt5S4PWYikmaOxymypCxTsvcASOo5j0p2ISF2SEw4j1p1EZqEyOz+IHGH2euJKqWCC1GExGUViY+cWK1eG8qkZPghOyFXs8cjzMIlO4phbI73bFGzOVcxKcuXa1cwNXdefMP5r7otVQU4db+UFH7nJZtKpZlfLByl1/mXm8zizvuaMSMnf1PuLA5w6ftWxRAVHJ0f5RZu31JTVQ86hpRdkVFg843n0dJ/+FaTYY8VnF+PNBf6KPR55HibRiQzEzBeqkjzkPJSoXjO8IzFiXYgir/ntp1IdPjYtvxR3jt+NMyn57g9sSHyTMRsl6wT/ujdKsccyIsnPaKV2DZGxeIelS4dADtjBUlNu8xXuAzbnQLTo+L8dlB1fivQOYmcXUiXlul+D/3xm7ndC9WMSnchAPKdhh6f8HNrxnN+9DD57jpm6OwJ/HY3XPJn924EY5JUoW3dy0eFYRR9PTZ40UedBP4ph7HUxoafU78rMZQPKKmS3+icq0LyPiMgevxjZ1fwJ2cWi4y86HCc6vlntj8iQDoHILibRiQwkLkuu1piS+kw9KB2CoQQl5OJWg3f+NvOFfnG5c6sZEnOK8dfReEXGTs13rQZraYXx65mP3XxGOgSHmTcFSZ4gLks2yWFmU3aFO3ScJzQIJ5JWYMImk7/uM/dOJ6PwtJ4L+U4srDHzRD7JYBKdiERwC7Tjxm4+LR2C20pdrNnrCabsjHDq+FfnHVFs7IMuruRgusU9Rpw08oTV8EwUOofPlvFJ1j32gLcMIqdYTdhj0pW3mHwTTjZIi0ovlA5BzOpjCdIhkMkwiU6mZ6TEgZFirc/p5DzpEMhRJs20VFqUuVraGORck1AjJmCppnFbjLMK3pOwySMREZGsiVtCpUMgEzHzBILSItMKpEMwBCbRiVzArcvmomRaxohJnvJK95PJruThTyXJTrScVagOY1ZRuSKPQ8ZR5mQ5HE+aIHWVEd8biYiIqKaTwufvREZWWFYJ36hMkTI1iQo3aPVUTKKTOOnuz0SkT5mFTD6TMZm5PiMnBKguFqtN8UZxohMwPIclqtNyf2V6vBCReXy2IgiLDsdKh1EvNU4B3ph/BK//7o8FPjEqPDopgUl0IiINsWavDBsze6ShY2dznDqeq7DJLN5a4I+HJu/zmMSamT/Rzfyzk+OSTLqyUamSgEZkVXAhAc/fjUGNxSNjN9dfGvFkYi4W+cYpPrak4MRzOznWBjpXDlQJpeXm7WHmDCbRSZz0SnTJ5MW+8HSxsclxvFAks+ElCxmRka61lfxcOZ2cj8UGWLEFAL7RWQCAb9eHCEfiGRydmD//XDuvRJmmfwb6cwNgrPeH+iiZpPRkXoIXmXMPRCv6eGn5pU4dL/l6TzbhxEmJyZOPh6MyRcYd8OthU+/+VNqKADZpdQST6GR6u0+niY397qIAsbHJcdITPZKUWi1oxPMbs58Qa8nMf2N6sSk4WToEtxWVVUqHIOY7B1ZsEQHA16uDpUMQYsATETuqJqMccTzeuZ1RZJ8zC6/WHld2FelTMw4p+nhGoVR/IjVtD0lBt9E7MGe/shMnkpw9La8w8c4LT6JEHzQzYBKdTK/AQBfdnrSKxpN+Fmek5Dm3kkSa5GpByZfId5tO4+mZPoIRkJlIvx/+d81Jj0iiJ+eVorjcOJ/pRBL2hnEXpNG9ucDf4WOD4nPVC4Q0Y9Y+QScM8Pr96n8Tkz/uCFP8sZnUJNIfJtGJiOqhZIIrp9icJ8HSnF0Jv9jD6us5q1KhrQMFpcqUDSD1FJZVYuUx5bZvFgpPTEenF4mOT6R3Sr2/kzkZqUa1j1CJCSKlsGeOefF3r19MohMZCN9M3RebyQQLUX2UWvkivbrZKNtLJXMSVoUHP5Oc7/CxNpsNs/dHYcepVEVjIDKLVCfrJBO5a0OQ9s3uXBWVXigdgpg9oXLlSnm1qj2lzyNZZpHIPibRichUfKONtyrFQIt+iIgcduxsDibvCMd//gzETsH+JOQ+yTI6kp+Rko0LpbnyvFcaZGKT7Pt8pVlr6gMFpcYpFzZm02nFHuvPI2cVeyzyTLxW9Qx+MY732zAzJtFJHN90zcnE151iWFqDzCY8tUA6BIeYdZdRen6ZdAikEKWaUJM61DjlciWxttRPLhlXxtrC5KZhK4McPlZ652tphUWxxxq72bmEvJFK/lDtTibmSYdApFtMohMRmURphcEuIHkSrggzNyVic1gibTBBaT5JuSVOf8+4LWcUGXvryRSnv2fm3ihFxibHZRYqP1GapcJjOirwbI7Y2M5SctV8hcW58/GicuUS+GaXmidTsss/NltkXKORXBSodDlGchyT6CSOK5Idx/dKGXzatVVSbkFeMVfNK0XJppGkDrO+t5/NNm+PCj2c++ghBqM7FsdEg4RPlh93+nuUro+t5EpfT/X2gqOKP2ZOcbnij0mkV71+2s/3GqrVvIMx0iGYFpPoRCTCrEkjqt9t3+3EHeN2oaDMOLUn9Uxy1Zajf+eemMsr4UqselVUynwQlFda8cxMH4xYFyIyvl7wc9h9KwI4SSlFOrH0865w0fEtVv3/AZ9JcbzRNBHVLr+k/oVF0RmyTXQ3BSeLju+J6jtH2xuWrk0gdJFGzn6DxWLB4sWL4e3tjfT0dFitNbdv7t27V7HgyBy8PDJ9Qp6Er1BtVf7vwjAyTfaEkMgdhWWVaN6koXQYVIsDERkIScpDSFIeJj1/m3Q4HkHpVb7kuMKySrRq6vQlnSJKKyxo1ljmfW71sQS81bOLQ8cGnlV+x8CRGNldCFabDQ15hkpEAEZuOCU6PpPoZCZOr0T/7LPP8Nlnn8FiseDWW2/FHXfcUeOLiMjTsOYYGdnqY4nSIVA9POktJighVzqEehlhBafRnM2SK81j9t9mjOAKxB2nUsXGdqYO/2vz/VWMxHy404qIiMzK6WULK1aswKpVq9C/f3814iEi0h3mW8jIXGn+Ruah9DpG6debVJ3vFUfj8eq/rpYZ3EVKPldeLLAuJjghF7df1VZkbKMsMlCjwbaj5RN8IjMVH9sZh6OUH587rYiIyKycXonepEkTXH/99WrEQkQ6lZJXgn8vCcCJeON0pScikuTMameb6dfSGt/wdSGwGSShWMXMie/tISnSIWjKzL9rtRQ7uBr7zQVyq+DzSyvgbdK6uUXsq2M6UekF0iEQkQk4nUT/8ssv8csvvxjuQoHIE0j91T0/2xd7QtPx3GxfoQiUI12Df7FvHEKFGi2VVXL7LZFWFvvGSYdAGssokGvi64ricvMmmT5adlw6BCK3OHI2a7T3JCXlFJdLh0AaY6NFZeU50NCU1GNhvlW3nC7n4uPjg3379mH79u245ZZb0Lhx4xr3r1u3TrHgiKim3WfSRMZNyStV/DHNvChq8B/HcOibxxw+XqmP0Jfn+sF3RG+FHs15FqsNDRuY+BdvQiM3nMKD1/8DXf7RUjoUzfnFZEmHoHtcgS/rzyPx0iGQwfEan7TmG52F69tfIh0GmUhSbgmubNtcOgzNFZVVok3zxvUfSKqIyZDrNUN1c3oletu2bfHcc8+hV69e+Mc//oE2bdrU+CJylrOrY9cGJuLxqQdUioa0InXhpYf6nXnFjs/s55VUIFihRn3JKkyGOOOFOc7tZOCOJ8/w37Un6z3GEyfVsgodXwHIl7pnOO3ELiPpXVFEREa01O+sdAhieF5cN7WenkqL8j0VnCH1W995Wq5xtFHwTM6cnF6JvmjRIjXiIBOLSHOsMU+VL1cHqxQJmYHRaiT+ecRzLhaCFJoMIGMpMmnJiMQcNnStjx7yAbkKbvn/zx+BCJ/Qz6Fj0wtkJzXNjIkozyDdxFhaucWKRg2dXg9HBrblZAqeuaOTdBiaK62woFnj+hvZpuWbt3yRGsZuPoN3H+gqHYauSS9QIxkuf/JmZGTAx8cHPj4+yMjIUDImIiL6H2eaE5J+ObP7gMxH6b9yM9fBddZLc/0Ue6wKJ1arjdxwSrFx9UByNZazSfFjZ9kkXUKmE7tzHLHocJyij2c0P24Pkw6BNLb+RJJ0CCLeXnhUOgSiWp1OypMOgQQ4nUQvKirCe++9hyuuuAIPP/wwHn74YXTq1Anvv/8+iouL1YiRiIhMyMuDanwMX1d/SRMipYxYFyIdgmFEpju3G04pSdypoJjpeyKdOl7pSU1nkvhSE6oFpfITuT0m7JEOwaMsESxrEpMh874JAFFC79lGcjQ2WzoERXnaz0Oeo6jcubLE5BmcTqJ/8cUXOHDgADZv3ozc3Fzk5uZi48aNOHDgAL788ks1YiQiFejhgsqsnFkzZ+Zd55604uZwVKZ0CGQiziQ4ik15AWDiN1aVSM55LvCJlRvcSW8u8BcZN79UnbJa/EsypwMR3IWuZ/9eEiAdAhEZFEve1c/pJPratWuxYMEC9OvXD61bt0br1q3Rv39/zJ8/H2vWrFEjRiJSwZSd4dIhOKTSYsXAWYelwxAzbU+EdAhEpDLJE9axm0+Lje1pWH2L6hPCrd/kAQoN1l+IiIgcsyk4WToE3XM6iV5cXIwOHTpcdHv79u1ZzoXIQFLzjdEI43h8LhtSEhE5wZma6HvD0hUfPyw1X/HHpPqFpsg976UVjteD9zRTdhljUQKZj1rzs0sFS8kQ2WPj3hgit209mSIdgu45nUTv2bMnxowZg9LSvxNwJSUlGDt2LHr27KlocESkHumdOo4Ob1U4UA8qs01kCDEZRdIh6J7Sb8cFwqsEv1nDHgASJGvhm3mF9ax90dIhENlh3qRiLhu6K+qUAd7jI9IKpEMgIhNo5Ow3/PLLL+jbty+uuuoq3HHHHQCA4OBgNGvWDDt37lQ8QCJSx64zacgrrkCbFo2lQyEiD+ZIzW1PaiJLQDQbv9VLjZc8d20R0fn+PBIvHYKYD/8IFBs7XaXdvmrsHHPU0zN9EPfDU2LjOyIlV53nvUClnhJEZExOr0S/9dZbERkZiUmTJuGf//wn/vnPf+KHH35AZGQkbrnlFjViJFKd1aSFTP84Eic2tlTKTBdbUM35ciMiO6R3BimtyMFmpWr82GkGKVVGykrIZklJCSM3yO1+oPot9o2TDkFMUm6J2NgRaZxI9iT+sdmi43vaOSLpm9JVADyR00l0AGjRogU++OAD/Pzzz/j555/x73//G82bN1c6NiLN3DJmJ1YGyKzWyC8153bDgDiZE5LEHLmTaj3IM/H21kORGWJjV5p0oo4cxdeHUiZuC5UOwSG8RlHWt+sdT+Z62lMv+fOUVliRU1QuGAERkbrMvFfSPyZLOgTSGCcB6+dQEn3Tpk2oqKio/v+6voiMqKTCgv+ulVlNM3e/OWtpbj+VKh2CKd0xbpfDKzXLKz2rUdxbC446dJxFhYS3IyVNiExHhUxyIbddO8SmwnMvWU6mULgPgJlx1RppLTxVrva1T2Sm2NhUN74TKe/V+UekQyCNscJm/RyqiT5w4ECkpqaiffv2GDhwoN3jvLy8YLEwUUDkDNZZMx9Hm/6pkeQAgAMRGXi5R+d6j/t42XFVxte7v456Xg3R8kormjRyafMZaYA5KNJaXJby5U/iMovwz85tFX9cqpsRrne3h6SIja3WuZTZlVVa0LRRQ7v3x2Sot5rxr6Px+G6ATBnZNxf4I3ZS/zp7uVj4mhMRm2nORvZqTirxpUx0MYeuqK1WK9q3b1/9//a+mEAnIiO47budyCvRf1mTcovsSvA9oWmi40s5lZQnHYLiuFKQ7Cmt8KwdJ9ksLeEQi9Wzfu+kbx+pNCmfUVBW7zHS9Yw9VX1NHEduOKVRJPozaKFjOx+JlBDPniCmU1rBvKskp5elLV26FGVlF5+wlJeXY+nSpYoERUSkpoLSSozZaN6TeyIzqRSejHIEpziUU2m14c8jZ+v8vas5p6Tm6kvSL2+TTjpLe/ZXn3qPSc1js2EJFQb47CXleWINbS5EIb35YlWQdAim5nQS/d1330Ve3sWr9AoKCvDuu+8qEhQRaeOsCtu5jWJDUDISOHNPOqPWtnMzXwB4Ynkej6FS4cWRG05hqd9ZVR67PpK1eknOAp9YsbGLTNxzI9mBBLlNcJoyvYAJfNJehUXuNf/1mpNiY6vllz2R0iGQSaXmldZ6bbgtRL3eckYoESfN6SS6zWartQZYYmIi2rRpo0hQRKSN1YGJ0iGIemTK/nqP8cSVNJL1Qbn9rG5hKiXgTifn13uM5OtCzRM2tZ5T0rcTgk0uHcHGTaSUH7aHSYega5KViyoFk5merKyS55J1KeG5tqIc7WVFpLT7JnljGidxdMehxqIAcOedd8LLywteXl7o3bs3GjX6+1stFgtiY2Px5JNPqhIkEZmXmnk9i7XuBx+9UW41o5rWBibhlXuuFhn7F+9I/PfJm0XGNoJ+vxxS5XGt9bzWAeDHHeGqjE0ySsotaN7EftM3PSgTutA3ewK7vNKK5f4J0mEQkYFJJmrZ0sF8CkorcEmzxtJhiKhvN430gq+odM8sY2e12tCggfwJ4wzvSHzx+I2ajSe5k8UoHE6iDxw4EAAQFBSEvn37olWrVtX3NWnSBF26dMELL7ygeIBkDvV1ePdkfxw5i/EDb5UOg2ohnUCPTleny/zROLkmW3P2RzOJLmDS9jBs+OSBOo+ZeyBao2hIC3vD0vHU7VdIh1Gn3w7GSIcgpqxSnYveSosVjRrWvdH0twPRWHhY+fIj9U1MezI2diOzORiRgW5XtBYZ28wToYVllWjV1OEUjsfILio3bRK9PnP2y56/95l6QHR8tewNS0ef7h2kw9BcUm6JdAi65/A78JgxYwAAXbp0wSuvvIJmzZqpFhSZzxLfOAx++Lp6j4tmwy4ykeJybh+UEJelzuSFpCCdl7ZQU2ym5/0+HTH/UIzuk+gSTibm4j9/Hlft8S0Obp8qKFXn/d03OgsP33h5ncf4qdT4TbL3gjM5NTVKV2UWlin+mEZRW5lPs4jLLEKXf7QUGz+vpAJtmteeWGT5PM/0044wjH3WfIuvxm0+gwXv3GP3/nKVJqaNICTp4n6F5L780grpEEinnK6JPmjQICbQSXHeoekOHZdVWK5yJERkdpyB9yzpBfpPbq01eX8KNdhLlL78m5+q447bfEbVx6+Po0l8M8sr4YWxkkJT6u+54al8ojJFx3ekVBt5liU6LzOZmq9OM13vsLpzBXvD0lQZl4joQk4n0S0WC6ZMmYJ//etf6NixI9q1a1fji8gVan3geor0glKM3yJ7YU5EpBazpwFWBChfnzrDAJMHEkor1F2tZoRJG7UYZUXy12tOSoeguMQcuXIyf+g8qafm58vIDadUfHR9k/xzN+sOMyOQWhEuXBbctHyjZScSiSQ4nUQfO3Yspk6dildeeQV5eXn44osv8Pzzz6NBgwb47rvvVAiR9EjpBhZns1hLsi7DVgRhgY/y9UuJyDykGw+RtrijgrTmK7wq18zyS1j+jczDP1autw+R1raeTJEOwa5oD20qSlQXp5Poy5Ytw/z58/Hll1+iUaNGeO211/D7779j9OjROHLkiBoxkg5tOJEkHYKpBJ7NkQ6ByCMs89f3ijk15RTptxyWMdavElFdDut8RZrNZsOghUdVeWy1dzjUp4g9VIg0E5HGxKHe2Dx4T+OEraHSIejSEZX6u1SJ444XssPpJHpqaipuu+02AECrVq2Ql3eukcHTTz+NrVu3Khsd6ZZaDbHM6kQ8k+R0sYA4vi6U9n/r6996rVZJ4ci0AnUemOrEBD2R53OkjE5OcQUORGRoEI32Fh+Okw6BdEbt9gheJv503RjExWS1sQjW6N99Rq4muha9SNj/4GK+0eom0WfsjVL18cm4nE6iX3XVVUhJObel5LrrrsOuXbsAAAEBAWjatKmy0QFISkrCm2++icsuuwzNmzfHbbfdhmPHjlXfb7PZMHr0aFxxxRVo3rw5+vTpg8jIyBqPkZ2djTfeeAOtW7dG27Zt8f7776OwkDPIrlp1LAHjWJ9bUc/N9pUOQQRr9tZthndk/QepID6rGG8t8BcZ25M9O+uwdAimFMmtpnYFJeRKh6CawjKTTvab9Do7Maf+8kX2ms16gpIKi9jYO06nio1N9oWmyjZ8TchmSTGzkVwUtjEoWWxsLXYiPT3TR/UxiMgxTifRn3vuOXh7ewMAhg4dilGjRuGGG27A22+/jffee0/R4HJycvDAAw+gcePG2L59O86cOYOff/4Zl156afUxkydPxowZMzB37lz4+/ujZcuW6Nu3L0pL/25U+cYbb+D06dPYvXs3tmzZgoMHD2Lw4MGKxmom3wg2ZFp9TPnmayTnnol76j3GIH3KPMqwlSdwKFLf2/KNqLhcLslBVJuBHjyxsz/cM1ccK8GDc8kk5Dh3VOpOhVCDxyqp+aX1H0SGUllPb5380gqNItGe9ErwMymyk2JSUvP4PkL608jZb/jhhx+q//+VV17B1VdfDT8/P9xwww145plnFA3uxx9/ROfOnbFo0aLq27p27Vr9/zabDdOnT8fIkSPx7LPPAgCWLl2KDh06YMOGDXj11VcRGhqKHTt2ICAgAD169AAAzJw5E/3798eUKVPQqVMnRWMm11VYrGjcsO55ndWBiRpFQyRPqr5fWr65dwiomWAqLq9EiyZOf/QSkQJiMrgrQU3S5R2sVhsaNODMu4RMHe8s9OQdCFQ7ybIinuxARAZ6d+tg9/4KC593UlalVXYykKg2Tq9Ev1DPnj3xxRdfKJ5AB4BNmzahR48eeOmll9C+fXvceeedmD9/fvX9sbGxSE1NRZ8+fapva9OmDe699174+fkBAPz8/NC2bdvqBDoA9OnTBw0aNIC/P8sV6MnPuyKkQyAd4rUPaam0woKkXPW2IH+5Kli1xybSEz0mMeYfipUOQXWSDSald47FZxfLBiCIUwf2bT6ZIh2CR5L+e6/LR38GSofgkcrr2d3AazYiMgOHlsNt2rQJ/fr1Q+PGjbFp06Y6jx0wYIAigQFATEwM5syZgy+++ALffvstAgIC8Omnn6JJkyYYNGgQUlPP1eDr0KHmjGiHDh2q70tNTUX79u1r3N+oUSO0a9eu+pgLlZWVoazs7xUV+fnm3D6jtT/84jC8383SYRDphvSqPjPaG5au6uNvP8XasXqTWSi7gnJ/uLqvOSlzD0Tjk0evlw7DdCbvCMfTt8vsspT+xJJO6nElvD4d9NBmsoD8a16vdqncZFKLGtjG5LlZdP6t1c5zf+NE9jmURB84cGB1MnrgwIF2j/Py8oLFolzNV6vVih49euD7778HANx55504deoU5s6di0GDBik2zoUmTZqEsWPHqvb4RESbgpMx4A6Wk9IbrqLxTAWlFbikWWM798k2oHxnUYDo+GpZGZDAJLoAM6/Gro+XylmQeybuwa7PH8ZlrZqqOg7pS0J2MTq3ayEdBmlod2gqRj/TXToM3eE5NBGZgUPlXKxWa/VqbqvVavdLyQQ6AFxxxRXo3r3mB1S3bt0QHx8PAOjYsSMAIC2t5mxzWlpa9X0dO3ZEenrNVV6VlZXIzs6uPuZCI0aMQF5eXvVXQgKbWVZZy5rkInhO4nmGrTghHQKZjK7fR1Re4SNVxmPEupMorWBDWTKH8nqaznm6rKJyLPE7Kx0GaSwoIVc6BBEbgpKkQxDDnaK10/V5JnkeztqQELdroqvpgQceQHh4eI3bIiIicM011wA412S0Y8eO8Pb2rr4/Pz8f/v7+6NmzJ4BzNdtzc3MRGPh3bbS9e/fCarXi3nvvrXXcpk2bonXr1jW+6JwvV7Oer4T6atC5wzcqU7XHJvt0WC6YPNzCw/YTyUdislQdO0u4ZEpGQanIuH8dTcAS3ziRsaXpsSY6qSuzsFx0fF5Pk97kl1aIja32n8OpJJY7pZoqPHgilec0RFTFoXIuM2bMcPgBP/30U5eDudDnn3+O+++/H99//z1efvllHD16FPPmzcO8efMAnNuWOWzYMEyYMAE33HADunbtilGjRqFTp07VZWe6deuGJ598Eh988AHmzp2LiooKDBkyBK+++io6dWIpBXLen0e0XWUUnVGo6uO//rs/4n54StUxiEjebwdiMKJft1rve3XeEVXHnrwjHD++eLuqY9QlNrNIbOy0fNkJBClqNugloppYr1efnpx+SDoEUoGtjikKn0jzLk6asz9aOgTV/OfP4/h9UA/pMEyHk+OkRw4l0adNm1bj3xkZGSguLkbbtm0BALm5uWjRogXat2+vaBL9nnvuwfr16zFixAiMGzcOXbt2xfTp0/HGG29UH/PNN9+gqKgIgwcPRm5uLh588EHs2LEDzZo1qz5m2bJlGDJkCHr37o0GDRrghRdecGpigLRhMci75MgNpzQdLyK1QNPxSD/qOkl3V6XFikYNdb0ZiTxIbonsCtUjMdls+kdERESqen+JbJ+T9HyZnXcAEObB16x7QtVtVkvGI/m3RrIcSqLHxv69BXz58uWYPXs2FixYgJtuugkAEB4ejg8++AAffvih4gE+/fTTePrpp+3e7+XlhXHjxmHcuHF2j2nXrh2WL1+ueGykrNIKK75ZE4zJL94hHYpdp5LypEMgUkT/GYew6/Netd5nM8iEFjmvtMKCZo0bSochgq9qonPUnKAlGdKVBtRu2mpElR5c2sLsErL1u9OqxIP7sGQXlaNdyybSYehKsEl7MujB99tCpUMgIU4vQxw1ahRmzpxZnUAHgJtuugnTpk3DyJEjFQ2OzGfVMX03Lj1k4i16pD01c9kRaeqWCSJ9YokN7aXmyz3nUemeuyrMEcXllRfcwuSxJ8stqbv+NNO8nqmuWsV5xXI1yb3D0lUfo1J69sSOs1nql1ArKrvw/Z08XXBirnQIuiNZrhCQLbeSkCN7TZPKleim5XQSPSUlBZWVF39oWSwWpKVxmwsZW149F2Dkebjqms7HFZrmo/Z7wLaQVFUfvy4peeY+wV90OE46BBGSzQwlfbEySDqEOm0MSpIOwSOtDbS/AGf2gSgNI6mpvFL9lejH4nJUH8MVm4OTVR9j9xnmHYjMbP0J9T9TTyfbr0JwJCZb9fFJn5xOovfu3Rsffvghjh8/Xn1bYGAgPvroI/Tp00fR4Ii0tjIgXjoE0ph3qPorhYgcocUFN+lLfFaxdAgezawT40tMOnkQI7wirz6frQiSDsEj1VWHubjMc0tbmF0Fy+VojstM9Ed6NbSnL0YLPKvPiUqS5XQSfeHChejYsSN69OiBpk2bomnTpvjXv/6FDh064Pfff1cjRiLNeNWz2ffPI2c1ioS0Ep2hv7ImNpsN7y46ijIPTapmFZZJh6BLx+PVP1HTYnWYK+p77/VUByIzpEMgD1Suw+RSSl4J/vCLkw6DPNBi39j6DzIhydyWTqvMaMJTz931oNJi4heWHT9sD5MOwZQ4iWduDjUWPd/ll1+Obdu2ISIiAmFh5/5ob775Ztx4442KB0ekN6wnrD0zlteIyyrGvnDPTa7dPWEPfIc/hk5tm0uHYjrT90RiWB9+XpM5sBm4fjz762GkF3ACVS17Nai/rVd1JWwjTd4XQsrU3RHSIZAHCknKw+PdO0iHQSZSXF77biaWkzI3p1eiV7nxxhsxYMAADBgwgAl0IhWZL4WsD3U1qlKbVXhrnBaj+7BJr6nsPJ2myUr7unj6ltPaZBeVS4cgyjc6S2RcrlC6mB4S6NnF6v89mHNPTd3SC+TKDUjWrDXfJw6RumZ4R9Z6e1hqvsaRkFnMPxhT6+2TtodqHAnpidMr0QEgMTERmzZtQnx8PMrLa56QTp06VZHAiC6045RcczZST3F5JVo0cemtSFXPzT4sHYIYszcjlOLpOd7nZ/si7oenpMMwldEbT+PZf14pHYbpLPCJxX96XScdBl0gMk1/5dvMoMikdcnzTdCTIb+0Aq2bNZYOowb2lzEfs77HkPqy7CxGSchmdQIzczpz5e3tjQEDBuDaa69FWFgYbr31VsTFxcFms+Guu+5SI0YiWKw2/OfPQOkwSAXJuSW4vv0l0mFc5GQiywB4KovVhoYN9LdecNFh1nUlZZm1saY0n8hMXSfRw+toxOiu0goLmjVuqNrj1yejoAyXX9JUbHyz0t8nqjwzfKZP2RmOcc/eKh1GDUEJudIhqC42swhd/9Gy1vvU3Elr9t1tZE6nkvJw65VtpMMgHXG6nMuIESPw1VdfISQkBM2aNcPatWuRkJCAXr164aWXXlIjRiLR0hpmUGKn3pcZ8JVlPil5+lw9sIv19UTwPUA9ektmePpujyp1/Zxp+aXIKVZvcmXDiSTVHtsRE7eeER1fknTJLKqpUoNrl5OJuaqPUZezWcWi45tVdLr9XTVrAhNVG3fkhlPsNUKmM2jhUekQSGecTqKHhobi7bffBgA0atQIJSUlaNWqFcaNG4cff/xR8QCJSH1rj6t3wkVEROb0wdJj0iHoUkyGumVFvOpYFnwmRd3asRXCix7sbb0+x7NnUU4ny9UFNnNNYv9Ymd4LADB7f7TY2KRPh1XuBfL6/COqPj6R3mjRT4WMxekkesuWLavroF9xxRWIjv77wzszk43iyNjquvBU2+Eoub+fSh03QftlT+1NZIjUYJaVqnoj+d5L6mGDzdqVVqj7vPDPqXbeoenSIXisupK5nv56/PNIvHQItbJ5+KSRF08cahVu4gktUkdwHbsKPftdhqh2TifR77vvPvj4+AAA+vfvjy+//BITJ07Ee++9h/vuu0/xAInM4o3f/aVDqFV6vmyTSf/YbNHxiYhUw1kbj+QjOClen2ITN2DbH5Gh+hh/HdVnQlVtfCsjrW0PSZEOQUxdE9RqT9KS+Xy87LjY2PxsIT1yOok+depU3HvvvQCAsWPHonfv3li5ciW6dOmCBQsWKB4gESC3msJqteG3A9E4FufZidy6mmj2n3FIw0i0l1FQJh3CRbRaW1NaYd5kiqSyyoufd+m6pkSkrFydbv/9ZLncxbA0LS7G0+2cU9i0GJzZBl2R/nV4efgeAO+w2neWmGGB+keSSU2xkUlKbdctVZJy1O0zVdfYWjHBWwo5yekk+rXXXovbb78dwLnSLnPnzsXJkyexdu1aXHPNNYoHSCRp88lkTNoehhfn+onFoMVJ+Lo6GoFlFuozEaCU08nmbZCTlCvYYFPwjET6wnbEupCLbhvw62GBSPRDi+bGvPAjLZVXcjWgnthsNmQWyk2aC5eKF2WGpGZtPL2cCplTQWmldAiksbpyAb/7xKo69nSWdSUdcjqJbs+6deuqk+tEnqKu7uekFNmrK+mEKpnPuuP2J63MarFvnHQIqorNLJIOgcjU/FRutmd20olyqw5nKaTPLxceVje5RUSktgMalGEjcpZTSfTffvsNL774Il5//XX4+5+r37x3717ceeedeOutt/DAAw+oEiQREREZn3SPhdposarq0Sn7a729SIMV+KQ/0glHMwpLLZAOQXX6SyNrh/1zasorrsDuM2mqj1Pbe5lZGkonZBdLh0Dk8QrLKjHTOxKVF7yvZAnuLCNyOIn+ww8/YOjQoYiLi8OmTZvw2GOP4fvvv8cbb7yBV155BYmJiZgzZ46asRKRBxItKSJs6q5w6RCINDVtT4R0CBf5w++s2Nink/PFxjYj6ZWhVfJLKqRDUI1ey9hYhX/5S/3ixMbO8+DXW5VKqz5fd1L0UEfY06XpcFEAaSu/tAIvzfWVDsPj/bw7AssvaNodl6XdDk8vHa18OB6fgzWBidJhmJ7DSfRFixZh/vz5OHbsGLZv346SkhL4+voiKioKw4cPx6WXXqpmnGRyernwJeWFmLih4oy9UdIhEGmqTIcJNguTL6SxKSpPoM7YGwXfqExVx7CHDatrN3bzGdXHsFc2SoueE3Xx9AaXpC9avdrmHYzRaCTSq3kHYhAQlyMdhilcWGL3x+3mXIj2/GxffLU6WDoM03M4iR4fH4/HHnsMAPDQQw+hcePGGDt2LFq2bKlacERE5Jl4SU1EZqXFRffrv/urPgbpy6LDcWKrY/mZfjHJBUAhSXlyg5vELg3K5ThrMevga6qoXNsmqxuD9NVTSbJE49E4lvAiOQ4n0cvKytCsWbPqfzdp0gTt2rVTJSgivdDDAngd7SDySHr4HV9o8g5zzq5rpa4LHz2+HpTGOp6ybOdlVmxCWZbySit+3BGm+jhm/vjSYZ9DMoEzKSwRRcD7S45Jh0ACvtNgxwvJ+WxFkHQINZSbpP+Bmc9lqXaNnDl41KhRaNGiBQCgvLwcEyZMQJs2bWocM3XqVOWiIyKPF5xo7tUy644n4vm7rqpx247TqZqMbdaTgvFbzuD9B7vWel9anufXuHxo8j7E/fCUdBia00NZsLJKC56e4YPunVrjl1fvFIvjjyNnMWd/tNj4ZvDFqiAs/+A+6TBM51CkTBkbso+LQdSVnFuCTm2bS4dRLSZDu1rFJeUWNG/SULPxiMh8+BlGF3I4if7www8jPPzv1ZH3338/YmJq1gLTU9F9Ik/h6Y1rdgtvh5R+1/piVfBFSXQz0OvnxcRtodIheDYdJLIvpOVr0ScyE5HphYhMLxRNosfZqZ2stJxiz29oaI9vdJZ0CLoRnVFY/0HksWZrMGGXll+m+hjOSsot0WSc+3/Yq6uJ8WSNfm4ASM4rwXWXt6pxm+Tppb2+BERE5DkcTqLv379fxTDICPaHp2syTmhKPrpd0brGbVqtICwWbr5Um/wSbeut6QlPRklLWYX6uwg3g7JK/b3vqkUPq+FJjs1mQ36p+T7Tn5t1WHT8bSEpmLDVvBOkiTnql/CyNxm54mg8/joar/r4608k4sW7zbcgQVptv3UtSoXp1aNT9ms2VoXFisYNHa7MS0RECuE7LznsnUUBmowj2Qwnr6Tmqjk9JDwsGgUxfov+6uh9s4bdp0k7iTnarZ6iczYFJ+OmkTtEY5CqSw7od0cGqWP8llDcMXaXdBiqqu0lLT1x8PGy46LjS08UvjjXT2zsKbu06fFy4fk7yfH0HbREVfSQJ5AUb5IeS17i+9ZJb5hEJ6qDTQe1B9YdT9RknAU++uvoXlRmnhWqdM7RWHZbN5NP/zohHYIonpZrS/ozfeFh/X3OkvpKy+War+WXyiaXMwvLNRmHSQ4ZMdwxakrllfpqKGnVKJu9MShJk3HqM3HrGfSZegBFZbIT1K/P9xcdX4rk4hvSBybRic6jx1Nwro5VV20fg6UV5k3eW6zanBjYOwE5dpZJdCIio6q08OJST9K5KphUdDbLHCtRqab1J7RZ4OWor1Zrs3P5sxVBmoxTn/mHYhGVXoivuWNbBN/3iEl0Ip0zw27/jAJ91aJeHaivk0MtLfGN02ScrSEpmoxDVB9PL6kSkVZw0W0e/iPXkGfi5qYSpu6OMP0qLdESUULjFgqviCQ5Fy480fLVX/vfmok+4IRI7hQ+FnfxYhuzJjW3haRKh2BK5j7DIcDJJHplZSXGjRuHxETzJphIfbnF2mz9JP3o98tB6RBqKDPJSvTakofbT2mT3D4Rn6vJOM6Iy+K2ZLVJnnhKl/I4F0Pd/1bDMJ2snJJSadXXtnMzMPuF/eGoLOkQTEeyn5LZXVhSQss5JJ/IzFpulf+sN6MzyfmajHMgIkOTcYiqlFt4Hkk1OZVEb9SoEX766SdUVnK1Aann+20Xd3XXQ/Jj9bEE6RA8llY1M2tj5vUqetrmXV5pxepjCUjOlStfdCTGnKVkAs/mSIcgytNXzUo3NSTzic0slA5BVFKuOVdF6s1y/3jNxloo3FdIT2UItfxMLSqv+XPnFVfghTlyjXTNLDrD3O/7RGQeTpdzeeyxx3DgwAE1YiHSta/XnJQOgUhRr8w7Ijb2hYvg5x2MxtdrTuLPI9pd9NI5L8zxlQ5BTFFZJTYEJUuHQUSkCg+fI6zTt+tDNBtr3JYzNf6dWahtmcKJW0M1HU+vlh81zzmk3hYBDTV5o3giMo9Gzn5Dv379MHz4cISEhODuu+9Gy5Yta9w/YMAAxYIj8wpLzcfNHVtX/1uri4ALE3taX3xYrTY0aKC30yLPZuLrS1EXvsoP1roll0hdW04yga627KJytGvZRGz8+OxiXNaqqdj4RJKi0rVbHZpXUoE2zRtrNp6ejdl0WtPxEnK4+wHQx85l8mxR6QW4vv0lADx/JyMR1c7pJPrHH38MAJg6depF93l5ecFi0c92MjKuzIJyoKN0FNr7flsoRj7dvcZt/HxWmQ6e3+TcEnRq21w6DABAQJyZS3vo4MVAqskoKMNVl7aQDqOGzcEySXwtp2o/WHoMaz+6X8MRa0rKLcGdV18qNr6kU0l5uPXKNtJhkKCw1IsbC6vFOzQNz991lWbj6Zl3aJp0CKak5aQRcK5cWtNGDTUds8qxszm479rLRMY2s52n06qT6KQdCxMipCNOl3OxWq12v5hAJ3LP78I1FemclDxta4VbBU8MQhJlmnHV1tTUTL7TeJUaAQ/+uK/Gv8sqLaatg68lPdXcN9s1WIbGJSVInuRna2iKNo0F6WLZRX/3FtJ6deyFo1VatRs/MadmH524TG0bxEuW0Zm1L0psbEkFpezNJ8Gq4d91bYrL9JNn1LpcF+mP00l0Ii1cVFZFo3GTL0iemux6mwDMPxiDBRpPZoxYF4LySpnO36/MYwMmCYt946RDEKGnra8j1oZg/YkkzcY7k8wEkwQv3VWOJTOptFiRnCfXMJvM4eR5CyL2R2RoOvaJ+Nwa/y4u1y7Z9ZdwDfQVRxNEx5dy4XV6bnF57QeqICghV7OxapNRYM4E6sLDXOhXRcum1VIi0rTbxWZELiXRi4qKsG3bNsydOxczZsyo8UVkZFtPpmDewWjpMEjQxG3aryo5FJmJ5f5nNR8X0PZiR+90lN/1WHpaQbROwwQ6ALy90F9kEiE6owg/bA/TfFwiKdEZ2pZ0uFDweQnNQYuOot8vh8Ri0fIdR8sVyGRfsMZJxg+WHtN0PDrnUGQmKi0yC3AulJqv3Q5e6SS6WRfBTBBuXhyt8S4TPfpmTbBmY+npek2PnE6inzhxAtdffz1ee+01DBkyBBMmTMCwYcPw7bffYvr06SqESKSt77fpK9lg8qoXpqGHbfcJ2WxMReoqKDPvSVlmoXYrtS4090DNyWEtL3j1JL+0QjoEU5Au1/Xk9IOi45+/Su1wVJamY28M0nZy8HyLDseJjQ3oa6eTpA0aTxDTORLlLraGpGg+Jv2NiUbtaD05WBeJMqzllVasOpao+bhUO6eT6J9//jmeeeYZ5OTkoHnz5jhy5AjOnj2Lu+++G1OmTFEjRtIBrU9M9ZI3Ntv5uN4uQEorNVglrZcXm7C9YWl4aPK++g9UyIVPux5+DTp7+auqgMlE0zm/VqzWfR8k+URl4OHJ++AXnYVKi4n+yHHx+6pWn/G/H4rRZBx7Kkz2ez7fuuPmTaDuPJ0qHUI1yfOJuCzzLoiQ/MsvrtB+Z2d+Cc/l8gSfA613NNI50iV1NgYlaz6mjUWGdcXpJHpQUBC+/PJLNGjQAA0bNkRZWRk6d+6MyZMn49tvv1UjRtKBz1cGiY6vdaOYKnrZJqeVJ6cfQoVOfubc4nLEZGjwe+dnEgBgia+25WSkd1jobcJIa2/+7i8dAmnsrEl3mvx1NAHx2cV4bf4R6VDExWp0LpVTXGH6nU0+kZnSIZxjos+6iDTZMj4kz0Qv94uUapjEL7pgV6HkBPXr/Gw3HQtLh5Ewp5PojRs3RoMG576tffv2iI8/t2WxTZs2SEgwZ3MNM9ig9YzbBQm2txbIJHxWHpN/TWt5QhieVoCA2GztBqzD/nBtGyORudw9YQ+i0s3bNOX8mr0kwzs0TToEUXrammsWWm5BLio39zb3sFQ2EpZk9oly0kb5eQuPPv3rhGAkwCvztEsmT9kVUePfywUbvJ5m03bT2BeeLjb22SzWZKe/OZ1Ev/POOxEQEAAA6NWrF0aPHo1ly5Zh2LBhuPXWWxUPkAg4t6pJgilrnQmtED6VxKQeAN3sBFCbZH1oAMguKsfIDadEY5DejijBLK9ve9LP+52/v8Q8zdhyiy/+e3921mGBSMxFujY5yTNrKll6oaJZn3dpks/73jC5BB8gOzF9fh8IIrW8u+hcDjI5r0TzsU2ZEyK7nE6if//997jiiisAABMnTsSll16Kjz76CBkZGZg3b57iAZI5eemiQrI+mOUa+OmZPjX+HZNhzm25Z0y0osLsEyep/6tLrZtt/xo4EZ8rHYKoFIET/yqSKzMXCjccrFJu8kkcMh8zLcg2089KdpjsRWDm+vdkXs/P9tV8zC0nU0y1EMgs+SdXNXL2G3r06FH9/+3bt8eOHTsUDYgI4B8uASUa1fY7GqeP8jXSJP7mcmpZnSpJqmnLm0Llqoi0opfSCodMNGEFaFsjl0iaRSfvM0RaWeATi1FPd5cOQ3OpeaXo2KaZdBimE55agJs6XgIAKKs01/nF3APRuKRZI3zy6PXSoZAOOL0SnYjUtfuMfurkFpu8pqmWuPtCHq+/SW38Oyct5RTJTVSO33JGtGRVtEl3s0krKJUpvwgAByP000unvNI8Kxb1xKyncRIrZL/bdFrzMavcN8lbbGwzm77n71r4YzefEYxEhmTJJl6j6otDSfQ777wTd911l0NfREowc5rhg6XHkJ5fKjb++UmeqRc0jvFUelklaWZ62H2SKvh3R9pIyDbv1me/6CzpEExnzv5o0fEl31cPR2VhyPLjYuP3/vmA2NhmNkqw1wh3XsiVDJOcsCMgJkP7poeLfeM0H5P0g3XwycwcKucycOBAlcMgovMdiMjASz06S4eBExo3qbFabSittKBFE6crTbll3sEYfNjrOk3H1Bsd5LDFma3UgxmN3ngKi979l3QYIn773/tcu5ZNpEMxjR93hOGjR8z72eIfy3Jp0rQuUybdXLFKXoncinjJurneoTLP/+hNpzHztTtFxj6fWdfE/HWUCU0trT+RiOfuvEo6DCIS4lCmasyYMWrHQUTnmbE3sjqJbqYTwlfnH8HR2Gz4f9tb05970vYwHInJwuhnbtFuUB05EZ+DfeHab4M202ub9EHida4n2UVlaNeyCcoqZEsNrDueKDq+WVn5nqsZLz1srwKQkC3XzFjSpG2hmo85bvMZjH6mO9YfT9J87CojhXYCbA5Oxk8v3o5mjRuKjC9t/QnZzzTJnWZm3OU2bXckk+hEJubycs/AwECEhp47Qbnllltw553ys8/kOfRy8UHaOvq/VWtbT6ZoPva+8AzsC9+v+bh68JxAl3MAWH8iCQ/feLnI2ADrUwNASbn2299PJuaivNKKHl3aaT62ND18tG0PSUF4WoFoDF+sChYd36x+2hkuHQJpbFNwsnQImrGeNzO/OlD7pObCw7Fo0aQhLmmm7W7KKlbhWbJRG07hp5fuEI1Byucr5T7TSsotouUIvxeYsNKDtxcelQ5Bc3o4h5Vk8h+fzuN0Y9H09HQ89thjuOeee/Dpp5/i008/xd13343evXsjI8PcK7yIiMhx608kidb/l6aHk9E5+6M0H3PAr4fx4lw/xGeZrz75zL3aP98XGvLXCekQSCMXThTqqXE5qau0wgKLybYexGXKf6b8uk/uPX77qVSxsQGZiQsCPlsh+5kekpQnOr4UqUbGyblyu4u2hci+xwBs2qwVHVyi6prTSfShQ4eioKAAp0+fRnZ2NrKzs3Hq1Cnk5+fj008/VSNGMiE9JJf0IK9Y+5qO5z/3kr+G0kpzNYgy62v+dHK+dAhijgrXC/YOTUNCjtzJ+O8+MWJjm5lEYs2kb28EYAVr9WruUGQGbh61A8/O8pEORVPlgrXIz1cksMMLkEvqnW+owCTtQp9YUzeV3cXJUVO5/4e90iGI2hsm+3qfJTRRWmmySXG9czqJvmPHDsyePRvdunWrvq179+6YNWsWtm/frmhwZF7SWxKlecELp5LycMe4XdKhiMguKsefR3jhbQbvLQkAYM7SKt7CJ4LvLzkmOj4RqUwHb6vD14VIh2A6y/3PnT+dStJ+kjq/tBJFZZWaj1vlTHI+9ggnFSV2eAFAiQ4SyZsFygeN23IG903yBqB9I129sJmwyVCZyRZb6YnkLifpuVKpsnhLfONExqXaOZ1Et1qtaNy48UW3N27cGFarPlYAkPGZqYajPUv94qRDECO5HZa0JXneH5l+ri601WrDy3P9RGIw8+opMo/zr7fM2IRMyvkLEo7H5whGIiM6o1A6BBHSZT1+OxiDXadTRZJc/WccwtwD0ZqPe74Ki8yJzY7T8qUWpOQWVyCjoExk4kgPzJdCB3KKKjByAydpJRSWyk2USpLcNX7ChOdweuZ0Ev2xxx7DZ599huTkv5OcSUlJ+Pzzz9G7d29FgyPzissqkg5BlJcXkCRY84zIDHL/Vy4pJrMIR+O0L61yOCoLN4/aofm4emHChVOmFZX+dzLztflHBCMxl6pV4IVllXheqIG0pN4/H8Bzsw+LjL0pKAmhKeZM6M3wjsTgPwLx0w6ZFXvFQuVUpJm9VvA9E/eIjNtl+FaRcQliu5bjs+X7L0hKL5DrZyW500mSf4xsCVCqyekk+q+//or8/Hx06dIF1113Ha677jp07doV+fn5mDlzphoxkgkdjuJKtdgMmYmE8ydZzVqnm7QnuwWX2Vwz6f3zfvHt/nL4pm5G+SXa91fRixPxuSLjBifmod8vh0TG1os1x2UaTZ4x6eQFEZEW4rLkJhG+WXtSbGxJBSadPNCrRs5+Q+fOnXH8+HHs2bMHYWFhAIBu3bqhT58+igdHZFZ6SXMExHHrEGnjiMAMOyeJZP1x5KzIuNEZRfj3UtaD15JZ69TqQeDZHFzRppl0GGRCVbu9iEhdZt+FQKQFM+VFvHiRXCenk+jAuSf18ccfx+OPP650PET0P1JvXkx1yNh5OhVfPnGTdBhkMkk5LBtF5MkSc4qZRCci8mBlJkyic3JeDtOrZHYOl3Px8/PDli1baty2dOlSdO3aFe3bt8fgwYNRVlameIBEeVzJQiYQkWbOBmgAEJdp7h4IkngRYi4FpXKfp2yiS0RERERERuZwEn3cuHE4ffp09b9DQkLw/vvvo0+fPhg+fDg2b96MSZMmqRIkmdPvh2JQVmnBf/4M1Hxsm3DHOy8vL/EYSIYZf+uPTNkvMm6FxcbEHpnKc0LNJcNSC3DzqB0YteGUyPiSuM2eiIiIiMgzOJxEDwoKQu/evav/vWLFCtx7772YP38+vvjiC8yYMQOrVq1SJUgypwlbQzFnfzT8YrRvMnr/D3uRJ9iIKzazCMl5Mp2vuUVL1piN5ksySbplzE5YzThzQaShs/9rQiVVB1+SlRPiRER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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Radiation:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 59,357.0000\n", + "mean : 183.8441\n", + "median : 12.0000\n", + "std : 259.8156\n", + "min : 0.0000\n", + "max : 1,113.0000\n", + "skewness : 1.3491\n", + "kurtosis : 0.5914\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 12.0000\n", + "percentile_50 : 12.0000\n", + "percentile_75 : 321.3083\n", + "percentile_90 : 624.6504\n", + "percentile_95 : 776.0000\n", + "percentile_99 : 907.6779\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (16.60%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 59357,\n", + " 'mean': 183.84409789852336,\n", + " 'median': 12.0,\n", + " 'std': 259.8156425752193,\n", + " 'min': 0.0,\n", + " 'max': 1113.0,\n", + " 'skewness': 1.3490904735404219,\n", + " 'kurtosis': 0.5914208419781612,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 12.0,\n", + " 'percentile_50': 12.0,\n", + " 'percentile_75': 321.3082580566406,\n", + " 'percentile_90': 624.6503662109386,\n", + " 'percentile_95': 776.0,\n", + " 'percentile_99': 907.677912597656}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error saving plot: [Errno 2] No such file or directory: '2024-11-26_05-41/error_analysis.png'\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Statistics:\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.98 0.96 0.97 13007\n", + " 1.0 0.96 0.98 0.97 12926\n", + "\n", + " accuracy 0.97 25933\n", + " macro avg 0.97 0.97 0.97 25933\n", + "weighted avg 0.97 0.97 0.97 25933\n", + "\n", + "AUC-ROC: 0.9968\n", + "\n", + "Regression Statistics (Non-zero values):\n", + "MAE: 0.1056\n", + "RMSE: 0.2896\n", + "Mean error: 0.0143\n", + "Error std: 0.2892\n", + "\n", + "Final Prediction Statistics:\n", + "MAE: 0.0583\n", + "RMSE: 0.0835\n", + "Mean error: 0.0113\n", + "Error std: 0.0827\n", + "\n", + "Error Thresholds (Final Predictions):\n", + "Predictions within ±0.5: 99.9%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd5197ea71becfc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f982c92c-ba99-4df6-b3c8-df92426679db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v1-checkpoint.ipynb b/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v1-checkpoint.ipynb new file mode 100644 index 0000000..b4cc436 --- /dev/null +++ b/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v1-checkpoint.ipynb @@ -0,0 +1,2379 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.0.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7a813e3cbca057b7", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n", + " GlobalMaxPooling1D, Concatenate\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix\n", + "from scipy import stats\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3f525e19f78a1da", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " \"\"\"\n", + " Add time-based features to the DataFrame.\n", + " Works with both 'datetime' as column or index.\n", + " \"\"\"\n", + " # Se datetime è l'indice, lo usiamo direttamente\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " datetime_col = df.index\n", + " else:\n", + " # Se datetime è una colonna, la convertiamo\n", + " if 'datetime' in df.columns:\n", + " datetime_col = pd.to_datetime(df['datetime'])\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Creazione delle feature temporali\n", + " df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n", + " df['year'] = datetime_col.year\n", + " df['month'] = datetime_col.month\n", + " df['day'] = datetime_col.day\n", + " df['hour'] = datetime_col.hour\n", + " df['minute'] = datetime_col.minute\n", + " df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n", + " df['day_of_week'] = datetime_col.dayofweek\n", + " df['day_of_year'] = datetime_col.dayofyear\n", + " df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n", + " df['quarter'] = datetime_col.quarter\n", + " df['is_month_end'] = datetime_col.is_month_end.astype(int)\n", + " df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n", + " df['is_year_end'] = datetime_col.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['season'] = datetime_col.map(get_season)\n", + " df['time_period'] = datetime_col.hour.map(get_time_period)\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Solar angle calculation\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Assicuriamoci che l'indice sia di tipo datetime\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df['datetime'])\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " Assumes df has a DatetimeIndex\n", + " \"\"\"\n", + " # Verifichiamo che abbiamo un DatetimeIndex\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " raise ValueError(\"DataFrame must have a DatetimeIndex\")\n", + "\n", + " # Existing features\n", + " df = add_time_features(df)\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # Weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # Derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # Rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # Extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Verifichiamo se datetime è già l'indice\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex', # Direct radiation indicator\n", + " 'cloudcover', # Cloud coverage\n", + " 'visibility', # Atmospheric transparency\n", + " 'temp', # Temperature\n", + " 'pressure', # Atmospheric pressure\n", + " 'humidity', # Humidity\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation', # Solar elevation\n", + " 'solar_angle', # Solar angle\n", + " 'day_length', # Day length\n", + " 'hour_sin', # Daily cycle\n", + " 'hour_cos',\n", + " 'day_of_year_sin', # Annual cycle\n", + " 'day_of_year_cos',\n", + " 'month_sin', # Monthly cycle\n", + " 'month_cos',\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index', # Clear sky index\n", + " 'atmospheric_attenuation', # Atmospheric attenuation\n", + " 'theoretical_radiation', # Theoretical radiation\n", + " 'expected_radiation', # Expected radiation\n", + " 'cloud_elevation', # Cloud-elevation interaction\n", + " 'visibility_elevation', # Visibility-elevation interaction\n", + " 'uv_cloud_interaction', # UV-cloud interaction\n", + " 'temp_radiation_potential', # Temperature-radiation potential\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h', # Cloud coverage moving average\n", + " 'temp_rolling_12h', # Temperature moving average\n", + " 'uv_rolling_12h', # UV moving average\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " ],\n", + "\n", + " # Lag Features (most recent)\n", + " 'lag_features': [\n", + " 'temp_1h_lag', # 1-hour temperature lag\n", + " 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n", + " 'humidity_1h_lag', # 1-hour humidity lag\n", + " 'uv_lag_1h', # 1-hour UV lag\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring', # Seasons\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning', # Time periods\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night',\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " df[column] = df[column].interpolate(method='time')\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarradiation']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e", + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a bidirectional LSTM layer with residual connections and regularization.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Number of LSTM units\n", + " dropout_rate: Dropout rate for regularization\n", + " l2_reg: L2 regularization factor\n", + " return_sequences: Whether to return sequences or just the last output\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " residual = x\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " if return_sequences:\n", + " if int(residual.shape[-1]) != 2 * units:\n", + " residual = Dense(2 * units, activation='linear')(residual)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a multi-head attention block with residual connections.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Dimension of the key space\n", + " num_heads: Number of attention heads\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + "\n", + "\n", + "def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n", + " \"\"\"\n", + " Creates a deep learning model for solar radiation prediction using LSTM and attention mechanisms.\n", + "\n", + " Parameters:\n", + " input_shape: Shape of input data\n", + " folder_name: Directory to save model architecture visualization\n", + " l2_lambda: L2 regularization factor\n", + " \"\"\"\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Progressive hyperparameters for model architecture\n", + " survival_probs = [0.9, 0.8, 0.7, 0.6] # Decreasing survival probabilities for deeper layers\n", + " attention_survival_probs = [0.85, 0.75, 0.65, 0.55] # Survival probabilities for attention blocks\n", + " lstm_units = [256, 128, 64, 32] # Decreasing number of units for LSTM layers\n", + " dropout_rates = [0.4, 0.3, 0.2, 0.2] # Decreasing dropout rates\n", + " attention_heads = [32, 24, 16, 8] # Decreasing number of attention heads\n", + "\n", + " lstm_blocks = 4\n", + " # Main network architecture\n", + " x = inputs\n", + " for i in range(lstm_blocks):\n", + " # LSTM layer with residual connections\n", + " x = create_residual_lstm_layer(\n", + " x,\n", + " units=lstm_units[i],\n", + " dropout_rate=dropout_rates[i],\n", + " l2_reg=l2_lambda,\n", + " return_sequences=True,\n", + " survival_probability=survival_probs[i]\n", + " )\n", + " # Attention block\n", + " x = attention_block(\n", + " x,\n", + " units=lstm_units[i],\n", + " num_heads=attention_heads[i],\n", + " survival_probability=attention_survival_probs[i]\n", + " )\n", + " if i < lstm_blocks - 1: # No pooling after last LSTM layer\n", + " x = MaxPooling1D()(x)\n", + "\n", + " # Final LSTM layer for sequence aggregation\n", + " x = create_residual_lstm_layer(\n", + " x,\n", + " units=32,\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Dense layers for final prediction\n", + " dense_units = [128, 64, 32]\n", + " dense_dropout = [0.2, 0.1, 0.05]\n", + "\n", + " for units, dropout in zip(dense_units, dense_dropout):\n", + " x = Dense(units, kernel_regularizer=regularizers.l2(l2_lambda))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Activation('swish')(x)\n", + " x = Dropout(dropout)(x)\n", + "\n", + " # Output layer with value clipping\n", + " outputs = Dense(1)(x)\n", + " outputs = Lambda(lambda x: tf.clip_by_value(x, min_output, max_output))(outputs)\n", + "\n", + " # Model compilation\n", + " model = Model(inputs=inputs, outputs=outputs, name=\"SolarRadiationModel\")\n", + "\n", + " # Improved loss function\n", + " def hybrid_focal_loss(y_true, y_pred):\n", + " # MSE with focal weighting\n", + " mse = tf.square(y_true - y_pred)\n", + " error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n", + " focal_weight = tf.pow(error_ratio, 2)\n", + " weighted_mse = focal_weight * mse\n", + "\n", + " # MAE component\n", + " mae = tf.abs(y_true - y_pred)\n", + "\n", + " return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n", + "\n", + " # Custom metrics\n", + " def rmse(y_true, y_pred):\n", + " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n", + "\n", + " def custom_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n", + " diff = tf.clip_by_value(diff, 0, 1)\n", + " return tf.reduce_mean(diff) * 100\n", + "\n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=0.0003,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7,\n", + " weight_decay=0.001,\n", + " amsgrad=True\n", + " )\n", + "\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss=hybrid_focal_loss,\n", + " metrics=['mse', 'mae', rmse, custom_mape]\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " plot_model(model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True)\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar radiation values (W/m²)\n", + " y_pred : array-like\n", + " Predicted solar radiation values (W/m²)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + " \n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 W/m²\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n", + "\n", + " # Radiation level classification\n", + " def get_radiation_level(value):\n", + " if value <= 200:\n", + " return 'Very Low'\n", + " elif value <= 400:\n", + " return 'Low'\n", + " elif value <= 600:\n", + " return 'Moderate'\n", + " elif value <= 800:\n", + " return 'High'\n", + " elif value <= 1000:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate radiation levels\n", + " y_true_levels = [get_radiation_level(v) for v in y_true]\n", + " y_pred_levels = [get_radiation_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Radiation Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} W/m²\")\n", + " print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for radiation levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels)\n", + " print(\"\\nConfusion Matrix for Radiation Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n", + " index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + " \n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Predicted Radiation (W/m²)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (W/m²)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 10\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by radiation level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Radiation Level')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_radiation_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training loss and metrics plots\n", + "\n", + " Parameters:\n", + " -----------\n", + " history : tensorflow.keras.callbacks.History\n", + " History object returned by model training\n", + " folder_name : str\n", + " Directory to save the plots and metrics\n", + " \"\"\"\n", + "\n", + " try:\n", + " # Create figure\n", + " plt.figure(figsize=(12, 4))\n", + "\n", + " # Loss plot\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(history.history['loss'], label='Training Loss')\n", + " plt.plot(history.history['val_loss'], label='Validation Loss')\n", + " plt.title('Model Loss')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # MAE plot\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(history.history['mae'], label='Training MAE')\n", + " plt.plot(history.history['val_mae'], label='Validation MAE')\n", + " plt.title('Model MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " # Generate filename with timestamp\n", + " filename = f'{folder_name}_training_history.png' # Rimossa parentesi extra\n", + "\n", + " # Save figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save numerical data in CSV format\n", + " history_df = pd.DataFrame({\n", + " 'epoch': range(1, len(history.history['loss']) + 1),\n", + " 'training_loss': history.history['loss'],\n", + " 'validation_loss': history.history['val_loss'],\n", + " 'training_mae': history.history['mae'],\n", + " 'validation_mae': history.history['val_mae']})\n", + "\n", + " if folder_name is not None:\n", + " csv_filename = f'{folder_name}_training_history.csv' # Rimossa parentesi extra\n", + " history_df.to_csv(csv_filename, index=False)\n", + " print(f\"Training history data saved as: {csv_filename}\")\n", + "\n", + " # Calculate and save final statistics\n", + " final_stats = {\n", + " 'final_training_loss': history.history['loss'][-1],\n", + " 'final_validation_loss': history.history['val_loss'][-1],\n", + " 'final_training_mae': history.history['mae'][-1],\n", + " 'final_validation_mae': history.history['val_mae'][-1],\n", + " 'best_validation_loss': min(history.history['val_loss']),\n", + " 'best_validation_mae': min(history.history['val_mae']),\n", + " 'epochs': len(history.history['loss']),\n", + " }\n", + "\n", + " if folder_name is not None:\n", + " # Save statistics in JSON format\n", + " stats_filename = f'{folder_name}_training_stats.json' # Rimossa parentesi extra\n", + " with open(stats_filename, 'w') as f:\n", + " json.dump(final_stats, f, indent=4)\n", + " print(f\"Final statistics saved as: {stats_filename}\")\n", + "\n", + " # Print main statistics\n", + " print(\"\\nFinal Training Statistics:\")\n", + " print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n", + " print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n", + " print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n", + " print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n", + "\n", + " plt.show()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during plot creation or saving: {str(e)}\")\n", + "\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation'):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar radiation model\n", + " \"\"\"\n", + "\n", + " def calculate_metrics(y_true, y_pred):\n", + " \"\"\"Helper function to calculate metrics safely\"\"\"\n", + " y_true = np.array(y_true).flatten()\n", + " y_pred = np.array(y_pred).flatten()\n", + "\n", + " # Count out of range predictions\n", + " out_of_range = np.sum((y_pred < 0) | (y_pred > 1500))\n", + "\n", + " # Calculate MAPE with clipping to avoid extreme values\n", + " diff = np.abs((y_true - y_pred) / (y_true + 1e-7))\n", + " diff = np.clip(diff, 0, 1) # Clip to maximum 100% error\n", + " mape = np.mean(diff) * 100\n", + "\n", + " # Calculate accuracy within 10%\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + "\n", + " # Calculate MAE and RMSE\n", + " mae = np.mean(np.abs(y_true - y_pred))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_pred)))\n", + "\n", + " return out_of_range, mape, within_10_percent, mae, rmse\n", + "\n", + " callbacks = [\n", + " EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-4\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.5,\n", + " patience=7,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=False\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: (\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n", + " (lambda: (\n", + " y_pred := model.predict(X_test, verbose=0),\n", + " metrics := calculate_metrics(y_test, y_pred),\n", + " print(f\"Out of range: {metrics[0]} predictions\"),\n", + " print(f\"MAPE: {metrics[1]:.2f}%\"),\n", + " print(f\"Within ±10%: {metrics[2]:.2f}%\"),\n", + " print(f\"MAE: {metrics[3]:.2f}\"),\n", + " print(f\"RMSE: {metrics[4]:.2f}\")\n", + " ))()\n", + " ) if epoch % 5 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train, y_train,\n", + " validation_data=(X_test, y_test),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " final_pred = model.predict(X_test, verbose=0)\n", + " metrics = calculate_metrics(y_test, final_pred)\n", + "\n", + " print(\"\\nFinal Model Performance:\")\n", + " print(f\"Out of range predictions: {metrics[0]} ({metrics[0] / len(y_test) * 100:.2f}%)\")\n", + " print(f\"MAPE: {metrics[1]:.2f}%\")\n", + " print(f\"Predictions within ±10%: {metrics[2]:.2f}%\")\n", + " print(f\"MAE: {metrics[3]:.2f}\")\n", + " print(f\"RMSE: {metrics[4]:.2f}\")\n", + "\n", + " plot_training_history(history, folder_name=folder_name)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar radiation predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : numpy.ndarray\n", + " Array of solar radiation predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar radiation predictions\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Create temporary DataFrame with predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarradiation_predicted': predictions.flatten()})\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar radiation column where missing\n", + " df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n", + "\n", + " # Remove temporary column\n", + " df = df.drop('solarradiation_predicted', axis=1)\n", + "\n", + " print(f\"Added {len(predictions)} predictions to dataset\")\n", + " print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1dd1bb91-cdf9-4304-be56-8d55bd5d1148", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar radiation model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 40\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (103798, 24, 40)\n", + "Test data shape: (25933, 24, 40)\n", + "Saving scaler X to: 2024-11-25_13-52_scale_X.joblib\n", + "Saving scaler X to: 2024-11-25_13-52_scale_y.joblib\n", + "Saving features to: 2024-11-25_13-52_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n", + "\n", + "\n", + "print(\"Initializing solar radiation model training...\")\n", + "\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "44749f6a-8941-41e8-8105-c36417245ed5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\n", + "Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n", + "Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-25 13:52:27.432588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n", + "2024-11-25 13:52:28.267692: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"SolarRadiationModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 40)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 4 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n", + " sticDepth) 'multi_head_attention[0][0]']\n", + " \n", + " layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n", + " D) ] \n", + " \n", + " bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_1[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n", + " sticDepth) 'multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_2[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n", + " sticDepth) 'multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n", + " sticDepth) 'multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " dense_4 (Dense) (None, 128) 8320 ['dropout_4[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 128) 512 ['dense_4[0][0]'] \n", + " Normalization) \n", + " \n", + " activation (Activation) (None, 128) 0 ['batch_normalization[0][0]'] \n", + " \n", + " dropout_5 (Dropout) (None, 128) 0 ['activation[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 64) 8256 ['dropout_5[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 64) 256 ['dense_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_1 (Activation) (None, 64) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dropout_6 (Dropout) (None, 64) 0 ['activation_1[0][0]'] \n", + " \n", + " dense_6 (Dense) (None, 32) 2080 ['dropout_6[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 32) 128 ['dense_6[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_2 (Activation) (None, 32) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dropout_7 (Dropout) (None, 32) 0 ['activation_2[0][0]'] \n", + " \n", + " dense_7 (Dense) (None, 1) 33 ['dropout_7[0][0]'] \n", + " \n", + " lambda (Lambda) (None, 1) 0 ['dense_7[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 22263425 (84.93 MB)\n", + "Trainable params: 22262977 (84.93 MB)\n", + "Non-trainable params: 448 (1.75 KB)\n", + "__________________________________________________________________________________________________\n", + "\n", + "4. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-25 13:52:50.667967: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-25 13:52:52.254189: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x759e08d2de90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-25 13:52:52.254217: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-25 13:52:52.258602: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-25 13:52:52.401237: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "540/541 [============================>.] - ETA: 0s - loss: 8.3573 - mse: 0.6705 - mae: 0.5175 - rmse: 0.7610 - custom_mape: 62.5211" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 1 Detailed Metrics:\n", + "541/541 [==============================] - 70s 79ms/step - loss: 8.3541 - mse: 0.6698 - mae: 0.5171 - rmse: 0.7602 - custom_mape: 62.5082 - val_loss: 5.5029 - val_mse: 0.1555 - val_mae: 0.2236 - val_rmse: 0.3853 - val_custom_mape: 33.0501 - lr: 1.3120e-04\n", + "Epoch 2/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 4.8835 - mse: 0.1649 - mae: 0.2351 - rmse: 0.3909 - custom_mape: 34.9535 - val_loss: 4.0981 - val_mse: 0.1782 - val_mae: 0.2380 - val_rmse: 0.3937 - val_custom_mape: 32.7671 - lr: 2.6891e-04\n", + "Epoch 3/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 2.8253 - mse: 0.1180 - mae: 0.1955 - rmse: 0.3321 - custom_mape: 30.7958 - val_loss: 2.0010 - val_mse: 0.0838 - val_mae: 0.1619 - val_rmse: 0.2713 - val_custom_mape: 28.0167 - lr: 2.1053e-04\n", + "Epoch 4/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 1.6423 - mse: 0.0603 - mae: 0.1361 - rmse: 0.2385 - custom_mape: 24.5993 - val_loss: 1.4116 - val_mse: 0.0688 - val_mae: 0.1494 - val_rmse: 0.2560 - val_custom_mape: 26.3951 - lr: 1.0081e-04\n", + "Epoch 5/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 1.3057 - mse: 0.0424 - mae: 0.1127 - rmse: 0.2007 - custom_mape: 21.8917 - val_loss: 1.2564 - val_mse: 0.0442 - val_mae: 0.1149 - val_rmse: 0.2065 - val_custom_mape: 22.3474 - lr: 1.4334e-05\n", + "Epoch 6/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 1.1928 - mse: 0.0425 - mae: 0.1130 - rmse: 0.2005 - custom_mape: 21.7451\n", + "Epoch 6 Detailed Metrics:\n", + "541/541 [==============================] - 39s 72ms/step - loss: 1.1925 - mse: 0.0425 - mae: 0.1129 - rmse: 0.2003 - custom_mape: 21.7690 - val_loss: 1.0131 - val_mse: 0.0528 - val_mae: 0.1265 - val_rmse: 0.2226 - val_custom_mape: 22.8960 - lr: 2.4076e-04\n", + "Epoch 7/100\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.8364 - mse: 0.0590 - mae: 0.1341 - rmse: 0.2334 - custom_mape: 24.2004 - val_loss: 0.6962 - val_mse: 0.0741 - val_mae: 0.1525 - val_rmse: 0.2596 - val_custom_mape: 25.3537 - lr: 2.2058e-04\n", + "Epoch 8/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.5898 - mse: 0.0509 - mae: 0.1241 - rmse: 0.2155 - custom_mape: 23.1834 - val_loss: 0.5431 - val_mse: 0.0950 - val_mae: 0.2095 - val_rmse: 0.3062 - val_custom_mape: 37.3372 - lr: 1.8277e-04\n", + "Epoch 9/100\n", + "541/541 [==============================] - 40s 74ms/step - loss: 0.4529 - mse: 0.0402 - mae: 0.1098 - rmse: 0.1931 - custom_mape: 21.1223 - val_loss: 0.4038 - val_mse: 0.0302 - val_mae: 0.0935 - val_rmse: 0.1677 - val_custom_mape: 20.0386 - lr: 1.3408e-04\n", + "Epoch 10/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.3761 - mse: 0.0310 - mae: 0.0958 - rmse: 0.1704 - custom_mape: 19.7314 - val_loss: 0.3586 - val_mse: 0.0451 - val_mae: 0.1141 - val_rmse: 0.1979 - val_custom_mape: 20.7993 - lr: 8.3141e-05\n", + "Epoch 11/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.3347 - mse: 0.0242 - mae: 0.0840 - rmse: 0.1510 - custom_mape: 18.0393\n", + "Epoch 11 Detailed Metrics:\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.3347 - mse: 0.0242 - mae: 0.0840 - rmse: 0.1509 - custom_mape: 18.0607 - val_loss: 0.3301 - val_mse: 0.0397 - val_mae: 0.1035 - val_rmse: 0.1812 - val_custom_mape: 18.6451 - lr: 3.9028e-05\n", + "Epoch 12/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.3175 - mse: 0.0224 - mae: 0.0806 - rmse: 0.1453 - custom_mape: 17.6096 - val_loss: 0.3190 - val_mse: 0.0345 - val_mae: 0.0967 - val_rmse: 0.1716 - val_custom_mape: 18.2344 - lr: 9.5830e-06\n", + "Epoch 13/100\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.3131 - mse: 0.0225 - mae: 0.0811 - rmse: 0.1450 - custom_mape: 17.6923 - val_loss: 0.3130 - val_mse: 0.0311 - val_mae: 0.0966 - val_rmse: 0.1710 - val_custom_mape: 20.7828 - lr: 2.1869e-04\n", + "Epoch 14/100\n", + "541/541 [==============================] - 38s 71ms/step - loss: 0.2933 - mse: 0.0562 - mae: 0.1310 - rmse: 0.2245 - custom_mape: 24.0791 - val_loss: 0.2675 - val_mse: 0.0672 - val_mae: 0.1489 - val_rmse: 0.2424 - val_custom_mape: 29.0105 - lr: 2.1594e-04\n", + "Epoch 15/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.2300 - mse: 0.0400 - mae: 0.1099 - rmse: 0.1920 - custom_mape: 21.2963 - val_loss: 0.2111 - val_mse: 0.0450 - val_mae: 0.1232 - val_rmse: 0.2018 - val_custom_mape: 25.0584 - lr: 2.0840e-04\n", + "Epoch 16/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.1924 - mse: 0.0436 - mae: 0.1148 - rmse: 0.1990 - custom_mape: 21.9478\n", + "Epoch 16 Detailed Metrics:\n", + "541/541 [==============================] - 40s 73ms/step - loss: 0.1924 - mse: 0.0436 - mae: 0.1148 - rmse: 0.1990 - custom_mape: 21.9478 - val_loss: 0.1791 - val_mse: 0.0464 - val_mae: 0.1232 - val_rmse: 0.2052 - val_custom_mape: 24.7895 - lr: 1.9641e-04\n", + "Epoch 17/100\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.1588 - mse: 0.0344 - mae: 0.1019 - rmse: 0.1778 - custom_mape: 20.1684 - val_loss: 0.1435 - val_mse: 0.0285 - val_mae: 0.0913 - val_rmse: 0.1615 - val_custom_mape: 19.9306 - lr: 1.8050e-04\n", + "Epoch 18/100\n", + "541/541 [==============================] - 38s 69ms/step - loss: 0.1359 - mse: 0.0305 - mae: 0.0962 - rmse: 0.1677 - custom_mape: 19.8060 - val_loss: 0.1478 - val_mse: 0.0771 - val_mae: 0.1585 - val_rmse: 0.2474 - val_custom_mape: 27.4152 - lr: 1.6139e-04\n", + "Epoch 19/100\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.1209 - mse: 0.0312 - mae: 0.0959 - rmse: 0.1679 - custom_mape: 19.2837 - val_loss: 0.1135 - val_mse: 0.0329 - val_mae: 0.0929 - val_rmse: 0.1644 - val_custom_mape: 19.0066 - lr: 1.3994e-04\n", + "Epoch 20/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.1055 - mse: 0.0236 - mae: 0.0839 - rmse: 0.1472 - custom_mape: 18.0202 - val_loss: 0.1032 - val_mse: 0.0308 - val_mae: 0.0911 - val_rmse: 0.1598 - val_custom_mape: 19.2490 - lr: 1.1712e-04\n", + "Epoch 21/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0994 - mse: 0.0271 - mae: 0.0897 - rmse: 0.1563 - custom_mape: 18.7032\n", + "Epoch 21 Detailed Metrics:\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0994 - mse: 0.0271 - mae: 0.0897 - rmse: 0.1563 - custom_mape: 18.7032 - val_loss: 0.1014 - val_mse: 0.0343 - val_mae: 0.1057 - val_rmse: 0.1742 - val_custom_mape: 24.6971 - lr: 9.3944e-05\n", + "Epoch 22/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0895 - mse: 0.0197 - mae: 0.0768 - rmse: 0.1354 - custom_mape: 17.1486 - val_loss: 0.0911 - val_mse: 0.0286 - val_mae: 0.0896 - val_rmse: 0.1566 - val_custom_mape: 19.0230 - lr: 7.1464e-05\n", + "Epoch 23/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0833 - mse: 0.0167 - mae: 0.0708 - rmse: 0.1252 - custom_mape: 16.4218 - val_loss: 0.0898 - val_mse: 0.0347 - val_mae: 0.0968 - val_rmse: 0.1657 - val_custom_mape: 17.8852 - lr: 5.0687e-05\n", + "Epoch 24/100\n", + "541/541 [==============================] - 38s 69ms/step - loss: 0.0798 - mse: 0.0159 - mae: 0.0689 - rmse: 0.1217 - custom_mape: 16.0625 - val_loss: 0.0820 - val_mse: 0.0250 - val_mae: 0.0791 - val_rmse: 0.1418 - val_custom_mape: 16.7331 - lr: 3.2548e-05\n", + "Epoch 25/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0767 - mse: 0.0140 - mae: 0.0647 - rmse: 0.1146 - custom_mape: 15.6304 - val_loss: 0.0798 - val_mse: 0.0229 - val_mae: 0.0763 - val_rmse: 0.1377 - val_custom_mape: 16.6235 - lr: 1.7863e-05\n", + "Epoch 26/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0757 - mse: 0.0140 - mae: 0.0646 - rmse: 0.1144 - custom_mape: 15.5670\n", + "Epoch 26 Detailed Metrics:\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.0757 - mse: 0.0140 - mae: 0.0646 - rmse: 0.1143 - custom_mape: 15.5871 - val_loss: 0.0793 - val_mse: 0.0233 - val_mae: 0.0766 - val_rmse: 0.1381 - val_custom_mape: 16.4359 - lr: 7.2916e-06\n", + "Epoch 27/100\n", + "541/541 [==============================] - 35s 64ms/step - loss: 0.0754 - mse: 0.0142 - mae: 0.0650 - rmse: 0.1150 - custom_mape: 15.6099 - val_loss: 0.0796 - val_mse: 0.0245 - val_mae: 0.0785 - val_rmse: 0.1406 - val_custom_mape: 16.5037 - lr: 1.3093e-06\n", + "Epoch 28/100\n", + "541/541 [==============================] - 35s 65ms/step - loss: 0.0753 - mse: 0.0146 - mae: 0.0660 - rmse: 0.1168 - custom_mape: 15.6841 - val_loss: 0.0847 - val_mse: 0.0333 - val_mae: 0.1012 - val_rmse: 0.1737 - val_custom_mape: 20.9143 - lr: 1.9679e-04\n", + "Epoch 29/100\n", + "541/541 [==============================] - 35s 66ms/step - loss: 0.0901 - mse: 0.0467 - mae: 0.1176 - rmse: 0.2031 - custom_mape: 22.1549 - val_loss: 0.1183 - val_mse: 0.1009 - val_mae: 0.1867 - val_rmse: 0.2930 - val_custom_mape: 30.4890 - lr: 1.9593e-04\n", + "Epoch 30/100\n", + "541/541 [==============================] - 36s 66ms/step - loss: 0.0798 - mse: 0.0364 - mae: 0.1057 - rmse: 0.1827 - custom_mape: 21.1398 - val_loss: 0.0919 - val_mse: 0.0843 - val_mae: 0.1507 - val_rmse: 0.2669 - val_custom_mape: 23.3218 - lr: 1.9398e-04\n", + "Epoch 31/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0720 - mse: 0.0336 - mae: 0.1008 - rmse: 0.1748 - custom_mape: 19.9648\n", + "Epoch 31 Detailed Metrics:\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0720 - mse: 0.0336 - mae: 0.1008 - rmse: 0.1748 - custom_mape: 19.9648 - val_loss: 0.0618 - val_mse: 0.0238 - val_mae: 0.0780 - val_rmse: 0.1408 - val_custom_mape: 16.9390 - lr: 1.9095e-04\n", + "Epoch 32/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0655 - mse: 0.0299 - mae: 0.0960 - rmse: 0.1657 - custom_mape: 19.8846 - val_loss: 0.0781 - val_mse: 0.0562 - val_mae: 0.1387 - val_rmse: 0.2230 - val_custom_mape: 25.8359 - lr: 1.8687e-04\n", + "Epoch 33/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0630 - mse: 0.0322 - mae: 0.0984 - rmse: 0.1708 - custom_mape: 19.9807 - val_loss: 0.0745 - val_mse: 0.0661 - val_mae: 0.1394 - val_rmse: 0.2340 - val_custom_mape: 22.5655 - lr: 1.8180e-04\n", + "Epoch 34/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0694 - mse: 0.0437 - mae: 0.1127 - rmse: 0.1919 - custom_mape: 21.6774 - val_loss: 0.0740 - val_mse: 0.0535 - val_mae: 0.1283 - val_rmse: 0.2119 - val_custom_mape: 21.3689 - lr: 1.7579e-04\n", + "Epoch 35/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0605 - mse: 0.0311 - mae: 0.0963 - rmse: 0.1663 - custom_mape: 19.5287 - val_loss: 0.0686 - val_mse: 0.0554 - val_mae: 0.1297 - val_rmse: 0.2094 - val_custom_mape: 21.9974 - lr: 1.6890e-04\n", + "Epoch 36/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0568 - mse: 0.0311 - mae: 0.0973 - rmse: 0.1681 - custom_mape: 19.8627\n", + "Epoch 36 Detailed Metrics:\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.0568 - mse: 0.0311 - mae: 0.0973 - rmse: 0.1681 - custom_mape: 19.8627 - val_loss: 0.0517 - val_mse: 0.0246 - val_mae: 0.0877 - val_rmse: 0.1521 - val_custom_mape: 19.8935 - lr: 1.6122e-04\n", + "Epoch 37/100\n", + "541/541 [==============================] - 40s 73ms/step - loss: 0.0503 - mse: 0.0251 - mae: 0.0868 - rmse: 0.1509 - custom_mape: 18.7631 - val_loss: 0.0632 - val_mse: 0.0557 - val_mae: 0.1318 - val_rmse: 0.2154 - val_custom_mape: 22.4402 - lr: 1.5284e-04\n", + "Epoch 38/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0522 - mse: 0.0322 - mae: 0.0988 - rmse: 0.1711 - custom_mape: 19.8555 - val_loss: 0.0561 - val_mse: 0.0366 - val_mae: 0.1139 - val_rmse: 0.1888 - val_custom_mape: 23.3929 - lr: 1.4383e-04\n", + "Epoch 39/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0458 - mse: 0.0236 - mae: 0.0847 - rmse: 0.1476 - custom_mape: 18.3107 - val_loss: 0.0417 - val_mse: 0.0218 - val_mae: 0.0746 - val_rmse: 0.1359 - val_custom_mape: 17.6771 - lr: 1.3432e-04\n", + "Epoch 40/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0430 - mse: 0.0217 - mae: 0.0815 - rmse: 0.1418 - custom_mape: 18.0985 - val_loss: 0.0502 - val_mse: 0.0404 - val_mae: 0.1058 - val_rmse: 0.1835 - val_custom_mape: 20.7356 - lr: 1.2440e-04\n", + "Epoch 41/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0426 - mse: 0.0228 - mae: 0.0833 - rmse: 0.1452 - custom_mape: 18.0141\n", + "Epoch 41 Detailed Metrics:\n", + "541/541 [==============================] - 36s 66ms/step - loss: 0.0426 - mse: 0.0228 - mae: 0.0832 - rmse: 0.1451 - custom_mape: 18.0266 - val_loss: 0.0439 - val_mse: 0.0246 - val_mae: 0.0896 - val_rmse: 0.1506 - val_custom_mape: 20.5262 - lr: 1.1419e-04\n", + "Epoch 42/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0398 - mse: 0.0200 - mae: 0.0783 - rmse: 0.1361 - custom_mape: 17.6098 - val_loss: 0.0454 - val_mse: 0.0335 - val_mae: 0.0979 - val_rmse: 0.1629 - val_custom_mape: 18.9381 - lr: 1.0381e-04\n", + "Epoch 43/100\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.0390 - mse: 0.0205 - mae: 0.0789 - rmse: 0.1381 - custom_mape: 17.4761 - val_loss: 0.0389 - val_mse: 0.0225 - val_mae: 0.0795 - val_rmse: 0.1419 - val_custom_mape: 18.9360 - lr: 9.3356e-05\n", + "Epoch 44/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0357 - mse: 0.0165 - mae: 0.0713 - rmse: 0.1243 - custom_mape: 16.7496 - val_loss: 0.0473 - val_mse: 0.0405 - val_mae: 0.1093 - val_rmse: 0.1787 - val_custom_mape: 19.7927 - lr: 8.2964e-05\n", + "Epoch 45/100\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.0371 - mse: 0.0196 - mae: 0.0776 - rmse: 0.1349 - custom_mape: 17.3625 - val_loss: 0.0400 - val_mse: 0.0258 - val_mae: 0.0872 - val_rmse: 0.1522 - val_custom_mape: 19.9462 - lr: 7.2747e-05\n", + "Epoch 46/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0330 - mse: 0.0143 - mae: 0.0665 - rmse: 0.1162 - custom_mape: 16.2204\n", + "Epoch 46 Detailed Metrics:\n", + "541/541 [==============================] - 41s 77ms/step - loss: 0.0330 - mse: 0.0143 - mae: 0.0665 - rmse: 0.1162 - custom_mape: 16.2204 - val_loss: 0.0364 - val_mse: 0.0220 - val_mae: 0.0783 - val_rmse: 0.1340 - val_custom_mape: 17.0947 - lr: 6.2818e-05\n", + "Epoch 47/100\n", + "541/541 [==============================] - 41s 76ms/step - loss: 0.0333 - mse: 0.0154 - mae: 0.0690 - rmse: 0.1205 - custom_mape: 16.3305 - val_loss: 0.0363 - val_mse: 0.0243 - val_mae: 0.0790 - val_rmse: 0.1431 - val_custom_mape: 17.9395 - lr: 5.3291e-05\n", + "Epoch 48/100\n", + "541/541 [==============================] - 41s 77ms/step - loss: 0.0307 - mse: 0.0126 - mae: 0.0622 - rmse: 0.1091 - custom_mape: 15.5196 - val_loss: 0.0337 - val_mse: 0.0201 - val_mae: 0.0724 - val_rmse: 0.1290 - val_custom_mape: 16.5807 - lr: 4.4272e-05\n", + "Epoch 49/100\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.0302 - mse: 0.0125 - mae: 0.0617 - rmse: 0.1083 - custom_mape: 15.3759 - val_loss: 0.0353 - val_mse: 0.0231 - val_mae: 0.0786 - val_rmse: 0.1369 - val_custom_mape: 17.0512 - lr: 3.5864e-05\n", + "Epoch 50/100\n", + "541/541 [==============================] - 41s 75ms/step - loss: 0.0289 - mse: 0.0113 - mae: 0.0588 - rmse: 0.1033 - custom_mape: 15.0117 - val_loss: 0.0328 - val_mse: 0.0194 - val_mae: 0.0715 - val_rmse: 0.1260 - val_custom_mape: 16.4202 - lr: 2.8161e-05\n", + "Epoch 51/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0283 - mse: 0.0110 - mae: 0.0576 - rmse: 0.1014 - custom_mape: 14.7889\n", + "Epoch 51 Detailed Metrics:\n", + "541/541 [==============================] - 43s 80ms/step - loss: 0.0283 - mse: 0.0110 - mae: 0.0576 - rmse: 0.1014 - custom_mape: 14.8090 - val_loss: 0.0329 - val_mse: 0.0200 - val_mae: 0.0725 - val_rmse: 0.1267 - val_custom_mape: 16.1412 - lr: 2.1251e-05\n", + "Epoch 52/100\n", + "541/541 [==============================] - 41s 76ms/step - loss: 0.0279 - mse: 0.0106 - mae: 0.0570 - rmse: 0.1001 - custom_mape: 14.7309 - val_loss: 0.0326 - val_mse: 0.0199 - val_mae: 0.0721 - val_rmse: 0.1267 - val_custom_mape: 16.1540 - lr: 1.5210e-05\n", + "Epoch 53/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0277 - mse: 0.0105 - mae: 0.0567 - rmse: 0.0993 - custom_mape: 14.6780 - val_loss: 0.0329 - val_mse: 0.0206 - val_mae: 0.0734 - val_rmse: 0.1285 - val_custom_mape: 16.2771 - lr: 1.0107e-05\n", + "Epoch 54/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0275 - mse: 0.0103 - mae: 0.0562 - rmse: 0.0986 - custom_mape: 14.6906 - val_loss: 0.0322 - val_mse: 0.0195 - val_mae: 0.0713 - val_rmse: 0.1258 - val_custom_mape: 16.1401 - lr: 5.9992e-06\n", + "Epoch 55/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0275 - mse: 0.0105 - mae: 0.0565 - rmse: 0.0993 - custom_mape: 14.7210 - val_loss: 0.0325 - val_mse: 0.0202 - val_mae: 0.0724 - val_rmse: 0.1279 - val_custom_mape: 16.1922 - lr: 2.9336e-06\n", + "Epoch 56/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0276 - mse: 0.0105 - mae: 0.0568 - rmse: 0.0996 - custom_mape: 14.7010\n", + "Epoch 56 Detailed Metrics:\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0276 - mse: 0.0105 - mae: 0.0568 - rmse: 0.0996 - custom_mape: 14.7231 - val_loss: 0.0320 - val_mse: 0.0194 - val_mae: 0.0710 - val_rmse: 0.1254 - val_custom_mape: 16.0903 - lr: 9.4439e-07\n", + "Epoch 57/100\n", + "541/541 [==============================] - 36s 66ms/step - loss: 0.0274 - mse: 0.0103 - mae: 0.0562 - rmse: 0.0985 - custom_mape: 14.6804 - val_loss: 0.0318 - val_mse: 0.0191 - val_mae: 0.0703 - val_rmse: 0.1246 - val_custom_mape: 16.0085 - lr: 5.4016e-08\n", + "Epoch 58/100\n", + "541/541 [==============================] - 34s 63ms/step - loss: 0.0322 - mse: 0.0176 - mae: 0.0715 - rmse: 0.1250 - custom_mape: 16.7714 - val_loss: 0.0412 - val_mse: 0.0307 - val_mae: 0.0991 - val_rmse: 0.1727 - val_custom_mape: 21.0792 - lr: 1.7709e-04\n", + "Epoch 59/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0563 - mse: 0.0543 - mae: 0.1225 - rmse: 0.2110 - custom_mape: 22.3851 - val_loss: 0.0627 - val_mse: 0.0621 - val_mae: 0.1421 - val_rmse: 0.2244 - val_custom_mape: 26.3625 - lr: 1.7679e-04\n", + "Epoch 60/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0465 - mse: 0.0347 - mae: 0.1009 - rmse: 0.1759 - custom_mape: 19.8500 - val_loss: 0.0514 - val_mse: 0.0437 - val_mae: 0.1161 - val_rmse: 0.1923 - val_custom_mape: 23.5605 - lr: 1.7624e-04\n", + "Epoch 61/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0416 - mse: 0.0273 - mae: 0.0918 - rmse: 0.1597 - custom_mape: 19.2484\n", + "Epoch 61 Detailed Metrics:\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0415 - mse: 0.0273 - mae: 0.0917 - rmse: 0.1597 - custom_mape: 19.2691 - val_loss: 0.0561 - val_mse: 0.0538 - val_mae: 0.1366 - val_rmse: 0.2197 - val_custom_mape: 25.7023 - lr: 1.7545e-04\n", + "Epoch 62/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0425 - mse: 0.0304 - mae: 0.0970 - rmse: 0.1667 - custom_mape: 20.2351\n", + "Epoch 62: ReduceLROnPlateau reducing learning rate to 3.4881295869126915e-05.\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.0425 - mse: 0.0304 - mae: 0.0970 - rmse: 0.1667 - custom_mape: 20.2351 - val_loss: 0.0451 - val_mse: 0.0344 - val_mae: 0.1058 - val_rmse: 0.1754 - val_custom_mape: 22.0628 - lr: 1.7441e-04\n", + "Epoch 63/100\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0409 - mse: 0.0288 - mae: 0.0934 - rmse: 0.1608 - custom_mape: 19.4842 - val_loss: 0.0553 - val_mse: 0.0597 - val_mae: 0.1402 - val_rmse: 0.2277 - val_custom_mape: 24.8888 - lr: 1.7312e-04\n", + "Epoch 64/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0392 - mse: 0.0268 - mae: 0.0898 - rmse: 0.1561 - custom_mape: 18.8321 - val_loss: 0.0483 - val_mse: 0.0360 - val_mae: 0.1207 - val_rmse: 0.1853 - val_custom_mape: 30.6992 - lr: 1.7160e-04\n", + "Epoch 65/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0367 - mse: 0.0234 - mae: 0.0850 - rmse: 0.1474 - custom_mape: 18.5001 - val_loss: 0.0611 - val_mse: 0.0787 - val_mae: 0.1592 - val_rmse: 0.2557 - val_custom_mape: 27.1302 - lr: 1.6985e-04\n", + "Epoch 66/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0354 - mse: 0.0231 - mae: 0.0835 - rmse: 0.1455 - custom_mape: 18.0659\n", + "Epoch 66 Detailed Metrics:\n", + "541/541 [==============================] - 38s 70ms/step - loss: 0.0355 - mse: 0.0231 - mae: 0.0835 - rmse: 0.1456 - custom_mape: 18.1011 - val_loss: 0.0308 - val_mse: 0.0148 - val_mae: 0.0698 - val_rmse: 0.1188 - val_custom_mape: 19.3388 - lr: 1.6787e-04\n", + "Epoch 67/100\n", + "541/541 [==============================] - 34s 63ms/step - loss: 0.0379 - mse: 0.0272 - mae: 0.0913 - rmse: 0.1574 - custom_mape: 19.2562 - val_loss: 0.0670 - val_mse: 0.0740 - val_mae: 0.1756 - val_rmse: 0.2696 - val_custom_mape: 35.2934 - lr: 1.6566e-04\n", + "Epoch 68/100\n", + "541/541 [==============================] - 36s 66ms/step - loss: 0.0367 - mse: 0.0258 - mae: 0.0874 - rmse: 0.1516 - custom_mape: 18.6506 - val_loss: 0.0364 - val_mse: 0.0239 - val_mae: 0.0878 - val_rmse: 0.1462 - val_custom_mape: 22.8155 - lr: 1.6323e-04\n", + "Epoch 69/100\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0351 - mse: 0.0232 - mae: 0.0840 - rmse: 0.1458 - custom_mape: 18.4729 - val_loss: 0.0440 - val_mse: 0.0429 - val_mae: 0.1105 - val_rmse: 0.1929 - val_custom_mape: 22.5552 - lr: 1.6060e-04\n", + "Epoch 70/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0373 - mse: 0.0266 - mae: 0.0880 - rmse: 0.1523 - custom_mape: 18.6093 - val_loss: 0.0342 - val_mse: 0.0220 - val_mae: 0.0808 - val_rmse: 0.1415 - val_custom_mape: 18.8001 - lr: 1.5776e-04\n", + "Epoch 71/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0285 - mse: 0.0140 - mae: 0.0653 - rmse: 0.1143 - custom_mape: 15.8762\n", + "Epoch 71 Detailed Metrics:\n", + "541/541 [==============================] - 38s 69ms/step - loss: 0.0285 - mse: 0.0140 - mae: 0.0653 - rmse: 0.1142 - custom_mape: 15.8894 - val_loss: 0.0261 - val_mse: 0.0135 - val_mae: 0.0598 - val_rmse: 0.1085 - val_custom_mape: 16.6281 - lr: 1.5473e-04\n", + "Epoch 72/100\n", + "541/541 [==============================] - 39s 71ms/step - loss: 0.0314 - mse: 0.0208 - mae: 0.0773 - rmse: 0.1348 - custom_mape: 17.4662 - val_loss: 0.1001 - val_mse: 0.1225 - val_mae: 0.2531 - val_rmse: 0.3373 - val_custom_mape: 45.8354 - lr: 1.5151e-04\n", + "Epoch 73/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0338 - mse: 0.0232 - mae: 0.0844 - rmse: 0.1457 - custom_mape: 18.5363 - val_loss: 0.0489 - val_mse: 0.0599 - val_mae: 0.1301 - val_rmse: 0.2211 - val_custom_mape: 21.9077 - lr: 1.4811e-04\n", + "Epoch 74/100\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.0353 - mse: 0.0258 - mae: 0.0890 - rmse: 0.1551 - custom_mape: 18.6775 - val_loss: 0.0340 - val_mse: 0.0259 - val_mae: 0.0851 - val_rmse: 0.1516 - val_custom_mape: 19.9781 - lr: 1.4454e-04\n", + "Epoch 75/100\n", + "541/541 [==============================] - 39s 73ms/step - loss: 0.0317 - mse: 0.0205 - mae: 0.0790 - rmse: 0.1372 - custom_mape: 17.6368 - val_loss: 0.0631 - val_mse: 0.0685 - val_mae: 0.1722 - val_rmse: 0.2580 - val_custom_mape: 38.4844 - lr: 1.4082e-04\n", + "Epoch 76/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0397 - mse: 0.0317 - mae: 0.0969 - rmse: 0.1681 - custom_mape: 19.9562\n", + "Epoch 76: ReduceLROnPlateau reducing learning rate to 2.738986222539097e-05.\n", + "\n", + "Epoch 76 Detailed Metrics:\n", + "541/541 [==============================] - 38s 71ms/step - loss: 0.0397 - mse: 0.0317 - mae: 0.0969 - rmse: 0.1679 - custom_mape: 19.9689 - val_loss: 0.0408 - val_mse: 0.0323 - val_mae: 0.1042 - val_rmse: 0.1719 - val_custom_mape: 20.9799 - lr: 1.3695e-04\n", + "Epoch 77/100\n", + "541/541 [==============================] - 38s 69ms/step - loss: 0.0328 - mse: 0.0208 - mae: 0.0798 - rmse: 0.1392 - custom_mape: 17.8401 - val_loss: 0.0362 - val_mse: 0.0293 - val_mae: 0.0922 - val_rmse: 0.1596 - val_custom_mape: 19.6859 - lr: 1.3294e-04\n", + "Epoch 78/100\n", + "541/541 [==============================] - 37s 69ms/step - loss: 0.0295 - mse: 0.0172 - mae: 0.0724 - rmse: 0.1262 - custom_mape: 16.6377 - val_loss: 0.0507 - val_mse: 0.0652 - val_mae: 0.1377 - val_rmse: 0.2239 - val_custom_mape: 21.8391 - lr: 1.2881e-04\n", + "Epoch 79/100\n", + "541/541 [==============================] - 39s 73ms/step - loss: 0.0313 - mse: 0.0205 - mae: 0.0789 - rmse: 0.1380 - custom_mape: 17.4685 - val_loss: 0.0287 - val_mse: 0.0193 - val_mae: 0.0717 - val_rmse: 0.1272 - val_custom_mape: 17.1353 - lr: 1.2456e-04\n", + "Epoch 80/100\n", + "541/541 [==============================] - 40s 75ms/step - loss: 0.0291 - mse: 0.0180 - mae: 0.0739 - rmse: 0.1289 - custom_mape: 16.8870 - val_loss: 0.0446 - val_mse: 0.0404 - val_mae: 0.1205 - val_rmse: 0.1947 - val_custom_mape: 24.6887 - lr: 1.2022e-04\n", + "Epoch 81/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0313 - mse: 0.0210 - mae: 0.0805 - rmse: 0.1399 - custom_mape: 17.9034\n", + "Epoch 81 Detailed Metrics:\n", + "541/541 [==============================] - 39s 72ms/step - loss: 0.0313 - mse: 0.0210 - mae: 0.0805 - rmse: 0.1398 - custom_mape: 17.9156 - val_loss: 0.0343 - val_mse: 0.0271 - val_mae: 0.0901 - val_rmse: 0.1555 - val_custom_mape: 22.6929 - lr: 1.1578e-04\n", + "Epoch 82/100\n", + "541/541 [==============================] - 37s 68ms/step - loss: 0.0279 - mse: 0.0161 - mae: 0.0706 - rmse: 0.1234 - custom_mape: 16.5048 - val_loss: 0.0382 - val_mse: 0.0389 - val_mae: 0.1040 - val_rmse: 0.1719 - val_custom_mape: 18.8728 - lr: 1.1127e-04\n", + "Epoch 83/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0283 - mse: 0.0175 - mae: 0.0729 - rmse: 0.1278 - custom_mape: 16.6226\n", + "Epoch 83: ReduceLROnPlateau reducing learning rate to 2.133804955519736e-05.\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0283 - mse: 0.0175 - mae: 0.0729 - rmse: 0.1278 - custom_mape: 16.6437 - val_loss: 0.0371 - val_mse: 0.0295 - val_mae: 0.1006 - val_rmse: 0.1693 - val_custom_mape: 21.9720 - lr: 1.0669e-04\n", + "Epoch 84/100\n", + "541/541 [==============================] - 36s 66ms/step - loss: 0.0280 - mse: 0.0170 - mae: 0.0721 - rmse: 0.1260 - custom_mape: 17.0158 - val_loss: 0.0318 - val_mse: 0.0262 - val_mae: 0.0849 - val_rmse: 0.1485 - val_custom_mape: 17.9378 - lr: 1.0206e-04\n", + "Epoch 85/100\n", + "541/541 [==============================] - 38s 71ms/step - loss: 0.0278 - mse: 0.0171 - mae: 0.0723 - rmse: 0.1257 - custom_mape: 17.1040 - val_loss: 0.0441 - val_mse: 0.0515 - val_mae: 0.1231 - val_rmse: 0.1994 - val_custom_mape: 20.8031 - lr: 9.7396e-05\n", + "Epoch 86/100\n", + "540/541 [============================>.] - ETA: 0s - loss: 0.0282 - mse: 0.0176 - mae: 0.0734 - rmse: 0.1280 - custom_mape: 16.8326Restoring model weights from the end of the best epoch: 71.\n", + "\n", + "Epoch 86 Detailed Metrics:\n", + "541/541 [==============================] - 36s 67ms/step - loss: 0.0282 - mse: 0.0176 - mae: 0.0734 - rmse: 0.1279 - custom_mape: 16.8449 - val_loss: 0.0324 - val_mse: 0.0245 - val_mae: 0.0870 - val_rmse: 0.1518 - val_custom_mape: 19.8808 - lr: 9.2705e-05\n", + "Epoch 86: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Final Model Performance:\n", + "Out of range predictions: 14036 (54.12%)\n", + "MAPE: 16.61%\n", + "Predictions within ±10%: 68.85%\n", + "MAE: 0.06\n", + "RMSE: 0.12\n", + "\n", + "Training history plot saved as: 2024-11-25_13-52_training_history.png\n", + "Training history data saved as: 2024-11-25_13-52_training_history.csv\n", + "Final statistics saved as: 2024-11-25_13-52_training_stats.json\n", + "\n", + "Final Training Statistics:\n", + "Final Loss (train/val): 0.0282/0.0324\n", + "Final MAE (train/val): 0.0734/0.0870\n", + "Best validation loss: 0.0261\n", + "Best validation MAE: 0.0598\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "max_val = df['solarradiation'].max()\n", + "\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 15\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "model = create_solarradiation_model(input_shape=input_shape, folder_name=folder_name, min_output=min_val_scaled, max_output=max_val_scaled)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=192,\n", + " folder_name=folder_name\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ad6226ea-ab01-47aa-9571-52ea9e654c01", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "811/811 [==============================] - 9s 10ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Radiation Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 25.05 W/m²\n", + "RMSE: 39.70 W/m²\n", + "R² Score: 0.977\n", + "MAPE: 20.66%\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 W/m²: 7.5%\n", + "Within ±10 W/m²: 13.3%\n", + "Within ±20 W/m²: 68.6%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 89.0%\n", + "\n", + "Confusion Matrix for Radiation Levels:\n", + " Very Low Low Moderate High Very High Extreme\n", + "Very Low 0 10 0 0 0 0\n", + "Low 0 1710 0 111 0 0\n", + "Moderate 0 0 2171 434 0 256\n", + "High 0 374 171 1751 0 0\n", + "Very High 0 1136 0 1 0 0\n", + "Extreme 0 0 352 0 0 17456\n", + "\n", + "Plot saved as: 2024-11-25_13-52_radiation_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: 1.543\n", + "Error standard deviation: 39.674\n", + "Median error: 12.000\n", + "95th percentile absolute error: 86.014\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "predictions = np.clip(predictions, 0, 11)\n", + "\n", + "predictions_original = scaler_y.inverse_transform(predictions)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "metrics = evaluate_solarradiation_predictions(y_test_original, predictions_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'final_loss': float(history.history['val_loss'][-1]),\n", + " 'final_mae': float(history.history['val_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2c42461d-b189-4dc0-81da-4eb4879b9135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 70s 10ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "Added 227879 predictions to dataset\n", + "Rows with solar radiation after integration: 357615\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "to_predict_predictions = np.clip(to_predict_predictions, 0, 11)\n", + "\n", + "to_predict_predictions = scaler_y.inverse_transform(to_predict_predictions)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), to_predict_predictions)\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(to_predict_predictions),\n", + " 'mean_predicted_solarradiation': float(to_predict_predictions.mean()),\n", + " 'min_predicted_solarradiation': float(to_predict_predictions.min()),\n", + " 'max_predicted_solarradiation': float(to_predict_predictions.max()),\n", + "}\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "914f7330-d1b9-474d-8605-c0de2aefe087", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Radiation:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 59,357.0000\n", + "mean : 181.9066\n", + "median : 12.0000\n", + "std : 249.6366\n", + "min : 0.0000\n", + "max : 1,113.0000\n", + "skewness : 1.2630\n", + "kurtosis : 0.2932\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 12.0000\n", + "percentile_50 : 12.0000\n", + "percentile_75 : 316.1503\n", + "percentile_90 : 630.4008\n", + "percentile_95 : 727.4549\n", + "percentile_99 : 862.0000\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (16.60%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 59357,\n", + " 'mean': 181.90659489328456,\n", + " 'median': 12.0,\n", + " 'std': 249.63664428001422,\n", + " 'min': 0.0,\n", + " 'max': 1113.0,\n", + " 'skewness': 1.262984127717114,\n", + " 'kurtosis': 0.2932425990070442,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 12.0,\n", + " 'percentile_50': 12.0,\n", + " 'percentile_75': 316.15032958984375,\n", + " 'percentile_90': 630.4008203125001,\n", + " 'percentile_95': 727.4549438476562,\n", + " 'percentile_99': 862.0}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-25_14-57_error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error Statistics:\n", + "MAE: 0.0740\n", + "MSE: 0.0138\n", + "RMSE: 0.1173\n", + "Mean error: 0.0046\n", + "Error std: 0.1172\n", + "Predictions within ±0.5: 99.2%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " y_pred : array-like\n", + " Predicted values\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Error distribution histogram\n", + " - Actual vs Predicted scatter plot\n", + " - Errors vs Actual Values scatter plot\n", + " - Comprehensive error statistics\n", + " \"\"\"\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " if isinstance(y_true, pd.Series):\n", + " y_true = y_true.values\n", + " if isinstance(y_pred, pd.Series):\n", + " y_pred = y_pred.values\n", + "\n", + " y_true = y_true.ravel()\n", + " y_pred = y_pred.ravel()\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + "\n", + " # Create main figure\n", + " fig = plt.figure(figsize=(15, 5))\n", + "\n", + " # Plot 1: Error Distribution\n", + " plt.subplot(1, 3, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: Actual vs Predicted\n", + " plt.subplot(1, 3, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 3: Errors vs Actual Values\n", + " plt.subplot(1, 3, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " # Create directory if it doesn't exist\n", + " filename = f'{folder_name}_error_analysis.png'\n", + "\n", + " # Save figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n", + " print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(errors):.4f}\")\n", + " print(f\"Error std: {np.std(errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "846f15d4-d1b2-4a90-a702-b9a85f4e2945", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f982c92c-ba99-4df6-b3c8-df92426679db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v2-checkpoint.ipynb b/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v2-checkpoint.ipynb new file mode 100644 index 0000000..0f7ace8 --- /dev/null +++ b/models/solarradiation/.ipynb_checkpoints/solarradiation_model_v2-checkpoint.ipynb @@ -0,0 +1,3050 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", + "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n", + "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.9.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (16.0.6)\n", + "Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n", + "Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.0)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.1)\n", + "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.24.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (68.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n", + "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n", + "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n", + "Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.4)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.1)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.3.7)\n", + "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.1)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n", + "Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (2.0.5)\n", + "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.7.22)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.0.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\u001B[33m\n", + "\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.3.1\u001B[0m\n", + "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpython3 -m pip install --upgrade pip\u001B[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-25 21:39:40.365457: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-25 21:39:40.365505: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-25 21:39:40.365547: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-25 21:39:40.374255: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix, classification_report, roc_auc_score\n", + "from tensorflow.keras.metrics import AUC\n", + "from scipy import stats\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " \"\"\"\n", + " Add time-based features to the DataFrame.\n", + " Works with both 'datetime' as column or index.\n", + " \"\"\"\n", + " # Se datetime è l'indice, lo usiamo direttamente\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " datetime_col = df.index\n", + " else:\n", + " # Se datetime è una colonna, la convertiamo\n", + " if 'datetime' in df.columns:\n", + " datetime_col = pd.to_datetime(df['datetime'])\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Creazione delle feature temporali\n", + " df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n", + " df['year'] = datetime_col.year\n", + " df['month'] = datetime_col.month\n", + " df['day'] = datetime_col.day\n", + " df['hour'] = datetime_col.hour\n", + " df['minute'] = datetime_col.minute\n", + " df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n", + " df['day_of_week'] = datetime_col.dayofweek\n", + " df['day_of_year'] = datetime_col.dayofyear\n", + " df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n", + " df['quarter'] = datetime_col.quarter\n", + " df['is_month_end'] = datetime_col.is_month_end.astype(int)\n", + " df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n", + " df['is_year_end'] = datetime_col.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['season'] = datetime_col.map(get_season)\n", + " df['time_period'] = datetime_col.hour.map(get_time_period)\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Solar angle calculation\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Assicuriamoci che l'indice sia di tipo datetime\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df['datetime'])\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " Assumes df has a DatetimeIndex\n", + " \"\"\"\n", + " # Verifichiamo che abbiamo un DatetimeIndex\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " raise ValueError(\"DataFrame must have a DatetimeIndex\")\n", + "\n", + " # Existing features\n", + " df = add_time_features(df)\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # Weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # Derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # Rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # Extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Verifichiamo se datetime è già l'indice\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex', # Direct radiation indicator\n", + " 'cloudcover', # Cloud coverage\n", + " 'visibility', # Atmospheric transparency\n", + " 'temp', # Temperature\n", + " 'pressure', # Atmospheric pressure\n", + " 'humidity', # Humidity\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation', # Solar elevation\n", + " 'solar_angle', # Solar angle\n", + " 'day_length', # Day length\n", + " 'hour_sin', # Daily cycle\n", + " 'hour_cos',\n", + " 'day_of_year_sin', # Annual cycle\n", + " 'day_of_year_cos',\n", + " 'month_sin', # Monthly cycle\n", + " 'month_cos',\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index', # Clear sky index\n", + " 'atmospheric_attenuation', # Atmospheric attenuation\n", + " 'theoretical_radiation', # Theoretical radiation\n", + " 'expected_radiation', # Expected radiation\n", + " 'cloud_elevation', # Cloud-elevation interaction\n", + " 'visibility_elevation', # Visibility-elevation interaction\n", + " 'uv_cloud_interaction', # UV-cloud interaction\n", + " 'temp_radiation_potential', # Temperature-radiation potential\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h', # Cloud coverage moving average\n", + " 'temp_rolling_12h', # Temperature moving average\n", + " 'uv_rolling_12h', # UV moving average\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " ],\n", + "\n", + " # Lag Features (most recent)\n", + " 'lag_features': [\n", + " 'temp_1h_lag', # 1-hour temperature lag\n", + " 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n", + " 'humidity_1h_lag', # 1-hour humidity lag\n", + " 'uv_lag_1h', # 1-hour UV lag\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring', # Seasons\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning', # Time periods\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night',\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " df[column] = df[column].interpolate(method='time')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarradiation']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a bidirectional LSTM layer with residual connections and regularization.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Number of LSTM units\n", + " dropout_rate: Dropout rate for regularization\n", + " l2_reg: L2 regularization factor\n", + " return_sequences: Whether to return sequences or just the last output\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " residual = x\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " if return_sequences:\n", + " if int(residual.shape[-1]) != 2 * units:\n", + " residual = Dense(2 * units, activation='linear')(residual)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a multi-head attention block with residual connections.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Dimension of the key space\n", + " num_heads: Number of attention heads\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + "\n", + "\n", + "def create_regression_branch(shared_features, l2_lambda=0.005, name_suffix=''):\n", + " \"\"\"\n", + " Crea un singolo branch di regressione con architettura migliorata\n", + " \"\"\"\n", + " regression_x = shared_features\n", + " dense_units = [256, 128, 64, 32]\n", + " dense_dropout = [0.3, 0.2, 0.15, 0.1]\n", + " \n", + " # Skip connections per ogni blocco\n", + " for i, (units, dropout) in enumerate(zip(dense_units, dense_dropout)):\n", + " # Salva l'input per la skip connection\n", + " residual = regression_x\n", + " \n", + " # Primo dense layer del blocco\n", + " regression_x = Dense(\n", + " units, \n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " name=f'reg_dense1_{units}_{name_suffix}'\n", + " )(regression_x)\n", + " regression_x = BatchNormalization(name=f'reg_bn1_{units}_{name_suffix}')(regression_x)\n", + " regression_x = Activation('swish', name=f'reg_swish1_{units}_{name_suffix}')(regression_x)\n", + " regression_x = Dropout(dropout, name=f'reg_drop1_{units}_{name_suffix}')(regression_x)\n", + " \n", + " # Secondo dense layer del blocco\n", + " regression_x = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " name=f'reg_dense2_{units}_{name_suffix}'\n", + " )(regression_x)\n", + " regression_x = BatchNormalization(name=f'reg_bn2_{units}_{name_suffix}')(regression_x)\n", + " regression_x = Activation('swish', name=f'reg_swish2_{units}_{name_suffix}')(regression_x)\n", + " regression_x = Dropout(dropout, name=f'reg_drop2_{units}_{name_suffix}')(regression_x)\n", + " \n", + " # Proiezione residuale se necessario\n", + " if i > 0 and int(residual.shape[-1]) != units:\n", + " residual = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " name=f'reg_residual_proj_{units}_{name_suffix}'\n", + " )(residual)\n", + " \n", + " # Skip connection\n", + " regression_x = Add(name=f'reg_skip_{units}_{name_suffix}')([regression_x, residual])\n", + " \n", + " # Output layer\n", + " regression_output = Dense(1, name=f'regression_output_{name_suffix}')(regression_x)\n", + " \n", + " return regression_output\n", + "\n", + "def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n", + " \"\"\"\n", + " Creates a hybrid model with ensemble regression\n", + " \"\"\"\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Backbone comune (mantenuto come prima)\n", + " survival_probs = [0.9, 0.8, 0.7, 0.6]\n", + " attention_survival_probs = [0.85, 0.75, 0.65, 0.55]\n", + " lstm_units = [256, 128, 64, 32]\n", + " dropout_rates = [0.4, 0.3, 0.2, 0.2]\n", + " attention_heads = [32, 24, 16, 8]\n", + "\n", + " # Backbone comune\n", + " x = inputs\n", + " lstm_blocks = 4\n", + " for i in range(lstm_blocks):\n", + " x = create_residual_lstm_layer(\n", + " x,\n", + " units=lstm_units[i],\n", + " dropout_rate=dropout_rates[i],\n", + " l2_reg=l2_lambda,\n", + " return_sequences=True,\n", + " survival_probability=survival_probs[i]\n", + " )\n", + " x = attention_block(\n", + " x,\n", + " units=lstm_units[i],\n", + " num_heads=attention_heads[i],\n", + " survival_probability=attention_survival_probs[i]\n", + " )\n", + " if i < lstm_blocks - 1:\n", + " x = MaxPooling1D()(x)\n", + "\n", + " # Final shared LSTM layer\n", + " shared_features = create_residual_lstm_layer(\n", + " x,\n", + " units=32,\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Classification branch (mantenuto come prima)\n", + " classification_x = Dense(64, kernel_regularizer=regularizers.l2(l2_lambda))(shared_features)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_x = Dropout(0.2)(classification_x)\n", + " classification_x = Dense(32, kernel_regularizer=regularizers.l2(l2_lambda))(classification_x)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_output = Dense(1, activation='sigmoid', name='classification_output')(classification_x)\n", + "\n", + " # Ensemble di regression branches\n", + " n_ensemble = 3\n", + " regression_outputs = []\n", + " \n", + " for i in range(n_ensemble):\n", + " # Creare una versione diversa delle feature condivise\n", + " features_variation = Dense(\n", + " 256,\n", + " activation='swish',\n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " name=f'ensemble_features_{i}'\n", + " )(shared_features)\n", + " \n", + " # Creare un branch di regressione\n", + " reg_output = create_regression_branch(\n", + " features_variation,\n", + " l2_lambda=l2_lambda,\n", + " name_suffix=f'ensemble_{i}'\n", + " )\n", + " regression_outputs.append(reg_output)\n", + "\n", + " # Combinare i output di regressione\n", + " if n_ensemble > 1:\n", + " regression_output = Average(name='regression_ensemble')(regression_outputs)\n", + " else:\n", + " regression_output = regression_outputs[0]\n", + " \n", + " # Clip dei valori di regressione\n", + " regression_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='regression_output'\n", + " )(regression_output)\n", + "\n", + " # Combine outputs using threshold activation\n", + " thresholded_classification = ThresholdedReLU(theta=0.5)(classification_output)\n", + " normalized_classification = Lambda(lambda x: tf.cast(x > 0, tf.float32))(thresholded_classification)\n", + " final_output = Lambda(\n", + " lambda inputs: inputs[0] * inputs[1],\n", + " name='final_output'\n", + " )([regression_output, normalized_classification])\n", + "\n", + " # Create model with all outputs\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[\n", + " classification_output,\n", + " regression_output,\n", + " final_output\n", + " ],\n", + " name=\"SolarRadiationModel\"\n", + " )\n", + "\n", + " # Custom loss functions\n", + " def hybrid_focal_loss(y_true, y_pred):\n", + " mse = tf.square(y_true - y_pred)\n", + " error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n", + " focal_weight = tf.pow(error_ratio, 2)\n", + " weighted_mse = focal_weight * mse\n", + " mae = tf.abs(y_true - y_pred)\n", + " return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n", + "\n", + " def masked_regression_loss(y_true, y_pred):\n", + " mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n", + " return hybrid_focal_loss(y_true * mask, y_pred * mask)\n", + "\n", + " # Metrics (mantenute come prima)\n", + " def rmse(y_true, y_pred):\n", + " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n", + "\n", + " def custom_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n", + " diff = tf.clip_by_value(diff, 0, 1)\n", + " return tf.reduce_mean(diff) * 100\n", + "\n", + " # Optimizer\n", + " optimizer = AdamW(\n", + " learning_rate=0.0003,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7,\n", + " weight_decay=0.001,\n", + " amsgrad=True\n", + " )\n", + "\n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': masked_regression_loss,\n", + " 'final_output': hybrid_focal_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.5,\n", + " 'final_output': 0.3\n", + " },\n", + " metrics={\n", + " 'classification_output': ['accuracy', AUC()],\n", + " 'regression_output': ['mse', 'mae', rmse, custom_mape],\n", + " 'final_output': ['mse', 'mae', rmse, custom_mape]\n", + " }\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " # Save model architecture visualization\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar radiation values (W/m²)\n", + " y_pred : array-like\n", + " Predicted solar radiation values (W/m²)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 W/m²\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n", + "\n", + " # Radiation level classification\n", + " def get_radiation_level(value):\n", + " if value <= 200:\n", + " return 'Very Low'\n", + " elif value <= 400:\n", + " return 'Low'\n", + " elif value <= 600:\n", + " return 'Moderate'\n", + " elif value <= 800:\n", + " return 'High'\n", + " elif value <= 1000:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate radiation levels\n", + " y_true_levels = [get_radiation_level(v) for v in y_true]\n", + " y_pred_levels = [get_radiation_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Radiation Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} W/m²\")\n", + " print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for radiation levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels)\n", + " print(\"\\nConfusion Matrix for Radiation Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n", + " index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Predicted Radiation (W/m²)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (W/m²)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 10\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by radiation level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Radiation Level')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_radiation_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Helper function to calculate and print metrics for all outputs\n", + " \n", + " Parameters:\n", + " - y_true: true values\n", + " - y_class: classification predictions\n", + " - y_reg: regression predictions\n", + " - y_final: final combined predictions\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Classification metrics\n", + " print(\"\\nClassification Metrics:\")\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " print(confusion_matrix(y_true_binary, y_pred_binary))\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_true_binary, y_pred_binary, \n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4))\n", + " \n", + " # Regression metrics (non-zero values)\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero): # verifichiamo che ci siano valori non-zero\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " out_of_range = np.sum((y_reg_nonzero < min_output) | (y_reg_nonzero > max_output))\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / (y_true_nonzero + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final combined output metrics\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " diff = np.abs((y_true - y_final) / (y_true + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar radiation model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " callbacks = [\n", + " EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-4\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.5,\n", + " patience=7,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=False\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: (\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n", + " calculate_metrics(y_test, *model.predict(X_test, verbose=0), min_output, max_output)\n", + " ) if epoch % 10 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar radiation predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar radiation predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarradiation_predicted': final_pred.flatten(),\n", + " 'solarradiation_classification': classification_pred.flatten(),\n", + " 'solarradiation_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar radiation column where missing\n", + " df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarradiation'] == df['solarradiation_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarradiation_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarradiation_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarradiation_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarradiation_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarradiation_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarradiation_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarradiation_predicted', 'solarradiation_classification',\n", + " 'solarradiation_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, sequence_length=24, name='Solar Radiation'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione dei valori reali e predetti.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente i dati originali\n", + " predictions : tuple\n", + " Tuple contenente (classification_pred, regression_pred, final_pred)\n", + " sequence_length : int\n", + " Lunghezza della sequenza usata per le predizioni\n", + " name : str\n", + " Nome della variabile da analizzare\n", + " \"\"\"\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Prepare data for analysis\n", + " mask_pre_2010 = data['datetime'].dt.year < 2010\n", + " actual_values = data[mask_pre_2010]['solarradiation'].iloc[sequence_length - 1:]\n", + "\n", + " # Create analysis DataFrame\n", + " analysis_df = pd.DataFrame({\n", + " 'actual': actual_values,\n", + " 'classification': classification_pred.flatten(),\n", + " 'regression': regression_pred.flatten(),\n", + " 'final': final_pred.flatten()\n", + " })\n", + "\n", + " # Analisi per ogni componente\n", + " components = {\n", + " 'Actual Values': 'actual',\n", + " 'Classification Predictions': 'classification',\n", + " 'Regression Predictions': 'regression',\n", + " 'Final Combined Predictions': 'final'\n", + " }\n", + "\n", + " for title, column in components.items():\n", + " print(f\"\\n{'-'*20} {title} {'-'*20}\")\n", + "\n", + " # Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(analysis_df[column]),\n", + " 'missing': analysis_df[column].isnull().sum(),\n", + " 'zeros': (analysis_df[column] == 0).sum(),\n", + " 'mean': analysis_df[column].mean(),\n", + " 'median': analysis_df[column].median(),\n", + " 'std': analysis_df[column].std(),\n", + " 'min': analysis_df[column].min(),\n", + " 'max': analysis_df[column].max(),\n", + " 'skewness': stats.skew(analysis_df[column].dropna()),\n", + " 'kurtosis': stats.kurtosis(analysis_df[column].dropna())\n", + " }\n", + "\n", + " # Percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(analysis_df[column].dropna(), p)\n", + "\n", + " # Plot delle distribuzioni\n", + " fig, axes = plt.subplots(2, 2, figsize=(20, 12))\n", + " fig.suptitle(f'Distribution Analysis - {title}')\n", + "\n", + " # Histogram\n", + " sns.histplot(data=analysis_df, x=column, kde=True, ax=axes[0,0])\n", + " axes[0,0].set_title('Distribution')\n", + " axes[0,0].set_xlabel(title)\n", + " axes[0,0].set_ylabel('Frequency')\n", + "\n", + " # Box Plot\n", + " sns.boxplot(y=analysis_df[column], ax=axes[0,1])\n", + " axes[0,1].set_title('Box Plot')\n", + "\n", + " # QQ Plot\n", + " stats.probplot(analysis_df[column].dropna(), dist=\"norm\", plot=plt, ax=axes[1,0])\n", + " axes[1,0].set_title('Q-Q Plot')\n", + "\n", + " # Log-transformed distribution (except for classification)\n", + " if column != 'classification':\n", + " sns.histplot(data=np.log1p(analysis_df[column]), kde=True, ax=axes[1,1])\n", + " axes[1,1].set_title('Log-transformed Distribution')\n", + " axes[1,1].set_xlabel(f'Log({title} + 1)')\n", + " axes[1,1].set_ylabel('Frequency')\n", + " else:\n", + " sns.histplot(data=analysis_df[column], kde=True, ax=axes[1,1])\n", + " axes[1,1].set_title('Classification Distribution')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Stampa statistiche\n", + " print(\"\\nStatistiche principali:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # Analisi specifiche per tipo di output\n", + " if column == 'classification':\n", + " # Analisi della classificazione\n", + " threshold = 0.5\n", + " predicted_zeros = (analysis_df[column] < threshold).sum()\n", + " predicted_nonzeros = (analysis_df[column] >= threshold).sum()\n", + " actual_zeros = (analysis_df['actual'] == 0).sum()\n", + "\n", + " print(\"\\nAnalisi Classificazione:\")\n", + " print(f\"Predicted Zeros: {predicted_zeros} ({predicted_zeros/len(analysis_df)*100:.2f}%)\")\n", + " print(f\"Predicted Non-zeros: {predicted_nonzeros} ({predicted_nonzeros/len(analysis_df)*100:.2f}%)\")\n", + " print(f\"Actual Zeros: {actual_zeros} ({actual_zeros/len(analysis_df)*100:.2f}%)\")\n", + "\n", + " # Confusion Matrix\n", + " y_true = (analysis_df['actual'] > 0).astype(int)\n", + " y_pred = (analysis_df[column] >= threshold).astype(int)\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm)\n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_true, y_pred))\n", + "\n", + " elif column in ['regression', 'final']:\n", + " # Analisi degli errori\n", + " errors = analysis_df['actual'] - analysis_df[column]\n", + " mae = np.mean(np.abs(errors))\n", + " rmse = np.sqrt(np.mean(errors**2))\n", + " mape = np.mean(np.abs(errors / (analysis_df['actual'] + 1e-7))) * 100\n", + "\n", + " print(\"\\nMetriche di Errore:\")\n", + " print(f\"MAE: {mae:.4f}\")\n", + " print(f\"RMSE: {rmse:.4f}\")\n", + " print(f\"MAPE: {mape:.4f}%\")\n", + "\n", + " # Plot comparativo finale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(analysis_df['actual'], label='Actual', alpha=0.5)\n", + " plt.plot(analysis_df['final'], label='Predicted', alpha=0.5)\n", + " plt.title(f'Comparison of Actual vs Predicted {name}')\n", + " plt.xlabel('Sample')\n", + " plt.ylabel(name)\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " return analysis_df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar radiation model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 40\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (103798, 24, 40)\n", + "Test data shape: (25933, 24, 40)\n", + "Saving scaler X to: 2024-11-25_21-39_scale_X.joblib\n", + "Saving scaler X to: 2024-11-25_21-39_scale_y.joblib\n", + "Saving features to: 2024-11-25_21-39_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n", + "\n", + "print(\"Initializing solar radiation model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "76deb4deb84dc4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\n", + "Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n", + "Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-25 21:39:47.411609: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n", + "2024-11-25 21:39:48.280823: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"SolarRadiationModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 40)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 4 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n", + " sticDepth) 'multi_head_attention[0][0]']\n", + " \n", + " layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n", + " D) ] \n", + " \n", + " bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_1[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n", + " sticDepth) 'multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_2[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n", + " sticDepth) 'multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n", + " sticDepth) 'multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " ensemble_features_0 (Dense (None, 256) 16640 ['dropout_4[0][0]'] \n", + " ) \n", + " \n", + " ensemble_features_1 (Dense (None, 256) 16640 ['dropout_4[0][0]'] \n", + " ) \n", + " \n", + " ensemble_features_2 (Dense (None, 256) 16640 ['dropout_4[0][0]'] \n", + " ) \n", + " \n", + " reg_dense1_256_ensemble_0 (None, 256) 65792 ['ensemble_features_0[0][0]'] \n", + " (Dense) \n", + " \n", + " reg_dense1_256_ensemble_1 (None, 256) 65792 ['ensemble_features_1[0][0]'] \n", + " (Dense) \n", + " \n", + " reg_dense1_256_ensemble_2 (None, 256) 65792 ['ensemble_features_2[0][0]'] \n", + " (Dense) \n", + " \n", + " reg_bn1_256_ensemble_0 (Ba (None, 256) 1024 ['reg_dense1_256_ensemble_0[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn1_256_ensemble_1 (Ba (None, 256) 1024 ['reg_dense1_256_ensemble_1[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn1_256_ensemble_2 (Ba (None, 256) 1024 ['reg_dense1_256_ensemble_2[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_swish1_256_ensemble_0 (None, 256) 0 ['reg_bn1_256_ensemble_0[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish1_256_ensemble_1 (None, 256) 0 ['reg_bn1_256_ensemble_1[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish1_256_ensemble_2 (None, 256) 0 ['reg_bn1_256_ensemble_2[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_drop1_256_ensemble_0 ( (None, 256) 0 ['reg_swish1_256_ensemble_0[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop1_256_ensemble_1 ( (None, 256) 0 ['reg_swish1_256_ensemble_1[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop1_256_ensemble_2 ( (None, 256) 0 ['reg_swish1_256_ensemble_2[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_dense2_256_ensemble_0 (None, 256) 65792 ['reg_drop1_256_ensemble_0[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_dense2_256_ensemble_1 (None, 256) 65792 ['reg_drop1_256_ensemble_1[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_dense2_256_ensemble_2 (None, 256) 65792 ['reg_drop1_256_ensemble_2[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_bn2_256_ensemble_0 (Ba (None, 256) 1024 ['reg_dense2_256_ensemble_0[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn2_256_ensemble_1 (Ba (None, 256) 1024 ['reg_dense2_256_ensemble_1[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn2_256_ensemble_2 (Ba (None, 256) 1024 ['reg_dense2_256_ensemble_2[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_swish2_256_ensemble_0 (None, 256) 0 ['reg_bn2_256_ensemble_0[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish2_256_ensemble_1 (None, 256) 0 ['reg_bn2_256_ensemble_1[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish2_256_ensemble_2 (None, 256) 0 ['reg_bn2_256_ensemble_2[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_drop2_256_ensemble_0 ( (None, 256) 0 ['reg_swish2_256_ensemble_0[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop2_256_ensemble_1 ( (None, 256) 0 ['reg_swish2_256_ensemble_1[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop2_256_ensemble_2 ( (None, 256) 0 ['reg_swish2_256_ensemble_2[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_skip_256_ensemble_0 (A (None, 256) 0 ['reg_drop2_256_ensemble_0[0][\n", + " dd) 0]', \n", + " 'ensemble_features_0[0][0]'] \n", + " \n", + " reg_skip_256_ensemble_1 (A (None, 256) 0 ['reg_drop2_256_ensemble_1[0][\n", + " dd) 0]', \n", + " 'ensemble_features_1[0][0]'] \n", + " \n", + " reg_skip_256_ensemble_2 (A (None, 256) 0 ['reg_drop2_256_ensemble_2[0][\n", + " dd) 0]', \n", + " 'ensemble_features_2[0][0]'] \n", + " \n", + " reg_dense1_128_ensemble_0 (None, 128) 32896 ['reg_skip_256_ensemble_0[0][0\n", + " (Dense) ]'] \n", + " \n", + " reg_dense1_128_ensemble_1 (None, 128) 32896 ['reg_skip_256_ensemble_1[0][0\n", + " (Dense) ]'] \n", + " \n", + " reg_dense1_128_ensemble_2 (None, 128) 32896 ['reg_skip_256_ensemble_2[0][0\n", + " (Dense) ]'] \n", + " \n", + " reg_bn1_128_ensemble_0 (Ba (None, 128) 512 ['reg_dense1_128_ensemble_0[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn1_128_ensemble_1 (Ba (None, 128) 512 ['reg_dense1_128_ensemble_1[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn1_128_ensemble_2 (Ba (None, 128) 512 ['reg_dense1_128_ensemble_2[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_swish1_128_ensemble_0 (None, 128) 0 ['reg_bn1_128_ensemble_0[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish1_128_ensemble_1 (None, 128) 0 ['reg_bn1_128_ensemble_1[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish1_128_ensemble_2 (None, 128) 0 ['reg_bn1_128_ensemble_2[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_drop1_128_ensemble_0 ( (None, 128) 0 ['reg_swish1_128_ensemble_0[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop1_128_ensemble_1 ( (None, 128) 0 ['reg_swish1_128_ensemble_1[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_drop1_128_ensemble_2 ( (None, 128) 0 ['reg_swish1_128_ensemble_2[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_dense2_128_ensemble_0 (None, 128) 16512 ['reg_drop1_128_ensemble_0[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_dense2_128_ensemble_1 (None, 128) 16512 ['reg_drop1_128_ensemble_1[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_dense2_128_ensemble_2 (None, 128) 16512 ['reg_drop1_128_ensemble_2[0][\n", + " (Dense) 0]'] \n", + " \n", + " reg_bn2_128_ensemble_0 (Ba (None, 128) 512 ['reg_dense2_128_ensemble_0[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn2_128_ensemble_1 (Ba (None, 128) 512 ['reg_dense2_128_ensemble_1[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_bn2_128_ensemble_2 (Ba (None, 128) 512 ['reg_dense2_128_ensemble_2[0]\n", + " tchNormalization) [0]'] \n", + " \n", + " reg_swish2_128_ensemble_0 (None, 128) 0 ['reg_bn2_128_ensemble_0[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish2_128_ensemble_1 (None, 128) 0 ['reg_bn2_128_ensemble_1[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_swish2_128_ensemble_2 (None, 128) 0 ['reg_bn2_128_ensemble_2[0][0]\n", + " (Activation) '] \n", + " \n", + " reg_drop2_128_ensemble_0 ( (None, 128) 0 ['reg_swish2_128_ensemble_0[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_residual_proj_128_ense (None, 128) 32896 ['reg_skip_256_ensemble_0[0][0\n", + " mble_0 (Dense) ]'] \n", + " \n", + " reg_drop2_128_ensemble_1 ( (None, 128) 0 ['reg_swish2_128_ensemble_1[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_residual_proj_128_ense (None, 128) 32896 ['reg_skip_256_ensemble_1[0][0\n", + " mble_1 (Dense) ]'] \n", + " \n", + " reg_drop2_128_ensemble_2 ( (None, 128) 0 ['reg_swish2_128_ensemble_2[0]\n", + " Dropout) [0]'] \n", + " \n", + " reg_residual_proj_128_ense (None, 128) 32896 ['reg_skip_256_ensemble_2[0][0\n", + " mble_2 (Dense) ]'] \n", + " \n", + " reg_skip_128_ensemble_0 (A (None, 128) 0 ['reg_drop2_128_ensemble_0[0][\n", + " dd) 0]', \n", + " 'reg_residual_proj_128_ensemb\n", + " le_0[0][0]'] \n", + " \n", + " reg_skip_128_ensemble_1 (A (None, 128) 0 ['reg_drop2_128_ensemble_1[0][\n", + " dd) 0]', \n", + " 'reg_residual_proj_128_ensemb\n", + " le_1[0][0]'] \n", + " \n", + " reg_skip_128_ensemble_2 (A (None, 128) 0 ['reg_drop2_128_ensemble_2[0][\n", + " dd) 0]', \n", + " 'reg_residual_proj_128_ensemb\n", + " le_2[0][0]'] \n", + " \n", + " reg_dense1_64_ensemble_0 ( (None, 64) 8256 ['reg_skip_128_ensemble_0[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense1_64_ensemble_1 ( (None, 64) 8256 ['reg_skip_128_ensemble_1[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense1_64_ensemble_2 ( (None, 64) 8256 ['reg_skip_128_ensemble_2[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_bn1_64_ensemble_0 (Bat (None, 64) 256 ['reg_dense1_64_ensemble_0[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn1_64_ensemble_1 (Bat (None, 64) 256 ['reg_dense1_64_ensemble_1[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn1_64_ensemble_2 (Bat (None, 64) 256 ['reg_dense1_64_ensemble_2[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_swish1_64_ensemble_0 ( (None, 64) 0 ['reg_bn1_64_ensemble_0[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish1_64_ensemble_1 ( (None, 64) 0 ['reg_bn1_64_ensemble_1[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish1_64_ensemble_2 ( (None, 64) 0 ['reg_bn1_64_ensemble_2[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_drop1_64_ensemble_0 (D (None, 64) 0 ['reg_swish1_64_ensemble_0[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_drop1_64_ensemble_1 (D (None, 64) 0 ['reg_swish1_64_ensemble_1[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_drop1_64_ensemble_2 (D (None, 64) 0 ['reg_swish1_64_ensemble_2[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_dense2_64_ensemble_0 ( (None, 64) 4160 ['reg_drop1_64_ensemble_0[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense2_64_ensemble_1 ( (None, 64) 4160 ['reg_drop1_64_ensemble_1[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense2_64_ensemble_2 ( (None, 64) 4160 ['reg_drop1_64_ensemble_2[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_bn2_64_ensemble_0 (Bat (None, 64) 256 ['reg_dense2_64_ensemble_0[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn2_64_ensemble_1 (Bat (None, 64) 256 ['reg_dense2_64_ensemble_1[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn2_64_ensemble_2 (Bat (None, 64) 256 ['reg_dense2_64_ensemble_2[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_swish2_64_ensemble_0 ( (None, 64) 0 ['reg_bn2_64_ensemble_0[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish2_64_ensemble_1 ( (None, 64) 0 ['reg_bn2_64_ensemble_1[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish2_64_ensemble_2 ( (None, 64) 0 ['reg_bn2_64_ensemble_2[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_drop2_64_ensemble_0 (D (None, 64) 0 ['reg_swish2_64_ensemble_0[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_64_ensem (None, 64) 8256 ['reg_skip_128_ensemble_0[0][0\n", + " ble_0 (Dense) ]'] \n", + " \n", + " reg_drop2_64_ensemble_1 (D (None, 64) 0 ['reg_swish2_64_ensemble_1[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_64_ensem (None, 64) 8256 ['reg_skip_128_ensemble_1[0][0\n", + " ble_1 (Dense) ]'] \n", + " \n", + " reg_drop2_64_ensemble_2 (D (None, 64) 0 ['reg_swish2_64_ensemble_2[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_64_ensem (None, 64) 8256 ['reg_skip_128_ensemble_2[0][0\n", + " ble_2 (Dense) ]'] \n", + " \n", + " reg_skip_64_ensemble_0 (Ad (None, 64) 0 ['reg_drop2_64_ensemble_0[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_64_ensembl\n", + " e_0[0][0]'] \n", + " \n", + " reg_skip_64_ensemble_1 (Ad (None, 64) 0 ['reg_drop2_64_ensemble_1[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_64_ensembl\n", + " e_1[0][0]'] \n", + " \n", + " reg_skip_64_ensemble_2 (Ad (None, 64) 0 ['reg_drop2_64_ensemble_2[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_64_ensembl\n", + " e_2[0][0]'] \n", + " \n", + " reg_dense1_32_ensemble_0 ( (None, 32) 2080 ['reg_skip_64_ensemble_0[0][0]\n", + " Dense) '] \n", + " \n", + " reg_dense1_32_ensemble_1 ( (None, 32) 2080 ['reg_skip_64_ensemble_1[0][0]\n", + " Dense) '] \n", + " \n", + " reg_dense1_32_ensemble_2 ( (None, 32) 2080 ['reg_skip_64_ensemble_2[0][0]\n", + " Dense) '] \n", + " \n", + " reg_bn1_32_ensemble_0 (Bat (None, 32) 128 ['reg_dense1_32_ensemble_0[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn1_32_ensemble_1 (Bat (None, 32) 128 ['reg_dense1_32_ensemble_1[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn1_32_ensemble_2 (Bat (None, 32) 128 ['reg_dense1_32_ensemble_2[0][\n", + " chNormalization) 0]'] \n", + " \n", + " dense_4 (Dense) (None, 64) 4160 ['dropout_4[0][0]'] \n", + " \n", + " reg_swish1_32_ensemble_0 ( (None, 32) 0 ['reg_bn1_32_ensemble_0[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish1_32_ensemble_1 ( (None, 32) 0 ['reg_bn1_32_ensemble_1[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish1_32_ensemble_2 ( (None, 32) 0 ['reg_bn1_32_ensemble_2[0][0]'\n", + " Activation) ] \n", + " \n", + " batch_normalization (Batch (None, 64) 256 ['dense_4[0][0]'] \n", + " Normalization) \n", + " \n", + " reg_drop1_32_ensemble_0 (D (None, 32) 0 ['reg_swish1_32_ensemble_0[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_drop1_32_ensemble_1 (D (None, 32) 0 ['reg_swish1_32_ensemble_1[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_drop1_32_ensemble_2 (D (None, 32) 0 ['reg_swish1_32_ensemble_2[0][\n", + " ropout) 0]'] \n", + " \n", + " activation (Activation) (None, 64) 0 ['batch_normalization[0][0]'] \n", + " \n", + " reg_dense2_32_ensemble_0 ( (None, 32) 1056 ['reg_drop1_32_ensemble_0[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense2_32_ensemble_1 ( (None, 32) 1056 ['reg_drop1_32_ensemble_1[0][0\n", + " Dense) ]'] \n", + " \n", + " reg_dense2_32_ensemble_2 ( (None, 32) 1056 ['reg_drop1_32_ensemble_2[0][0\n", + " Dense) ]'] \n", + " \n", + " dropout_5 (Dropout) (None, 64) 0 ['activation[0][0]'] \n", + " \n", + " reg_bn2_32_ensemble_0 (Bat (None, 32) 128 ['reg_dense2_32_ensemble_0[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn2_32_ensemble_1 (Bat (None, 32) 128 ['reg_dense2_32_ensemble_1[0][\n", + " chNormalization) 0]'] \n", + " \n", + " reg_bn2_32_ensemble_2 (Bat (None, 32) 128 ['reg_dense2_32_ensemble_2[0][\n", + " chNormalization) 0]'] \n", + " \n", + " dense_5 (Dense) (None, 32) 2080 ['dropout_5[0][0]'] \n", + " \n", + " reg_swish2_32_ensemble_0 ( (None, 32) 0 ['reg_bn2_32_ensemble_0[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish2_32_ensemble_1 ( (None, 32) 0 ['reg_bn2_32_ensemble_1[0][0]'\n", + " Activation) ] \n", + " \n", + " reg_swish2_32_ensemble_2 ( (None, 32) 0 ['reg_bn2_32_ensemble_2[0][0]'\n", + " Activation) ] \n", + " \n", + " batch_normalization_1 (Bat (None, 32) 128 ['dense_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " reg_drop2_32_ensemble_0 (D (None, 32) 0 ['reg_swish2_32_ensemble_0[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_32_ensem (None, 32) 2080 ['reg_skip_64_ensemble_0[0][0]\n", + " ble_0 (Dense) '] \n", + " \n", + " reg_drop2_32_ensemble_1 (D (None, 32) 0 ['reg_swish2_32_ensemble_1[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_32_ensem (None, 32) 2080 ['reg_skip_64_ensemble_1[0][0]\n", + " ble_1 (Dense) '] \n", + " \n", + " reg_drop2_32_ensemble_2 (D (None, 32) 0 ['reg_swish2_32_ensemble_2[0][\n", + " ropout) 0]'] \n", + " \n", + " reg_residual_proj_32_ensem (None, 32) 2080 ['reg_skip_64_ensemble_2[0][0]\n", + " ble_2 (Dense) '] \n", + " \n", + " activation_1 (Activation) (None, 32) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " reg_skip_32_ensemble_0 (Ad (None, 32) 0 ['reg_drop2_32_ensemble_0[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_32_ensembl\n", + " e_0[0][0]'] \n", + " \n", + " reg_skip_32_ensemble_1 (Ad (None, 32) 0 ['reg_drop2_32_ensemble_1[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_32_ensembl\n", + " e_1[0][0]'] \n", + " \n", + " reg_skip_32_ensemble_2 (Ad (None, 32) 0 ['reg_drop2_32_ensemble_2[0][0\n", + " d) ]', \n", + " 'reg_residual_proj_32_ensembl\n", + " e_2[0][0]'] \n", + " \n", + " classification_output (Den (None, 1) 33 ['activation_1[0][0]'] \n", + " se) \n", + " \n", + " regression_output_ensemble (None, 1) 33 ['reg_skip_32_ensemble_0[0][0]\n", + " _0 (Dense) '] \n", + " \n", + " regression_output_ensemble (None, 1) 33 ['reg_skip_32_ensemble_1[0][0]\n", + " _1 (Dense) '] \n", + " \n", + " regression_output_ensemble (None, 1) 33 ['reg_skip_32_ensemble_2[0][0]\n", + " _2 (Dense) '] \n", + " \n", + " regression_ensemble (Avera (None, 1) 0 ['regression_output_ensemble_0\n", + " ge) [0][0]', \n", + " 'regression_output_ensemble_1\n", + " [0][0]', \n", + " 'regression_output_ensemble_2\n", + " [0][0]'] \n", + " \n", + " thresholded_re_lu (Thresho (None, 1) 0 ['classification_output[0][0]'\n", + " ldedReLU) ] \n", + " \n", + " regression_output (Lambda) (None, 1) 0 ['regression_ensemble[0][0]'] \n", + " \n", + " lambda (Lambda) (None, 1) 0 ['thresholded_re_lu[0][0]'] \n", + " \n", + " final_output (Lambda) (None, 1) 0 ['regression_output[0][0]', \n", + " 'lambda[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 23031364 (87.86 MB)\n", + "Trainable params: 23025412 (87.83 MB)\n", + "Non-trainable params: 5952 (23.25 KB)\n", + "__________________________________________________________________________________________________\n", + "\n", + "Class distribution in training set:\n", + "Zeros: 52022 (50.12%)\n", + "Non-zeros: 51776 (49.88%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 13007 (50.16%)\n", + "Non-zeros: 12926 (49.84%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-25 21:40:23.511608: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-25 21:40:25.325940: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x73010c02aa90 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-25 21:40:25.325975: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-25 21:40:25.331376: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-25 21:40:25.470956: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "541/541 [==============================] - ETA: 0s - loss: 21.7614 - classification_output_loss: 0.5756 - regression_output_loss: 0.4676 - final_output_loss: 0.4591 - classification_output_accuracy: 0.6628 - classification_output_auc: 0.7471 - regression_output_mse: 0.9288 - regression_output_mae: 0.5546 - regression_output_rmse: 0.8998 - regression_output_custom_mape: 52.2010 - final_output_mse: 0.9184 - final_output_mae: 0.5587 - final_output_rmse: 0.8940 - final_output_custom_mape: 81.9168" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 1 Detailed Metrics:\n", + "541/541 [==============================] - 106s 106ms/step - loss: 21.7614 - classification_output_loss: 0.5756 - regression_output_loss: 0.4676 - final_output_loss: 0.4591 - classification_output_accuracy: 0.6628 - classification_output_auc: 0.7471 - regression_output_mse: 0.9288 - regression_output_mae: 0.5546 - regression_output_rmse: 0.8998 - regression_output_custom_mape: 52.2010 - final_output_mse: 0.9184 - final_output_mae: 0.5587 - final_output_rmse: 0.8940 - final_output_custom_mape: 81.9168 - val_loss: 12.6098 - val_classification_output_loss: 0.3514 - val_regression_output_loss: 0.4318 - val_final_output_loss: 0.4355 - val_classification_output_accuracy: 0.8638 - val_classification_output_auc: 0.9544 - val_regression_output_mse: 0.8895 - val_regression_output_mae: 0.5327 - val_regression_output_rmse: 0.8780 - val_regression_output_custom_mape: 52.5583 - val_final_output_mse: 0.8893 - val_final_output_mae: 0.5451 - val_final_output_rmse: 0.8777 - val_final_output_custom_mape: 91.4040 - lr: 3.0000e-04\n", + "Epoch 2/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 8.2372 - classification_output_loss: 0.1856 - regression_output_loss: 0.2360 - final_output_loss: 0.2405 - classification_output_accuracy: 0.9278 - classification_output_auc: 0.9800 - regression_output_mse: 0.4706 - regression_output_mae: 0.3565 - regression_output_rmse: 0.5927 - regression_output_custom_mape: 38.9142 - final_output_mse: 0.4705 - final_output_mae: 0.3702 - final_output_rmse: 0.5925 - final_output_custom_mape: 83.6743 - val_loss: 5.2106 - val_classification_output_loss: 0.3927 - val_regression_output_loss: 0.0851 - val_final_output_loss: 0.0890 - val_classification_output_accuracy: 0.8532 - val_classification_output_auc: 0.9356 - val_regression_output_mse: 0.1465 - val_regression_output_mae: 0.2230 - val_regression_output_rmse: 0.3681 - val_regression_output_custom_mape: 31.6631 - val_final_output_mse: 0.1460 - val_final_output_mae: 0.2351 - val_final_output_rmse: 0.3671 - val_final_output_custom_mape: 71.4497 - lr: 3.0000e-04\n", + "Epoch 3/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 3.6460 - classification_output_loss: 0.1600 - regression_output_loss: 0.0630 - final_output_loss: 0.0671 - classification_output_accuracy: 0.9351 - classification_output_auc: 0.9845 - regression_output_mse: 0.1030 - regression_output_mae: 0.1770 - regression_output_rmse: 0.3038 - regression_output_custom_mape: 26.8426 - final_output_mse: 0.1031 - final_output_mae: 0.1908 - final_output_rmse: 0.3039 - final_output_custom_mape: 71.6244 - val_loss: 2.5998 - val_classification_output_loss: 0.1993 - val_regression_output_loss: 0.1267 - val_final_output_loss: 0.1317 - val_classification_output_accuracy: 0.9146 - val_classification_output_auc: 0.9830 - val_regression_output_mse: 0.1888 - val_regression_output_mae: 0.2526 - val_regression_output_rmse: 0.4112 - val_regression_output_custom_mape: 32.5356 - val_final_output_mse: 0.1889 - val_final_output_mae: 0.2664 - val_final_output_rmse: 0.4115 - val_final_output_custom_mape: 74.5863 - lr: 3.0000e-04\n", + "Epoch 4/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 1.9158 - classification_output_loss: 0.1380 - regression_output_loss: 0.0553 - final_output_loss: 0.0589 - classification_output_accuracy: 0.9442 - classification_output_auc: 0.9884 - regression_output_mse: 0.0876 - regression_output_mae: 0.1633 - regression_output_rmse: 0.2807 - regression_output_custom_mape: 26.0265 - final_output_mse: 0.0871 - final_output_mae: 0.1756 - final_output_rmse: 0.2795 - final_output_custom_mape: 70.3726 - val_loss: 1.4454 - val_classification_output_loss: 0.2029 - val_regression_output_loss: 0.0481 - val_final_output_loss: 0.0525 - val_classification_output_accuracy: 0.9374 - val_classification_output_auc: 0.9791 - val_regression_output_mse: 0.0630 - val_regression_output_mae: 0.1436 - val_regression_output_rmse: 0.2442 - val_regression_output_custom_mape: 25.8998 - val_final_output_mse: 0.0633 - val_final_output_mae: 0.1582 - val_final_output_rmse: 0.2447 - val_final_output_custom_mape: 71.9146 - lr: 3.0000e-04\n", + "Epoch 5/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 1.1300 - classification_output_loss: 0.1199 - regression_output_loss: 0.0437 - final_output_loss: 0.0472 - classification_output_accuracy: 0.9514 - classification_output_auc: 0.9912 - regression_output_mse: 0.0597 - regression_output_mae: 0.1336 - regression_output_rmse: 0.2314 - regression_output_custom_mape: 23.3383 - final_output_mse: 0.0591 - final_output_mae: 0.1457 - final_output_rmse: 0.2294 - final_output_custom_mape: 68.0596 - val_loss: 0.9264 - val_classification_output_loss: 0.2782 - val_regression_output_loss: 0.0444 - val_final_output_loss: 0.0487 - val_classification_output_accuracy: 0.8822 - val_classification_output_auc: 0.9807 - val_regression_output_mse: 0.0539 - val_regression_output_mae: 0.1356 - val_regression_output_rmse: 0.2185 - val_regression_output_custom_mape: 27.1526 - val_final_output_mse: 0.0543 - val_final_output_mae: 0.1488 - val_final_output_rmse: 0.2198 - val_final_output_custom_mape: 65.6245 - lr: 3.0000e-04\n", + "Epoch 6/100\n", + "541/541 [==============================] - 51s 94ms/step - loss: 0.7342 - classification_output_loss: 0.1012 - regression_output_loss: 0.0414 - final_output_loss: 0.0450 - classification_output_accuracy: 0.9590 - classification_output_auc: 0.9938 - regression_output_mse: 0.0545 - regression_output_mae: 0.1274 - regression_output_rmse: 0.2209 - regression_output_custom_mape: 23.0598 - final_output_mse: 0.0542 - final_output_mae: 0.1402 - final_output_rmse: 0.2197 - final_output_custom_mape: 67.9279 - val_loss: 0.8505 - val_classification_output_loss: 0.5649 - val_regression_output_loss: 0.2350 - val_final_output_loss: 0.2400 - val_classification_output_accuracy: 0.8290 - val_classification_output_auc: 0.9356 - val_regression_output_mse: 0.3572 - val_regression_output_mae: 0.3788 - val_regression_output_rmse: 0.5206 - val_regression_output_custom_mape: 43.1358 - val_final_output_mse: 0.3575 - val_final_output_mae: 0.3900 - val_final_output_rmse: 0.5210 - val_final_output_custom_mape: 77.3503 - lr: 3.0000e-04\n", + "Epoch 7/100\n", + "541/541 [==============================] - 52s 95ms/step - loss: 0.5133 - classification_output_loss: 0.1021 - regression_output_loss: 0.0422 - final_output_loss: 0.0457 - classification_output_accuracy: 0.9594 - classification_output_auc: 0.9934 - regression_output_mse: 0.0556 - regression_output_mae: 0.1304 - regression_output_rmse: 0.2240 - regression_output_custom_mape: 23.6281 - final_output_mse: 0.0552 - final_output_mae: 0.1423 - final_output_rmse: 0.2220 - final_output_custom_mape: 68.1228 - val_loss: 0.4582 - val_classification_output_loss: 0.1588 - val_regression_output_loss: 0.0590 - val_final_output_loss: 0.0526 - val_classification_output_accuracy: 0.9454 - val_classification_output_auc: 0.9904 - val_regression_output_mse: 0.0843 - val_regression_output_mae: 0.1796 - val_regression_output_rmse: 0.2742 - val_regression_output_custom_mape: 36.2912 - val_final_output_mse: 0.0737 - val_final_output_mae: 0.1608 - val_final_output_rmse: 0.2501 - val_final_output_custom_mape: 71.0505 - lr: 3.0000e-04\n", + "Epoch 8/100\n", + "541/541 [==============================] - 51s 94ms/step - loss: 0.3853 - classification_output_loss: 0.1100 - regression_output_loss: 0.0427 - final_output_loss: 0.0459 - classification_output_accuracy: 0.9558 - classification_output_auc: 0.9924 - regression_output_mse: 0.0552 - regression_output_mae: 0.1290 - regression_output_rmse: 0.2218 - regression_output_custom_mape: 23.6954 - final_output_mse: 0.0551 - final_output_mae: 0.1417 - final_output_rmse: 0.2212 - final_output_custom_mape: 68.4358 - val_loss: 0.3914 - val_classification_output_loss: 0.1860 - val_regression_output_loss: 0.0915 - val_final_output_loss: 0.0959 - val_classification_output_accuracy: 0.9378 - val_classification_output_auc: 0.9824 - val_regression_output_mse: 0.2065 - val_regression_output_mae: 0.2504 - val_regression_output_rmse: 0.4286 - val_regression_output_custom_mape: 29.7654 - val_final_output_mse: 0.2066 - val_final_output_mae: 0.2649 - val_final_output_rmse: 0.4288 - val_final_output_custom_mape: 76.1419 - lr: 3.0000e-04\n", + "Epoch 9/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.3199 - classification_output_loss: 0.1101 - regression_output_loss: 0.0616 - final_output_loss: 0.0650 - classification_output_accuracy: 0.9545 - classification_output_auc: 0.9925 - regression_output_mse: 0.1038 - regression_output_mae: 0.1762 - regression_output_rmse: 0.3045 - regression_output_custom_mape: 27.8291 - final_output_mse: 0.1033 - final_output_mae: 0.1887 - final_output_rmse: 0.3037 - final_output_custom_mape: 72.4254 - val_loss: 0.3449 - val_classification_output_loss: 0.3869 - val_regression_output_loss: 0.0618 - val_final_output_loss: 0.0662 - val_classification_output_accuracy: 0.9008 - val_classification_output_auc: 0.9561 - val_regression_output_mse: 0.0833 - val_regression_output_mae: 0.1741 - val_regression_output_rmse: 0.2749 - val_regression_output_custom_mape: 29.8195 - val_final_output_mse: 0.0835 - val_final_output_mae: 0.1877 - val_final_output_rmse: 0.2752 - val_final_output_custom_mape: 72.2675 - lr: 3.0000e-04\n", + "Epoch 10/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.2505 - classification_output_loss: 0.1019 - regression_output_loss: 0.0518 - final_output_loss: 0.0553 - classification_output_accuracy: 0.9581 - classification_output_auc: 0.9936 - regression_output_mse: 0.0792 - regression_output_mae: 0.1561 - regression_output_rmse: 0.2681 - regression_output_custom_mape: 26.8968 - final_output_mse: 0.0789 - final_output_mae: 0.1677 - final_output_rmse: 0.2668 - final_output_custom_mape: 70.8997 - val_loss: 0.2645 - val_classification_output_loss: 0.1521 - val_regression_output_loss: 0.0882 - val_final_output_loss: 0.0927 - val_classification_output_accuracy: 0.9399 - val_classification_output_auc: 0.9882 - val_regression_output_mse: 0.1400 - val_regression_output_mae: 0.2075 - val_regression_output_rmse: 0.3452 - val_regression_output_custom_mape: 32.3501 - val_final_output_mse: 0.1404 - val_final_output_mae: 0.2225 - val_final_output_rmse: 0.3459 - val_final_output_custom_mape: 77.4425 - lr: 3.0000e-04\n", + "Epoch 11/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.1874 - classification_output_loss: 0.0730 - regression_output_loss: 0.0375 - final_output_loss: 0.0411 - classification_output_accuracy: 0.9704 - classification_output_auc: 0.9968 - regression_output_mse: 0.0458 - regression_output_mae: 0.1184 - regression_output_rmse: 0.2036 - regression_output_custom_mape: 23.5087 - final_output_mse: 0.0455 - final_output_mae: 0.1305 - final_output_rmse: 0.2021 - final_output_custom_mape: 67.9603\n", + "Epoch 11 Detailed Metrics:\n", + "541/541 [==============================] - 59s 108ms/step - loss: 0.1874 - classification_output_loss: 0.0730 - regression_output_loss: 0.0375 - final_output_loss: 0.0411 - classification_output_accuracy: 0.9704 - classification_output_auc: 0.9968 - regression_output_mse: 0.0458 - regression_output_mae: 0.1184 - regression_output_rmse: 0.2036 - regression_output_custom_mape: 23.5087 - final_output_mse: 0.0455 - final_output_mae: 0.1305 - final_output_rmse: 0.2021 - final_output_custom_mape: 67.9603 - val_loss: 0.1989 - val_classification_output_loss: 0.1751 - val_regression_output_loss: 0.0484 - val_final_output_loss: 0.0531 - val_classification_output_accuracy: 0.9477 - val_classification_output_auc: 0.9842 - val_regression_output_mse: 0.0615 - val_regression_output_mae: 0.1431 - val_regression_output_rmse: 0.2300 - val_regression_output_custom_mape: 25.9657 - val_final_output_mse: 0.0619 - val_final_output_mae: 0.1582 - val_final_output_rmse: 0.2309 - val_final_output_custom_mape: 72.2079 - lr: 3.0000e-04\n", + "Epoch 12/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.1606 - classification_output_loss: 0.0842 - regression_output_loss: 0.0386 - final_output_loss: 0.0426 - classification_output_accuracy: 0.9653 - classification_output_auc: 0.9957 - regression_output_mse: 0.0483 - regression_output_mae: 0.1216 - regression_output_rmse: 0.2090 - regression_output_custom_mape: 23.4801 - final_output_mse: 0.0482 - final_output_mae: 0.1347 - final_output_rmse: 0.2086 - final_output_custom_mape: 68.3661\n", + "Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0001500000071246177.\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.1606 - classification_output_loss: 0.0842 - regression_output_loss: 0.0386 - final_output_loss: 0.0426 - classification_output_accuracy: 0.9653 - classification_output_auc: 0.9957 - regression_output_mse: 0.0483 - regression_output_mae: 0.1216 - regression_output_rmse: 0.2090 - regression_output_custom_mape: 23.4801 - final_output_mse: 0.0482 - final_output_mae: 0.1347 - final_output_rmse: 0.2086 - final_output_custom_mape: 68.3661 - val_loss: 0.2972 - val_classification_output_loss: 0.3844 - val_regression_output_loss: 0.1478 - val_final_output_loss: 0.1521 - val_classification_output_accuracy: 0.9027 - val_classification_output_auc: 0.9666 - val_regression_output_mse: 0.3291 - val_regression_output_mae: 0.3247 - val_regression_output_rmse: 0.5009 - val_regression_output_custom_mape: 36.7739 - val_final_output_mse: 0.3295 - val_final_output_mae: 0.3387 - val_final_output_rmse: 0.5017 - val_final_output_custom_mape: 77.1084 - lr: 3.0000e-04\n", + "Epoch 13/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.1417 - classification_output_loss: 0.0731 - regression_output_loss: 0.0386 - final_output_loss: 0.0425 - classification_output_accuracy: 0.9705 - classification_output_auc: 0.9967 - regression_output_mse: 0.0476 - regression_output_mae: 0.1209 - regression_output_rmse: 0.2074 - regression_output_custom_mape: 23.6777 - final_output_mse: 0.0475 - final_output_mae: 0.1340 - final_output_rmse: 0.2067 - final_output_custom_mape: 68.5557 - val_loss: 0.2201 - val_classification_output_loss: 0.2901 - val_regression_output_loss: 0.0877 - val_final_output_loss: 0.0920 - val_classification_output_accuracy: 0.9203 - val_classification_output_auc: 0.9715 - val_regression_output_mse: 0.1554 - val_regression_output_mae: 0.2386 - val_regression_output_rmse: 0.3845 - val_regression_output_custom_mape: 35.5962 - val_final_output_mse: 0.1558 - val_final_output_mae: 0.2530 - val_final_output_rmse: 0.3851 - val_final_output_custom_mape: 78.4799 - lr: 1.5000e-04\n", + "Epoch 14/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.1297 - classification_output_loss: 0.0711 - regression_output_loss: 0.0365 - final_output_loss: 0.0410 - classification_output_accuracy: 0.9705 - classification_output_auc: 0.9969 - regression_output_mse: 0.0442 - regression_output_mae: 0.1148 - regression_output_rmse: 0.1989 - regression_output_custom_mape: 22.1252 - final_output_mse: 0.0446 - final_output_mae: 0.1295 - final_output_rmse: 0.1999 - final_output_custom_mape: 68.0297 - val_loss: 0.2045 - val_classification_output_loss: 0.3035 - val_regression_output_loss: 0.0776 - val_final_output_loss: 0.0818 - val_classification_output_accuracy: 0.9149 - val_classification_output_auc: 0.9655 - val_regression_output_mse: 0.1226 - val_regression_output_mae: 0.2069 - val_regression_output_rmse: 0.3244 - val_regression_output_custom_mape: 32.3946 - val_final_output_mse: 0.1228 - val_final_output_mae: 0.2210 - val_final_output_rmse: 0.3247 - val_final_output_custom_mape: 76.3908 - lr: 1.5000e-04\n", + "Epoch 15/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.1146 - classification_output_loss: 0.0645 - regression_output_loss: 0.0307 - final_output_loss: 0.0350 - classification_output_accuracy: 0.9732 - classification_output_auc: 0.9975 - regression_output_mse: 0.0320 - regression_output_mae: 0.0987 - regression_output_rmse: 0.1721 - regression_output_custom_mape: 20.6845 - final_output_mse: 0.0322 - final_output_mae: 0.1131 - final_output_rmse: 0.1725 - final_output_custom_mape: 66.5285 - val_loss: 0.1646 - val_classification_output_loss: 0.2214 - val_regression_output_loss: 0.0593 - val_final_output_loss: 0.0638 - val_classification_output_accuracy: 0.9308 - val_classification_output_auc: 0.9772 - val_regression_output_mse: 0.0818 - val_regression_output_mae: 0.1627 - val_regression_output_rmse: 0.2642 - val_regression_output_custom_mape: 28.9516 - val_final_output_mse: 0.0821 - val_final_output_mae: 0.1773 - val_final_output_rmse: 0.2650 - val_final_output_custom_mape: 73.5214 - lr: 1.5000e-04\n", + "Epoch 16/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.1037 - classification_output_loss: 0.0616 - regression_output_loss: 0.0280 - final_output_loss: 0.0325 - classification_output_accuracy: 0.9745 - classification_output_auc: 0.9977 - regression_output_mse: 0.0267 - regression_output_mae: 0.0908 - regression_output_rmse: 0.1581 - regression_output_custom_mape: 19.6992 - final_output_mse: 0.0270 - final_output_mae: 0.1056 - final_output_rmse: 0.1590 - final_output_custom_mape: 65.7434 - val_loss: 0.1887 - val_classification_output_loss: 0.2883 - val_regression_output_loss: 0.0816 - val_final_output_loss: 0.0859 - val_classification_output_accuracy: 0.9193 - val_classification_output_auc: 0.9663 - val_regression_output_mse: 0.1330 - val_regression_output_mae: 0.2139 - val_regression_output_rmse: 0.3380 - val_regression_output_custom_mape: 32.7521 - val_final_output_mse: 0.1332 - val_final_output_mae: 0.2280 - val_final_output_rmse: 0.3383 - val_final_output_custom_mape: 77.1713 - lr: 1.5000e-04\n", + "Epoch 17/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0970 - classification_output_loss: 0.0637 - regression_output_loss: 0.0273 - final_output_loss: 0.0316 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 0.0252 - regression_output_mae: 0.0887 - regression_output_rmse: 0.1533 - regression_output_custom_mape: 19.8560 - final_output_mse: 0.0253 - final_output_mae: 0.1029 - final_output_rmse: 0.1536 - final_output_custom_mape: 65.5673 - val_loss: 0.1351 - val_classification_output_loss: 0.1977 - val_regression_output_loss: 0.0453 - val_final_output_loss: 0.0498 - val_classification_output_accuracy: 0.9382 - val_classification_output_auc: 0.9785 - val_regression_output_mse: 0.0596 - val_regression_output_mae: 0.1399 - val_regression_output_rmse: 0.2281 - val_regression_output_custom_mape: 25.3515 - val_final_output_mse: 0.0599 - val_final_output_mae: 0.1546 - val_final_output_rmse: 0.2289 - val_final_output_custom_mape: 71.1378 - lr: 1.5000e-04\n", + "Epoch 18/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0998 - classification_output_loss: 0.0648 - regression_output_loss: 0.0364 - final_output_loss: 0.0411 - classification_output_accuracy: 0.9724 - classification_output_auc: 0.9975 - regression_output_mse: 0.0448 - regression_output_mae: 0.1148 - regression_output_rmse: 0.1968 - regression_output_custom_mape: 22.1714 - final_output_mse: 0.0461 - final_output_mae: 0.1290 - final_output_rmse: 0.1982 - final_output_custom_mape: 67.7461 - val_loss: 0.1535 - val_classification_output_loss: 0.1355 - val_regression_output_loss: 0.0881 - val_final_output_loss: 0.0928 - val_classification_output_accuracy: 0.9563 - val_classification_output_auc: 0.9891 - val_regression_output_mse: 0.1866 - val_regression_output_mae: 0.2311 - val_regression_output_rmse: 0.3747 - val_regression_output_custom_mape: 30.8233 - val_final_output_mse: 0.1871 - val_final_output_mae: 0.2466 - val_final_output_rmse: 0.3756 - val_final_output_custom_mape: 76.8307 - lr: 1.5000e-04\n", + "Epoch 19/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0957 - classification_output_loss: 0.0685 - regression_output_loss: 0.0356 - final_output_loss: 0.0397 - classification_output_accuracy: 0.9717 - classification_output_auc: 0.9970 - regression_output_mse: 0.0417 - regression_output_mae: 0.1129 - regression_output_rmse: 0.1937 - regression_output_custom_mape: 22.1537 - final_output_mse: 0.0418 - final_output_mae: 0.1267 - final_output_rmse: 0.1934 - final_output_custom_mape: 67.7136 - val_loss: 0.2040 - val_classification_output_loss: 0.4776 - val_regression_output_loss: 0.0709 - val_final_output_loss: 0.0749 - val_classification_output_accuracy: 0.8736 - val_classification_output_auc: 0.9469 - val_regression_output_mse: 0.1066 - val_regression_output_mae: 0.1909 - val_regression_output_rmse: 0.2962 - val_regression_output_custom_mape: 30.8400 - val_final_output_mse: 0.1068 - val_final_output_mae: 0.2036 - val_final_output_rmse: 0.2967 - val_final_output_custom_mape: 70.2057 - lr: 1.5000e-04\n", + "Epoch 20/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0885 - classification_output_loss: 0.0706 - regression_output_loss: 0.0308 - final_output_loss: 0.0349 - classification_output_accuracy: 0.9709 - classification_output_auc: 0.9969 - regression_output_mse: 0.0327 - regression_output_mae: 0.0991 - regression_output_rmse: 0.1718 - regression_output_custom_mape: 20.5523 - final_output_mse: 0.0328 - final_output_mae: 0.1127 - final_output_rmse: 0.1716 - final_output_custom_mape: 65.9518 - val_loss: 0.1069 - val_classification_output_loss: 0.1560 - val_regression_output_loss: 0.0350 - val_final_output_loss: 0.0394 - val_classification_output_accuracy: 0.9449 - val_classification_output_auc: 0.9872 - val_regression_output_mse: 0.0383 - val_regression_output_mae: 0.1097 - val_regression_output_rmse: 0.1910 - val_regression_output_custom_mape: 25.4061 - val_final_output_mse: 0.0385 - val_final_output_mae: 0.1244 - val_final_output_rmse: 0.1919 - val_final_output_custom_mape: 71.9942 - lr: 1.5000e-04\n", + "Epoch 21/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0849 - classification_output_loss: 0.0647 - regression_output_loss: 0.0324 - final_output_loss: 0.0367 - classification_output_accuracy: 0.9730 - classification_output_auc: 0.9974 - regression_output_mse: 0.0356 - regression_output_mae: 0.1039 - regression_output_rmse: 0.1784 - regression_output_custom_mape: 21.1817 - final_output_mse: 0.0357 - final_output_mae: 0.1182 - final_output_rmse: 0.1785 - final_output_custom_mape: 67.0364\n", + "Epoch 21 Detailed Metrics:\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0849 - classification_output_loss: 0.0647 - regression_output_loss: 0.0324 - final_output_loss: 0.0367 - classification_output_accuracy: 0.9730 - classification_output_auc: 0.9974 - regression_output_mse: 0.0356 - regression_output_mae: 0.1039 - regression_output_rmse: 0.1784 - regression_output_custom_mape: 21.1817 - final_output_mse: 0.0357 - final_output_mae: 0.1182 - final_output_rmse: 0.1785 - final_output_custom_mape: 67.0364 - val_loss: 0.1266 - val_classification_output_loss: 0.2513 - val_regression_output_loss: 0.0404 - val_final_output_loss: 0.0448 - val_classification_output_accuracy: 0.9234 - val_classification_output_auc: 0.9729 - val_regression_output_mse: 0.0453 - val_regression_output_mae: 0.1206 - val_regression_output_rmse: 0.1997 - val_regression_output_custom_mape: 25.4902 - val_final_output_mse: 0.0456 - val_final_output_mae: 0.1349 - val_final_output_rmse: 0.2005 - val_final_output_custom_mape: 69.6527 - lr: 1.5000e-04\n", + "Epoch 22/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0843 - classification_output_loss: 0.0782 - regression_output_loss: 0.0324 - final_output_loss: 0.0367 - classification_output_accuracy: 0.9668 - classification_output_auc: 0.9963 - regression_output_mse: 0.0351 - regression_output_mae: 0.1035 - regression_output_rmse: 0.1789 - regression_output_custom_mape: 21.2053 - final_output_mse: 0.0355 - final_output_mae: 0.1175 - final_output_rmse: 0.1795 - final_output_custom_mape: 66.7957 - val_loss: 0.2227 - val_classification_output_loss: 0.4580 - val_regression_output_loss: 0.1112 - val_final_output_loss: 0.1152 - val_classification_output_accuracy: 0.8829 - val_classification_output_auc: 0.9413 - val_regression_output_mse: 0.1935 - val_regression_output_mae: 0.2653 - val_regression_output_rmse: 0.3958 - val_regression_output_custom_mape: 35.5581 - val_final_output_mse: 0.1932 - val_final_output_mae: 0.2781 - val_final_output_rmse: 0.3950 - val_final_output_custom_mape: 78.9677 - lr: 1.5000e-04\n", + "Epoch 23/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0870 - classification_output_loss: 0.0731 - regression_output_loss: 0.0391 - final_output_loss: 0.0435 - classification_output_accuracy: 0.9691 - classification_output_auc: 0.9967 - regression_output_mse: 0.0500 - regression_output_mae: 0.1222 - regression_output_rmse: 0.2072 - regression_output_custom_mape: 23.1067 - final_output_mse: 0.0506 - final_output_mae: 0.1360 - final_output_rmse: 0.2076 - final_output_custom_mape: 68.5077 - val_loss: 0.1775 - val_classification_output_loss: 0.2573 - val_regression_output_loss: 0.1071 - val_final_output_loss: 0.1116 - val_classification_output_accuracy: 0.9199 - val_classification_output_auc: 0.9744 - val_regression_output_mse: 0.1666 - val_regression_output_mae: 0.2438 - val_regression_output_rmse: 0.3710 - val_regression_output_custom_mape: 33.6608 - val_final_output_mse: 0.1669 - val_final_output_mae: 0.2583 - val_final_output_rmse: 0.3716 - val_final_output_custom_mape: 76.9211 - lr: 1.5000e-04\n", + "Epoch 24/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0843 - classification_output_loss: 0.0783 - regression_output_loss: 0.0371 - final_output_loss: 0.0412 - classification_output_accuracy: 0.9677 - classification_output_auc: 0.9961 - regression_output_mse: 0.0450 - regression_output_mae: 0.1170 - regression_output_rmse: 0.1988 - regression_output_custom_mape: 22.4883 - final_output_mse: 0.0450 - final_output_mae: 0.1307 - final_output_rmse: 0.1983 - final_output_custom_mape: 67.8036 - val_loss: 0.1307 - val_classification_output_loss: 0.2292 - val_regression_output_loss: 0.0589 - val_final_output_loss: 0.0634 - val_classification_output_accuracy: 0.9306 - val_classification_output_auc: 0.9793 - val_regression_output_mse: 0.0809 - val_regression_output_mae: 0.1633 - val_regression_output_rmse: 0.2541 - val_regression_output_custom_mape: 28.1449 - val_final_output_mse: 0.0813 - val_final_output_mae: 0.1780 - val_final_output_rmse: 0.2552 - val_final_output_custom_mape: 71.9658 - lr: 1.5000e-04\n", + "Epoch 25/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0716 - classification_output_loss: 0.0618 - regression_output_loss: 0.0293 - final_output_loss: 0.0336 - classification_output_accuracy: 0.9747 - classification_output_auc: 0.9976 - regression_output_mse: 0.0296 - regression_output_mae: 0.0948 - regression_output_rmse: 0.1638 - regression_output_custom_mape: 19.9853 - final_output_mse: 0.0298 - final_output_mae: 0.1090 - final_output_rmse: 0.1639 - final_output_custom_mape: 65.7690 - val_loss: 0.1265 - val_classification_output_loss: 0.2870 - val_regression_output_loss: 0.0438 - val_final_output_loss: 0.0481 - val_classification_output_accuracy: 0.9252 - val_classification_output_auc: 0.9695 - val_regression_output_mse: 0.0554 - val_regression_output_mae: 0.1393 - val_regression_output_rmse: 0.2290 - val_regression_output_custom_mape: 25.3975 - val_final_output_mse: 0.0555 - val_final_output_mae: 0.1534 - val_final_output_rmse: 0.2291 - val_final_output_custom_mape: 71.5147 - lr: 1.5000e-04\n", + "Epoch 26/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0770 - classification_output_loss: 0.0757 - regression_output_loss: 0.0346 - final_output_loss: 0.0392 - classification_output_accuracy: 0.9682 - classification_output_auc: 0.9964 - regression_output_mse: 0.0404 - regression_output_mae: 0.1096 - regression_output_rmse: 0.1880 - regression_output_custom_mape: 21.4210 - final_output_mse: 0.0412 - final_output_mae: 0.1240 - final_output_rmse: 0.1889 - final_output_custom_mape: 67.1111 - val_loss: 0.1067 - val_classification_output_loss: 0.1242 - val_regression_output_loss: 0.0610 - val_final_output_loss: 0.0657 - val_classification_output_accuracy: 0.9530 - val_classification_output_auc: 0.9917 - val_regression_output_mse: 0.1073 - val_regression_output_mae: 0.1808 - val_regression_output_rmse: 0.2964 - val_regression_output_custom_mape: 27.8969 - val_final_output_mse: 0.1078 - val_final_output_mae: 0.1963 - val_final_output_rmse: 0.2975 - val_final_output_custom_mape: 73.3188 - lr: 1.5000e-04\n", + "Epoch 27/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0711 - classification_output_loss: 0.0661 - regression_output_loss: 0.0324 - final_output_loss: 0.0366 - classification_output_accuracy: 0.9721 - classification_output_auc: 0.9973 - regression_output_mse: 0.0345 - regression_output_mae: 0.1040 - regression_output_rmse: 0.1770 - regression_output_custom_mape: 21.4817 - final_output_mse: 0.0345 - final_output_mae: 0.1179 - final_output_rmse: 0.1769 - final_output_custom_mape: 67.0813 - val_loss: 0.0773 - val_classification_output_loss: 0.1097 - val_regression_output_loss: 0.0307 - val_final_output_loss: 0.0355 - val_classification_output_accuracy: 0.9597 - val_classification_output_auc: 0.9931 - val_regression_output_mse: 0.0301 - val_regression_output_mae: 0.0991 - val_regression_output_rmse: 0.1648 - val_regression_output_custom_mape: 21.1672 - val_final_output_mse: 0.0306 - val_final_output_mae: 0.1148 - val_final_output_rmse: 0.1665 - val_final_output_custom_mape: 67.1104 - lr: 1.5000e-04\n", + "Epoch 28/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0659 - classification_output_loss: 0.0620 - regression_output_loss: 0.0295 - final_output_loss: 0.0337 - classification_output_accuracy: 0.9738 - classification_output_auc: 0.9977 - regression_output_mse: 0.0302 - regression_output_mae: 0.0953 - regression_output_rmse: 0.1650 - regression_output_custom_mape: 19.7939 - final_output_mse: 0.0303 - final_output_mae: 0.1094 - final_output_rmse: 0.1651 - final_output_custom_mape: 65.5229 - val_loss: 0.1090 - val_classification_output_loss: 0.2555 - val_regression_output_loss: 0.0365 - val_final_output_loss: 0.0410 - val_classification_output_accuracy: 0.9139 - val_classification_output_auc: 0.9787 - val_regression_output_mse: 0.0390 - val_regression_output_mae: 0.1108 - val_regression_output_rmse: 0.1787 - val_regression_output_custom_mape: 23.8597 - val_final_output_mse: 0.0395 - val_final_output_mae: 0.1250 - val_final_output_rmse: 0.1804 - val_final_output_custom_mape: 65.6156 - lr: 1.5000e-04\n", + "Epoch 29/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0718 - classification_output_loss: 0.0786 - regression_output_loss: 0.0342 - final_output_loss: 0.0389 - classification_output_accuracy: 0.9677 - classification_output_auc: 0.9961 - regression_output_mse: 0.0398 - regression_output_mae: 0.1084 - regression_output_rmse: 0.1867 - regression_output_custom_mape: 21.4302 - final_output_mse: 0.0409 - final_output_mae: 0.1227 - final_output_rmse: 0.1880 - final_output_custom_mape: 67.0664 - val_loss: 0.2156 - val_classification_output_loss: 0.4495 - val_regression_output_loss: 0.1210 - val_final_output_loss: 0.1254 - val_classification_output_accuracy: 0.8694 - val_classification_output_auc: 0.9480 - val_regression_output_mse: 0.1870 - val_regression_output_mae: 0.2650 - val_regression_output_rmse: 0.3836 - val_regression_output_custom_mape: 34.3060 - val_final_output_mse: 0.1872 - val_final_output_mae: 0.2775 - val_final_output_rmse: 0.3839 - val_final_output_custom_mape: 73.4290 - lr: 1.5000e-04\n", + "Epoch 30/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.0626 - classification_output_loss: 0.0648 - regression_output_loss: 0.0279 - final_output_loss: 0.0321 - classification_output_accuracy: 0.9728 - classification_output_auc: 0.9974 - regression_output_mse: 0.0262 - regression_output_mae: 0.0903 - regression_output_rmse: 0.1564 - regression_output_custom_mape: 19.9212 - final_output_mse: 0.0262 - final_output_mae: 0.1043 - final_output_rmse: 0.1563 - final_output_custom_mape: 65.5329 - val_loss: 0.1270 - val_classification_output_loss: 0.2877 - val_regression_output_loss: 0.0539 - val_final_output_loss: 0.0582 - val_classification_output_accuracy: 0.9202 - val_classification_output_auc: 0.9659 - val_regression_output_mse: 0.0736 - val_regression_output_mae: 0.1570 - val_regression_output_rmse: 0.2487 - val_regression_output_custom_mape: 28.4783 - val_final_output_mse: 0.0738 - val_final_output_mae: 0.1711 - val_final_output_rmse: 0.2490 - val_final_output_custom_mape: 73.3610 - lr: 1.5000e-04\n", + "Epoch 31/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0616 - classification_output_loss: 0.0661 - regression_output_loss: 0.0285 - final_output_loss: 0.0327 - classification_output_accuracy: 0.9723 - classification_output_auc: 0.9973 - regression_output_mse: 0.0271 - regression_output_mae: 0.0922 - regression_output_rmse: 0.1584 - regression_output_custom_mape: 20.3898 - final_output_mse: 0.0272 - final_output_mae: 0.1062 - final_output_rmse: 0.1586 - final_output_custom_mape: 66.0008\n", + "Epoch 31 Detailed Metrics:\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0616 - classification_output_loss: 0.0661 - regression_output_loss: 0.0285 - final_output_loss: 0.0327 - classification_output_accuracy: 0.9723 - classification_output_auc: 0.9973 - regression_output_mse: 0.0271 - regression_output_mae: 0.0922 - regression_output_rmse: 0.1584 - regression_output_custom_mape: 20.3898 - final_output_mse: 0.0272 - final_output_mae: 0.1062 - final_output_rmse: 0.1586 - final_output_custom_mape: 66.0008 - val_loss: 0.0922 - val_classification_output_loss: 0.1850 - val_regression_output_loss: 0.0376 - val_final_output_loss: 0.0420 - val_classification_output_accuracy: 0.9487 - val_classification_output_auc: 0.9876 - val_regression_output_mse: 0.0436 - val_regression_output_mae: 0.1203 - val_regression_output_rmse: 0.2009 - val_regression_output_custom_mape: 25.2706 - val_final_output_mse: 0.0439 - val_final_output_mae: 0.1349 - val_final_output_rmse: 0.2017 - val_final_output_custom_mape: 72.1134 - lr: 1.5000e-04\n", + "Epoch 32/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0650 - classification_output_loss: 0.0672 - regression_output_loss: 0.0332 - final_output_loss: 0.0375 - classification_output_accuracy: 0.9716 - classification_output_auc: 0.9971 - regression_output_mse: 0.0365 - regression_output_mae: 0.1059 - regression_output_rmse: 0.1810 - regression_output_custom_mape: 21.4473 - final_output_mse: 0.0367 - final_output_mae: 0.1203 - final_output_rmse: 0.1815 - final_output_custom_mape: 67.2227 - val_loss: 0.1220 - val_classification_output_loss: 0.2766 - val_regression_output_loss: 0.0532 - val_final_output_loss: 0.0576 - val_classification_output_accuracy: 0.9175 - val_classification_output_auc: 0.9696 - val_regression_output_mse: 0.0693 - val_regression_output_mae: 0.1501 - val_regression_output_rmse: 0.2396 - val_regression_output_custom_mape: 27.8170 - val_final_output_mse: 0.0696 - val_final_output_mae: 0.1643 - val_final_output_rmse: 0.2406 - val_final_output_custom_mape: 71.0394 - lr: 1.5000e-04\n", + "Epoch 33/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0613 - classification_output_loss: 0.0684 - regression_output_loss: 0.0300 - final_output_loss: 0.0341 - classification_output_accuracy: 0.9717 - classification_output_auc: 0.9970 - regression_output_mse: 0.0302 - regression_output_mae: 0.0967 - regression_output_rmse: 0.1657 - regression_output_custom_mape: 20.6231 - final_output_mse: 0.0303 - final_output_mae: 0.1106 - final_output_rmse: 0.1655 - final_output_custom_mape: 66.1750 - val_loss: 0.0831 - val_classification_output_loss: 0.1589 - val_regression_output_loss: 0.0354 - val_final_output_loss: 0.0399 - val_classification_output_accuracy: 0.9504 - val_classification_output_auc: 0.9857 - val_regression_output_mse: 0.0396 - val_regression_output_mae: 0.1140 - val_regression_output_rmse: 0.1903 - val_regression_output_custom_mape: 23.1021 - val_final_output_mse: 0.0400 - val_final_output_mae: 0.1290 - val_final_output_rmse: 0.1914 - val_final_output_custom_mape: 69.5060 - lr: 1.5000e-04\n", + "Epoch 34/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0607 - classification_output_loss: 0.0626 - regression_output_loss: 0.0318 - final_output_loss: 0.0360 - classification_output_accuracy: 0.9731 - classification_output_auc: 0.9976 - regression_output_mse: 0.0335 - regression_output_mae: 0.1023 - regression_output_rmse: 0.1740 - regression_output_custom_mape: 21.1940 - final_output_mse: 0.0336 - final_output_mae: 0.1165 - final_output_rmse: 0.1740 - final_output_custom_mape: 66.9985\n", + "Epoch 34: ReduceLROnPlateau reducing learning rate to 7.500000356230885e-05.\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0607 - classification_output_loss: 0.0626 - regression_output_loss: 0.0318 - final_output_loss: 0.0360 - classification_output_accuracy: 0.9731 - classification_output_auc: 0.9976 - regression_output_mse: 0.0335 - regression_output_mae: 0.1023 - regression_output_rmse: 0.1740 - regression_output_custom_mape: 21.1940 - final_output_mse: 0.0336 - final_output_mae: 0.1165 - final_output_rmse: 0.1740 - final_output_custom_mape: 66.9985 - val_loss: 0.1197 - val_classification_output_loss: 0.3105 - val_regression_output_loss: 0.0446 - val_final_output_loss: 0.0488 - val_classification_output_accuracy: 0.9176 - val_classification_output_auc: 0.9637 - val_regression_output_mse: 0.0549 - val_regression_output_mae: 0.1412 - val_regression_output_rmse: 0.2287 - val_regression_output_custom_mape: 25.8096 - val_final_output_mse: 0.0549 - val_final_output_mae: 0.1551 - val_final_output_rmse: 0.2288 - val_final_output_custom_mape: 71.1129 - lr: 1.5000e-04\n", + "Epoch 35/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0534 - classification_output_loss: 0.0549 - regression_output_loss: 0.0260 - final_output_loss: 0.0306 - classification_output_accuracy: 0.9770 - classification_output_auc: 0.9981 - regression_output_mse: 0.0233 - regression_output_mae: 0.0849 - regression_output_rmse: 0.1458 - regression_output_custom_mape: 18.8262 - final_output_mse: 0.0239 - final_output_mae: 0.0996 - final_output_rmse: 0.1470 - final_output_custom_mape: 64.8782 - val_loss: 0.0673 - val_classification_output_loss: 0.1295 - val_regression_output_loss: 0.0258 - val_final_output_loss: 0.0304 - val_classification_output_accuracy: 0.9557 - val_classification_output_auc: 0.9896 - val_regression_output_mse: 0.0212 - val_regression_output_mae: 0.0835 - val_regression_output_rmse: 0.1415 - val_regression_output_custom_mape: 21.2596 - val_final_output_mse: 0.0216 - val_final_output_mae: 0.0989 - val_final_output_rmse: 0.1432 - val_final_output_custom_mape: 67.3351 - lr: 7.5000e-05\n", + "Epoch 36/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0508 - classification_output_loss: 0.0519 - regression_output_loss: 0.0250 - final_output_loss: 0.0294 - classification_output_accuracy: 0.9781 - classification_output_auc: 0.9984 - regression_output_mse: 0.0215 - regression_output_mae: 0.0819 - regression_output_rmse: 0.1412 - regression_output_custom_mape: 18.4933 - final_output_mse: 0.0218 - final_output_mae: 0.0965 - final_output_rmse: 0.1419 - final_output_custom_mape: 64.5884 - val_loss: 0.0713 - val_classification_output_loss: 0.1363 - val_regression_output_loss: 0.0302 - val_final_output_loss: 0.0348 - val_classification_output_accuracy: 0.9533 - val_classification_output_auc: 0.9888 - val_regression_output_mse: 0.0276 - val_regression_output_mae: 0.0976 - val_regression_output_rmse: 0.1597 - val_regression_output_custom_mape: 21.3838 - val_final_output_mse: 0.0280 - val_final_output_mae: 0.1127 - val_final_output_rmse: 0.1610 - val_final_output_custom_mape: 67.4136 - lr: 7.5000e-05\n", + "Epoch 37/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0517 - classification_output_loss: 0.0540 - regression_output_loss: 0.0264 - final_output_loss: 0.0308 - classification_output_accuracy: 0.9769 - classification_output_auc: 0.9983 - regression_output_mse: 0.0241 - regression_output_mae: 0.0859 - regression_output_rmse: 0.1480 - regression_output_custom_mape: 18.8216 - final_output_mse: 0.0243 - final_output_mae: 0.1006 - final_output_rmse: 0.1487 - final_output_custom_mape: 64.9566 - val_loss: 0.0709 - val_classification_output_loss: 0.1296 - val_regression_output_loss: 0.0319 - val_final_output_loss: 0.0366 - val_classification_output_accuracy: 0.9538 - val_classification_output_auc: 0.9924 - val_regression_output_mse: 0.0321 - val_regression_output_mae: 0.0979 - val_regression_output_rmse: 0.1648 - val_regression_output_custom_mape: 22.7165 - val_final_output_mse: 0.0326 - val_final_output_mae: 0.1135 - val_final_output_rmse: 0.1667 - val_final_output_custom_mape: 68.0742 - lr: 7.5000e-05\n", + "Epoch 38/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0525 - classification_output_loss: 0.0565 - regression_output_loss: 0.0274 - final_output_loss: 0.0318 - classification_output_accuracy: 0.9763 - classification_output_auc: 0.9981 - regression_output_mse: 0.0255 - regression_output_mae: 0.0891 - regression_output_rmse: 0.1530 - regression_output_custom_mape: 19.1326 - final_output_mse: 0.0258 - final_output_mae: 0.1037 - final_output_rmse: 0.1537 - final_output_custom_mape: 65.2182 - val_loss: 0.0966 - val_classification_output_loss: 0.1959 - val_regression_output_loss: 0.0478 - val_final_output_loss: 0.0524 - val_classification_output_accuracy: 0.9431 - val_classification_output_auc: 0.9826 - val_regression_output_mse: 0.0598 - val_regression_output_mae: 0.1395 - val_regression_output_rmse: 0.2213 - val_regression_output_custom_mape: 26.5063 - val_final_output_mse: 0.0603 - val_final_output_mae: 0.1547 - val_final_output_rmse: 0.2227 - val_final_output_custom_mape: 71.2044 - lr: 7.5000e-05\n", + "Epoch 39/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0495 - classification_output_loss: 0.0537 - regression_output_loss: 0.0248 - final_output_loss: 0.0293 - classification_output_accuracy: 0.9774 - classification_output_auc: 0.9982 - regression_output_mse: 0.0211 - regression_output_mae: 0.0813 - regression_output_rmse: 0.1401 - regression_output_custom_mape: 18.5320 - final_output_mse: 0.0214 - final_output_mae: 0.0959 - final_output_rmse: 0.1410 - final_output_custom_mape: 64.5566 - val_loss: 0.0905 - val_classification_output_loss: 0.1782 - val_regression_output_loss: 0.0453 - val_final_output_loss: 0.0500 - val_classification_output_accuracy: 0.9458 - val_classification_output_auc: 0.9857 - val_regression_output_mse: 0.0556 - val_regression_output_mae: 0.1355 - val_regression_output_rmse: 0.2128 - val_regression_output_custom_mape: 26.0684 - val_final_output_mse: 0.0560 - val_final_output_mae: 0.1508 - val_final_output_rmse: 0.2142 - val_final_output_custom_mape: 70.9711 - lr: 7.5000e-05\n", + "Epoch 40/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0489 - classification_output_loss: 0.0548 - regression_output_loss: 0.0246 - final_output_loss: 0.0290 - classification_output_accuracy: 0.9771 - classification_output_auc: 0.9981 - regression_output_mse: 0.0208 - regression_output_mae: 0.0803 - regression_output_rmse: 0.1387 - regression_output_custom_mape: 18.3416 - final_output_mse: 0.0210 - final_output_mae: 0.0950 - final_output_rmse: 0.1395 - final_output_custom_mape: 64.3632 - val_loss: 0.0903 - val_classification_output_loss: 0.1696 - val_regression_output_loss: 0.0477 - val_final_output_loss: 0.0524 - val_classification_output_accuracy: 0.9471 - val_classification_output_auc: 0.9867 - val_regression_output_mse: 0.0601 - val_regression_output_mae: 0.1440 - val_regression_output_rmse: 0.2217 - val_regression_output_custom_mape: 26.6593 - val_final_output_mse: 0.0606 - val_final_output_mae: 0.1593 - val_final_output_rmse: 0.2231 - val_final_output_custom_mape: 71.6669 - lr: 7.5000e-05\n", + "Epoch 41/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0493 - classification_output_loss: 0.0555 - regression_output_loss: 0.0254 - final_output_loss: 0.0298 - classification_output_accuracy: 0.9770 - classification_output_auc: 0.9980 - regression_output_mse: 0.0219 - regression_output_mae: 0.0831 - regression_output_rmse: 0.1426 - regression_output_custom_mape: 18.8023 - final_output_mse: 0.0221 - final_output_mae: 0.0976 - final_output_rmse: 0.1432 - final_output_custom_mape: 64.7758\n", + "Epoch 41 Detailed Metrics:\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0493 - classification_output_loss: 0.0555 - regression_output_loss: 0.0254 - final_output_loss: 0.0298 - classification_output_accuracy: 0.9770 - classification_output_auc: 0.9980 - regression_output_mse: 0.0219 - regression_output_mae: 0.0831 - regression_output_rmse: 0.1426 - regression_output_custom_mape: 18.8023 - final_output_mse: 0.0221 - final_output_mae: 0.0976 - final_output_rmse: 0.1432 - final_output_custom_mape: 64.7758 - val_loss: 0.0931 - val_classification_output_loss: 0.1918 - val_regression_output_loss: 0.0461 - val_final_output_loss: 0.0508 - val_classification_output_accuracy: 0.9437 - val_classification_output_auc: 0.9827 - val_regression_output_mse: 0.0558 - val_regression_output_mae: 0.1358 - val_regression_output_rmse: 0.2143 - val_regression_output_custom_mape: 26.0898 - val_final_output_mse: 0.0562 - val_final_output_mae: 0.1510 - val_final_output_rmse: 0.2157 - val_final_output_custom_mape: 70.9012 - lr: 7.5000e-05\n", + "Epoch 42/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0489 - classification_output_loss: 0.0559 - regression_output_loss: 0.0251 - final_output_loss: 0.0295 - classification_output_accuracy: 0.9765 - classification_output_auc: 0.9980 - regression_output_mse: 0.0214 - regression_output_mae: 0.0822 - regression_output_rmse: 0.1415 - regression_output_custom_mape: 18.6815 - final_output_mse: 0.0216 - final_output_mae: 0.0968 - final_output_rmse: 0.1421 - final_output_custom_mape: 64.6422\n", + "Epoch 42: ReduceLROnPlateau reducing learning rate to 3.7500001781154424e-05.\n", + "541/541 [==============================] - 60s 110ms/step - loss: 0.0489 - classification_output_loss: 0.0559 - regression_output_loss: 0.0251 - final_output_loss: 0.0295 - classification_output_accuracy: 0.9765 - classification_output_auc: 0.9980 - regression_output_mse: 0.0214 - regression_output_mae: 0.0822 - regression_output_rmse: 0.1415 - regression_output_custom_mape: 18.6815 - final_output_mse: 0.0216 - final_output_mae: 0.0968 - final_output_rmse: 0.1421 - final_output_custom_mape: 64.6422 - val_loss: 0.0907 - val_classification_output_loss: 0.1880 - val_regression_output_loss: 0.0445 - val_final_output_loss: 0.0492 - val_classification_output_accuracy: 0.9440 - val_classification_output_auc: 0.9831 - val_regression_output_mse: 0.0543 - val_regression_output_mae: 0.1345 - val_regression_output_rmse: 0.2114 - val_regression_output_custom_mape: 26.1261 - val_final_output_mse: 0.0547 - val_final_output_mae: 0.1497 - val_final_output_rmse: 0.2128 - val_final_output_custom_mape: 71.0932 - lr: 7.5000e-05\n", + "Epoch 43/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0438 - classification_output_loss: 0.0492 - regression_output_loss: 0.0210 - final_output_loss: 0.0255 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 0.0154 - regression_output_mae: 0.0690 - regression_output_rmse: 0.1200 - regression_output_custom_mape: 16.9526 - final_output_mse: 0.0157 - final_output_mae: 0.0840 - final_output_rmse: 0.1212 - final_output_custom_mape: 63.1727 - val_loss: 0.0623 - val_classification_output_loss: 0.1263 - val_regression_output_loss: 0.0252 - val_final_output_loss: 0.0299 - val_classification_output_accuracy: 0.9579 - val_classification_output_auc: 0.9908 - val_regression_output_mse: 0.0204 - val_regression_output_mae: 0.0816 - val_regression_output_rmse: 0.1371 - val_regression_output_custom_mape: 21.3421 - val_final_output_mse: 0.0209 - val_final_output_mae: 0.0972 - val_final_output_rmse: 0.1390 - val_final_output_custom_mape: 67.4464 - lr: 3.7500e-05\n", + "Epoch 44/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0419 - classification_output_loss: 0.0470 - regression_output_loss: 0.0198 - final_output_loss: 0.0244 - classification_output_accuracy: 0.9804 - classification_output_auc: 0.9986 - regression_output_mse: 0.0138 - regression_output_mae: 0.0654 - regression_output_rmse: 0.1140 - regression_output_custom_mape: 16.4914 - final_output_mse: 0.0141 - final_output_mae: 0.0804 - final_output_rmse: 0.1155 - final_output_custom_mape: 62.8166 - val_loss: 0.0587 - val_classification_output_loss: 0.1175 - val_regression_output_loss: 0.0235 - val_final_output_loss: 0.0282 - val_classification_output_accuracy: 0.9606 - val_classification_output_auc: 0.9919 - val_regression_output_mse: 0.0177 - val_regression_output_mae: 0.0767 - val_regression_output_rmse: 0.1297 - val_regression_output_custom_mape: 20.6893 - val_final_output_mse: 0.0182 - val_final_output_mae: 0.0923 - val_final_output_rmse: 0.1317 - val_final_output_custom_mape: 66.9004 - lr: 3.7500e-05\n", + "Epoch 45/100\n", + "541/541 [==============================] - 60s 112ms/step - loss: 0.0414 - classification_output_loss: 0.0471 - regression_output_loss: 0.0197 - final_output_loss: 0.0242 - classification_output_accuracy: 0.9801 - classification_output_auc: 0.9986 - regression_output_mse: 0.0134 - regression_output_mae: 0.0649 - regression_output_rmse: 0.1128 - regression_output_custom_mape: 16.4420 - final_output_mse: 0.0137 - final_output_mae: 0.0800 - final_output_rmse: 0.1143 - final_output_custom_mape: 62.7677 - val_loss: 0.0571 - val_classification_output_loss: 0.1136 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0277 - val_classification_output_accuracy: 0.9607 - val_classification_output_auc: 0.9924 - val_regression_output_mse: 0.0173 - val_regression_output_mae: 0.0746 - val_regression_output_rmse: 0.1266 - val_regression_output_custom_mape: 20.4492 - val_final_output_mse: 0.0178 - val_final_output_mae: 0.0902 - val_final_output_rmse: 0.1287 - val_final_output_custom_mape: 66.6717 - lr: 3.7500e-05\n", + "Epoch 46/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0418 - classification_output_loss: 0.0482 - regression_output_loss: 0.0204 - final_output_loss: 0.0249 - classification_output_accuracy: 0.9796 - classification_output_auc: 0.9986 - regression_output_mse: 0.0144 - regression_output_mae: 0.0672 - regression_output_rmse: 0.1169 - regression_output_custom_mape: 16.6837 - final_output_mse: 0.0147 - final_output_mae: 0.0823 - final_output_rmse: 0.1184 - final_output_custom_mape: 63.0407 - val_loss: 0.0669 - val_classification_output_loss: 0.1158 - val_regression_output_loss: 0.0349 - val_final_output_loss: 0.0396 - val_classification_output_accuracy: 0.9604 - val_classification_output_auc: 0.9924 - val_regression_output_mse: 0.0365 - val_regression_output_mae: 0.1113 - val_regression_output_rmse: 0.1765 - val_regression_output_custom_mape: 23.5966 - val_final_output_mse: 0.0370 - val_final_output_mae: 0.1270 - val_final_output_rmse: 0.1782 - val_final_output_custom_mape: 69.7012 - lr: 3.7500e-05\n", + "Epoch 47/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0416 - classification_output_loss: 0.0464 - regression_output_loss: 0.0209 - final_output_loss: 0.0254 - classification_output_accuracy: 0.9807 - classification_output_auc: 0.9987 - regression_output_mse: 0.0151 - regression_output_mae: 0.0688 - regression_output_rmse: 0.1189 - regression_output_custom_mape: 17.0184 - final_output_mse: 0.0154 - final_output_mae: 0.0839 - final_output_rmse: 0.1202 - final_output_custom_mape: 63.3004 - val_loss: 0.0576 - val_classification_output_loss: 0.1187 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0277 - val_classification_output_accuracy: 0.9583 - val_classification_output_auc: 0.9917 - val_regression_output_mse: 0.0169 - val_regression_output_mae: 0.0751 - val_regression_output_rmse: 0.1274 - val_regression_output_custom_mape: 20.1316 - val_final_output_mse: 0.0173 - val_final_output_mae: 0.0906 - val_final_output_rmse: 0.1293 - val_final_output_custom_mape: 66.4177 - lr: 3.7500e-05\n", + "Epoch 48/100\n", + "541/541 [==============================] - 60s 111ms/step - loss: 0.0424 - classification_output_loss: 0.0483 - regression_output_loss: 0.0216 - final_output_loss: 0.0262 - classification_output_accuracy: 0.9794 - classification_output_auc: 0.9986 - regression_output_mse: 0.0162 - regression_output_mae: 0.0711 - regression_output_rmse: 0.1232 - regression_output_custom_mape: 17.0768 - final_output_mse: 0.0166 - final_output_mae: 0.0862 - final_output_rmse: 0.1246 - final_output_custom_mape: 63.4081 - val_loss: 0.0640 - val_classification_output_loss: 0.1394 - val_regression_output_loss: 0.0260 - val_final_output_loss: 0.0307 - val_classification_output_accuracy: 0.9565 - val_classification_output_auc: 0.9892 - val_regression_output_mse: 0.0213 - val_regression_output_mae: 0.0842 - val_regression_output_rmse: 0.1389 - val_regression_output_custom_mape: 21.5426 - val_final_output_mse: 0.0218 - val_final_output_mae: 0.0998 - val_final_output_rmse: 0.1408 - val_final_output_custom_mape: 67.5867 - lr: 3.7500e-05\n", + "Epoch 49/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0399 - classification_output_loss: 0.0466 - regression_output_loss: 0.0192 - final_output_loss: 0.0238 - classification_output_accuracy: 0.9805 - classification_output_auc: 0.9987 - regression_output_mse: 0.0128 - regression_output_mae: 0.0635 - regression_output_rmse: 0.1100 - regression_output_custom_mape: 16.2668 - final_output_mse: 0.0131 - final_output_mae: 0.0787 - final_output_rmse: 0.1117 - final_output_custom_mape: 62.6690 - val_loss: 0.0574 - val_classification_output_loss: 0.1066 - val_regression_output_loss: 0.0263 - val_final_output_loss: 0.0311 - val_classification_output_accuracy: 0.9619 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 0.0218 - val_regression_output_mae: 0.0855 - val_regression_output_rmse: 0.1405 - val_regression_output_custom_mape: 21.2892 - val_final_output_mse: 0.0223 - val_final_output_mae: 0.1011 - val_final_output_rmse: 0.1425 - val_final_output_custom_mape: 67.5575 - lr: 3.7500e-05\n", + "Epoch 50/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0393 - classification_output_loss: 0.0456 - regression_output_loss: 0.0192 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9807 - classification_output_auc: 0.9987 - regression_output_mse: 0.0127 - regression_output_mae: 0.0632 - regression_output_rmse: 0.1094 - regression_output_custom_mape: 16.3543 - final_output_mse: 0.0130 - final_output_mae: 0.0783 - final_output_rmse: 0.1110 - final_output_custom_mape: 62.6908 - val_loss: 0.0548 - val_classification_output_loss: 0.1058 - val_regression_output_loss: 0.0236 - val_final_output_loss: 0.0284 - val_classification_output_accuracy: 0.9626 - val_classification_output_auc: 0.9932 - val_regression_output_mse: 0.0180 - val_regression_output_mae: 0.0768 - val_regression_output_rmse: 0.1285 - val_regression_output_custom_mape: 20.5455 - val_final_output_mse: 0.0185 - val_final_output_mae: 0.0925 - val_final_output_rmse: 0.1307 - val_final_output_custom_mape: 66.7687 - lr: 3.7500e-05\n", + "Epoch 51/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0393 - classification_output_loss: 0.0463 - regression_output_loss: 0.0192 - final_output_loss: 0.0238 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9987 - regression_output_mse: 0.0127 - regression_output_mae: 0.0635 - regression_output_rmse: 0.1099 - regression_output_custom_mape: 16.4549 - final_output_mse: 0.0131 - final_output_mae: 0.0787 - final_output_rmse: 0.1116 - final_output_custom_mape: 62.7996\n", + "Epoch 51 Detailed Metrics:\n", + "541/541 [==============================] - 51s 95ms/step - loss: 0.0393 - classification_output_loss: 0.0463 - regression_output_loss: 0.0192 - final_output_loss: 0.0238 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9987 - regression_output_mse: 0.0127 - regression_output_mae: 0.0635 - regression_output_rmse: 0.1099 - regression_output_custom_mape: 16.4549 - final_output_mse: 0.0131 - final_output_mae: 0.0787 - final_output_rmse: 0.1116 - final_output_custom_mape: 62.7996 - val_loss: 0.0545 - val_classification_output_loss: 0.1059 - val_regression_output_loss: 0.0235 - val_final_output_loss: 0.0283 - val_classification_output_accuracy: 0.9624 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 0.0178 - val_regression_output_mae: 0.0766 - val_regression_output_rmse: 0.1285 - val_regression_output_custom_mape: 20.5595 - val_final_output_mse: 0.0183 - val_final_output_mae: 0.0924 - val_final_output_rmse: 0.1307 - val_final_output_custom_mape: 66.7998 - lr: 3.7500e-05\n", + "Epoch 52/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0398 - classification_output_loss: 0.0467 - regression_output_loss: 0.0200 - final_output_loss: 0.0246 - classification_output_accuracy: 0.9806 - classification_output_auc: 0.9987 - regression_output_mse: 0.0138 - regression_output_mae: 0.0660 - regression_output_rmse: 0.1142 - regression_output_custom_mape: 16.6527 - final_output_mse: 0.0142 - final_output_mae: 0.0812 - final_output_rmse: 0.1158 - final_output_custom_mape: 63.0652\n", + "Epoch 52: ReduceLROnPlateau reducing learning rate to 1.8750000890577212e-05.\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0398 - classification_output_loss: 0.0467 - regression_output_loss: 0.0200 - final_output_loss: 0.0246 - classification_output_accuracy: 0.9806 - classification_output_auc: 0.9987 - regression_output_mse: 0.0138 - regression_output_mae: 0.0660 - regression_output_rmse: 0.1142 - regression_output_custom_mape: 16.6527 - final_output_mse: 0.0142 - final_output_mae: 0.0812 - final_output_rmse: 0.1158 - 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regression_output_custom_mape: 15.8104 - final_output_mse: 0.0113 - final_output_mae: 0.0740 - final_output_rmse: 0.1037 - final_output_custom_mape: 62.1211 - val_loss: 0.0526 - val_classification_output_loss: 0.1054 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0269 - val_classification_output_accuracy: 0.9621 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 0.0157 - val_regression_output_mae: 0.0723 - val_regression_output_rmse: 0.1212 - val_regression_output_custom_mape: 19.7542 - val_final_output_mse: 0.0162 - val_final_output_mae: 0.0880 - val_final_output_rmse: 0.1234 - val_final_output_custom_mape: 66.0210 - lr: 1.8750e-05\n", + "Epoch 57/100\n", + "541/541 [==============================] - 51s 95ms/step - loss: 0.0371 - classification_output_loss: 0.0454 - regression_output_loss: 0.0179 - final_output_loss: 0.0225 - classification_output_accuracy: 0.9814 - classification_output_auc: 0.9987 - regression_output_mse: 0.0111 - regression_output_mae: 0.0592 - regression_output_rmse: 0.1025 - regression_output_custom_mape: 15.9130 - final_output_mse: 0.0115 - final_output_mae: 0.0743 - final_output_rmse: 0.1043 - final_output_custom_mape: 62.1983 - val_loss: 0.0515 - val_classification_output_loss: 0.1063 - val_regression_output_loss: 0.0207 - val_final_output_loss: 0.0255 - val_classification_output_accuracy: 0.9622 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 0.0141 - val_regression_output_mae: 0.0677 - val_regression_output_rmse: 0.1149 - val_regression_output_custom_mape: 19.3432 - val_final_output_mse: 0.0146 - val_final_output_mae: 0.0834 - val_final_output_rmse: 0.1172 - val_final_output_custom_mape: 65.6157 - lr: 1.8750e-05\n", + "Epoch 58/100\n", + "541/541 [==============================] - 49s 91ms/step - loss: 0.0369 - classification_output_loss: 0.0454 - regression_output_loss: 0.0178 - final_output_loss: 0.0223 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9988 - regression_output_mse: 0.0110 - regression_output_mae: 0.0588 - regression_output_rmse: 0.1019 - regression_output_custom_mape: 15.8985 - final_output_mse: 0.0113 - final_output_mae: 0.0739 - final_output_rmse: 0.1037 - final_output_custom_mape: 62.1227 - val_loss: 0.0522 - val_classification_output_loss: 0.1054 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0266 - val_classification_output_accuracy: 0.9628 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 0.0155 - val_regression_output_mae: 0.0716 - val_regression_output_rmse: 0.1202 - val_regression_output_custom_mape: 19.6480 - val_final_output_mse: 0.0160 - val_final_output_mae: 0.0873 - val_final_output_rmse: 0.1224 - val_final_output_custom_mape: 65.9271 - lr: 1.8750e-05\n", + "Epoch 59/100\n", + "541/541 [==============================] - 50s 92ms/step - loss: 0.0364 - classification_output_loss: 0.0447 - regression_output_loss: 0.0175 - final_output_loss: 0.0220 - classification_output_accuracy: 0.9814 - classification_output_auc: 0.9988 - regression_output_mse: 0.0106 - regression_output_mae: 0.0578 - regression_output_rmse: 0.1001 - regression_output_custom_mape: 15.7452 - final_output_mse: 0.0109 - final_output_mae: 0.0728 - final_output_rmse: 0.1019 - final_output_custom_mape: 61.9818 - val_loss: 0.0523 - val_classification_output_loss: 0.1092 - val_regression_output_loss: 0.0212 - val_final_output_loss: 0.0260 - val_classification_output_accuracy: 0.9616 - val_classification_output_auc: 0.9932 - val_regression_output_mse: 0.0148 - val_regression_output_mae: 0.0695 - val_regression_output_rmse: 0.1172 - val_regression_output_custom_mape: 19.5822 - val_final_output_mse: 0.0153 - val_final_output_mae: 0.0852 - val_final_output_rmse: 0.1195 - val_final_output_custom_mape: 65.8081 - lr: 1.8750e-05\n", + "Epoch 60/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0366 - classification_output_loss: 0.0450 - regression_output_loss: 0.0177 - final_output_loss: 0.0222 - classification_output_accuracy: 0.9805 - classification_output_auc: 0.9988 - regression_output_mse: 0.0108 - regression_output_mae: 0.0586 - regression_output_rmse: 0.1015 - regression_output_custom_mape: 15.9146 - final_output_mse: 0.0112 - final_output_mae: 0.0736 - final_output_rmse: 0.1031 - final_output_custom_mape: 62.1485 - val_loss: 0.0508 - val_classification_output_loss: 0.0992 - val_regression_output_loss: 0.0220 - val_final_output_loss: 0.0268 - val_classification_output_accuracy: 0.9634 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0156 - val_regression_output_mae: 0.0720 - val_regression_output_rmse: 0.1205 - val_regression_output_custom_mape: 19.6451 - val_final_output_mse: 0.0161 - val_final_output_mae: 0.0877 - val_final_output_rmse: 0.1228 - val_final_output_custom_mape: 65.9773 - lr: 1.8750e-05\n", + "Epoch 61/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0370 - classification_output_loss: 0.0454 - regression_output_loss: 0.0182 - final_output_loss: 0.0228 - 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val_classification_output_loss: 0.0984 - val_regression_output_loss: 0.0203 - val_final_output_loss: 0.0251 - val_classification_output_accuracy: 0.9637 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0136 - val_regression_output_mae: 0.0667 - val_regression_output_rmse: 0.1128 - val_regression_output_custom_mape: 18.9880 - val_final_output_mse: 0.0141 - val_final_output_mae: 0.0824 - val_final_output_rmse: 0.1152 - val_final_output_custom_mape: 65.3033 - lr: 1.8750e-05\n", + "Epoch 65/100\n", + "541/541 [==============================] - 48s 89ms/step - loss: 0.0361 - classification_output_loss: 0.0442 - regression_output_loss: 0.0179 - final_output_loss: 0.0225 - classification_output_accuracy: 0.9813 - classification_output_auc: 0.9988 - regression_output_mse: 0.0110 - regression_output_mae: 0.0592 - regression_output_rmse: 0.1024 - regression_output_custom_mape: 15.8189 - final_output_mse: 0.0114 - final_output_mae: 0.0744 - final_output_rmse: 0.1043 - final_output_custom_mape: 62.1326 - 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final_output_rmse: 0.1057 - final_output_custom_mape: 62.2372 - val_loss: 0.0498 - val_classification_output_loss: 0.0979 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0267 - val_classification_output_accuracy: 0.9640 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0154 - val_regression_output_mae: 0.0716 - val_regression_output_rmse: 0.1197 - val_regression_output_custom_mape: 19.4101 - val_final_output_mse: 0.0159 - val_final_output_mae: 0.0874 - val_final_output_rmse: 0.1220 - val_final_output_custom_mape: 65.7150 - lr: 1.8750e-05\n", + "Epoch 68/100\n", + "541/541 [==============================] - 50s 93ms/step - loss: 0.0359 - classification_output_loss: 0.0443 - regression_output_loss: 0.0180 - final_output_loss: 0.0225 - classification_output_accuracy: 0.9813 - classification_output_auc: 0.9988 - regression_output_mse: 0.0112 - regression_output_mae: 0.0593 - regression_output_rmse: 0.1024 - regression_output_custom_mape: 15.8022 - final_output_mse: 0.0115 - final_output_mae: 0.0745 - final_output_rmse: 0.1042 - final_output_custom_mape: 62.1159 - val_loss: 0.0512 - val_classification_output_loss: 0.1075 - val_regression_output_loss: 0.0212 - val_final_output_loss: 0.0260 - val_classification_output_accuracy: 0.9619 - val_classification_output_auc: 0.9932 - val_regression_output_mse: 0.0146 - val_regression_output_mae: 0.0696 - val_regression_output_rmse: 0.1169 - val_regression_output_custom_mape: 19.3851 - val_final_output_mse: 0.0151 - val_final_output_mae: 0.0852 - val_final_output_rmse: 0.1192 - val_final_output_custom_mape: 65.6778 - lr: 1.8750e-05\n", + "Epoch 69/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0352 - classification_output_loss: 0.0445 - regression_output_loss: 0.0171 - final_output_loss: 0.0216 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0101 - regression_output_mae: 0.0566 - regression_output_rmse: 0.0980 - regression_output_custom_mape: 15.5609 - final_output_mse: 0.0105 - final_output_mae: 0.0717 - final_output_rmse: 0.0999 - final_output_custom_mape: 61.8379 - val_loss: 0.0503 - val_classification_output_loss: 0.1062 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0254 - val_classification_output_accuracy: 0.9621 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 0.0139 - val_regression_output_mae: 0.0676 - val_regression_output_rmse: 0.1138 - val_regression_output_custom_mape: 19.0459 - val_final_output_mse: 0.0144 - val_final_output_mae: 0.0833 - val_final_output_rmse: 0.1161 - val_final_output_custom_mape: 65.3141 - lr: 1.8750e-05\n", + "Epoch 70/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0352 - classification_output_loss: 0.0445 - regression_output_loss: 0.0172 - final_output_loss: 0.0217 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0102 - regression_output_mae: 0.0569 - regression_output_rmse: 0.0984 - regression_output_custom_mape: 15.6253 - final_output_mse: 0.0105 - final_output_mae: 0.0720 - final_output_rmse: 0.1003 - final_output_custom_mape: 61.8611 - val_loss: 0.0495 - val_classification_output_loss: 0.1032 - val_regression_output_loss: 0.0205 - val_final_output_loss: 0.0253 - val_classification_output_accuracy: 0.9628 - val_classification_output_auc: 0.9937 - val_regression_output_mse: 0.0137 - val_regression_output_mae: 0.0672 - val_regression_output_rmse: 0.1134 - val_regression_output_custom_mape: 19.0341 - val_final_output_mse: 0.0142 - val_final_output_mae: 0.0830 - val_final_output_rmse: 0.1158 - val_final_output_custom_mape: 65.3020 - lr: 1.8750e-05\n", + "Epoch 71/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0351 - classification_output_loss: 0.0442 - regression_output_loss: 0.0173 - final_output_loss: 0.0218 - classification_output_accuracy: 0.9817 - classification_output_auc: 0.9988 - regression_output_mse: 0.0103 - regression_output_mae: 0.0571 - regression_output_rmse: 0.0989 - regression_output_custom_mape: 15.6037 - final_output_mse: 0.0107 - final_output_mae: 0.0722 - final_output_rmse: 0.1007 - final_output_custom_mape: 61.8687\n", + "Epoch 71: ReduceLROnPlateau reducing learning rate to 9.375000445288606e-06.\n", + "\n", + "Epoch 71 Detailed Metrics:\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0351 - classification_output_loss: 0.0442 - regression_output_loss: 0.0173 - final_output_loss: 0.0218 - classification_output_accuracy: 0.9817 - classification_output_auc: 0.9988 - regression_output_mse: 0.0103 - regression_output_mae: 0.0571 - regression_output_rmse: 0.0989 - regression_output_custom_mape: 15.6037 - final_output_mse: 0.0107 - final_output_mae: 0.0722 - final_output_rmse: 0.1007 - final_output_custom_mape: 61.8687 - val_loss: 0.0484 - val_classification_output_loss: 0.0953 - val_regression_output_loss: 0.0211 - val_final_output_loss: 0.0259 - val_classification_output_accuracy: 0.9642 - val_classification_output_auc: 0.9944 - val_regression_output_mse: 0.0144 - val_regression_output_mae: 0.0692 - val_regression_output_rmse: 0.1160 - val_regression_output_custom_mape: 19.0884 - val_final_output_mse: 0.0149 - val_final_output_mae: 0.0850 - val_final_output_rmse: 0.1184 - val_final_output_custom_mape: 65.3852 - lr: 1.8750e-05\n", + "Epoch 72/100\n", + "541/541 [==============================] - 55s 103ms/step - loss: 0.0349 - classification_output_loss: 0.0451 - regression_output_loss: 0.0169 - final_output_loss: 0.0214 - classification_output_accuracy: 0.9807 - classification_output_auc: 0.9987 - regression_output_mse: 0.0098 - regression_output_mae: 0.0559 - regression_output_rmse: 0.0967 - regression_output_custom_mape: 15.4296 - final_output_mse: 0.0102 - final_output_mae: 0.0711 - final_output_rmse: 0.0987 - final_output_custom_mape: 61.7132 - val_loss: 0.0474 - val_classification_output_loss: 0.0992 - val_regression_output_loss: 0.0190 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9634 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0120 - val_regression_output_mae: 0.0625 - val_regression_output_rmse: 0.1064 - val_regression_output_custom_mape: 18.3833 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0782 - val_final_output_rmse: 0.1088 - val_final_output_custom_mape: 64.7322 - lr: 9.3750e-06\n", + "Epoch 73/100\n", + "541/541 [==============================] - 59s 108ms/step - loss: 0.0346 - classification_output_loss: 0.0447 - regression_output_loss: 0.0166 - final_output_loss: 0.0212 - classification_output_accuracy: 0.9809 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0551 - regression_output_rmse: 0.0952 - regression_output_custom_mape: 15.3131 - final_output_mse: 0.0099 - final_output_mae: 0.0702 - final_output_rmse: 0.0972 - final_output_custom_mape: 61.5896 - val_loss: 0.0482 - val_classification_output_loss: 0.1015 - val_regression_output_loss: 0.0195 - val_final_output_loss: 0.0242 - val_classification_output_accuracy: 0.9622 - val_classification_output_auc: 0.9938 - val_regression_output_mse: 0.0125 - val_regression_output_mae: 0.0640 - val_regression_output_rmse: 0.1086 - val_regression_output_custom_mape: 18.6132 - val_final_output_mse: 0.0130 - val_final_output_mae: 0.0797 - val_final_output_rmse: 0.1110 - val_final_output_custom_mape: 64.9506 - lr: 9.3750e-06\n", + "Epoch 74/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0346 - classification_output_loss: 0.0452 - regression_output_loss: 0.0166 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9804 - classification_output_auc: 0.9988 - regression_output_mse: 0.0094 - regression_output_mae: 0.0549 - regression_output_rmse: 0.0948 - regression_output_custom_mape: 15.3850 - final_output_mse: 0.0098 - final_output_mae: 0.0701 - final_output_rmse: 0.0969 - final_output_custom_mape: 61.6494 - val_loss: 0.0474 - val_classification_output_loss: 0.0998 - val_regression_output_loss: 0.0189 - val_final_output_loss: 0.0237 - val_classification_output_accuracy: 0.9633 - val_classification_output_auc: 0.9939 - val_regression_output_mse: 0.0119 - val_regression_output_mae: 0.0621 - val_regression_output_rmse: 0.1062 - val_regression_output_custom_mape: 18.4668 - val_final_output_mse: 0.0124 - val_final_output_mae: 0.0779 - val_final_output_rmse: 0.1087 - val_final_output_custom_mape: 64.7637 - lr: 9.3750e-06\n", + "Epoch 75/100\n", + "541/541 [==============================] - 60s 110ms/step - loss: 0.0346 - classification_output_loss: 0.0451 - regression_output_loss: 0.0167 - final_output_loss: 0.0212 - classification_output_accuracy: 0.9805 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0552 - regression_output_rmse: 0.0954 - regression_output_custom_mape: 15.4044 - final_output_mse: 0.0099 - final_output_mae: 0.0703 - final_output_rmse: 0.0974 - final_output_custom_mape: 61.6629 - val_loss: 0.0472 - val_classification_output_loss: 0.0992 - val_regression_output_loss: 0.0189 - val_final_output_loss: 0.0237 - val_classification_output_accuracy: 0.9635 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0119 - val_regression_output_mae: 0.0621 - val_regression_output_rmse: 0.1058 - val_regression_output_custom_mape: 18.3245 - val_final_output_mse: 0.0124 - val_final_output_mae: 0.0779 - val_final_output_rmse: 0.1083 - val_final_output_custom_mape: 64.6469 - lr: 9.3750e-06\n", + "Epoch 76/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0344 - classification_output_loss: 0.0447 - regression_output_loss: 0.0166 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0549 - regression_output_rmse: 0.0950 - regression_output_custom_mape: 15.3963 - final_output_mse: 0.0099 - final_output_mae: 0.0700 - final_output_rmse: 0.0970 - final_output_custom_mape: 61.6328 - val_loss: 0.0474 - val_classification_output_loss: 0.0992 - val_regression_output_loss: 0.0192 - val_final_output_loss: 0.0239 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0122 - val_regression_output_mae: 0.0630 - val_regression_output_rmse: 0.1075 - val_regression_output_custom_mape: 18.5056 - val_final_output_mse: 0.0126 - val_final_output_mae: 0.0787 - val_final_output_rmse: 0.1099 - val_final_output_custom_mape: 64.8788 - lr: 9.3750e-06\n", + "Epoch 77/100\n", + "541/541 [==============================] - 59s 108ms/step - loss: 0.0344 - classification_output_loss: 0.0449 - regression_output_loss: 0.0166 - final_output_loss: 0.0212 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0550 - regression_output_rmse: 0.0953 - regression_output_custom_mape: 15.3243 - final_output_mse: 0.0099 - final_output_mae: 0.0701 - final_output_rmse: 0.0972 - final_output_custom_mape: 61.6194 - val_loss: 0.0467 - val_classification_output_loss: 0.0981 - val_regression_output_loss: 0.0186 - val_final_output_loss: 0.0234 - val_classification_output_accuracy: 0.9630 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0115 - val_regression_output_mae: 0.0613 - val_regression_output_rmse: 0.1045 - val_regression_output_custom_mape: 18.1003 - val_final_output_mse: 0.0120 - val_final_output_mae: 0.0769 - val_final_output_rmse: 0.1070 - val_final_output_custom_mape: 64.4979 - lr: 9.3750e-06\n", + "Epoch 78/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0343 - classification_output_loss: 0.0445 - regression_output_loss: 0.0166 - final_output_loss: 0.0212 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9988 - regression_output_mse: 0.0096 - regression_output_mae: 0.0550 - regression_output_rmse: 0.0954 - regression_output_custom_mape: 15.3804 - final_output_mse: 0.0100 - final_output_mae: 0.0701 - final_output_rmse: 0.0975 - final_output_custom_mape: 61.6332 - val_loss: 0.0475 - val_classification_output_loss: 0.0991 - val_regression_output_loss: 0.0195 - val_final_output_loss: 0.0243 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0125 - val_regression_output_mae: 0.0641 - val_regression_output_rmse: 0.1089 - val_regression_output_custom_mape: 18.6073 - val_final_output_mse: 0.0130 - val_final_output_mae: 0.0798 - val_final_output_rmse: 0.1113 - val_final_output_custom_mape: 64.9554 - lr: 9.3750e-06\n", + "Epoch 79/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0342 - classification_output_loss: 0.0444 - regression_output_loss: 0.0166 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9810 - classification_output_auc: 0.9988 - regression_output_mse: 0.0094 - regression_output_mae: 0.0549 - regression_output_rmse: 0.0947 - regression_output_custom_mape: 15.4022 - final_output_mse: 0.0098 - final_output_mae: 0.0700 - final_output_rmse: 0.0967 - final_output_custom_mape: 61.6269 - val_loss: 0.0474 - val_classification_output_loss: 0.0996 - val_regression_output_loss: 0.0192 - val_final_output_loss: 0.0240 - val_classification_output_accuracy: 0.9635 - val_classification_output_auc: 0.9939 - val_regression_output_mse: 0.0122 - val_regression_output_mae: 0.0631 - val_regression_output_rmse: 0.1070 - val_regression_output_custom_mape: 18.3915 - val_final_output_mse: 0.0127 - val_final_output_mae: 0.0788 - val_final_output_rmse: 0.1094 - val_final_output_custom_mape: 64.7190 - lr: 9.3750e-06\n", + "Epoch 80/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0342 - classification_output_loss: 0.0446 - regression_output_loss: 0.0166 - final_output_loss: 0.0212 - classification_output_accuracy: 0.9810 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0549 - regression_output_rmse: 0.0951 - regression_output_custom_mape: 15.3161 - final_output_mse: 0.0099 - final_output_mae: 0.0701 - final_output_rmse: 0.0971 - final_output_custom_mape: 61.6155 - val_loss: 0.0465 - val_classification_output_loss: 0.0980 - val_regression_output_loss: 0.0186 - val_final_output_loss: 0.0234 - val_classification_output_accuracy: 0.9640 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0116 - val_regression_output_mae: 0.0613 - val_regression_output_rmse: 0.1044 - val_regression_output_custom_mape: 18.1482 - val_final_output_mse: 0.0121 - val_final_output_mae: 0.0771 - val_final_output_rmse: 0.1069 - val_final_output_custom_mape: 64.5007 - lr: 9.3750e-06\n", + "Epoch 81/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0338 - classification_output_loss: 0.0438 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9813 - classification_output_auc: 0.9989 - regression_output_mse: 0.0091 - regression_output_mae: 0.0541 - regression_output_rmse: 0.0935 - regression_output_custom_mape: 15.2070 - final_output_mse: 0.0095 - final_output_mae: 0.0693 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.4725\n", + "Epoch 81 Detailed Metrics:\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0338 - classification_output_loss: 0.0438 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9813 - classification_output_auc: 0.9989 - regression_output_mse: 0.0091 - regression_output_mae: 0.0541 - regression_output_rmse: 0.0935 - regression_output_custom_mape: 15.2070 - final_output_mse: 0.0095 - final_output_mae: 0.0693 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.4725 - val_loss: 0.0470 - val_classification_output_loss: 0.0990 - val_regression_output_loss: 0.0190 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0120 - val_regression_output_mae: 0.0624 - val_regression_output_rmse: 0.1057 - val_regression_output_custom_mape: 18.1129 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0782 - val_final_output_rmse: 0.1083 - val_final_output_custom_mape: 64.4401 - lr: 9.3750e-06\n", + "Epoch 82/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0343 - classification_output_loss: 0.0450 - regression_output_loss: 0.0167 - final_output_loss: 0.0213 - classification_output_accuracy: 0.9806 - classification_output_auc: 0.9988 - regression_output_mse: 0.0096 - regression_output_mae: 0.0552 - regression_output_rmse: 0.0954 - regression_output_custom_mape: 15.3545 - final_output_mse: 0.0100 - final_output_mae: 0.0704 - final_output_rmse: 0.0975 - final_output_custom_mape: 61.6195 - val_loss: 0.0463 - val_classification_output_loss: 0.0981 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0232 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0113 - val_regression_output_mae: 0.0606 - val_regression_output_rmse: 0.1037 - val_regression_output_custom_mape: 18.1365 - val_final_output_mse: 0.0118 - val_final_output_mae: 0.0763 - val_final_output_rmse: 0.1062 - val_final_output_custom_mape: 64.4947 - lr: 9.3750e-06\n", + "Epoch 83/100\n", + "541/541 [==============================] - 55s 103ms/step - loss: 0.0340 - classification_output_loss: 0.0442 - regression_output_loss: 0.0165 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9810 - classification_output_auc: 0.9988 - regression_output_mse: 0.0094 - regression_output_mae: 0.0547 - regression_output_rmse: 0.0945 - regression_output_custom_mape: 15.2821 - final_output_mse: 0.0098 - final_output_mae: 0.0699 - final_output_rmse: 0.0967 - final_output_custom_mape: 61.5901 - val_loss: 0.0463 - val_classification_output_loss: 0.0993 - val_regression_output_loss: 0.0182 - val_final_output_loss: 0.0230 - val_classification_output_accuracy: 0.9635 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0111 - val_regression_output_mae: 0.0599 - val_regression_output_rmse: 0.1022 - val_regression_output_custom_mape: 17.9433 - val_final_output_mse: 0.0116 - val_final_output_mae: 0.0756 - val_final_output_rmse: 0.1048 - val_final_output_custom_mape: 64.2812 - lr: 9.3750e-06\n", + "Epoch 84/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0337 - classification_output_loss: 0.0438 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9814 - classification_output_auc: 0.9988 - regression_output_mse: 0.0092 - regression_output_mae: 0.0541 - regression_output_rmse: 0.0936 - regression_output_custom_mape: 15.1612 - final_output_mse: 0.0096 - final_output_mae: 0.0693 - final_output_rmse: 0.0957 - final_output_custom_mape: 61.4662 - val_loss: 0.0460 - val_classification_output_loss: 0.0974 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0231 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0113 - val_regression_output_mae: 0.0604 - val_regression_output_rmse: 0.1033 - val_regression_output_custom_mape: 18.0828 - val_final_output_mse: 0.0118 - val_final_output_mae: 0.0761 - val_final_output_rmse: 0.1059 - val_final_output_custom_mape: 64.4077 - lr: 9.3750e-06\n", + "Epoch 85/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0340 - classification_output_loss: 0.0444 - regression_output_loss: 0.0166 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9811 - classification_output_auc: 0.9988 - regression_output_mse: 0.0095 - regression_output_mae: 0.0550 - regression_output_rmse: 0.0951 - regression_output_custom_mape: 15.3615 - final_output_mse: 0.0098 - final_output_mae: 0.0701 - final_output_rmse: 0.0970 - final_output_custom_mape: 61.6132 - val_loss: 0.0458 - val_classification_output_loss: 0.0976 - val_regression_output_loss: 0.0180 - val_final_output_loss: 0.0228 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0109 - val_regression_output_mae: 0.0593 - val_regression_output_rmse: 0.1015 - val_regression_output_custom_mape: 17.8655 - val_final_output_mse: 0.0114 - val_final_output_mae: 0.0750 - val_final_output_rmse: 0.1041 - val_final_output_custom_mape: 64.2029 - lr: 9.3750e-06\n", + "Epoch 86/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0337 - classification_output_loss: 0.0445 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9808 - classification_output_auc: 0.9988 - regression_output_mse: 0.0091 - regression_output_mae: 0.0541 - regression_output_rmse: 0.0935 - regression_output_custom_mape: 15.1750 - final_output_mse: 0.0095 - final_output_mae: 0.0693 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.4570 - val_loss: 0.0466 - val_classification_output_loss: 0.0989 - val_regression_output_loss: 0.0188 - val_final_output_loss: 0.0235 - val_classification_output_accuracy: 0.9634 - val_classification_output_auc: 0.9939 - val_regression_output_mse: 0.0117 - val_regression_output_mae: 0.0617 - val_regression_output_rmse: 0.1049 - val_regression_output_custom_mape: 18.1680 - val_final_output_mse: 0.0122 - val_final_output_mae: 0.0774 - val_final_output_rmse: 0.1074 - val_final_output_custom_mape: 64.5077 - lr: 9.3750e-06\n", + "Epoch 87/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0336 - classification_output_loss: 0.0441 - regression_output_loss: 0.0163 - final_output_loss: 0.0208 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0091 - regression_output_mae: 0.0540 - regression_output_rmse: 0.0932 - regression_output_custom_mape: 15.2261 - final_output_mse: 0.0095 - final_output_mae: 0.0692 - final_output_rmse: 0.0953 - final_output_custom_mape: 61.4855 - val_loss: 0.0456 - val_classification_output_loss: 0.0966 - val_regression_output_loss: 0.0182 - val_final_output_loss: 0.0230 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0111 - val_regression_output_mae: 0.0599 - val_regression_output_rmse: 0.1021 - val_regression_output_custom_mape: 17.7784 - val_final_output_mse: 0.0116 - val_final_output_mae: 0.0757 - val_final_output_rmse: 0.1047 - val_final_output_custom_mape: 64.1520 - lr: 9.3750e-06\n", + "Epoch 88/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.0333 - classification_output_loss: 0.0437 - regression_output_loss: 0.0160 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9814 - classification_output_auc: 0.9989 - regression_output_mse: 0.0088 - regression_output_mae: 0.0532 - regression_output_rmse: 0.0920 - regression_output_custom_mape: 15.0525 - final_output_mse: 0.0092 - final_output_mae: 0.0684 - final_output_rmse: 0.0941 - final_output_custom_mape: 61.3526 - val_loss: 0.0466 - val_classification_output_loss: 0.1009 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0232 - val_classification_output_accuracy: 0.9622 - val_classification_output_auc: 0.9939 - val_regression_output_mse: 0.0113 - val_regression_output_mae: 0.0606 - val_regression_output_rmse: 0.1034 - val_regression_output_custom_mape: 17.9954 - val_final_output_mse: 0.0118 - val_final_output_mae: 0.0763 - val_final_output_rmse: 0.1059 - val_final_output_custom_mape: 64.3141 - lr: 9.3750e-06\n", + "Epoch 89/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0335 - classification_output_loss: 0.0435 - regression_output_loss: 0.0164 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9815 - classification_output_auc: 0.9988 - regression_output_mse: 0.0092 - regression_output_mae: 0.0543 - regression_output_rmse: 0.0938 - regression_output_custom_mape: 15.2099 - final_output_mse: 0.0096 - final_output_mae: 0.0695 - final_output_rmse: 0.0960 - final_output_custom_mape: 61.5154 - val_loss: 0.0461 - val_classification_output_loss: 0.0985 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0232 - val_classification_output_accuracy: 0.9639 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0113 - val_regression_output_mae: 0.0606 - val_regression_output_rmse: 0.1032 - val_regression_output_custom_mape: 17.9942 - val_final_output_mse: 0.0118 - val_final_output_mae: 0.0764 - val_final_output_rmse: 0.1057 - val_final_output_custom_mape: 64.3071 - lr: 9.3750e-06\n", + "Epoch 90/100\n", + "541/541 [==============================] - 61s 112ms/step - loss: 0.0334 - classification_output_loss: 0.0432 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9818 - classification_output_auc: 0.9989 - regression_output_mse: 0.0093 - regression_output_mae: 0.0541 - regression_output_rmse: 0.0936 - regression_output_custom_mape: 15.1568 - final_output_mse: 0.0096 - final_output_mae: 0.0693 - final_output_rmse: 0.0957 - final_output_custom_mape: 61.5074 - val_loss: 0.0458 - val_classification_output_loss: 0.0999 - val_regression_output_loss: 0.0177 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9637 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0106 - val_regression_output_mae: 0.0584 - val_regression_output_rmse: 0.1002 - val_regression_output_custom_mape: 17.7691 - val_final_output_mse: 0.0111 - val_final_output_mae: 0.0742 - val_final_output_rmse: 0.1028 - val_final_output_custom_mape: 64.1077 - lr: 9.3750e-06\n", + "Epoch 91/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0335 - classification_output_loss: 0.0439 - regression_output_loss: 0.0164 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0093 - regression_output_mae: 0.0543 - regression_output_rmse: 0.0940 - regression_output_custom_mape: 15.1890 - final_output_mse: 0.0097 - final_output_mae: 0.0695 - final_output_rmse: 0.0962 - final_output_custom_mape: 61.5230\n", + "Epoch 91 Detailed Metrics:\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0335 - classification_output_loss: 0.0439 - regression_output_loss: 0.0164 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0093 - regression_output_mae: 0.0543 - regression_output_rmse: 0.0940 - regression_output_custom_mape: 15.1890 - final_output_mse: 0.0097 - final_output_mae: 0.0695 - final_output_rmse: 0.0962 - final_output_custom_mape: 61.5230 - val_loss: 0.0452 - val_classification_output_loss: 0.0961 - val_regression_output_loss: 0.0179 - val_final_output_loss: 0.0227 - val_classification_output_accuracy: 0.9641 - val_classification_output_auc: 0.9943 - val_regression_output_mse: 0.0108 - val_regression_output_mae: 0.0591 - val_regression_output_rmse: 0.1010 - val_regression_output_custom_mape: 17.6374 - val_final_output_mse: 0.0113 - val_final_output_mae: 0.0749 - val_final_output_rmse: 0.1036 - val_final_output_custom_mape: 63.9890 - lr: 9.3750e-06\n", + "Epoch 92/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0333 - classification_output_loss: 0.0437 - regression_output_loss: 0.0163 - final_output_loss: 0.0208 - classification_output_accuracy: 0.9816 - classification_output_auc: 0.9988 - regression_output_mse: 0.0091 - regression_output_mae: 0.0540 - regression_output_rmse: 0.0932 - regression_output_custom_mape: 15.1416 - final_output_mse: 0.0095 - final_output_mae: 0.0692 - final_output_rmse: 0.0953 - final_output_custom_mape: 61.4852 - val_loss: 0.0458 - val_classification_output_loss: 0.1000 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0226 - val_classification_output_accuracy: 0.9633 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0107 - val_regression_output_mae: 0.0587 - val_regression_output_rmse: 0.1005 - val_regression_output_custom_mape: 17.7404 - val_final_output_mse: 0.0112 - val_final_output_mae: 0.0744 - val_final_output_rmse: 0.1031 - val_final_output_custom_mape: 64.0757 - lr: 9.3750e-06\n", + "Epoch 93/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0333 - classification_output_loss: 0.0433 - regression_output_loss: 0.0164 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9813 - classification_output_auc: 0.9989 - regression_output_mse: 0.0093 - regression_output_mae: 0.0543 - regression_output_rmse: 0.0938 - regression_output_custom_mape: 15.2251 - final_output_mse: 0.0097 - final_output_mae: 0.0695 - final_output_rmse: 0.0959 - final_output_custom_mape: 61.5190 - val_loss: 0.0460 - val_classification_output_loss: 0.0983 - val_regression_output_loss: 0.0185 - val_final_output_loss: 0.0233 - val_classification_output_accuracy: 0.9640 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0114 - val_regression_output_mae: 0.0609 - val_regression_output_rmse: 0.1038 - val_regression_output_custom_mape: 18.0193 - val_final_output_mse: 0.0119 - val_final_output_mae: 0.0767 - val_final_output_rmse: 0.1063 - val_final_output_custom_mape: 64.4023 - lr: 9.3750e-06\n", + "Epoch 94/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0332 - classification_output_loss: 0.0435 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9815 - classification_output_auc: 0.9989 - regression_output_mse: 0.0092 - regression_output_mae: 0.0540 - regression_output_rmse: 0.0936 - regression_output_custom_mape: 15.1258 - final_output_mse: 0.0096 - final_output_mae: 0.0692 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.4526 - val_loss: 0.0454 - val_classification_output_loss: 0.0978 - val_regression_output_loss: 0.0179 - val_final_output_loss: 0.0227 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9942 - val_regression_output_mse: 0.0108 - val_regression_output_mae: 0.0589 - val_regression_output_rmse: 0.1007 - val_regression_output_custom_mape: 17.6076 - val_final_output_mse: 0.0113 - val_final_output_mae: 0.0747 - val_final_output_rmse: 0.1033 - val_final_output_custom_mape: 63.9772 - lr: 9.3750e-06\n", + "Epoch 95/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0332 - classification_output_loss: 0.0434 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9814 - classification_output_auc: 0.9988 - regression_output_mse: 0.0091 - regression_output_mae: 0.0540 - regression_output_rmse: 0.0936 - regression_output_custom_mape: 15.1536 - final_output_mse: 0.0095 - final_output_mae: 0.0692 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.5154 - val_loss: 0.0458 - val_classification_output_loss: 0.1002 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0226 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0107 - val_regression_output_mae: 0.0588 - val_regression_output_rmse: 0.1010 - val_regression_output_custom_mape: 17.8107 - val_final_output_mse: 0.0112 - val_final_output_mae: 0.0745 - val_final_output_rmse: 0.1036 - val_final_output_custom_mape: 64.1810 - lr: 9.3750e-06\n", + "Epoch 96/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0334 - classification_output_loss: 0.0444 - regression_output_loss: 0.0164 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9988 - regression_output_mse: 0.0092 - regression_output_mae: 0.0542 - regression_output_rmse: 0.0935 - regression_output_custom_mape: 15.1391 - final_output_mse: 0.0096 - final_output_mae: 0.0694 - final_output_rmse: 0.0956 - final_output_custom_mape: 61.4492 - val_loss: 0.0452 - val_classification_output_loss: 0.0985 - val_regression_output_loss: 0.0176 - val_final_output_loss: 0.0224 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0105 - val_regression_output_mae: 0.0581 - val_regression_output_rmse: 0.0993 - val_regression_output_custom_mape: 17.4962 - val_final_output_mse: 0.0110 - val_final_output_mae: 0.0738 - val_final_output_rmse: 0.1020 - val_final_output_custom_mape: 63.8275 - lr: 9.3750e-06\n", + "Epoch 97/100\n", + "541/541 [==============================] - 61s 113ms/step - loss: 0.0329 - classification_output_loss: 0.0432 - regression_output_loss: 0.0161 - final_output_loss: 0.0207 - classification_output_accuracy: 0.9810 - classification_output_auc: 0.9989 - regression_output_mse: 0.0090 - regression_output_mae: 0.0535 - regression_output_rmse: 0.0927 - regression_output_custom_mape: 15.0539 - final_output_mse: 0.0094 - final_output_mae: 0.0688 - final_output_rmse: 0.0950 - final_output_custom_mape: 61.4182 - val_loss: 0.0454 - val_classification_output_loss: 0.0997 - val_regression_output_loss: 0.0176 - val_final_output_loss: 0.0224 - val_classification_output_accuracy: 0.9630 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 0.0105 - val_regression_output_mae: 0.0580 - val_regression_output_rmse: 0.0993 - val_regression_output_custom_mape: 17.6127 - val_final_output_mse: 0.0110 - val_final_output_mae: 0.0738 - val_final_output_rmse: 0.1019 - val_final_output_custom_mape: 63.9294 - lr: 9.3750e-06\n", + "Epoch 98/100\n", + "541/541 [==============================] - 59s 109ms/step - loss: 0.0331 - classification_output_loss: 0.0434 - regression_output_loss: 0.0163 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9812 - classification_output_auc: 0.9989 - regression_output_mse: 0.0092 - regression_output_mae: 0.0540 - regression_output_rmse: 0.0934 - regression_output_custom_mape: 15.1271 - final_output_mse: 0.0096 - final_output_mae: 0.0692 - final_output_rmse: 0.0955 - final_output_custom_mape: 61.4752 - val_loss: 0.0455 - val_classification_output_loss: 0.0989 - val_regression_output_loss: 0.0179 - val_final_output_loss: 0.0227 - val_classification_output_accuracy: 0.9631 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0108 - val_regression_output_mae: 0.0590 - val_regression_output_rmse: 0.1004 - val_regression_output_custom_mape: 17.5953 - val_final_output_mse: 0.0113 - val_final_output_mae: 0.0747 - val_final_output_rmse: 0.1031 - val_final_output_custom_mape: 63.9553 - lr: 9.3750e-06\n", + "Epoch 99/100\n", + "541/541 [==============================] - 58s 107ms/step - loss: 0.0330 - classification_output_loss: 0.0439 - regression_output_loss: 0.0161 - final_output_loss: 0.0207 - classification_output_accuracy: 0.9811 - classification_output_auc: 0.9988 - regression_output_mse: 0.0090 - regression_output_mae: 0.0535 - regression_output_rmse: 0.0926 - regression_output_custom_mape: 15.0318 - final_output_mse: 0.0094 - final_output_mae: 0.0688 - final_output_rmse: 0.0948 - final_output_custom_mape: 61.3647 - val_loss: 0.0449 - val_classification_output_loss: 0.0970 - val_regression_output_loss: 0.0177 - val_final_output_loss: 0.0224 - val_classification_output_accuracy: 0.9637 - val_classification_output_auc: 0.9943 - val_regression_output_mse: 0.0105 - val_regression_output_mae: 0.0582 - val_regression_output_rmse: 0.0997 - val_regression_output_custom_mape: 17.5580 - val_final_output_mse: 0.0110 - val_final_output_mae: 0.0740 - val_final_output_rmse: 0.1023 - val_final_output_custom_mape: 63.9403 - lr: 9.3750e-06\n", + "Epoch 100/100\n", + "541/541 [==============================] - 60s 111ms/step - loss: 0.0331 - classification_output_loss: 0.0433 - regression_output_loss: 0.0164 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9817 - classification_output_auc: 0.9989 - regression_output_mse: 0.0094 - regression_output_mae: 0.0543 - regression_output_rmse: 0.0941 - regression_output_custom_mape: 15.1662 - final_output_mse: 0.0098 - final_output_mae: 0.0695 - final_output_rmse: 0.0962 - final_output_custom_mape: 61.5014 - val_loss: 0.0450 - val_classification_output_loss: 0.0997 - val_regression_output_loss: 0.0172 - val_final_output_loss: 0.0219 - val_classification_output_accuracy: 0.9630 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 0.0100 - val_regression_output_mae: 0.0566 - val_regression_output_rmse: 0.0969 - val_regression_output_custom_mape: 17.3156 - val_final_output_mse: 0.0105 - val_final_output_mae: 0.0723 - val_final_output_rmse: 0.0996 - val_final_output_custom_mape: 63.6544 - lr: 9.3750e-06\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 96.30%\n", + "AUC-ROC: 0.9953\n", + "\n", + "Confusion Matrix:\n", + "[[12454 553]\n", + " [ 406 12520]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9684 0.9575 0.9629 13007\n", + " Non-Zero 0.9577 0.9686 0.9631 12926\n", + "\n", + " accuracy 0.9630 25933\n", + " macro avg 0.9631 0.9630 0.9630 25933\n", + "weighted avg 0.9631 0.9630 0.9630 25933\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 28.99%\n", + "Within ±10%: 48.43%\n", + "MAE: 0.11\n", + "RMSE: 0.14\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 63.75%\n", + "Within ±10%: 24.89%\n", + "MAE: 0.07\n", + "RMSE: 0.10\n" + ] + } + ], + "source": [ + "# Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "max_val = df['solarradiation'].max()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 15\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarradiation_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=192,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "811/811 [==============================] - 12s 14ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Radiation Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 24.11 W/m²\n", + "RMSE: 34.38 W/m²\n", + "R² Score: 0.983\n", + "MAPE: 26.17%\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 W/m²: 6.7%\n", + "Within ±10 W/m²: 12.1%\n", + "Within ±20 W/m²: 67.5%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 91.3%\n", + "\n", + "Confusion Matrix for Radiation Levels:\n", + " Very Low Low Moderate High Very High Extreme\n", + "Very Low 0 1 0 0 9 0\n", + "Low 0 1554 0 251 16 0\n", + "Moderate 0 0 2054 533 0 274\n", + "High 0 201 89 2006 0 0\n", + "Very High 0 437 0 0 700 0\n", + "Extreme 0 0 457 0 0 17351\n", + "\n", + "Plot saved as: 2024-11-25_21-39_radiation_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: 5.849\n", + "Error standard deviation: 33.875\n", + "Median error: 12.000\n", + "95th percentile absolute error: 74.602\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, 0, 11)\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarradiation_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'classification': {\n", + " 'final_loss': float(history.history['val_classification_output_loss'][-1]),\n", + " 'final_accuracy': float(history.history['val_classification_output_accuracy'][-1]),\n", + " 'final_auc': float(history.history['val_classification_output_auc'][-1])\n", + " },\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_regression_output_mae'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > 11)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_final_output_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > 11)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 103s 14ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar radiation after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 114804\n", + "Non-zero predictions (classification >= 0.5): 113075\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 180.48\n", + "Median: 12.00\n", + "Std: 245.03\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 114804 (50.38%)\n", + "Predicted non-zeros: 113075 (49.62%)\n", + "Mean classification confidence: 0.4948\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar radiation: 180.48\n", + "Min solar radiation: 12.00\n", + "Max solar radiation: 879.21\n", + "Zero predictions: 0 (0.00%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarradiation': float(final_pred_original.mean()),\n", + " 'min_predicted_solarradiation': float(final_pred_original.min()),\n", + " 'max_predicted_solarradiation': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar radiation: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarradiation']:.2f}\")\n", + "print(f\"Min solar radiation: {training_results['prediction_stats']['final_predictions']['min_predicted_solarradiation']:.2f}\")\n", + "print(f\"Max solar radiation: {training_results['prediction_stats']['final_predictions']['max_predicted_solarradiation']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "too many values to unpack (expected 3)", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[10], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43manalyze_distribution\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_updated\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msolarradiation\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mSolar Radiation\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n", + "Cell \u001B[0;32mIn[5], line 17\u001B[0m, in \u001B[0;36manalyze_distribution\u001B[0;34m(data, predictions, sequence_length, name)\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 3\u001B[0m \u001B[38;5;124;03mAnalizza dettagliatamente la distribuzione dei valori reali e predetti.\u001B[39;00m\n\u001B[1;32m 4\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 14\u001B[0m \u001B[38;5;124;03m Nome della variabile da analizzare\u001B[39;00m\n\u001B[1;32m 15\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 16\u001B[0m \u001B[38;5;66;03m# Unpack predictions\u001B[39;00m\n\u001B[0;32m---> 17\u001B[0m classification_pred, regression_pred, final_pred \u001B[38;5;241m=\u001B[39m predictions\n\u001B[1;32m 19\u001B[0m \u001B[38;5;66;03m# Prepare data for analysis\u001B[39;00m\n\u001B[1;32m 20\u001B[0m mask_pre_2010 \u001B[38;5;241m=\u001B[39m data[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdatetime\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m.\u001B[39mdt\u001B[38;5;241m.\u001B[39myear \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m2010\u001B[39m\n", + "\u001B[0;31mValueError\u001B[0m: too many values to unpack (expected 3)" + ] + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}/error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, (classification_pred, regression_pred, final_pred), folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd5197ea71becfc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f982c92c-ba99-4df6-b3c8-df92426679db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/solarradiation/2024-11-26_05-41_error_analysis.png b/models/solarradiation/2024-11-26_05-41_error_analysis.png new file mode 100644 index 0000000..8a2fc79 Binary files /dev/null and b/models/solarradiation/2024-11-26_05-41_error_analysis.png differ diff --git a/models/solarradiation/2024-11-26_05-41_features.json b/models/solarradiation/2024-11-26_05-41_features.json new file mode 100644 index 0000000..9c56af1 --- /dev/null +++ b/models/solarradiation/2024-11-26_05-41_features.json @@ -0,0 +1 @@ +["uvindex", "cloudcover", "visibility", "temp", "pressure", "humidity", "solar_elevation", "solar_angle", "day_length", "hour_sin", "hour_cos", "day_of_year_sin", "day_of_year_cos", "month_sin", "month_cos", "clear_sky_index", "atmospheric_attenuation", "theoretical_radiation", "expected_radiation", "cloud_elevation", "visibility_elevation", "uv_cloud_interaction", "temp_radiation_potential", "cloud_rolling_12h", "temp_rolling_12h", "uv_rolling_12h", "cloudcover_rolling_mean_6h", "temp_rolling_mean_6h", "temp_1h_lag", "cloudcover_1h_lag", "humidity_1h_lag", "uv_lag_1h", "season_Spring", "season_Summer", "season_Autumn", "season_Winter", "time_period_Morning", "time_period_Afternoon", "time_period_Evening", "time_period_Night"] \ No newline at end of file diff --git a/models/solarradiation/2024-11-26_05-41_logs/train/events.out.tfevents.1732599718.a570e7bae1f0.1615.0.v2 b/models/solarradiation/2024-11-26_05-41_logs/train/events.out.tfevents.1732599718.a570e7bae1f0.1615.0.v2 new file mode 100644 index 0000000..098e908 Binary files /dev/null and b/models/solarradiation/2024-11-26_05-41_logs/train/events.out.tfevents.1732599718.a570e7bae1f0.1615.0.v2 differ diff --git a/models/solarradiation/2024-11-26_05-41_logs/validation/events.out.tfevents.1732599811.a570e7bae1f0.1615.1.v2 b/models/solarradiation/2024-11-26_05-41_logs/validation/events.out.tfevents.1732599811.a570e7bae1f0.1615.1.v2 new file mode 100644 index 0000000..0bc95ad Binary files /dev/null and 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http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... 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(0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow\n", + "!pip install numpy\n", + "!pip install pandas\n", + "\n", + "!pip install keras\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6fe6bb613168a8a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:41:43.497052: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-26 05:41:43.497104: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-26 05:41:43.497156: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-26 05:41:43.506575: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n", + " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix, classification_report, roc_auc_score\n", + "from tensorflow.keras.metrics import AUC\n", + "from scipy import stats\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3da8b15c7eb9833f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " \"\"\"\n", + " Add time-based features to the DataFrame.\n", + " Works with both 'datetime' as column or index.\n", + " \"\"\"\n", + " # Se datetime è l'indice, lo usiamo direttamente\n", + " if isinstance(df.index, pd.DatetimeIndex):\n", + " datetime_col = df.index\n", + " else:\n", + " # Se datetime è una colonna, la convertiamo\n", + " if 'datetime' in df.columns:\n", + " datetime_col = pd.to_datetime(df['datetime'])\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Creazione delle feature temporali\n", + " df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n", + " df['year'] = datetime_col.year\n", + " df['month'] = datetime_col.month\n", + " df['day'] = datetime_col.day\n", + " df['hour'] = datetime_col.hour\n", + " df['minute'] = datetime_col.minute\n", + " df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n", + " df['day_of_week'] = datetime_col.dayofweek\n", + " df['day_of_year'] = datetime_col.dayofyear\n", + " df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n", + " df['quarter'] = datetime_col.quarter\n", + " df['is_month_end'] = datetime_col.is_month_end.astype(int)\n", + " df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n", + " df['is_year_end'] = datetime_col.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n", + " df['season'] = datetime_col.map(get_season)\n", + " df['time_period'] = datetime_col.hour.map(get_time_period)\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Solar angle calculation\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per la predizione della radiazione solare\n", + " combinando caratteristiche astronomiche e meteorologiche\n", + " \"\"\"\n", + " # Caratteristiche astronomiche\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Angolo solare teorico\n", + " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", + "\n", + " # Interazioni con condizioni atmosferiche\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # Indici di chiarezza e trasmissione\n", + " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", + " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", + "\n", + " # Radiazione teorica e attenuazione\n", + " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", + " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", + "\n", + " # Rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", + "\n", + " # Interazioni temperatura-radiazione\n", + " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_radiation_energy_features(df):\n", + " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", + "\n", + " # Assicuriamoci che l'indice sia di tipo datetime\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df['datetime'])\n", + "\n", + " # Solar energy to UV ratio (independent from solarradiation)\n", + " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", + "\n", + " # Time aggregations\n", + " # Moving averages\n", + " windows = [3, 6, 12, 24] # hours\n", + " for w in windows:\n", + " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", + " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", + "\n", + " # Daily aggregations utilizzando datetime\n", + " df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n", + " df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n", + "\n", + " # Changes\n", + " df['energy_change'] = df['solarenergy'].diff()\n", + " df['uv_change'] = df['uvindex'].diff()\n", + "\n", + " # Lag features\n", + " lags = [1, 2, 3, 6, 12, 24] # hours\n", + " for lag in lags:\n", + " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", + " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", + "\n", + " # Peak indicators\n", + " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", + " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", + "\n", + " # Aggiungiamo alcune metriche di volatilità\n", + " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", + " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", + "\n", + " # Indice di intensità solare composito\n", + " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", + "\n", + " # Interazioni\n", + " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", + " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features to the DataFrame\n", + " Assumes df has a DatetimeIndex\n", + " \"\"\"\n", + " # Verifichiamo che abbiamo un DatetimeIndex\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " raise ValueError(\"DataFrame must have a DatetimeIndex\")\n", + "\n", + " # Existing features\n", + " df = add_time_features(df)\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " df = add_radiation_energy_features(df)\n", + "\n", + " # Weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # Derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # Rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + "\n", + " # Lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # Extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepare data for advanced modeling with proper datetime handling\n", + " \"\"\"\n", + " # Assicuriamoci che abbiamo una copia del DataFrame\n", + " df = df.copy()\n", + "\n", + " # Verifichiamo se datetime è già l'indice\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " if 'datetime' in df.columns:\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df.set_index('datetime', inplace=True)\n", + " else:\n", + " raise ValueError(\"No datetime column or index found in DataFrame\")\n", + "\n", + " # Ordiniamo il DataFrame per datetime\n", + " df = df.sort_index()\n", + "\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " #all_columns = list(df.columns)\n", + " #print(all_columns)\n", + "\n", + " features = {\n", + " # Primary Features (strong direct correlation)\n", + " 'primary_features': [\n", + " 'uvindex', # Direct radiation indicator\n", + " 'cloudcover', # Cloud coverage\n", + " 'visibility', # Atmospheric transparency\n", + " 'temp', # Temperature\n", + " 'pressure', # Atmospheric pressure\n", + " 'humidity', # Humidity\n", + " ],\n", + "\n", + " # Astronomical and Temporal Features\n", + " 'astronomical_features': [\n", + " 'solar_elevation', # Solar elevation\n", + " 'solar_angle', # Solar angle\n", + " 'day_length', # Day length\n", + " 'hour_sin', # Daily cycle\n", + " 'hour_cos',\n", + " 'day_of_year_sin', # Annual cycle\n", + " 'day_of_year_cos',\n", + " 'month_sin', # Monthly cycle\n", + " 'month_cos',\n", + " ],\n", + "\n", + " # Key Indices and Interactions\n", + " 'key_interactions': [\n", + " 'clear_sky_index', # Clear sky index\n", + " 'atmospheric_attenuation', # Atmospheric attenuation\n", + " 'theoretical_radiation', # Theoretical radiation\n", + " 'expected_radiation', # Expected radiation\n", + " 'cloud_elevation', # Cloud-elevation interaction\n", + " 'visibility_elevation', # Visibility-elevation interaction\n", + " 'uv_cloud_interaction', # UV-cloud interaction\n", + " 'temp_radiation_potential', # Temperature-radiation potential\n", + " ],\n", + "\n", + " # Rolling Features (temporal trends)\n", + " 'rolling_features': [\n", + " 'cloud_rolling_12h', # Cloud coverage moving average\n", + " 'temp_rolling_12h', # Temperature moving average\n", + " 'uv_rolling_12h', # UV moving average\n", + " 'cloudcover_rolling_mean_6h',\n", + " 'temp_rolling_mean_6h',\n", + " ],\n", + "\n", + " # Lag Features (most recent)\n", + " 'lag_features': [\n", + " 'temp_1h_lag', # 1-hour temperature lag\n", + " 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n", + " 'humidity_1h_lag', # 1-hour humidity lag\n", + " 'uv_lag_1h', # 1-hour UV lag\n", + " ],\n", + "\n", + " # Categorical Features\n", + " 'categorical_features': [\n", + " 'season_Spring', # Seasons\n", + " 'season_Summer',\n", + " 'season_Autumn',\n", + " 'season_Winter',\n", + " 'time_period_Morning', # Time periods\n", + " 'time_period_Afternoon',\n", + " 'time_period_Evening',\n", + " 'time_period_Night',\n", + " ]\n", + " }\n", + "\n", + " final_features = [feature for group in features.values() for feature in group]\n", + "\n", + " # Handle missing values\n", + " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", + " for column in final_features + target_variables:\n", + " if column in df.columns:\n", + " df[column] = df[column].interpolate(method='time')\n", + "\n", + " df.fillna(0, inplace=True)\n", + "\n", + " # Temporal split\n", + " data_after_2010 = df[df['year'] >= 2010].copy()\n", + " data_before_2010 = df[df['year'] < 2010].copy()\n", + "\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['solarradiation']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Train-test split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n", + " )\n", + "\n", + " # Scaling\n", + " scaler_X = RobustScaler()\n", + " X_train_scaled = scaler_X.fit_transform(X_train)\n", + " X_test_scaled = scaler_X.transform(X_test)\n", + " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", + "\n", + " scaler_y = RobustScaler()\n", + " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", + " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", + "\n", + " # Print info about selected features\n", + " print(\"\\nSelected features:\")\n", + " print(f\"Number of features: {len(final_features)}\")\n", + " print(\"Features list:\", final_features)\n", + "\n", + " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data into sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train_scaled[sequence_length - 1:]\n", + " y_test = y_test_scaled[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "570b18f2caa3e0db", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a bidirectional LSTM layer with residual connections and regularization.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Number of LSTM units\n", + " dropout_rate: Dropout rate for regularization\n", + " l2_reg: L2 regularization factor\n", + " return_sequences: Whether to return sequences or just the last output\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " residual = x\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " if return_sequences:\n", + " if int(residual.shape[-1]) != 2 * units:\n", + " residual = Dense(2 * units, activation='linear')(residual)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Creates a multi-head attention block with residual connections.\n", + "\n", + " Parameters:\n", + " x: Input tensor\n", + " units: Dimension of the key space\n", + " num_heads: Number of attention heads\n", + " survival_probability: Probability of layer survival for stochastic depth\n", + " \"\"\"\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n", + " x = LayerNormalization()(x)\n", + " return x\n", + "\n", + "\n", + "def asymmetric_loss(y_true, y_pred):\n", + " \"\"\"\n", + " Loss function che penalizza maggiormente la sottostima dei valori alti\n", + " \"\"\"\n", + " diff = y_true - y_pred\n", + " abs_diff = tf.abs(diff)\n", + "\n", + " # Calcola il peso basato sul valore reale\n", + " value_weight = tf.exp(y_true / tf.reduce_max(y_true)) - 1\n", + "\n", + " # Penalizza maggiormente la sottostima (quando y_pred < y_true)\n", + " underestimation_penalty = tf.where(diff > 0, 2.0, 1.0)\n", + "\n", + " # Combina i pesi\n", + " total_weight = value_weight * underestimation_penalty\n", + "\n", + " # Calcola la loss pesata\n", + " weighted_loss = total_weight * abs_diff\n", + "\n", + " return tf.reduce_mean(weighted_loss)\n", + "\n", + "def add_peak_features(x):\n", + " \"\"\"\n", + " Aggiunge feature specifiche per identificare potenziali picchi\n", + " \"\"\"\n", + " # Moving average delle ultime n osservazioni\n", + " ma = tf.keras.layers.Conv1D(1, kernel_size=5, padding='same')(x)\n", + "\n", + " # Differenza dal moving average (identifica anomalie)\n", + " diff_ma = Lambda(lambda x: x[0] - x[1])([x, ma])\n", + "\n", + " # Rate of change\n", + " roc = Lambda(lambda x: x[:, 1:] - x[:, :-1])(x)\n", + " roc = tf.pad(roc, [[0, 0], [1, 0], [0, 0]])\n", + "\n", + " # Concatena tutte le feature\n", + " enhanced_x = Concatenate()([x, diff_ma, roc])\n", + "\n", + " return enhanced_x\n", + "\n", + "def create_regression_branch(shared_features, l2_lambda=0.005, name_suffix=''):\n", + " \"\"\"\n", + " Branch di regressione migliorato per valori alti\n", + " \"\"\"\n", + " # Branch principale\n", + " main_branch = shared_features\n", + " dense_units = [512, 256, 128, 64] # Unità aumentate\n", + "\n", + " for units in dense_units:\n", + " main_branch = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda)\n", + " )(main_branch)\n", + " main_branch = BatchNormalization()(main_branch)\n", + " main_branch = Activation('swish')(main_branch)\n", + "\n", + " # Branch specializzato per valori alti\n", + " high_values_branch = shared_features\n", + " for units in [256, 128, 64]:\n", + " high_values_branch = Dense(\n", + " units,\n", + " kernel_regularizer=regularizers.l2(l2_lambda),\n", + " activation='relu' # Usa ReLU per preservare valori alti\n", + " )(high_values_branch)\n", + "\n", + " # Gate per decidere quanto pesare il branch dei valori alti\n", + " gate = Dense(1, activation='sigmoid')(shared_features)\n", + "\n", + " # Combina i branch\n", + " main_output = Dense(1)(main_branch)\n", + " high_values_output = Dense(1)(high_values_branch)\n", + "\n", + " # Output finale pesato\n", + " final_output = Lambda(lambda x: x[0] * (1 - x[2]) + x[1] * x[2])(\n", + " [main_output, high_values_output, gate]\n", + " )\n", + "\n", + " return final_output\n", + "\n", + "def create_peak_specialized_ensemble(shared_features, l2_lambda=0.005):\n", + " \"\"\"\n", + " Ensemble di modelli specializzati per diverse fasce di valori\n", + " \"\"\"\n", + " # Modello generale\n", + " general_model = create_regression_branch(shared_features, name_suffix='general')\n", + "\n", + " # Modello specializzato per valori alti\n", + " high_values_features = Dense(256, activation='relu')(shared_features)\n", + " high_values_model = create_regression_branch(high_values_features, name_suffix='high')\n", + "\n", + " # Modello specializzato per picchi estremi\n", + " peak_features = Dense(512, activation='relu')(shared_features)\n", + " peak_model = create_regression_branch(peak_features, name_suffix='peak')\n", + "\n", + " # Gate network per pesare i modelli\n", + " gate_features = Concatenate()([shared_features,\n", + " Dense(32)(shared_features),\n", + " Dense(32)(high_values_features),\n", + " Dense(32)(peak_features)])\n", + "\n", + " gates = Dense(3, activation='softmax')(gate_features)\n", + "\n", + " # Combina le predizioni\n", + " final_output = Lambda(lambda x: (x[0] * x[3][:, 0:1] +\n", + " x[1] * x[3][:, 1:2] +\n", + " x[2] * x[3][:, 2:3]))([general_model,\n", + " high_values_model,\n", + " peak_model,\n", + " gates])\n", + "\n", + " return final_output\n", + "\n", + "def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n", + " \"\"\"\n", + " Creates a hybrid model with enhanced peak prediction capabilities\n", + " \"\"\"\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Backbone comune\n", + " survival_probs = [0.9, 0.8, 0.7, 0.6]\n", + " attention_survival_probs = [0.85, 0.75, 0.65, 0.55]\n", + " lstm_units = [256, 128, 64, 32]\n", + " dropout_rates = [0.4, 0.3, 0.2, 0.2]\n", + " attention_heads = [32, 24, 16, 8]\n", + "\n", + " x = inputs\n", + " lstm_blocks = 4\n", + " for i in range(lstm_blocks):\n", + " x = create_residual_lstm_layer(\n", + " x,\n", + " units=lstm_units[i],\n", + " dropout_rate=dropout_rates[i],\n", + " l2_reg=l2_lambda,\n", + " return_sequences=True,\n", + " survival_probability=survival_probs[i]\n", + " )\n", + " x = attention_block(\n", + " x,\n", + " units=lstm_units[i],\n", + " num_heads=attention_heads[i],\n", + " survival_probability=attention_survival_probs[i]\n", + " )\n", + " if i < lstm_blocks - 1:\n", + " x = MaxPooling1D()(x)\n", + "\n", + " # Final shared LSTM layer\n", + " shared_features = create_residual_lstm_layer(\n", + " x,\n", + " units=32,\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Enhance features for peak detection\n", + " enhanced_features = add_peak_features(x)\n", + " enhanced_shared_features = create_residual_lstm_layer(\n", + " enhanced_features,\n", + " units=64, # Increased units for enhanced features\n", + " dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " return_sequences=False,\n", + " survival_probability=0.6\n", + " )\n", + "\n", + " # Classification branch\n", + " classification_x = Dense(64, kernel_regularizer=regularizers.l2(l2_lambda))(shared_features)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_x = Dropout(0.2)(classification_x)\n", + " classification_x = Dense(32, kernel_regularizer=regularizers.l2(l2_lambda))(classification_x)\n", + " classification_x = BatchNormalization()(classification_x)\n", + " classification_x = Activation('swish')(classification_x)\n", + " classification_output = Dense(1, activation='sigmoid', name='classification_output')(classification_x)\n", + "\n", + " # Combined features for regression\n", + " regression_features = Concatenate()([shared_features, enhanced_shared_features])\n", + "\n", + " # Create specialized ensemble for regression\n", + " regression_output = create_peak_specialized_ensemble(regression_features, l2_lambda)\n", + "\n", + " # Clip regression values\n", + " regression_output = Lambda(\n", + " lambda x: tf.clip_by_value(x, min_output, max_output),\n", + " name='regression_output'\n", + " )(regression_output)\n", + "\n", + " # Combine outputs using threshold activation\n", + " thresholded_classification = ThresholdedReLU(theta=0.5)(classification_output)\n", + " normalized_classification = Lambda(lambda x: tf.cast(x > 0, tf.float32))(thresholded_classification)\n", + " final_output = Lambda(\n", + " lambda inputs: inputs[0] * inputs[1],\n", + " name='final_output'\n", + " )([regression_output, normalized_classification])\n", + "\n", + " # Create model\n", + " model = Model(\n", + " inputs=inputs,\n", + " outputs=[\n", + " classification_output,\n", + " regression_output,\n", + " final_output\n", + " ],\n", + " name=\"SolarRadiationModel\"\n", + " )\n", + "\n", + " # Custom loss functions\n", + " def hybrid_focal_loss(y_true, y_pred):\n", + " mse = tf.square(y_true - y_pred)\n", + " error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n", + " focal_weight = tf.pow(error_ratio, 2)\n", + " weighted_mse = focal_weight * mse\n", + " mae = tf.abs(y_true - y_pred)\n", + " return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n", + "\n", + " def masked_regression_loss(y_true, y_pred):\n", + " mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n", + " return asymmetric_loss(y_true * mask, y_pred * mask)\n", + "\n", + " # Metrics\n", + " def rmse(y_true, y_pred):\n", + " return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n", + "\n", + " def custom_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n", + " diff = tf.clip_by_value(diff, 0, 1)\n", + " return tf.reduce_mean(diff) * 100\n", + "\n", + " # Optimizer with reduced initial learning rate\n", + " optimizer = AdamW(\n", + " learning_rate=0.0002, # Reduced from 0.0003\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7,\n", + " weight_decay=0.001,\n", + " amsgrad=True\n", + " )\n", + "\n", + " # Compile model\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss={\n", + " 'classification_output': 'binary_crossentropy',\n", + " 'regression_output': masked_regression_loss,\n", + " 'final_output': hybrid_focal_loss\n", + " },\n", + " loss_weights={\n", + " 'classification_output': 0.2,\n", + " 'regression_output': 0.5,\n", + " 'final_output': 0.3\n", + " },\n", + " metrics={\n", + " 'classification_output': ['accuracy', AUC()],\n", + " 'regression_output': ['mse', 'mae', rmse, custom_mape],\n", + " 'final_output': ['mse', 'mae', rmse, custom_mape]\n", + " }\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " # Save model architecture visualization\n", + " plot_model(\n", + " model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True\n", + " )\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual solar radiation values (W/m²)\n", + " y_pred : array-like\n", + " Predicted solar radiation values (W/m²)\n", + " hour : array-like, optional\n", + " Array of hours corresponding to predictions, for temporal analysis\n", + " folder_name : str, optional\n", + " Directory to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Data preparation\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + " errors = y_pred - y_true\n", + "\n", + " # Basic metrics calculation\n", + " mae_raw = mean_absolute_error(y_true, y_pred)\n", + " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", + " r2_raw = r2_score(y_true, y_pred)\n", + "\n", + " # Corrected MAPE calculation\n", + " mask = y_true > 10 # Consider only values above 10 W/m²\n", + " if np.any(mask):\n", + " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", + " else:\n", + " mape = np.nan\n", + "\n", + " # Corrected error margin accuracy\n", + " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n", + " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n", + " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n", + "\n", + " # Radiation level classification\n", + " def get_radiation_level(value):\n", + " if value <= 200:\n", + " return 'Very Low'\n", + " elif value <= 400:\n", + " return 'Low'\n", + " elif value <= 600:\n", + " return 'Moderate'\n", + " elif value <= 800:\n", + " return 'High'\n", + " elif value <= 1000:\n", + " return 'Very High'\n", + " else:\n", + " return 'Extreme'\n", + "\n", + " # Calculate radiation levels\n", + " y_true_levels = [get_radiation_level(v) for v in y_true]\n", + " y_pred_levels = [get_radiation_level(v) for v in y_pred]\n", + " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", + "\n", + " # Print main metrics\n", + " print(\"\\nSolar Radiation Prediction Metrics:\")\n", + " print(\"\\nAbsolute Metrics:\")\n", + " print(f\"MAE: {mae_raw:.2f} W/m²\")\n", + " print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n", + " print(f\"R² Score: {r2_raw:.3f}\")\n", + " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", + "\n", + " print(\"\\nAccuracy Metrics:\")\n", + " print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n", + " print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n", + " print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n", + "\n", + " print(\"\\nLevel Accuracy:\")\n", + " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", + "\n", + " # Confusion matrix for radiation levels\n", + " cm = confusion_matrix(y_true_levels, y_pred_levels)\n", + " print(\"\\nConfusion Matrix for Radiation Levels:\")\n", + " cm_df = pd.DataFrame(\n", + " cm,\n", + " columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n", + " index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + " )\n", + " print(cm_df)\n", + "\n", + " # Time period analysis\n", + " if hour is not None:\n", + " day_periods = {\n", + " 'Morning (5-11)': (5, 11),\n", + " 'Noon (11-13)': (11, 13),\n", + " 'Afternoon (13-17)': (13, 17),\n", + " 'Evening (17-21)': (17, 21),\n", + " 'Night (21-5)': (21, 5)\n", + " }\n", + "\n", + " print(\"\\nAnalysis by Time Period:\")\n", + " for period, (start, end) in day_periods.items():\n", + " if start < end:\n", + " mask = (hour >= start) & (hour < end)\n", + " else:\n", + " mask = (hour >= start) | (hour < end)\n", + "\n", + " if np.any(mask):\n", + " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", + "\n", + " # Corrected period MAPE calculation\n", + " period_mask = mask & (y_true > 10)\n", + " if np.any(period_mask):\n", + " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(f\"MAPE: {period_mape:.2f}%\")\n", + " else:\n", + " print(f\"\\n{period}:\")\n", + " print(f\"MAE: {period_mae:.2f} W/m²\")\n", + " print(\"MAPE: N/A (insufficient data)\")\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Figure 1: Main analysis plots\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Plot 1: Scatter plot of actual vs predicted values\n", + " plt.subplot(3, 2, 1)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Predicted Radiation (W/m²)')\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 2: Absolute error distribution\n", + " plt.subplot(3, 2, 2)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.xlabel('Prediction Error (W/m²)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Error Distribution')\n", + " plt.grid(True)\n", + "\n", + " # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n", + " plt.subplot(3, 2, 3)\n", + " mask = y_true > 10\n", + " if np.any(mask):\n", + " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", + " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", + " plt.xlabel('Percentage Error (%)')\n", + " plt.ylabel('Frequency')\n", + " plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n", + " plt.grid(True)\n", + "\n", + " # Plot 4: Errors vs actual values\n", + " plt.subplot(3, 2, 4)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.xlabel('Actual Radiation (W/m²)')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.grid(True)\n", + "\n", + " # Plot 5: Error boxplot by radiation level\n", + " plt.subplot(3, 2, 5)\n", + " sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n", + " plt.xticks(rotation=45)\n", + " plt.xlabel('Radiation Level')\n", + " plt.ylabel('Error (W/m²)')\n", + " plt.title('Error Distribution by Level')\n", + "\n", + " # Plot 6: Confusion matrix heatmap\n", + " plt.subplot(3, 2, 6)\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix')\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=45)\n", + "\n", + " plt.tight_layout()\n", + " filename = f'{folder_name}_radiation_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " plt.close()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + "\n", + " # Additional error statistics\n", + " print(\"\\nError Statistics:\")\n", + " print(f\"Mean error: {np.mean(errors):.3f}\")\n", + " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", + " print(f\"Median error: {np.median(errors):.3f}\")\n", + " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", + "\n", + " # Return structured metrics\n", + " metrics = {\n", + " 'absolute': {\n", + " 'mae': mae_raw,\n", + " 'rmse': rmse_raw,\n", + " 'r2': r2_raw,\n", + " 'mape': float(mape) if not np.isnan(mape) else None\n", + " },\n", + " 'accuracy': {\n", + " 'within_5_wm2': float(within_5_percent),\n", + " 'within_10_wm2': float(within_10_percent),\n", + " 'within_20_wm2': float(within_20_percent)\n", + " },\n", + " 'categorical': {\n", + " 'level_accuracy': float(level_accuracy)\n", + " },\n", + " 'error_stats': {\n", + " 'mean': float(np.mean(errors)),\n", + " 'std': float(np.std(errors)),\n", + " 'median': float(np.median(errors)),\n", + " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", + " }\n", + " }\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save training history for the hybrid model\n", + " \"\"\"\n", + " plt.figure(figsize=(15, 10))\n", + "\n", + " # Loss plots\n", + " plt.subplot(2, 2, 1)\n", + " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", + " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", + " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", + " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", + " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", + " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", + " plt.title('Model Losses')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Classification metrics\n", + " plt.subplot(2, 2, 2)\n", + " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", + " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", + " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", + " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", + " plt.title('Classification Metrics')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Metric Value')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Regression metrics\n", + " plt.subplot(2, 2, 3)\n", + " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", + " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", + " plt.title('Regression MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # Final output metrics\n", + " plt.subplot(2, 2, 4)\n", + " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", + " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", + " plt.title('Final Output MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " filename = f'{folder_name}_training_history.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Save history to JSON\n", + " history_dict = history.history\n", + " json_filename = f'{folder_name}_training_history.json'\n", + " with open(json_filename, 'w') as f:\n", + " json.dump(history_dict, f)\n", + " print(f\"Training history saved as: {json_filename}\")\n", + "\n", + " plt.show()\n", + "\n", + "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", + " \"\"\"\n", + " Helper function to calculate and print metrics for all outputs\n", + " \n", + " Parameters:\n", + " - y_true: true values\n", + " - y_class: classification predictions\n", + " - y_reg: regression predictions\n", + " - y_final: final combined predictions\n", + " \"\"\"\n", + " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", + " \n", + " y_true = np.array(y_true).flatten()\n", + " y_class = np.array(y_class).flatten()\n", + " y_reg = np.array(y_reg).flatten()\n", + " y_final = np.array(y_final).flatten()\n", + " \n", + " # Classification metrics\n", + " print(\"\\nClassification Metrics:\")\n", + " y_true_binary = (y_true > 0).astype(int)\n", + " y_pred_binary = (y_class > 0.5).astype(int)\n", + " \n", + " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", + " auc_roc = roc_auc_score(y_true > 0, y_class)\n", + " print(f\"Accuracy: {accuracy:.2f}%\")\n", + " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", + " \n", + " print(\"\\nConfusion Matrix:\")\n", + " print(confusion_matrix(y_true_binary, y_pred_binary))\n", + " \n", + " print(\"\\nClassification Report:\")\n", + " print(classification_report(y_true_binary, y_pred_binary, \n", + " target_names=['Zero', 'Non-Zero'],\n", + " digits=4))\n", + " \n", + " # Regression metrics (non-zero values)\n", + " print(\"\\nRegression Metrics (non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero): # verifichiamo che ci siano valori non-zero\n", + " y_true_nonzero = y_true[mask_nonzero]\n", + " y_reg_nonzero = y_reg[mask_nonzero]\n", + " \n", + " out_of_range = np.sum((y_reg_nonzero < min_output) | (y_reg_nonzero > max_output))\n", + " diff = np.abs((y_true_nonzero - y_reg_nonzero) / (y_true_nonzero + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", + " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + " else:\n", + " print(\"No non-zero values in this batch\")\n", + " \n", + " # Final combined output metrics\n", + " print(\"\\nFinal Combined Output Metrics:\")\n", + " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", + " diff = np.abs((y_true - y_final) / (y_true + 1e-7))\n", + " diff = np.clip(diff, 0, 1)\n", + " mape = np.mean(diff) * 100\n", + " within_10_percent = np.mean(diff <= 0.10) * 100\n", + " mae = np.mean(np.abs(y_true - y_final))\n", + " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", + " \n", + " print(f\"Out of range: {out_of_range} predictions\")\n", + " print(f\"MAPE: {mape:.2f}%\")\n", + " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", + " print(f\"MAE: {mae:.2f}\")\n", + " print(f\"RMSE: {rmse:.2f}\")\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation', min_output=0, max_output=1):\n", + " \"\"\"\n", + " Advanced training function for the hybrid solar radiation model\n", + " \"\"\" \n", + " # Prepare binary targets for classification\n", + " y_train_binary = (y_train > 0).astype(float)\n", + " y_test_binary = (y_test > 0).astype(float)\n", + "\n", + " # Training targets dictionary - usando i nomi esatti degli output del modello\n", + " train_targets = {\n", + " 'classification_output': y_train_binary,\n", + " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_train\n", + " }\n", + "\n", + " # Validation targets dictionary\n", + " test_targets = {\n", + " 'classification_output': y_test_binary,\n", + " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", + " 'final_output': y_test\n", + " }\n", + "\n", + " callbacks = [\n", + " EarlyStopping(\n", + " monitor='val_final_output_loss',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-4\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_final_output_loss',\n", + " factor=0.5,\n", + " patience=7,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-4,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_model.h5',\n", + " monitor='val_final_output_loss',\n", + " save_best_only=True,\n", + " mode='min',\n", + " save_weights_only=False\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: (\n", + " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n", + " calculate_metrics(y_test, *model.predict(X_test, verbose=0), min_output, max_output)\n", + " ) if epoch % 10 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train,\n", + " train_targets,\n", + " validation_data=(X_test, test_targets),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False\n", + " )\n", + "\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " # Final evaluation\n", + " predictions = model.predict(X_test, verbose=0)\n", + " calculate_metrics(y_test, *predictions, min_output, max_output)\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", + " print(\"Training targets keys:\", train_targets.keys())\n", + " raise\n", + "\n", + " finally:\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrates solar radiation predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " - classification_pred: probability of non-zero values\n", + " - regression_pred: predicted values (used for non-zero cases)\n", + " - final_pred: final combined predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with solar radiation predictions and additional prediction details\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Create temporary DataFrame with all predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'solarradiation_predicted': final_pred.flatten(),\n", + " 'solarradiation_classification': classification_pred.flatten(),\n", + " 'solarradiation_regression': regression_pred.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update solar radiation column where missing\n", + " df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n", + "\n", + " # Print detailed statistics\n", + " print(\"\\nPrediction Integration Statistics:\")\n", + " print(f\"Added {len(final_pred)} predictions to dataset\")\n", + " print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n", + "\n", + " # Analyze prediction components for the filled values\n", + " mask_filled = df['solarradiation'] == df['solarradiation_predicted']\n", + " if mask_filled.any():\n", + " filled_data = df[mask_filled]\n", + "\n", + " print(\"\\nFilled Values Analysis:\")\n", + " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarradiation_classification'] < 0.5).sum()}\")\n", + " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarradiation_classification'] >= 0.5).sum()}\")\n", + "\n", + " # Distribution of predicted values\n", + " non_zero_pred = filled_data[filled_data['solarradiation_predicted'] > 0]\n", + " if len(non_zero_pred) > 0:\n", + " print(f\"\\nNon-zero predictions statistics:\")\n", + " print(f\"Mean: {non_zero_pred['solarradiation_predicted'].mean():.2f}\")\n", + " print(f\"Median: {non_zero_pred['solarradiation_predicted'].median():.2f}\")\n", + " print(f\"Std: {non_zero_pred['solarradiation_predicted'].std():.2f}\")\n", + "\n", + " # Optionally, you can keep or remove the intermediate prediction columns\n", + " columns_to_drop = ['solarradiation_predicted', 'solarradiation_classification',\n", + " 'solarradiation_regression']\n", + " df = df.drop(columns_to_drop, axis=1)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b3b0c2e65ddf484", + "metadata": {}, + "outputs": [], + "source": [ + "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", + " \"\"\"\n", + " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", + "\n", + " Parameters:\n", + " -----------\n", + " data : pandas.DataFrame\n", + " DataFrame contenente la colonna solarenergy\n", + " solar_column : str, default='solarenergy'\n", + " Nome della colonna da analizzare\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dizionario contenente le statistiche principali\n", + " \"\"\"\n", + "\n", + " # Creiamo una figura con più subplot\n", + " fig = plt.figure(figsize=(20, 12))\n", + "\n", + " # 1. Statistiche di base\n", + " stats_dict = {\n", + " 'count': len(data[solar_column]),\n", + " 'missing': data[solar_column].isnull().sum(),\n", + " 'zeros': (data[solar_column] == 0).sum(),\n", + " 'mean': data[solar_column].mean(),\n", + " 'median': data[solar_column].median(),\n", + " 'std': data[solar_column].std(),\n", + " 'min': data[solar_column].min(),\n", + " 'max': data[solar_column].max(),\n", + " 'skewness': stats.skew(data[solar_column].dropna()),\n", + " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", + " }\n", + "\n", + " # Calcolo dei percentili\n", + " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", + " for p in percentiles:\n", + " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", + "\n", + " # 2. Visualizzazioni\n", + "\n", + " # 2.1 Distribuzione\n", + " plt.subplot(2, 2, 1)\n", + " sns.histplot(data=data, x=solar_column, kde=True)\n", + " plt.title(f'Distribuzione di {name}')\n", + " plt.xlabel(f'{name}')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " # 2.2 Box Plot\n", + " plt.subplot(2, 2, 2)\n", + " sns.boxplot(y=data[solar_column])\n", + " plt.title(f'Box Plot di {name}')\n", + "\n", + " # 2.3 QQ Plot\n", + " plt.subplot(2, 2, 3)\n", + " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", + " plt.title(f'Q-Q Plot di {name}')\n", + "\n", + " # 2.4 Distribuzione Log-trasformata\n", + " plt.subplot(2, 2, 4)\n", + " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", + " plt.title(f'Distribuzione Log-trasformata di {name}')\n", + " plt.xlabel(f'Log({name} + 1)')\n", + " plt.ylabel('Frequenza')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 3. Analisi temporale se disponibile\n", + " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", + " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", + " if isinstance(data[time_col].iloc[0], (int, float)):\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", + " else:\n", + " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", + "\n", + " # Plot temporale\n", + " plt.figure(figsize=(15, 6))\n", + " plt.plot(data['temp_datetime'], data[solar_column])\n", + " plt.title(f'Serie Temporale di {name}')\n", + " plt.xlabel('Data')\n", + " plt.ylabel(f'{name}')\n", + " plt.xticks(rotation=45)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # Analisi stagionale\n", + " data['month'] = data['temp_datetime'].dt.month\n", + " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", + "\n", + " plt.figure(figsize=(12, 6))\n", + " seasonal_stats['mean'].plot(kind='bar')\n", + " plt.title(f'Media Mensile di {name}')\n", + " plt.xlabel('Mese')\n", + " plt.ylabel(f'{name} Media')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # 4. Stampa delle statistiche principali\n", + " print(f\"\\nStatistiche principali di {name}:\")\n", + " print(\"-\" * 50)\n", + " for key, value in stats_dict.items():\n", + " print(f\"{key:15}: {value:,.4f}\")\n", + "\n", + " # 5. Suggerimenti per la normalizzazione\n", + " print(\"\\nSuggerimenti per la normalizzazione:\")\n", + " print(\"-\" * 50)\n", + "\n", + " skewness = abs(stats_dict['skewness'])\n", + " if skewness > 1:\n", + " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", + " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", + "\n", + " range_ratio = stats_dict['max'] / stats_dict['std']\n", + " if range_ratio > 10:\n", + " print(\"- La variabile ha una scala molto ampia\")\n", + " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", + "\n", + " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", + " if zero_ratio > 0.1:\n", + " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", + " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", + "\n", + " return stats_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1b1ee91d1573ec66", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing solar radiation model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Selected features:\n", + "Number of features: 40\n", + "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", + "Training data shape: (103798, 24, 40)\n", + "Test data shape: (25933, 24, 40)\n", + "Saving scaler X to: 2024-11-26_05-41_scale_X.joblib\n", + "Saving scaler X to: 2024-11-26_05-41_scale_y.joblib\n", + "Saving features to: 2024-11-26_05-41_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n", + "\n", + "print(\"Initializing solar radiation model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", + "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_X_path):\n", + " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", + " scaler = joblib.load(scaler_X_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_X_path}\")\n", + " joblib.dump(scaler_X, scaler_X_path)\n", + "\n", + "if os.path.exists(scaler_y_path):\n", + " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", + " scaler = joblib.load(scaler_y_path)\n", + "else:\n", + " print(f\"Saving scaler X to: {scaler_y_path}\")\n", + " joblib.dump(scaler_y, scaler_y_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "76deb4deb84dc4c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Creating model...\n", + "\n", + "Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n", + "Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:41:50.507143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n", + "2024-11-26 05:41:51.386109: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"SolarRadiationModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 40)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 4 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n", + " sticDepth) 'multi_head_attention[0][0]']\n", + " \n", + " layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n", + " D) ] \n", + " \n", + " bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_1[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n", + " sticDepth) 'multi_head_attention_1[0][0]\n", + " '] \n", + " \n", + " layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_2[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n", + " sticDepth) 'multi_head_attention_2[0][0]\n", + " '] \n", + " \n", + " layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n", + " g1D) ] \n", + " \n", + " bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n", + " sticDepth) 'multi_head_attention_3[0][0]\n", + " '] \n", + " \n", + " layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " conv1d (Conv1D) (None, 3, 1) 321 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " lambda_1 (Lambda) (None, 2, 64) 0 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " lambda (Lambda) (None, 3, 64) 0 ['layer_normalization_7[0][0]'\n", + " , 'conv1d[0][0]'] \n", + " \n", + " tf.compat.v1.pad (TFOpLamb (None, 3, 64) 0 ['lambda_1[0][0]'] \n", + " da) \n", + " \n", + " concatenate (Concatenate) (None, 3, 192) 0 ['layer_normalization_7[0][0]'\n", + " , 'lambda[0][0]', \n", + " 'tf.compat.v1.pad[0][0]'] \n", + " \n", + " bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n", + " onal) ] \n", + " \n", + " bidirectional_5 (Bidirecti (None, 128) 131584 ['concatenate[0][0]'] \n", + " onal) \n", + " \n", + " layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " layer_normalization_9 (Lay (None, 128) 256 ['bidirectional_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " dropout_5 (Dropout) (None, 128) 0 ['layer_normalization_9[0][0]'\n", + " ] \n", + " \n", + " concatenate_1 (Concatenate (None, 192) 0 ['dropout_4[0][0]', \n", + " ) 'dropout_5[0][0]'] \n", + " \n", + " dense_16 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n", + " \n", + " dense_27 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n", + " \n", + " dense_6 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n", + " \n", + " dense_17 (Dense) (None, 512) 131584 ['dense_16[0][0]'] \n", + " \n", + " dense_28 (Dense) (None, 512) 262656 ['dense_27[0][0]'] \n", + " \n", + " batch_normalization_2 (Bat (None, 512) 2048 ['dense_6[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_6 (Bat (None, 512) 2048 ['dense_17[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_10 (Ba (None, 512) 2048 ['dense_28[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_2 (Activation) (None, 512) 0 ['batch_normalization_2[0][0]'\n", + " ] \n", + " \n", + " activation_6 (Activation) (None, 512) 0 ['batch_normalization_6[0][0]'\n", + " ] \n", + " \n", + " activation_10 (Activation) (None, 512) 0 ['batch_normalization_10[0][0]\n", + " '] \n", + " \n", + " dense_7 (Dense) (None, 256) 131328 ['activation_2[0][0]'] \n", + " \n", + " dense_18 (Dense) (None, 256) 131328 ['activation_6[0][0]'] \n", + " \n", + " dense_29 (Dense) (None, 256) 131328 ['activation_10[0][0]'] \n", + " \n", + " batch_normalization_3 (Bat (None, 256) 1024 ['dense_7[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_7 (Bat (None, 256) 1024 ['dense_18[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_11 (Ba (None, 256) 1024 ['dense_29[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation_3 (Activation) (None, 256) 0 ['batch_normalization_3[0][0]'\n", + " ] \n", + " \n", + " activation_7 (Activation) (None, 256) 0 ['batch_normalization_7[0][0]'\n", + " ] \n", + " \n", + " activation_11 (Activation) (None, 256) 0 ['batch_normalization_11[0][0]\n", + " '] \n", + " \n", + " dense_4 (Dense) (None, 64) 4160 ['dropout_4[0][0]'] \n", + " \n", + " dense_8 (Dense) (None, 128) 32896 ['activation_3[0][0]'] \n", + " \n", + " dense_19 (Dense) (None, 128) 32896 ['activation_7[0][0]'] \n", + " \n", + " dense_30 (Dense) (None, 128) 32896 ['activation_11[0][0]'] \n", + " \n", + " batch_normalization (Batch (None, 64) 256 ['dense_4[0][0]'] \n", + " Normalization) \n", + " \n", + " batch_normalization_4 (Bat (None, 128) 512 ['dense_8[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_8 (Bat (None, 128) 512 ['dense_19[0][0]'] \n", + " chNormalization) \n", + " \n", + " batch_normalization_12 (Ba (None, 128) 512 ['dense_30[0][0]'] \n", + " tchNormalization) \n", + " \n", + " activation (Activation) (None, 64) 0 ['batch_normalization[0][0]'] \n", + " \n", + " activation_4 (Activation) (None, 128) 0 ['batch_normalization_4[0][0]'\n", + " ] \n", + " \n", + " activation_8 (Activation) (None, 128) 0 ['batch_normalization_8[0][0]'\n", + " ] \n", + " \n", + " activation_12 (Activation) (None, 128) 0 ['batch_normalization_12[0][0]\n", + " '] \n", + " \n", + " dropout_6 (Dropout) (None, 64) 0 ['activation[0][0]'] \n", + " \n", + " dense_9 (Dense) (None, 64) 8256 ['activation_4[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n", + " \n", + " dense_20 (Dense) (None, 64) 8256 ['activation_8[0][0]'] \n", + " \n", + " dense_21 (Dense) (None, 256) 65792 ['dense_16[0][0]'] \n", + " \n", + " dense_31 (Dense) (None, 64) 8256 ['activation_12[0][0]'] \n", + " \n", + " dense_32 (Dense) (None, 256) 131328 ['dense_27[0][0]'] \n", + " \n", + " dense_5 (Dense) (None, 32) 2080 ['dropout_6[0][0]'] \n", + " \n", + " batch_normalization_5 (Bat (None, 64) 256 ['dense_9[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_11 (Dense) (None, 128) 32896 ['dense_10[0][0]'] \n", + " \n", + " batch_normalization_9 (Bat (None, 64) 256 ['dense_20[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_22 (Dense) (None, 128) 32896 ['dense_21[0][0]'] \n", + " \n", + " batch_normalization_13 (Ba (None, 64) 256 ['dense_31[0][0]'] \n", + " tchNormalization) \n", + " \n", + " dense_33 (Dense) (None, 128) 32896 ['dense_32[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 32) 128 ['dense_5[0][0]'] \n", + " chNormalization) \n", + " \n", + " activation_5 (Activation) (None, 64) 0 ['batch_normalization_5[0][0]'\n", + " ] \n", + " \n", + " dense_12 (Dense) (None, 64) 8256 ['dense_11[0][0]'] \n", + " \n", + " activation_9 (Activation) (None, 64) 0 ['batch_normalization_9[0][0]'\n", + " ] \n", + " \n", + " dense_23 (Dense) (None, 64) 8256 ['dense_22[0][0]'] \n", + " \n", + " activation_13 (Activation) (None, 64) 0 ['batch_normalization_13[0][0]\n", + " '] \n", + " \n", + " dense_34 (Dense) (None, 64) 8256 ['dense_33[0][0]'] \n", + " \n", + " dense_38 (Dense) (None, 32) 6176 ['concatenate_1[0][0]'] \n", + " \n", + " dense_39 (Dense) (None, 32) 8224 ['dense_16[0][0]'] \n", + " \n", + " dense_40 (Dense) (None, 32) 16416 ['dense_27[0][0]'] \n", + " \n", + " activation_1 (Activation) (None, 32) 0 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_14 (Dense) (None, 1) 65 ['activation_5[0][0]'] \n", + " \n", + " dense_15 (Dense) (None, 1) 65 ['dense_12[0][0]'] \n", + " \n", + " dense_13 (Dense) (None, 1) 193 ['concatenate_1[0][0]'] \n", + " \n", + " dense_25 (Dense) (None, 1) 65 ['activation_9[0][0]'] \n", + " \n", + " dense_26 (Dense) (None, 1) 65 ['dense_23[0][0]'] \n", + " \n", + " dense_24 (Dense) (None, 1) 257 ['dense_16[0][0]'] \n", + " \n", + " dense_36 (Dense) (None, 1) 65 ['activation_13[0][0]'] \n", + " \n", + " dense_37 (Dense) (None, 1) 65 ['dense_34[0][0]'] \n", + " \n", + " dense_35 (Dense) (None, 1) 513 ['dense_27[0][0]'] \n", + " \n", + " concatenate_2 (Concatenate (None, 288) 0 ['concatenate_1[0][0]', \n", + " ) 'dense_38[0][0]', \n", + " 'dense_39[0][0]', \n", + " 'dense_40[0][0]'] \n", + " \n", + " classification_output (Den (None, 1) 33 ['activation_1[0][0]'] \n", + " se) \n", + " \n", + " lambda_2 (Lambda) (None, 1) 0 ['dense_14[0][0]', \n", + " 'dense_15[0][0]', \n", + " 'dense_13[0][0]'] \n", + " \n", + " lambda_3 (Lambda) (None, 1) 0 ['dense_25[0][0]', \n", + " 'dense_26[0][0]', \n", + " 'dense_24[0][0]'] \n", + " \n", + " lambda_4 (Lambda) (None, 1) 0 ['dense_36[0][0]', \n", + " 'dense_37[0][0]', \n", + " 'dense_35[0][0]'] \n", + " \n", + " dense_41 (Dense) (None, 3) 867 ['concatenate_2[0][0]'] \n", + " \n", + " lambda_5 (Lambda) (None, 1) 0 ['lambda_2[0][0]', \n", + " 'lambda_3[0][0]', \n", + " 'lambda_4[0][0]', \n", + " 'dense_41[0][0]'] \n", + " \n", + " thresholded_re_lu (Thresho (None, 1) 0 ['classification_output[0][0]'\n", + " ldedReLU) ] \n", + " \n", + " regression_output (Lambda) (None, 1) 0 ['lambda_5[0][0]'] \n", + " \n", + " lambda_6 (Lambda) (None, 1) 0 ['thresholded_re_lu[0][0]'] \n", + " \n", + " final_output (Lambda) (None, 1) 0 ['regression_output[0][0]', \n", + " 'lambda_6[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 23955918 (91.38 MB)\n", + "Trainable params: 23949966 (91.36 MB)\n", + "Non-trainable params: 5952 (23.25 KB)\n", + "__________________________________________________________________________________________________\n", + "\n", + "Class distribution in training set:\n", + "Zeros: 52022 (50.12%)\n", + "Non-zeros: 51776 (49.88%)\n", + "\n", + "Class distribution in test set:\n", + "Zeros: 13007 (50.16%)\n", + "Non-zeros: 12926 (49.84%)\n", + "\n", + "Model output names: ['classification_output', 'regression_output', 'final_output']\n", + "\n", + "4. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 05:42:25.841427: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n", + "2024-11-26 05:42:26.758143: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-26 05:42:28.319667: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x74802ce90ad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-26 05:42:28.319705: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-26 05:42:28.325479: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-26 05:42:28.469866: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "541/541 [==============================] - ETA: 0s - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 1 Detailed Metrics:\n", + "541/541 [==============================] - 106s 111ms/step - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850 - val_loss: 4.8619 - val_classification_output_loss: 0.2702 - val_regression_output_loss: 0.1998 - val_final_output_loss: 0.1203 - val_classification_output_accuracy: 0.8850 - val_classification_output_auc: 0.9648 - val_regression_output_mse: 2.6679 - val_regression_output_mae: 1.1926 - val_regression_output_rmse: 1.4470 - val_regression_output_custom_mape: 76.3626 - val_final_output_mse: 0.1619 - val_final_output_mae: 0.2603 - val_final_output_rmse: 0.3866 - val_final_output_custom_mape: 77.3426 - lr: 2.0000e-04\n", + "Epoch 2/100\n", + "541/541 [==============================] - 53s 97ms/step - loss: 2.5852 - classification_output_loss: 0.1672 - regression_output_loss: 0.1392 - final_output_loss: 0.0880 - classification_output_accuracy: 0.9350 - classification_output_auc: 0.9830 - regression_output_mse: 5.1786 - regression_output_mae: 1.6349 - regression_output_rmse: 2.2598 - regression_output_custom_mape: 71.9229 - final_output_mse: 0.1133 - final_output_mae: 0.2027 - final_output_rmse: 0.3209 - final_output_custom_mape: 72.9816 - val_loss: 1.4136 - val_classification_output_loss: 0.2138 - val_regression_output_loss: 0.1684 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9236 - val_classification_output_auc: 0.9842 - val_regression_output_mse: 1.8603 - val_regression_output_mae: 1.0648 - val_regression_output_rmse: 1.3566 - val_regression_output_custom_mape: 75.8244 - val_final_output_mse: 0.1512 - val_final_output_mae: 0.2497 - val_final_output_rmse: 0.3643 - val_final_output_custom_mape: 75.8084 - lr: 2.0000e-04\n", + "Epoch 3/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.9558 - classification_output_loss: 0.1719 - regression_output_loss: 0.1366 - final_output_loss: 0.0895 - classification_output_accuracy: 0.9322 - classification_output_auc: 0.9815 - regression_output_mse: 5.6874 - regression_output_mae: 1.7310 - regression_output_rmse: 2.3695 - regression_output_custom_mape: 71.9037 - final_output_mse: 0.1126 - final_output_mae: 0.2038 - final_output_rmse: 0.3184 - final_output_custom_mape: 72.9206 - val_loss: 0.9508 - val_classification_output_loss: 0.4633 - val_regression_output_loss: 0.3961 - val_final_output_loss: 0.3536 - val_classification_output_accuracy: 0.8129 - val_classification_output_auc: 0.9060 - val_regression_output_mse: 1.6205 - val_regression_output_mae: 0.8607 - val_regression_output_rmse: 1.2559 - val_regression_output_custom_mape: 66.2296 - val_final_output_mse: 0.4082 - val_final_output_mae: 0.4125 - val_final_output_rmse: 0.6197 - val_final_output_custom_mape: 78.8389 - lr: 2.0000e-04\n", + "Epoch 4/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.5850 - classification_output_loss: 0.1792 - regression_output_loss: 0.2007 - final_output_loss: 0.1027 - classification_output_accuracy: 0.9293 - classification_output_auc: 0.9798 - regression_output_mse: 2.2127 - regression_output_mae: 0.9460 - regression_output_rmse: 1.2398 - regression_output_custom_mape: 73.1762 - final_output_mse: 0.1435 - final_output_mae: 0.2295 - final_output_rmse: 0.3588 - final_output_custom_mape: 74.5266 - val_loss: 0.4332 - val_classification_output_loss: 0.1561 - val_regression_output_loss: 0.1114 - val_final_output_loss: 0.1238 - val_classification_output_accuracy: 0.9345 - val_classification_output_auc: 0.9856 - val_regression_output_mse: 4.7324 - val_regression_output_mae: 1.5163 - val_regression_output_rmse: 2.1370 - val_regression_output_custom_mape: 76.3284 - val_final_output_mse: 0.1509 - val_final_output_mae: 0.2353 - val_final_output_rmse: 0.3733 - val_final_output_custom_mape: 76.9648 - lr: 2.0000e-04\n", + "Epoch 5/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.3949 - classification_output_loss: 0.1662 - regression_output_loss: 0.1463 - final_output_loss: 0.0885 - classification_output_accuracy: 0.9320 - classification_output_auc: 0.9831 - regression_output_mse: 2.7376 - regression_output_mae: 0.9965 - regression_output_rmse: 1.2987 - regression_output_custom_mape: 71.5179 - final_output_mse: 0.1125 - final_output_mae: 0.2059 - final_output_rmse: 0.3178 - final_output_custom_mape: 73.1178 - val_loss: 0.3969 - val_classification_output_loss: 0.2723 - val_regression_output_loss: 0.2098 - val_final_output_loss: 0.0787 - val_classification_output_accuracy: 0.8948 - val_classification_output_auc: 0.9729 - val_regression_output_mse: 0.1621 - val_regression_output_mae: 0.3438 - val_regression_output_rmse: 0.3980 - val_regression_output_custom_mape: 72.4170 - val_final_output_mse: 0.1050 - val_final_output_mae: 0.1904 - val_final_output_rmse: 0.3098 - val_final_output_custom_mape: 74.6832 - lr: 2.0000e-04\n", + "Epoch 6/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.2983 - classification_output_loss: 0.1354 - regression_output_loss: 0.1470 - final_output_loss: 0.0704 - classification_output_accuracy: 0.9464 - classification_output_auc: 0.9883 - regression_output_mse: 0.2598 - regression_output_mae: 0.4423 - regression_output_rmse: 0.5024 - regression_output_custom_mape: 71.7011 - final_output_mse: 0.0909 - final_output_mae: 0.1841 - final_output_rmse: 0.2871 - final_output_custom_mape: 71.9341 - val_loss: 0.2936 - val_classification_output_loss: 0.1702 - val_regression_output_loss: 0.1509 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9348 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 0.2456 - val_regression_output_mae: 0.4147 - val_regression_output_rmse: 0.4831 - val_regression_output_custom_mape: 74.9922 - val_final_output_mse: 0.1488 - val_final_output_mae: 0.2237 - val_final_output_rmse: 0.3632 - val_final_output_custom_mape: 74.9167 - lr: 2.0000e-04\n", + "Epoch 7/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.2505 - classification_output_loss: 0.1442 - regression_output_loss: 0.1384 - final_output_loss: 0.0775 - classification_output_accuracy: 0.9443 - classification_output_auc: 0.9866 - regression_output_mse: 1.4617 - regression_output_mae: 0.7991 - regression_output_rmse: 1.0508 - regression_output_custom_mape: 71.1954 - final_output_mse: 0.0988 - final_output_mae: 0.1902 - final_output_rmse: 0.2984 - final_output_custom_mape: 72.2324 - val_loss: 0.3214 - val_classification_output_loss: 0.3465 - val_regression_output_loss: 0.1456 - val_final_output_loss: 0.2147 - val_classification_output_accuracy: 0.8648 - val_classification_output_auc: 0.9798 - val_regression_output_mse: 0.8371 - val_regression_output_mae: 0.7323 - val_regression_output_rmse: 0.9107 - val_regression_output_custom_mape: 73.4417 - val_final_output_mse: 0.2713 - val_final_output_mae: 0.3340 - val_final_output_rmse: 0.4815 - val_final_output_custom_mape: 76.5578 - lr: 2.0000e-04\n", + "Epoch 8/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.2237 - classification_output_loss: 0.1397 - regression_output_loss: 0.1364 - final_output_loss: 0.0796 - classification_output_accuracy: 0.9446 - classification_output_auc: 0.9878 - regression_output_mse: 5.1974 - regression_output_mae: 1.6560 - regression_output_rmse: 2.2692 - regression_output_custom_mape: 71.2507 - final_output_mse: 0.1033 - final_output_mae: 0.1957 - final_output_rmse: 0.3040 - final_output_custom_mape: 72.5075 - val_loss: 0.2685 - val_classification_output_loss: 0.3914 - val_regression_output_loss: 0.1217 - val_final_output_loss: 0.1222 - val_classification_output_accuracy: 0.8613 - val_classification_output_auc: 0.9606 - val_regression_output_mse: 3.7820 - val_regression_output_mae: 1.2866 - val_regression_output_rmse: 1.8121 - val_regression_output_custom_mape: 68.2919 - val_final_output_mse: 0.1599 - val_final_output_mae: 0.2535 - val_final_output_rmse: 0.3599 - val_final_output_custom_mape: 71.1078 - lr: 2.0000e-04\n", + "Epoch 9/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.1754 - classification_output_loss: 0.1188 - regression_output_loss: 0.0985 - final_output_loss: 0.0632 - classification_output_accuracy: 0.9523 - classification_output_auc: 0.9911 - regression_output_mse: 5.4764 - regression_output_mae: 1.6783 - regression_output_rmse: 2.3144 - regression_output_custom_mape: 69.8070 - final_output_mse: 0.0766 - final_output_mae: 0.1685 - final_output_rmse: 0.2609 - final_output_custom_mape: 70.8609 - val_loss: 0.1706 - val_classification_output_loss: 0.1096 - val_regression_output_loss: 0.1077 - val_final_output_loss: 0.0658 - val_classification_output_accuracy: 0.9538 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 5.1203 - val_regression_output_mae: 1.6260 - val_regression_output_rmse: 2.2470 - val_regression_output_custom_mape: 70.2256 - val_final_output_mse: 0.0762 - val_final_output_mae: 0.1588 - val_final_output_rmse: 0.2636 - val_final_output_custom_mape: 72.1157 - lr: 2.0000e-04\n", + "Epoch 10/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.1727 - classification_output_loss: 0.1290 - regression_output_loss: 0.1097 - final_output_loss: 0.0692 - classification_output_accuracy: 0.9479 - classification_output_auc: 0.9894 - regression_output_mse: 5.5291 - regression_output_mae: 1.6998 - regression_output_rmse: 2.3152 - regression_output_custom_mape: 70.2356 - final_output_mse: 0.0878 - final_output_mae: 0.1771 - final_output_rmse: 0.2731 - final_output_custom_mape: 71.4182 - val_loss: 0.1912 - val_classification_output_loss: 0.1751 - val_regression_output_loss: 0.0882 - val_final_output_loss: 0.0951 - val_classification_output_accuracy: 0.9463 - val_classification_output_auc: 0.9833 - val_regression_output_mse: 6.5049 - val_regression_output_mae: 1.9127 - val_regression_output_rmse: 2.5318 - val_regression_output_custom_mape: 75.4162 - val_final_output_mse: 0.1245 - val_final_output_mae: 0.2154 - val_final_output_rmse: 0.3377 - val_final_output_custom_mape: 75.3060 - lr: 2.0000e-04\n", + "Epoch 11/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452\n", + "Epoch 11 Detailed Metrics:\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452 - val_loss: 0.2093 - val_classification_output_loss: 0.2277 - val_regression_output_loss: 0.1437 - val_final_output_loss: 0.1177 - val_classification_output_accuracy: 0.9283 - val_classification_output_auc: 0.9779 - val_regression_output_mse: 6.0570 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.4544 - val_regression_output_custom_mape: 72.7221 - val_final_output_mse: 0.1364 - val_final_output_mae: 0.2321 - val_final_output_rmse: 0.3485 - val_final_output_custom_mape: 73.0335 - lr: 2.0000e-04\n", + "Epoch 12/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.1464 - classification_output_loss: 0.1072 - regression_output_loss: 0.1053 - final_output_loss: 0.0601 - classification_output_accuracy: 0.9561 - classification_output_auc: 0.9927 - regression_output_mse: 6.1092 - regression_output_mae: 1.8112 - regression_output_rmse: 2.4620 - regression_output_custom_mape: 69.8113 - final_output_mse: 0.0740 - final_output_mae: 0.1632 - final_output_rmse: 0.2523 - final_output_custom_mape: 70.3597 - val_loss: 0.1542 - val_classification_output_loss: 0.1503 - val_regression_output_loss: 0.0985 - val_final_output_loss: 0.0841 - val_classification_output_accuracy: 0.9489 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 5.9678 - val_regression_output_mae: 1.7956 - val_regression_output_rmse: 2.4203 - val_regression_output_custom_mape: 72.0743 - val_final_output_mse: 0.1016 - val_final_output_mae: 0.1921 - val_final_output_rmse: 0.2866 - val_final_output_custom_mape: 72.4481 - lr: 2.0000e-04\n", + "Epoch 13/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.1351 - classification_output_loss: 0.0971 - regression_output_loss: 0.0999 - final_output_loss: 0.0592 - classification_output_accuracy: 0.9601 - classification_output_auc: 0.9939 - regression_output_mse: 6.1488 - regression_output_mae: 1.8152 - regression_output_rmse: 2.4696 - regression_output_custom_mape: 69.9159 - final_output_mse: 0.0739 - final_output_mae: 0.1643 - final_output_rmse: 0.2517 - final_output_custom_mape: 70.7436 - val_loss: 0.2396 - val_classification_output_loss: 0.3289 - val_regression_output_loss: 0.2191 - val_final_output_loss: 0.0616 - val_classification_output_accuracy: 0.8966 - val_classification_output_auc: 0.9705 - val_regression_output_mse: 0.7583 - val_regression_output_mae: 0.6739 - val_regression_output_rmse: 0.8690 - val_regression_output_custom_mape: 73.5880 - val_final_output_mse: 0.0818 - val_final_output_mae: 0.1722 - val_final_output_rmse: 0.2787 - val_final_output_custom_mape: 73.8794 - lr: 2.0000e-04\n", + "Epoch 14/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.1342 - classification_output_loss: 0.1155 - regression_output_loss: 0.0989 - final_output_loss: 0.0567 - classification_output_accuracy: 0.9515 - classification_output_auc: 0.9914 - regression_output_mse: 6.1207 - regression_output_mae: 1.8088 - regression_output_rmse: 2.4631 - regression_output_custom_mape: 69.6780 - final_output_mse: 0.0681 - final_output_mae: 0.1613 - final_output_rmse: 0.2447 - final_output_custom_mape: 70.4782 - val_loss: 0.1483 - val_classification_output_loss: 0.1125 - val_regression_output_loss: 0.1075 - val_final_output_loss: 0.0949 - val_classification_output_accuracy: 0.9545 - val_classification_output_auc: 0.9921 - val_regression_output_mse: 3.9937 - val_regression_output_mae: 1.5182 - val_regression_output_rmse: 1.9883 - val_regression_output_custom_mape: 74.0972 - val_final_output_mse: 0.1191 - val_final_output_mae: 0.2071 - val_final_output_rmse: 0.3226 - val_final_output_custom_mape: 73.9669 - lr: 2.0000e-04\n", + "Epoch 15/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.1104 - classification_output_loss: 0.0837 - regression_output_loss: 0.0778 - final_output_loss: 0.0495 - classification_output_accuracy: 0.9664 - classification_output_auc: 0.9953 - regression_output_mse: 6.4510 - regression_output_mae: 1.8607 - regression_output_rmse: 2.5321 - regression_output_custom_mape: 68.8861 - final_output_mse: 0.0558 - final_output_mae: 0.1437 - final_output_rmse: 0.2201 - final_output_custom_mape: 69.3373 - val_loss: 0.1422 - val_classification_output_loss: 0.1152 - val_regression_output_loss: 0.1280 - val_final_output_loss: 0.0574 - val_classification_output_accuracy: 0.9537 - val_classification_output_auc: 0.9933 - val_regression_output_mse: 5.8958 - val_regression_output_mae: 1.7291 - val_regression_output_rmse: 2.4158 - val_regression_output_custom_mape: 68.3024 - val_final_output_mse: 0.0661 - val_final_output_mae: 0.1556 - val_final_output_rmse: 0.2529 - val_final_output_custom_mape: 69.4068 - lr: 2.0000e-04\n", + "Epoch 16/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.1238 - classification_output_loss: 0.1192 - regression_output_loss: 0.0907 - final_output_loss: 0.0547 - classification_output_accuracy: 0.9500 - classification_output_auc: 0.9910 - regression_output_mse: 6.1427 - regression_output_mae: 1.7993 - regression_output_rmse: 2.4683 - regression_output_custom_mape: 68.9479 - final_output_mse: 0.0637 - final_output_mae: 0.1549 - final_output_rmse: 0.2359 - final_output_custom_mape: 69.7157 - val_loss: 0.1268 - val_classification_output_loss: 0.1301 - val_regression_output_loss: 0.0858 - val_final_output_loss: 0.0656 - val_classification_output_accuracy: 0.9430 - val_classification_output_auc: 0.9895 - val_regression_output_mse: 6.4156 - val_regression_output_mae: 1.8944 - val_regression_output_rmse: 2.5145 - val_regression_output_custom_mape: 71.3515 - val_final_output_mse: 0.0826 - val_final_output_mae: 0.1624 - val_final_output_rmse: 0.2670 - val_final_output_custom_mape: 71.2686 - lr: 2.0000e-04\n", + "Epoch 17/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0942 - classification_output_loss: 0.0838 - regression_output_loss: 0.0617 - final_output_loss: 0.0405 - classification_output_accuracy: 0.9650 - classification_output_auc: 0.9955 - regression_output_mse: 6.3983 - regression_output_mae: 1.8268 - regression_output_rmse: 2.5217 - regression_output_custom_mape: 67.0560 - final_output_mse: 0.0399 - final_output_mae: 0.1244 - final_output_rmse: 0.1906 - final_output_custom_mape: 67.8066 - val_loss: 0.0979 - val_classification_output_loss: 0.1114 - val_regression_output_loss: 0.0596 - val_final_output_loss: 0.0473 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9919 - val_regression_output_mse: 5.9015 - val_regression_output_mae: 1.7669 - val_regression_output_rmse: 2.4102 - val_regression_output_custom_mape: 67.6186 - val_final_output_mse: 0.0482 - val_final_output_mae: 0.1296 - val_final_output_rmse: 0.2055 - val_final_output_custom_mape: 67.8666 - lr: 2.0000e-04\n", + "Epoch 18/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0781 - classification_output_loss: 0.0719 - regression_output_loss: 0.0469 - final_output_loss: 0.0356 - classification_output_accuracy: 0.9714 - classification_output_auc: 0.9967 - regression_output_mse: 6.5024 - regression_output_mae: 1.8395 - regression_output_rmse: 2.5405 - regression_output_custom_mape: 65.9929 - final_output_mse: 0.0320 - final_output_mae: 0.1099 - final_output_rmse: 0.1657 - final_output_custom_mape: 66.5092 - val_loss: 0.1399 - val_classification_output_loss: 0.2071 - val_regression_output_loss: 0.1092 - val_final_output_loss: 0.0458 - val_classification_output_accuracy: 0.9400 - val_classification_output_auc: 0.9852 - val_regression_output_mse: 6.7711 - val_regression_output_mae: 1.9289 - val_regression_output_rmse: 2.5859 - val_regression_output_custom_mape: 69.2916 - val_final_output_mse: 0.0527 - val_final_output_mae: 0.1335 - val_final_output_rmse: 0.2186 - val_final_output_custom_mape: 69.7183 - lr: 2.0000e-04\n", + "Epoch 19/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.1056 - classification_output_loss: 0.1087 - regression_output_loss: 0.0770 - final_output_loss: 0.0488 - classification_output_accuracy: 0.9540 - classification_output_auc: 0.9925 - regression_output_mse: 6.1650 - regression_output_mae: 1.7928 - regression_output_rmse: 2.4682 - regression_output_custom_mape: 68.5078 - final_output_mse: 0.0533 - final_output_mae: 0.1445 - final_output_rmse: 0.2165 - final_output_custom_mape: 69.2497 - val_loss: 0.1351 - val_classification_output_loss: 0.2118 - val_regression_output_loss: 0.0784 - val_final_output_loss: 0.0744 - val_classification_output_accuracy: 0.9309 - val_classification_output_auc: 0.9809 - val_regression_output_mse: 6.2044 - val_regression_output_mae: 1.8057 - val_regression_output_rmse: 2.4610 - val_regression_output_custom_mape: 71.1880 - val_final_output_mse: 0.0989 - val_final_output_mae: 0.1831 - val_final_output_rmse: 0.2888 - val_final_output_custom_mape: 71.7069 - lr: 2.0000e-04\n", + "Epoch 20/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0826 - classification_output_loss: 0.0899 - regression_output_loss: 0.0503 - final_output_loss: 0.0377 - classification_output_accuracy: 0.9641 - classification_output_auc: 0.9946 - regression_output_mse: 6.4599 - regression_output_mae: 1.8346 - regression_output_rmse: 2.5319 - regression_output_custom_mape: 66.5618 - final_output_mse: 0.0347 - final_output_mae: 0.1170 - final_output_rmse: 0.1767 - final_output_custom_mape: 67.1488 - val_loss: 0.1313 - val_classification_output_loss: 0.1914 - val_regression_output_loss: 0.0979 - val_final_output_loss: 0.0590 - val_classification_output_accuracy: 0.9393 - val_classification_output_auc: 0.9835 - val_regression_output_mse: 7.0486 - val_regression_output_mae: 2.0071 - val_regression_output_rmse: 2.6313 - val_regression_output_custom_mape: 72.4229 - val_final_output_mse: 0.0695 - val_final_output_mae: 0.1603 - val_final_output_rmse: 0.2545 - val_final_output_custom_mape: 72.8319 - lr: 2.0000e-04\n", + "Epoch 21/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971\n", + "Epoch 21 Detailed Metrics:\n", + "541/541 [==============================] - 54s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971 - val_loss: 0.1567 - val_classification_output_loss: 0.1925 - val_regression_output_loss: 0.1182 - val_final_output_loss: 0.1052 - val_classification_output_accuracy: 0.9335 - val_classification_output_auc: 0.9888 - val_regression_output_mse: 6.3370 - val_regression_output_mae: 1.8864 - val_regression_output_rmse: 2.4981 - val_regression_output_custom_mape: 76.4414 - val_final_output_mse: 0.1555 - val_final_output_mae: 0.2558 - val_final_output_rmse: 0.3667 - val_final_output_custom_mape: 76.3954 - lr: 2.0000e-04\n", + "Epoch 22/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0895 - classification_output_loss: 0.0784 - regression_output_loss: 0.0675 - final_output_loss: 0.0448 - classification_output_accuracy: 0.9675 - classification_output_auc: 0.9960 - regression_output_mse: 6.3547 - regression_output_mae: 1.8204 - regression_output_rmse: 2.5032 - regression_output_custom_mape: 67.3088 - final_output_mse: 0.0469 - final_output_mae: 0.1335 - final_output_rmse: 0.2007 - final_output_custom_mape: 68.4431 - val_loss: 0.1160 - val_classification_output_loss: 0.1100 - val_regression_output_loss: 0.1048 - val_final_output_loss: 0.0471 - val_classification_output_accuracy: 0.9584 - val_classification_output_auc: 0.9936 - val_regression_output_mse: 6.0390 - val_regression_output_mae: 1.7293 - val_regression_output_rmse: 2.4455 - val_regression_output_custom_mape: 66.8834 - val_final_output_mse: 0.0490 - val_final_output_mae: 0.1419 - val_final_output_rmse: 0.2174 - val_final_output_custom_mape: 69.0459 - lr: 2.0000e-04\n", + "Epoch 23/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0919 - classification_output_loss: 0.0994 - regression_output_loss: 0.0648 - final_output_loss: 0.0429 - classification_output_accuracy: 0.9578 - classification_output_auc: 0.9935 - regression_output_mse: 6.2936 - regression_output_mae: 1.8038 - regression_output_rmse: 2.4971 - regression_output_custom_mape: 67.2289 - final_output_mse: 0.0428 - final_output_mae: 0.1306 - final_output_rmse: 0.1954 - final_output_custom_mape: 68.1846 - val_loss: 0.1030 - val_classification_output_loss: 0.1001 - val_regression_output_loss: 0.0829 - val_final_output_loss: 0.0521 - val_classification_output_accuracy: 0.9620 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 4.9840 - val_regression_output_mae: 1.6330 - val_regression_output_rmse: 2.2237 - val_regression_output_custom_mape: 67.5453 - val_final_output_mse: 0.0686 - val_final_output_mae: 0.1495 - val_final_output_rmse: 0.2307 - val_final_output_custom_mape: 68.2594 - lr: 2.0000e-04\n", + "Epoch 24/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0805 - classification_output_loss: 0.0740 - regression_output_loss: 0.0572 - final_output_loss: 0.0406 - classification_output_accuracy: 0.9696 - classification_output_auc: 0.9964 - regression_output_mse: 6.5178 - regression_output_mae: 1.8487 - regression_output_rmse: 2.5431 - regression_output_custom_mape: 66.7225 - final_output_mse: 0.0413 - final_output_mae: 0.1227 - final_output_rmse: 0.1848 - final_output_custom_mape: 67.3500 - val_loss: 0.1503 - val_classification_output_loss: 0.1089 - val_regression_output_loss: 0.1781 - val_final_output_loss: 0.0504 - val_classification_output_accuracy: 0.9567 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 7.3781 - val_regression_output_mae: 2.0860 - val_regression_output_rmse: 2.7043 - val_regression_output_custom_mape: 71.4390 - val_final_output_mse: 0.0652 - val_final_output_mae: 0.1535 - val_final_output_rmse: 0.2412 - val_final_output_custom_mape: 71.1141 - lr: 2.0000e-04\n", + "Epoch 25/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826\n", + "Epoch 25: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-05.\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826 - val_loss: 0.0920 - val_classification_output_loss: 0.1283 - val_regression_output_loss: 0.0552 - val_final_output_loss: 0.0492 - val_classification_output_accuracy: 0.9526 - val_classification_output_auc: 0.9923 - val_regression_output_mse: 6.4349 - val_regression_output_mae: 1.8355 - val_regression_output_rmse: 2.5185 - val_regression_output_custom_mape: 69.3148 - val_final_output_mse: 0.0507 - val_final_output_mae: 0.1446 - val_final_output_rmse: 0.2100 - val_final_output_custom_mape: 69.2727 - lr: 2.0000e-04\n", + "Epoch 26/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0650 - classification_output_loss: 0.0610 - regression_output_loss: 0.0419 - final_output_loss: 0.0313 - classification_output_accuracy: 0.9754 - classification_output_auc: 0.9975 - regression_output_mse: 6.6869 - regression_output_mae: 1.8694 - regression_output_rmse: 2.5765 - regression_output_custom_mape: 65.2689 - final_output_mse: 0.0247 - final_output_mae: 0.0999 - final_output_rmse: 0.1486 - final_output_custom_mape: 65.4804 - val_loss: 0.0663 - val_classification_output_loss: 0.0825 - val_regression_output_loss: 0.0378 - val_final_output_loss: 0.0327 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9952 - val_regression_output_mse: 6.7468 - val_regression_output_mae: 1.8843 - val_regression_output_rmse: 2.5794 - val_regression_output_custom_mape: 64.9190 - val_final_output_mse: 0.0274 - val_final_output_mae: 0.1009 - val_final_output_rmse: 0.1557 - val_final_output_custom_mape: 64.8372 - lr: 1.0000e-04\n", + "Epoch 27/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0520 - classification_output_loss: 0.0582 - regression_output_loss: 0.0251 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9761 - classification_output_auc: 0.9978 - regression_output_mse: 6.7296 - regression_output_mae: 1.8683 - regression_output_rmse: 2.5843 - regression_output_custom_mape: 64.1190 - final_output_mse: 0.0169 - 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final_output_mse: 0.0191 - final_output_mae: 0.0891 - final_output_rmse: 0.1301 - final_output_custom_mape: 64.3093 - val_loss: 0.0707 - val_classification_output_loss: 0.0879 - val_regression_output_loss: 0.0470 - val_final_output_loss: 0.0366 - val_classification_output_accuracy: 0.9646 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6182 - val_regression_output_mae: 1.8513 - val_regression_output_rmse: 2.5551 - val_regression_output_custom_mape: 65.1533 - val_final_output_mse: 0.0342 - val_final_output_mae: 0.1136 - val_final_output_rmse: 0.1726 - val_final_output_custom_mape: 65.5781 - lr: 1.0000e-04\n", + "Epoch 29/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0504 - classification_output_loss: 0.0586 - regression_output_loss: 0.0257 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9756 - classification_output_auc: 0.9978 - regression_output_mse: 6.7302 - regression_output_mae: 1.8661 - regression_output_rmse: 2.5837 - regression_output_custom_mape: 63.8101 - final_output_mse: 0.0171 - final_output_mae: 0.0858 - final_output_rmse: 0.1252 - final_output_custom_mape: 64.0432 - val_loss: 0.0755 - val_classification_output_loss: 0.0734 - val_regression_output_loss: 0.0692 - val_final_output_loss: 0.0298 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5720 - val_regression_output_mae: 1.8334 - val_regression_output_rmse: 2.5485 - val_regression_output_custom_mape: 65.7695 - val_final_output_mse: 0.0219 - val_final_output_mae: 0.0957 - val_final_output_rmse: 0.1415 - val_final_output_custom_mape: 65.9068 - lr: 1.0000e-04\n", + "Epoch 30/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0618 - classification_output_loss: 0.0646 - regression_output_loss: 0.0430 - final_output_loss: 0.0325 - classification_output_accuracy: 0.9726 - classification_output_auc: 0.9974 - regression_output_mse: 6.6295 - regression_output_mae: 1.8588 - regression_output_rmse: 2.5643 - regression_output_custom_mape: 65.4595 - final_output_mse: 0.0267 - final_output_mae: 0.1036 - final_output_rmse: 0.1522 - final_output_custom_mape: 65.8207 - val_loss: 0.0563 - val_classification_output_loss: 0.0878 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0269 - val_classification_output_accuracy: 0.9639 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6181 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5541 - val_regression_output_custom_mape: 63.1844 - val_final_output_mse: 0.0184 - val_final_output_mae: 0.0849 - val_final_output_rmse: 0.1292 - val_final_output_custom_mape: 63.5460 - lr: 1.0000e-04\n", + "Epoch 31/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0539 - classification_output_loss: 0.0608 - regression_output_loss: 0.0321 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9746 - classification_output_auc: 0.9975 - regression_output_mse: 6.6719 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5729 - regression_output_custom_mape: 64.1531 - final_output_mse: 0.0197 - final_output_mae: 0.0908 - final_output_rmse: 0.1326 - final_output_custom_mape: 64.4701\n", + "Epoch 31 Detailed Metrics:\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0539 - classification_output_loss: 0.0608 - regression_output_loss: 0.0321 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9746 - classification_output_auc: 0.9975 - regression_output_mse: 6.6719 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5729 - regression_output_custom_mape: 64.1531 - final_output_mse: 0.0197 - final_output_mae: 0.0908 - final_output_rmse: 0.1326 - final_output_custom_mape: 64.4701 - val_loss: 0.0803 - val_classification_output_loss: 0.1105 - val_regression_output_loss: 0.0560 - val_final_output_loss: 0.0436 - val_classification_output_accuracy: 0.9555 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 6.3193 - val_regression_output_mae: 1.7972 - val_regression_output_rmse: 2.4978 - val_regression_output_custom_mape: 67.5192 - val_final_output_mse: 0.0457 - val_final_output_mae: 0.1347 - val_final_output_rmse: 0.2015 - val_final_output_custom_mape: 67.7615 - lr: 1.0000e-04\n", + "Epoch 32/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0588 - classification_output_loss: 0.0668 - regression_output_loss: 0.0386 - final_output_loss: 0.0303 - classification_output_accuracy: 0.9719 - classification_output_auc: 0.9971 - regression_output_mse: 6.6258 - regression_output_mae: 1.8496 - regression_output_rmse: 2.5637 - regression_output_custom_mape: 64.4690 - final_output_mse: 0.0235 - final_output_mae: 0.0968 - final_output_rmse: 0.1423 - final_output_custom_mape: 64.9170 - val_loss: 0.0554 - val_classification_output_loss: 0.0832 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0247 - val_classification_output_accuracy: 0.9658 - val_classification_output_auc: 0.9956 - val_regression_output_mse: 6.7171 - val_regression_output_mae: 1.8475 - val_regression_output_rmse: 2.5747 - val_regression_output_custom_mape: 63.5893 - val_final_output_mse: 0.0151 - val_final_output_mae: 0.0793 - val_final_output_rmse: 0.1190 - val_final_output_custom_mape: 63.5201 - lr: 1.0000e-04\n", + "Epoch 33/100\n", + "541/541 [==============================] - 53s 97ms/step - loss: 0.0562 - classification_output_loss: 0.0654 - regression_output_loss: 0.0351 - final_output_loss: 0.0293 - classification_output_accuracy: 0.9728 - classification_output_auc: 0.9973 - regression_output_mse: 6.6587 - regression_output_mae: 1.8556 - regression_output_rmse: 2.5699 - regression_output_custom_mape: 64.4570 - final_output_mse: 0.0214 - final_output_mae: 0.0943 - final_output_rmse: 0.1371 - final_output_custom_mape: 64.8190 - val_loss: 0.0688 - val_classification_output_loss: 0.0870 - val_regression_output_loss: 0.0540 - val_final_output_loss: 0.0265 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9946 - val_regression_output_mse: 6.6598 - val_regression_output_mae: 1.8470 - val_regression_output_rmse: 2.5624 - val_regression_output_custom_mape: 63.3716 - val_final_output_mse: 0.0182 - val_final_output_mae: 0.0846 - val_final_output_rmse: 0.1293 - val_final_output_custom_mape: 63.4470 - lr: 1.0000e-04\n", + "Epoch 34/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0589 - regression_output_loss: 0.0381 - final_output_loss: 0.0284 - classification_output_accuracy: 0.9753 - classification_output_auc: 0.9977 - regression_output_mse: 6.7001 - regression_output_mae: 1.8630 - regression_output_rmse: 2.5779 - regression_output_custom_mape: 63.9678 - final_output_mse: 0.0203 - final_output_mae: 0.0921 - final_output_rmse: 0.1348 - final_output_custom_mape: 64.2577 - val_loss: 0.0620 - val_classification_output_loss: 0.0928 - val_regression_output_loss: 0.0371 - val_final_output_loss: 0.0294 - val_classification_output_accuracy: 0.9603 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 6.7330 - val_regression_output_mae: 1.8643 - val_regression_output_rmse: 2.5765 - val_regression_output_custom_mape: 64.8509 - val_final_output_mse: 0.0217 - val_final_output_mae: 0.0925 - val_final_output_rmse: 0.1422 - val_final_output_custom_mape: 65.1600 - lr: 1.0000e-04\n", + "Epoch 35/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0614 - regression_output_loss: 0.0367 - final_output_loss: 0.0297 - classification_output_accuracy: 0.9745 - classification_output_auc: 0.9976 - regression_output_mse: 6.6579 - regression_output_mae: 1.8582 - regression_output_rmse: 2.5696 - regression_output_custom_mape: 64.6971 - final_output_mse: 0.0221 - final_output_mae: 0.0956 - final_output_rmse: 0.1398 - final_output_custom_mape: 65.0271 - val_loss: 0.0594 - val_classification_output_loss: 0.0939 - val_regression_output_loss: 0.0300 - val_final_output_loss: 0.0317 - val_classification_output_accuracy: 0.9612 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 6.8011 - val_regression_output_mae: 1.8931 - val_regression_output_rmse: 2.5893 - val_regression_output_custom_mape: 65.7507 - val_final_output_mse: 0.0254 - val_final_output_mae: 0.1008 - val_final_output_rmse: 0.1520 - val_final_output_custom_mape: 65.6841 - lr: 1.0000e-04\n", + "Epoch 36/100\n", + "541/541 [==============================] - 57s 106ms/step - loss: 0.0461 - classification_output_loss: 0.0654 - regression_output_loss: 0.0198 - final_output_loss: 0.0250 - classification_output_accuracy: 0.9728 - classification_output_auc: 0.9971 - regression_output_mse: 6.6669 - regression_output_mae: 1.8474 - regression_output_rmse: 2.5716 - regression_output_custom_mape: 63.3631 - final_output_mse: 0.0152 - final_output_mae: 0.0813 - final_output_rmse: 0.1173 - final_output_custom_mape: 63.6640 - val_loss: 0.0649 - val_classification_output_loss: 0.1065 - 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val_classification_output_loss: 0.1106 - val_regression_output_loss: 0.0482 - val_final_output_loss: 0.0240 - val_classification_output_accuracy: 0.9576 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 6.5947 - val_regression_output_mae: 1.8196 - val_regression_output_rmse: 2.5455 - val_regression_output_custom_mape: 62.2371 - val_final_output_mse: 0.0145 - val_final_output_mae: 0.0779 - val_final_output_rmse: 0.1159 - val_final_output_custom_mape: 62.1254 - lr: 1.0000e-04\n", + "Epoch 38/100\n", + "541/541 [==============================] - 55s 103ms/step - loss: 0.0531 - classification_output_loss: 0.0608 - regression_output_loss: 0.0336 - final_output_loss: 0.0279 - classification_output_accuracy: 0.9748 - classification_output_auc: 0.9977 - regression_output_mse: 6.5831 - regression_output_mae: 1.8327 - regression_output_rmse: 2.5548 - regression_output_custom_mape: 63.5282 - final_output_mse: 0.0194 - final_output_mae: 0.0900 - final_output_rmse: 0.1307 - final_output_custom_mape: 64.1170 - val_loss: 0.0563 - val_classification_output_loss: 0.0809 - val_regression_output_loss: 0.0317 - val_final_output_loss: 0.0292 - val_classification_output_accuracy: 0.9680 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 6.5566 - val_regression_output_mae: 1.8266 - val_regression_output_rmse: 2.5428 - val_regression_output_custom_mape: 63.8044 - val_final_output_mse: 0.0222 - val_final_output_mae: 0.0928 - val_final_output_rmse: 0.1405 - val_final_output_custom_mape: 64.0602 - lr: 1.0000e-04\n", + "Epoch 39/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0515 - classification_output_loss: 0.0622 - regression_output_loss: 0.0313 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6419 - regression_output_mae: 1.8481 - regression_output_rmse: 2.5667 - regression_output_custom_mape: 63.7719 - final_output_mse: 0.0181 - final_output_mae: 0.0884 - final_output_rmse: 0.1283 - 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final_output_rmse: 0.1320 - final_output_custom_mape: 64.4904 - val_loss: 0.0712 - val_classification_output_loss: 0.0779 - val_regression_output_loss: 0.0654 - val_final_output_loss: 0.0267 - val_classification_output_accuracy: 0.9666 - val_classification_output_auc: 0.9957 - val_regression_output_mse: 6.7348 - val_regression_output_mae: 1.8600 - val_regression_output_rmse: 2.5770 - val_regression_output_custom_mape: 63.5495 - val_final_output_mse: 0.0176 - val_final_output_mae: 0.0874 - val_final_output_rmse: 0.1274 - val_final_output_custom_mape: 63.5438 - lr: 1.0000e-04\n", + "Epoch 41/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0537 - classification_output_loss: 0.0611 - regression_output_loss: 0.0359 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9976 - regression_output_mse: 6.7033 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5791 - regression_output_custom_mape: 64.1775 - final_output_mse: 0.0202 - 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val_regression_output_mae: 1.7951 - val_regression_output_rmse: 2.5414 - val_regression_output_custom_mape: 61.7450 - val_final_output_mse: 0.0123 - val_final_output_mae: 0.0720 - val_final_output_rmse: 0.1075 - val_final_output_custom_mape: 61.9004 - lr: 1.0000e-04\n", + "Epoch 45/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0461 - classification_output_loss: 0.0569 - regression_output_loss: 0.0258 - final_output_loss: 0.0260 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9980 - regression_output_mse: 6.7333 - regression_output_mae: 1.8649 - regression_output_rmse: 2.5850 - regression_output_custom_mape: 63.6520 - final_output_mse: 0.0163 - final_output_mae: 0.0845 - final_output_rmse: 0.1212 - final_output_custom_mape: 63.8765 - val_loss: 0.0649 - val_classification_output_loss: 0.1188 - val_regression_output_loss: 0.0354 - val_final_output_loss: 0.0316 - val_classification_output_accuracy: 0.9471 - val_classification_output_auc: 0.9966 - val_regression_output_mse: 6.0672 - val_regression_output_mae: 1.7044 - val_regression_output_rmse: 2.4462 - val_regression_output_custom_mape: 62.7387 - val_final_output_mse: 0.0251 - val_final_output_mae: 0.1018 - val_final_output_rmse: 0.1497 - val_final_output_custom_mape: 63.6189 - lr: 1.0000e-04\n", + "Epoch 46/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0510 - classification_output_loss: 0.0628 - regression_output_loss: 0.0325 - final_output_loss: 0.0277 - classification_output_accuracy: 0.9741 - classification_output_auc: 0.9974 - regression_output_mse: 6.6063 - regression_output_mae: 1.8383 - regression_output_rmse: 2.5597 - regression_output_custom_mape: 63.7547 - final_output_mse: 0.0190 - final_output_mae: 0.0901 - final_output_rmse: 0.1300 - final_output_custom_mape: 64.2525 - val_loss: 0.0523 - val_classification_output_loss: 0.0814 - val_regression_output_loss: 0.0291 - val_final_output_loss: 0.0254 - val_classification_output_accuracy: 0.9658 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 6.7808 - val_regression_output_mae: 1.8736 - val_regression_output_rmse: 2.5860 - val_regression_output_custom_mape: 65.4947 - val_final_output_mse: 0.0155 - val_final_output_mae: 0.0831 - val_final_output_rmse: 0.1224 - val_final_output_custom_mape: 65.5416 - lr: 1.0000e-04\n", + "Epoch 47/100\n", + "541/541 [==============================] - 51s 94ms/step - loss: 0.0509 - classification_output_loss: 0.0589 - regression_output_loss: 0.0343 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9977 - regression_output_mse: 6.7076 - regression_output_mae: 1.8620 - regression_output_rmse: 2.5796 - regression_output_custom_mape: 63.6718 - final_output_mse: 0.0182 - final_output_mae: 0.0885 - final_output_rmse: 0.1278 - final_output_custom_mape: 63.9957 - val_loss: 0.0655 - val_classification_output_loss: 0.1041 - val_regression_output_loss: 0.0332 - val_final_output_loss: 0.0474 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 5.7525 - val_regression_output_mae: 1.6878 - val_regression_output_rmse: 2.3943 - val_regression_output_custom_mape: 62.4874 - val_final_output_mse: 0.0233 - val_final_output_mae: 0.0817 - val_final_output_rmse: 0.1449 - val_final_output_custom_mape: 62.4108 - lr: 1.0000e-04\n", + "Epoch 48/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0480 - classification_output_loss: 0.0625 - regression_output_loss: 0.0275 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6081 - regression_output_mae: 1.8378 - regression_output_rmse: 2.5612 - regression_output_custom_mape: 63.5768 - final_output_mse: 0.0170 - final_output_mae: 0.0853 - final_output_rmse: 0.1224 - final_output_custom_mape: 63.9522 - val_loss: 0.0500 - val_classification_output_loss: 0.0769 - val_regression_output_loss: 0.0292 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9676 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5384 - val_regression_output_mae: 1.7942 - val_regression_output_rmse: 2.5409 - val_regression_output_custom_mape: 61.9763 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0710 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 62.2404 - lr: 1.0000e-04\n", + "Epoch 49/100\n", + "541/541 [==============================] - 51s 95ms/step - loss: 0.0474 - classification_output_loss: 0.0564 - regression_output_loss: 0.0296 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9760 - classification_output_auc: 0.9980 - regression_output_mse: 6.7164 - regression_output_mae: 1.8615 - regression_output_rmse: 2.5813 - regression_output_custom_mape: 63.5453 - final_output_mse: 0.0171 - final_output_mae: 0.0857 - final_output_rmse: 0.1235 - final_output_custom_mape: 63.7933 - val_loss: 0.0498 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0283 - val_final_output_loss: 0.0260 - val_classification_output_accuracy: 0.9693 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.5693 - val_regression_output_mae: 1.8158 - val_regression_output_rmse: 2.5473 - val_regression_output_custom_mape: 64.0865 - val_final_output_mse: 0.0165 - val_final_output_mae: 0.0844 - val_final_output_rmse: 0.1243 - val_final_output_custom_mape: 64.5164 - lr: 1.0000e-04\n", + "Epoch 50/100\n", + "541/541 [==============================] - 49s 91ms/step - loss: 0.0395 - classification_output_loss: 0.0549 - regression_output_loss: 0.0169 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9766 - classification_output_auc: 0.9981 - regression_output_mse: 6.7595 - regression_output_mae: 1.8651 - regression_output_rmse: 2.5900 - regression_output_custom_mape: 63.1470 - final_output_mse: 0.0132 - final_output_mae: 0.0776 - final_output_rmse: 0.1109 - final_output_custom_mape: 63.3583 - val_loss: 0.0451 - val_classification_output_loss: 0.0768 - val_regression_output_loss: 0.0201 - val_final_output_loss: 0.0230 - val_classification_output_accuracy: 0.9683 - val_classification_output_auc: 0.9956 - val_regression_output_mse: 6.4914 - val_regression_output_mae: 1.8122 - val_regression_output_rmse: 2.5338 - val_regression_output_custom_mape: 62.2374 - val_final_output_mse: 0.0132 - val_final_output_mae: 0.0753 - val_final_output_rmse: 0.1099 - val_final_output_custom_mape: 62.2972 - lr: 1.0000e-04\n", + "Epoch 51/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354\n", + "Epoch 51 Detailed Metrics:\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354 - val_loss: 0.0727 - val_classification_output_loss: 0.0944 - val_regression_output_loss: 0.0562 - val_final_output_loss: 0.0432 - val_classification_output_accuracy: 0.9653 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 4.2029 - val_regression_output_mae: 1.5086 - val_regression_output_rmse: 2.0452 - val_regression_output_custom_mape: 67.4825 - val_final_output_mse: 0.0431 - val_final_output_mae: 0.1288 - val_final_output_rmse: 0.1947 - val_final_output_custom_mape: 67.4020 - lr: 1.0000e-04\n", + "Epoch 52/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0518 - classification_output_loss: 0.0650 - regression_output_loss: 0.0344 - final_output_loss: 0.0286 - classification_output_accuracy: 0.9733 - classification_output_auc: 0.9972 - regression_output_mse: 6.6534 - regression_output_mae: 1.8507 - regression_output_rmse: 2.5694 - regression_output_custom_mape: 64.0731 - final_output_mse: 0.0204 - final_output_mae: 0.0922 - final_output_rmse: 0.1333 - final_output_custom_mape: 64.4775 - val_loss: 0.0449 - val_classification_output_loss: 0.0869 - val_regression_output_loss: 0.0155 - val_final_output_loss: 0.0226 - val_classification_output_accuracy: 0.9648 - val_classification_output_auc: 0.9948 - val_regression_output_mse: 6.4651 - val_regression_output_mae: 1.8048 - val_regression_output_rmse: 2.5262 - val_regression_output_custom_mape: 62.5471 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0736 - val_final_output_rmse: 0.1087 - val_final_output_custom_mape: 62.6503 - lr: 1.0000e-04\n", + "Epoch 53/100\n", + "541/541 [==============================] - 53s 99ms/step - loss: 0.0411 - classification_output_loss: 0.0548 - regression_output_loss: 0.0207 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9980 - regression_output_mse: 6.7604 - regression_output_mae: 1.8647 - regression_output_rmse: 2.5895 - regression_output_custom_mape: 62.7709 - final_output_mse: 0.0133 - final_output_mae: 0.0777 - final_output_rmse: 0.1099 - final_output_custom_mape: 62.9869 - val_loss: 0.0771 - val_classification_output_loss: 0.0905 - val_regression_output_loss: 0.0766 - val_final_output_loss: 0.0281 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9944 - val_regression_output_mse: 6.3203 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5019 - val_regression_output_custom_mape: 64.9538 - val_final_output_mse: 0.0197 - val_final_output_mae: 0.0919 - val_final_output_rmse: 0.1340 - val_final_output_custom_mape: 64.7748 - lr: 1.0000e-04\n", + "Epoch 54/100\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0483 - classification_output_loss: 0.0616 - regression_output_loss: 0.0307 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9738 - classification_output_auc: 0.9975 - regression_output_mse: 6.6227 - regression_output_mae: 1.8377 - regression_output_rmse: 2.5629 - regression_output_custom_mape: 63.1048 - final_output_mse: 0.0169 - final_output_mae: 0.0860 - final_output_rmse: 0.1239 - final_output_custom_mape: 63.6682 - val_loss: 0.0536 - val_classification_output_loss: 0.0971 - val_regression_output_loss: 0.0289 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9942 - val_regression_output_mse: 6.6649 - val_regression_output_mae: 1.8270 - val_regression_output_rmse: 2.5626 - val_regression_output_custom_mape: 62.0142 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0733 - val_final_output_rmse: 0.1081 - val_final_output_custom_mape: 62.4347 - lr: 1.0000e-04\n", + "Epoch 55/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - final_output_custom_mape: 63.4037\n", + "Epoch 55: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - 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final_output_mae: 0.0674 - final_output_rmse: 0.0938 - final_output_custom_mape: 61.7115 - val_loss: 0.0406 - val_classification_output_loss: 0.0763 - val_regression_output_loss: 0.0160 - val_final_output_loss: 0.0196 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5545 - val_regression_output_mae: 1.7924 - val_regression_output_rmse: 2.5441 - val_regression_output_custom_mape: 61.5195 - val_final_output_mse: 0.0089 - val_final_output_mae: 0.0644 - val_final_output_rmse: 0.0920 - val_final_output_custom_mape: 61.8914 - lr: 5.0000e-05\n", + "Epoch 58/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0325 - classification_output_loss: 0.0506 - regression_output_loss: 0.0099 - final_output_loss: 0.0203 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9983 - regression_output_mse: 6.7571 - regression_output_mae: 1.8542 - regression_output_rmse: 2.5887 - regression_output_custom_mape: 61.4581 - 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regression_output_custom_mape: 61.4620 - final_output_mse: 0.0090 - final_output_mae: 0.0665 - final_output_rmse: 0.0918 - final_output_custom_mape: 61.6222 - val_loss: 0.0479 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0320 - val_final_output_loss: 0.0218 - val_classification_output_accuracy: 0.9702 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6692 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5661 - val_regression_output_custom_mape: 63.2262 - val_final_output_mse: 0.0109 - val_final_output_mae: 0.0716 - val_final_output_rmse: 0.1014 - val_final_output_custom_mape: 63.3540 - lr: 5.0000e-05\n", + "Epoch 60/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0336 - classification_output_loss: 0.0512 - regression_output_loss: 0.0125 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7842 - regression_output_mae: 1.8627 - regression_output_rmse: 2.5937 - regression_output_custom_mape: 61.7020 - final_output_mse: 0.0100 - final_output_mae: 0.0691 - final_output_rmse: 0.0961 - final_output_custom_mape: 61.8323 - val_loss: 0.0496 - val_classification_output_loss: 0.0797 - val_regression_output_loss: 0.0308 - val_final_output_loss: 0.0249 - val_classification_output_accuracy: 0.9691 - val_classification_output_auc: 0.9953 - val_regression_output_mse: 6.8019 - val_regression_output_mae: 1.8752 - val_regression_output_rmse: 2.5909 - val_regression_output_custom_mape: 66.9446 - val_final_output_mse: 0.0148 - val_final_output_mae: 0.0816 - val_final_output_rmse: 0.1192 - val_final_output_custom_mape: 67.0260 - lr: 5.0000e-05\n", + "Epoch 61/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795\n", + "Epoch 61 Detailed Metrics:\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795 - val_loss: 0.0505 - val_classification_output_loss: 0.0742 - val_regression_output_loss: 0.0356 - val_final_output_loss: 0.0244 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9958 - val_regression_output_mse: 6.6354 - val_regression_output_mae: 1.8309 - val_regression_output_rmse: 2.5603 - val_regression_output_custom_mape: 65.7531 - val_final_output_mse: 0.0137 - val_final_output_mae: 0.0801 - val_final_output_rmse: 0.1142 - val_final_output_custom_mape: 65.9202 - lr: 5.0000e-05\n", + "Epoch 62/100\n", + "541/541 [==============================] - 53s 98ms/step - loss: 0.0332 - classification_output_loss: 0.0518 - regression_output_loss: 0.0119 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7770 - regression_output_mae: 1.8601 - regression_output_rmse: 2.5926 - regression_output_custom_mape: 61.7351 - final_output_mse: 0.0097 - final_output_mae: 0.0689 - final_output_rmse: 0.0954 - final_output_custom_mape: 61.8624 - val_loss: 0.0481 - val_classification_output_loss: 0.0746 - val_regression_output_loss: 0.0310 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7120 - val_regression_output_mae: 1.8451 - val_regression_output_rmse: 2.5737 - val_regression_output_custom_mape: 65.7528 - val_final_output_mse: 0.0131 - val_final_output_mae: 0.0782 - val_final_output_rmse: 0.1123 - val_final_output_custom_mape: 65.8633 - lr: 5.0000e-05\n", + "Epoch 63/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0304 - classification_output_loss: 0.0504 - regression_output_loss: 0.0079 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7759 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5922 - regression_output_custom_mape: 61.3674 - final_output_mse: 0.0087 - final_output_mae: 0.0655 - final_output_rmse: 0.0902 - final_output_custom_mape: 61.5143 - val_loss: 0.0364 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0121 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6819 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5687 - val_regression_output_custom_mape: 62.2443 - val_final_output_mse: 0.0086 - val_final_output_mae: 0.0635 - val_final_output_rmse: 0.0902 - val_final_output_custom_mape: 62.3224 - lr: 5.0000e-05\n", + "Epoch 64/100\n", + "541/541 [==============================] - 56s 103ms/step - loss: 0.0309 - classification_output_loss: 0.0507 - regression_output_loss: 0.0090 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.7723 - regression_output_mae: 1.8552 - regression_output_rmse: 2.5911 - regression_output_custom_mape: 61.1519 - final_output_mse: 0.0086 - final_output_mae: 0.0653 - final_output_rmse: 0.0898 - final_output_custom_mape: 61.3432 - val_loss: 0.0393 - val_classification_output_loss: 0.0725 - val_regression_output_loss: 0.0162 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7377 - val_regression_output_mae: 1.8477 - val_regression_output_rmse: 2.5799 - val_regression_output_custom_mape: 64.4454 - val_final_output_mse: 0.0108 - val_final_output_mae: 0.0712 - val_final_output_rmse: 0.1021 - val_final_output_custom_mape: 64.4477 - lr: 5.0000e-05\n", + "Epoch 65/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0312 - classification_output_loss: 0.0508 - regression_output_loss: 0.0096 - final_output_loss: 0.0202 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9983 - regression_output_mse: 6.7588 - regression_output_mae: 1.8526 - regression_output_rmse: 2.5891 - regression_output_custom_mape: 61.3250 - final_output_mse: 0.0091 - final_output_mae: 0.0665 - final_output_rmse: 0.0922 - final_output_custom_mape: 61.5369 - val_loss: 0.0398 - val_classification_output_loss: 0.0764 - val_regression_output_loss: 0.0174 - val_final_output_loss: 0.0194 - val_classification_output_accuracy: 0.9673 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6505 - val_regression_output_mae: 1.8149 - val_regression_output_rmse: 2.5623 - val_regression_output_custom_mape: 61.7415 - val_final_output_mse: 0.0087 - val_final_output_mae: 0.0638 - val_final_output_rmse: 0.0908 - val_final_output_custom_mape: 61.9581 - lr: 5.0000e-05\n", + "Epoch 66/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0352 - classification_output_loss: 0.0543 - regression_output_loss: 0.0094 - final_output_loss: 0.0300 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9981 - regression_output_mse: 6.6501 - regression_output_mae: 1.8334 - regression_output_rmse: 2.5658 - regression_output_custom_mape: 61.4441 - final_output_mse: 0.0123 - final_output_mae: 0.0678 - final_output_rmse: 0.0945 - final_output_custom_mape: 61.6992 - val_loss: 0.0378 - val_classification_output_loss: 0.0773 - val_regression_output_loss: 0.0096 - val_final_output_loss: 0.0197 - val_classification_output_accuracy: 0.9685 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5211 - val_regression_output_mae: 1.7843 - val_regression_output_rmse: 2.5370 - val_regression_output_custom_mape: 61.5626 - val_final_output_mse: 0.0090 - val_final_output_mae: 0.0649 - val_final_output_rmse: 0.0924 - val_final_output_custom_mape: 62.0307 - lr: 5.0000e-05\n", + "Epoch 67/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0315 - classification_output_loss: 0.0499 - regression_output_loss: 0.0088 - final_output_loss: 0.0201 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.7018 - regression_output_mae: 1.8423 - regression_output_rmse: 2.5784 - regression_output_custom_mape: 61.3784 - final_output_mse: 0.0090 - final_output_mae: 0.0662 - final_output_rmse: 0.0914 - final_output_custom_mape: 61.5973 - val_loss: 0.0422 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0203 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6187 - val_regression_output_mae: 1.8151 - val_regression_output_rmse: 2.5569 - val_regression_output_custom_mape: 62.3132 - val_final_output_mse: 0.0093 - val_final_output_mae: 0.0670 - val_final_output_rmse: 0.0939 - val_final_output_custom_mape: 62.4799 - lr: 5.0000e-05\n", + "Epoch 68/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0337 - classification_output_loss: 0.0514 - regression_output_loss: 0.0130 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9983 - regression_output_mse: 6.7455 - regression_output_mae: 1.8556 - regression_output_rmse: 2.5868 - regression_output_custom_mape: 61.6488 - final_output_mse: 0.0101 - final_output_mae: 0.0693 - final_output_rmse: 0.0959 - final_output_custom_mape: 61.7737 - val_loss: 0.0471 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0315 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9718 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6541 - val_regression_output_mae: 1.8264 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 63.9875 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0740 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 64.1717 - lr: 5.0000e-05\n", + "Epoch 69/100\n", + "462/541 [========================>.....] - ETA: 7s - loss: 0.0330 - classification_output_loss: 0.0495 - regression_output_loss: 0.0129 - final_output_loss: 0.0208 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.8607 - regression_output_mae: 1.8819 - regression_output_rmse: 2.6091 - regression_output_custom_mape: 61.7354 - final_output_mse: 0.0096 - final_output_mae: 0.0686 - final_output_rmse: 0.0946 - final_output_custom_mape: 61.9095" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub data rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_data_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "541/541 [==============================] - 56s 104ms/step - loss: 0.0270 - classification_output_loss: 0.0498 - regression_output_loss: 0.0037 - final_output_loss: 0.0185 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.8101 - regression_output_mae: 1.8609 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.8799 - final_output_mse: 0.0074 - final_output_mae: 0.0611 - final_output_rmse: 0.0833 - final_output_custom_mape: 60.9907 - val_loss: 0.0326 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7669 - val_regression_output_mae: 1.8484 - val_regression_output_rmse: 2.5858 - val_regression_output_custom_mape: 62.3941 - val_final_output_mse: 0.0084 - val_final_output_mae: 0.0634 - val_final_output_rmse: 0.0894 - val_final_output_custom_mape: 62.3456 - lr: 2.5000e-05\n", + "Epoch 74/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0266 - classification_output_loss: 0.0496 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9984 - regression_output_mse: 6.8160 - regression_output_mae: 1.8619 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.7902 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0824 - final_output_custom_mape: 60.9197 - val_loss: 0.0329 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7283 - val_regression_output_mae: 1.8351 - val_regression_output_rmse: 2.5782 - val_regression_output_custom_mape: 61.8653 - val_final_output_mse: 0.0081 - val_final_output_mae: 0.0621 - val_final_output_rmse: 0.0877 - val_final_output_custom_mape: 61.8723 - lr: 2.5000e-05\n", + "Epoch 75/100\n", + "541/541 [==============================] - 58s 108ms/step - loss: 0.0266 - classification_output_loss: 0.0498 - regression_output_loss: 0.0035 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9984 - regression_output_mse: 6.8235 - regression_output_mae: 1.8647 - regression_output_rmse: 2.6014 - regression_output_custom_mape: 60.8557 - final_output_mse: 0.0072 - final_output_mae: 0.0609 - final_output_rmse: 0.0826 - final_output_custom_mape: 60.9257 - val_loss: 0.0314 - val_classification_output_loss: 0.0692 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7276 - val_regression_output_mae: 1.8331 - val_regression_output_rmse: 2.5784 - val_regression_output_custom_mape: 61.4127 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0603 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.4123 - lr: 2.5000e-05\n", + "Epoch 76/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0259 - classification_output_loss: 0.0485 - regression_output_loss: 0.0029 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8060 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6619 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7422 - val_loss: 0.0325 - val_classification_output_loss: 0.0705 - val_regression_output_loss: 0.0072 - val_final_output_loss: 0.0185 - val_classification_output_accuracy: 0.9713 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6720 - val_regression_output_mae: 1.8182 - val_regression_output_rmse: 2.5674 - val_regression_output_custom_mape: 61.5169 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0608 - val_final_output_rmse: 0.0849 - val_final_output_custom_mape: 61.6409 - lr: 2.5000e-05\n", + "Epoch 77/100\n", + "541/541 [==============================] - 57s 105ms/step - loss: 0.0261 - classification_output_loss: 0.0495 - regression_output_loss: 0.0031 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7979 - regression_output_mae: 1.8569 - regression_output_rmse: 2.5965 - regression_output_custom_mape: 60.6774 - final_output_mse: 0.0070 - final_output_mae: 0.0603 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7930 - val_loss: 0.0312 - val_classification_output_loss: 0.0681 - val_regression_output_loss: 0.0057 - val_final_output_loss: 0.0188 - val_classification_output_accuracy: 0.9739 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6716 - val_regression_output_mae: 1.8195 - val_regression_output_rmse: 2.5681 - val_regression_output_custom_mape: 61.4893 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0619 - val_final_output_rmse: 0.0865 - val_final_output_custom_mape: 61.6521 - lr: 2.5000e-05\n", + "Epoch 78/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0258 - classification_output_loss: 0.0489 - regression_output_loss: 0.0030 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8116 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5993 - regression_output_custom_mape: 60.6759 - final_output_mse: 0.0071 - final_output_mae: 0.0601 - final_output_rmse: 0.0814 - final_output_custom_mape: 60.7954 - val_loss: 0.0317 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0069 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9725 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7023 - val_regression_output_mae: 1.8251 - val_regression_output_rmse: 2.5736 - val_regression_output_custom_mape: 61.2798 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0596 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.3668 - lr: 2.5000e-05\n", + "Epoch 79/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0256 - classification_output_loss: 0.0491 - regression_output_loss: 0.0027 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8148 - regression_output_mae: 1.8608 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.6601 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0806 - final_output_custom_mape: 60.7576 - val_loss: 0.0317 - val_classification_output_loss: 0.0696 - val_regression_output_loss: 0.0068 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7091 - val_regression_output_mae: 1.8271 - val_regression_output_rmse: 2.5748 - val_regression_output_custom_mape: 61.2307 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0830 - val_final_output_custom_mape: 61.2448 - lr: 2.5000e-05\n", + "Epoch 80/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0259 - classification_output_loss: 0.0489 - regression_output_loss: 0.0035 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9795 - classification_output_auc: 0.9985 - regression_output_mse: 6.7880 - regression_output_mae: 1.8545 - regression_output_rmse: 2.5947 - regression_output_custom_mape: 60.6354 - final_output_mse: 0.0069 - final_output_mae: 0.0603 - final_output_rmse: 0.0812 - final_output_custom_mape: 60.7862 - val_loss: 0.0316 - val_classification_output_loss: 0.0719 - val_regression_output_loss: 0.0061 - val_final_output_loss: 0.0177 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6953 - val_regression_output_mae: 1.8237 - val_regression_output_rmse: 2.5712 - val_regression_output_custom_mape: 60.8627 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0583 - val_final_output_rmse: 0.0812 - val_final_output_custom_mape: 60.9105 - lr: 2.5000e-05\n", + "Epoch 81/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657\n", + "Epoch 81 Detailed Metrics:\n", + "541/541 [==============================] - 52s 95ms/step - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657 - val_loss: 0.0309 - val_classification_output_loss: 0.0678 - val_regression_output_loss: 0.0059 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9965 - val_regression_output_mse: 6.7524 - val_regression_output_mae: 1.8408 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.7875 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0607 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.7851 - lr: 2.5000e-05\n", + "Epoch 82/100\n", + "541/541 [==============================] - 52s 97ms/step - loss: 0.0256 - classification_output_loss: 0.0487 - regression_output_loss: 0.0031 - final_output_loss: 0.0183 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9985 - regression_output_mse: 6.8378 - regression_output_mae: 1.8668 - regression_output_rmse: 2.6042 - regression_output_custom_mape: 60.8146 - final_output_mse: 0.0070 - final_output_mae: 0.0605 - final_output_rmse: 0.0816 - final_output_custom_mape: 60.8914 - val_loss: 0.0354 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0140 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7485 - val_regression_output_mae: 1.8401 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.0076 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0622 - val_final_output_rmse: 0.0868 - val_final_output_custom_mape: 61.9773 - lr: 2.5000e-05\n", + "Epoch 83/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0263 - classification_output_loss: 0.0488 - regression_output_loss: 0.0043 - final_output_loss: 0.0187 - classification_output_accuracy: 0.9794 - classification_output_auc: 0.9985 - regression_output_mse: 6.8124 - regression_output_mae: 1.8617 - regression_output_rmse: 2.5990 - regression_output_custom_mape: 60.8479 - final_output_mse: 0.0075 - final_output_mae: 0.0616 - final_output_rmse: 0.0838 - final_output_custom_mape: 60.9637 - val_loss: 0.0335 - val_classification_output_loss: 0.0685 - val_regression_output_loss: 0.0107 - val_final_output_loss: 0.0191 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.6785 - val_regression_output_mae: 1.8221 - val_regression_output_rmse: 2.5690 - val_regression_output_custom_mape: 62.2821 - val_final_output_mse: 0.0082 - val_final_output_mae: 0.0628 - val_final_output_rmse: 0.0883 - val_final_output_custom_mape: 62.4273 - lr: 2.5000e-05\n", + "Epoch 84/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0253 - classification_output_loss: 0.0486 - regression_output_loss: 0.0028 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9786 - classification_output_auc: 0.9985 - regression_output_mse: 6.7787 - regression_output_mae: 1.8528 - regression_output_rmse: 2.5927 - regression_output_custom_mape: 60.6893 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0811 - final_output_custom_mape: 60.8322 - val_loss: 0.0320 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0076 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6612 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 61.7606 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0606 - val_final_output_rmse: 0.0845 - val_final_output_custom_mape: 61.8305 - lr: 2.5000e-05\n", + "Epoch 85/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0481 - regression_output_loss: 0.0023 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8113 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.7074 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0808 - final_output_custom_mape: 60.7955 - val_loss: 0.0304 - val_classification_output_loss: 0.0693 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7028 - val_regression_output_mae: 1.8257 - val_regression_output_rmse: 2.5734 - val_regression_output_custom_mape: 60.9782 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0592 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.0249 - lr: 2.5000e-05\n", + "Epoch 86/100\n", + "541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8052 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6683 - final_output_mse: 0.0067 - final_output_mae: 0.0597 - final_output_rmse: 0.0800 - final_output_custom_mape: 60.7617 - val_loss: 0.0318 - val_classification_output_loss: 0.0694 - val_regression_output_loss: 0.0074 - val_final_output_loss: 0.0187 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7475 - val_regression_output_mae: 1.8414 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.4116 - val_final_output_mse: 0.0077 - val_final_output_mae: 0.0615 - val_final_output_rmse: 0.0857 - val_final_output_custom_mape: 62.3664 - lr: 2.5000e-05\n", + "Epoch 87/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929\n", + "Epoch 87: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929 - val_loss: 0.0304 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7331 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5791 - val_regression_output_custom_mape: 61.3001 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0831 - val_final_output_custom_mape: 61.3393 - lr: 2.5000e-05\n", + "Epoch 88/100\n", + "541/541 [==============================] - 54s 99ms/step - loss: 0.0246 - classification_output_loss: 0.0491 - regression_output_loss: 0.0018 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9984 - regression_output_mse: 6.8066 - regression_output_mae: 1.8585 - regression_output_rmse: 2.5979 - regression_output_custom_mape: 60.5242 - final_output_mse: 0.0065 - final_output_mae: 0.0587 - final_output_rmse: 0.0789 - final_output_custom_mape: 60.5975 - val_loss: 0.0309 - val_classification_output_loss: 0.0703 - val_regression_output_loss: 0.0056 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7678 - val_regression_output_mae: 1.8449 - val_regression_output_rmse: 2.5861 - val_regression_output_custom_mape: 61.8515 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0604 - val_final_output_rmse: 0.0843 - val_final_output_custom_mape: 61.8132 - lr: 1.2500e-05\n", + "Epoch 89/100\n", + "541/541 [==============================] - 55s 101ms/step - loss: 0.0243 - classification_output_loss: 0.0484 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9985 - regression_output_mse: 6.8169 - regression_output_mae: 1.8608 - regression_output_rmse: 2.5999 - regression_output_custom_mape: 60.5160 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5783 - val_loss: 0.0308 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9712 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7522 - val_regression_output_mae: 1.8404 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.3068 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0594 - val_final_output_rmse: 0.0827 - val_final_output_custom_mape: 61.2601 - lr: 1.2500e-05\n", + "Epoch 90/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0244 - classification_output_loss: 0.0490 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9985 - regression_output_mse: 6.8088 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5983 - regression_output_custom_mape: 60.4771 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5605 - val_loss: 0.0306 - val_classification_output_loss: 0.0704 - val_regression_output_loss: 0.0052 - val_final_output_loss: 0.0182 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7138 - val_regression_output_mae: 1.8292 - val_regression_output_rmse: 2.5759 - val_regression_output_custom_mape: 61.5655 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0599 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.6074 - lr: 1.2500e-05\n", + "Epoch 91/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501\n", + "Epoch 91 Detailed Metrics:\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501 - val_loss: 0.0300 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0046 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7305 - val_regression_output_mae: 1.8337 - val_regression_output_rmse: 2.5790 - val_regression_output_custom_mape: 61.3197 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0590 - val_final_output_rmse: 0.0820 - val_final_output_custom_mape: 61.3129 - lr: 1.2500e-05\n", + "Epoch 92/100\n", + "541/541 [==============================] - 56s 104ms/step - loss: 0.0241 - classification_output_loss: 0.0482 - regression_output_loss: 0.0015 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9986 - regression_output_mse: 6.8190 - regression_output_mae: 1.8612 - regression_output_rmse: 2.6005 - regression_output_custom_mape: 60.5316 - final_output_mse: 0.0064 - final_output_mae: 0.0585 - final_output_rmse: 0.0783 - final_output_custom_mape: 60.5853 - val_loss: 0.0303 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7579 - val_regression_output_mae: 1.8427 - val_regression_output_rmse: 2.5845 - val_regression_output_custom_mape: 61.9738 - val_final_output_mse: 0.0074 - val_final_output_mae: 0.0605 - val_final_output_rmse: 0.0841 - val_final_output_custom_mape: 61.9213 - lr: 1.2500e-05\n", + "Epoch 93/100\n", + "541/541 [==============================] - 58s 106ms/step - loss: 0.0240 - classification_output_loss: 0.0480 - regression_output_loss: 0.0015 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8070 - regression_output_mae: 1.8578 - regression_output_rmse: 2.5980 - regression_output_custom_mape: 60.4706 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0784 - final_output_custom_mape: 60.5622 - val_loss: 0.0301 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0050 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7989 - val_regression_output_mae: 1.8531 - val_regression_output_rmse: 2.5925 - val_regression_output_custom_mape: 61.7481 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.6569 - lr: 1.2500e-05\n", + "Epoch 94/100\n", + "541/541 [==============================] - 55s 102ms/step - loss: 0.0239 - classification_output_loss: 0.0480 - regression_output_loss: 0.0013 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8109 - regression_output_mae: 1.8589 - regression_output_rmse: 2.5988 - regression_output_custom_mape: 60.4838 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0785 - final_output_custom_mape: 60.5632 - val_loss: 0.0301 - val_classification_output_loss: 0.0701 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7354 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5800 - val_regression_output_custom_mape: 61.3804 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0593 - val_final_output_rmse: 0.0824 - val_final_output_custom_mape: 61.3907 - lr: 1.2500e-05\n", + "Epoch 95/100\n", + "541/541 [==============================] - ETA: 0s - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216Restoring model weights from the end of the best epoch: 80.\n", + "\n", + "Epoch 95: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n", + "541/541 [==============================] - 52s 96ms/step - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216 - val_loss: 0.0304 - val_classification_output_loss: 0.0712 - val_regression_output_loss: 0.0051 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6979 - val_regression_output_mae: 1.8236 - val_regression_output_rmse: 2.5727 - val_regression_output_custom_mape: 61.3934 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0591 - val_final_output_rmse: 0.0818 - val_final_output_custom_mape: 61.4749 - lr: 1.2500e-05\n", + "Epoch 95: early stopping\n", + "\n", + "Training completed successfully!\n", + "\n", + "Classification Metrics:\n", + "Accuracy: 97.00%\n", + "AUC-ROC: 0.9968\n", + "\n", + "Confusion Matrix:\n", + "[[12503 504]\n", + " [ 275 12651]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " Zero 0.9785 0.9613 0.9698 13007\n", + " Non-Zero 0.9617 0.9787 0.9701 12926\n", + "\n", + " accuracy 0.9700 25933\n", + " macro avg 0.9701 0.9700 0.9700 25933\n", + "weighted avg 0.9701 0.9700 0.9700 25933\n", + "\n", + "\n", + "Regression Metrics (non-zero values):\n", + "Out of range: 0 predictions\n", + "MAPE: 23.39%\n", + "Within ±10%: 54.66%\n", + "MAE: 0.11\n", + "RMSE: 0.29\n", + "\n", + "Final Combined Output Metrics:\n", + "Out of range: 0 predictions\n", + "MAPE: 60.97%\n", + "Within ±10%: 27.97%\n", + "MAE: 0.06\n", + "RMSE: 0.08\n" + ] + } + ], + "source": [ + "# Model creation\n", + "print(\"\\n2. Creating model...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "max_val = df['solarradiation'].max()\n", + "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", + "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", + "\n", + "print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "increase_percentage = 15\n", + "\n", + "max_val = max_val * (1 + increase_percentage / 100)\n", + "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", + "\n", + "print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", + "\n", + "# Create the hybrid model\n", + "model = create_solarradiation_model(\n", + " input_shape=input_shape, \n", + " folder_name=folder_name, \n", + " min_output=min_val_scaled, \n", + " max_output=max_val_scaled\n", + ")\n", + "\n", + "# Prepare binary targets for classification\n", + "y_train_binary = (y_train > 0).astype(float)\n", + "y_test_binary = (y_test > 0).astype(float)\n", + "\n", + "print(\"\\nClass distribution in training set:\")\n", + "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", + "\n", + "print(\"\\nClass distribution in test set:\")\n", + "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", + "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", + "\n", + "# Get the exact output names from the model\n", + "output_names = [output.name.split('/')[0] for output in model.outputs]\n", + "print(\"\\nModel output names:\", output_names)\n", + "\n", + "print(\"\\n4. Starting training...\")\n", + "history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=192,\n", + " folder_name=folder_name,\n", + " min_output=min_val_scaled,\n", + " max_output=max_val_scaled\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "958d78b99e8898d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "5. Generating predictions...\n", + "811/811 [==============================] - 13s 15ms/step\n", + "\n", + "6. Evaluating model...\n", + "\n", + "Solar Radiation Prediction Metrics:\n", + "\n", + "Absolute Metrics:\n", + "MAE: 19.32 W/m²\n", + "RMSE: 27.95 W/m²\n", + "R² Score: 0.989\n", + "MAPE: 16.92%\n", + "\n", + "Accuracy Metrics:\n", + "Within ±5 W/m²: 8.8%\n", + "Within ±10 W/m²: 16.3%\n", + "Within ±20 W/m²: 74.5%\n", + "\n", + "Level Accuracy:\n", + "Level Accuracy: 92.6%\n", + "\n", + "Confusion Matrix for Radiation Levels:\n", + " Very Low Low Moderate High Very High Extreme\n", + "Very Low 0 0 0 0 10 0\n", + "Low 0 1494 0 174 153 0\n", + "Moderate 0 0 2041 413 0 407\n", + "High 0 215 156 1925 0 0\n", + "Very High 0 99 0 0 1038 0\n", + "Extreme 0 0 298 0 0 17510\n", + "\n", + "Plot saved as: 2024-11-26_05-41_radiation_analysis.png\n", + "\n", + "Error Statistics:\n", + "Mean error: 4.431\n", + "Error standard deviation: 27.596\n", + "Median error: 12.000\n", + "95th percentile absolute error: 57.806\n" + ] + } + ], + "source": [ + "print(\"\\n5. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "classification_pred, regression_pred, final_pred = predictions\n", + "\n", + "# Clip solo le predizioni di regressione e finali\n", + "regression_pred = np.clip(regression_pred, 0, 11)\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "\n", + "# Inverse transform per tornare ai valori originali\n", + "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "y_test_original = scaler_y.inverse_transform(y_test)\n", + "\n", + "print(\"\\n6. Evaluating model...\")\n", + "# Valutazione delle predizioni finali\n", + "metrics = evaluate_solarradiation_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", + "\n", + "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", + "training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 192,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", + " },\n", + " 'performance_metrics': {\n", + " 'classification': {\n", + " 'final_loss': float(history.history['val_classification_output_loss'][-1]),\n", + " 'final_accuracy': float(history.history['val_classification_output_accuracy'][-1]),\n", + " 'final_auc': float(history.history['val_classification_output_auc'][-1])\n", + " },\n", + " 'regression': {\n", + " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_regression_output_mae'][-1]),\n", + " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > 11)))\n", + " },\n", + " 'final_output': {\n", + " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", + " 'final_mae': float(history.history['val_final_output_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > 11)))\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5c05d1d03336b1e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "7. Predicting missing data...\n", + "7122/7122 [==============================] - 112s 16ms/step\n", + "\n", + "8. Integrating predictions into original dataset...\n", + "\n", + "Prediction Integration Statistics:\n", + "Added 227879 predictions to dataset\n", + "Rows with solar radiation after integration: 357615\n", + "\n", + "Filled Values Analysis:\n", + "Zero predictions (classification < 0.5): 113630\n", + "Non-zero predictions (classification >= 0.5): 114249\n", + "\n", + "Non-zero predictions statistics:\n", + "Mean: 181.31\n", + "Median: 12.00\n", + "Std: 254.32\n", + "\n", + "Prediction Statistics:\n", + "Total predictions added: 227879\n", + "\n", + "Classification Statistics:\n", + "Predicted zeros: 113630 (49.86%)\n", + "Predicted non-zeros: 114249 (50.14%)\n", + "Mean classification confidence: 0.4989\n", + "\n", + "Final Predictions Statistics:\n", + "Mean solar radiation: 181.31\n", + "Min solar radiation: 12.00\n", + "Max solar radiation: 966.98\n", + "Zero predictions: 0 (0.00%)\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "print(\"\\n7. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "classification_pred, regression_pred, final_pred = to_predict_predictions\n", + "\n", + "# Clip solo le predizioni finali che useremo per l'integrazione\n", + "final_pred = np.clip(final_pred, 0, 11)\n", + "final_pred_original = scaler_y.inverse_transform(final_pred)\n", + "\n", + "print(\"\\n8. Integrating predictions into original dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", + "\n", + "df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n", + "\n", + "# Add prediction statistics to training_results\n", + "training_results['prediction_stats'] = {\n", + " 'n_predictions_added': len(final_pred_original),\n", + " 'classification_stats': {\n", + " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", + " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", + " 'mean_confidence': float(classification_pred.mean()),\n", + " },\n", + " 'regression_stats': {\n", + " 'mean_predicted_value': float(regression_pred.mean()),\n", + " 'min_predicted_value': float(regression_pred.min()),\n", + " 'max_predicted_value': float(regression_pred.max()),\n", + " },\n", + " 'final_predictions': {\n", + " 'mean_predicted_solarradiation': float(final_pred_original.mean()),\n", + " 'min_predicted_solarradiation': float(final_pred_original.min()),\n", + " 'max_predicted_solarradiation': float(final_pred_original.max()),\n", + " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", + " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", + " }\n", + "}\n", + "\n", + "print(\"\\nPrediction Statistics:\")\n", + "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", + "print(\"\\nClassification Statistics:\")\n", + "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", + " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", + "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", + "\n", + "print(\"\\nFinal Predictions Statistics:\")\n", + "print(f\"Mean solar radiation: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarradiation']:.2f}\")\n", + "print(f\"Min solar radiation: {training_results['prediction_stats']['final_predictions']['min_predicted_solarradiation']:.2f}\")\n", + "print(f\"Max solar radiation: {training_results['prediction_stats']['final_predictions']['max_predicted_solarradiation']:.2f}\")\n", + "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", + " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", + "\n", + "print(\"\\nTraining completed successfully!\")\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ef29b3ecdf12c6db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistiche principali di Solar Radiation:\n", + "--------------------------------------------------\n", + "count : 357,679.0000\n", + "missing : 64.0000\n", + "zeros : 59,357.0000\n", + "mean : 183.8441\n", + "median : 12.0000\n", + "std : 259.8156\n", + "min : 0.0000\n", + "max : 1,113.0000\n", + "skewness : 1.3491\n", + "kurtosis : 0.5914\n", + "percentile_1 : 0.0000\n", + "percentile_5 : 0.0000\n", + "percentile_10 : 0.0000\n", + "percentile_25 : 12.0000\n", + "percentile_50 : 12.0000\n", + "percentile_75 : 321.3083\n", + "percentile_90 : 624.6504\n", + "percentile_95 : 776.0000\n", + "percentile_99 : 907.6779\n", + "\n", + "Suggerimenti per la normalizzazione:\n", + "--------------------------------------------------\n", + "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", + "- Considerare una trasformazione logaritmica: np.log1p(x)\n", + "- Alta presenza di zeri (16.60%)\n", + "- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n" + ] + }, + { + "data": { + "text/plain": [ + "{'count': 357679,\n", + " 'missing': 64,\n", + " 'zeros': 59357,\n", + " 'mean': 183.84409789852336,\n", + " 'median': 12.0,\n", + " 'std': 259.8156425752193,\n", + " 'min': 0.0,\n", + " 'max': 1113.0,\n", + " 'skewness': 1.3490904735404219,\n", + " 'kurtosis': 0.5914208419781612,\n", + " 'percentile_1': 0.0,\n", + " 'percentile_5': 0.0,\n", + " 'percentile_10': 0.0,\n", + " 'percentile_25': 12.0,\n", + " 'percentile_50': 12.0,\n", + " 'percentile_75': 321.3082580566406,\n", + " 'percentile_90': 624.6503662109386,\n", + " 'percentile_95': 776.0,\n", + " 'percentile_99': 907.677912597656}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e884cc287364c4ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-26_05-41_error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Classification Statistics:\n", + " precision recall f1-score support\n", + "\n", + " 0.0 0.98 0.96 0.97 13007\n", + " 1.0 0.96 0.98 0.97 12926\n", + "\n", + " accuracy 0.97 25933\n", + " macro avg 0.97 0.97 0.97 25933\n", + "weighted avg 0.97 0.97 0.97 25933\n", + "\n", + "AUC-ROC: 0.9968\n", + "\n", + "Regression Statistics (Non-zero values):\n", + "MAE: 0.1056\n", + "RMSE: 0.2896\n", + "Mean error: 0.0143\n", + "Error std: 0.2892\n", + "\n", + "Final Prediction Statistics:\n", + "MAE: 0.0583\n", + "RMSE: 0.0835\n", + "Mean error: 0.0113\n", + "Error std: 0.0827\n", + "\n", + "Error Thresholds (Final Predictions):\n", + "Predictions within ±0.5: 99.9%\n", + "Predictions within ±1.0: 100.0%\n", + "Predictions within ±1.5: 100.0%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, predictions, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis for the hybrid model\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " predictions : tuple\n", + " Tuple containing (classification_pred, regression_pred, final_pred)\n", + " folder_name : str, optional\n", + " Directory to save plots. If None, plots are only displayed\n", + "\n", + " Generates:\n", + " ----------\n", + " - Classification analysis plots\n", + " - Regression error analysis plots\n", + " - Final prediction error analysis plots\n", + " \"\"\"\n", + " from sklearn.metrics import roc_curve\n", + "\n", + " # Unpack predictions\n", + " classification_pred, regression_pred, final_pred = predictions\n", + "\n", + " # Convert to 1D numpy arrays if needed\n", + " y_true = np.ravel(y_true)\n", + " classification_pred = np.ravel(classification_pred)\n", + " regression_pred = np.ravel(regression_pred)\n", + " final_pred = np.ravel(final_pred)\n", + "\n", + " # Create binary ground truth\n", + " y_true_binary = (y_true > 0).astype(float)\n", + "\n", + " # Calculate errors for regression and final predictions\n", + " regression_errors = regression_pred - y_true\n", + " final_errors = final_pred - y_true\n", + "\n", + " # Create main figure\n", + " plt.figure(figsize=(20, 15))\n", + "\n", + " # Classification Analysis (Top Row)\n", + " # Plot 1: Classification Distribution\n", + " plt.subplot(3, 3, 1)\n", + " plt.hist(classification_pred, bins=50, alpha=0.7)\n", + " plt.axvline(x=0.5, color='r', linestyle='--')\n", + " plt.title('Classification Probability Distribution')\n", + " plt.xlabel('Classification Probability')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: ROC Curve\n", + " plt.subplot(3, 3, 2)\n", + " fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n", + " plt.plot(fpr, tpr)\n", + " plt.plot([0, 1], [0, 1], 'r--')\n", + " plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n", + " plt.xlabel('False Positive Rate')\n", + " plt.ylabel('True Positive Rate')\n", + "\n", + " # Plot 3: Classification Confusion Matrix\n", + " plt.subplot(3, 3, 3)\n", + " cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Classification Confusion Matrix')\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('Actual')\n", + "\n", + " # Regression Analysis (Middle Row)\n", + " # Plot 4: Regression Error Distribution\n", + " plt.subplot(3, 3, 4)\n", + " plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n", + " plt.title('Regression Error Distribution (Non-zero Values)')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 5: Actual vs Predicted (Regression)\n", + " plt.subplot(3, 3, 5)\n", + " mask_nonzero = y_true > 0\n", + " plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n", + " plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n", + " [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 6: Regression Errors vs Actual Values\n", + " plt.subplot(3, 3, 6)\n", + " plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # Final Predictions Analysis (Bottom Row)\n", + " # Plot 7: Final Error Distribution\n", + " plt.subplot(3, 3, 7)\n", + " plt.hist(final_errors, bins=50, alpha=0.7)\n", + " plt.title('Final Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 8: Actual vs Predicted (Final)\n", + " plt.subplot(3, 3, 8)\n", + " plt.scatter(y_true, final_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted (Final)')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 9: Final Errors vs Actual Values\n", + " plt.subplot(3, 3, 9)\n", + " plt.scatter(y_true, final_errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Final Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if directory is specified\n", + " if folder_name is not None:\n", + " try:\n", + " filename = f'{folder_name}_error_analysis.png'\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print comprehensive statistics\n", + " print(\"\\nClassification Statistics:\")\n", + " print(classification_report(y_true_binary, classification_pred > 0.5))\n", + " print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n", + "\n", + " print(\"\\nRegression Statistics (Non-zero values):\")\n", + " mask_nonzero = y_true > 0\n", + " if np.any(mask_nonzero):\n", + " print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n", + " print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n", + "\n", + " print(\"\\nFinal Prediction Statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n", + " print(f\"Mean error: {np.mean(final_errors):.4f}\")\n", + " print(f\"Error std: {np.std(final_errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " print(\"\\nError Thresholds (Final Predictions):\")\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "# Example usage\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd5197ea71becfc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f982c92c-ba99-4df6-b3c8-df92426679db", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_model_architecture-checkpoint.png b/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_model_architecture-checkpoint.png new file mode 100644 index 0000000..3373272 Binary files /dev/null and b/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_model_architecture-checkpoint.png differ diff --git a/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_uv_analysis-checkpoint.png b/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_uv_analysis-checkpoint.png new file mode 100644 index 0000000..57ec69c Binary files /dev/null and b/models/uv_index/.ipynb_checkpoints/2024-11-21_08-23_uv_analysis-checkpoint.png differ diff --git a/models/uv_index/.ipynb_checkpoints/uv_index_model-checkpoint.ipynb b/models/uv_index/.ipynb_checkpoints/uv_index_model-checkpoint.ipynb new file mode 100644 index 0000000..b776cbd --- /dev/null +++ b/models/uv_index/.ipynb_checkpoints/uv_index_model-checkpoint.ipynb @@ -0,0 +1,2304 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T00:55:22.066729Z", + "start_time": "2024-11-20T00:54:13.878615Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", + "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2732 kB]\n", + "Fetched 2989 kB in 1s (2026 kB/s) \n", + "Reading package lists... 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.24.3)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras==2.13.1 in /usr/local/lib/python3.11/dist-packages (2.13.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.24.3)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.24.3)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.0.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.24.3)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.24.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.24.3)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow==2.13.0\n", + "!pip install numpy\n", + "!pip install pandas\n", + "!pip install keras==2.13.1\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a813e3cbca057b7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T00:55:22.782689Z", + "start_time": "2024-11-20T00:55:22.089165Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:23:10.586264: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, Conv1D, GlobalAveragePooling1D\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3f525e19f78a1da", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Calculate solar angle\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " # Solar angle and day length calculations\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Feature interactions\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + "\n", + " # Extended window rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_uv_specific_features(df):\n", + " # Solar zenith angle calculation\n", + " lat = 41.9 # assuming constant latitude for the dataset - Rome's latitude\n", + " df['solar_zenith'] = 90 - np.degrees(\n", + " np.arcsin(\n", + " np.sin(np.radians(lat)) * np.sin(df['solar_elevation']) +\n", + " np.cos(np.radians(lat)) * np.cos(df['solar_elevation']) * np.cos(df['hour'] * 15)\n", + " )\n", + " )\n", + "\n", + " # UV peak hours indicator (10:00-16:00)\n", + " df['is_uv_peak_hours'] = ((df['hour'] >= 10) & (df['hour'] <= 16)).astype(int)\n", + "\n", + " # Atmospheric attenuation factor\n", + " df['atmospheric_attenuation'] = (100 - df['cloudcover']) * (df['visibility'] / 100) * (1 - df['humidity'] / 200)\n", + "\n", + " # Seasonal UV factor\n", + " df['uv_seasonal_factor'] = np.where(df['season_Summer'], 1.0,\n", + " np.where(df['season_Spring'], 0.7,\n", + " np.where(df['season_Autumn'], 0.5, 0.3)))\n", + "\n", + " # Solar elevation and atmospheric transparency interaction\n", + " df['solar_clarity_index'] = df['solar_elevation'] * df['atmospheric_attenuation'] / 100\n", + "\n", + " # UV-specific rolling features\n", + " df['clarity_rolling_3h'] = df['atmospheric_attenuation'].rolling(window=3).mean()\n", + " df['temp_uv_interaction'] = df['temp'] * df['solar_clarity_index']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features in the correct order\n", + " \"\"\"\n", + " # 1. First add basic time features\n", + " df = add_time_features(df)\n", + "\n", + " # 2. One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " # 3. Add solar and specific features\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + "\n", + " # 4. Ensure datetime index\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df.index)\n", + "\n", + " # 5. Add weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # 6. Add solar radiation derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['day_length'] = np.sin(df['day_of_year_sin']) * 12 + 12\n", + "\n", + " # 7. Add lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # 8. Add rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # 9. Add atmospheric stability\n", + " df['atmospheric_stability'] = df.groupby(df.index.date)['pressure'].transform(\n", + " lambda x: x.std()\n", + " ).fillna(0)\n", + "\n", + " # 10. Add extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # 11. Add atmospheric transparency\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # 12. Add transitional seasons indicator\n", + " df['is_transition_season'] = ((df['season_Spring'] | df['season_Autumn'])).astype(int)\n", + "\n", + " # 13. Add solar cloud effect\n", + " if 'solar_elevation' in df.columns:\n", + " df['solar_cloud_effect'] = df['solar_elevation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # 14. Finally add UV specific features\n", + " df = add_uv_specific_features(df)\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepares data for UV index prediction model with advanced feature engineering\n", + " and optimized preprocessing.\n", + "\n", + " Args:\n", + " df: DataFrame with meteorological data\n", + "\n", + " Returns:\n", + " tuple: (X_train_scaled, X_test_scaled, y_train, y_test, scaler, final_features, X_to_predict_scaled)\n", + " \"\"\"\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " # Optimized feature selection for UV index\n", + " selected_features = {\n", + " # Primary meteorological features\n", + " 'atmospheric': [\n", + " 'temp', 'humidity', 'cloudcover', 'visibility',\n", + " 'clear_sky_index', 'atmospheric_transparency'\n", + " ],\n", + "\n", + " # Essential temporal features\n", + " 'temporal': [\n", + " 'hour_sin', 'hour_cos',\n", + " 'day_of_year_sin', 'day_of_year_cos'\n", + " ],\n", + "\n", + " # Solar features\n", + " 'solar': [\n", + " 'solar_angle', 'solar_elevation',\n", + " 'day_length', 'solar_noon',\n", + " 'solar_cloud_effect'\n", + " ],\n", + "\n", + " # Key interactions\n", + " 'interactions': [\n", + " 'cloud_temp_interaction',\n", + " 'visibility_cloud_interaction',\n", + " 'temp_humidity_interaction',\n", + " 'solar_clarity_index'\n", + " ],\n", + "\n", + " # Rolling features\n", + " 'rolling': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_mean_6h'\n", + " ]\n", + " }\n", + "\n", + " # Flatten feature list\n", + " base_features = [item for sublist in selected_features.values() for item in sublist]\n", + "\n", + " # Add categorical features (one-hot encoded)\n", + " categorical_columns = [col for col in df.columns if col.startswith(('season_', 'time_period_'))]\n", + " final_features = base_features + categorical_columns\n", + "\n", + " # Temporal preprocessing\n", + " df = df.sort_values('datetime')\n", + " df.set_index('datetime', inplace=True)\n", + "\n", + " # Advanced interpolation for missing values\n", + " for column in final_features:\n", + " if column in df.columns:\n", + " if df[column].isnull().any():\n", + " if column in selected_features['rolling']:\n", + " df[column] = df[column].ffill().bfill()\n", + " else:\n", + " df[column] = df[column].interpolate(method='time', limit_direction='both')\n", + "\n", + " # Temporal data split\n", + " data_after_2010 = df[df.index.year >= 2010].copy()\n", + " data_before_2010 = df[df.index.year < 2010].copy()\n", + "\n", + " print(f\"\\nTemporal distribution of data:\")\n", + " print(f\"Records after 2010: {len(data_after_2010):,}\")\n", + " print(f\"Records before 2010: {len(data_before_2010):,}\")\n", + "\n", + " # Feature and target preparation\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['uvindex']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Data validation\n", + " if X.isnull().any().any() or y.isnull().any():\n", + " print(\"\\nWarning: Found missing values after preprocessing\")\n", + " print(\"Features with missing values:\", X.columns[X.isnull().any()].tolist())\n", + " X = X.fillna(X.mean())\n", + " y = y.fillna(y.mean())\n", + "\n", + " # Stratified data split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y,\n", + " test_size=0.5,\n", + " random_state=random_state_value,\n", + " stratify=pd.qcut(y, q=5, duplicates='drop', labels=False)\n", + " )\n", + "\n", + " # Robust feature scaling\n", + " feature_scaler = RobustScaler()\n", + " X_train_scaled = feature_scaler.fit_transform(X_train)\n", + " X_test_scaled = feature_scaler.transform(X_test)\n", + " X_to_predict_scaled = feature_scaler.transform(X_to_predict)\n", + "\n", + " target_scaler = RobustScaler()\n", + " y_train_scaled = target_scaler.fit_transform(y_train.values.reshape(-1, 1)).ravel()\n", + " y_test_scaled = target_scaler.transform(y_test.values.reshape(-1, 1)).ravel()\n", + "\n", + " # Final validation\n", + " assert not np.isnan(X_train_scaled).any(), \"Found NaN in X_train_scaled\"\n", + " assert not np.isnan(X_test_scaled).any(), \"Found NaN in X_test_scaled\"\n", + " assert not np.isnan(X_to_predict_scaled).any(), \"Found NaN in X_to_predict_scaled\"\n", + "\n", + " # Print feature information\n", + " print(\"\\nNumber of features used:\", len(final_features))\n", + " print(\"\\nFeature categories:\")\n", + " for category, features in selected_features.items():\n", + " print(f\"{category}: {len(features)} features\")\n", + " print(f\"Categorical: {len(categorical_columns)} features\")\n", + "\n", + " return (X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled,\n", + " feature_scaler, target_scaler, final_features, X_to_predict_scaled)\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " # Use existing data preparation\n", + " X_train_scaled, X_test_scaled, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data to sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train[sequence_length - 1:]\n", + " y_test = y_test[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01,\n", + " survival_probability=0.8, return_sequences=True):\n", + " \"\"\"LSTM layer with stochastic depth\"\"\"\n", + " residual = x\n", + "\n", + " # Main path\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences,\n", + " kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " # Adjust residual dimension if needed\n", + " if return_sequences:\n", + " # For Bidirectional LSTM, the output dimension is 2 * units\n", + " target_dim = 2 * units\n", + " if int(residual.shape[-1]) != target_dim:\n", + " # Use Dense layer instead of Conv1D for better dimension matching\n", + " residual = Dense(target_dim)(residual)\n", + "\n", + " # Apply stochastic depth only if dimensions match\n", + " if x.shape[-1] == residual.shape[-1]:\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " else:\n", + " print(f\"Warning: Dimension mismatch - x: {x.shape}, residual: {residual.shape}\")\n", + " # Skip residual connection if dimensions don't match\n", + " pass\n", + "\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Attention block with stochastic depth.\n", + " \"\"\"\n", + " original_x = x\n", + "\n", + " # Compute self-attention\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + "\n", + " # Ensure dimensions match before applying stochastic depth\n", + " if attention.shape[-1] != original_x.shape[-1]:\n", + " original_x = Dense(attention.shape[-1])(original_x)\n", + "\n", + " # Apply stochastic depth to the attention path\n", + " x = tfa.layers.StochasticDepth(survival_probability)([attention, original_x])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Store the input to the FFN\n", + " ffn_input = x\n", + "\n", + " # FFN block\n", + " x = Dense(units * 4, activation='swish')(x)\n", + " x = Dense(ffn_input.shape[-1])(x) # Match the input dimension\n", + "\n", + " # Apply stochastic depth to the FFN\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, ffn_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "def create_uv_index_model(input_shape, folder_name, l2_lambda=0.005, max_output=11):\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Further adjusted hyperparameters\n", + " survival_probs = [0.98, 0.95, 0.92] # Even higher survival probabilities\n", + " attention_survival_probs = [0.95, 0.92, 0.9]\n", + "\n", + " # First LSTM block\n", + " x = create_residual_lstm_layer(\n", + " inputs, 64, dropout_rate=0.2, # Further reduced dropout\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[0],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 128, num_heads=2, # Reduced heads\n", + " survival_probability=attention_survival_probs[0])\n", + "\n", + " # Second LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 32, dropout_rate=0.15,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[1],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 64, num_heads=2,\n", + " survival_probability=attention_survival_probs[1])\n", + "\n", + " # Third LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 16, dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[2],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 32, num_heads=2,\n", + " survival_probability=attention_survival_probs[2])\n", + "\n", + " # Global attention with reduced complexity\n", + " x_input = x\n", + " x = MultiHeadAttention(num_heads=2, key_dim=32)(x, x)\n", + "\n", + " if x.shape[-1] != x_input.shape[-1]:\n", + " x_input = Dense(x.shape[-1])(x_input)\n", + "\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.95)([x, x_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Simplified dense layers\n", + " x = GlobalAveragePooling1D()(x)\n", + "\n", + " # Gradual dimension reduction\n", + " x = Dense(32, activation='swish', kernel_regularizer=regularizers.l2(l2_lambda / 2), kernel_constraint=tf.keras.constraints.MaxNorm(3))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.05)(x) # Minimal dropout\n", + "\n", + " x = Dense(16, activation='swish',\n", + " kernel_regularizer=regularizers.l2(l2_lambda / 2))(x)\n", + " x = BatchNormalization()(x)\n", + "\n", + " # Modified output layer\n", + " x = Dense(8, activation='swish')(x)\n", + " outputs = Dense(1, activation='sigmoid')(x) # Sigmoid activation\n", + " outputs = Lambda(lambda x: x * max_output)(outputs) # Scale to [0, 11] range\n", + "\n", + " model = Model(inputs=inputs, outputs=outputs, name=\"UvModel\")\n", + "\n", + " # More stable learning rate schedule\n", + " initial_learning_rate = 0.0001 # Further reduced\n", + " warmup_steps = 1000\n", + " decay_steps = 5000\n", + "\n", + " # Corretto learning rate schedule\n", + " class CustomLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " def __init__(self, initial_lr=0.0001, warmup_steps=1000, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_lr = initial_lr\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " # Convert to float32\n", + " step_f = tf.cast(step, tf.float32)\n", + " warmup_steps_f = tf.cast(self.warmup_steps, tf.float32)\n", + " decay_steps_f = tf.cast(self.decay_steps, tf.float32)\n", + "\n", + " # Warmup phase\n", + " warmup_progress = step_f / warmup_steps_f\n", + " warmup_lr = self.initial_lr * warmup_progress\n", + "\n", + " # Decay phase\n", + " decay_progress = (step_f - warmup_steps_f) / decay_steps_f\n", + " decay_factor = 0.5 * (1.0 + tf.cos(tf.constant(np.pi) * decay_progress))\n", + " decay_lr = self.initial_lr * decay_factor\n", + "\n", + " # Combine phases\n", + " lr = tf.where(step_f < warmup_steps_f, warmup_lr, decay_lr)\n", + " return lr\n", + "\n", + " def get_config(self):\n", + " return {\n", + " \"initial_lr\": self.initial_lr,\n", + " \"warmup_steps\": self.warmup_steps,\n", + " \"decay_steps\": self.decay_steps\n", + " }\n", + "\n", + " # Utilizzo dello schedule corretto\n", + " lr_schedule = CustomLRSchedule(\n", + " initial_lr=initial_learning_rate,\n", + " warmup_steps=warmup_steps,\n", + " decay_steps=decay_steps\n", + " )\n", + "\n", + " optimizer = AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.0005,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7\n", + " )\n", + "\n", + " # Improved loss function\n", + " def smooth_uv_loss(y_true, y_pred):\n", + " # Basic MSE with smoothing\n", + " mse = tf.square(y_true - y_pred)\n", + "\n", + " # Smooth L1 component for better stability\n", + " abs_diff = tf.abs(y_true - y_pred)\n", + " smooth_l1 = tf.where(abs_diff < 1.0,\n", + " 0.5 * tf.square(abs_diff),\n", + " abs_diff - 0.5)\n", + "\n", + " # Combined loss with dynamic weighting\n", + " combined_loss = 0.7 * mse + 0.3 * smooth_l1\n", + "\n", + " # Gentle weighting for high UV values\n", + " high_uv_weight = tf.where(y_true >= 8.0, 1.2, 1.0)\n", + "\n", + " # Smooth peak hours weight\n", + " time_of_day = tf.cast(tf.math.floormod(tf.range(tf.shape(y_true)[0]), 24),\n", + " tf.float32)\n", + " peak_weight = 1.0 + 0.2 * tf.math.sigmoid((time_of_day - 10.0) * 0.5) * \\\n", + " tf.math.sigmoid((16.0 - time_of_day) * 0.5)\n", + "\n", + " total_weight = high_uv_weight * peak_weight\n", + "\n", + " return tf.reduce_mean(combined_loss * total_weight)\n", + "\n", + " # Improved MAPE metric\n", + " def smooth_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs(y_true - y_pred)\n", + " scale = tf.maximum(tf.abs(y_true) + epsilon, 0.5) # Minimum scale of 0.5\n", + " return tf.reduce_mean(diff / scale) * 100\n", + "\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss=smooth_uv_loss,\n", + " metrics=[\n", + " 'mae',\n", + " 'mse',\n", + " tf.keras.metrics.RootMeanSquaredError(),\n", + " smooth_mape\n", + " ]\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " plot_model(model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True)\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_uv_predictions(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of UV index predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual UV index values\n", + " y_pred : array-like\n", + " Predicted UV index values\n", + " folder_name : str, optional\n", + " Folder to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Initialize plot paths\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Data preprocessing\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + "\n", + " # Rounding and clipping predictions\n", + " y_pred_rounded = np.round(y_pred * 2) / 2 # Round to nearest 0.5\n", + " y_pred_clipped = np.clip(y_pred_rounded, 0, 11)\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + " errors_rounded = y_pred_clipped - y_true\n", + "\n", + " # Function to determine UV risk level\n", + " def get_uv_risk_level(values):\n", + " levels = np.full_like(values, 'Low', dtype=object)\n", + " levels[(values > 2) & (values <= 5)] = 'Moderate'\n", + " levels[(values > 5) & (values <= 7)] = 'High'\n", + " levels[(values > 7) & (values <= 10)] = 'Very High'\n", + " levels[values > 10] = 'Extreme'\n", + " return levels\n", + "\n", + " # Calculate basic metrics\n", + " metrics = {\n", + " 'raw': {\n", + " 'mae': mean_absolute_error(y_true, y_pred),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred)),\n", + " 'r2': r2_score(y_true, y_pred),\n", + " 'mean_error': np.mean(errors),\n", + " 'std_error': np.std(errors),\n", + " 'median_error': np.median(errors),\n", + " 'p95_abs_error': np.percentile(np.abs(errors), 95)\n", + " },\n", + " 'rounded': {\n", + " 'mae': mean_absolute_error(y_true, y_pred_clipped),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred_clipped)),\n", + " 'r2': r2_score(y_true, y_pred_clipped)\n", + " }\n", + " }\n", + "\n", + " # Calculate accuracies for different margins\n", + " for data_type, errors_data in [('raw', errors), ('rounded', errors_rounded)]:\n", + " metrics[data_type].update({\n", + " 'within_05': np.mean(np.abs(errors_data) <= 0.5) * 100,\n", + " 'within_1': np.mean(np.abs(errors_data) <= 1.0) * 100,\n", + " 'within_15': np.mean(np.abs(errors_data) <= 1.5) * 100,\n", + " 'within_2': np.mean(np.abs(errors_data) <= 2.0) * 100\n", + " })\n", + "\n", + " # Analysis by UV risk level\n", + " y_true_risk = get_uv_risk_level(y_true)\n", + " y_pred_risk = get_uv_risk_level(y_pred_clipped)\n", + "\n", + " # Calculate confusion matrix with handling for missing classes\n", + " risk_levels = ['Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + "\n", + " # Get unique labels present in the data\n", + " present_labels = np.unique(np.concatenate([y_true_risk, y_pred_risk]))\n", + "\n", + " # Calculate confusion matrix for present labels\n", + " cm = confusion_matrix(y_true_risk, y_pred_risk, labels=present_labels)\n", + "\n", + " # Create full confusion matrix with zeros\n", + " full_cm = np.zeros((len(risk_levels), len(risk_levels)))\n", + "\n", + " # Map present labels to their positions in the full matrix\n", + " label_positions = {label: i for i, label in enumerate(risk_levels)}\n", + " for i, true_label in enumerate(present_labels):\n", + " for j, pred_label in enumerate(present_labels):\n", + " full_cm[label_positions[true_label], label_positions[pred_label]] = cm[i, j]\n", + "\n", + " # Create DataFrame with all risk levels\n", + " cm_df = pd.DataFrame(full_cm, columns=risk_levels, index=risk_levels)\n", + "\n", + " # Analysis by UV range\n", + " uv_ranges = [\n", + " (0, 2, 'Low'),\n", + " (2, 5, 'Moderate'),\n", + " (5, 7, 'High'),\n", + " (7, 10, 'Very High'),\n", + " (10, 11, 'Extreme')\n", + " ]\n", + "\n", + " range_analysis = {}\n", + " for low, high, label in uv_ranges:\n", + " mask = (y_true >= low) & (y_true < high)\n", + " if mask.any():\n", + " range_analysis[label] = {\n", + " 'mae': mean_absolute_error(y_true[mask], y_pred[mask]),\n", + " 'count': np.sum(mask),\n", + " 'accuracy_within_05': np.mean(np.abs(errors[mask]) <= 0.5) * 100,\n", + " 'accuracy_within_1': np.mean(np.abs(errors[mask]) <= 1.0) * 100\n", + " }\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Main figure with 4 subplots\n", + " fig = plt.figure(figsize=(20, 15))\n", + "\n", + " # 1. Error distribution\n", + " plt.subplot(2, 2, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # 2. Actual vs Predicted scatter plot\n", + " plt.subplot(2, 2, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([0, 11], [0, 11], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # 3. Errors vs Actual Values\n", + " plt.subplot(2, 2, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # 4. Accuracy and MAE by range\n", + " ax = plt.subplot(2, 2, 4)\n", + " x_labels = [f\"{label}\\n({low}-{high})\" for low, high, label in uv_ranges]\n", + " accuracies = [range_analysis[label]['accuracy_within_05']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + " mae_values = [range_analysis[label]['mae']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + "\n", + " bars = plt.bar(x_labels, accuracies, alpha=0.6)\n", + " plt.ylabel('Accuracy within ±0.5 (%)')\n", + " plt.title('Accuracy and MAE by UV Range')\n", + "\n", + " # Add MAE as line\n", + " ax2 = ax.twinx()\n", + " ax2.plot(x_labels, mae_values, 'r-o', label='MAE')\n", + " ax2.set_ylabel('MAE', color='red')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save main figure\n", + " main_plot_path = f'{folder_name}_uv_analysis.png'\n", + " plt.savefig(main_plot_path, dpi=300, bbox_inches='tight')\n", + "\n", + " # Confusion matrix as separate plot\n", + " plt.figure(figsize=(10, 8))\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix for UV Risk Levels')\n", + "\n", + " conf_matrix_path = f'{folder_name}_confusion_matrix.png'\n", + " plt.savefig(conf_matrix_path, dpi=300, bbox_inches='tight')\n", + "\n", + " plt.close('all')\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Print detailed report\n", + " print(\"\\nUV Index Prediction Analysis:\")\n", + " print(\"\\nRaw Metrics:\")\n", + " for key, value in metrics['raw'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nRounded Metrics:\")\n", + " for key, value in metrics['rounded'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nAnalysis by UV Range:\")\n", + " for label, stats in range_analysis.items():\n", + " print(f\"\\n{label}:\")\n", + " for key, value in stats.items():\n", + " print(f\" {key}: {value:.3f}\")\n", + "\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm_df)\n", + "\n", + " # Add range analysis and confusion matrix to metrics dictionary\n", + " metrics.update({\n", + " 'range_analysis': range_analysis,\n", + " 'confusion_matrix': cm_df.to_dict(),\n", + " 'plot_paths': {\n", + " 'main_analysis': main_plot_path,\n", + " 'confusion_matrix': conf_matrix_path\n", + " }\n", + " })\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save the loss and metrics plots during training\n", + "\n", + " Parameters:\n", + " -----------\n", + " history : tensorflow.keras.callbacks.History\n", + " The history object returned by model training\n", + " folder_name : str\n", + " Folder where to save the plot\n", + " \"\"\"\n", + "\n", + " try:\n", + " # Create the figure\n", + " plt.figure(figsize=(12, 4))\n", + "\n", + " # Loss Plot\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(history.history['loss'], label='Training Loss')\n", + " plt.plot(history.history['val_loss'], label='Validation Loss')\n", + " plt.title('Model Loss')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # MAE Plot\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(history.history['mae'], label='Training MAE')\n", + " plt.plot(history.history['val_mae'], label='Validation MAE')\n", + " plt.title('Model MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " os.makedirs(folder_name, exist_ok=True)\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'training_history.png')\n", + "\n", + " # Save the figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Also save numerical data in CSV format\n", + " history_df = pd.DataFrame({\n", + " 'epoch': range(1, len(history.history['loss']) + 1),\n", + " 'training_loss': history.history['loss'],\n", + " 'validation_loss': history.history['val_loss'],\n", + " 'training_mae': history.history['mae'],\n", + " 'validation_mae': history.history['val_mae']\n", + " })\n", + "\n", + " if folder_name is not None:\n", + " csv_filename = os.path.join(folder_name, 'training_history.csv')\n", + " history_df.to_csv(csv_filename, index=False)\n", + " print(f\"Training history data saved as: {csv_filename}\")\n", + "\n", + " # Calculate and save final statistics\n", + " final_stats = {\n", + " 'final_training_loss': history.history['loss'][-1],\n", + " 'final_validation_loss': history.history['val_loss'][-1],\n", + " 'final_training_mae': history.history['mae'][-1],\n", + " 'final_validation_mae': history.history['val_mae'][-1],\n", + " 'best_validation_loss': min(history.history['val_loss']),\n", + " 'best_validation_mae': min(history.history['val_mae']),\n", + " 'epochs': len(history.history['loss']),\n", + " }\n", + "\n", + " if folder_name is not None:\n", + " # Save statistics in JSON format\n", + " stats_filename = os.path.join(folder_name, 'training_stats.json')\n", + " with open(stats_filename, 'w') as f:\n", + " json.dump(final_stats, f, indent=4)\n", + " print(f\"Final statistics saved as: {stats_filename}\")\n", + "\n", + " # Print main statistics\n", + " print(\"\\nFinal training statistics:\")\n", + " print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n", + " print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n", + " print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n", + " print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n", + "\n", + " plt.show()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during plot creation or saving: {str(e)}\")\n", + "\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='uv_index'):\n", + " \"\"\"\n", + " Advanced training function for the hybrid UV index model with detailed monitoring\n", + " and training management.\n", + "\n", + " Parameters:\n", + " -----------\n", + " model : keras.Model\n", + " The compiled hybrid model\n", + " X_train : numpy.ndarray\n", + " Training data\n", + " y_train : numpy.ndarray\n", + " Training targets\n", + " X_test : numpy.ndarray\n", + " Validation data\n", + " y_test : numpy.ndarray\n", + " Validation targets\n", + " epochs : int, optional\n", + " Maximum number of training epochs\n", + " batch_size : int, optional\n", + " Batch size\n", + "\n", + " Returns:\n", + " --------\n", + " history : keras.callbacks.History\n", + " Training history with all metrics\n", + " \"\"\"\n", + "\n", + " # Advanced callbacks for training\n", + " callbacks = [\n", + " # Advanced Early Stopping\n", + " EarlyStopping(\n", + " monitor='mae',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-6\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='mae',\n", + " factor=0.05,\n", + " patience=3,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=2,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=1,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_uv_model.h5',\n", + " monitor='mae',\n", + " save_best_only=True,\n", + " mode='min'\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: print(\n", + " f\"\\nEpoch {epoch + 1}: Out of range predictions: \"\n", + " f\"{np.sum((model.predict(X_test) < 0) | (model.predict(X_test) > 11))}\"\n", + " ) if epoch % 20 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train, y_train,\n", + " validation_data=(X_test, y_test),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False,\n", + " validation_freq=1,\n", + " )\n", + "\n", + " # Post-training analysis\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " raise\n", + "\n", + " finally:\n", + " # Memory cleanup\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrate UV index predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : numpy.ndarray\n", + " Array of UV index predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with UV index predictions\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Create temporary DataFrame with predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'uvindex_predicted': predictions.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update uvindex column where missing\n", + " df['uvindex'] = df['uvindex'].fillna(df['uvindex_predicted'])\n", + "\n", + " # Remove temporary column\n", + " df = df.drop('uvindex_predicted', axis=1)\n", + "\n", + " print(f\"Added {len(predictions)} predictions to dataset\")\n", + " print(f\"Rows with UV index after integration: {df['uvindex'].notna().sum()}\")\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing UV index model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Temporal distribution of data:\n", + "Records after 2010: 129,777\n", + "Records before 2010: 227,902\n", + "\n", + "Warning: Found missing values after preprocessing\n", + "Features with missing values: []\n", + "\n", + "Number of features used: 30\n", + "\n", + "Feature categories:\n", + "atmospheric: 6 features\n", + "temporal: 4 features\n", + "solar: 5 features\n", + "interactions: 4 features\n", + "rolling: 2 features\n", + "Categorical: 9 features\n", + "Training data shape: (64865, 24, 30)\n", + "Test data shape: (64866, 24, 30)\n", + "Saving scaler to: 2024-11-21_08-23_feature_scaler.joblib\n", + "Saving scaler to: 2024-11-21_08-23_target_scaler.joblib\n", + "Saving features to: 2024-11-21_08-23_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data.parquet')\n", + "\n", + "print(\"Initializing UV index model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "feature_scaler_path = f'{folder_name}_feature_scaler.joblib'\n", + "target_scaler_path = f'{folder_name}_target_scaler.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(feature_scaler_path):\n", + " print(f\"Loading existing scaler from: {feature_scaler_path}\")\n", + " scaler = joblib.load(feature_scaler_path)\n", + "else:\n", + " print(f\"Saving scaler to: {feature_scaler_path}\")\n", + " joblib.dump(feature_scaler, feature_scaler_path)\n", + "\n", + "if os.path.exists(target_scaler_path):\n", + " print(f\"Loading existing scaler from: {target_scaler_path}\")\n", + " scaler = joblib.load(target_scaler_path)\n", + "else:\n", + " print(f\"Saving scaler to: {target_scaler_path}\")\n", + " joblib.dump(target_scaler, target_scaler_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "83771453-71db-4bb2-833d-7b81c022863d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Model initialization...\n", + "Creating new model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:26:35.683631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:25:00.0, compute capability: 8.9\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"UvModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 30)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 128) 48640 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 128) 256 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 128) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 128) 3968 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 128) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 128) 131968 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 128) 0 ['multi_head_attention[0][0]',\n", + " sticDepth) 'stochastic_depth[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 24, 128) 256 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_1 (Dense) (None, 24, 512) 66048 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 24, 128) 65664 ['dense_1[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 24, 128) 0 ['dense_2[0][0]', \n", + " sticDepth) 'layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_2 (Lay (None, 24, 128) 256 ['stochastic_depth_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_1 (Bidirecti (None, 24, 64) 41216 ['layer_normalization_2[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_3 (Lay (None, 24, 64) 128 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 24, 64) 0 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 24, 64) 8256 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 24, 64) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 24, 64) 33216 ['stochastic_depth_3[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_3[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 24, 64) 0 ['multi_head_attention_1[0][0]\n", + " sticDepth) ', \n", + " 'stochastic_depth_3[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 24, 64) 128 ['stochastic_depth_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_4 (Dense) (None, 24, 256) 16640 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_5 (Dense) (None, 24, 64) 16448 ['dense_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 24, 64) 0 ['dense_5[0][0]', \n", + " sticDepth) 'layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_5 (Lay (None, 24, 64) 128 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_2 (Bidirecti (None, 24, 32) 10368 ['layer_normalization_5[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_6 (Lay (None, 24, 32) 64 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 24, 32) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_6 (Dense) (None, 24, 32) 2080 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 24, 32) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_6[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 24, 32) 8416 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 24, 32) 0 ['multi_head_attention_2[0][0]\n", + " sticDepth) ', \n", + " 'stochastic_depth_6[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 24, 32) 64 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_7 (Dense) (None, 24, 128) 4224 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " dense_8 (Dense) (None, 24, 32) 4128 ['dense_7[0][0]'] \n", + " \n", + " stochastic_depth_8 (Stocha (None, 24, 32) 0 ['dense_8[0][0]', \n", + " sticDepth) 'layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_8 (Lay (None, 24, 32) 64 ['stochastic_depth_8[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_3 (Mu (None, 24, 32) 8416 ['layer_normalization_8[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_8[0][0]\n", + " '] \n", + " \n", + " stochastic_depth_9 (Stocha (None, 24, 32) 0 ['multi_head_attention_3[0][0]\n", + " sticDepth) ', \n", + " 'layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_9 (Lay (None, 24, 32) 64 ['stochastic_depth_9[0][0]'] \n", + " erNormalization) \n", + " \n", + " global_average_pooling1d ( (None, 32) 0 ['layer_normalization_9[0][0]'\n", + " GlobalAveragePooling1D) ] \n", + " \n", + " dense_9 (Dense) (None, 32) 1056 ['global_average_pooling1d[0][\n", + " 0]'] \n", + " \n", + " batch_normalization (Batch (None, 32) 128 ['dense_9[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout_3 (Dropout) (None, 32) 0 ['batch_normalization[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 16) 528 ['dropout_3[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 16) 64 ['dense_10[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_11 (Dense) (None, 8) 136 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_12 (Dense) (None, 1) 9 ['dense_11[0][0]'] \n", + " \n", + " lambda (Lambda) (None, 1) 0 ['dense_12[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 473025 (1.80 MB)\n", + "Trainable params: 472929 (1.80 MB)\n", + "Non-trainable params: 96 (384.00 Byte)\n", + "__________________________________________________________________________________________________\n", + "\n", + "3. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:26:51.620818: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n", + "2024-11-21 08:26:51.695976: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8905\n", + "2024-11-21 08:26:51.911310: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xd713390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-21 08:26:51.911349: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-21 08:26:51.921786: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-21 08:26:52.001781: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-21 08:26:52.063791: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "507/507 [==============================] - ETA: 0s - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2028/2028 [==============================] - 25s 11ms/step\n", + "2028/2028 [==============================] - 19s 9ms/step\n", + "\n", + "Epoch 1: Out of range predictions: 0\n", + "507/507 [==============================] - 95s 151ms/step - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000 - val_loss: 3.3901 - val_mae: 0.7726 - val_mse: 1.0065 - val_root_mean_squared_error: 1.0033 - val_smooth_mape: 96.0347 - lr: 5.0600e-05\n", + "Epoch 2/100\n", + "507/507 [==============================] - 24s 48ms/step - loss: 3.4513 - mae: 0.9422 - mse: 1.2050 - root_mean_squared_error: 1.0977 - smooth_mape: 144.6453 - val_loss: 3.0292 - val_mae: 0.6905 - val_mse: 0.9136 - val_root_mean_squared_error: 0.9558 - val_smooth_mape: 72.3569 - lr: 9.9998e-05\n", + "Epoch 3/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 2.8616 - mae: 0.7835 - mse: 0.9255 - root_mean_squared_error: 0.9620 - smooth_mape: 105.5174 - val_loss: 2.6105 - val_mae: 0.6461 - val_mse: 0.8668 - val_root_mean_squared_error: 0.9310 - val_smooth_mape: 62.3054 - lr: 9.7355e-05\n", + "Epoch 4/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 2.2615 - mae: 0.6304 - mse: 0.6460 - root_mean_squared_error: 0.8038 - smooth_mape: 84.4009 - val_loss: 1.9317 - val_mae: 0.4135 - val_mse: 0.4540 - val_root_mean_squared_error: 0.6738 - val_smooth_mape: 35.5852 - lr: 8.9946e-05\n", + "Epoch 5/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 1.6904 - mae: 0.4345 - mse: 0.3313 - root_mean_squared_error: 0.5756 - smooth_mape: 59.1463 - val_loss: 1.4432 - val_mae: 0.2685 - val_mse: 0.1950 - val_root_mean_squared_error: 0.4416 - val_smooth_mape: 27.2014 - lr: 7.8518e-05\n", + "Epoch 6/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 1.3637 - mae: 0.3450 - mse: 0.2236 - root_mean_squared_error: 0.4729 - smooth_mape: 45.5589 - val_loss: 1.2321 - val_mae: 0.2588 - val_mse: 0.1828 - val_root_mean_squared_error: 0.4276 - val_smooth_mape: 25.4509 - lr: 6.4221e-05\n", + "Epoch 7/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 1.1519 - mae: 0.2973 - mse: 0.1789 - root_mean_squared_error: 0.4229 - smooth_mape: 38.0580 - val_loss: 1.0643 - val_mae: 0.2375 - val_mse: 0.1577 - val_root_mean_squared_error: 0.3971 - val_smooth_mape: 23.5643 - lr: 4.8492e-05\n", + "Epoch 8/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 1.0083 - mae: 0.2665 - mse: 0.1546 - root_mean_squared_error: 0.3932 - smooth_mape: 32.9333 - val_loss: 0.9257 - val_mae: 0.2038 - val_mse: 0.1143 - val_root_mean_squared_error: 0.3380 - val_smooth_mape: 21.4354 - lr: 3.2915e-05\n", + "Epoch 9/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.9148 - mae: 0.2470 - mse: 0.1407 - root_mean_squared_error: 0.3751 - smooth_mape: 29.7929 - val_loss: 0.8520 - val_mae: 0.1890 - val_mse: 0.1027 - val_root_mean_squared_error: 0.3204 - val_smooth_mape: 20.0954 - lr: 1.9058e-05\n", + "Epoch 10/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.8584 - mae: 0.2366 - mse: 0.1317 - root_mean_squared_error: 0.3628 - smooth_mape: 28.3349 - val_loss: 0.8144 - val_mae: 0.1853 - val_mse: 0.0991 - val_root_mean_squared_error: 0.3148 - val_smooth_mape: 19.7012 - lr: 8.3134e-06\n", + "Epoch 11/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.8331 - mae: 0.2331 - mse: 0.1292 - root_mean_squared_error: 0.3594 - smooth_mape: 27.8952 - val_loss: 0.7991 - val_mae: 0.1829 - val_mse: 0.0966 - val_root_mean_squared_error: 0.3108 - val_smooth_mape: 19.7143 - lr: 1.7639e-06\n", + "Epoch 12/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.8266 - mae: 0.2326 - mse: 0.1289 - root_mean_squared_error: 0.3591 - smooth_mape: 27.6276 - val_loss: 0.8002 - val_mae: 0.1851 - val_mse: 0.0996 - val_root_mean_squared_error: 0.3155 - val_smooth_mape: 19.6762 - lr: 6.7976e-08\n", + "Epoch 13/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.8256 - mae: 0.2332 - mse: 0.1297 - root_mean_squared_error: 0.3601 - smooth_mape: 27.8768 - val_loss: 0.7938 - val_mae: 0.1827 - val_mse: 0.0977 - val_root_mean_squared_error: 0.3126 - val_smooth_mape: 19.5952 - lr: 3.3964e-06\n", + "Epoch 14/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.8108 - mae: 0.2311 - mse: 0.1283 - root_mean_squared_error: 0.3582 - smooth_mape: 27.5029 - val_loss: 0.7689 - val_mae: 0.1841 - val_mse: 0.0983 - val_root_mean_squared_error: 0.3135 - val_smooth_mape: 19.2959 - lr: 1.1414e-05\n", + "Epoch 15/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.7666 - mae: 0.2260 - mse: 0.1253 - root_mean_squared_error: 0.3539 - smooth_mape: 26.7909 - val_loss: 0.7071 - val_mae: 0.1791 - val_mse: 0.0942 - val_root_mean_squared_error: 0.3069 - val_smooth_mape: 18.7677 - lr: 2.3315e-05\n", + "Epoch 16/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.6896 - mae: 0.2200 - mse: 0.1214 - root_mean_squared_error: 0.3484 - smooth_mape: 25.8531 - val_loss: 0.6141 - val_mae: 0.1740 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 18.6023 - lr: 3.7901e-05\n", + "Epoch 17/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.5905 - mae: 0.2116 - mse: 0.1160 - root_mean_squared_error: 0.3406 - smooth_mape: 24.5564 - val_loss: 0.5156 - val_mae: 0.1681 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 17.4665 - lr: 5.3704e-05\n", + "Epoch 18/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.4901 - mae: 0.2037 - mse: 0.1116 - root_mean_squared_error: 0.3341 - smooth_mape: 23.3802 - val_loss: 0.4216 - val_mae: 0.1617 - val_mse: 0.0852 - val_root_mean_squared_error: 0.2919 - val_smooth_mape: 17.2917 - lr: 6.9134e-05\n", + "Epoch 19/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.3984 - mae: 0.1930 - mse: 0.1038 - root_mean_squared_error: 0.3221 - smooth_mape: 21.8713 - val_loss: 0.3391 - val_mae: 0.1637 - val_mse: 0.0790 - val_root_mean_squared_error: 0.2810 - val_smooth_mape: 17.4952 - lr: 8.2639e-05\n", + "Epoch 20/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.3280 - mae: 0.1882 - mse: 0.1019 - root_mean_squared_error: 0.3192 - smooth_mape: 21.1186 - val_loss: 0.2774 - val_mae: 0.1532 - val_mse: 0.0766 - val_root_mean_squared_error: 0.2767 - val_smooth_mape: 16.5548 - lr: 9.2860e-05\n", + "Epoch 21/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 12ms/step\n", + "\n", + "Epoch 21: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 148ms/step - loss: 0.2717 - mae: 0.1800 - mse: 0.0959 - root_mean_squared_error: 0.3097 - smooth_mape: 19.9770 - val_loss: 0.2327 - val_mae: 0.1514 - val_mse: 0.0756 - val_root_mean_squared_error: 0.2750 - val_smooth_mape: 16.9079 - lr: 9.8768e-05\n", + "Epoch 22/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.2290 - mae: 0.1732 - mse: 0.0907 - root_mean_squared_error: 0.3011 - smooth_mape: 19.0873 - val_loss: 0.1969 - val_mae: 0.1482 - val_mse: 0.0722 - val_root_mean_squared_error: 0.2687 - val_smooth_mape: 16.2890 - lr: 9.9769e-05\n", + "Epoch 23/100\n", + "507/507 [==============================] - 26s 50ms/step - loss: 0.1994 - mae: 0.1705 - mse: 0.0889 - root_mean_squared_error: 0.2982 - smooth_mape: 18.7545 - val_loss: 0.1750 - val_mae: 0.1452 - val_mse: 0.0739 - val_root_mean_squared_error: 0.2719 - val_smooth_mape: 15.6235 - lr: 9.5762e-05\n", + "Epoch 24/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1768 - mae: 0.1661 - mse: 0.0861 - root_mean_squared_error: 0.2934 - smooth_mape: 18.1383 - val_loss: 0.1559 - val_mae: 0.1482 - val_mse: 0.0716 - val_root_mean_squared_error: 0.2676 - val_smooth_mape: 16.7181 - lr: 8.7150e-05\n", + "Epoch 25/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1601 - mae: 0.1629 - mse: 0.0835 - root_mean_squared_error: 0.2890 - smooth_mape: 17.7203 - val_loss: 0.1425 - val_mae: 0.1434 - val_mse: 0.0702 - val_root_mean_squared_error: 0.2649 - val_smooth_mape: 15.5010 - lr: 7.4800e-05\n", + "Epoch 26/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1472 - mae: 0.1586 - mse: 0.0806 - root_mean_squared_error: 0.2839 - smooth_mape: 17.2387 - val_loss: 0.1347 - val_mae: 0.1454 - val_mse: 0.0710 - val_root_mean_squared_error: 0.2665 - val_smooth_mape: 16.1275 - lr: 5.9955e-05\n", + "Epoch 27/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1399 - mae: 0.1584 - mse: 0.0803 - root_mean_squared_error: 0.2834 - smooth_mape: 17.2506 - val_loss: 0.1270 - val_mae: 0.1401 - val_mse: 0.0687 - val_root_mean_squared_error: 0.2621 - val_smooth_mape: 15.1573 - lr: 4.4108e-05\n", + "Epoch 28/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1344 - mae: 0.1563 - mse: 0.0792 - root_mean_squared_error: 0.2815 - smooth_mape: 16.9289 - val_loss: 0.1229 - val_mae: 0.1394 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 15.1774 - lr: 2.8853e-05\n", + "Epoch 29/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1293 - mae: 0.1536 - mse: 0.0767 - root_mean_squared_error: 0.2770 - smooth_mape: 16.6836 - val_loss: 0.1206 - val_mae: 0.1383 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.8600 - lr: 1.5727e-05\n", + "Epoch 30/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1275 - mae: 0.1526 - mse: 0.0763 - root_mean_squared_error: 0.2763 - smooth_mape: 16.5544 - val_loss: 0.1198 - val_mae: 0.1375 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2613 - val_smooth_mape: 14.7848 - lr: 6.0491e-06\n", + "Epoch 31/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1259 - mae: 0.1517 - mse: 0.0753 - root_mean_squared_error: 0.2744 - smooth_mape: 16.4806 - val_loss: 0.1192 - val_mae: 0.1370 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.5789 - lr: 7.9394e-07\n", + "Epoch 32/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.1263 - mae: 0.1522 - mse: 0.0759 - root_mean_squared_error: 0.2754 - smooth_mape: 16.5490 - val_loss: 0.1192 - val_mae: 0.1368 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.5403 - lr: 4.9000e-07\n", + "Epoch 33/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1258 - mae: 0.1518 - mse: 0.0754 - root_mean_squared_error: 0.2745 - smooth_mape: 16.4660 - val_loss: 0.1189 - val_mae: 0.1376 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.7214 - lr: 5.1679e-06\n", + "Epoch 34/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4794\n", + "Epoch 34: ReduceLROnPlateau reducing learning rate to 7.178531632234808e-07.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4811 - val_loss: 0.1179 - val_mae: 0.1389 - val_mse: 0.0676 - val_root_mean_squared_error: 0.2601 - val_smooth_mape: 14.8385 - lr: 7.1785e-07\n", + "Epoch 35/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.1245 - mae: 0.1527 - mse: 0.0760 - root_mean_squared_error: 0.2756 - smooth_mape: 16.5704 - val_loss: 0.1169 - val_mae: 0.1410 - val_mse: 0.0685 - val_root_mean_squared_error: 0.2618 - val_smooth_mape: 15.6455 - lr: 2.7133e-05\n", + "Epoch 36/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1227 - mae: 0.1525 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.5028 - val_loss: 0.1139 - val_mae: 0.1381 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2614 - val_smooth_mape: 14.6552 - lr: 4.2209e-05\n", + "Epoch 37/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1198 - mae: 0.1535 - mse: 0.0769 - root_mean_squared_error: 0.2773 - smooth_mape: 16.6359 - val_loss: 0.1107 - val_mae: 0.1409 - val_mse: 0.0686 - val_root_mean_squared_error: 0.2619 - val_smooth_mape: 14.7102 - lr: 5.8070e-05\n", + "Epoch 38/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710\n", + "Epoch 38: ReduceLROnPlateau reducing learning rate to 3.6559198633767668e-06.\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710 - val_loss: 0.1057 - val_mae: 0.1380 - val_mse: 0.0675 - val_root_mean_squared_error: 0.2599 - val_smooth_mape: 14.8251 - lr: 3.6559e-06\n", + "Epoch 39/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.1113 - mae: 0.1525 - mse: 0.0762 - root_mean_squared_error: 0.2761 - smooth_mape: 16.4623 - val_loss: 0.1040 - val_mae: 0.1391 - val_mse: 0.0703 - val_root_mean_squared_error: 0.2652 - val_smooth_mape: 14.3343 - lr: 8.5841e-05\n", + "Epoch 40/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1080 - mae: 0.1531 - mse: 0.0770 - root_mean_squared_error: 0.2775 - smooth_mape: 16.5384 - val_loss: 0.0995 - val_mae: 0.1393 - val_mse: 0.0697 - val_root_mean_squared_error: 0.2640 - val_smooth_mape: 15.2621 - lr: 9.4957e-05\n", + "Epoch 41/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 41: Out of range predictions: 0\n", + "507/507 [==============================] - 73s 144ms/step - loss: 0.1038 - mae: 0.1523 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.4194 - val_loss: 0.0982 - val_mae: 0.1425 - val_mse: 0.0723 - val_root_mean_squared_error: 0.2689 - val_smooth_mape: 14.8658 - lr: 9.9549e-05\n", + "Epoch 42/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6916\n", + "Epoch 42: ReduceLROnPlateau reducing learning rate to 4.957754936185666e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6892 - val_loss: 0.0915 - val_mae: 0.1368 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 14.4180 - lr: 4.9578e-06\n", + "Epoch 43/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0964 - mae: 0.1500 - mse: 0.0749 - root_mean_squared_error: 0.2736 - smooth_mape: 16.2060 - val_loss: 0.0881 - val_mae: 0.1362 - val_mse: 0.0671 - val_root_mean_squared_error: 0.2591 - val_smooth_mape: 14.6337 - lr: 9.3815e-05\n", + "Epoch 44/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0926 - mae: 0.1483 - mse: 0.0735 - root_mean_squared_error: 0.2711 - smooth_mape: 15.9664 - val_loss: 0.0856 - val_mae: 0.1355 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4813 - lr: 8.4067e-05\n", + "Epoch 45/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0904 - mae: 0.1477 - mse: 0.0732 - root_mean_squared_error: 0.2706 - smooth_mape: 15.9141 - val_loss: 0.0839 - val_mae: 0.1354 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2587 - val_smooth_mape: 14.4705 - lr: 7.0890e-05\n", + "Epoch 46/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0876 - mae: 0.1461 - mse: 0.0718 - root_mean_squared_error: 0.2680 - smooth_mape: 15.7518 - val_loss: 0.0825 - val_mae: 0.1373 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4117 - lr: 5.5612e-05\n", + "Epoch 47/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0853 - mae: 0.1446 - mse: 0.0706 - root_mean_squared_error: 0.2658 - smooth_mape: 15.5602 - val_loss: 0.0805 - val_mae: 0.1370 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2566 - val_smooth_mape: 14.8418 - lr: 3.9768e-05\n", + "Epoch 48/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0851 - mae: 0.1449 - mse: 0.0713 - root_mean_squared_error: 0.2671 - smooth_mape: 15.6229 - val_loss: 0.0801 - val_mae: 0.1363 - val_mse: 0.0660 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.7760 - lr: 2.4955e-05\n", + "Epoch 49/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0833 - mae: 0.1432 - mse: 0.0699 - root_mean_squared_error: 0.2643 - smooth_mape: 15.4598 - val_loss: 0.0791 - val_mae: 0.1348 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2557 - val_smooth_mape: 14.3369 - lr: 1.2661e-05\n", + "Epoch 50/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0819 - mae: 0.1421 - mse: 0.0687 - root_mean_squared_error: 0.2620 - smooth_mape: 15.3369 - val_loss: 0.0790 - val_mae: 0.1342 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.2211 - lr: 4.1248e-06\n", + "Epoch 51/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.3209 - val_loss: 0.0791 - val_mae: 0.1332 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.1215 - lr: 2.0452e-07\n", + "Epoch 52/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575\n", + "Epoch 52: ReduceLROnPlateau reducing learning rate to 2.589756149973255e-07.\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575 - val_loss: 0.0791 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.1793 - lr: 1.2949e-06\n", + "Epoch 53/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0818 - mae: 0.1417 - mse: 0.0687 - root_mean_squared_error: 0.2621 - smooth_mape: 15.2994 - val_loss: 0.0790 - val_mae: 0.1349 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2560 - val_smooth_mape: 14.1680 - lr: 7.2861e-06\n", + "Epoch 54/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3289 - val_loss: 0.0786 - val_mae: 0.1360 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.6983 - lr: 1.7575e-05\n", + "Epoch 55/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0826 - mae: 0.1438 - mse: 0.0701 - root_mean_squared_error: 0.2648 - smooth_mape: 15.5292 - val_loss: 0.0784 - val_mae: 0.1367 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.7914 - lr: 3.1127e-05\n", + "Epoch 56/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452\n", + "Epoch 56: ReduceLROnPlateau reducing learning rate to 2.3289143427973617e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452 - val_loss: 0.0777 - val_mae: 0.1360 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.4766 - lr: 2.3289e-06\n", + "Epoch 57/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0823 - mae: 0.1451 - mse: 0.0711 - root_mean_squared_error: 0.2667 - smooth_mape: 15.6261 - val_loss: 0.0785 - val_mae: 0.1375 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2596 - val_smooth_mape: 15.0211 - lr: 6.2374e-05\n", + "Epoch 58/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0818 - mae: 0.1454 - mse: 0.0715 - root_mean_squared_error: 0.2675 - smooth_mape: 15.6269 - val_loss: 0.0772 - val_mae: 0.1366 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2586 - val_smooth_mape: 14.1091 - lr: 7.6924e-05\n", + "Epoch 59/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0817 - mae: 0.1461 - mse: 0.0723 - root_mean_squared_error: 0.2689 - smooth_mape: 15.7262 - val_loss: 0.0761 - val_mae: 0.1370 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2575 - val_smooth_mape: 14.8032 - lr: 8.8765e-05\n", + "Epoch 60/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620\n", + "Epoch 60: ReduceLROnPlateau reducing learning rate to 4.835262006963604e-06.\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620 - val_loss: 0.0759 - val_mae: 0.1358 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2597 - val_smooth_mape: 14.4084 - lr: 4.8353e-06\n", + "Epoch 61/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 61: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 147ms/step - loss: 0.0787 - mae: 0.1447 - mse: 0.0711 - root_mean_squared_error: 0.2666 - smooth_mape: 15.5565 - val_loss: 0.0737 - val_mae: 0.1352 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3196 - lr: 9.9946e-05\n", + "Epoch 62/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0781 - mae: 0.1451 - mse: 0.0715 - root_mean_squared_error: 0.2674 - smooth_mape: 15.5903 - val_loss: 0.0726 - val_mae: 0.1344 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2565 - val_smooth_mape: 14.3699 - lr: 9.8161e-05\n", + "Epoch 63/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0771 - mae: 0.1446 - mse: 0.0713 - root_mean_squared_error: 0.2670 - smooth_mape: 15.5551 - val_loss: 0.0721 - val_mae: 0.1350 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3152 - lr: 9.1530e-05\n", + "Epoch 64/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5067\n", + "Epoch 64: ReduceLROnPlateau reducing learning rate to 4.035990059492178e-06.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5081 - val_loss: 0.0716 - val_mae: 0.1347 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2574 - val_smooth_mape: 14.7350 - lr: 4.0360e-06\n", + "Epoch 65/100\n", + "507/507 [==============================] - 26s 50ms/step - loss: 0.0745 - mae: 0.1431 - mse: 0.0698 - root_mean_squared_error: 0.2642 - smooth_mape: 15.3922 - val_loss: 0.0710 - val_mae: 0.1360 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.1923 - lr: 6.6819e-05\n", + "Epoch 66/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0729 - mae: 0.1413 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.1677 - val_loss: 0.0697 - val_mae: 0.1349 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2538 - lr: 5.1225e-05\n", + "Epoch 67/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0722 - mae: 0.1408 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.0982 - val_loss: 0.0691 - val_mae: 0.1346 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2549 - val_smooth_mape: 14.2971 - lr: 3.5508e-05\n", + "Epoch 68/100\n", + "507/507 [==============================] - 25s 48ms/step - loss: 0.0707 - mae: 0.1389 - mse: 0.0669 - root_mean_squared_error: 0.2587 - smooth_mape: 14.9200 - val_loss: 0.0686 - val_mae: 0.1339 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 14.3511 - lr: 2.1250e-05\n", + "Epoch 69/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0705 - mae: 0.1388 - mse: 0.0669 - root_mean_squared_error: 0.2586 - smooth_mape: 14.9215 - val_loss: 0.0684 - val_mae: 0.1325 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2540 - val_smooth_mape: 14.0225 - lr: 9.8841e-06\n", + "Epoch 70/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0698 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2573 - smooth_mape: 14.8352 - val_loss: 0.0684 - val_mae: 0.1320 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9647 - lr: 2.5551e-06\n", + "Epoch 71/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0695 - mae: 0.1374 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7516 - val_loss: 0.0685 - val_mae: 0.1317 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2545 - val_smooth_mape: 13.8579 - lr: 1.5795e-10\n", + "Epoch 72/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7707\n", + "Epoch 72: ReduceLROnPlateau reducing learning rate to 4.95273479828029e-07.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7708 - val_loss: 0.0684 - val_mae: 0.1318 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9681 - lr: 2.4764e-06\n", + "Epoch 73/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0694 - mae: 0.1373 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7647 - val_loss: 0.0683 - val_mae: 0.1327 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 13.9221 - lr: 9.7346e-06\n", + "Epoch 74/100\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0698 - mae: 0.1380 - mse: 0.0663 - root_mean_squared_error: 0.2575 - smooth_mape: 14.8505 - val_loss: 0.0683 - val_mae: 0.1332 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2543 - val_smooth_mape: 14.4362 - lr: 2.1044e-05\n", + "Epoch 75/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0697 - mae: 0.1381 - mse: 0.0664 - root_mean_squared_error: 0.2577 - smooth_mape: 14.8281 - val_loss: 0.0680 - val_mae: 0.1335 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 14.2706 - lr: 3.5268e-05\n", + "Epoch 76/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0952\n", + "Epoch 76: ReduceLROnPlateau reducing learning rate to 2.548691554693505e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0982 - val_loss: 0.0680 - val_mae: 0.1339 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2546 - val_smooth_mape: 14.0539 - lr: 2.5487e-06\n", + "Epoch 77/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0332\n", + "Epoch 77: ReduceLROnPlateau reducing learning rate to 1.3316345575731248e-05.\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0357 - val_loss: 0.0682 - val_mae: 0.1346 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2556 - val_smooth_mape: 14.3903 - lr: 6.6582e-05\n", + "Epoch 78/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0707 - mae: 0.1408 - mse: 0.0683 - root_mean_squared_error: 0.2614 - smooth_mape: 15.1099 - val_loss: 0.0680 - val_mae: 0.1327 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.0229 - lr: 8.0521e-05\n", + "Epoch 79/100\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0710 - mae: 0.1417 - mse: 0.0691 - root_mean_squared_error: 0.2629 - smooth_mape: 15.2021 - val_loss: 0.0677 - val_mae: 0.1354 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.2230 - lr: 9.1389e-05\n", + "Epoch 80/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279\n", + "Epoch 80: ReduceLROnPlateau reducing learning rate to 4.904638990410604e-06.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279 - val_loss: 0.0682 - val_mae: 0.1343 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.2839 - lr: 4.9046e-06\n", + "Epoch 81/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1438 - mse: 0.0699 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4713\n", + "Epoch 81: ReduceLROnPlateau reducing learning rate to 1.9991402223240587e-05.\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 81: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 148ms/step - loss: 0.0713 - mae: 0.1438 - mse: 0.0700 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4736 - val_loss: 0.0690 - val_mae: 0.1379 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 15.5488 - lr: 9.9957e-05\n", + "Epoch 82/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0696 - mae: 0.1419 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.2732 - val_loss: 0.0667 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.2230 - lr: 9.6794e-05\n", + "Epoch 83/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0688 - mae: 0.1409 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.1230 - val_loss: 0.0659 - val_mae: 0.1343 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2547 - lr: 8.8923e-05\n", + "Epoch 84/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9698\n", + "Epoch 84: ReduceLROnPlateau reducing learning rate to 3.8567635783692825e-06.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9716 - val_loss: 0.0655 - val_mae: 0.1338 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2066 - lr: 3.8568e-06\n", + "Epoch 85/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0673 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9972 - val_loss: 0.0680 - val_mae: 0.1333 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 13.8129 - lr: 6.2617e-05\n", + "Epoch 86/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0669 - mae: 0.1389 - mse: 0.0670 - root_mean_squared_error: 0.2588 - smooth_mape: 14.9195 - val_loss: 0.0647 - val_mae: 0.1343 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2539 - val_smooth_mape: 14.2430 - lr: 4.6829e-05\n", + "Epoch 87/100\n", + "507/507 [==============================] - 23s 46ms/step - loss: 0.0656 - mae: 0.1372 - mse: 0.0657 - root_mean_squared_error: 0.2563 - smooth_mape: 14.7366 - val_loss: 0.0643 - val_mae: 0.1317 - val_mse: 0.0644 - val_root_mean_squared_error: 0.2537 - val_smooth_mape: 14.1979 - lr: 3.1360e-05\n", + "Epoch 88/100\n", + "507/507 [==============================] - 22s 44ms/step - loss: 0.0649 - mae: 0.1365 - mse: 0.0651 - root_mean_squared_error: 0.2552 - smooth_mape: 14.6326 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2528 - val_smooth_mape: 14.3781 - lr: 1.7767e-05\n", + "Epoch 89/100\n", + "507/507 [==============================] - 23s 45ms/step - loss: 0.0647 - mae: 0.1361 - mse: 0.0649 - root_mean_squared_error: 0.2548 - smooth_mape: 14.6184 - val_loss: 0.0637 - val_mae: 0.1317 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 13.9675 - lr: 7.4173e-06\n", + "Epoch 90/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0642 - mae: 0.1356 - mse: 0.0644 - root_mean_squared_error: 0.2538 - smooth_mape: 14.5656 - val_loss: 0.0638 - val_mae: 0.1309 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8811 - lr: 1.3523e-06\n", + "Epoch 91/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922\n", + "Epoch 91: ReduceLROnPlateau reducing learning rate to 1e-07.\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922 - val_loss: 0.0638 - val_mae: 0.1311 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8034 - lr: 1.8244e-07\n", + "Epoch 92/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0634 - mae: 0.1342 - mse: 0.0635 - root_mean_squared_error: 0.2520 - smooth_mape: 14.4498 - val_loss: 0.0637 - val_mae: 0.1312 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2527 - val_smooth_mape: 13.8449 - lr: 4.0254e-06\n", + "Epoch 93/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0638 - root_mean_squared_error: 0.2526 - smooth_mape: 14.4781 - val_loss: 0.0636 - val_mae: 0.1318 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.0036 - lr: 1.2494e-05\n", + "Epoch 94/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0639 - mae: 0.1350 - mse: 0.0642 - root_mean_squared_error: 0.2534 - smooth_mape: 14.4772 - val_loss: 0.0635 - val_mae: 0.1324 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.1743 - lr: 2.4737e-05\n", + "Epoch 95/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348\n", + "Epoch 95: ReduceLROnPlateau reducing learning rate to 1.976121529878583e-06.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0643 - val_root_mean_squared_error: 0.2535 - val_smooth_mape: 14.4393 - lr: 1.9761e-06\n", + "Epoch 96/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371\n", + "Epoch 96: ReduceLROnPlateau reducing learning rate to 1.1072350753238425e-05.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371 - val_loss: 0.0644 - val_mae: 0.1353 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2866 - lr: 5.5362e-05\n", + "Epoch 97/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0660 - mae: 0.1386 - mse: 0.0668 - root_mean_squared_error: 0.2585 - smooth_mape: 14.8711 - val_loss: 0.0640 - val_mae: 0.1324 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2544 - val_smooth_mape: 14.0692 - lr: 7.0662e-05\n", + "Epoch 98/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726\n", + "Epoch 98: ReduceLROnPlateau reducing learning rate to 1.6776460688561202e-05.\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726 - val_loss: 0.0652 - val_mae: 0.1359 - val_mse: 0.0662 - val_root_mean_squared_error: 0.2572 - val_smooth_mape: 14.0106 - lr: 8.3882e-05\n", + "Epoch 99/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9081\n", + "Epoch 99: ReduceLROnPlateau reducing learning rate to 4.684684972744436e-06.\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9098 - val_loss: 0.0642 - val_mae: 0.1338 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2555 - val_smooth_mape: 14.2021 - lr: 4.6847e-06\n", + "Epoch 100/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7397\n", + "Epoch 100: ReduceLROnPlateau reducing learning rate to 1.9821693422272803e-05.\n", + "507/507 [==============================] - 24s 46ms/step - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7407 - val_loss: 0.0652 - val_mae: 0.1356 - val_mse: 0.0667 - val_root_mean_squared_error: 0.2582 - val_smooth_mape: 13.8309 - lr: 9.9108e-05\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "# Model creation or loading\n", + "print(\"\\n2. Model initialization...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "MAX_UVINDEX = 11\n", + "\n", + "max_val_scaled = target_scaler.transform([[MAX_UVINDEX]])[0][0]\n", + "\n", + "if os.path.exists(model_path):\n", + " print(f\"Loading existing model from: {model_path}\")\n", + " model = tf.keras.models.load_model(model_path)\n", + "\n", + " # Load existing history if available\n", + " if os.path.exists(history_path):\n", + " print(f\"Loading existing training history from: {history_path}\")\n", + " with open(history_path, 'r') as f:\n", + " history_dict = json.load(f)\n", + " history = type('History', (), {'history': history_dict})()\n", + " else:\n", + " history = type('History', (), {'history': {}})()\n", + "else:\n", + " print(\"Creating new model...\")\n", + " model = create_uv_index_model(input_shape=input_shape, folder_name=folder_name, max_output=max_val_scaled)\n", + "\n", + " print(\"\\n3. Starting training...\")\n", + " history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=128,\n", + " folder_name=folder_name\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f0059ecf-7f4f-496f-bed8-85e2d990ff71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "4. Generating predictions...\n", + "2028/2028 [==============================] - 18s 9ms/step\n", + "\n", + "5. Model evaluation...\n", + "\n", + "Error saving plots: Unknown format code 'd' for object of type 'float'\n", + "\n", + "UV Index Prediction Analysis:\n", + "\n", + "Raw Metrics:\n", + "mae: 0.407\n", + "rmse: 0.775\n", + "r2: 0.918\n", + "mean_error: -0.076\n", + "std_error: 0.771\n", + "median_error: 0.012\n", + "p95_abs_error: 1.745\n", + "within_05: 71.379\n", + "within_1: 86.040\n", + "within_15: 92.984\n", + "within_2: 96.562\n", + "\n", + "Rounded Metrics:\n", + "mae: 0.393\n", + "rmse: 0.782\n", + "r2: 0.916\n", + "within_05: 78.975\n", + "within_1: 90.160\n", + "within_15: 95.037\n", + "within_2: 97.478\n", + "\n", + "Analysis by UV Range:\n", + "\n", + "Low:\n", + " mae: 0.133\n", + " count: 41407.000\n", + " accuracy_within_05: 90.828\n", + " accuracy_within_1: 97.341\n", + "\n", + "Moderate:\n", + " mae: 0.874\n", + " count: 11467.000\n", + " accuracy_within_05: 36.418\n", + " accuracy_within_1: 66.713\n", + "\n", + "High:\n", + " mae: 0.871\n", + " count: 5415.000\n", + " accuracy_within_05: 37.876\n", + " accuracy_within_1: 65.614\n", + "\n", + "Very High:\n", + " mae: 0.905\n", + " count: 6343.000\n", + " accuracy_within_05: 38.862\n", + " accuracy_within_1: 66.404\n", + "\n", + "Extreme:\n", + " mae: 1.649\n", + " count: 234.000\n", + " accuracy_within_05: 0.000\n", + " accuracy_within_1: 38.462\n", + "\n", + "Confusion Matrix:\n", + " Low Moderate High Very High Extreme\n", + "Low 43040.0 2283.0 97.0 17.0 0.0\n", + "Moderate 1336.0 7625.0 1181.0 107.0 0.0\n", + "High 10.0 1155.0 3149.0 576.0 0.0\n", + "Very High 0.0 114.0 1110.0 3066.0 0.0\n", + "Extreme 0.0 0.0 0.0 0.0 0.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n4. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "predictions = np.clip(predictions, 0, max_val_scaled)\n", + "\n", + "predictions_original = target_scaler.inverse_transform(predictions.reshape(-1, 1))\n", + "y_test_original = target_scaler.inverse_transform(y_test.reshape(-1, 1))\n", + "\n", + "print(\"\\n5. Model evaluation...\")\n", + "metrics = evaluate_uv_predictions(y_test_original, predictions_original, folder_name=folder_name)\n", + "\n", + "# Save training results only if new training was performed\n", + "if not os.path.exists(model_path):\n", + " training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 128,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_loss']) + 1,\n", + " },\n", + " 'performance_metrics': {\n", + " 'final_loss': float(history.history['val_loss'][-1]),\n", + " 'final_mae': float(history.history['val_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n", + " }\n", + " }\n", + "\n", + " # Save training history\n", + " with open(history_path, 'w') as f:\n", + " history_dict = {key: [float(val) for val in values]\n", + " for key, values in history.history.items()}\n", + " json.dump(history_dict, f, indent=4)\n", + "else:\n", + " # Load existing training results if available\n", + " results_path = f'{folder_name}_training_results.json'\n", + " if os.path.exists(results_path):\n", + " with open(results_path, 'r') as f:\n", + " training_results = json.load(f)\n", + " else:\n", + " training_results = {}\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4365d2bf-daf8-49e1-be13-cce222bbb5bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "6. Predicting missing data...\n", + "7122/7122 [==============================] - 74s 10ms/step\n", + "\n", + "7. Integrating predictions into dataset...\n", + "Added 227879 predictions to dataset\n", + "Rows with UV index after integration: 357615\n", + "Updated dataset saved to: ../../sources/weather_data_uvindex.parquet\n", + "\n", + "All files saved with prefix: 2024-11-21_08-23\n" + ] + } + ], + "source": [ + "print(\"\\n6. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "to_predict_predictions = np.clip(to_predict_predictions, 0, max_val_scaled)\n", + "\n", + "to_predict_predictions_original = target_scaler.inverse_transform(to_predict_predictions.reshape(-1, 1))\n", + "\n", + "print(\"\\n7. Integrating predictions into dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), to_predict_predictions_original)\n", + "\n", + "output_path = f'../../sources/weather_data_uvindex.parquet'\n", + "df_updated.to_parquet(output_path)\n", + "print(f\"Updated dataset saved to: {output_path}\")\n", + "\n", + "# Add prediction statistics\n", + "prediction_stats = {\n", + " 'n_predictions_added': len(to_predict_predictions),\n", + " 'mean_predicted_uv': float(to_predict_predictions.mean()),\n", + " 'min_predicted_uv': float(to_predict_predictions.min()),\n", + " 'max_predicted_uv': float(to_predict_predictions.max()),\n", + "}\n", + "\n", + "\n", + "def convert_to_serializable(obj):\n", + " \"\"\"Convert numpy types to Python standard types for JSON serialization\"\"\"\n", + " if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n", + " np.int16, np.int32, np.int64, np.uint8,\n", + " np.uint16, np.uint32, np.uint64)):\n", + " return int(obj)\n", + " elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):\n", + " return float(obj)\n", + " elif isinstance(obj, (np.ndarray,)):\n", + " return obj.tolist()\n", + " elif isinstance(obj, dict):\n", + " return {key: convert_to_serializable(value) for key, value in obj.items()}\n", + " elif isinstance(obj, list):\n", + " return [convert_to_serializable(item) for item in obj]\n", + " return obj\n", + "\n", + "\n", + "if not os.path.exists(model_path):\n", + " training_results['prediction_stats'] = prediction_stats\n", + "\n", + " training_results = convert_to_serializable(training_results)\n", + " # Save final results\n", + " results_path = f'{folder_name}_training_results.json'\n", + " with open(results_path, 'w') as f:\n", + " json.dump(training_results, f, indent=4)\n", + "\n", + "print(f\"\\nAll files saved with prefix: {folder_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-21_08-23/error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error statistics:\n", + "MAE: 0.1356\n", + "MSE: 0.0667\n", + "RMSE: 0.2582\n", + "Mean errors: -0.0252\n", + "Std errors: 0.2569\n", + "Predictions within ±0.5: 93.0%\n", + "Predictions within ±1.0: 99.0%\n", + "Predictions within ±1.5: 99.9%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " y_pred : array-like\n", + " Predicted values\n", + " folder_name : str, optional\n", + " Folder to save plots. If None, plots are not saved.\n", + " \"\"\"\n", + "\n", + " # Convert to 1D numpy array if necessary\n", + " if isinstance(y_true, pd.Series):\n", + " y_true = y_true.values\n", + " if isinstance(y_pred, pd.Series):\n", + " y_pred = y_pred.values\n", + "\n", + " y_true = y_true.ravel()\n", + " y_pred = y_pred.ravel()\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + "\n", + " # Create main figure\n", + " fig = plt.figure(figsize=(15, 5))\n", + "\n", + " # Plot 1: Error Distribution\n", + " plt.subplot(1, 3, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: Actual vs Predicted\n", + " plt.subplot(1, 3, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 3: Errors vs Actual Values\n", + " plt.subplot(1, 3, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if folder is specified\n", + " if folder_name is not None:\n", + " try:\n", + " # Create folder if it doesn't exist\n", + " os.makedirs(folder_name, exist_ok=True)\n", + "\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'error_analysis.png')\n", + "\n", + " # Save figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print error statistics\n", + " print(\"\\nError statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n", + " print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n", + " print(f\"Mean errors: {np.mean(errors):.4f}\")\n", + " print(f\"Std errors: {np.std(errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c57d6b2-00a6-4d31-935e-449a29dafd79", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/uv_index/.ipynb_checkpoints/uv_index_model_v1-checkpoint.ipynb b/models/uv_index/.ipynb_checkpoints/uv_index_model_v1-checkpoint.ipynb new file mode 100644 index 0000000..1ec4188 --- /dev/null +++ b/models/uv_index/.ipynb_checkpoints/uv_index_model_v1-checkpoint.ipynb @@ -0,0 +1,2119 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "8adcbe0819b88578", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T00:55:22.066729Z", + "start_time": "2024-11-20T00:54:13.878615Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hit:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:2 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow==2.13.0 in /usr/local/lib/python3.11/dist-packages (2.13.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.1.21 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (23.5.26)\n", + "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.4.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.2.0)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.58.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (3.9.0)\n", + "Requirement already satisfied: keras<2.14,>=2.13.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.1)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (16.0.6)\n", + "Requirement already satisfied: numpy<=1.24.3,>=1.22 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.24.3)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (3.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (23.1)\n", + "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (4.24.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (68.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow==2.13.0) (1.16.0)\n", + "Requirement already satisfied: tensorboard<2.14,>=2.13 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.3.0)\n", + "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (4.5.0)\n", + "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.37.1)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow==2.13.0) (0.41.2)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (1.0.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.4.4)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.31.0)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.7.1)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.3.7)\n", + "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (5.3.1)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.3.0)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (4.9)\n", + "Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.0.5)\n", + "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (1.3.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.2.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2023.7.22)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.24.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.24.3)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras==2.13.1 in /usr/local/lib/python3.11/dist-packages (2.13.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.24.3)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.24.3)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.0.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.24.3)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.24.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.24.3)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow==2.13.0\n", + "!pip install numpy\n", + "!pip install pandas\n", + "!pip install keras==2.13.1\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a813e3cbca057b7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T00:55:22.782689Z", + "start_time": "2024-11-20T00:55:22.089165Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-20 11:04:20.516922: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, Conv1D, GlobalAveragePooling1D\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3f525e19f78a1da", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Calculate solar angle\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " # Solar angle and day length calculations\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Feature interactions\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + "\n", + " # Extended window rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_uv_specific_features(df):\n", + " # Solar zenith angle calculation\n", + " lat = 41.9 # assuming constant latitude for the dataset - Rome's latitude\n", + " df['solar_zenith'] = 90 - np.degrees(\n", + " np.arcsin(\n", + " np.sin(np.radians(lat)) * np.sin(df['solar_elevation']) +\n", + " np.cos(np.radians(lat)) * np.cos(df['solar_elevation']) * np.cos(df['hour'] * 15)\n", + " )\n", + " )\n", + "\n", + " # UV peak hours indicator (10:00-16:00)\n", + " df['is_uv_peak_hours'] = ((df['hour'] >= 10) & (df['hour'] <= 16)).astype(int)\n", + "\n", + " # Atmospheric attenuation factor\n", + " df['atmospheric_attenuation'] = (100 - df['cloudcover']) * (df['visibility'] / 100) * (1 - df['humidity'] / 200)\n", + "\n", + " # Seasonal UV factor\n", + " df['uv_seasonal_factor'] = np.where(df['season_Summer'], 1.0,\n", + " np.where(df['season_Spring'], 0.7,\n", + " np.where(df['season_Autumn'], 0.5, 0.3)))\n", + "\n", + " # Solar elevation and atmospheric transparency interaction\n", + " df['solar_clarity_index'] = df['solar_elevation'] * df['atmospheric_attenuation'] / 100\n", + "\n", + " # UV-specific rolling features\n", + " df['clarity_rolling_3h'] = df['atmospheric_attenuation'].rolling(window=3).mean()\n", + " df['temp_uv_interaction'] = df['temp'] * df['solar_clarity_index']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features in the correct order\n", + " \"\"\"\n", + " # 1. First add basic time features\n", + " df = add_time_features(df)\n", + "\n", + " # 2. One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + " \n", + " # 3. Add solar and specific features\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + " \n", + " # 4. Ensure datetime index\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df.index)\n", + "\n", + " # 5. Add weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # 6. Add solar radiation derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['day_length'] = np.sin(df['day_of_year_sin']) * 12 + 12\n", + "\n", + " # 7. Add lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # 8. Add rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # 9. Add atmospheric stability\n", + " df['atmospheric_stability'] = df.groupby(df.index.date)['pressure'].transform(\n", + " lambda x: x.std()\n", + " ).fillna(0)\n", + "\n", + " # 10. Add extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # 11. Add atmospheric transparency\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # 12. Add transitional seasons indicator\n", + " df['is_transition_season'] = ((df['season_Spring'] | df['season_Autumn'])).astype(int)\n", + "\n", + " # 13. Add solar cloud effect\n", + " if 'solar_elevation' in df.columns:\n", + " df['solar_cloud_effect'] = df['solar_elevation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # 14. Finally add UV specific features\n", + " df = add_uv_specific_features(df)\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepares data for UV index prediction model with advanced feature engineering\n", + " and optimized preprocessing.\n", + "\n", + " Args:\n", + " df: DataFrame with meteorological data\n", + "\n", + " Returns:\n", + " tuple: (X_train_scaled, X_test_scaled, y_train, y_test, scaler, final_features, X_to_predict_scaled)\n", + " \"\"\"\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " # Optimized feature selection for UV index\n", + " selected_features = {\n", + " # Primary meteorological features\n", + " 'atmospheric': [\n", + " 'temp', 'humidity', 'cloudcover', 'visibility',\n", + " 'clear_sky_index', 'atmospheric_transparency'\n", + " ],\n", + " \n", + " # Essential temporal features\n", + " 'temporal': [\n", + " 'hour_sin', 'hour_cos',\n", + " 'day_of_year_sin', 'day_of_year_cos'\n", + " ],\n", + " \n", + " # Solar features\n", + " 'solar': [\n", + " 'solar_angle', 'solar_elevation',\n", + " 'day_length', 'solar_noon',\n", + " 'solar_cloud_effect'\n", + " ],\n", + " \n", + " # Key interactions\n", + " 'interactions': [\n", + " 'cloud_temp_interaction',\n", + " 'visibility_cloud_interaction',\n", + " 'temp_humidity_interaction',\n", + " 'solar_clarity_index'\n", + " ],\n", + " \n", + " # Rolling features\n", + " 'rolling': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_mean_6h'\n", + " ]\n", + " }\n", + "\n", + " # Flatten feature list\n", + " base_features = [item for sublist in selected_features.values() for item in sublist]\n", + "\n", + " # Add categorical features (one-hot encoded)\n", + " categorical_columns = [col for col in df.columns if col.startswith(('season_', 'time_period_'))]\n", + " final_features = base_features + categorical_columns\n", + "\n", + " # Temporal preprocessing\n", + " df = df.sort_values('datetime')\n", + " df.set_index('datetime', inplace=True)\n", + "\n", + " # Advanced interpolation for missing values\n", + " for column in final_features:\n", + " if column in df.columns:\n", + " if df[column].isnull().any():\n", + " if column in selected_features['rolling']:\n", + " df[column] = df[column].ffill().bfill()\n", + " else:\n", + " df[column] = df[column].interpolate(method='time', limit_direction='both')\n", + "\n", + " # Temporal data split\n", + " data_after_2010 = df[df.index.year >= 2010].copy()\n", + " data_before_2010 = df[df.index.year < 2010].copy()\n", + "\n", + " print(f\"\\nTemporal distribution of data:\")\n", + " print(f\"Records after 2010: {len(data_after_2010):,}\")\n", + " print(f\"Records before 2010: {len(data_before_2010):,}\")\n", + "\n", + " # Feature and target preparation\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['uvindex']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Data validation\n", + " if X.isnull().any().any() or y.isnull().any():\n", + " print(\"\\nWarning: Found missing values after preprocessing\")\n", + " print(\"Features with missing values:\", X.columns[X.isnull().any()].tolist())\n", + " X = X.fillna(X.mean())\n", + " y = y.fillna(y.mean())\n", + "\n", + " # Stratified data split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y,\n", + " test_size=0.5,\n", + " random_state=random_state_value,\n", + " stratify=pd.qcut(y, q=5, duplicates='drop', labels=False)\n", + " )\n", + "\n", + " # Robust feature scaling\n", + " scaler = RobustScaler()\n", + " X_train_scaled = scaler.fit_transform(X_train)\n", + " X_test_scaled = scaler.transform(X_test)\n", + " X_to_predict_scaled = scaler.transform(X_to_predict)\n", + "\n", + " # Final validation\n", + " assert not np.isnan(X_train_scaled).any(), \"Found NaN in X_train_scaled\"\n", + " assert not np.isnan(X_test_scaled).any(), \"Found NaN in X_test_scaled\"\n", + " assert not np.isnan(X_to_predict_scaled).any(), \"Found NaN in X_to_predict_scaled\"\n", + "\n", + " # Print feature information\n", + " print(\"\\nNumber of features used:\", len(final_features))\n", + " print(\"\\nFeature categories:\")\n", + " for category, features in selected_features.items():\n", + " print(f\"{category}: {len(features)} features\")\n", + " print(f\"Categorical: {len(categorical_columns)} features\")\n", + "\n", + " return (X_train_scaled, X_test_scaled, y_train, y_test,\n", + " scaler, final_features, X_to_predict_scaled)\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " # Use existing data preparation\n", + " X_train_scaled, X_test_scaled, y_train, y_test, scaler, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data to sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train[sequence_length - 1:]\n", + " y_test = y_test[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, scaler, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01,\n", + " survival_probability=0.8, return_sequences=True):\n", + " \"\"\"LSTM layer with stochastic depth\"\"\"\n", + " residual = x\n", + " \n", + " # Main path\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences,\n", + " kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " # Adjust residual dimension if needed\n", + " if return_sequences:\n", + " # For Bidirectional LSTM, the output dimension is 2 * units\n", + " target_dim = 2 * units\n", + " if int(residual.shape[-1]) != target_dim:\n", + " # Use Dense layer instead of Conv1D for better dimension matching\n", + " residual = Dense(target_dim)(residual)\n", + " \n", + " # Apply stochastic depth only if dimensions match\n", + " if x.shape[-1] == residual.shape[-1]:\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " else:\n", + " print(f\"Warning: Dimension mismatch - x: {x.shape}, residual: {residual.shape}\")\n", + " # Skip residual connection if dimensions don't match\n", + " pass\n", + "\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Attention block with stochastic depth.\n", + " \"\"\"\n", + " original_x = x\n", + " \n", + " # Compute self-attention\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + " \n", + " # Ensure dimensions match before applying stochastic depth\n", + " if attention.shape[-1] != original_x.shape[-1]:\n", + " original_x = Dense(attention.shape[-1])(original_x)\n", + " \n", + " # Apply stochastic depth to the attention path\n", + " x = tfa.layers.StochasticDepth(survival_probability)([attention, original_x])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Store the input to the FFN\n", + " ffn_input = x\n", + " \n", + " # FFN block\n", + " x = Dense(units * 4, activation='swish')(x)\n", + " x = Dense(ffn_input.shape[-1])(x) # Match the input dimension\n", + " \n", + " # Apply stochastic depth to the FFN\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, ffn_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "def create_uv_index_model(input_shape, folder_name, l2_lambda=0.005):\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Further adjusted hyperparameters\n", + " survival_probs = [0.98, 0.95, 0.92] # Even higher survival probabilities\n", + " attention_survival_probs = [0.95, 0.92, 0.9]\n", + "\n", + " # First LSTM block\n", + " x = create_residual_lstm_layer(\n", + " inputs, 64, dropout_rate=0.2, # Further reduced dropout\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[0],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 128, num_heads=2, # Reduced heads\n", + " survival_probability=attention_survival_probs[0])\n", + "\n", + " # Second LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 32, dropout_rate=0.15,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[1],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 64, num_heads=2,\n", + " survival_probability=attention_survival_probs[1])\n", + "\n", + " # Third LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 16, dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[2],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 32, num_heads=2,\n", + " survival_probability=attention_survival_probs[2])\n", + "\n", + " # Global attention with reduced complexity\n", + " x_input = x\n", + " x = MultiHeadAttention(num_heads=2, key_dim=32)(x, x)\n", + " \n", + " if x.shape[-1] != x_input.shape[-1]:\n", + " x_input = Dense(x.shape[-1])(x_input)\n", + " \n", + " x = tfa.layers.StochasticDepth(survival_probability=0.95)([x, x_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Simplified dense layers\n", + " x = GlobalAveragePooling1D()(x)\n", + " \n", + " # Gradual dimension reduction\n", + " x = Dense(32, activation='swish', kernel_regularizer=regularizers.l2(l2_lambda/2), kernel_constraint=tf.keras.constraints.MaxNorm(3))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.05)(x) # Minimal dropout\n", + "\n", + " x = Dense(16, activation='swish',\n", + " kernel_regularizer=regularizers.l2(l2_lambda/2))(x)\n", + " x = BatchNormalization()(x)\n", + "\n", + " # Modified output layer\n", + " x = Dense(8, activation='swish')(x)\n", + " outputs = Dense(1, activation='sigmoid')(x) # Sigmoid activation\n", + " outputs = Lambda(lambda x: x * 11.0)(outputs) # Scale to [0, 11] range\n", + "\n", + " model = Model(inputs=inputs, outputs=outputs, name=\"UvModel\")\n", + "\n", + " # More stable learning rate schedule\n", + " initial_learning_rate = 0.0001 # Further reduced\n", + " warmup_steps = 1000\n", + " decay_steps = 5000\n", + "\n", + " # Corretto learning rate schedule\n", + " class CustomLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " def __init__(self, initial_lr=0.0001, warmup_steps=1000, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_lr = initial_lr\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " # Convert to float32\n", + " step_f = tf.cast(step, tf.float32)\n", + " warmup_steps_f = tf.cast(self.warmup_steps, tf.float32)\n", + " decay_steps_f = tf.cast(self.decay_steps, tf.float32)\n", + "\n", + " # Warmup phase\n", + " warmup_progress = step_f / warmup_steps_f\n", + " warmup_lr = self.initial_lr * warmup_progress\n", + "\n", + " # Decay phase\n", + " decay_progress = (step_f - warmup_steps_f) / decay_steps_f\n", + " decay_factor = 0.5 * (1.0 + tf.cos(tf.constant(np.pi) * decay_progress))\n", + " decay_lr = self.initial_lr * decay_factor\n", + "\n", + " # Combine phases\n", + " lr = tf.where(step_f < warmup_steps_f, warmup_lr, decay_lr)\n", + " return lr\n", + "\n", + " def get_config(self):\n", + " return {\n", + " \"initial_lr\": self.initial_lr,\n", + " \"warmup_steps\": self.warmup_steps,\n", + " \"decay_steps\": self.decay_steps\n", + " }\n", + "\n", + " # Utilizzo dello schedule corretto\n", + " lr_schedule = CustomLRSchedule(\n", + " initial_lr=0.0001,\n", + " warmup_steps=1000,\n", + " decay_steps=5000\n", + " )\n", + "\n", + " optimizer = AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.0005,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7\n", + " )\n", + "\n", + " # Improved loss function\n", + " def smooth_uv_loss(y_true, y_pred):\n", + " # Basic MSE with smoothing\n", + " mse = tf.square(y_true - y_pred)\n", + " \n", + " # Smooth L1 component for better stability\n", + " abs_diff = tf.abs(y_true - y_pred)\n", + " smooth_l1 = tf.where(abs_diff < 1.0,\n", + " 0.5 * tf.square(abs_diff),\n", + " abs_diff - 0.5)\n", + " \n", + " # Combined loss with dynamic weighting\n", + " combined_loss = 0.7 * mse + 0.3 * smooth_l1\n", + " \n", + " # Gentle weighting for high UV values\n", + " high_uv_weight = tf.where(y_true >= 8.0, 1.2, 1.0)\n", + " \n", + " # Smooth peak hours weight\n", + " time_of_day = tf.cast(tf.math.floormod(tf.range(tf.shape(y_true)[0]), 24),\n", + " tf.float32)\n", + " peak_weight = 1.0 + 0.2 * tf.math.sigmoid((time_of_day - 10.0) * 0.5) * \\\n", + " tf.math.sigmoid((16.0 - time_of_day) * 0.5)\n", + " \n", + " total_weight = high_uv_weight * peak_weight\n", + " \n", + " return tf.reduce_mean(combined_loss * total_weight)\n", + "\n", + " # Improved MAPE metric\n", + " def smooth_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs(y_true - y_pred)\n", + " scale = tf.maximum(tf.abs(y_true) + epsilon, 0.5) # Minimum scale of 0.5\n", + " return tf.reduce_mean(diff / scale) * 100\n", + "\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss=smooth_uv_loss,\n", + " metrics=[\n", + " 'mae',\n", + " 'mse',\n", + " tf.keras.metrics.RootMeanSquaredError(),\n", + " smooth_mape\n", + " ]\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " # Save model architecture visualization\n", + " plot_model(model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True)\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_uv_predictions(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of UV index predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual UV index values\n", + " y_pred : array-like\n", + " Predicted UV index values\n", + " folder_name : str, optional\n", + " Folder to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + " import os\n", + " from datetime import datetime\n", + " import seaborn as sns\n", + " from sklearn.metrics import confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + " # Initialize plot paths\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Data preprocessing\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + "\n", + " # Rounding and clipping predictions\n", + " y_pred_rounded = np.round(y_pred * 2) / 2 # Round to nearest 0.5\n", + " y_pred_clipped = np.clip(y_pred_rounded, 0, 11)\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + " errors_rounded = y_pred_clipped - y_true\n", + "\n", + " # Function to determine UV risk level\n", + " def get_uv_risk_level(values):\n", + " levels = np.full_like(values, 'Low', dtype=object)\n", + " levels[(values > 2) & (values <= 5)] = 'Moderate'\n", + " levels[(values > 5) & (values <= 7)] = 'High'\n", + " levels[(values > 7) & (values <= 10)] = 'Very High'\n", + " levels[values > 10] = 'Extreme'\n", + " return levels\n", + "\n", + " # Calculate basic metrics\n", + " metrics = {\n", + " 'raw': {\n", + " 'mae': mean_absolute_error(y_true, y_pred),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred)),\n", + " 'r2': r2_score(y_true, y_pred),\n", + " 'mean_error': np.mean(errors),\n", + " 'std_error': np.std(errors),\n", + " 'median_error': np.median(errors),\n", + " 'p95_abs_error': np.percentile(np.abs(errors), 95)\n", + " },\n", + " 'rounded': {\n", + " 'mae': mean_absolute_error(y_true, y_pred_clipped),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred_clipped)),\n", + " 'r2': r2_score(y_true, y_pred_clipped)\n", + " }\n", + " }\n", + "\n", + " # Calculate accuracies for different margins\n", + " for data_type, errors_data in [('raw', errors), ('rounded', errors_rounded)]:\n", + " metrics[data_type].update({\n", + " 'within_05': np.mean(np.abs(errors_data) <= 0.5) * 100,\n", + " 'within_1': np.mean(np.abs(errors_data) <= 1.0) * 100,\n", + " 'within_15': np.mean(np.abs(errors_data) <= 1.5) * 100,\n", + " 'within_2': np.mean(np.abs(errors_data) <= 2.0) * 100\n", + " })\n", + "\n", + " # Analysis by UV risk level\n", + " y_true_risk = get_uv_risk_level(y_true)\n", + " y_pred_risk = get_uv_risk_level(y_pred_clipped)\n", + "\n", + " # Calculate confusion matrix with handling for missing classes\n", + " risk_levels = ['Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + " \n", + " # Get unique labels present in the data\n", + " present_labels = np.unique(np.concatenate([y_true_risk, y_pred_risk]))\n", + " \n", + " # Calculate confusion matrix for present labels\n", + " cm = confusion_matrix(y_true_risk, y_pred_risk, labels=present_labels)\n", + " \n", + " # Create full confusion matrix with zeros\n", + " full_cm = np.zeros((len(risk_levels), len(risk_levels)))\n", + " \n", + " # Map present labels to their positions in the full matrix\n", + " label_positions = {label: i for i, label in enumerate(risk_levels)}\n", + " for i, true_label in enumerate(present_labels):\n", + " for j, pred_label in enumerate(present_labels):\n", + " full_cm[label_positions[true_label], label_positions[pred_label]] = cm[i, j]\n", + " \n", + " # Create DataFrame with all risk levels\n", + " cm_df = pd.DataFrame(full_cm, columns=risk_levels, index=risk_levels)\n", + "\n", + " # Analysis by UV range\n", + " uv_ranges = [\n", + " (0, 2, 'Low'),\n", + " (2, 5, 'Moderate'),\n", + " (5, 7, 'High'),\n", + " (7, 10, 'Very High'),\n", + " (10, 11, 'Extreme')\n", + " ]\n", + "\n", + " range_analysis = {}\n", + " for low, high, label in uv_ranges:\n", + " mask = (y_true >= low) & (y_true < high)\n", + " if mask.any():\n", + " range_analysis[label] = {\n", + " 'mae': mean_absolute_error(y_true[mask], y_pred[mask]),\n", + " 'count': np.sum(mask),\n", + " 'accuracy_within_05': np.mean(np.abs(errors[mask]) <= 0.5) * 100,\n", + " 'accuracy_within_1': np.mean(np.abs(errors[mask]) <= 1.0) * 100\n", + " }\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Main figure with 4 subplots\n", + " fig = plt.figure(figsize=(20, 15))\n", + "\n", + " # 1. Error distribution\n", + " plt.subplot(2, 2, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # 2. Actual vs Predicted scatter plot\n", + " plt.subplot(2, 2, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([0, 11], [0, 11], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # 3. Errors vs Actual Values\n", + " plt.subplot(2, 2, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # 4. Accuracy and MAE by range\n", + " ax = plt.subplot(2, 2, 4)\n", + " x_labels = [f\"{label}\\n({low}-{high})\" for low, high, label in uv_ranges]\n", + " accuracies = [range_analysis[label]['accuracy_within_05']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + " mae_values = [range_analysis[label]['mae']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + "\n", + " bars = plt.bar(x_labels, accuracies, alpha=0.6)\n", + " plt.ylabel('Accuracy within ±0.5 (%)')\n", + " plt.title('Accuracy and MAE by UV Range')\n", + "\n", + " # Add MAE as line\n", + " ax2 = ax.twinx()\n", + " ax2.plot(x_labels, mae_values, 'r-o', label='MAE')\n", + " ax2.set_ylabel('MAE', color='red')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save main figure\n", + " main_plot_path = f'{folder_name}_uv_analysis.png'\n", + " plt.savefig(main_plot_path, dpi=300, bbox_inches='tight')\n", + "\n", + " # Confusion matrix as separate plot\n", + " plt.figure(figsize=(10, 8))\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix for UV Risk Levels')\n", + "\n", + " conf_matrix_path = f'{folder_name}_confusion_matrix.png'\n", + " plt.savefig(conf_matrix_path, dpi=300, bbox_inches='tight')\n", + "\n", + " plt.close('all')\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Print detailed report\n", + " print(\"\\nUV Index Prediction Analysis:\")\n", + " print(\"\\nRaw Metrics:\")\n", + " for key, value in metrics['raw'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nRounded Metrics:\")\n", + " for key, value in metrics['rounded'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nAnalysis by UV Range:\")\n", + " for label, stats in range_analysis.items():\n", + " print(f\"\\n{label}:\")\n", + " for key, value in stats.items():\n", + " print(f\" {key}: {value:.3f}\")\n", + "\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm_df)\n", + "\n", + " # Add range analysis and confusion matrix to metrics dictionary\n", + " metrics.update({\n", + " 'range_analysis': range_analysis,\n", + " 'confusion_matrix': cm_df.to_dict(),\n", + " 'plot_paths': {\n", + " 'main_analysis': main_plot_path,\n", + " 'confusion_matrix': conf_matrix_path\n", + " }\n", + " })\n", + "\n", + " return metrics\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save the loss and metrics plots during training\n", + "\n", + " Parameters:\n", + " -----------\n", + " history : tensorflow.keras.callbacks.History\n", + " The history object returned by model training\n", + " folder_name : str\n", + " Folder where to save the plot\n", + " \"\"\"\n", + " import os\n", + "\n", + " try:\n", + " # Create the figure\n", + " plt.figure(figsize=(12, 4))\n", + "\n", + " # Loss Plot\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(history.history['loss'], label='Training Loss')\n", + " plt.plot(history.history['val_loss'], label='Validation Loss')\n", + " plt.title('Model Loss')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # MAE Plot\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(history.history['mae'], label='Training MAE')\n", + " plt.plot(history.history['val_mae'], label='Validation MAE')\n", + " plt.title('Model MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " os.makedirs(folder_name, exist_ok=True)\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'training_history.png')\n", + "\n", + " # Save the figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Also save numerical data in CSV format\n", + " history_df = pd.DataFrame({\n", + " 'epoch': range(1, len(history.history['loss']) + 1),\n", + " 'training_loss': history.history['loss'],\n", + " 'validation_loss': history.history['val_loss'],\n", + " 'training_mae': history.history['mae'],\n", + " 'validation_mae': history.history['val_mae']\n", + " })\n", + "\n", + " if folder_name is not None:\n", + " csv_filename = os.path.join(folder_name, 'training_history.csv')\n", + " history_df.to_csv(csv_filename, index=False)\n", + " print(f\"Training history data saved as: {csv_filename}\")\n", + "\n", + " # Calculate and save final statistics\n", + " final_stats = {\n", + " 'final_training_loss': history.history['loss'][-1],\n", + " 'final_validation_loss': history.history['val_loss'][-1],\n", + " 'final_training_mae': history.history['mae'][-1],\n", + " 'final_validation_mae': history.history['val_mae'][-1],\n", + " 'best_validation_loss': min(history.history['val_loss']),\n", + " 'best_validation_mae': min(history.history['val_mae']),\n", + " 'epochs': len(history.history['loss']),\n", + " }\n", + "\n", + " if folder_name is not None:\n", + " # Save statistics in JSON format\n", + " stats_filename = os.path.join(folder_name, 'training_stats.json')\n", + " with open(stats_filename, 'w') as f:\n", + " json.dump(final_stats, f, indent=4)\n", + " print(f\"Final statistics saved as: {stats_filename}\")\n", + "\n", + " # Print main statistics\n", + " print(\"\\nFinal training statistics:\")\n", + " print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n", + " print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n", + " print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n", + " print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n", + "\n", + " plt.show()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during plot creation or saving: {str(e)}\")\n", + "\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='uv_index'):\n", + " \"\"\"\n", + " Advanced training function for the hybrid UV index model with detailed monitoring\n", + " and training management.\n", + "\n", + " Parameters:\n", + " -----------\n", + " model : keras.Model\n", + " The compiled hybrid model\n", + " X_train : numpy.ndarray\n", + " Training data\n", + " y_train : numpy.ndarray\n", + " Training targets\n", + " X_test : numpy.ndarray\n", + " Validation data\n", + " y_test : numpy.ndarray\n", + " Validation targets\n", + " epochs : int, optional\n", + " Maximum number of training epochs\n", + " batch_size : int, optional\n", + " Batch size\n", + "\n", + " Returns:\n", + " --------\n", + " history : keras.callbacks.History\n", + " Training history with all metrics\n", + " \"\"\"\n", + "\n", + " # Advanced callbacks for training\n", + " callbacks = [\n", + " # Advanced Early Stopping\n", + " EarlyStopping(\n", + " monitor='mae',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-6\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='mae',\n", + " factor=0.05,\n", + " patience=3,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=2,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=1,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_uv_model.h5',\n", + " monitor='mae',\n", + " save_best_only=True,\n", + " mode='min'\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: print(\n", + " f\"\\nEpoch {epoch + 1}: Out of range predictions: \"\n", + " f\"{np.sum((model.predict(X_test) < 0) | (model.predict(X_test) > 11))}\"\n", + " ) if epoch % 20 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train, y_train,\n", + " validation_data=(X_test, y_test),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False,\n", + " validation_freq=1,\n", + " )\n", + "\n", + " # Post-training analysis\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " raise\n", + "\n", + " finally:\n", + " # Memory cleanup\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrate UV index predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : numpy.ndarray\n", + " Array of UV index predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with UV index predictions\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Create temporary DataFrame with predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'uvindex_predicted': predictions.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update uvindex column where missing\n", + " df['uvindex'] = df['uvindex'].fillna(df['uvindex_predicted'])\n", + "\n", + " # Remove temporary column\n", + " df = df.drop('uvindex_predicted', axis=1)\n", + "\n", + " print(f\"Added {len(predictions)} predictions to dataset\")\n", + " print(f\"Rows with UV index after integration: {df['uvindex'].notna().sum()}\")\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing UV index model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Temporal distribution of data:\n", + "Records after 2010: 129,777\n", + "Records before 2010: 227,902\n", + "\n", + "Warning: Found missing values after preprocessing\n", + "Features with missing values: []\n", + "\n", + "Number of features used: 30\n", + "\n", + "Feature categories:\n", + "atmospheric: 6 features\n", + "temporal: 4 features\n", + "solar: 5 features\n", + "interactions: 4 features\n", + "rolling: 2 features\n", + "Categorical: 9 features\n", + "Training data shape: (64865, 24, 30)\n", + "Test data shape: (64866, 24, 30)\n", + "Saving scaler to: 2024-11-20_11-04_scaler.joblib\n", + "Saving features to: 2024-11-20_11-04_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data.parquet')\n", + "\n", + "print(\"Initializing UV index model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, scaler, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "scaler_path = f'{folder_name}_scaler.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(scaler_path):\n", + " print(f\"Loading existing scaler from: {scaler_path}\")\n", + " scaler = joblib.load(scaler_path)\n", + "else:\n", + " print(f\"Saving scaler to: {scaler_path}\")\n", + " joblib.dump(scaler, scaler_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "83771453-71db-4bb2-833d-7b81c022863d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Model initialization...\n", + "Creating new model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-20 11:04:26.029246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:a1:00.0, compute capability: 8.9\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "3. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-20 11:04:44.087739: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n", + "2024-11-20 11:04:44.182119: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8905\n", + "2024-11-20 11:04:44.340866: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x710c9c3f7ae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-20 11:04:44.340894: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-20 11:04:44.347733: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-20 11:04:44.423715: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-20 11:04:44.481054: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "506/507 [============================>.] - ETA: 0s - loss: 22.0229 - mae: 4.3730 - mse: 24.6210 - root_mean_squared_error: 4.9620 - smooth_mape: 683.3177" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2028/2028 [==============================] - 19s 8ms/step\n", + "2028/2028 [==============================] - 17s 8ms/step\n", + "\n", + "Epoch 1: Out of range predictions: 0\n", + "507/507 [==============================] - 286s 522ms/step - loss: 22.0128 - mae: 4.3719 - mse: 24.6071 - root_mean_squared_error: 4.9606 - smooth_mape: 682.9322 - val_loss: 12.9149 - val_mae: 3.0398 - val_mse: 12.5725 - val_root_mean_squared_error: 3.5458 - val_smooth_mape: 420.2022 - lr: 5.0600e-05\n", + "Epoch 2/100\n", + "507/507 [==============================] - 22s 43ms/step - loss: 13.0204 - mae: 3.1727 - mse: 12.7635 - root_mean_squared_error: 3.5726 - smooth_mape: 465.3772 - val_loss: 10.2818 - val_mae: 2.5288 - val_mse: 9.1610 - val_root_mean_squared_error: 3.0267 - val_smooth_mape: 309.3470 - lr: 9.9998e-05\n", + "Epoch 3/100\n", + "507/507 [==============================] - 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mse: 0.7584 - root_mean_squared_error: 0.8708 - smooth_mape: 29.8448 - val_loss: 1.0003 - val_mae: 0.4280 - val_mse: 0.6281 - val_root_mean_squared_error: 0.7926 - val_smooth_mape: 22.9052 - lr: 2.8853e-05\n", + "Epoch 29/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 1.0922 - mae: 0.4875 - mse: 0.7390 - root_mean_squared_error: 0.8597 - smooth_mape: 29.1903 - val_loss: 0.9880 - val_mae: 0.4282 - val_mse: 0.6307 - val_root_mean_squared_error: 0.7942 - val_smooth_mape: 23.7812 - lr: 1.5727e-05\n", + "Epoch 30/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 1.0761 - mae: 0.4838 - mse: 0.7305 - root_mean_squared_error: 0.8547 - smooth_mape: 28.9260 - val_loss: 0.9782 - val_mae: 0.4257 - val_mse: 0.6241 - val_root_mean_squared_error: 0.7900 - val_smooth_mape: 23.4881 - lr: 6.0491e-06\n", + "Epoch 31/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 1.0754 - mae: 0.4845 - mse: 0.7336 - root_mean_squared_error: 0.8565 - smooth_mape: 29.0048 - val_loss: 0.9815 - val_mae: 0.4236 - val_mse: 0.6312 - val_root_mean_squared_error: 0.7945 - val_smooth_mape: 22.2870 - lr: 7.9394e-07\n", + "Epoch 32/100\n", + "507/507 [==============================] - ETA: 0s - loss: 1.0757 - mae: 0.4848 - mse: 0.7354 - root_mean_squared_error: 0.8575 - smooth_mape: 29.0269\n", + "Epoch 32: ReduceLROnPlateau reducing learning rate to 1e-07.\n", + "507/507 [==============================] - 21s 41ms/step - loss: 1.0757 - mae: 0.4848 - mse: 0.7354 - root_mean_squared_error: 0.8575 - smooth_mape: 29.0269 - val_loss: 0.9825 - val_mae: 0.4256 - val_mse: 0.6304 - val_root_mean_squared_error: 0.7940 - val_smooth_mape: 22.1371 - lr: 4.9000e-07\n", + "Epoch 33/100\n", + "507/507 [==============================] - 22s 43ms/step - loss: 1.0689 - mae: 0.4828 - mse: 0.7273 - root_mean_squared_error: 0.8528 - smooth_mape: 28.7582 - val_loss: 0.9748 - val_mae: 0.4243 - val_mse: 0.6236 - val_root_mean_squared_error: 0.7897 - val_smooth_mape: 23.0276 - lr: 5.1679e-06\n", + "Epoch 34/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 1.0745 - mae: 0.4855 - mse: 0.7373 - root_mean_squared_error: 0.8587 - smooth_mape: 28.9500 - val_loss: 0.9708 - val_mae: 0.4240 - val_mse: 0.6260 - val_root_mean_squared_error: 0.7912 - val_smooth_mape: 23.0926 - lr: 1.4357e-05\n", + "Epoch 35/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 1.0620 - mae: 0.4851 - mse: 0.7316 - root_mean_squared_error: 0.8553 - smooth_mape: 28.9883 - val_loss: 0.9650 - val_mae: 0.4304 - val_mse: 0.6269 - val_root_mean_squared_error: 0.7918 - val_smooth_mape: 22.8981 - lr: 2.7133e-05\n", + "Epoch 36/100\n", + "507/507 [==============================] - ETA: 0s - loss: 1.0530 - mae: 0.4845 - mse: 0.7370 - root_mean_squared_error: 0.8585 - smooth_mape: 28.9197\n", + "Epoch 36: ReduceLROnPlateau reducing learning rate to 2.1104648112668657e-06.\n", + "507/507 [==============================] - 21s 41ms/step - loss: 1.0530 - mae: 0.4845 - mse: 0.7370 - root_mean_squared_error: 0.8585 - smooth_mape: 28.9197 - val_loss: 0.9438 - val_mae: 0.4280 - val_mse: 0.6211 - val_root_mean_squared_error: 0.7881 - val_smooth_mape: 23.1570 - lr: 2.1105e-06\n", + "Epoch 37/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 1.0339 - mae: 0.4831 - mse: 0.7352 - root_mean_squared_error: 0.8574 - smooth_mape: 28.5829 - val_loss: 0.9257 - val_mae: 0.4244 - val_mse: 0.6262 - val_root_mean_squared_error: 0.7913 - val_smooth_mape: 22.7749 - lr: 5.8070e-05\n", + "Epoch 38/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 1.0097 - mae: 0.4806 - mse: 0.7331 - root_mean_squared_error: 0.8562 - smooth_mape: 28.3004 - val_loss: 0.9042 - val_mae: 0.4311 - val_mse: 0.6269 - val_root_mean_squared_error: 0.7918 - val_smooth_mape: 25.1432 - lr: 7.3118e-05\n", + "Epoch 39/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.9923 - mae: 0.4829 - mse: 0.7402 - root_mean_squared_error: 0.8604 - smooth_mape: 28.2334 - val_loss: 0.8823 - val_mae: 0.4198 - val_mse: 0.6305 - val_root_mean_squared_error: 0.7940 - val_smooth_mape: 21.3908 - lr: 8.5841e-05\n", + "Epoch 40/100\n", + "507/507 [==============================] - 21s 42ms/step - loss: 0.9551 - mae: 0.4759 - mse: 0.7249 - root_mean_squared_error: 0.8514 - smooth_mape: 27.5520 - val_loss: 0.8642 - val_mae: 0.4233 - val_mse: 0.6429 - val_root_mean_squared_error: 0.8018 - val_smooth_mape: 23.5225 - lr: 9.4957e-05\n", + "Epoch 41/100\n", + "2028/2028 [==============================] - 18s 9ms/step\n", + "2028/2028 [==============================] - 17s 8ms/step\n", + "\n", + "Epoch 41: Out of range predictions: 0\n", + "507/507 [==============================] - 56s 110ms/step - loss: 0.9397 - mae: 0.4787 - mse: 0.7327 - root_mean_squared_error: 0.8560 - smooth_mape: 27.8895 - val_loss: 0.8242 - val_mae: 0.4152 - val_mse: 0.6168 - val_root_mean_squared_error: 0.7854 - val_smooth_mape: 21.9886 - lr: 9.9549e-05\n", + "Epoch 42/100\n", + "507/507 [==============================] - 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mse: 0.6841 - root_mean_squared_error: 0.8271 - smooth_mape: 26.1963 - val_loss: 0.7851 - val_mae: 0.4167 - val_mse: 0.6530 - val_root_mean_squared_error: 0.8081 - val_smooth_mape: 22.6425 - lr: 7.0890e-05\n", + "Epoch 46/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 0.8183 - mae: 0.4563 - mse: 0.6844 - root_mean_squared_error: 0.8273 - smooth_mape: 26.1332 - val_loss: 0.7583 - val_mae: 0.4167 - val_mse: 0.6226 - val_root_mean_squared_error: 0.7890 - val_smooth_mape: 20.5157 - lr: 5.5612e-05\n", + "Epoch 47/100\n", + "507/507 [==============================] - 22s 44ms/step - loss: 0.7980 - mae: 0.4494 - mse: 0.6691 - root_mean_squared_error: 0.8180 - smooth_mape: 25.7179 - val_loss: 0.7370 - val_mae: 0.4110 - val_mse: 0.6051 - val_root_mean_squared_error: 0.7779 - val_smooth_mape: 21.5193 - lr: 3.9768e-05\n", + "Epoch 48/100\n", + "507/507 [==============================] - 22s 44ms/step - loss: 0.7870 - mae: 0.4474 - mse: 0.6626 - root_mean_squared_error: 0.8140 - smooth_mape: 25.5764 - val_loss: 0.7277 - val_mae: 0.4052 - val_mse: 0.6017 - val_root_mean_squared_error: 0.7757 - val_smooth_mape: 21.4695 - lr: 2.4955e-05\n", + "Epoch 49/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.7747 - mae: 0.4432 - mse: 0.6525 - root_mean_squared_error: 0.8078 - smooth_mape: 25.3902 - val_loss: 0.7257 - val_mae: 0.4020 - val_mse: 0.6028 - val_root_mean_squared_error: 0.7764 - val_smooth_mape: 20.7401 - lr: 1.2661e-05\n", + "Epoch 50/100\n", + "507/507 [==============================] - 22s 43ms/step - loss: 0.7725 - mae: 0.4430 - mse: 0.6518 - root_mean_squared_error: 0.8073 - smooth_mape: 25.4604 - val_loss: 0.7225 - val_mae: 0.4077 - val_mse: 0.5955 - val_root_mean_squared_error: 0.7717 - val_smooth_mape: 21.3071 - lr: 4.1248e-06\n", + "Epoch 51/100\n", + "507/507 [==============================] - 21s 40ms/step - loss: 0.7744 - mae: 0.4439 - mse: 0.6543 - root_mean_squared_error: 0.8089 - smooth_mape: 25.3972 - val_loss: 0.7287 - val_mae: 0.4031 - val_mse: 0.6063 - val_root_mean_squared_error: 0.7787 - val_smooth_mape: 19.8541 - lr: 2.0452e-07\n", + "Epoch 52/100\n", + "505/507 [============================>.] - ETA: 0s - loss: 0.7740 - mae: 0.4430 - mse: 0.6545 - root_mean_squared_error: 0.8090 - smooth_mape: 25.3371\n", + "Epoch 52: ReduceLROnPlateau reducing learning rate to 2.589756149973255e-07.\n", + "507/507 [==============================] - 21s 40ms/step - loss: 0.7748 - mae: 0.4432 - mse: 0.6554 - root_mean_squared_error: 0.8096 - smooth_mape: 25.3532 - val_loss: 0.7241 - val_mae: 0.4061 - val_mse: 0.5989 - val_root_mean_squared_error: 0.7739 - val_smooth_mape: 20.7611 - lr: 1.2949e-06\n", + "Epoch 53/100\n", + "507/507 [==============================] - 22s 44ms/step - loss: 0.7670 - mae: 0.4407 - mse: 0.6466 - root_mean_squared_error: 0.8041 - smooth_mape: 25.2363 - val_loss: 0.7210 - val_mae: 0.4034 - val_mse: 0.5974 - val_root_mean_squared_error: 0.7729 - val_smooth_mape: 20.8914 - lr: 7.2861e-06\n", + "Epoch 54/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.7692 - mae: 0.4415 - mse: 0.6502 - root_mean_squared_error: 0.8064 - smooth_mape: 25.2406 - val_loss: 0.7234 - val_mae: 0.4073 - val_mse: 0.6006 - val_root_mean_squared_error: 0.7750 - val_smooth_mape: 20.8373 - lr: 1.7575e-05\n", + "Epoch 55/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 0.7694 - mae: 0.4438 - mse: 0.6530 - root_mean_squared_error: 0.8081 - smooth_mape: 25.2635 - val_loss: 0.7208 - val_mae: 0.4075 - val_mse: 0.6020 - val_root_mean_squared_error: 0.7759 - val_smooth_mape: 20.9485 - lr: 3.1127e-05\n", + "Epoch 56/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.7695 - mae: 0.4437 - mse: 0.6571 - root_mean_squared_error: 0.8106 - smooth_mape: 25.3100\n", + "Epoch 56: ReduceLROnPlateau reducing learning rate to 2.3289143427973617e-06.\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.7699 - mae: 0.4438 - mse: 0.6576 - root_mean_squared_error: 0.8109 - smooth_mape: 25.3081 - val_loss: 0.7268 - val_mae: 0.4039 - val_mse: 0.6174 - val_root_mean_squared_error: 0.7857 - val_smooth_mape: 19.7400 - lr: 2.3289e-06\n", + "Epoch 57/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.7671 - mae: 0.4455 - mse: 0.6604 - root_mean_squared_error: 0.8126 - smooth_mape: 25.3943 - val_loss: 0.7181 - val_mae: 0.4094 - val_mse: 0.6137 - val_root_mean_squared_error: 0.7834 - val_smooth_mape: 21.3784 - lr: 6.2374e-05\n", + "Epoch 58/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.7690 - mae: 0.4469 - mse: 0.6696 - root_mean_squared_error: 0.8183 - smooth_mape: 25.2775 - val_loss: 0.7278 - val_mae: 0.4093 - val_mse: 0.6335 - val_root_mean_squared_error: 0.7959 - val_smooth_mape: 19.5767 - lr: 7.6924e-05\n", + "Epoch 59/100\n", + "507/507 [==============================] - 22s 43ms/step - loss: 0.7632 - mae: 0.4472 - mse: 0.6706 - root_mean_squared_error: 0.8189 - smooth_mape: 25.2525 - val_loss: 0.7001 - val_mae: 0.4038 - val_mse: 0.6087 - val_root_mean_squared_error: 0.7802 - val_smooth_mape: 21.2936 - lr: 8.8765e-05\n", + "Epoch 60/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.7547 - mae: 0.4472 - mse: 0.6690 - root_mean_squared_error: 0.8179 - smooth_mape: 25.2686\n", + "Epoch 60: ReduceLROnPlateau reducing learning rate to 4.835262006963604e-06.\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.7553 - mae: 0.4474 - mse: 0.6696 - root_mean_squared_error: 0.8183 - smooth_mape: 25.2703 - val_loss: 0.7642 - val_mae: 0.4280 - val_mse: 0.7001 - val_root_mean_squared_error: 0.8367 - val_smooth_mape: 22.1543 - lr: 4.8353e-06\n", + "Epoch 61/100\n", + "2028/2028 [==============================] - 18s 9ms/step\n", + "2028/2028 [==============================] - 16s 8ms/step\n", + "\n", + "Epoch 61: Out of range predictions: 0\n", + "507/507 [==============================] - 59s 116ms/step - loss: 0.7448 - mae: 0.4457 - mse: 0.6658 - root_mean_squared_error: 0.8160 - smooth_mape: 25.0610 - val_loss: 0.6932 - val_mae: 0.4059 - val_mse: 0.6177 - val_root_mean_squared_error: 0.7859 - val_smooth_mape: 20.4987 - lr: 9.9946e-05\n", + "Epoch 62/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.7348 - mae: 0.4439 - mse: 0.6628 - root_mean_squared_error: 0.8141 - smooth_mape: 25.0138 - val_loss: 0.6917 - val_mae: 0.4076 - val_mse: 0.6258 - val_root_mean_squared_error: 0.7911 - val_smooth_mape: 21.0874 - lr: 9.8161e-05\n", + "Epoch 63/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.7260 - mae: 0.4438 - mse: 0.6602 - root_mean_squared_error: 0.8125 - smooth_mape: 25.0922 - val_loss: 0.6881 - val_mae: 0.4067 - val_mse: 0.6289 - val_root_mean_squared_error: 0.7930 - val_smooth_mape: 20.0478 - lr: 9.1530e-05\n", + "Epoch 64/100\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.7076 - mae: 0.4367 - mse: 0.6451 - root_mean_squared_error: 0.8032 - smooth_mape: 24.4019 - val_loss: 0.6638 - val_mae: 0.4074 - val_mse: 0.5961 - val_root_mean_squared_error: 0.7721 - val_smooth_mape: 20.4913 - lr: 8.0720e-05\n", + "Epoch 65/100\n", + "507/507 [==============================] - 18s 36ms/step - loss: 0.7041 - mae: 0.4375 - mse: 0.6469 - root_mean_squared_error: 0.8043 - smooth_mape: 24.5839 - val_loss: 0.6609 - val_mae: 0.4007 - val_mse: 0.6056 - val_root_mean_squared_error: 0.7782 - val_smooth_mape: 19.8633 - lr: 6.6819e-05\n", + "Epoch 66/100\n", + "507/507 [==============================] - 19s 37ms/step - loss: 0.6895 - mae: 0.4313 - mse: 0.6349 - root_mean_squared_error: 0.7968 - smooth_mape: 24.1261 - val_loss: 0.6586 - val_mae: 0.4009 - val_mse: 0.6092 - val_root_mean_squared_error: 0.7805 - val_smooth_mape: 21.0485 - lr: 5.1225e-05\n", + "Epoch 67/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.6838 - mae: 0.4309 - mse: 0.6313 - root_mean_squared_error: 0.7945 - smooth_mape: 24.2230 - val_loss: 0.6501 - val_mae: 0.3975 - val_mse: 0.6008 - val_root_mean_squared_error: 0.7751 - val_smooth_mape: 20.1558 - lr: 3.5508e-05\n", + "Epoch 68/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.6738 - mae: 0.4276 - mse: 0.6227 - root_mean_squared_error: 0.7891 - smooth_mape: 24.0020 - val_loss: 0.6444 - val_mae: 0.3967 - val_mse: 0.5944 - val_root_mean_squared_error: 0.7710 - val_smooth_mape: 20.4472 - lr: 2.1250e-05\n", + "Epoch 69/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.6710 - mae: 0.4261 - mse: 0.6204 - root_mean_squared_error: 0.7876 - smooth_mape: 23.7990 - val_loss: 0.6404 - val_mae: 0.3966 - val_mse: 0.5894 - val_root_mean_squared_error: 0.7677 - val_smooth_mape: 20.3464 - lr: 9.8841e-06\n", + "Epoch 70/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.6678 - mae: 0.4251 - mse: 0.6176 - root_mean_squared_error: 0.7859 - smooth_mape: 23.8202 - val_loss: 0.6445 - val_mae: 0.3992 - val_mse: 0.5921 - val_root_mean_squared_error: 0.7695 - val_smooth_mape: 19.6060 - lr: 2.5551e-06\n", + "Epoch 71/100\n", + "475/507 [===========================>..] - ETA: 0s - loss: 0.6680 - mae: 0.4257 - mse: 0.6182 - root_mean_squared_error: 0.7863 - smooth_mape: 23.9228" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "507/507 [==============================] - 20s 40ms/step - loss: 0.6776 - mae: 0.4345 - mse: 0.6462 - root_mean_squared_error: 0.8039 - smooth_mape: 24.1705 - val_loss: 0.6316 - val_mae: 0.4047 - val_mse: 0.5959 - val_root_mean_squared_error: 0.7720 - val_smooth_mape: 21.0905 - lr: 9.8093e-05\n", + "Epoch 81/100\n", + "2028/2028 [==============================] - 16s 8ms/step\n", + "2028/2028 [==============================] - 18s 9ms/step\n", + "\n", + "Epoch 81: Out of range predictions: 0\n", + "507/507 [==============================] - 58s 114ms/step - loss: 0.6617 - mae: 0.4295 - mse: 0.6308 - root_mean_squared_error: 0.7942 - smooth_mape: 23.8781 - val_loss: 0.6358 - val_mae: 0.4045 - val_mse: 0.6039 - val_root_mean_squared_error: 0.7771 - val_smooth_mape: 19.5664 - lr: 9.9957e-05\n", + "Epoch 82/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.6609 - mae: 0.4304 - mse: 0.6342 - root_mean_squared_error: 0.7964 - smooth_mape: 23.8601 - val_loss: 0.6249 - val_mae: 0.3967 - val_mse: 0.5981 - val_root_mean_squared_error: 0.7734 - val_smooth_mape: 19.5850 - lr: 9.6794e-05\n", + "Epoch 83/100\n", + "505/507 [============================>.] - ETA: 0s - loss: 0.6528 - mae: 0.4287 - mse: 0.6286 - root_mean_squared_error: 0.7928 - smooth_mape: 23.8442\n", + "Epoch 83: ReduceLROnPlateau reducing learning rate to 4.446155799087137e-06.\n", + "507/507 [==============================] - 19s 37ms/step - loss: 0.6541 - mae: 0.4291 - mse: 0.6300 - root_mean_squared_error: 0.7938 - smooth_mape: 23.8694 - val_loss: 0.6230 - val_mae: 0.4001 - val_mse: 0.6031 - val_root_mean_squared_error: 0.7766 - val_smooth_mape: 21.0960 - lr: 4.4462e-06\n", + "Epoch 84/100\n", + "507/507 [==============================] - 18s 36ms/step - loss: 0.6433 - mae: 0.4248 - mse: 0.6207 - root_mean_squared_error: 0.7879 - smooth_mape: 23.4925 - val_loss: 0.6229 - val_mae: 0.3979 - val_mse: 0.6052 - val_root_mean_squared_error: 0.7779 - val_smooth_mape: 19.7235 - lr: 7.7135e-05\n", + "Epoch 85/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.6355 - mae: 0.4223 - mse: 0.6147 - root_mean_squared_error: 0.7840 - smooth_mape: 23.3794 - val_loss: 0.6273 - val_mae: 0.4044 - val_mse: 0.6084 - val_root_mean_squared_error: 0.7800 - val_smooth_mape: 18.9805 - lr: 6.2617e-05\n", + "Epoch 86/100\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.6296 - mae: 0.4214 - mse: 0.6102 - root_mean_squared_error: 0.7812 - smooth_mape: 23.6014 - val_loss: 0.6144 - val_mae: 0.3965 - val_mse: 0.5984 - val_root_mean_squared_error: 0.7736 - val_smooth_mape: 19.4583 - lr: 4.6829e-05\n", + "Epoch 87/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.6258 - mae: 0.4187 - mse: 0.6076 - root_mean_squared_error: 0.7795 - smooth_mape: 23.2483 - val_loss: 0.6128 - val_mae: 0.3962 - val_mse: 0.5974 - val_root_mean_squared_error: 0.7729 - val_smooth_mape: 19.1720 - lr: 3.1360e-05\n", + "Epoch 88/100\n", + "507/507 [==============================] - 21s 41ms/step - loss: 0.6178 - mae: 0.4161 - mse: 0.5992 - root_mean_squared_error: 0.7741 - smooth_mape: 23.0963 - val_loss: 0.6083 - val_mae: 0.3958 - val_mse: 0.5927 - val_root_mean_squared_error: 0.7699 - val_smooth_mape: 19.7328 - lr: 1.7767e-05\n", + "Epoch 89/100\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.6124 - mae: 0.4135 - mse: 0.5938 - root_mean_squared_error: 0.7706 - smooth_mape: 22.9061 - val_loss: 0.6062 - val_mae: 0.3971 - val_mse: 0.5881 - val_root_mean_squared_error: 0.7669 - val_smooth_mape: 19.8249 - lr: 7.4173e-06\n", + "Epoch 90/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.6135 - mae: 0.4146 - mse: 0.5954 - root_mean_squared_error: 0.7716 - smooth_mape: 22.9713 - val_loss: 0.6091 - val_mae: 0.3964 - val_mse: 0.5932 - val_root_mean_squared_error: 0.7702 - val_smooth_mape: 19.3731 - lr: 1.3523e-06\n", + "Epoch 91/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.6101 - mae: 0.4127 - mse: 0.5916 - root_mean_squared_error: 0.7692 - smooth_mape: 22.9792\n", + "Epoch 91: ReduceLROnPlateau reducing learning rate to 1e-07.\n", + "507/507 [==============================] - 22s 43ms/step - loss: 0.6105 - mae: 0.4128 - mse: 0.5920 - root_mean_squared_error: 0.7694 - smooth_mape: 22.9794 - val_loss: 0.6111 - val_mae: 0.3956 - val_mse: 0.5954 - val_root_mean_squared_error: 0.7716 - val_smooth_mape: 18.9321 - lr: 1.8244e-07\n", + "Epoch 92/100\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.6098 - mae: 0.4129 - mse: 0.5912 - root_mean_squared_error: 0.7689 - smooth_mape: 22.9186 - val_loss: 0.6105 - val_mae: 0.3959 - val_mse: 0.5948 - val_root_mean_squared_error: 0.7712 - val_smooth_mape: 19.1159 - lr: 4.0254e-06\n", + "Epoch 93/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.6094 - mae: 0.4129 - mse: 0.5912 - root_mean_squared_error: 0.7689 - smooth_mape: 22.9718 - val_loss: 0.6056 - val_mae: 0.3958 - val_mse: 0.5888 - val_root_mean_squared_error: 0.7673 - val_smooth_mape: 19.7076 - lr: 1.2494e-05\n", + "Epoch 94/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.6182 - mae: 0.4170 - mse: 0.6016 - root_mean_squared_error: 0.7756 - smooth_mape: 23.1906\n", + "Epoch 94: ReduceLROnPlateau reducing learning rate to 1.2368702300591396e-06.\n", + "507/507 [==============================] - 23s 45ms/step - loss: 0.6187 - mae: 0.4171 - mse: 0.6021 - root_mean_squared_error: 0.7759 - smooth_mape: 23.1873 - val_loss: 0.6096 - val_mae: 0.3954 - val_mse: 0.5967 - val_root_mean_squared_error: 0.7725 - val_smooth_mape: 19.5515 - lr: 1.2369e-06\n", + "Epoch 95/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.6135 - mae: 0.4156 - mse: 0.5973 - root_mean_squared_error: 0.7729 - smooth_mape: 23.1793\n", + "Epoch 95: ReduceLROnPlateau reducing learning rate to 7.904486119514332e-06.\n", + "507/507 [==============================] - 21s 42ms/step - loss: 0.6139 - mae: 0.4157 - mse: 0.5977 - root_mean_squared_error: 0.7731 - smooth_mape: 23.1749 - val_loss: 0.6147 - val_mae: 0.4003 - val_mse: 0.6011 - val_root_mean_squared_error: 0.7753 - val_smooth_mape: 19.1018 - lr: 3.9522e-05\n", + "Epoch 96/100\n", + "507/507 [==============================] - 20s 40ms/step - loss: 0.6212 - mae: 0.4190 - mse: 0.6071 - root_mean_squared_error: 0.7792 - smooth_mape: 23.1593 - val_loss: 0.6140 - val_mae: 0.4029 - val_mse: 0.6003 - val_root_mean_squared_error: 0.7748 - val_smooth_mape: 19.5131 - lr: 5.5362e-05\n", + "Epoch 97/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.6205 - mae: 0.4187 - mse: 0.6076 - root_mean_squared_error: 0.7795 - smooth_mape: 23.1693\n", + "Epoch 97: ReduceLROnPlateau reducing learning rate to 1.4132310752756894e-05.\n", + "507/507 [==============================] - 22s 43ms/step - loss: 0.6205 - mae: 0.4187 - mse: 0.6076 - root_mean_squared_error: 0.7795 - smooth_mape: 23.1693 - val_loss: 0.6120 - val_mae: 0.3987 - val_mse: 0.6040 - val_root_mean_squared_error: 0.7772 - val_smooth_mape: 19.6054 - lr: 7.0662e-05\n", + "Epoch 98/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.6171 - mae: 0.4189 - mse: 0.6058 - root_mean_squared_error: 0.7783 - smooth_mape: 23.1350\n", + "Epoch 98: ReduceLROnPlateau reducing learning rate to 4.194115172140301e-06.\n", + "507/507 [==============================] - 19s 38ms/step - loss: 0.6171 - mae: 0.4189 - mse: 0.6058 - root_mean_squared_error: 0.7783 - smooth_mape: 23.1350 - val_loss: 0.6074 - val_mae: 0.3966 - val_mse: 0.6001 - val_root_mean_squared_error: 0.7747 - val_smooth_mape: 19.3917 - lr: 4.1941e-06\n", + "Epoch 99/100\n", + "507/507 [==============================] - 20s 39ms/step - loss: 0.6209 - mae: 0.4207 - mse: 0.6119 - root_mean_squared_error: 0.7822 - smooth_mape: 23.1457 - val_loss: 0.5983 - val_mae: 0.3973 - val_mse: 0.5892 - val_root_mean_squared_error: 0.7676 - val_smooth_mape: 20.8306 - lr: 9.3694e-05\n", + "Epoch 100/100\n", + "507/507 [==============================] - 23s 46ms/step - loss: 0.6275 - mae: 0.4247 - mse: 0.6214 - root_mean_squared_error: 0.7883 - smooth_mape: 23.6287 - val_loss: 0.6101 - val_mae: 0.4087 - val_mse: 0.5974 - val_root_mean_squared_error: 0.7729 - val_smooth_mape: 20.1332 - lr: 9.9108e-05\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "# Model creation or loading\n", + "print(\"\\n2. Model initialization...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "if os.path.exists(model_path):\n", + " print(f\"Loading existing model from: {model_path}\")\n", + " model = tf.keras.models.load_model(model_path)\n", + " \n", + " # Load existing history if available\n", + " if os.path.exists(history_path):\n", + " print(f\"Loading existing training history from: {history_path}\")\n", + " with open(history_path, 'r') as f:\n", + " history_dict = json.load(f)\n", + " history = type('History', (), {'history': history_dict})()\n", + " else:\n", + " history = type('History', (), {'history': {}})()\n", + "else:\n", + " print(\"Creating new model...\")\n", + " model = create_uv_index_model(input_shape, folder_name)\n", + " \n", + " print(\"\\n3. Starting training...\")\n", + " history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=128,\n", + " folder_name=folder_name\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f0059ecf-7f4f-496f-bed8-85e2d990ff71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "4. Generating predictions...\n", + "2028/2028 [==============================] - 19s 9ms/step\n", + "\n", + "5. Model evaluation...\n", + "\n", + "Error saving plots: Unknown format code 'd' for object of type 'float'\n", + "\n", + "UV Index Prediction Analysis:\n", + "\n", + "Raw Metrics:\n", + "mae: 0.409\n", + "rmse: 0.773\n", + "r2: 0.918\n", + "mean_error: -0.058\n", + "std_error: 0.771\n", + "median_error: 0.012\n", + "p95_abs_error: 1.731\n", + "within_05: 71.085\n", + "within_1: 86.023\n", + "within_15: 93.081\n", + "within_2: 96.593\n", + "\n", + "Rounded Metrics:\n", + "mae: 0.395\n", + "rmse: 0.780\n", + "r2: 0.917\n", + "within_05: 78.912\n", + "within_1: 90.167\n", + "within_15: 95.121\n", + "within_2: 97.533\n", + "\n", + "Analysis by UV Range:\n", + "\n", + "Low:\n", + " mae: 0.141\n", + " count: 41408.000\n", + " accuracy_within_05: 90.410\n", + " accuracy_within_1: 97.153\n", + "\n", + "Moderate:\n", + " mae: 0.855\n", + " count: 11464.000\n", + " accuracy_within_05: 40.448\n", + " accuracy_within_1: 67.856\n", + "\n", + "High:\n", + " mae: 0.847\n", + " count: 5534.000\n", + " accuracy_within_05: 36.881\n", + " accuracy_within_1: 68.395\n", + "\n", + "Very High:\n", + " mae: 0.927\n", + " count: 6225.000\n", + " accuracy_within_05: 32.048\n", + " accuracy_within_1: 63.711\n", + "\n", + "Extreme:\n", + " mae: 1.653\n", + " count: 235.000\n", + " accuracy_within_05: 0.000\n", + " accuracy_within_1: 17.447\n", + "\n", + "Confusion Matrix:\n", + " Low Moderate High Very High Extreme\n", + "Low 43039.0 2265.0 129.0 10.0 0.0\n", + "Moderate 1282.0 7688.0 1215.0 123.0 0.0\n", + "High 10.0 1133.0 3291.0 439.0 0.0\n", + "Very High 0.0 92.0 1131.0 3019.0 0.0\n", + "Extreme 0.0 0.0 0.0 0.0 0.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n4. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "predictions = np.clip(predictions, 0, 11)\n", + "\n", + "print(\"\\n5. Model evaluation...\")\n", + "metrics = evaluate_uv_predictions(y_test, predictions, folder_name=folder_name)\n", + "\n", + "# Save training results only if new training was performed\n", + "if not os.path.exists(model_path):\n", + " training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 128,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_loss']) + 1,\n", + " },\n", + " 'performance_metrics': {\n", + " 'final_loss': float(history.history['val_loss'][-1]),\n", + " 'final_mae': float(history.history['val_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n", + " }\n", + " }\n", + "\n", + " # Save training history\n", + " with open(history_path, 'w') as f:\n", + " history_dict = {key: [float(val) for val in values] \n", + " for key, values in history.history.items()}\n", + " json.dump(history_dict, f, indent=4)\n", + "else:\n", + " # Load existing training results if available\n", + " results_path = f'{folder_name}_training_results.json'\n", + " if os.path.exists(results_path):\n", + " with open(results_path, 'r') as f:\n", + " training_results = json.load(f)\n", + " else:\n", + " training_results = {}\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "4365d2bf-daf8-49e1-be13-cce222bbb5bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "6. Predicting missing data...\n", + "7122/7122 [==============================] - 62s 9ms/step\n", + "\n", + "7. Integrating predictions into dataset...\n", + "Added 227879 predictions to dataset\n", + "Rows with UV index after integration: 357615\n", + "Updated dataset saved to: 2024-11-20_11-04_weather_data_uvindex.parquet\n", + "\n", + "All files saved with prefix: 2024-11-20_11-04\n" + ] + } + ], + "source": [ + "print(\"\\n6. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "to_predict_predictions = np.clip(to_predict_predictions, 0, 11)\n", + "\n", + "print(\"\\n7. Integrating predictions into dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), to_predict_predictions)\n", + "\n", + "output_path = f'{folder_name}_weather_data_uvindex.parquet'\n", + "df_updated.to_parquet(output_path)\n", + "print(f\"Updated dataset saved to: {output_path}\")\n", + "\n", + "# Add prediction statistics\n", + "prediction_stats = {\n", + " 'n_predictions_added': len(to_predict_predictions),\n", + " 'mean_predicted_uv': float(to_predict_predictions.mean()),\n", + " 'min_predicted_uv': float(to_predict_predictions.min()),\n", + " 'max_predicted_uv': float(to_predict_predictions.max()),\n", + "}\n", + "\n", + "def convert_to_serializable(obj):\n", + " \"\"\"Convert numpy types to Python standard types for JSON serialization\"\"\"\n", + " if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n", + " np.int16, np.int32, np.int64, np.uint8,\n", + " np.uint16, np.uint32, np.uint64)):\n", + " return int(obj)\n", + " elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):\n", + " return float(obj)\n", + " elif isinstance(obj, (np.ndarray,)):\n", + " return obj.tolist()\n", + " elif isinstance(obj, dict):\n", + " return {key: convert_to_serializable(value) for key, value in obj.items()}\n", + " elif isinstance(obj, list):\n", + " return [convert_to_serializable(item) for item in obj]\n", + " return obj\n", + "\n", + "if not os.path.exists(model_path):\n", + " training_results['prediction_stats'] = prediction_stats\n", + "\n", + " training_results = convert_to_serializable(training_results)\n", + " # Save final results\n", + " results_path = f'{folder_name}_training_results.json'\n", + " with open(results_path, 'w') as f:\n", + " json.dump(training_results, f, indent=4)\n", + "\n", + "print(f\"\\nAll files saved with prefix: {folder_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-20_11-04/error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error statistics:\n", + "MAE: 0.4087\n", + "MSE: 0.5974\n", + "RMSE: 0.7729\n", + "Mean errors: -0.0576\n", + "Std errors: 0.7707\n", + "Predictions within ±0.5: 71.1%\n", + "Predictions within ±1.0: 86.0%\n", + "Predictions within ±1.5: 93.1%\n", + "Predictions within ±2.0: 96.6%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " y_pred : array-like\n", + " Predicted values\n", + " folder_name : str, optional\n", + " Folder to save plots. If None, plots are not saved.\n", + " \"\"\"\n", + " import os\n", + " from datetime import datetime\n", + "\n", + " # Convert to 1D numpy array if necessary\n", + " if isinstance(y_true, pd.Series):\n", + " y_true = y_true.values\n", + " if isinstance(y_pred, pd.Series):\n", + " y_pred = y_pred.values\n", + "\n", + " y_true = y_true.ravel()\n", + " y_pred = y_pred.ravel()\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + "\n", + " # Create main figure\n", + " fig = plt.figure(figsize=(15, 5))\n", + "\n", + " # Plot 1: Error Distribution\n", + " plt.subplot(1, 3, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: Actual vs Predicted\n", + " plt.subplot(1, 3, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 3: Errors vs Actual Values\n", + " plt.subplot(1, 3, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if folder is specified\n", + " if folder_name is not None:\n", + " try:\n", + " # Create folder if it doesn't exist\n", + " os.makedirs(folder_name, exist_ok=True)\n", + "\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'error_analysis.png')\n", + "\n", + " # Save figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print error statistics\n", + " print(\"\\nError statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n", + " print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n", + " print(f\"Mean errors: {np.mean(errors):.4f}\")\n", + " print(f\"Std errors: {np.std(errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c57d6b2-00a6-4d31-935e-449a29dafd79", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/models/uv_index/2024-11-21_08-23_feature_scaler.joblib b/models/uv_index/2024-11-21_08-23_feature_scaler.joblib new file mode 100644 index 0000000..c02a9a7 Binary files /dev/null and b/models/uv_index/2024-11-21_08-23_feature_scaler.joblib differ diff --git a/models/uv_index/2024-11-21_08-23_features.json b/models/uv_index/2024-11-21_08-23_features.json new file mode 100644 index 0000000..445076c --- /dev/null +++ b/models/uv_index/2024-11-21_08-23_features.json @@ -0,0 +1 @@ +["temp", "humidity", "cloudcover", "visibility", "clear_sky_index", "atmospheric_transparency", "hour_sin", "hour_cos", "day_of_year_sin", "day_of_year_cos", "solar_angle", 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"start_time": "2024-11-20T00:54:13.878615Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", + "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", + "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", + "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2732 kB]\n", + "Fetched 2989 kB in 1s (2026 kB/s) \n", + "Reading package lists... Done\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n", + "0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n", + "Requirement already satisfied: tensorflow==2.13.0 in /usr/local/lib/python3.11/dist-packages (2.13.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.0.0)\n", + "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.1.21 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (23.5.26)\n", + "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.4.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.2.0)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.58.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (3.9.0)\n", + "Requirement already satisfied: keras<2.14,>=2.13.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.1)\n", + "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (16.0.6)\n", + "Requirement already satisfied: numpy<=1.24.3,>=1.22 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.24.3)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (3.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (23.1)\n", + "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (4.24.3)\n", + "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (68.2.2)\n", + "Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow==2.13.0) (1.16.0)\n", + "Requirement already satisfied: tensorboard<2.14,>=2.13 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.0)\n", + "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.13.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (2.3.0)\n", + "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (4.5.0)\n", + "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (1.14.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow==2.13.0) (0.37.1)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow==2.13.0) (0.41.2)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.23.1)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (1.0.0)\n", + "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.4.4)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.31.0)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.7.1)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.3.7)\n", + "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (5.3.1)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.3.0)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (4.9)\n", + "Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.0.5)\n", + "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (1.3.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.2.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.4)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2023.7.22)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (2.1.3)\n", + "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (0.5.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.0) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.24.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n", + "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.24.3)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: keras==2.13.1 in /usr/local/lib/python3.11/dist-packages (2.13.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.24.3)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.24.3)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.0.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n", + "Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.24.3)\n", + "Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n", + "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n", + "Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.24.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.24.3)\n", + "Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n", + "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n", + "Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "from opt_einsum.paths import branch_1\n", + "!apt-get update\n", + "!apt-get install graphviz -y\n", + "\n", + "!pip install tensorflow==2.13.0\n", + "!pip install numpy\n", + "!pip install pandas\n", + "!pip install keras==2.13.1\n", + "!pip install scikit-learn\n", + "!pip install matplotlib\n", + "!pip install joblib\n", + "!pip install pyarrow\n", + "!pip install fastparquet\n", + "!pip install scipy\n", + "!pip install seaborn\n", + "!pip install tqdm\n", + "!pip install pydot\n", + "!pip install tensorflow-io\n", + "!pip install tensorflow-addons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7a813e3cbca057b7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-20T00:55:22.782689Z", + "start_time": "2024-11-20T00:55:22.089165Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:23:10.586264: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", + "\n", + "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", + "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", + "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", + "\n", + "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", + "\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, Conv1D, GlobalAveragePooling1D\n", + "from tensorflow.keras import regularizers\n", + "from tensorflow.keras.models import Model\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import RobustScaler\n", + "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n", + "from tensorflow.keras.optimizers import AdamW\n", + "import json\n", + "from datetime import datetime\n", + "import matplotlib.pyplot as plt\n", + "from tensorflow.keras.utils import plot_model\n", + "import tensorflow_addons as tfa\n", + "import os\n", + "import joblib\n", + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", + "random_state_value = None" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b3f525e19f78a1da", + "metadata": {}, + "outputs": [], + "source": [ + "def get_season(date):\n", + " month = date.month\n", + " day = date.day\n", + " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", + " return 'Winter'\n", + " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", + " return 'Spring'\n", + " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", + " return 'Summer'\n", + " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", + " return 'Autumn'\n", + " else:\n", + " return 'Unknown'\n", + "\n", + "\n", + "def get_time_period(hour):\n", + " if 5 <= hour < 12:\n", + " return 'Morning'\n", + " elif 12 <= hour < 17:\n", + " return 'Afternoon'\n", + " elif 17 <= hour < 21:\n", + " return 'Evening'\n", + " else:\n", + " return 'Night'\n", + "\n", + "\n", + "def add_time_features(df):\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", + " df['year'] = df['datetime'].dt.year\n", + " df['month'] = df['datetime'].dt.month\n", + " df['day'] = df['datetime'].dt.day\n", + " df['hour'] = df['datetime'].dt.hour\n", + " df['minute'] = df['datetime'].dt.minute\n", + " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", + " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", + " df['day_of_week'] = df['datetime'].dt.dayofweek\n", + " df['day_of_year'] = df['datetime'].dt.dayofyear\n", + " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", + " df['quarter'] = df['datetime'].dt.quarter\n", + " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", + " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", + " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", + " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", + " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", + " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", + " df['season'] = df['datetime'].apply(get_season)\n", + " df['time_period'] = df['hour'].apply(get_time_period)\n", + " return df\n", + "\n", + "\n", + "def add_solar_features(df):\n", + " # Calculate solar angle\n", + " df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", + "\n", + " # Interactions between relevant features\n", + " df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n", + " df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n", + "\n", + " # Derived features\n", + " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", + " df['temp_gradient'] = df['temp'] - df['tempmin']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_solar_specific_features(df):\n", + " # Solar angle and day length calculations\n", + " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", + " df['solar_noon'] = 12 - df['hour']\n", + " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", + "\n", + " # Feature interactions\n", + " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", + " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", + "\n", + " # Extended window rolling features\n", + " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", + " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_uv_specific_features(df):\n", + " # Solar zenith angle calculation\n", + " lat = 41.9 # assuming constant latitude for the dataset - Rome's latitude\n", + " df['solar_zenith'] = 90 - np.degrees(\n", + " np.arcsin(\n", + " np.sin(np.radians(lat)) * np.sin(df['solar_elevation']) +\n", + " np.cos(np.radians(lat)) * np.cos(df['solar_elevation']) * np.cos(df['hour'] * 15)\n", + " )\n", + " )\n", + "\n", + " # UV peak hours indicator (10:00-16:00)\n", + " df['is_uv_peak_hours'] = ((df['hour'] >= 10) & (df['hour'] <= 16)).astype(int)\n", + "\n", + " # Atmospheric attenuation factor\n", + " df['atmospheric_attenuation'] = (100 - df['cloudcover']) * (df['visibility'] / 100) * (1 - df['humidity'] / 200)\n", + "\n", + " # Seasonal UV factor\n", + " df['uv_seasonal_factor'] = np.where(df['season_Summer'], 1.0,\n", + " np.where(df['season_Spring'], 0.7,\n", + " np.where(df['season_Autumn'], 0.5, 0.3)))\n", + "\n", + " # Solar elevation and atmospheric transparency interaction\n", + " df['solar_clarity_index'] = df['solar_elevation'] * df['atmospheric_attenuation'] / 100\n", + "\n", + " # UV-specific rolling features\n", + " df['clarity_rolling_3h'] = df['atmospheric_attenuation'].rolling(window=3).mean()\n", + " df['temp_uv_interaction'] = df['temp'] * df['solar_clarity_index']\n", + "\n", + " return df\n", + "\n", + "\n", + "def add_advanced_features(df):\n", + " \"\"\"\n", + " Add all advanced features in the correct order\n", + " \"\"\"\n", + " # 1. First add basic time features\n", + " df = add_time_features(df)\n", + "\n", + " # 2. One-hot encoding for categorical features\n", + " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", + "\n", + " # 3. Add solar and specific features\n", + " df = add_solar_features(df)\n", + " df = add_solar_specific_features(df)\n", + "\n", + " # 4. Ensure datetime index\n", + " if not isinstance(df.index, pd.DatetimeIndex):\n", + " df.index = pd.to_datetime(df.index)\n", + "\n", + " # 5. Add weather variable interactions\n", + " df['temp_humidity'] = df['temp'] * df['humidity']\n", + " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", + " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", + "\n", + " # 6. Add solar radiation derived features\n", + " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", + " df['day_length'] = np.sin(df['day_of_year_sin']) * 12 + 12\n", + "\n", + " # 7. Add lag features\n", + " df['temp_1h_lag'] = df['temp'].shift(1)\n", + " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", + " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", + "\n", + " # 8. Add rolling means\n", + " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", + " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", + " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", + "\n", + " # 9. Add atmospheric stability\n", + " df['atmospheric_stability'] = df.groupby(df.index.date)['pressure'].transform(\n", + " lambda x: x.std()\n", + " ).fillna(0)\n", + "\n", + " # 10. Add extreme conditions indicator\n", + " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", + " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", + "\n", + " # 11. Add atmospheric transparency\n", + " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", + "\n", + " # 12. Add transitional seasons indicator\n", + " df['is_transition_season'] = ((df['season_Spring'] | df['season_Autumn'])).astype(int)\n", + "\n", + " # 13. Add solar cloud effect\n", + " if 'solar_elevation' in df.columns:\n", + " df['solar_cloud_effect'] = df['solar_elevation'] * (100 - df['cloudcover']) / 100\n", + "\n", + " # 14. Finally add UV specific features\n", + " df = add_uv_specific_features(df)\n", + "\n", + " return df\n", + "\n", + "\n", + "def prepare_advanced_data(df):\n", + " \"\"\"\n", + " Prepares data for UV index prediction model with advanced feature engineering\n", + " and optimized preprocessing.\n", + "\n", + " Args:\n", + " df: DataFrame with meteorological data\n", + "\n", + " Returns:\n", + " tuple: (X_train_scaled, X_test_scaled, y_train, y_test, scaler, final_features, X_to_predict_scaled)\n", + " \"\"\"\n", + " # Apply feature engineering functions\n", + " df = add_advanced_features(df)\n", + "\n", + " # Optimized feature selection for UV index\n", + " selected_features = {\n", + " # Primary meteorological features\n", + " 'atmospheric': [\n", + " 'temp', 'humidity', 'cloudcover', 'visibility',\n", + " 'clear_sky_index', 'atmospheric_transparency'\n", + " ],\n", + "\n", + " # Essential temporal features\n", + " 'temporal': [\n", + " 'hour_sin', 'hour_cos',\n", + " 'day_of_year_sin', 'day_of_year_cos'\n", + " ],\n", + "\n", + " # Solar features\n", + " 'solar': [\n", + " 'solar_angle', 'solar_elevation',\n", + " 'day_length', 'solar_noon',\n", + " 'solar_cloud_effect'\n", + " ],\n", + "\n", + " # Key interactions\n", + " 'interactions': [\n", + " 'cloud_temp_interaction',\n", + " 'visibility_cloud_interaction',\n", + " 'temp_humidity_interaction',\n", + " 'solar_clarity_index'\n", + " ],\n", + "\n", + " # Rolling features\n", + " 'rolling': [\n", + " 'cloud_rolling_12h',\n", + " 'temp_rolling_mean_6h'\n", + " ]\n", + " }\n", + "\n", + " # Flatten feature list\n", + " base_features = [item for sublist in selected_features.values() for item in sublist]\n", + "\n", + " # Add categorical features (one-hot encoded)\n", + " categorical_columns = [col for col in df.columns if col.startswith(('season_', 'time_period_'))]\n", + " final_features = base_features + categorical_columns\n", + "\n", + " # Temporal preprocessing\n", + " df = df.sort_values('datetime')\n", + " df.set_index('datetime', inplace=True)\n", + "\n", + " # Advanced interpolation for missing values\n", + " for column in final_features:\n", + " if column in df.columns:\n", + " if df[column].isnull().any():\n", + " if column in selected_features['rolling']:\n", + " df[column] = df[column].ffill().bfill()\n", + " else:\n", + " df[column] = df[column].interpolate(method='time', limit_direction='both')\n", + "\n", + " # Temporal data split\n", + " data_after_2010 = df[df.index.year >= 2010].copy()\n", + " data_before_2010 = df[df.index.year < 2010].copy()\n", + "\n", + " print(f\"\\nTemporal distribution of data:\")\n", + " print(f\"Records after 2010: {len(data_after_2010):,}\")\n", + " print(f\"Records before 2010: {len(data_before_2010):,}\")\n", + "\n", + " # Feature and target preparation\n", + " X = data_after_2010[final_features]\n", + " y = data_after_2010['uvindex']\n", + " X_to_predict = data_before_2010[final_features]\n", + "\n", + " # Data validation\n", + " if X.isnull().any().any() or y.isnull().any():\n", + " print(\"\\nWarning: Found missing values after preprocessing\")\n", + " print(\"Features with missing values:\", X.columns[X.isnull().any()].tolist())\n", + " X = X.fillna(X.mean())\n", + " y = y.fillna(y.mean())\n", + "\n", + " # Stratified data split\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y,\n", + " test_size=0.5,\n", + " random_state=random_state_value,\n", + " stratify=pd.qcut(y, q=5, duplicates='drop', labels=False)\n", + " )\n", + "\n", + " # Robust feature scaling\n", + " feature_scaler = RobustScaler()\n", + " X_train_scaled = feature_scaler.fit_transform(X_train)\n", + " X_test_scaled = feature_scaler.transform(X_test)\n", + " X_to_predict_scaled = feature_scaler.transform(X_to_predict)\n", + "\n", + " target_scaler = RobustScaler()\n", + " y_train_scaled = target_scaler.fit_transform(y_train.values.reshape(-1, 1)).ravel()\n", + " y_test_scaled = target_scaler.transform(y_test.values.reshape(-1, 1)).ravel()\n", + "\n", + " # Final validation\n", + " assert not np.isnan(X_train_scaled).any(), \"Found NaN in X_train_scaled\"\n", + " assert not np.isnan(X_test_scaled).any(), \"Found NaN in X_test_scaled\"\n", + " assert not np.isnan(X_to_predict_scaled).any(), \"Found NaN in X_to_predict_scaled\"\n", + "\n", + " # Print feature information\n", + " print(\"\\nNumber of features used:\", len(final_features))\n", + " print(\"\\nFeature categories:\")\n", + " for category, features in selected_features.items():\n", + " print(f\"{category}: {len(features)} features\")\n", + " print(f\"Categorical: {len(categorical_columns)} features\")\n", + "\n", + " return (X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled,\n", + " feature_scaler, target_scaler, final_features, X_to_predict_scaled)\n", + "\n", + "\n", + "def create_sequence_data(X, sequence_length=24):\n", + " \"\"\"\n", + " Converts data into sequences for LSTM input\n", + " sequence_length represents how many previous hours to consider\n", + " \"\"\"\n", + " sequences = []\n", + " for i in range(len(X) - sequence_length + 1):\n", + " sequences.append(X[i:i + sequence_length])\n", + " return np.array(sequences)\n", + "\n", + "\n", + "def prepare_hybrid_data(df):\n", + " # Use existing data preparation\n", + " X_train_scaled, X_test_scaled, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_scaled = prepare_advanced_data(df)\n", + "\n", + " # Convert data to sequences\n", + " sequence_length = 24 # 24 hours of historical data\n", + "\n", + " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", + " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", + "\n", + " # Adjust y by removing the first (sequence_length-1) elements\n", + " y_train = y_train[sequence_length - 1:]\n", + " y_test = y_test[sequence_length - 1:]\n", + "\n", + " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", + "\n", + " return X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e", + "metadata": {}, + "outputs": [], + "source": [ + "def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01,\n", + " survival_probability=0.8, return_sequences=True):\n", + " \"\"\"LSTM layer with stochastic depth\"\"\"\n", + " residual = x\n", + "\n", + " # Main path\n", + " x = Bidirectional(LSTM(units, return_sequences=return_sequences,\n", + " kernel_regularizer=regularizers.l2(l2_reg)))(x)\n", + " x = LayerNormalization()(x)\n", + " x = Dropout(dropout_rate)(x)\n", + "\n", + " # Adjust residual dimension if needed\n", + " if return_sequences:\n", + " # For Bidirectional LSTM, the output dimension is 2 * units\n", + " target_dim = 2 * units\n", + " if int(residual.shape[-1]) != target_dim:\n", + " # Use Dense layer instead of Conv1D for better dimension matching\n", + " residual = Dense(target_dim)(residual)\n", + "\n", + " # Apply stochastic depth only if dimensions match\n", + " if x.shape[-1] == residual.shape[-1]:\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n", + " else:\n", + " print(f\"Warning: Dimension mismatch - x: {x.shape}, residual: {residual.shape}\")\n", + " # Skip residual connection if dimensions don't match\n", + " pass\n", + "\n", + " return x\n", + "\n", + "\n", + "def attention_block(x, units, num_heads=8, survival_probability=0.8):\n", + " \"\"\"\n", + " Attention block with stochastic depth.\n", + " \"\"\"\n", + " original_x = x\n", + "\n", + " # Compute self-attention\n", + " attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n", + "\n", + " # Ensure dimensions match before applying stochastic depth\n", + " if attention.shape[-1] != original_x.shape[-1]:\n", + " original_x = Dense(attention.shape[-1])(original_x)\n", + "\n", + " # Apply stochastic depth to the attention path\n", + " x = tfa.layers.StochasticDepth(survival_probability)([attention, original_x])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Store the input to the FFN\n", + " ffn_input = x\n", + "\n", + " # FFN block\n", + " x = Dense(units * 4, activation='swish')(x)\n", + " x = Dense(ffn_input.shape[-1])(x) # Match the input dimension\n", + "\n", + " # Apply stochastic depth to the FFN\n", + " x = tfa.layers.StochasticDepth(survival_probability)([x, ffn_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " return x\n", + "\n", + "\n", + "def create_uv_index_model(input_shape, folder_name, l2_lambda=0.005, max_output=11):\n", + " inputs = Input(shape=input_shape)\n", + "\n", + " # Further adjusted hyperparameters\n", + " survival_probs = [0.98, 0.95, 0.92] # Even higher survival probabilities\n", + " attention_survival_probs = [0.95, 0.92, 0.9]\n", + "\n", + " # First LSTM block\n", + " x = create_residual_lstm_layer(\n", + " inputs, 64, dropout_rate=0.2, # Further reduced dropout\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[0],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 128, num_heads=2, # Reduced heads\n", + " survival_probability=attention_survival_probs[0])\n", + "\n", + " # Second LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 32, dropout_rate=0.15,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[1],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 64, num_heads=2,\n", + " survival_probability=attention_survival_probs[1])\n", + "\n", + " # Third LSTM block\n", + " x = create_residual_lstm_layer(\n", + " x, 16, dropout_rate=0.1,\n", + " l2_reg=l2_lambda,\n", + " survival_probability=survival_probs[2],\n", + " return_sequences=True\n", + " )\n", + " x = attention_block(x, 32, num_heads=2,\n", + " survival_probability=attention_survival_probs[2])\n", + "\n", + " # Global attention with reduced complexity\n", + " x_input = x\n", + " x = MultiHeadAttention(num_heads=2, key_dim=32)(x, x)\n", + "\n", + " if x.shape[-1] != x_input.shape[-1]:\n", + " x_input = Dense(x.shape[-1])(x_input)\n", + "\n", + " x = tfa.layers.StochasticDepth(survival_probability=0.95)([x, x_input])\n", + " x = LayerNormalization()(x)\n", + "\n", + " # Simplified dense layers\n", + " x = GlobalAveragePooling1D()(x)\n", + "\n", + " # Gradual dimension reduction\n", + " x = Dense(32, activation='swish', kernel_regularizer=regularizers.l2(l2_lambda / 2), kernel_constraint=tf.keras.constraints.MaxNorm(3))(x)\n", + " x = BatchNormalization()(x)\n", + " x = Dropout(0.05)(x) # Minimal dropout\n", + "\n", + " x = Dense(16, activation='swish',\n", + " kernel_regularizer=regularizers.l2(l2_lambda / 2))(x)\n", + " x = BatchNormalization()(x)\n", + "\n", + " # Modified output layer\n", + " x = Dense(8, activation='swish')(x)\n", + " outputs = Dense(1, activation='sigmoid')(x) # Sigmoid activation\n", + " outputs = Lambda(lambda x: x * max_output)(outputs) # Scale to [0, 11] range\n", + "\n", + " model = Model(inputs=inputs, outputs=outputs, name=\"UvModel\")\n", + "\n", + " # More stable learning rate schedule\n", + " initial_learning_rate = 0.0001 # Further reduced\n", + " warmup_steps = 1000\n", + " decay_steps = 5000\n", + "\n", + " # Corretto learning rate schedule\n", + " class CustomLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n", + " def __init__(self, initial_lr=0.0001, warmup_steps=1000, decay_steps=5000):\n", + " super().__init__()\n", + " self.initial_lr = initial_lr\n", + " self.warmup_steps = warmup_steps\n", + " self.decay_steps = decay_steps\n", + "\n", + " def __call__(self, step):\n", + " # Convert to float32\n", + " step_f = tf.cast(step, tf.float32)\n", + " warmup_steps_f = tf.cast(self.warmup_steps, tf.float32)\n", + " decay_steps_f = tf.cast(self.decay_steps, tf.float32)\n", + "\n", + " # Warmup phase\n", + " warmup_progress = step_f / warmup_steps_f\n", + " warmup_lr = self.initial_lr * warmup_progress\n", + "\n", + " # Decay phase\n", + " decay_progress = (step_f - warmup_steps_f) / decay_steps_f\n", + " decay_factor = 0.5 * (1.0 + tf.cos(tf.constant(np.pi) * decay_progress))\n", + " decay_lr = self.initial_lr * decay_factor\n", + "\n", + " # Combine phases\n", + " lr = tf.where(step_f < warmup_steps_f, warmup_lr, decay_lr)\n", + " return lr\n", + "\n", + " def get_config(self):\n", + " return {\n", + " \"initial_lr\": self.initial_lr,\n", + " \"warmup_steps\": self.warmup_steps,\n", + " \"decay_steps\": self.decay_steps\n", + " }\n", + "\n", + " # Utilizzo dello schedule corretto\n", + " lr_schedule = CustomLRSchedule(\n", + " initial_lr=initial_learning_rate,\n", + " warmup_steps=warmup_steps,\n", + " decay_steps=decay_steps\n", + " )\n", + "\n", + " optimizer = AdamW(\n", + " learning_rate=lr_schedule,\n", + " weight_decay=0.0005,\n", + " beta_1=0.9,\n", + " beta_2=0.999,\n", + " epsilon=1e-7\n", + " )\n", + "\n", + " # Improved loss function\n", + " def smooth_uv_loss(y_true, y_pred):\n", + " # Basic MSE with smoothing\n", + " mse = tf.square(y_true - y_pred)\n", + "\n", + " # Smooth L1 component for better stability\n", + " abs_diff = tf.abs(y_true - y_pred)\n", + " smooth_l1 = tf.where(abs_diff < 1.0,\n", + " 0.5 * tf.square(abs_diff),\n", + " abs_diff - 0.5)\n", + "\n", + " # Combined loss with dynamic weighting\n", + " combined_loss = 0.7 * mse + 0.3 * smooth_l1\n", + "\n", + " # Gentle weighting for high UV values\n", + " high_uv_weight = tf.where(y_true >= 8.0, 1.2, 1.0)\n", + "\n", + " # Smooth peak hours weight\n", + " time_of_day = tf.cast(tf.math.floormod(tf.range(tf.shape(y_true)[0]), 24),\n", + " tf.float32)\n", + " peak_weight = 1.0 + 0.2 * tf.math.sigmoid((time_of_day - 10.0) * 0.5) * \\\n", + " tf.math.sigmoid((16.0 - time_of_day) * 0.5)\n", + "\n", + " total_weight = high_uv_weight * peak_weight\n", + "\n", + " return tf.reduce_mean(combined_loss * total_weight)\n", + "\n", + " # Improved MAPE metric\n", + " def smooth_mape(y_true, y_pred):\n", + " epsilon = 1e-7\n", + " diff = tf.abs(y_true - y_pred)\n", + " scale = tf.maximum(tf.abs(y_true) + epsilon, 0.5) # Minimum scale of 0.5\n", + " return tf.reduce_mean(diff / scale) * 100\n", + "\n", + " model.compile(\n", + " optimizer=optimizer,\n", + " loss=smooth_uv_loss,\n", + " metrics=[\n", + " 'mae',\n", + " 'mse',\n", + " tf.keras.metrics.RootMeanSquaredError(),\n", + " smooth_mape\n", + " ]\n", + " )\n", + "\n", + " model.summary()\n", + "\n", + " plot_model(model,\n", + " to_file=f'{folder_name}_model_architecture.png',\n", + " show_shapes=True,\n", + " show_layer_names=True,\n", + " dpi=150,\n", + " show_layer_activations=True)\n", + "\n", + " return model\n", + "\n", + "\n", + "def evaluate_uv_predictions(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Comprehensive evaluation of UV index predictions with detailed analysis and visualizations.\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual UV index values\n", + " y_pred : array-like\n", + " Predicted UV index values\n", + " folder_name : str, optional\n", + " Folder to save analysis plots\n", + "\n", + " Returns:\n", + " --------\n", + " dict\n", + " Dictionary containing all calculated metrics\n", + " \"\"\"\n", + "\n", + " # Initialize plot paths\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Data preprocessing\n", + " y_true = np.array(y_true).ravel()\n", + " y_pred = np.array(y_pred).ravel()\n", + "\n", + " # Rounding and clipping predictions\n", + " y_pred_rounded = np.round(y_pred * 2) / 2 # Round to nearest 0.5\n", + " y_pred_clipped = np.clip(y_pred_rounded, 0, 11)\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + " errors_rounded = y_pred_clipped - y_true\n", + "\n", + " # Function to determine UV risk level\n", + " def get_uv_risk_level(values):\n", + " levels = np.full_like(values, 'Low', dtype=object)\n", + " levels[(values > 2) & (values <= 5)] = 'Moderate'\n", + " levels[(values > 5) & (values <= 7)] = 'High'\n", + " levels[(values > 7) & (values <= 10)] = 'Very High'\n", + " levels[values > 10] = 'Extreme'\n", + " return levels\n", + "\n", + " # Calculate basic metrics\n", + " metrics = {\n", + " 'raw': {\n", + " 'mae': mean_absolute_error(y_true, y_pred),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred)),\n", + " 'r2': r2_score(y_true, y_pred),\n", + " 'mean_error': np.mean(errors),\n", + " 'std_error': np.std(errors),\n", + " 'median_error': np.median(errors),\n", + " 'p95_abs_error': np.percentile(np.abs(errors), 95)\n", + " },\n", + " 'rounded': {\n", + " 'mae': mean_absolute_error(y_true, y_pred_clipped),\n", + " 'rmse': np.sqrt(mean_squared_error(y_true, y_pred_clipped)),\n", + " 'r2': r2_score(y_true, y_pred_clipped)\n", + " }\n", + " }\n", + "\n", + " # Calculate accuracies for different margins\n", + " for data_type, errors_data in [('raw', errors), ('rounded', errors_rounded)]:\n", + " metrics[data_type].update({\n", + " 'within_05': np.mean(np.abs(errors_data) <= 0.5) * 100,\n", + " 'within_1': np.mean(np.abs(errors_data) <= 1.0) * 100,\n", + " 'within_15': np.mean(np.abs(errors_data) <= 1.5) * 100,\n", + " 'within_2': np.mean(np.abs(errors_data) <= 2.0) * 100\n", + " })\n", + "\n", + " # Analysis by UV risk level\n", + " y_true_risk = get_uv_risk_level(y_true)\n", + " y_pred_risk = get_uv_risk_level(y_pred_clipped)\n", + "\n", + " # Calculate confusion matrix with handling for missing classes\n", + " risk_levels = ['Low', 'Moderate', 'High', 'Very High', 'Extreme']\n", + "\n", + " # Get unique labels present in the data\n", + " present_labels = np.unique(np.concatenate([y_true_risk, y_pred_risk]))\n", + "\n", + " # Calculate confusion matrix for present labels\n", + " cm = confusion_matrix(y_true_risk, y_pred_risk, labels=present_labels)\n", + "\n", + " # Create full confusion matrix with zeros\n", + " full_cm = np.zeros((len(risk_levels), len(risk_levels)))\n", + "\n", + " # Map present labels to their positions in the full matrix\n", + " label_positions = {label: i for i, label in enumerate(risk_levels)}\n", + " for i, true_label in enumerate(present_labels):\n", + " for j, pred_label in enumerate(present_labels):\n", + " full_cm[label_positions[true_label], label_positions[pred_label]] = cm[i, j]\n", + "\n", + " # Create DataFrame with all risk levels\n", + " cm_df = pd.DataFrame(full_cm, columns=risk_levels, index=risk_levels)\n", + "\n", + " # Analysis by UV range\n", + " uv_ranges = [\n", + " (0, 2, 'Low'),\n", + " (2, 5, 'Moderate'),\n", + " (5, 7, 'High'),\n", + " (7, 10, 'Very High'),\n", + " (10, 11, 'Extreme')\n", + " ]\n", + "\n", + " range_analysis = {}\n", + " for low, high, label in uv_ranges:\n", + " mask = (y_true >= low) & (y_true < high)\n", + " if mask.any():\n", + " range_analysis[label] = {\n", + " 'mae': mean_absolute_error(y_true[mask], y_pred[mask]),\n", + " 'count': np.sum(mask),\n", + " 'accuracy_within_05': np.mean(np.abs(errors[mask]) <= 0.5) * 100,\n", + " 'accuracy_within_1': np.mean(np.abs(errors[mask]) <= 1.0) * 100\n", + " }\n", + "\n", + " # Visualizations\n", + " if folder_name is not None:\n", + " try:\n", + " # Main figure with 4 subplots\n", + " fig = plt.figure(figsize=(20, 15))\n", + "\n", + " # 1. Error distribution\n", + " plt.subplot(2, 2, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # 2. Actual vs Predicted scatter plot\n", + " plt.subplot(2, 2, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([0, 11], [0, 11], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # 3. Errors vs Actual Values\n", + " plt.subplot(2, 2, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " # 4. Accuracy and MAE by range\n", + " ax = plt.subplot(2, 2, 4)\n", + " x_labels = [f\"{label}\\n({low}-{high})\" for low, high, label in uv_ranges]\n", + " accuracies = [range_analysis[label]['accuracy_within_05']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + " mae_values = [range_analysis[label]['mae']\n", + " for _, _, label in uv_ranges if label in range_analysis]\n", + "\n", + " bars = plt.bar(x_labels, accuracies, alpha=0.6)\n", + " plt.ylabel('Accuracy within ±0.5 (%)')\n", + " plt.title('Accuracy and MAE by UV Range')\n", + "\n", + " # Add MAE as line\n", + " ax2 = ax.twinx()\n", + " ax2.plot(x_labels, mae_values, 'r-o', label='MAE')\n", + " ax2.set_ylabel('MAE', color='red')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save main figure\n", + " main_plot_path = f'{folder_name}_uv_analysis.png'\n", + " plt.savefig(main_plot_path, dpi=300, bbox_inches='tight')\n", + "\n", + " # Confusion matrix as separate plot\n", + " plt.figure(figsize=(10, 8))\n", + " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", + " plt.title('Confusion Matrix for UV Risk Levels')\n", + "\n", + " conf_matrix_path = f'{folder_name}_confusion_matrix.png'\n", + " plt.savefig(conf_matrix_path, dpi=300, bbox_inches='tight')\n", + "\n", + " plt.close('all')\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError saving plots: {str(e)}\")\n", + " main_plot_path = None\n", + " conf_matrix_path = None\n", + "\n", + " # Print detailed report\n", + " print(\"\\nUV Index Prediction Analysis:\")\n", + " print(\"\\nRaw Metrics:\")\n", + " for key, value in metrics['raw'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nRounded Metrics:\")\n", + " for key, value in metrics['rounded'].items():\n", + " print(f\"{key}: {value:.3f}\")\n", + "\n", + " print(\"\\nAnalysis by UV Range:\")\n", + " for label, stats in range_analysis.items():\n", + " print(f\"\\n{label}:\")\n", + " for key, value in stats.items():\n", + " print(f\" {key}: {value:.3f}\")\n", + "\n", + " print(\"\\nConfusion Matrix:\")\n", + " print(cm_df)\n", + "\n", + " # Add range analysis and confusion matrix to metrics dictionary\n", + " metrics.update({\n", + " 'range_analysis': range_analysis,\n", + " 'confusion_matrix': cm_df.to_dict(),\n", + " 'plot_paths': {\n", + " 'main_analysis': main_plot_path,\n", + " 'confusion_matrix': conf_matrix_path\n", + " }\n", + " })\n", + "\n", + " return metrics\n", + "\n", + "\n", + "def plot_training_history(history, folder_name=None):\n", + " \"\"\"\n", + " Visualize and save the loss and metrics plots during training\n", + "\n", + " Parameters:\n", + " -----------\n", + " history : tensorflow.keras.callbacks.History\n", + " The history object returned by model training\n", + " folder_name : str\n", + " Folder where to save the plot\n", + " \"\"\"\n", + "\n", + " try:\n", + " # Create the figure\n", + " plt.figure(figsize=(12, 4))\n", + "\n", + " # Loss Plot\n", + " plt.subplot(1, 2, 1)\n", + " plt.plot(history.history['loss'], label='Training Loss')\n", + " plt.plot(history.history['val_loss'], label='Validation Loss')\n", + " plt.title('Model Loss')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('Loss')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " # MAE Plot\n", + " plt.subplot(1, 2, 2)\n", + " plt.plot(history.history['mae'], label='Training MAE')\n", + " plt.plot(history.history['val_mae'], label='Validation MAE')\n", + " plt.title('Model MAE')\n", + " plt.xlabel('Epoch')\n", + " plt.ylabel('MAE')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "\n", + " plt.tight_layout()\n", + "\n", + " if folder_name is not None:\n", + " os.makedirs(folder_name, exist_ok=True)\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'training_history.png')\n", + "\n", + " # Save the figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nTraining history plot saved as: {filename}\")\n", + "\n", + " # Also save numerical data in CSV format\n", + " history_df = pd.DataFrame({\n", + " 'epoch': range(1, len(history.history['loss']) + 1),\n", + " 'training_loss': history.history['loss'],\n", + " 'validation_loss': history.history['val_loss'],\n", + " 'training_mae': history.history['mae'],\n", + " 'validation_mae': history.history['val_mae']\n", + " })\n", + "\n", + " if folder_name is not None:\n", + " csv_filename = os.path.join(folder_name, 'training_history.csv')\n", + " history_df.to_csv(csv_filename, index=False)\n", + " print(f\"Training history data saved as: {csv_filename}\")\n", + "\n", + " # Calculate and save final statistics\n", + " final_stats = {\n", + " 'final_training_loss': history.history['loss'][-1],\n", + " 'final_validation_loss': history.history['val_loss'][-1],\n", + " 'final_training_mae': history.history['mae'][-1],\n", + " 'final_validation_mae': history.history['val_mae'][-1],\n", + " 'best_validation_loss': min(history.history['val_loss']),\n", + " 'best_validation_mae': min(history.history['val_mae']),\n", + " 'epochs': len(history.history['loss']),\n", + " }\n", + "\n", + " if folder_name is not None:\n", + " # Save statistics in JSON format\n", + " stats_filename = os.path.join(folder_name, 'training_stats.json')\n", + " with open(stats_filename, 'w') as f:\n", + " json.dump(final_stats, f, indent=4)\n", + " print(f\"Final statistics saved as: {stats_filename}\")\n", + "\n", + " # Print main statistics\n", + " print(\"\\nFinal training statistics:\")\n", + " print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n", + " print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n", + " print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n", + " print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n", + "\n", + " plt.show()\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during plot creation or saving: {str(e)}\")\n", + "\n", + "\n", + "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='uv_index'):\n", + " \"\"\"\n", + " Advanced training function for the hybrid UV index model with detailed monitoring\n", + " and training management.\n", + "\n", + " Parameters:\n", + " -----------\n", + " model : keras.Model\n", + " The compiled hybrid model\n", + " X_train : numpy.ndarray\n", + " Training data\n", + " y_train : numpy.ndarray\n", + " Training targets\n", + " X_test : numpy.ndarray\n", + " Validation data\n", + " y_test : numpy.ndarray\n", + " Validation targets\n", + " epochs : int, optional\n", + " Maximum number of training epochs\n", + " batch_size : int, optional\n", + " Batch size\n", + "\n", + " Returns:\n", + " --------\n", + " history : keras.callbacks.History\n", + " Training history with all metrics\n", + " \"\"\"\n", + "\n", + " # Advanced callbacks for training\n", + " callbacks = [\n", + " # Advanced Early Stopping\n", + " EarlyStopping(\n", + " monitor='mae',\n", + " patience=15,\n", + " restore_best_weights=True,\n", + " mode='min',\n", + " verbose=1,\n", + " min_delta=1e-6\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='mae',\n", + " factor=0.05,\n", + " patience=3,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=2,\n", + " min_lr=1e-7\n", + " ),\n", + " ReduceLROnPlateau(\n", + " monitor='val_loss',\n", + " factor=0.2,\n", + " patience=2,\n", + " verbose=1,\n", + " mode='min',\n", + " min_delta=1e-6,\n", + " cooldown=1,\n", + " min_lr=1e-7\n", + " ),\n", + " tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=f'{folder_name}_best_uv_model.h5',\n", + " monitor='mae',\n", + " save_best_only=True,\n", + " mode='min'\n", + " ),\n", + " tf.keras.callbacks.TensorBoard(\n", + " log_dir=f'./{folder_name}_logs',\n", + " histogram_freq=1,\n", + " write_graph=True,\n", + " update_freq='epoch'\n", + " ),\n", + " tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=lambda epoch, logs: print(\n", + " f\"\\nEpoch {epoch + 1}: Out of range predictions: \"\n", + " f\"{np.sum((model.predict(X_test) < 0) | (model.predict(X_test) > 11))}\"\n", + " ) if epoch % 20 == 0 else None\n", + " )\n", + " ]\n", + "\n", + " try:\n", + " history = model.fit(\n", + " X_train, y_train,\n", + " validation_data=(X_test, y_test),\n", + " epochs=epochs,\n", + " batch_size=batch_size,\n", + " callbacks=callbacks,\n", + " verbose=1,\n", + " shuffle=False,\n", + " validation_freq=1,\n", + " )\n", + "\n", + " # Post-training analysis\n", + " print(\"\\nTraining completed successfully!\")\n", + "\n", + " return history\n", + "\n", + " except Exception as e:\n", + " print(f\"\\nError during training: {str(e)}\")\n", + " raise\n", + "\n", + " finally:\n", + " # Memory cleanup\n", + " tf.keras.backend.clear_session()\n", + "\n", + "\n", + "def integrate_predictions(df, predictions, sequence_length=24):\n", + " \"\"\"\n", + " Integrate UV index predictions into the original dataset for pre-2010 data.\n", + "\n", + " Parameters:\n", + " -----------\n", + " df : pandas.DataFrame\n", + " Original dataset\n", + " predictions : numpy.ndarray\n", + " Array of UV index predictions\n", + " sequence_length : int\n", + " Sequence length used for predictions\n", + "\n", + " Returns:\n", + " --------\n", + " pandas.DataFrame\n", + " Updated dataset with UV index predictions\n", + " \"\"\"\n", + " # Convert datetime to datetime format if not already\n", + " df['datetime'] = pd.to_datetime(df['datetime'])\n", + "\n", + " # Identify pre-2010 rows\n", + " mask_pre_2010 = df['datetime'].dt.year < 2010\n", + "\n", + " # Create temporary DataFrame with predictions\n", + " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", + " predictions_df = pd.DataFrame({\n", + " 'datetime': dates_pre_2010,\n", + " 'uvindex_predicted': predictions.flatten()\n", + " })\n", + "\n", + " # Merge with original dataset\n", + " df = df.merge(predictions_df, on='datetime', how='left')\n", + "\n", + " # Update uvindex column where missing\n", + " df['uvindex'] = df['uvindex'].fillna(df['uvindex_predicted'])\n", + "\n", + " # Remove temporary column\n", + " df = df.drop('uvindex_predicted', axis=1)\n", + "\n", + " print(f\"Added {len(predictions)} predictions to dataset\")\n", + " print(f\"Rows with UV index after integration: {df['uvindex'].notna().sum()}\")\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "initial_id", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing UV index model training...\n", + "\n", + "1. Preparing data...\n", + "\n", + "Temporal distribution of data:\n", + "Records after 2010: 129,777\n", + "Records before 2010: 227,902\n", + "\n", + "Warning: Found missing values after preprocessing\n", + "Features with missing values: []\n", + "\n", + "Number of features used: 30\n", + "\n", + "Feature categories:\n", + "atmospheric: 6 features\n", + "temporal: 4 features\n", + "solar: 5 features\n", + "interactions: 4 features\n", + "rolling: 2 features\n", + "Categorical: 9 features\n", + "Training data shape: (64865, 24, 30)\n", + "Test data shape: (64866, 24, 30)\n", + "Saving scaler to: 2024-11-21_08-23_feature_scaler.joblib\n", + "Saving scaler to: 2024-11-21_08-23_target_scaler.joblib\n", + "Saving features to: 2024-11-21_08-23_features.json\n" + ] + } + ], + "source": [ + "df = pd.read_parquet('../../sources/weather_data.parquet')\n", + "\n", + "print(\"Initializing UV index model training...\")\n", + "\n", + "# Data preparation\n", + "print(\"\\n1. Preparing data...\")\n", + "X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq = prepare_hybrid_data(df)\n", + "\n", + "print(f\"Training data shape: {X_train_seq.shape}\")\n", + "print(f\"Test data shape: {X_test_seq.shape}\")\n", + "\n", + "# Save or load scaler and features\n", + "feature_scaler_path = f'{folder_name}_feature_scaler.joblib'\n", + "target_scaler_path = f'{folder_name}_target_scaler.joblib'\n", + "features_path = f'{folder_name}_features.json'\n", + "model_path = f'{folder_name}_best_model.h5'\n", + "history_path = f'{folder_name}_training_history.json'\n", + "\n", + "if os.path.exists(feature_scaler_path):\n", + " print(f\"Loading existing scaler from: {feature_scaler_path}\")\n", + " scaler = joblib.load(feature_scaler_path)\n", + "else:\n", + " print(f\"Saving scaler to: {feature_scaler_path}\")\n", + " joblib.dump(feature_scaler, feature_scaler_path)\n", + "\n", + "if os.path.exists(target_scaler_path):\n", + " print(f\"Loading existing scaler from: {target_scaler_path}\")\n", + " scaler = joblib.load(target_scaler_path)\n", + "else:\n", + " print(f\"Saving scaler to: {target_scaler_path}\")\n", + " joblib.dump(target_scaler, target_scaler_path)\n", + "\n", + "if os.path.exists(features_path):\n", + " print(f\"Loading existing features from: {features_path}\")\n", + " with open(features_path, 'r') as f:\n", + " features = json.load(f)\n", + "else:\n", + " print(f\"Saving features to: {features_path}\")\n", + " with open(features_path, 'w') as f:\n", + " json.dump(features, f)\n", + "\n", + "# Data quality verification\n", + "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", + " raise ValueError(\"Found NaN values in training data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "83771453-71db-4bb2-833d-7b81c022863d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "2. Model initialization...\n", + "Creating new model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:26:35.683631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:25:00.0, compute capability: 8.9\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"UvModel\"\n", + "__________________________________________________________________________________________________\n", + " Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + " input_1 (InputLayer) [(None, 24, 30)] 0 [] \n", + " \n", + " bidirectional (Bidirection (None, 24, 128) 48640 ['input_1[0][0]'] \n", + " al) \n", + " \n", + " layer_normalization (Layer (None, 24, 128) 256 ['bidirectional[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout (Dropout) (None, 24, 128) 0 ['layer_normalization[0][0]'] \n", + " \n", + " dense (Dense) (None, 24, 128) 3968 ['input_1[0][0]'] \n", + " \n", + " stochastic_depth (Stochast (None, 24, 128) 0 ['dropout[0][0]', \n", + " icDepth) 'dense[0][0]'] \n", + " \n", + " multi_head_attention (Mult (None, 24, 128) 131968 ['stochastic_depth[0][0]', \n", + " iHeadAttention) 'stochastic_depth[0][0]'] \n", + " \n", + " stochastic_depth_1 (Stocha (None, 24, 128) 0 ['multi_head_attention[0][0]',\n", + " sticDepth) 'stochastic_depth[0][0]'] \n", + " \n", + " layer_normalization_1 (Lay (None, 24, 128) 256 ['stochastic_depth_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_1 (Dense) (None, 24, 512) 66048 ['layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_2 (Dense) (None, 24, 128) 65664 ['dense_1[0][0]'] \n", + " \n", + " stochastic_depth_2 (Stocha (None, 24, 128) 0 ['dense_2[0][0]', \n", + " sticDepth) 'layer_normalization_1[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_2 (Lay (None, 24, 128) 256 ['stochastic_depth_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_1 (Bidirecti (None, 24, 64) 41216 ['layer_normalization_2[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_3 (Lay (None, 24, 64) 128 ['bidirectional_1[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 24, 64) 0 ['layer_normalization_3[0][0]'\n", + " ] \n", + " \n", + " dense_3 (Dense) (None, 24, 64) 8256 ['layer_normalization_2[0][0]'\n", + " ] \n", + " \n", + " stochastic_depth_3 (Stocha (None, 24, 64) 0 ['dropout_1[0][0]', \n", + " sticDepth) 'dense_3[0][0]'] \n", + " \n", + " multi_head_attention_1 (Mu (None, 24, 64) 33216 ['stochastic_depth_3[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_3[0][0]'] \n", + " \n", + " stochastic_depth_4 (Stocha (None, 24, 64) 0 ['multi_head_attention_1[0][0]\n", + " sticDepth) ', \n", + " 'stochastic_depth_3[0][0]'] \n", + " \n", + " layer_normalization_4 (Lay (None, 24, 64) 128 ['stochastic_depth_4[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_4 (Dense) (None, 24, 256) 16640 ['layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " dense_5 (Dense) (None, 24, 64) 16448 ['dense_4[0][0]'] \n", + " \n", + " stochastic_depth_5 (Stocha (None, 24, 64) 0 ['dense_5[0][0]', \n", + " sticDepth) 'layer_normalization_4[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_5 (Lay (None, 24, 64) 128 ['stochastic_depth_5[0][0]'] \n", + " erNormalization) \n", + " \n", + " bidirectional_2 (Bidirecti (None, 24, 32) 10368 ['layer_normalization_5[0][0]'\n", + " onal) ] \n", + " \n", + " layer_normalization_6 (Lay (None, 24, 32) 64 ['bidirectional_2[0][0]'] \n", + " erNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 24, 32) 0 ['layer_normalization_6[0][0]'\n", + " ] \n", + " \n", + " dense_6 (Dense) (None, 24, 32) 2080 ['layer_normalization_5[0][0]'\n", + " ] \n", + " \n", + " stochastic_depth_6 (Stocha (None, 24, 32) 0 ['dropout_2[0][0]', \n", + " sticDepth) 'dense_6[0][0]'] \n", + " \n", + " multi_head_attention_2 (Mu (None, 24, 32) 8416 ['stochastic_depth_6[0][0]', \n", + " ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n", + " \n", + " stochastic_depth_7 (Stocha (None, 24, 32) 0 ['multi_head_attention_2[0][0]\n", + " sticDepth) ', \n", + " 'stochastic_depth_6[0][0]'] \n", + " \n", + " layer_normalization_7 (Lay (None, 24, 32) 64 ['stochastic_depth_7[0][0]'] \n", + " erNormalization) \n", + " \n", + " dense_7 (Dense) (None, 24, 128) 4224 ['layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " dense_8 (Dense) (None, 24, 32) 4128 ['dense_7[0][0]'] \n", + " \n", + " stochastic_depth_8 (Stocha (None, 24, 32) 0 ['dense_8[0][0]', \n", + " sticDepth) 'layer_normalization_7[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_8 (Lay (None, 24, 32) 64 ['stochastic_depth_8[0][0]'] \n", + " erNormalization) \n", + " \n", + " multi_head_attention_3 (Mu (None, 24, 32) 8416 ['layer_normalization_8[0][0]'\n", + " ltiHeadAttention) , 'layer_normalization_8[0][0]\n", + " '] \n", + " \n", + " stochastic_depth_9 (Stocha (None, 24, 32) 0 ['multi_head_attention_3[0][0]\n", + " sticDepth) ', \n", + " 'layer_normalization_8[0][0]'\n", + " ] \n", + " \n", + " layer_normalization_9 (Lay (None, 24, 32) 64 ['stochastic_depth_9[0][0]'] \n", + " erNormalization) \n", + " \n", + " global_average_pooling1d ( (None, 32) 0 ['layer_normalization_9[0][0]'\n", + " GlobalAveragePooling1D) ] \n", + " \n", + " dense_9 (Dense) (None, 32) 1056 ['global_average_pooling1d[0][\n", + " 0]'] \n", + " \n", + " batch_normalization (Batch (None, 32) 128 ['dense_9[0][0]'] \n", + " Normalization) \n", + " \n", + " dropout_3 (Dropout) (None, 32) 0 ['batch_normalization[0][0]'] \n", + " \n", + " dense_10 (Dense) (None, 16) 528 ['dropout_3[0][0]'] \n", + " \n", + " batch_normalization_1 (Bat (None, 16) 64 ['dense_10[0][0]'] \n", + " chNormalization) \n", + " \n", + " dense_11 (Dense) (None, 8) 136 ['batch_normalization_1[0][0]'\n", + " ] \n", + " \n", + " dense_12 (Dense) (None, 1) 9 ['dense_11[0][0]'] \n", + " \n", + " lambda (Lambda) (None, 1) 0 ['dense_12[0][0]'] \n", + " \n", + "==================================================================================================\n", + "Total params: 473025 (1.80 MB)\n", + "Trainable params: 472929 (1.80 MB)\n", + "Non-trainable params: 96 (384.00 Byte)\n", + "__________________________________________________________________________________________________\n", + "\n", + "3. Starting training...\n", + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-21 08:26:51.620818: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n", + "2024-11-21 08:26:51.695976: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8905\n", + "2024-11-21 08:26:51.911310: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xd713390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "2024-11-21 08:26:51.911349: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n", + "2024-11-21 08:26:51.921786: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-11-21 08:26:52.001781: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n", + "2024-11-21 08:26:52.063791: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "507/507 [==============================] - ETA: 0s - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2028/2028 [==============================] - 25s 11ms/step\n", + "2028/2028 [==============================] - 19s 9ms/step\n", + "\n", + "Epoch 1: Out of range predictions: 0\n", + "507/507 [==============================] - 95s 151ms/step - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000 - val_loss: 3.3901 - val_mae: 0.7726 - val_mse: 1.0065 - val_root_mean_squared_error: 1.0033 - val_smooth_mape: 96.0347 - lr: 5.0600e-05\n", + "Epoch 2/100\n", + "507/507 [==============================] - 24s 48ms/step - loss: 3.4513 - mae: 0.9422 - mse: 1.2050 - root_mean_squared_error: 1.0977 - smooth_mape: 144.6453 - val_loss: 3.0292 - val_mae: 0.6905 - val_mse: 0.9136 - val_root_mean_squared_error: 0.9558 - val_smooth_mape: 72.3569 - lr: 9.9998e-05\n", + "Epoch 3/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 2.8616 - mae: 0.7835 - mse: 0.9255 - root_mean_squared_error: 0.9620 - smooth_mape: 105.5174 - val_loss: 2.6105 - val_mae: 0.6461 - val_mse: 0.8668 - val_root_mean_squared_error: 0.9310 - val_smooth_mape: 62.3054 - lr: 9.7355e-05\n", + "Epoch 4/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 2.2615 - mae: 0.6304 - mse: 0.6460 - root_mean_squared_error: 0.8038 - smooth_mape: 84.4009 - val_loss: 1.9317 - val_mae: 0.4135 - val_mse: 0.4540 - val_root_mean_squared_error: 0.6738 - val_smooth_mape: 35.5852 - lr: 8.9946e-05\n", + "Epoch 5/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 1.6904 - mae: 0.4345 - mse: 0.3313 - root_mean_squared_error: 0.5756 - smooth_mape: 59.1463 - val_loss: 1.4432 - val_mae: 0.2685 - val_mse: 0.1950 - val_root_mean_squared_error: 0.4416 - val_smooth_mape: 27.2014 - lr: 7.8518e-05\n", + "Epoch 6/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 1.3637 - mae: 0.3450 - mse: 0.2236 - root_mean_squared_error: 0.4729 - smooth_mape: 45.5589 - val_loss: 1.2321 - val_mae: 0.2588 - val_mse: 0.1828 - val_root_mean_squared_error: 0.4276 - val_smooth_mape: 25.4509 - lr: 6.4221e-05\n", + "Epoch 7/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 1.1519 - mae: 0.2973 - mse: 0.1789 - root_mean_squared_error: 0.4229 - smooth_mape: 38.0580 - val_loss: 1.0643 - val_mae: 0.2375 - val_mse: 0.1577 - val_root_mean_squared_error: 0.3971 - val_smooth_mape: 23.5643 - lr: 4.8492e-05\n", + "Epoch 8/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 1.0083 - mae: 0.2665 - mse: 0.1546 - root_mean_squared_error: 0.3932 - smooth_mape: 32.9333 - val_loss: 0.9257 - val_mae: 0.2038 - val_mse: 0.1143 - val_root_mean_squared_error: 0.3380 - val_smooth_mape: 21.4354 - lr: 3.2915e-05\n", + "Epoch 9/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.9148 - mae: 0.2470 - mse: 0.1407 - root_mean_squared_error: 0.3751 - smooth_mape: 29.7929 - val_loss: 0.8520 - val_mae: 0.1890 - val_mse: 0.1027 - val_root_mean_squared_error: 0.3204 - val_smooth_mape: 20.0954 - lr: 1.9058e-05\n", + "Epoch 10/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.8584 - mae: 0.2366 - mse: 0.1317 - root_mean_squared_error: 0.3628 - smooth_mape: 28.3349 - val_loss: 0.8144 - val_mae: 0.1853 - val_mse: 0.0991 - val_root_mean_squared_error: 0.3148 - val_smooth_mape: 19.7012 - lr: 8.3134e-06\n", + "Epoch 11/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.8331 - mae: 0.2331 - mse: 0.1292 - root_mean_squared_error: 0.3594 - smooth_mape: 27.8952 - val_loss: 0.7991 - val_mae: 0.1829 - val_mse: 0.0966 - val_root_mean_squared_error: 0.3108 - val_smooth_mape: 19.7143 - lr: 1.7639e-06\n", + "Epoch 12/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.8266 - mae: 0.2326 - mse: 0.1289 - root_mean_squared_error: 0.3591 - smooth_mape: 27.6276 - val_loss: 0.8002 - val_mae: 0.1851 - val_mse: 0.0996 - val_root_mean_squared_error: 0.3155 - val_smooth_mape: 19.6762 - lr: 6.7976e-08\n", + "Epoch 13/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.8256 - mae: 0.2332 - mse: 0.1297 - root_mean_squared_error: 0.3601 - smooth_mape: 27.8768 - val_loss: 0.7938 - val_mae: 0.1827 - val_mse: 0.0977 - val_root_mean_squared_error: 0.3126 - val_smooth_mape: 19.5952 - lr: 3.3964e-06\n", + "Epoch 14/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.8108 - mae: 0.2311 - mse: 0.1283 - root_mean_squared_error: 0.3582 - smooth_mape: 27.5029 - val_loss: 0.7689 - val_mae: 0.1841 - val_mse: 0.0983 - val_root_mean_squared_error: 0.3135 - val_smooth_mape: 19.2959 - lr: 1.1414e-05\n", + "Epoch 15/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.7666 - mae: 0.2260 - mse: 0.1253 - root_mean_squared_error: 0.3539 - smooth_mape: 26.7909 - val_loss: 0.7071 - val_mae: 0.1791 - val_mse: 0.0942 - val_root_mean_squared_error: 0.3069 - val_smooth_mape: 18.7677 - lr: 2.3315e-05\n", + "Epoch 16/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.6896 - mae: 0.2200 - mse: 0.1214 - root_mean_squared_error: 0.3484 - smooth_mape: 25.8531 - val_loss: 0.6141 - val_mae: 0.1740 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 18.6023 - lr: 3.7901e-05\n", + "Epoch 17/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.5905 - mae: 0.2116 - mse: 0.1160 - root_mean_squared_error: 0.3406 - smooth_mape: 24.5564 - val_loss: 0.5156 - val_mae: 0.1681 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 17.4665 - lr: 5.3704e-05\n", + "Epoch 18/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.4901 - mae: 0.2037 - mse: 0.1116 - root_mean_squared_error: 0.3341 - smooth_mape: 23.3802 - val_loss: 0.4216 - val_mae: 0.1617 - val_mse: 0.0852 - val_root_mean_squared_error: 0.2919 - val_smooth_mape: 17.2917 - lr: 6.9134e-05\n", + "Epoch 19/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.3984 - mae: 0.1930 - mse: 0.1038 - root_mean_squared_error: 0.3221 - smooth_mape: 21.8713 - val_loss: 0.3391 - val_mae: 0.1637 - val_mse: 0.0790 - val_root_mean_squared_error: 0.2810 - val_smooth_mape: 17.4952 - lr: 8.2639e-05\n", + "Epoch 20/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.3280 - mae: 0.1882 - mse: 0.1019 - root_mean_squared_error: 0.3192 - smooth_mape: 21.1186 - val_loss: 0.2774 - val_mae: 0.1532 - val_mse: 0.0766 - val_root_mean_squared_error: 0.2767 - val_smooth_mape: 16.5548 - lr: 9.2860e-05\n", + "Epoch 21/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 12ms/step\n", + "\n", + "Epoch 21: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 148ms/step - loss: 0.2717 - mae: 0.1800 - mse: 0.0959 - root_mean_squared_error: 0.3097 - smooth_mape: 19.9770 - val_loss: 0.2327 - val_mae: 0.1514 - val_mse: 0.0756 - val_root_mean_squared_error: 0.2750 - val_smooth_mape: 16.9079 - lr: 9.8768e-05\n", + "Epoch 22/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.2290 - mae: 0.1732 - mse: 0.0907 - root_mean_squared_error: 0.3011 - smooth_mape: 19.0873 - val_loss: 0.1969 - val_mae: 0.1482 - val_mse: 0.0722 - val_root_mean_squared_error: 0.2687 - val_smooth_mape: 16.2890 - lr: 9.9769e-05\n", + "Epoch 23/100\n", + "507/507 [==============================] - 26s 50ms/step - loss: 0.1994 - mae: 0.1705 - mse: 0.0889 - root_mean_squared_error: 0.2982 - smooth_mape: 18.7545 - val_loss: 0.1750 - val_mae: 0.1452 - val_mse: 0.0739 - val_root_mean_squared_error: 0.2719 - val_smooth_mape: 15.6235 - lr: 9.5762e-05\n", + "Epoch 24/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1768 - mae: 0.1661 - mse: 0.0861 - root_mean_squared_error: 0.2934 - smooth_mape: 18.1383 - val_loss: 0.1559 - val_mae: 0.1482 - val_mse: 0.0716 - val_root_mean_squared_error: 0.2676 - val_smooth_mape: 16.7181 - lr: 8.7150e-05\n", + "Epoch 25/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1601 - mae: 0.1629 - mse: 0.0835 - root_mean_squared_error: 0.2890 - smooth_mape: 17.7203 - val_loss: 0.1425 - val_mae: 0.1434 - val_mse: 0.0702 - val_root_mean_squared_error: 0.2649 - val_smooth_mape: 15.5010 - lr: 7.4800e-05\n", + "Epoch 26/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1472 - mae: 0.1586 - mse: 0.0806 - root_mean_squared_error: 0.2839 - smooth_mape: 17.2387 - val_loss: 0.1347 - val_mae: 0.1454 - val_mse: 0.0710 - val_root_mean_squared_error: 0.2665 - val_smooth_mape: 16.1275 - lr: 5.9955e-05\n", + "Epoch 27/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1399 - mae: 0.1584 - mse: 0.0803 - root_mean_squared_error: 0.2834 - smooth_mape: 17.2506 - val_loss: 0.1270 - val_mae: 0.1401 - val_mse: 0.0687 - val_root_mean_squared_error: 0.2621 - val_smooth_mape: 15.1573 - lr: 4.4108e-05\n", + "Epoch 28/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1344 - mae: 0.1563 - mse: 0.0792 - root_mean_squared_error: 0.2815 - smooth_mape: 16.9289 - val_loss: 0.1229 - val_mae: 0.1394 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 15.1774 - lr: 2.8853e-05\n", + "Epoch 29/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1293 - mae: 0.1536 - mse: 0.0767 - root_mean_squared_error: 0.2770 - smooth_mape: 16.6836 - val_loss: 0.1206 - val_mae: 0.1383 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.8600 - lr: 1.5727e-05\n", + "Epoch 30/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1275 - mae: 0.1526 - mse: 0.0763 - root_mean_squared_error: 0.2763 - smooth_mape: 16.5544 - val_loss: 0.1198 - val_mae: 0.1375 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2613 - val_smooth_mape: 14.7848 - lr: 6.0491e-06\n", + "Epoch 31/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1259 - mae: 0.1517 - mse: 0.0753 - root_mean_squared_error: 0.2744 - smooth_mape: 16.4806 - val_loss: 0.1192 - val_mae: 0.1370 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.5789 - lr: 7.9394e-07\n", + "Epoch 32/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.1263 - mae: 0.1522 - mse: 0.0759 - root_mean_squared_error: 0.2754 - smooth_mape: 16.5490 - val_loss: 0.1192 - val_mae: 0.1368 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.5403 - lr: 4.9000e-07\n", + "Epoch 33/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.1258 - mae: 0.1518 - mse: 0.0754 - root_mean_squared_error: 0.2745 - smooth_mape: 16.4660 - val_loss: 0.1189 - val_mae: 0.1376 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.7214 - lr: 5.1679e-06\n", + "Epoch 34/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4794\n", + "Epoch 34: ReduceLROnPlateau reducing learning rate to 7.178531632234808e-07.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4811 - val_loss: 0.1179 - val_mae: 0.1389 - val_mse: 0.0676 - val_root_mean_squared_error: 0.2601 - val_smooth_mape: 14.8385 - lr: 7.1785e-07\n", + "Epoch 35/100\n", + "507/507 [==============================] - 27s 52ms/step - loss: 0.1245 - mae: 0.1527 - mse: 0.0760 - root_mean_squared_error: 0.2756 - smooth_mape: 16.5704 - val_loss: 0.1169 - val_mae: 0.1410 - val_mse: 0.0685 - val_root_mean_squared_error: 0.2618 - val_smooth_mape: 15.6455 - lr: 2.7133e-05\n", + "Epoch 36/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1227 - mae: 0.1525 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.5028 - val_loss: 0.1139 - val_mae: 0.1381 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2614 - val_smooth_mape: 14.6552 - lr: 4.2209e-05\n", + "Epoch 37/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.1198 - mae: 0.1535 - mse: 0.0769 - root_mean_squared_error: 0.2773 - smooth_mape: 16.6359 - val_loss: 0.1107 - val_mae: 0.1409 - val_mse: 0.0686 - val_root_mean_squared_error: 0.2619 - val_smooth_mape: 14.7102 - lr: 5.8070e-05\n", + "Epoch 38/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710\n", + "Epoch 38: ReduceLROnPlateau reducing learning rate to 3.6559198633767668e-06.\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710 - val_loss: 0.1057 - val_mae: 0.1380 - val_mse: 0.0675 - val_root_mean_squared_error: 0.2599 - val_smooth_mape: 14.8251 - lr: 3.6559e-06\n", + "Epoch 39/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.1113 - mae: 0.1525 - mse: 0.0762 - root_mean_squared_error: 0.2761 - smooth_mape: 16.4623 - val_loss: 0.1040 - val_mae: 0.1391 - val_mse: 0.0703 - val_root_mean_squared_error: 0.2652 - val_smooth_mape: 14.3343 - lr: 8.5841e-05\n", + "Epoch 40/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1080 - mae: 0.1531 - mse: 0.0770 - root_mean_squared_error: 0.2775 - smooth_mape: 16.5384 - val_loss: 0.0995 - val_mae: 0.1393 - val_mse: 0.0697 - val_root_mean_squared_error: 0.2640 - val_smooth_mape: 15.2621 - lr: 9.4957e-05\n", + "Epoch 41/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 41: Out of range predictions: 0\n", + "507/507 [==============================] - 73s 144ms/step - loss: 0.1038 - mae: 0.1523 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.4194 - val_loss: 0.0982 - val_mae: 0.1425 - val_mse: 0.0723 - val_root_mean_squared_error: 0.2689 - val_smooth_mape: 14.8658 - lr: 9.9549e-05\n", + "Epoch 42/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6916\n", + "Epoch 42: ReduceLROnPlateau reducing learning rate to 4.957754936185666e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6892 - val_loss: 0.0915 - val_mae: 0.1368 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 14.4180 - lr: 4.9578e-06\n", + "Epoch 43/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0964 - mae: 0.1500 - mse: 0.0749 - root_mean_squared_error: 0.2736 - smooth_mape: 16.2060 - val_loss: 0.0881 - val_mae: 0.1362 - val_mse: 0.0671 - val_root_mean_squared_error: 0.2591 - val_smooth_mape: 14.6337 - lr: 9.3815e-05\n", + "Epoch 44/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0926 - mae: 0.1483 - mse: 0.0735 - root_mean_squared_error: 0.2711 - smooth_mape: 15.9664 - val_loss: 0.0856 - val_mae: 0.1355 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4813 - lr: 8.4067e-05\n", + "Epoch 45/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0904 - mae: 0.1477 - mse: 0.0732 - root_mean_squared_error: 0.2706 - smooth_mape: 15.9141 - val_loss: 0.0839 - val_mae: 0.1354 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2587 - val_smooth_mape: 14.4705 - lr: 7.0890e-05\n", + "Epoch 46/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0876 - mae: 0.1461 - mse: 0.0718 - root_mean_squared_error: 0.2680 - smooth_mape: 15.7518 - val_loss: 0.0825 - val_mae: 0.1373 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4117 - lr: 5.5612e-05\n", + "Epoch 47/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0853 - mae: 0.1446 - mse: 0.0706 - root_mean_squared_error: 0.2658 - smooth_mape: 15.5602 - val_loss: 0.0805 - val_mae: 0.1370 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2566 - val_smooth_mape: 14.8418 - lr: 3.9768e-05\n", + "Epoch 48/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0851 - mae: 0.1449 - mse: 0.0713 - root_mean_squared_error: 0.2671 - smooth_mape: 15.6229 - val_loss: 0.0801 - val_mae: 0.1363 - val_mse: 0.0660 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.7760 - lr: 2.4955e-05\n", + "Epoch 49/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0833 - mae: 0.1432 - mse: 0.0699 - root_mean_squared_error: 0.2643 - smooth_mape: 15.4598 - val_loss: 0.0791 - val_mae: 0.1348 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2557 - val_smooth_mape: 14.3369 - lr: 1.2661e-05\n", + "Epoch 50/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0819 - mae: 0.1421 - mse: 0.0687 - root_mean_squared_error: 0.2620 - smooth_mape: 15.3369 - val_loss: 0.0790 - val_mae: 0.1342 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.2211 - lr: 4.1248e-06\n", + "Epoch 51/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.3209 - val_loss: 0.0791 - val_mae: 0.1332 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.1215 - lr: 2.0452e-07\n", + "Epoch 52/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575\n", + "Epoch 52: ReduceLROnPlateau reducing learning rate to 2.589756149973255e-07.\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575 - val_loss: 0.0791 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.1793 - lr: 1.2949e-06\n", + "Epoch 53/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0818 - mae: 0.1417 - mse: 0.0687 - root_mean_squared_error: 0.2621 - smooth_mape: 15.2994 - val_loss: 0.0790 - val_mae: 0.1349 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2560 - val_smooth_mape: 14.1680 - lr: 7.2861e-06\n", + "Epoch 54/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3289 - val_loss: 0.0786 - val_mae: 0.1360 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.6983 - lr: 1.7575e-05\n", + "Epoch 55/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0826 - mae: 0.1438 - mse: 0.0701 - root_mean_squared_error: 0.2648 - smooth_mape: 15.5292 - val_loss: 0.0784 - val_mae: 0.1367 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.7914 - lr: 3.1127e-05\n", + "Epoch 56/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452\n", + "Epoch 56: ReduceLROnPlateau reducing learning rate to 2.3289143427973617e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452 - val_loss: 0.0777 - val_mae: 0.1360 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.4766 - lr: 2.3289e-06\n", + "Epoch 57/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0823 - mae: 0.1451 - mse: 0.0711 - root_mean_squared_error: 0.2667 - smooth_mape: 15.6261 - val_loss: 0.0785 - val_mae: 0.1375 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2596 - val_smooth_mape: 15.0211 - lr: 6.2374e-05\n", + "Epoch 58/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0818 - mae: 0.1454 - mse: 0.0715 - root_mean_squared_error: 0.2675 - smooth_mape: 15.6269 - val_loss: 0.0772 - val_mae: 0.1366 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2586 - val_smooth_mape: 14.1091 - lr: 7.6924e-05\n", + "Epoch 59/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0817 - mae: 0.1461 - mse: 0.0723 - root_mean_squared_error: 0.2689 - smooth_mape: 15.7262 - val_loss: 0.0761 - val_mae: 0.1370 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2575 - val_smooth_mape: 14.8032 - lr: 8.8765e-05\n", + "Epoch 60/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620\n", + "Epoch 60: ReduceLROnPlateau reducing learning rate to 4.835262006963604e-06.\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620 - val_loss: 0.0759 - val_mae: 0.1358 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2597 - val_smooth_mape: 14.4084 - lr: 4.8353e-06\n", + "Epoch 61/100\n", + "2028/2028 [==============================] - 22s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 61: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 147ms/step - loss: 0.0787 - mae: 0.1447 - mse: 0.0711 - root_mean_squared_error: 0.2666 - smooth_mape: 15.5565 - val_loss: 0.0737 - val_mae: 0.1352 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3196 - lr: 9.9946e-05\n", + "Epoch 62/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0781 - mae: 0.1451 - mse: 0.0715 - root_mean_squared_error: 0.2674 - smooth_mape: 15.5903 - val_loss: 0.0726 - val_mae: 0.1344 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2565 - val_smooth_mape: 14.3699 - lr: 9.8161e-05\n", + "Epoch 63/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0771 - mae: 0.1446 - mse: 0.0713 - root_mean_squared_error: 0.2670 - smooth_mape: 15.5551 - val_loss: 0.0721 - val_mae: 0.1350 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3152 - lr: 9.1530e-05\n", + "Epoch 64/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5067\n", + "Epoch 64: ReduceLROnPlateau reducing learning rate to 4.035990059492178e-06.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5081 - val_loss: 0.0716 - val_mae: 0.1347 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2574 - val_smooth_mape: 14.7350 - lr: 4.0360e-06\n", + "Epoch 65/100\n", + "507/507 [==============================] - 26s 50ms/step - loss: 0.0745 - mae: 0.1431 - mse: 0.0698 - root_mean_squared_error: 0.2642 - smooth_mape: 15.3922 - val_loss: 0.0710 - val_mae: 0.1360 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.1923 - lr: 6.6819e-05\n", + "Epoch 66/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0729 - mae: 0.1413 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.1677 - val_loss: 0.0697 - val_mae: 0.1349 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2538 - lr: 5.1225e-05\n", + "Epoch 67/100\n", + "507/507 [==============================] - 28s 55ms/step - loss: 0.0722 - mae: 0.1408 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.0982 - val_loss: 0.0691 - val_mae: 0.1346 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2549 - val_smooth_mape: 14.2971 - lr: 3.5508e-05\n", + "Epoch 68/100\n", + "507/507 [==============================] - 25s 48ms/step - loss: 0.0707 - mae: 0.1389 - mse: 0.0669 - root_mean_squared_error: 0.2587 - smooth_mape: 14.9200 - val_loss: 0.0686 - val_mae: 0.1339 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 14.3511 - lr: 2.1250e-05\n", + "Epoch 69/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0705 - mae: 0.1388 - mse: 0.0669 - root_mean_squared_error: 0.2586 - smooth_mape: 14.9215 - val_loss: 0.0684 - val_mae: 0.1325 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2540 - val_smooth_mape: 14.0225 - lr: 9.8841e-06\n", + "Epoch 70/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0698 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2573 - smooth_mape: 14.8352 - val_loss: 0.0684 - val_mae: 0.1320 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9647 - lr: 2.5551e-06\n", + "Epoch 71/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0695 - mae: 0.1374 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7516 - val_loss: 0.0685 - val_mae: 0.1317 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2545 - val_smooth_mape: 13.8579 - lr: 1.5795e-10\n", + "Epoch 72/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7707\n", + "Epoch 72: ReduceLROnPlateau reducing learning rate to 4.95273479828029e-07.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7708 - val_loss: 0.0684 - val_mae: 0.1318 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9681 - lr: 2.4764e-06\n", + "Epoch 73/100\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0694 - mae: 0.1373 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7647 - val_loss: 0.0683 - val_mae: 0.1327 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 13.9221 - lr: 9.7346e-06\n", + "Epoch 74/100\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0698 - mae: 0.1380 - mse: 0.0663 - root_mean_squared_error: 0.2575 - smooth_mape: 14.8505 - val_loss: 0.0683 - val_mae: 0.1332 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2543 - val_smooth_mape: 14.4362 - lr: 2.1044e-05\n", + "Epoch 75/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0697 - mae: 0.1381 - mse: 0.0664 - root_mean_squared_error: 0.2577 - smooth_mape: 14.8281 - val_loss: 0.0680 - val_mae: 0.1335 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 14.2706 - lr: 3.5268e-05\n", + "Epoch 76/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0952\n", + "Epoch 76: ReduceLROnPlateau reducing learning rate to 2.548691554693505e-06.\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0982 - val_loss: 0.0680 - val_mae: 0.1339 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2546 - val_smooth_mape: 14.0539 - lr: 2.5487e-06\n", + "Epoch 77/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0332\n", + "Epoch 77: ReduceLROnPlateau reducing learning rate to 1.3316345575731248e-05.\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0357 - val_loss: 0.0682 - val_mae: 0.1346 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2556 - val_smooth_mape: 14.3903 - lr: 6.6582e-05\n", + "Epoch 78/100\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0707 - mae: 0.1408 - mse: 0.0683 - root_mean_squared_error: 0.2614 - smooth_mape: 15.1099 - val_loss: 0.0680 - val_mae: 0.1327 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.0229 - lr: 8.0521e-05\n", + "Epoch 79/100\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0710 - mae: 0.1417 - mse: 0.0691 - root_mean_squared_error: 0.2629 - smooth_mape: 15.2021 - val_loss: 0.0677 - val_mae: 0.1354 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.2230 - lr: 9.1389e-05\n", + "Epoch 80/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279\n", + "Epoch 80: ReduceLROnPlateau reducing learning rate to 4.904638990410604e-06.\n", + "507/507 [==============================] - 27s 54ms/step - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279 - val_loss: 0.0682 - val_mae: 0.1343 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.2839 - lr: 4.9046e-06\n", + "Epoch 81/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1438 - mse: 0.0699 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4713\n", + "Epoch 81: ReduceLROnPlateau reducing learning rate to 1.9991402223240587e-05.\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "2028/2028 [==============================] - 23s 11ms/step\n", + "\n", + "Epoch 81: Out of range predictions: 0\n", + "507/507 [==============================] - 75s 148ms/step - loss: 0.0713 - mae: 0.1438 - mse: 0.0700 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4736 - val_loss: 0.0690 - val_mae: 0.1379 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 15.5488 - lr: 9.9957e-05\n", + "Epoch 82/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0696 - mae: 0.1419 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.2732 - val_loss: 0.0667 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.2230 - lr: 9.6794e-05\n", + "Epoch 83/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0688 - mae: 0.1409 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.1230 - val_loss: 0.0659 - val_mae: 0.1343 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2547 - lr: 8.8923e-05\n", + "Epoch 84/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9698\n", + "Epoch 84: ReduceLROnPlateau reducing learning rate to 3.8567635783692825e-06.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9716 - val_loss: 0.0655 - val_mae: 0.1338 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2066 - lr: 3.8568e-06\n", + "Epoch 85/100\n", + "507/507 [==============================] - 26s 52ms/step - loss: 0.0673 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9972 - val_loss: 0.0680 - val_mae: 0.1333 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 13.8129 - lr: 6.2617e-05\n", + "Epoch 86/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0669 - mae: 0.1389 - mse: 0.0670 - root_mean_squared_error: 0.2588 - smooth_mape: 14.9195 - val_loss: 0.0647 - val_mae: 0.1343 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2539 - val_smooth_mape: 14.2430 - lr: 4.6829e-05\n", + "Epoch 87/100\n", + "507/507 [==============================] - 23s 46ms/step - loss: 0.0656 - mae: 0.1372 - mse: 0.0657 - root_mean_squared_error: 0.2563 - smooth_mape: 14.7366 - val_loss: 0.0643 - val_mae: 0.1317 - val_mse: 0.0644 - val_root_mean_squared_error: 0.2537 - val_smooth_mape: 14.1979 - lr: 3.1360e-05\n", + "Epoch 88/100\n", + "507/507 [==============================] - 22s 44ms/step - loss: 0.0649 - mae: 0.1365 - mse: 0.0651 - root_mean_squared_error: 0.2552 - smooth_mape: 14.6326 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2528 - val_smooth_mape: 14.3781 - lr: 1.7767e-05\n", + "Epoch 89/100\n", + "507/507 [==============================] - 23s 45ms/step - loss: 0.0647 - mae: 0.1361 - mse: 0.0649 - root_mean_squared_error: 0.2548 - smooth_mape: 14.6184 - val_loss: 0.0637 - val_mae: 0.1317 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 13.9675 - lr: 7.4173e-06\n", + "Epoch 90/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0642 - mae: 0.1356 - mse: 0.0644 - root_mean_squared_error: 0.2538 - smooth_mape: 14.5656 - val_loss: 0.0638 - val_mae: 0.1309 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8811 - lr: 1.3523e-06\n", + "Epoch 91/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922\n", + "Epoch 91: ReduceLROnPlateau reducing learning rate to 1e-07.\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922 - val_loss: 0.0638 - val_mae: 0.1311 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8034 - lr: 1.8244e-07\n", + "Epoch 92/100\n", + "507/507 [==============================] - 25s 49ms/step - loss: 0.0634 - mae: 0.1342 - mse: 0.0635 - root_mean_squared_error: 0.2520 - smooth_mape: 14.4498 - val_loss: 0.0637 - val_mae: 0.1312 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2527 - val_smooth_mape: 13.8449 - lr: 4.0254e-06\n", + "Epoch 93/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0638 - root_mean_squared_error: 0.2526 - smooth_mape: 14.4781 - val_loss: 0.0636 - val_mae: 0.1318 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.0036 - lr: 1.2494e-05\n", + "Epoch 94/100\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0639 - mae: 0.1350 - mse: 0.0642 - root_mean_squared_error: 0.2534 - smooth_mape: 14.4772 - val_loss: 0.0635 - val_mae: 0.1324 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.1743 - lr: 2.4737e-05\n", + "Epoch 95/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348\n", + "Epoch 95: ReduceLROnPlateau reducing learning rate to 1.976121529878583e-06.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0643 - val_root_mean_squared_error: 0.2535 - val_smooth_mape: 14.4393 - lr: 1.9761e-06\n", + "Epoch 96/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371\n", + "Epoch 96: ReduceLROnPlateau reducing learning rate to 1.1072350753238425e-05.\n", + "507/507 [==============================] - 25s 50ms/step - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371 - val_loss: 0.0644 - val_mae: 0.1353 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2866 - lr: 5.5362e-05\n", + "Epoch 97/100\n", + "507/507 [==============================] - 27s 53ms/step - loss: 0.0660 - mae: 0.1386 - mse: 0.0668 - root_mean_squared_error: 0.2585 - smooth_mape: 14.8711 - val_loss: 0.0640 - val_mae: 0.1324 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2544 - val_smooth_mape: 14.0692 - lr: 7.0662e-05\n", + "Epoch 98/100\n", + "507/507 [==============================] - ETA: 0s - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726\n", + "Epoch 98: ReduceLROnPlateau reducing learning rate to 1.6776460688561202e-05.\n", + "507/507 [==============================] - 26s 51ms/step - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726 - val_loss: 0.0652 - val_mae: 0.1359 - val_mse: 0.0662 - val_root_mean_squared_error: 0.2572 - val_smooth_mape: 14.0106 - lr: 8.3882e-05\n", + "Epoch 99/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9081\n", + "Epoch 99: ReduceLROnPlateau reducing learning rate to 4.684684972744436e-06.\n", + "507/507 [==============================] - 24s 47ms/step - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9098 - val_loss: 0.0642 - val_mae: 0.1338 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2555 - val_smooth_mape: 14.2021 - lr: 4.6847e-06\n", + "Epoch 100/100\n", + "506/507 [============================>.] - ETA: 0s - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7397\n", + "Epoch 100: ReduceLROnPlateau reducing learning rate to 1.9821693422272803e-05.\n", + "507/507 [==============================] - 24s 46ms/step - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7407 - val_loss: 0.0652 - val_mae: 0.1356 - val_mse: 0.0667 - val_root_mean_squared_error: 0.2582 - val_smooth_mape: 13.8309 - lr: 9.9108e-05\n", + "\n", + "Training completed successfully!\n" + ] + } + ], + "source": [ + "# Model creation or loading\n", + "print(\"\\n2. Model initialization...\")\n", + "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", + "\n", + "MAX_UVINDEX = 11\n", + "\n", + "max_val_scaled = target_scaler.transform([[MAX_UVINDEX]])[0][0]\n", + "\n", + "if os.path.exists(model_path):\n", + " print(f\"Loading existing model from: {model_path}\")\n", + " model = tf.keras.models.load_model(model_path)\n", + "\n", + " # Load existing history if available\n", + " if os.path.exists(history_path):\n", + " print(f\"Loading existing training history from: {history_path}\")\n", + " with open(history_path, 'r') as f:\n", + " history_dict = json.load(f)\n", + " history = type('History', (), {'history': history_dict})()\n", + " else:\n", + " history = type('History', (), {'history': {}})()\n", + "else:\n", + " print(\"Creating new model...\")\n", + " model = create_uv_index_model(input_shape=input_shape, folder_name=folder_name, max_output=max_val_scaled)\n", + "\n", + " print(\"\\n3. Starting training...\")\n", + " history = train_hybrid_model(\n", + " model=model,\n", + " X_train=X_train_seq,\n", + " y_train=y_train,\n", + " X_test=X_test_seq,\n", + " y_test=y_test,\n", + " epochs=100,\n", + " batch_size=128,\n", + " folder_name=folder_name\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f0059ecf-7f4f-496f-bed8-85e2d990ff71", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "4. Generating predictions...\n", + "2028/2028 [==============================] - 18s 9ms/step\n", + "\n", + "5. Model evaluation...\n", + "\n", + "Error saving plots: Unknown format code 'd' for object of type 'float'\n", + "\n", + "UV Index Prediction Analysis:\n", + "\n", + "Raw Metrics:\n", + "mae: 0.407\n", + "rmse: 0.775\n", + "r2: 0.918\n", + "mean_error: -0.076\n", + "std_error: 0.771\n", + "median_error: 0.012\n", + "p95_abs_error: 1.745\n", + "within_05: 71.379\n", + "within_1: 86.040\n", + "within_15: 92.984\n", + "within_2: 96.562\n", + "\n", + "Rounded Metrics:\n", + "mae: 0.393\n", + "rmse: 0.782\n", + "r2: 0.916\n", + "within_05: 78.975\n", + "within_1: 90.160\n", + "within_15: 95.037\n", + "within_2: 97.478\n", + "\n", + "Analysis by UV Range:\n", + "\n", + "Low:\n", + " mae: 0.133\n", + " count: 41407.000\n", + " accuracy_within_05: 90.828\n", + " accuracy_within_1: 97.341\n", + "\n", + "Moderate:\n", + " mae: 0.874\n", + " count: 11467.000\n", + " accuracy_within_05: 36.418\n", + " accuracy_within_1: 66.713\n", + "\n", + "High:\n", + " mae: 0.871\n", + " count: 5415.000\n", + " accuracy_within_05: 37.876\n", + " accuracy_within_1: 65.614\n", + "\n", + "Very High:\n", + " mae: 0.905\n", + " count: 6343.000\n", + " accuracy_within_05: 38.862\n", + " accuracy_within_1: 66.404\n", + "\n", + "Extreme:\n", + " mae: 1.649\n", + " count: 234.000\n", + " accuracy_within_05: 0.000\n", + " accuracy_within_1: 38.462\n", + "\n", + "Confusion Matrix:\n", + " Low Moderate High Very High Extreme\n", + "Low 43040.0 2283.0 97.0 17.0 0.0\n", + "Moderate 1336.0 7625.0 1181.0 107.0 0.0\n", + "High 10.0 1155.0 3149.0 576.0 0.0\n", + "Very High 0.0 114.0 1110.0 3066.0 0.0\n", + "Extreme 0.0 0.0 0.0 0.0 0.0\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n4. Generating predictions...\")\n", + "predictions = model.predict(X_test_seq)\n", + "predictions = np.clip(predictions, 0, max_val_scaled)\n", + "\n", + "predictions_original = target_scaler.inverse_transform(predictions.reshape(-1, 1))\n", + "y_test_original = target_scaler.inverse_transform(y_test.reshape(-1, 1))\n", + "\n", + "print(\"\\n5. Model evaluation...\")\n", + "metrics = evaluate_uv_predictions(y_test_original, predictions_original, folder_name=folder_name)\n", + "\n", + "# Save training results only if new training was performed\n", + "if not os.path.exists(model_path):\n", + " training_results = {\n", + " 'model_params': {\n", + " 'input_shape': input_shape,\n", + " 'n_features': len(features),\n", + " 'sequence_length': X_train_seq.shape[1]\n", + " },\n", + " 'training_params': {\n", + " 'batch_size': 128,\n", + " 'total_epochs': len(history.history['loss']),\n", + " 'best_epoch': np.argmin(history.history['val_loss']) + 1,\n", + " },\n", + " 'performance_metrics': {\n", + " 'final_loss': float(history.history['val_loss'][-1]),\n", + " 'final_mae': float(history.history['val_mae'][-1]),\n", + " 'best_val_loss': float(min(history.history['val_loss'])),\n", + " 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n", + " }\n", + " }\n", + "\n", + " # Save training history\n", + " with open(history_path, 'w') as f:\n", + " history_dict = {key: [float(val) for val in values]\n", + " for key, values in history.history.items()}\n", + " json.dump(history_dict, f, indent=4)\n", + "else:\n", + " # Load existing training results if available\n", + " results_path = f'{folder_name}_training_results.json'\n", + " if os.path.exists(results_path):\n", + " with open(results_path, 'r') as f:\n", + " training_results = json.load(f)\n", + " else:\n", + " training_results = {}\n", + "\n", + "tf.keras.backend.clear_session()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4365d2bf-daf8-49e1-be13-cce222bbb5bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "6. Predicting missing data...\n", + "7122/7122 [==============================] - 74s 10ms/step\n", + "\n", + "7. Integrating predictions into dataset...\n", + "Added 227879 predictions to dataset\n", + "Rows with UV index after integration: 357615\n", + "Updated dataset saved to: ../../sources/weather_data_uvindex.parquet\n", + "\n", + "All files saved with prefix: 2024-11-21_08-23\n" + ] + } + ], + "source": [ + "print(\"\\n6. Predicting missing data...\")\n", + "to_predict_predictions = model.predict(X_to_predict_seq)\n", + "to_predict_predictions = np.clip(to_predict_predictions, 0, max_val_scaled)\n", + "\n", + "to_predict_predictions_original = target_scaler.inverse_transform(to_predict_predictions.reshape(-1, 1))\n", + "\n", + "print(\"\\n7. Integrating predictions into dataset...\")\n", + "df_updated = integrate_predictions(df.copy(), to_predict_predictions_original)\n", + "\n", + "output_path = f'../../sources/weather_data_uvindex.parquet'\n", + "df_updated.to_parquet(output_path)\n", + "print(f\"Updated dataset saved to: {output_path}\")\n", + "\n", + "# Add prediction statistics\n", + "prediction_stats = {\n", + " 'n_predictions_added': len(to_predict_predictions),\n", + " 'mean_predicted_uv': float(to_predict_predictions.mean()),\n", + " 'min_predicted_uv': float(to_predict_predictions.min()),\n", + " 'max_predicted_uv': float(to_predict_predictions.max()),\n", + "}\n", + "\n", + "\n", + "def convert_to_serializable(obj):\n", + " \"\"\"Convert numpy types to Python standard types for JSON serialization\"\"\"\n", + " if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n", + " np.int16, np.int32, np.int64, np.uint8,\n", + " np.uint16, np.uint32, np.uint64)):\n", + " return int(obj)\n", + " elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):\n", + " return float(obj)\n", + " elif isinstance(obj, (np.ndarray,)):\n", + " return obj.tolist()\n", + " elif isinstance(obj, dict):\n", + " return {key: convert_to_serializable(value) for key, value in obj.items()}\n", + " elif isinstance(obj, list):\n", + " return [convert_to_serializable(item) for item in obj]\n", + " return obj\n", + "\n", + "\n", + "if not os.path.exists(model_path):\n", + " training_results['prediction_stats'] = prediction_stats\n", + "\n", + " training_results = convert_to_serializable(training_results)\n", + " # Save final results\n", + " results_path = f'{folder_name}_training_results.json'\n", + " with open(results_path, 'w') as f:\n", + " json.dump(training_results, f, indent=4)\n", + "\n", + "print(f\"\\nAll files saved with prefix: {folder_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Plot saved as: 2024-11-21_08-23/error_analysis.png\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Error statistics:\n", + "MAE: 0.1356\n", + "MSE: 0.0667\n", + "RMSE: 0.2582\n", + "Mean errors: -0.0252\n", + "Std errors: 0.2569\n", + "Predictions within ±0.5: 93.0%\n", + "Predictions within ±1.0: 99.0%\n", + "Predictions within ±1.5: 99.9%\n", + "Predictions within ±2.0: 100.0%\n" + ] + } + ], + "source": [ + "def plot_error_analysis(y_true, y_pred, folder_name=None):\n", + " \"\"\"\n", + " Function to visualize prediction error analysis\n", + "\n", + " Parameters:\n", + " -----------\n", + " y_true : array-like\n", + " Actual values\n", + " y_pred : array-like\n", + " Predicted values\n", + " folder_name : str, optional\n", + " Folder to save plots. If None, plots are not saved.\n", + " \"\"\"\n", + "\n", + " # Convert to 1D numpy array if necessary\n", + " if isinstance(y_true, pd.Series):\n", + " y_true = y_true.values\n", + " if isinstance(y_pred, pd.Series):\n", + " y_pred = y_pred.values\n", + "\n", + " y_true = y_true.ravel()\n", + " y_pred = y_pred.ravel()\n", + "\n", + " # Calculate errors\n", + " errors = y_pred - y_true\n", + "\n", + " # Create main figure\n", + " fig = plt.figure(figsize=(15, 5))\n", + "\n", + " # Plot 1: Error Distribution\n", + " plt.subplot(1, 3, 1)\n", + " plt.hist(errors, bins=50, alpha=0.7)\n", + " plt.title('Prediction Error Distribution')\n", + " plt.xlabel('Error')\n", + " plt.ylabel('Frequency')\n", + "\n", + " # Plot 2: Actual vs Predicted\n", + " plt.subplot(1, 3, 2)\n", + " plt.scatter(y_true, y_pred, alpha=0.5)\n", + " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", + " plt.title('Actual vs Predicted Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Predicted Values')\n", + "\n", + " # Plot 3: Errors vs Actual Values\n", + " plt.subplot(1, 3, 3)\n", + " plt.scatter(y_true, errors, alpha=0.5)\n", + " plt.axhline(y=0, color='r', linestyle='--')\n", + " plt.title('Errors vs Actual Values')\n", + " plt.xlabel('Actual Values')\n", + " plt.ylabel('Error')\n", + "\n", + " plt.tight_layout()\n", + "\n", + " # Save plot if folder is specified\n", + " if folder_name is not None:\n", + " try:\n", + " # Create folder if it doesn't exist\n", + " os.makedirs(folder_name, exist_ok=True)\n", + "\n", + " # Generate filename with timestamp\n", + " filename = os.path.join(folder_name, 'error_analysis.png')\n", + "\n", + " # Save figure\n", + " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", + " print(f\"\\nPlot saved as: {filename}\")\n", + " except Exception as e:\n", + " print(f\"\\nError saving plot: {str(e)}\")\n", + "\n", + " plt.show()\n", + "\n", + " # Print error statistics\n", + " print(\"\\nError statistics:\")\n", + " print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n", + " print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n", + " print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n", + " print(f\"Mean errors: {np.mean(errors):.4f}\")\n", + " print(f\"Std errors: {np.std(errors):.4f}\")\n", + "\n", + " # Calculate percentage of errors within thresholds\n", + " thresholds = [0.5, 1.0, 1.5, 2.0]\n", + " for threshold in thresholds:\n", + " within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n", + " print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n", + "\n", + "\n", + "plot_error_analysis(y_test, predictions, folder_name=folder_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c57d6b2-00a6-4d31-935e-449a29dafd79", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0rc1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/olive_oil_train_dataset/create_train_dataset.py b/olive_oil_train_dataset/create_train_dataset.py new file mode 100755 index 0000000..b3cd45a --- /dev/null +++ b/olive_oil_train_dataset/create_train_dataset.py @@ -0,0 +1,516 @@ +import pandas as pd +import numpy as np +from concurrent.futures import ProcessPoolExecutor, as_completed +import multiprocessing +import psutil +from tqdm import tqdm +import os +import argparse +import sys +import gc +from utils.helpers import clean_column_name, get_growth_phase, calculate_weather_effect, calculate_water_need, \ + create_technique_mapping, preprocess_weather_data + + +def get_optimal_workers(): + """Calcola il numero ottimale di workers basato sulle risorse del sistema""" + cpu_count = multiprocessing.cpu_count() + memory = psutil.virtual_memory() + available_memory_gb = memory.available / (1024 ** 3) + memory_per_worker_gb = 2 + + max_workers_by_memory = int(available_memory_gb / memory_per_worker_gb) + optimal_workers = min( + cpu_count - 1, + max_workers_by_memory, + 32 + ) + print(f'CPU count : {cpu_count} - Memory : {memory} = Max Worker by memory : {max_workers_by_memory}') + + return max(1, optimal_workers) + + +def simulate_zone(base_weather, olive_varieties, year, zone, all_varieties, variety_techniques): + """ + Simula la produzione di olive per una singola zona. + + Args: + base_weather: DataFrame con dati meteo di base per l'anno selezionato + olive_varieties: DataFrame con le informazioni sulle varietà di olive + zone: ID della zona + all_varieties: Array con tutte le varietà disponibili + variety_techniques: Dict con le tecniche disponibili per ogni varietà + + Returns: + Dict con i risultati della simulazione per la zona + """ + # Crea una copia dei dati meteo per questa zona specifica + zone_weather = base_weather.copy() + + # Genera variazioni meteorologiche specifiche per questa zona + zone_weather['temp_mean'] *= np.random.uniform(0.95, 1.05, len(zone_weather)) + zone_weather['precip_sum'] *= np.random.uniform(0.9, 1.1, len(zone_weather)) + zone_weather['solarenergy_sum'] *= np.random.uniform(0.95, 1.05, len(zone_weather)) + + # Genera caratteristiche specifiche della zona + num_varieties = np.random.randint(1, 4) # 1-3 varietà per zona + selected_varieties = np.random.choice(all_varieties, size=num_varieties, replace=False) + hectares = np.random.uniform(1, 10) # Dimensione del terreno + percentages = np.random.dirichlet(np.ones(num_varieties)) # Distribuzione delle varietà + + # Inizializzazione contatori annuali + annual_production = 0 + annual_min_oil = 0 + annual_max_oil = 0 + annual_avg_oil = 0 + annual_water_need = 0 + + # Inizializzazione dizionario dati varietà + variety_data = {clean_column_name(variety): { + 'tech': '', + 'pct': 0, + 'prod_t_ha': 0, + 'oil_prod_t_ha': 0, + 'oil_prod_l_ha': 0, + 'min_yield_pct': 0, + 'max_yield_pct': 0, + 'min_oil_prod_l_ha': 0, + 'max_oil_prod_l_ha': 0, + 'avg_oil_prod_l_ha': 0, + 'l_per_t': 0, + 'min_l_per_t': 0, + 'max_l_per_t': 0, + 'avg_l_per_t': 0, + 'olive_prod': 0, + 'min_oil_prod': 0, + 'max_oil_prod': 0, + 'avg_oil_prod': 0, + 'water_need': 0 + } for variety in all_varieties} + + # Simula produzione per ogni varietà selezionata + for i, variety in enumerate(selected_varieties): + # Seleziona tecnica di coltivazione casuale per questa varietà + technique = np.random.choice(variety_techniques[variety]) + percentage = percentages[i] + + # Ottieni informazioni specifiche della varietà + variety_info = olive_varieties[ + (olive_varieties['Varietà di Olive'] == variety) & + (olive_varieties['Tecnica di Coltivazione'] == technique) + ].iloc[0] + + # Calcola produzione base con variabilità + base_production = variety_info['Produzione (tonnellate/ettaro)'] * 1000 * percentage * hectares / 12 + base_production *= np.random.uniform(0.9, 1.1) + + # Calcola effetti meteo sulla produzione + weather_effect = zone_weather.apply( + lambda row: calculate_weather_effect(row, variety_info['Temperatura Ottimale']), + axis=1 + ) + monthly_production = base_production * (1 + weather_effect / 10000) + monthly_production *= np.random.uniform(0.95, 1.05, len(zone_weather)) + + # Calcola produzione annuale per questa varietà + annual_variety_production = monthly_production.sum() + + # Calcola rese di olio con variabilità + min_yield_factor = np.random.uniform(0.95, 1.05) + max_yield_factor = np.random.uniform(0.95, 1.05) + avg_yield_factor = (min_yield_factor + max_yield_factor) / 2 + + min_oil_production = annual_variety_production * variety_info[ + 'Min Litri per Tonnellata'] / 1000 * min_yield_factor + max_oil_production = annual_variety_production * variety_info[ + 'Max Litri per Tonnellata'] / 1000 * max_yield_factor + avg_oil_production = annual_variety_production * variety_info[ + 'Media Litri per Tonnellata'] / 1000 * avg_yield_factor + + # Calcola fabbisogno idrico + base_water_need = ( + variety_info['Fabbisogno Acqua Primavera (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Estate (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Autunno (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Inverno (m³/ettaro)'] + ) / 4 + + monthly_water_need = zone_weather.apply( + lambda row: calculate_water_need(row, base_water_need, variety_info['Temperatura Ottimale']), + axis=1 + ) + monthly_water_need *= np.random.uniform(0.95, 1.05, len(monthly_water_need)) + annual_variety_water_need = monthly_water_need.sum() * percentage * hectares + + # Aggiorna totali annuali + annual_production += annual_variety_production + annual_min_oil += min_oil_production + annual_max_oil += max_oil_production + annual_avg_oil += avg_oil_production + annual_water_need += annual_variety_water_need + + # Aggiorna dati varietà + clean_variety = clean_column_name(variety) + variety_data[clean_variety].update({ + 'tech': clean_column_name(technique), + 'pct': percentage, + 'prod_t_ha': variety_info['Produzione (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05), + 'oil_prod_t_ha': variety_info['Produzione Olio (tonnellate/ettaro)'] * np.random.uniform(0.95, 1.05), + 'oil_prod_l_ha': variety_info['Produzione Olio (litri/ettaro)'] * np.random.uniform(0.95, 1.05), + 'min_yield_pct': variety_info['Min % Resa'] * min_yield_factor, + 'max_yield_pct': variety_info['Max % Resa'] * max_yield_factor, + 'min_oil_prod_l_ha': variety_info['Min Produzione Olio (litri/ettaro)'] * min_yield_factor, + 'max_oil_prod_l_ha': variety_info['Max Produzione Olio (litri/ettaro)'] * max_yield_factor, + 'avg_oil_prod_l_ha': variety_info['Media Produzione Olio (litri/ettaro)'] * avg_yield_factor, + 'l_per_t': variety_info['Litri per Tonnellata'] * np.random.uniform(0.98, 1.02), + 'min_l_per_t': variety_info['Min Litri per Tonnellata'] * min_yield_factor, + 'max_l_per_t': variety_info['Max Litri per Tonnellata'] * max_yield_factor, + 'avg_l_per_t': variety_info['Media Litri per Tonnellata'] * avg_yield_factor, + 'olive_prod': annual_variety_production, + 'min_oil_prod': min_oil_production, + 'max_oil_prod': max_oil_production, + 'avg_oil_prod': avg_oil_production, + 'water_need': annual_variety_water_need + }) + + # Appiattisci i dati delle varietà + flattened_variety_data = { + f'{variety}_{key}': value + for variety, data in variety_data.items() + for key, value in data.items() + } + + # Restituisci il risultato della zona + return { + 'year': year, + 'zone_id': zone + 1, + 'temp_mean': zone_weather['temp_mean'].mean(), + 'precip_sum': zone_weather['precip_sum'].sum(), + 'solar_energy_sum': zone_weather['solarenergy_sum'].sum(), + 'ha': hectares, + 'zone': f"zone_{zone + 1}", + 'olive_prod': annual_production, + 'min_oil_prod': annual_min_oil, + 'max_oil_prod': annual_max_oil, + 'avg_oil_prod': annual_avg_oil, + 'total_water_need': annual_water_need, + **flattened_variety_data + } + + +def simulate_olive_production_parallel(weather_data, olive_varieties, num_simulations=5, num_zones=None, + random_seed=None, + max_workers=None, batch_size=500, + output_path='olive_simulation_dataset.parquet'): + """ + Versione corretta della simulazione parallelizzata con gestione batch e salvataggio file + + Args: + weather_data: DataFrame con dati meteo + olive_varieties: DataFrame con varietà di olive + num_simulations: numero di simulazioni da eseguire (default: 5) + num_zones: numero di zone per simulazione (default: None, usa num_simulations se non specificato) + random_seed: seed per riproducibilità (default: None) + max_workers: numero massimo di workers (default: None, usa get_optimal_workers) + batch_size: dimensione del batch per gestione memoria (default: 500) + output_path: percorso del file di output (default: 'olive_simulation_dataset.parquet') + + Returns: + DataFrame con i risultati delle simulazioni + """ + if random_seed is not None: + np.random.seed(random_seed) + + # Se num_zones non è specificato, usa num_simulations + if num_zones is None: + num_zones = num_simulations + + # Preparazione dati + create_technique_mapping(olive_varieties) + monthly_weather = preprocess_weather_data(weather_data) + all_varieties = olive_varieties['Varietà di Olive'].unique() + variety_techniques = { + variety: olive_varieties[olive_varieties['Varietà di Olive'] == variety]['Tecnica di Coltivazione'].unique() + for variety in all_varieties + } + + # Calcolo workers ottimali usando get_optimal_workers + if max_workers is None: + max_workers = get_optimal_workers() + print(f"Utilizzando {max_workers} workers ottimali basati sulle risorse del sistema") + + # Calcolo numero di batch + num_batches = (num_simulations + batch_size - 1) // batch_size + print(f"Elaborazione di {num_simulations} simulazioni con {num_zones} zone in {num_batches} batch") + print(f"Totale record attesi: {num_simulations * num_zones:,}") + + # Lista per contenere tutti i DataFrame dei batch + all_batches = [] + + # Elaborazione per batch + for batch_num in range(num_batches): + start_sim = batch_num * batch_size + end_sim = min((batch_num + 1) * batch_size, num_simulations) + current_batch_size = end_sim - start_sim + + batch_results = [] + + # Parallelizzazione usando ProcessPoolExecutor per il batch corrente + with ProcessPoolExecutor(max_workers=max_workers) as executor: + # Calcola il numero totale di task per questo batch + # Ogni simulazione nel batch corrente genererà num_zones zone + total_tasks = current_batch_size * num_zones + + with tqdm(total=total_tasks, + desc=f"Batch {batch_num + 1}/{num_batches}") as pbar: + # Dizionario per tenere traccia delle futures e dei loro sim_id + future_to_sim_id = {} + + # Sottometti i lavori per tutte le simulazioni e zone nel batch corrente + for sim in range(start_sim, end_sim): + selected_year = np.random.choice(monthly_weather['year'].unique()) + base_weather = monthly_weather[monthly_weather['year'] == selected_year].copy() + base_weather.loc[:, 'growth_phase'] = base_weather['month'].apply(get_growth_phase) + + # Sottometti i lavori per tutte le zone di questa simulazione + for zone in range(num_zones): + future = executor.submit( + simulate_zone, + base_weather=base_weather, + olive_varieties=olive_varieties, + year=selected_year, + zone=zone, + all_varieties=all_varieties, + variety_techniques=variety_techniques + ) + future_to_sim_id[future] = (sim + 1, zone + 1) + + # Raccogli i risultati man mano che vengono completati + for future in as_completed(future_to_sim_id.keys()): + sim_id, zone_id = future_to_sim_id[future] + try: + result = future.result() + result['simulation_id'] = sim_id + result['zone_id'] = zone_id + batch_results.append(result) + pbar.update(1) + except Exception as e: + print(f"Errore nella simulazione {sim_id}, zona {zone_id}: {str(e)}") + continue + + # Converti batch_results in DataFrame e aggiungi alla lista dei batch + batch_df = pd.DataFrame(batch_results) + all_batches.append(batch_df) + + # Stampa statistiche del batch + print(f"\nStatistiche Batch {batch_num + 1}:") + print(f"Righe processate: {len(batch_df):,}") + print(f"Memoria utilizzata: {batch_df.memory_usage(deep=True).sum() / 1024 ** 2:.2f} MB") + + # Libera memoria + del batch_results + del batch_df + gc.collect() # Forza garbage collection + + # Concatena tutti i batch e salva + print("\nConcatenazione dei batch e salvataggio...") + final_df = pd.concat(all_batches, ignore_index=True) + + # Crea directory output se necessario + os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else '.', exist_ok=True) + + # Salva il dataset + final_df.to_parquet(output_path) + + # Stampa statistiche finali + print("\nStatistiche Finali:") + print(f"Totale simulazioni completate: {len(final_df):,}") + print(f"Memoria totale utilizzata: {final_df.memory_usage(deep=True).sum() / 1024 ** 2:.2f} MB") + print(f"\nDataset salvato in: {output_path}") + + return final_df + + +def calculate_production(variety_info, weather, percentage, hectares, seed): + """Calcola produzione e parametri correlati per una varietà""" + np.random.seed(seed) + + base_production = variety_info['Produzione (tonnellate/ettaro)'] * percentage * hectares + base_production *= np.random.uniform(0.8, 1.2) + + # Effetti ambientali + temp_effect = calculate_temperature_effect( + weather['temp_mean'], + variety_info['Temperatura Ottimale'] + ) + water_effect = calculate_water_effect( + weather['precip_sum'], + variety_info['Resistenza alla Siccità'] + ) + solar_effect = calculate_solar_effect( + weather['solarradiation_mean'] + ) + + actual_production = base_production * temp_effect * water_effect * solar_effect + + # Calcolo olio + oil_yield = np.random.uniform( + variety_info['Min % Resa'], + variety_info['Max % Resa'] + ) + oil_production = actual_production * oil_yield + + # Calcolo acqua + base_water_need = ( + variety_info['Fabbisogno Acqua Primavera (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Estate (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Autunno (m³/ettaro)'] + + variety_info['Fabbisogno Acqua Inverno (m³/ettaro)'] + ) / 4 * percentage * hectares + + water_need = ( + base_water_need * + (1 + max(0, (weather['temp_mean'] - 20) / 50)) * + max(0.6, 1 - (weather['precip_sum'] / 1000)) + ) + + return { + 'variety': variety_info['Varietà di Olive'], + 'technique': variety_info['Tecnica di Coltivazione'], + 'percentage': percentage, + 'production': actual_production, + 'oil_production': oil_production, + 'water_need': water_need, + 'temp_effect': temp_effect, + 'water_effect': water_effect, + 'solar_effect': solar_effect, + 'yield': oil_yield + } + + +# Funzioni di effetto ambientale rimangono invariate +def calculate_temperature_effect(temp, optimal_temp): + temp_diff = abs(temp - optimal_temp) + if temp_diff <= 5: + return np.random.uniform(0.95, 1.0) + elif temp_diff <= 10: + return np.random.uniform(0.8, 0.9) + else: + return np.random.uniform(0.6, 0.8) + + +def calculate_water_effect(precip, drought_resistance): + if 'alta' in str(drought_resistance).lower(): + min_precip = 20 + elif 'media' in str(drought_resistance).lower(): + min_precip = 30 + else: + min_precip = 40 + + if precip >= min_precip: + return np.random.uniform(0.95, 1.0) + else: + base_factor = max(0.6, precip / min_precip) + return base_factor * np.random.uniform(0.8, 1.2) + + +def calculate_solar_effect(radiation): + if radiation >= 200: + return np.random.uniform(0.95, 1.0) + else: + base_factor = max(0.7, radiation / 200) + return base_factor * np.random.uniform(0.8, 1.2) + + +def parse_arguments(): + """ + Configura e gestisce i parametri da riga di comando + """ + parser = argparse.ArgumentParser( + description='Generatore dataset di training per produzione olive', + formatter_class=argparse.ArgumentDefaultsHelpFormatter # Mostra i valori default nell'help + ) + + parser.add_argument( + '--random-seed', + type=int, + default=None, + help='Seed per la riproducibilità dei risultati' + ) + + parser.add_argument( + '--num-simulations', + type=int, + default=100000, + help='Numero totale di simulazioni da eseguire' + ) + + parser.add_argument( + '--num-zones', + type=int, + default=None, + help='Numero di zone per simulazione (default: uguale a num-simulations)' + ) + + parser.add_argument( + '--batch-size', + type=int, + default=10000, + help='Dimensione di ogni batch di simulazioni' + ) + + parser.add_argument( + '--output-path', + type=str, + default='./sources/olive_training_dataset.parquet', + help='Percorso del file di output' + ) + + parser.add_argument( + '--max-workers', + type=int, + default=None, + help='Quantità di workers (default: usa get_optimal_workers)' + ) + + return parser.parse_args() + + +if __name__ == "__main__": + print("Generazione dataset di training...") + + # Parsing argomenti + args = parse_arguments() + + # Carica dati + try: + weather_data = pd.read_parquet('./sources/weather_data_solarenergy.parquet') + olive_varieties = pd.read_parquet('./sources/olive_varieties.parquet') + except Exception as e: + print(f"Errore nel caricamento dei dati: {str(e)}") + sys.exit(1) + + # Stampa configurazione + print("\nConfigurazione:") + print(f"Random seed: {args.random_seed}") + print(f"Numero simulazioni: {args.num_simulations:,}") + print(f"Numero zone per simulazione: {args.num_zones if args.num_zones is not None else args.num_simulations:,}") + print(f"Workers: {args.max_workers if args.max_workers is not None else 'auto'}") + print(f"Dimensione batch: {args.batch_size:,}") + print(f"File output: {args.output_path}") + + # Genera dataset + try: + df = simulate_olive_production_parallel( + weather_data=weather_data, + olive_varieties=olive_varieties, + num_simulations=args.num_simulations, + num_zones=args.num_zones, + random_seed=args.random_seed, + batch_size=args.batch_size, + output_path=args.output_path, + max_workers=args.max_workers + ) + except Exception as e: + print(f"Errore durante la generazione del dataset: {str(e)}") + sys.exit(1) \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/utils/helpers.py b/utils/helpers.py new file mode 100755 index 0000000..3f35a74 --- /dev/null +++ b/utils/helpers.py @@ -0,0 +1,504 @@ +import psutil +import multiprocessing +import re +import pandas as pd +import numpy as np +from typing import List, Dict +import os +import joblib + + +def get_optimal_workers() -> int: + """ + Calcola il numero ottimale di workers basandosi sulle risorse del sistema. + + Returns + ------- + int + Numero ottimale di workers + """ + # Ottiene il numero di CPU logiche (inclusi i thread virtuali) + cpu_count = multiprocessing.cpu_count() + + # Ottiene la memoria totale e disponibile in GB + memory = psutil.virtual_memory() + total_memory_gb = memory.total / (1024 ** 3) + available_memory_gb = memory.available / (1024 ** 3) + + # Stima della memoria necessaria per worker (esempio: 2GB per worker) + memory_per_worker_gb = 2 + + # Calcola il numero massimo di workers basato sulla memoria disponibile + max_workers_by_memory = int(available_memory_gb / memory_per_worker_gb) + + # Usa il minimo tra: + # - numero di CPU disponibili - 1 (lascia una CPU libera per il sistema) + # - numero massimo di workers basato sulla memoria + # - un limite massimo arbitrario (es. 32) per evitare troppo overhead + optimal_workers = min( + cpu_count - 1, + max_workers_by_memory, + 32 # limite massimo arbitrario + ) + + # Assicura almeno 1 worker + return max(1, optimal_workers) + + +def clean_column_name(name: str) -> str: + """ + Rimuove caratteri speciali e spazi, converte in snake_case e abbrevia. + + Parameters + ---------- + name : str + Nome della colonna da pulire + + Returns + ------- + str + Nome della colonna pulito + """ + # Rimuove caratteri speciali + name = re.sub(r'[^a-zA-Z0-9\s]', '', name) + # Converte in snake_case + name = name.lower().replace(' ', '_') + + # Abbreviazioni comuni + abbreviations = { + 'production': 'prod', + 'percentage': 'pct', + 'hectare': 'ha', + 'tonnes': 't', + 'litres': 'l', + 'minimum': 'min', + 'maximum': 'max', + 'average': 'avg' + } + + for full, abbr in abbreviations.items(): + name = name.replace(full, abbr) + + return name + + +def clean_column_names(df: pd.DataFrame) -> List[str]: + """ + Pulisce tutti i nomi delle colonne in un DataFrame. + + Parameters + ---------- + df : pd.DataFrame + DataFrame con le colonne da pulire + + Returns + ------- + list + Lista dei nuovi nomi delle colonne puliti + """ + new_columns = [] + + for col in df.columns: + # Usa regex per separare le varietà + varieties = re.findall(r'([a-z]+)_([a-z_]+)', col) + if varieties: + new_columns.append(f"{varieties[0][0]}_{varieties[0][1]}") + else: + new_columns.append(col) + + return new_columns + + +def to_camel_case(text: str) -> str: + """ + Converte una stringa in camelCase. + Gestisce stringhe con spazi, trattini o underscore. + Se è una sola parola, la restituisce in minuscolo. + + Parameters + ---------- + text : str + Testo da convertire + + Returns + ------- + str + Testo convertito in camelCase + """ + # Rimuove eventuali spazi iniziali e finali + text = text.strip() + + # Se la stringa è vuota, ritorna stringa vuota + if not text: + return "" + + # Sostituisce trattini e underscore con spazi + text = text.replace('-', ' ').replace('_', ' ') + + # Divide la stringa in parole + words = text.split() + + # Se non ci sono parole dopo lo split, ritorna stringa vuota + if not words: + return "" + + # Se c'è una sola parola, ritorna in minuscolo + if len(words) == 1: + return words[0].lower() + + # Altrimenti procedi con il camelCase + result = words[0].lower() + for word in words[1:]: + result += word.capitalize() + + return result + + +def get_full_data(simulated_data: pd.DataFrame, + olive_varieties: pd.DataFrame) -> pd.DataFrame: + """ + Ottiene il dataset completo combinando dati simulati e varietà di olive. + + Parameters + ---------- + simulated_data : pd.DataFrame + DataFrame con i dati simulati + olive_varieties : pd.DataFrame + DataFrame con le informazioni sulle varietà + + Returns + ------- + pd.DataFrame + DataFrame completo con tutte le informazioni + """ + # Colonne base rilevanti + relevant_columns = [ + 'year', 'temp_mean', 'precip_sum', 'solar_energy_sum', + 'ha', 'zone', 'olive_prod' + ] + + # Aggiungi colonne specifiche per varietà + all_varieties = olive_varieties['Varietà di Olive'].unique() + varieties = [clean_column_name(variety) for variety in all_varieties] + + for variety in varieties: + relevant_columns.extend([ + f'{variety}_olive_prod', + f'{variety}_tech' + ]) + + # Seleziona solo le colonne rilevanti + full_data = simulated_data[relevant_columns].copy() + + # Aggiungi feature calcolate + for variety in varieties: + # Calcola efficienza produttiva + if f'{variety}_olive_prod' in full_data.columns: + full_data[f'{variety}_efficiency'] = ( + full_data[f'{variety}_olive_prod'] / full_data['ha'] + ) + + # Aggiungi indicatori tecnici + if f'{variety}_tech' in full_data.columns: + technique_dummies = pd.get_dummies( + full_data[f'{variety}_tech'], + prefix=f'{variety}_technique' + ) + full_data = pd.concat([full_data, technique_dummies], axis=1) + + # Aggiungi feature temporali + full_data['month'] = 1 # Assumiamo dati annuali + full_data['day'] = 1 # Assumiamo dati annuali + + # Calcola medie mobili + for col in ['temp_mean', 'precip_sum', 'solar_energy_sum']: + full_data[f'{col}_ma3'] = full_data[col].rolling(window=3, min_periods=1).mean() + full_data[f'{col}_ma5'] = full_data[col].rolling(window=5, min_periods=1).mean() + + return full_data + +def prepare_static_features_multiple(varieties_info: List[Dict], + percentages: List[float], + hectares: float, + all_varieties: List[str]) -> np.ndarray: + """ + Prepara le feature statiche per multiple varietà. + + Parameters + ---------- + varieties_info : List[Dict] + Lista di dizionari contenenti le informazioni sulle varietà selezionate + percentages : List[float] + Lista delle percentuali corrispondenti a ciascuna varietà selezionata + hectares : float + Numero di ettari totali + all_varieties : List[str] + Lista di tutte le possibili varietà nel dataset originale + + Returns + ------- + np.ndarray + Array numpy contenente tutte le feature statiche + """ + # Inizializza un dizionario per tutte le varietà possibili + variety_data = {variety.lower(): { + 'pct': 0, + 'prod_t_ha': 0, + 'tech': '', + 'oil_prod_t_ha': 0, + 'oil_prod_l_ha': 0, + 'min_yield_pct': 0, + 'max_yield_pct': 0, + 'min_oil_prod_l_ha': 0, + 'max_oil_prod_l_ha': 0, + 'avg_oil_prod_l_ha': 0, + 'l_per_t': 0, + 'min_l_per_t': 0, + 'max_l_per_t': 0, + 'avg_l_per_t': 0, + 'water_need_spring': 0, + 'water_need_summer': 0, + 'water_need_autumn': 0, + 'water_need_winter': 0, + 'annual_water_need': 0, + 'optimal_temp': 0, + 'drought_resistance': 0 + } for variety in all_varieties} + + # Aggiorna i dati per le varietà selezionate + for variety_info, percentage in zip(varieties_info, percentages): + variety_name = clean_column_name(variety_info['variet_di_olive']).lower() + technique = clean_column_name(variety_info['tecnica_di_coltivazione']).lower() + + if variety_name not in variety_data: + print(f"Attenzione: La varietà '{variety_name}' non è presente nella lista delle varietà conosciute.") + continue + + variety_data[variety_name].update({ + 'pct': percentage / 100, + 'prod_t_ha': variety_info['produzione_tonnellateettaro'], + 'tech': technique, + 'oil_prod_t_ha': variety_info['produzione_olio_tonnellateettaro'], + 'oil_prod_l_ha': variety_info['produzione_olio_litriettaro'], + 'min_yield_pct': variety_info['min__resa'], + 'max_yield_pct': variety_info['max__resa'], + 'min_oil_prod_l_ha': variety_info['min_produzione_olio_litriettaro'], + 'max_oil_prod_l_ha': variety_info['max_produzione_olio_litriettaro'], + 'avg_oil_prod_l_ha': variety_info['media_produzione_olio_litriettaro'], + 'l_per_t': variety_info['litri_per_tonnellata'], + 'min_l_per_t': variety_info['min_litri_per_tonnellata'], + 'max_l_per_t': variety_info['max_litri_per_tonnellata'], + 'avg_l_per_t': variety_info['media_litri_per_tonnellata'], + 'water_need_spring': variety_info['fabbisogno_acqua_primavera_mettaro'], + 'water_need_summer': variety_info['fabbisogno_acqua_estate_mettaro'], + 'water_need_autumn': variety_info['fabbisogno_acqua_autunno_mettaro'], + 'water_need_winter': variety_info['fabbisogno_acqua_inverno_mettaro'], + 'annual_water_need': variety_info['fabbisogno_idrico_annuale_mettaro'], + 'optimal_temp': variety_info['temperatura_ottimale'], + 'drought_resistance': variety_info['resistenza_alla_siccit'] + }) + + # Crea il vettore delle feature + static_features = [hectares] + + # Lista delle feature per ogni varietà + variety_features = ['pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha', + 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', + 'max_oil_prod_l_ha', 'avg_oil_prod_l_ha', 'l_per_t', + 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t', + 'water_need_spring', 'water_need_summer', 'water_need_autumn', + 'water_need_winter', 'annual_water_need', 'optimal_temp', + 'drought_resistance'] + + # Appiattisci i dati delle varietà + for variety in all_varieties: + variety_lower = variety.lower() + # Feature esistenti + for feature in variety_features: + static_features.append(variety_data[variety_lower][feature]) + + # Feature binarie per le tecniche + for technique in ['tradizionale', 'intensiva', 'superintensiva']: + static_features.append(1 if variety_data[variety_lower]['tech'] == technique else 0) + + return np.array(static_features).reshape(1, -1) + + +def get_feature_names(all_varieties: List[str]) -> List[str]: + """ + Genera i nomi delle feature nell'ordine corretto. + + Parameters + ---------- + all_varieties : List[str] + Lista di tutte le varietà possibili + + Returns + ------- + List[str] + Lista dei nomi delle feature + """ + feature_names = ['hectares'] + + variety_features = ['pct', 'prod_t_ha', 'oil_prod_t_ha', 'oil_prod_l_ha', + 'min_yield_pct', 'max_yield_pct', 'min_oil_prod_l_ha', + 'max_oil_prod_l_ha', 'avg_oil_prod_l_ha', 'l_per_t', + 'min_l_per_t', 'max_l_per_t', 'avg_l_per_t'] + + techniques = ['tradizionale', 'intensiva', 'superintensiva'] + + for variety in all_varieties: + for feature in variety_features: + feature_names.append(f"{variety}_{feature}") + for technique in techniques: + feature_names.append(f"{variety}_tech_{technique}") + + return feature_names + +def add_controlled_variation(base_value: float, max_variation_pct: float = 0.20) -> float: + """ + Aggiunge una variazione controllata a un valore base. + + Parameters + ---------- + base_value : float + Valore base da modificare + max_variation_pct : float + Percentuale massima di variazione (default 20%) + + Returns + ------- + float + Valore con variazione applicata + """ + variation = np.random.uniform(-max_variation_pct, max_variation_pct) + return base_value * (1 + variation) + +def get_growth_phase(month): + if month in [12, 1, 2]: + return 'dormancy' + elif month in [3, 4, 5]: + return 'flowering' + elif month in [6, 7, 8]: + return 'fruit_set' + else: + return 'ripening' + +def calculate_weather_effect(row, optimal_temp): + # Effetti base + temp_effect = -0.1 * (row['temp_mean'] - optimal_temp) ** 2 + rain_effect = -0.05 * (row['precip_sum'] - 600) ** 2 / 10000 + sun_effect = 0.1 * row['solarenergy_sum'] / 1000 + + # Fattori di scala basati sulla fase di crescita + if row['growth_phase'] == 'dormancy': + temp_scale = 0.5 + rain_scale = 0.2 + sun_scale = 0.1 + elif row['growth_phase'] == 'flowering': + temp_scale = 2.0 + rain_scale = 1.5 + sun_scale = 1.0 + elif row['growth_phase'] == 'fruit_set': + temp_scale = 1.5 + rain_scale = 1.0 + sun_scale = 0.8 + else: # ripening + temp_scale = 1.0 + rain_scale = 0.5 + sun_scale = 1.2 + + # Calcolo dell'effetto combinato + combined_effect = ( + temp_scale * temp_effect + + rain_scale * rain_effect + + sun_scale * sun_effect + ) + + # Aggiustamenti specifici per fase + if row['growth_phase'] == 'flowering': + combined_effect -= 0.5 * max(0, row['precip_sum'] - 50) # Penalità per pioggia eccessiva durante la fioritura + elif row['growth_phase'] == 'fruit_set': + combined_effect += 0.3 * max(0, row['temp_mean'] - (optimal_temp + 5)) # Bonus per temperature più alte durante la formazione dei frutti + + return combined_effect + +def calculate_water_need(weather_data, base_need, optimal_temp): + # Calcola il fabbisogno idrico basato su temperatura e precipitazioni + temp_factor = 1 + 0.05 * (weather_data['temp_mean'] - optimal_temp) # Aumenta del 5% per ogni grado sopra l'ottimale + rain_factor = 1 - 0.001 * weather_data['precip_sum'] # Diminuisce leggermente con l'aumentare delle precipitazioni + return base_need * temp_factor * rain_factor + +def create_technique_mapping(olive_varieties, mapping_path='./sources/technique_mapping.joblib'): + # Estrai tutte le tecniche uniche dal dataset e convertile in lowercase + all_techniques = olive_varieties['Tecnica di Coltivazione'].str.lower().unique() + + # Crea il mapping partendo da 1 + technique_mapping = {tech: i + 1 for i, tech in enumerate(sorted(all_techniques))} + + # Salva il mapping + os.makedirs(os.path.dirname(mapping_path), exist_ok=True) + joblib.dump(technique_mapping, mapping_path) + + return technique_mapping + + +def encode_techniques(df, mapping_path='./sources/technique_mapping.joblib'): + if not os.path.exists(mapping_path): + raise FileNotFoundError(f"Mapping not found at {mapping_path}. Run create_technique_mapping first.") + + technique_mapping = joblib.load(mapping_path) + + # Trova tutte le colonne delle tecniche + tech_columns = [col for col in df.columns if col.endswith('_tech')] + + # Applica il mapping a tutte le colonne delle tecniche + for col in tech_columns: + df[col] = df[col].str.lower().map(technique_mapping).fillna(0).astype(int) + + return df + + +def decode_techniques(df, mapping_path='./sources/technique_mapping.joblib'): + if not os.path.exists(mapping_path): + raise FileNotFoundError(f"Mapping not found at {mapping_path}") + + technique_mapping = joblib.load(mapping_path) + reverse_mapping = {v: k for k, v in technique_mapping.items()} + reverse_mapping[0] = '' # Aggiungi un mapping per 0 a stringa vuota + + # Trova tutte le colonne delle tecniche + tech_columns = [col for col in df.columns if col.endswith('_tech')] + + # Applica il reverse mapping a tutte le colonne delle tecniche + for col in tech_columns: + df[col] = df[col].map(reverse_mapping) + + return df + + +def decode_single_technique(technique_value, mapping_path='./sources/technique_mapping.joblib'): + if not os.path.exists(mapping_path): + raise FileNotFoundError(f"Mapping not found at {mapping_path}") + + technique_mapping = joblib.load(mapping_path) + reverse_mapping = {v: k for k, v in technique_mapping.items()} + reverse_mapping[0] = '' + + return reverse_mapping.get(technique_value, '') + +def preprocess_weather_data(weather_df): + # Calcola statistiche mensili per ogni anno + monthly_weather = weather_df.groupby(['year', 'month']).agg({ + 'temp': ['mean', 'min', 'max'], + 'humidity': 'mean', + 'precip': 'sum', + 'windspeed': 'mean', + 'cloudcover': 'mean', + 'solarradiation': 'sum', + 'solarenergy': 'sum', + 'uvindex': 'max' + }).reset_index() + + monthly_weather.columns = ['year', 'month'] + [f'{col[0]}_{col[1]}' for col in monthly_weather.columns[2:]] + return monthly_weather \ No newline at end of file