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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8adcbe0819b88578",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"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",
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"Building dependency tree... Done\n",
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"graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"\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",
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"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",
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"\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",
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"\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",
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"\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",
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"\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": {
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GG29UmTJl1KJFC23ZsqXAv+birEbFcrqtpo/sdumb3SfMHgcAAAAATEcwDkj0iAMAAAAo8datW6f+/fvrxx9/VExMjDIyMtSxY0elpqY61gwePFhfffWVPv/8c61bt07Hjx/XQw895LifmZmprl27Kj09XRs3btSCBQsUFRWl8PBwx5pDhw6pa9euat++vXbu3KlBgwbpmWee0cqVKx1rPv30U4WFhWns2LHavn27GjdurJCQEJ08ebJovhnFxL23GieefbWLYBwAAAAAbHa73W72ECVFSkqKvL29lZycLC8vL7PHgWQckz59ujR5svFnyegRf/11jkwHAAAAcM2Kw89/p06dUrVq1bRu3Tq1bdtWycnJqlq1qhYtWqSHH35YkhQXF6d69epp06ZNatmypVasWKF7771Xx48fl6+vcZR3ZGSkXn75ZZ06dUoeHh56+eWX9fXXX2vPnj2Oz+rZs6fOnDmj6OhoSVKLFi10++23680335QkZWVlKSAgQC+++KJGjBiR47xpaWlKS0tzPE9JSVFAQIBL/zOoXqOmhs9flau1EX2DdeLo4Ty9f0LyeQVNXi27XfphxN263qfstYwJAAAAAC4rLz9/s2McJZPdbhyRTo84AAAAAOQoOTlZklSpUiVJUmxsrDIyMhQcHOxYU7duXdWsWVObNm2SJG3atEmNGjVyhOKSFBISopSUFO3du9ex5uL3yF6T/R7p6emKjY11WuPm5qbg4GDHmpxMmjRJ3t7ejkdAQEB+vvxiwc+7jG6/0fjn9/Wu4yZPAwAAAADmIhhHybN1q9S6tfTYY0aPeECA9PHH9IgDAAAAwP9kZWVp0KBBuvPOO9WwYUNJUkJCgjw8POTj4+O01tfXVwkJCY41F4fi2fez711pTUpKis6dO6c//vhDmZmZOa7Jfo+cjBw5UsnJyY7HkSNH8v6FF0P3NTb+4vdyjlMHAAAAUMIRjKPkOHbMOCb9jjukjRv/6RHfv1/q2ZMecQAAAAD4n/79+2vPnj365JNPzB4l1zw9PeXl5eX0gNS5oZ/cbNKuo8n6/c/Uq78AAAAAAIopgnEUf+fOGQH4LbdIH35oXHvySenAAWn0aKksHWsAAAAAkG3AgAFavny51q5dqxo1ajiu+/n5KT09XWfOnHFan5iYKD8/P8eaxMTES+5n37vSGi8vL5UtW1ZVqlRRqVKlclyT/R7IvSrXearVzVUksWscAAAAQMlGMI7iK7tHvE6dS3vEFyyQrr/e7AkBAAAAwGXY7XYNGDBAS5Ys0Zo1a1SrVi2n+82aNVPp0qW1evVqx7X9+/fr8OHDCgoKkiQFBQVp9+7dOnnypGNNTEyMvLy8VL9+fceai98je032e3h4eKhZs2ZOa7KysrR69WrHGuTNfY2rS5K++omecQAAAAAlF8E4iid6xAEAAAAgT/r376+PPvpIixYtUoUKFZSQkKCEhASdO3dOkuTt7a3Q0FCFhYVp7dq1io2NVd++fRUUFKSWLVtKkjp27Kj69evriSee0E8//aSVK1dq9OjR6t+/vzw9PSVJ//nPf/Trr79q+PDhiouL09tvv63PPvtMgwcPdswSFhamd999VwsWLNC+ffv0/PPPKzU1VX379i36b0wxENLAT+5uNsUl/KWDJ/8yexwAAAAAMIW72QMABerYMemVV/45Mr1cOWnECGnoUI5MBwAAAIArmDt3riSpXbt2Ttfnz5+vp556SpI0c+ZMubm5qXv37kpLS1NISIjefvttx9pSpUpp+fLlev755xUUFKTy5curT58+mjBhgmNNrVq19PXXX2vw4MGaPXu2atSooffee08hISGONT169NCpU6cUHh6uhIQENWnSRNHR0fL19S28b0Ax5lPOQ21vqao1cSf11U8nNPieCmaPBAAAAABFzma32+1mD1FSpKSkyNvbW8nJyfLy8jJ7nOLl3Dlp+nRp0iTjyHTJ6BF//XWOTAcAAABQ5Pj5z3xW+GdQvUZNDZ+/KldrI/oG68TRw9f8WYu3H1XYZz+pVpXyWjPkLtlstmt+LwAAAABwFXn52Y+j1GFtdrv06adS3brSmDFGKB4UJG3eTI84AAAAAAD/07GBn8p7lNKhP1K16dc/zR4HAAAAAIocwTisK7tHvGdP6fDhf3rEf/hBuuMOs6cDAAAAAMBlXOfprgduM/7y+MIfr33nOQAAAABYFcE4rOf4calPHyP83rjR6BGfMEGKizNCco6DAwAAAADgEo+3uEGStHJvgk6mnDd5GgAAAAAoWgTjsI5z56SJE6XAQOnDD41rTz4pHThgHKNerpy58wEAAAAA4MLq+3upaU0fXciy69OtR8weBwAAAACKFME4XB894gAAAAAAFIjHWxq7xj/ecliZWXaTpwEAAACAokMwDte2bZvUpo1zj/iiRfSIAwAAAABwDbo0qq6K5UrrePJ5rYk7afY4AAAAAFBkCMbhmo4fl556Srr9diMEv7hH/LHH6BEHAAAAAOAalCldSo80D5AkffTj7yZPAwAAAABFh2AcruXcOem116RbbjGOSZekJ56Q9u+nRxwAAAAAgALQ646akqT18ad0+M+/TZ4GAAAAAIoGwThcw8U94qNHS6mp//SIf/ihVKOG2RMCAAAAAFAs3FilvNoEVpHdLi3cwq5xAAAAACUDwTjMR484AAAAAABF6vGWN0iSPtt6RKlpF0yeBgAAAAAKH8E4zJNTj/j48fSIAwAAAABQyDrUraYbK5fT6b8zNP+HQ2aPAwAAAACFjmAcRS+nHvHHHzd6xMPD6REHAAAAAKCQuZdy0+B7bpEkvbP+V535O93kiQAAAACgcBGMo+jY7dJnn0n16v3TI96ypfTjj9J//0uPOAAAAAAARei+W/1V16+C/jp/Qe+s/9XscQAAAACgUBGMo2hk94j36CH9/rsRgi9aJG3cKLVoYfZ0AAAAAACUOG5uNg3tWEeSNP+HQzr513mTJwIAAACAwkMwjsJ1/LjUt++lPeL799MjDgAAAACAyTrUq6bbavrofEaW3lpz0OxxAAAAAKDQEIyjcFzcIx4VZVyjRxwAAAAAAJdis9k0LMTYNb5oy2EdSfrb5IkAAAAAoHAQjKNg0SMOAAAAAICltLq5ilrXrqKMTLtmr443exwAAAAAKBQE4yg4sbFS27bOPeILF9IjDgAAAACAixv6v13jX24/qm2/JZk8DQAAAAAUPIJx5N/FPeLffy+VLSuNG2ccm96rFz3iAAAAAAC4uCYBPuretIbsdmnwZzv11/kMs0cCAAAAgAJFMI5rd+6c9Prr//SI2+1Gj/iBA9LYsfSIAwAAAABgIWPvr6/rfcrqSNI5jf/qZ7PHAQAAAIACRTCOvLu4R3zUKHrEAQAAAOD/27vv+Kaq/g/gn5uk6d50L4YUkdECZRQEBIsgUkFFEHikIPjIw7bsIQVUQAREBQUHQx8Ziqwfez2syoZWESiWAmW0pVC6d3J/f6S9bdpSktI2aft5v15pknNPTr7JuWly7/eec4lqARszE3wx0B+CAGy5cBf7LscZOiQiIiIiIqJKw8Q46YfnESciIiIiIiKqtdo1cMCoro0AANO3/oWE1GwDR0RERERERFQ5FIYOoKZ4/fXXERERgQcPHsDe3h5BQUH47LPP4O7ubujQqkdcHDBzJrB+vWbEuLk5MG0aMGUKp0wnIiIiIiIiqkU+DPLF8euJ+Pt+Kib/Fon1w9tBJhMAAK0D2iEuPl6ndtxcXXHx/NmqDJWIiIiIiEhnTIzrqFu3bpg5cybc3Nxw7949TJ48Gf3798cff/xh6NCqVnY2sGyZ5lziGRmasiFDgEWLOGU6ERERERERUS2kVMiwfKA/+nx9Eif+eYiZ2/7CgjdaQCYTEBcfj6lrD+nUzuLhQVUcKRERERERke6YGNfRhx9+KN328fHB9OnT0a9fP+Tl5cHExMSAkVURUQS2bNGMCL99W1PWvj2wfLnmfOJEREREREREVGs1drHG0gF+GL/xEjaduwNThQxzX29m6LCIiIiIiIgqjInxCkhKSsIvv/yCjh07lpsUz8nJQU5OjnQ/NTW1OsJ7dhcuABMnAidPau57eACffQYMGgTIeFp6IiIiIiIiorqgT0t35OSpMXlLJNafug1TE7mhQyIiIiIiIqowZjn1MG3aNFhaWsLR0RGxsbHYsWNHufUXLlwIW1tb6eLl5VVNkVZQXBzw3ntA27aapLi5OTB3LhAVpZk+nUlxIiIiIiIiojrlrTae+LRfCwDAd8djIPcLhiiKBo6KiIiIiIhIf3U60zl9+nQIglDu5dq1a1L9KVOm4NKlSzhw4ADkcjmGDh1a7sbgjBkzkJKSIl3u3LlTHS+r4jZtAtau1UyjPmQIcP06EBYGWFoaOjIiIiIiIiIiMpDB7b0xN/gFAICiRW/s+zseOfkqA0dFRERERESknzo9lfqkSZMwbNiwcus0bNhQul2vXj3Uq1cPvr6+aNq0Kby8vHD69GkEBgaW+VhTU1OYmppWZshVa8wY4Nw5YPx4nkeciIiIiIiIiCTDOjWAIAiYs/1PXE9IR0JqDno3d4WzjZmhQyMiIiIiItJJnU6MOzk5wcnJqUKPVavVAKB1DvEaT6kENmwwdBREREREREREZIRCOtbHjNHD4NhvBlKy8vDr+bt4sXE9+HnaQhAEQ4dHRERERERUrjqdGNfVmTNncO7cObz44ouwt7fHjRs38NFHH6FRo0ZPHC1ORERERERERFTbiA9jMLidNw5eSUDMwwwcu56Iq3Gp6PRcPXg7WBg6PCIiIiIioieq0+cY15WFhQW2bt2Kl19+GU2aNMGIESPQsmVLHDt2rGZNlU5ERERERERE9IzMTOTo09INXX2doJTL8CAtB9su3cP2S/eQmFaLZtYjIiIiIqJahSPGddCiRQscOXLE0GEQERERERERERkFQRDg72UHXxcrnL2ZhL/upeB2UiZun42Fj6MF/DztAHB6dSIiIiIiMh4cMU5ERERERERERBVioVTgpSbOeLeDD3ydrQAAtx9lYmfkfSj7zsN3x2/gQVq2gaMkIiIiIiLiiHEiIiIiIiIiIqP36NEjuHl661zfzdUVF8+frcKItNlZKPFqCzd0yMzFX3dTcCUuFTnWTliw5xoW7r2G9g0c0KelO15t7gpHK56WjoiIiIiIqh8T40RERERERERERk6tVmPq2kM615/2eiudE+mVmUS3t1Cii68TAhs54stF89D27bGIuJOM0zFJOB2ThDk7LqONjz26+jrhpSbOeMHNBjJZ1U253jqgHeLi43WuX90HFBARERERUfVhYpyIiIiIiIiIqJbRJ5GuTxL90aNHOtUzkcugjg7H9jEbcScpE3v+isOuP+Pw170UnLv1GOduPcaSA9dRz8oUHRs5ol0DB7Rv4IDnnK0gCJWXKI+Lj9frgILFw4Mq7bmJiIiIiMi4MDFORERERERERFSH6ZNEn9LHT+/2vRws8EHXRvigayPcScrEseuJOBqViD9uPMTD9BzsjLyPnZH3AQAOlkq08rJDC09btPS0RXMPWzhbm+n9nMZGn5HrHLVORERERFQ1mBgnIiIiIiIiIqJq4eVggX918MG/OvggN1+NC7cf4+zNJJy99QgXbj9GUkYuDl97gMPXHkiPcbI2ha+LFXxdrNHExRoNnazg7WABZ2vTKp2GvTLpM3LdWEatM5lPRERERLUNE+NERERERERERFTtlAoZAhs5IrCRI4DGyM1X4+/7KYi8k4w/76Xgr7spiE5MR2JaDhLTchAe/ajU473szeHtYAEvBwvp2sPOHC42ZnC0VBrmhdUSxpDMZ3KenlVVrUNVuW7WtJiNoV192yYiorqLiXEiIiIiIiIiIqp0jx490vnc5YUJjVbe9mjlbS+VZ+Tk43pCGv5JSEdUQhquJ6Th1qMM3E/ORm6+GjcSM3AjMaPMNuUyAco3F2Lj2VhYmipgqZRrrkvctjCR15iR53VNTUvOA1WXnDOG5GNVJh6rKo6qWoeqct009phFUYQoAmLB7biEBwj98YCmAIXlhbfFgsdo7n819k0kZeRCLbWhaUgEipUVtJuWhw++2Qux4MFiwR+xWBzFrZk1AtEP0gEAggAIAARBKLgGBAgQCv7V93m9Lx4kJhYFBlH7drEndHFywuGD+7XaAjTtQdB+rs5duiI+PqFEO2U/R2pKCmxsbIvqlcNYDq4g4yWKIvLVIvJUauTmq5GrUiNPJSKv4HZuvhp5BWWFt3NVauSrSq9/06bPwOPk5MKWAVU+RFUeoMoH1HmASnMRVfmAKg+uDja4dO50tb5eomfBxDgREREREREREVU6fc5d/qQkjKWpolSyHADyVGrEJWfjzuNMxCZpLncKLveSs/EoIwcqtQjBwg4P0nKAtJxyn99MIYOZUg6TVyZj5PpzsLdQwsFSCXtLJewtTGBlagILUzkslQpYFlwX3rdQyiEITKzXVvokE4GqS9Abe7L0WRkqDlEUoRJFqNUAlJZ4kJpdlFBSFSWS8lRqKcGUpxIh826Fa/GpUKtR8HixxLWmXKUWoWj7Dqb//ify1UXLVWoR6oJrlRrSbbUowuTlCdhy4a5W8lgUiyWORUBdUKbsOx+dFx+RykVRlBLTRY8DABHK/ovx3fEYqY6mFAXJ5qIEtghAOegr+M7aCxEi1CXaLcl0yEqs/N8Nnd5v0wFL0Prjg7rVfeMTrPvjlk51AUD5ehiClh3TrfKLE2GqY7vJANp8ouP/gG4zdW7X6QnlgvRHusKj/Dz4zt5bZpK/+AEABbl6pHSeDFtLa6mCgKLvqOKPgwAkPkqEz7j/ouxEPopuQ4RcLoePt7fWcxV+/5WM7Z9//kFeXl6xVyYWrXDFb4uASpUPuVxe4oULRW+ApnUAgImJCZo0aVIUVbGVsuQBGTeibyAvLxcQVZoPgqgudRFFdcGHSoX83FwoFPKi5eridcWCuprbVhbmGBYyFHIZIBOEYhdAJiu6rS747KrVms+SShQhiprPulpEQXnBMrUoJbFz89XIyVdJSW7N/eLL1LifkAg1ZIBcAcgVEARZWauU/lq+DRM9qj8G4DPpdyA3E2JOhuY6NxPIydBcZ6dCzEyGmJkCJysFzhzZCzMTeeXESlQBTIwTEREREREREZFBVWR0ubejBbwdLdCpjDr5KjUepueidecgvDX9K2TkqpCRk6+5FN7OzUdmrgqiCGTnq5Gdr4bMuREOXX1QRovlU8plUMgFyGUCTOQyKGSC5lJw26TPR/jv6duapEGxUYeygoSCrFiZSfdxCFlzVrNzXRAKdrBrRsALggC5oHkeecFzFF3LIJcBcplMu1xetFxR8nHyJ5TLZBBcfHHvcZYU5xOvAcDcFg/SsqGQyTTxybXjlAngwQM1gFiQnClMqBZPtKpFETCzQXxKtlYSV1UiqVPyvlad4klkqZ4mMSzzCcC1+FTpeYsnmVVq7YSzvM3bmL39L+Tli9Kox7wnJLNNXpuFn07dKhVf8TgLmQ5YgnYLDuv0Xpl0+Tf2/53w9IoA5E26YtO5Ozr3g8ztedxLztKprmDthDtJOtY1s0ZWnkq3unIT5KrUOtV9FsVHW8sKE7gFZdnZWVCammnqFSsvelzR/xQRIrLSU2Fvb6+13hbmX0sm9jOzsqBQmhY+uOjAAOmPLuO4q0ZZMQhyE+Tm694fgqklsnWsL7dy0LldNYCbD8uepaUUa1fomqbVJ52rAnAlLlXHGFz0alufVG02gFXHdDsgpMqYWqG8b1a5rOi7OP3xA9jXc9F8Jxcrl5Xx3Rzz13k0bBEAQPPZUKuBfLUaKrVmVHrxa1XBP1HBxAwwMYNgWf76lALg+Y/2QcxOh5iVDDEzGchIgpj+EGJaouaS/hDIz+FsBlRlmBgnIiIiIiIiIiKDqozR5cUp5DK42ppBTIpFQyerJz+vKCI7T4WsXBWy89TY+MVsLPlyJZIycvE4IxePM/PwODMX6Tn5yMzNR0aOdoK9UK5Kjdxy8k0yO3c8ysjV6fXJ3F/AseuJOtWtSsoeH2LLxbs61TV9axHafVp+QrH4DvriF5lQlJDXuggCTHrPxMazseUm5QvvKzqPxPiNl7QeXzJBr7WsjDKZTCg2TbQmaSp/4RWcu5WkNbJWGpVYbHRi4Uhbeat+WLjnqjS6VzvRXJDcLRglqCo2ylgtlkwalx5F/PuFu9JI4TJHEkPzHCavz0PnxUegLjESWWq3WMJZOegrfHXknzJHApfq5/6focNC3RLH+jLpPELnRLOiaXf893SsTnVl9p54nJn39IqF9QXARC6DUi6DiUIGE7lQdF8ug4lCc//CuXPweb6ldoJJWo+glXQ6/X//xbRJH0oHqcgE7fW/+PonlwFjx43H66Nmaa3nUuK42HoPAdjw6QT8345tJeoWjdotfvvloFfw3iffA9AeYVx4VXzk76opQ3DhzKkntlX8s9e0eUtMWLFNa2RyYZ3CBoWCJUtGvoJ7t29JBwmVx83TGxP1nK0h4q5u64U+bS8eHoT7d25rfcaKpnPX/p/Q4DlffLh6t9bnSSyW5S4+Un/uoK4I23C0qJ7WY6CVsP920mCcP3ta+v9U8rmLj5wWAXTu2g0jFqzVnoa+xHMUxrZ8wjuYsHyT1vOWjL3wcb8smIjtW3/XarfklPiF9wcO/hcGTFpU4vUUa7dY8n/dxxMx7KPlAKCd6C052h3Ali9m4pef15caqa65XTSSHgDefmcw3p78WYkZF4oOAJJmQyhYvvXbhej7wXTpdajFkv9ji/7/Ht26Hl3eGKr9v7jEqQEgatbzv07sxb8GDyo64K3EqPLit79cvhxd3xxW+juq2HeVQiaDXCbgqwkDMHnlFq0D2+TF2ir+GZvS5w2E7oqELqaEvYapw3SrqxZFTO//Imb+/D9k56uRk6f5PZWdr0JOnhrZeSpk5OYjPUfzGyo5LQOCQgnBzAqCmRVg71lmu+Ymcjy8/w8mbroEbwcLeDtawsfRAj4OFnCyNuXBdvRMmBgnIiIiIiIiIqIaQ5/R5Y8ePSp3uUwQYKFUwEKp2UWmvhOJQe10a1utFpGdr0J6Tj7yVCJUKhF5BSOq8lRFI6vyVSL69X8bA6cu1Z7m+Am3/++7hfhq+XJputXCaZYLp2JVlUhs5qtEqNTqUqO4NKO7UHqZqrBOWY8pKFeJuHItCo7uPlLioNQ1iu7n5+dDJpeXm1xVqUWoIGqG++lI5uClmQpfB3KfNtgZeV/3xnWkaP0G/rhR/nqkVb9ZT6w+HlPpccjcnsddHUcRy2ycdR9FLDfRKSkOAKKoholcXjSLgZTMLZpGWC4rKi9M+hbOeFBURyjVxulTp+Dzgr+U2Cov2Xx2zyZMmjAOymKJ66Jktvb9If/6FwZP/Vx6fPF45SXaXvZ+T8Tdua3Te+HmOQhvDtYtsRr+5y6Me/kb3d5kAKNvnYevi7VOdcWHMWjjY//0igDElPtwtNJxou+MJLjbmetWNzcTprpOiyyKkMlqXkJLEIonXMuJX5UHE7luY5TV2WnSd89TZT6Gh679AUBMTYCDpVKnunkPbsLFxky3dhNvoF0D3UaYi3FX4eNoqVPd7JhzaFTOQWzFqe//jZeaOOsWQ/w11NcxBgD4OXIfWnl/plPdHSd+Qpdpk3Sqe+nz/2LhlgU61V02Yg8CQkN1qpv38LbO/VxVZIIAMScddha6xTGljx/mb7uI9GzNQYbpOflIzc5DSlbRJTtPjaw8FWRODbE9ovT3upmJDD4OlvB2tEB9x4KkuYMF6jtawt3ODAodP4NUdzExTkRERERERERENYY+o8un9PGrsjhkMu2kennEhH/g7WChU7s7bp7FW23KHkFVndw8/4WheoyojLsbq5WwLxwNrVIVjYouPvWqVr0nlL09aAj6hy4qms5bXTStd8nRfAd+/hofz59fqp2S53Uufilcll9sWm/pfL0FIxV//fVXtOjcq2gEbLHRr9I5dotN83xu/xaMHvVvrXYKp4guHNVbmCwuPtpYM00+tJPBZY0iRtHoYQiATBrNWzQV9YaFE7Brx7ZibUBrVLJMKEoIt23fAaOXbdIaAVz42qURygWv7fP3euC2jiNy9eX28UC8OUS39e1UxA582ONrneqK8dfgYa9bQvHRw4eVdtBNybq6tluVbdfmdquy7aqMuSpiMJY4anMM9GzMTeQwN5HDybrsA3Vy8lVIycrDT0s+wuwFyxCblInYpAzcfpSJ+8lZyM5TIyohDVEJaaUeq5AJ8LA3h09BstzH0UJz29EC3g4WPLc5AWBinIiIiIiIiIiICIB+O9xTU1JgY2urc7uGjkGfuoVx6EsmEyCDgMra7yzGXUWDerqN9tt3/Rjee7FB5TxxMRtCg9HjgxCd659e+Dtm9v6i0uPQaxRxYgxae+s2ihiZybAyrVm7iKsqMVZVB93o025Vtl2b263Kto3hYKyqXIeqKo7aHIO+mMzXj6lCDmdrOdSxl/CflxppLctTqXH3cRZuP8pAbFImbj/KxO1HmqR5bFImcvLVBWWZZbbtamMGb0cLeNiZw83WDG525nC3NYObrTk87MxhY67gNO11QM361UNEREREREREdcbKlSvx+eefIz4+Hn5+fvj666/Rrl07Q4dFtZi+O9yNIQlTFXUL6+uqNh9QUJWjVo3hvTCG91jfOGpiYoyI6i7+z6o8JnIZGtSzLHXQXOuAdkiNTwAsbCFYOUGwrgfB2hmCVcG1dT0ISgvEp2YjPjX7ie1bKOVwszWDs7UZnKxN4WRtinpWpsVuK+FkbQoHCyWnbK/BmBgnIiIiIiIiIqOzefNmhIaGYtWqVWjfvj2WL1+Onj17IioqCs7Oup1bkoiqR20/oEAfxhCHsYycNYYRrkREVDPpe5DXZzsvPXG5KIrIzlcjJTMPqz6eDGsXbwiW9oCFPQRLewgW9hDMrJGZq8KNxAzcSMx46nNamylgZ2ECO3Ol5tpCCTtzE9hbmMDG3ASWpgpYKOWwMlXA0lQBS6UClqZyzW1TBSxM5JDJODrdEJgYJyIiIiIiIiKjs2zZMrz//vsYPnw4AGDVqlXYvXs31qxZg+nTpxs4OiIiIiIiqiqVeZCXIAiac5vbypFx7QTmLoksVSdPpUZ6Tj7SsvORmZuPzFwV9m78EeYOLhDMbABzG821mRUEQYa0bE3dO8iq0OsDNCPUCxPoSrkMSkXBpeC2acF9E7lMe7lCBrkgQC4TIAhCwW3N65TLBMgEQCYIkBXeLyiTF5TJZAIEAIJQcIFQ8D5pv2eCdFtTp5W3HdztzCv8eo0FE+PVSBRFAEBqaqqBIyEiIiIiIqKqVLjdV7gdSPrJzc3FhQsXMGPGDKlMJpMhKCgIp06dKvMxOTk5yMnJke6npKQAMO5tcLVajeyMdJ3qiqKoc11967OuccVhDHWNJY6aVtdY4qhpdY0lDmOoayxx1LS6xhKHMdQ1ljhqWl1jiaOm1TWWOCqrrjkAc1MApjIAMmw4/hMm/fqHVh21WkS2SoXcPDWy81XIKbjOzlPjwOYfYW5bD1CaAwolBIUZoFACJqZFtxVmEGSaadjTc4D0NJ3CNgpL3m6JXs3dDB1GmfTZ/hZEbqVXm7t378LLy8vQYRAREREREVE1uXPnDjw9PQ0dRo1z//59eHh44I8//kBgYKBUPnXqVBw7dgxnzpwp9Zi5c+di3rx51RkmERERERERGQldtr85Yrwaubu7486dO7C2toYg8NwB1Sk1NRVeXl64c+cObGxsDB0OkV64/lJNx3WYajKuv1STcf01LFEUkZaWBnd3d0OHUmfMmDEDoaGh0n21Wo2kpCQ4Ojoa5TY4P6PGh31inNgvxod9YnzYJ8aHfWJ82CfGh31inGpiv+iz/c3EeDWSyWQcKWBgNjY2NeaDTFQS11+q6bgOU03G9ZdqMq6/hmNra2voEGqsevXqQS6XIyEhQas8ISEBrq6uZT7G1NQUpqamWmV2dnZVFWKl4WfU+LBPjBP7xfiwT4wP+8T4sE+MD/vE+LBPjFNN6xddt79lVRwHEREREREREZFelEol2rRpg8OHD0tlarUahw8f1ppanYiIiIiIiEhXHDFOREREREREREYnNDQUISEhCAgIQLt27bB8+XJkZGRg+PDhhg6NiIiIiIiIaiAmxqlOMDU1RVhYWKlp9YhqAq6/VNNxHaaajOsv1WRcf6mmGzhwIBITEzFnzhzEx8fD398f+/btg4uLi6FDqxT8jBof9olxYr8YH/aJ8WGfGB/2ifFhnxgf9olxqu39IoiiKBo6CCIiIiIiIiIiIiIiIiIioqrCc4wTEREREREREREREREREVGtxsQ4ERERERERERERERERERHVakyMExERERERERERERERERFRrcbEOBERERERERERERERERER1WpMjFOdlZOTA39/fwiCgIiICEOHQ6STW7duYcSIEWjQoAHMzc3RqFEjhIWFITc319ChEZVp5cqVqF+/PszMzNC+fXucPXvW0CER6WThwoVo27YtrK2t4ezsjH79+iEqKsrQYRFVyKJFiyAIAiZOnGjoUIjqHH1/C/322294/vnnYWZmhhYtWmDPnj3VFGndoU+ffP/99+jcuTPs7e1hb2+PoKAg/p6tAhXdZti0aRMEQUC/fv2qNsA6St9+SU5OxpgxY+Dm5gZTU1P4+vryf1gl07dPli9fjiZNmsDc3BxeXl748MMPkZ2dXU3R1n7Hjx9HcHAw3N3dIQgCtm/f/tTHHD16FK1bt4apqSmee+45rFu3rsrjrEv07ZOtW7eiR48ecHJygo2NDQIDA7F///7qCbaOqMjnpFB4eDgUCgX8/f2rLL66qCJ9kpOTg1mzZsHHxwempqaoX78+1qxZU/XBVhEmxqnOmjp1Ktzd3Q0dBpFerl27BrVajdWrV+Pvv//GF198gVWrVmHmzJmGDo2olM2bNyM0NBRhYWG4ePEi/Pz80LNnTzx48MDQoRE91bFjxzBmzBicPn0aBw8eRF5eHl555RVkZGQYOjQivZw7dw6rV69Gy5YtDR0KUZ2j72+hP/74A4MGDcKIESNw6dIl9OvXD/369cPly5erOfLaS98+OXr0KAYNGoT//e9/OHXqFLy8vPDKK6/g3r171Rx57VXRbYZbt25h8uTJ6Ny5czVFWrfo2y+5ubno0aMHbt26hS1btiAqKgrff/89PDw8qjny2kvfPtmwYQOmT5+OsLAwXL16FT/++CM2b97M/UeVKCMjA35+fli5cqVO9W/evInXXnsN3bp1Q0REBCZOnIiRI0cyEVuJ9O2T48ePo0ePHtizZw8uXLiAbt26ITg4GJcuXariSOsOffukUHJyMoYOHYqXX365iiKruyrSJwMGDMDhw4fx448/IioqChs3bkSTJk2qMMqqJYiiKBo6CKLqtnfvXoSGhuL3339Hs2bNcOnSJR55RDXW559/jm+//RYxMTGGDoVIS/v27dG2bVusWLECAKBWq+Hl5YVx48Zh+vTpBo6OSD+JiYlwdnbGsWPH0KVLF0OHQ6ST9PR0tG7dGt988w0++eQT+Pv7Y/ny5YYOi6jO0Pe30MCBA5GRkYFdu3ZJZR06dIC/vz9WrVpVbXHXZs/6+1SlUsHe3h4rVqzA0KFDqzrcOqEifaJSqdClSxe89957OHHiBJKTk/UagUZPp2+/rFq1Cp9//jmuXbsGExOT6g63TtC3T8aOHYurV6/i8OHDUtmkSZNw5swZnDx5stririsEQcC2bdvKncFi2rRp2L17t9YBb++88w6Sk5Oxb9++aoiybtGlT8rSrFkzDBw4EHPmzKmawOowffrknXfeQePGjSGXy7F9+3bO+FtFdOmTffv24Z133kFMTAwcHByqL7gqxBHjVOckJCTg/fffx88//wwLCwtDh0P0zFJSUmrNlxLVHrm5ubhw4QKCgoKkMplMhqCgIJw6dcqAkRFVTEpKCgDw/y3VKGPGjMFrr72m9b+YiKpHRX4LnTp1qtTntWfPnvztVEkq4/dpZmYm8vLy+HugklS0T+bPnw9nZ2eMGDGiOsKscyrSLzt37kRgYCDGjBkDFxcXNG/eHAsWLIBKpaqusGu1ivRJx44dceHCBWm69ZiYGOzZswe9e/eulpipNH7PGz+1Wo20tDR+zxvY2rVrERMTg7CwMEOHQtB8xwcEBGDx4sXw8PCAr68vJk+ejKysLEOHVmEKQwdAVJ1EUcSwYcMwatQoBAQE4NatW4YOieiZREdH4+uvv8aSJUsMHQqRlocPH0KlUsHFxUWr3MXFBdeuXTNQVEQVo1arMXHiRHTq1AnNmzc3dDhEOtm0aRMuXryIc+fOGToUojqpIr+F4uPjy6wfHx9fZXHWJZXx+3TatGlwd3fnAUeVpCJ9cvLkSfz4448cOVaFKtIvMTExOHLkCIYMGYI9e/YgOjoao0ePRl5eHhMblaAifTJ48GA8fPgQL774IkRRRH5+PkaNGsWp1A3oSd/zqampyMrKgrm5uYEio0JLlixBeno6BgwYYOhQ6qx//vkH06dPx4kTJ6BQMH1pDGJiYnDy5EmYmZlh27ZtePjwIUaPHo1Hjx5h7dq1hg6vQjhinGqF6dOnQxCEci/Xrl3D119/jbS0NMyYMcPQIRNp0XUdLu7evXvo1asX3n77bbz//vsGipyIqPYbM2YMLl++jE2bNhk6FCKd3LlzBxMmTMAvv/wCMzMzQ4dDRFQrLFq0CJs2bcK2bdv4v9VA0tLS8O677+L7779HvXr1DB0OFaNWq+Hs7IzvvvsObdq0wcCBAzFr1iyeBsKAjh49igULFuCbb77BxYsXsXXrVuzevRsff/yxoUMjMkobNmzAvHnz8Ouvv8LZ2dnQ4dRJKpUKgwcPxrx58+Dr62vocKiAWq2GIAj45Zdf0K5dO/Tu3RvLli3D+vXra+yocR5yQbXCpEmTMGzYsHLrNGzYEEeOHMGpU6dgamqqtSwgIABDhgzB+vXrqzBKoifTdR0udP/+fXTr1g0dO3bEd999V8XREemvXr16kMvlSEhI0CpPSEiAq6urgaIi0t/YsWOxa9cuHD9+HJ6enoYOh0gnFy5cwIMHD9C6dWupTKVS4fjx41ixYgVycnIgl8sNGCFR7VeR30Kurq787VSFnuX36ZIlS7Bo0SIcOnQILVu2rMow6xR9++TGjRu4desWgoODpTK1Wg0AUCgUiIqKQqNGjao26DqgIp8VNzc3mJiYaP2+aNq0KeLj45GbmwulUlmlMdd2FemTjz76CO+++y5GjhwJAGjRogUyMjLw73//G7NmzYJMxvFy1e1J3/M2NjYcLW5gmzZtwsiRI/Hbb79xVhgDSktLw/nz53Hp0iWMHTsWgOZ7XhRFKBQKHDhwAN27dzdwlHWPm5sbPDw8YGtrK5U1bdoUoiji7t27aNy4sQGjqxh+A1Kt4OTkhOeff77ci1KpxFdffYXIyEhEREQgIiICe/bsAQBs3rwZn376qYFfBdVluq7DgGak+EsvvYQ2bdpg7dq13Jgho6RUKtGmTRscPnxYKlOr1Th8+DACAwMNGBmRbkRRxNixY7Ft2zYcOXIEDRo0MHRIRDp7+eWX8ddff0m/eSMiIqQDQSMiIpgUJ6oGFfktFBgYqFUfAA4ePMjfTpWkor9PFy9ejI8//hj79u1DQEBAdYRaZ+jbJ88//3yp77fXX38d3bp1Q0REBLy8vKoz/FqrIp+VTp06ITo6WjpQAQCuX78ONzc3JsUrQUX6JDMzs9T+osLfgKIoVl2w9ET8njdOGzduxPDhw7Fx40a89tprhg6nTrOxsSn1PT9q1Cg0adIEERERaN++vaFDrJM6deqE+/fvIz09XSq7fv06ZDJZjR1AwhHjVKd4e3tr3beysgIANGrUqMZ+iKluKUyK+/j4YMmSJUhMTJSWcSQJGZvQ0FCEhIQgICAA7dq1w/Lly5GRkYHhw4cbOjSipxozZgw2bNiAHTt2wNraWjq/q62tLY/mJ6NnbW2N5s2ba5VZWlrC0dGxVDkRVZ2n/RYaOnQoPDw8sHDhQgDAhAkT0LVrVyxduhSvvfYaNm3ahPPnz3OGqEqkb5989tlnmDNnDjZs2ID69etLvwesrKyk/Qn0bPTpEzMzs1LfY3Z2dgDA77dKpu9n5T//+Q9WrFiBCRMmYNy4cfjnn3+wYMECjB8/3pAvo1bRt0+Cg4OxbNkytGrVCu3bt0d0dDQ++ugjBAcH8yDJSpKeno7o6Gjp/s2bNxEREQEHBwd4e3tjxowZuHfvHn766ScAwKhRo7BixQpMnToV7733Ho4cOYJff/0Vu3fvNtRLqHX07ZMNGzYgJCQEX375Jdq3by99z5ubm2uNjqWK06dPZDJZqe9zZ2fnMr//qeL0/ZwMHjwYH3/8MYYPH4558+bh4cOHmDJlCt57770au3+MiXEiohrk4MGDiI6ORnR0dKmDOXjELxmbgQMHIjExEXPmzEF8fDz8/f2xb98+uLi4GDo0oqf69ttvAQAvvfSSVvnatWufeuoLIiIi4Om/hWJjY7VG83Xs2BEbNmzA7NmzMXPmTDRu3Bjbt2/njsBKpG+ffPvtt8jNzUX//v212gkLC8PcuXOrM/RaS98+oeqhb794eXlh//79+PDDD9GyZUt4eHhgwoQJmDZtmqFeQq2jb5/Mnj0bgiBg9uzZuHfvHpycnBAcHMwZMyvR+fPn0a1bN+l+aGgoACAkJATr1q1DXFwcYmNjpeUNGjTA7t278eGHH+LLL7+Ep6cnfvjhB/Ts2bPaY6+t9O2T7777Dvn5+RgzZgzGjBkjlRfWp2enb59Q1dO3T6ysrHDw4EGMGzcOAQEBcHR0xIABA/DJJ59Ue+yVRRCZSSEiIiIiIiIiIiIiIiIiolqMh1wSEREREREREREREREREVGtxsQ4ERERERERERERERERERHVakyMExERERERERERERERERFRrcbEOBERERERERERERERERER1WpMjBMRERERERERERERERERUa3GxDgREREREREREREREREREdVqTIwTEREREREREREREREREVGtxsQ4ERERERERERERERERERHVakyMExERVYOjR49CEAQkJycbOhS9CIKA7du3V1p79evXx/Llyyutvep269YtCIKAiIgIADW3X4mIiIiIiKi0qKgouLq6Ii0trdLaLLkdSYY3ffp0jBs3ztBhEBGRATAxTkRE9IwEQSj3MnfuXEOH+FRz586Fv79/qfK4uDi8+uqr1R+QERg2bBj69eunVebl5YW4uDg0b97cMEERERERERHVQWVtn1WFGTNmYNy4cbC2tpbKvv/+e/j5+cHKygp2dnZo1aoVFi5cWOWx6GLdunVl7ocwMzMzdGgGExcXh8GDB8PX1xcymQwTJ04sVWfy5MlYv349YmJiqj9AIiIyKCbGiYiInlFcXJx0Wb58OWxsbLTKJk+ebLDYcnNzn+nxrq6uMDU1raRoaj65XA5XV1coFApDh0JERERERESVKDY2Frt27cKwYcOksjVr1mDixIkYP348IiIiEB4ejqlTpyI9Pb1aYytv277kPoi4uDjcvn3bYPFUtrlz52r1ydPk5OTAyckJs2fPhp+fX5l16tWrh549e+Lbb7+tpCiJiKimYGKciIjoGbm6ukoXW1tbCIKgVWZlZSXVvXDhAgICAmBhYYGOHTsiKipKq60dO3agdevWMDMzQ8OGDTFv3jzk5+dLy2NjY9G3b19YWVnBxsYGAwYMQEJCgrS8cOT3Dz/8gAYNGkhHiScnJ2PkyJFwcnKCjY0NunfvjsjISACaI8znzZuHyMhI6ejydevWASg9lfrdu3cxaNAgODg4wNLSEgEBAThz5gwA4MaNG+jbty9cXFxgZWWFtm3b4tChQ3q9lyqVCqGhobCzs4OjoyOmTp2KkJAQrZEBZU3H7u/vrzUyf9myZWjRogUsLS3h5eWF0aNHa+24WLduHezs7LB//340bdoUVlZW6NWrF+Li4qT3cf369dixY4f0nhw9elSnKfBOnjyJzp07w9zcHF5eXhg/fjwyMjKk5d988w0aN24MMzMzuLi4oH///nq9R0RERERERKTt2LFjaNeuHUxNTeHm5obp06drbUunpaVhyJAhsLS0hJubG7744gu89NJLWqOJf/31V/j5+cHDw0Mq27lzJwYMGIARI0bgueeeQ7NmzTBo0CB8+umnUh21Wo358+fD09MTpqam8Pf3x759+54Yq0qlwogRI9CgQQOYm5ujSZMm+PLLL7XqFI6Q//TTT+Hu7o4mTZo8sb2S+yBcXV3h4uIiLX/ppZcwfvx4TJ06FQ4ODnB1dS01s115+wyAJ+9ruHbtGl588UWYmZnhhRdewKFDh7T2I3Tv3h1jx47Veq7ExEQolUocPnz4ia/pWdSvXx9ffvklhg4dCltb2yfWCw4OxqZNm6okBiIiMl5MjBMREVWjWbNmYenSpTh//jwUCgXee+89admJEycwdOhQTJgwAVeuXMHq1auxbt06aYNbrVajb9++SEpKwrFjx3Dw4EHExMRg4MCBWs8RHR2N33//HVu3bpUSuG+//TYePHiAvXv34sKFC2jdujVefvllJCUlYeDAgZg0aRKaNWsmHV1esk0ASE9PR9euXXHv3j3s3LkTkZGRmDp1KtRqtbS8d+/eOHz4MC5duoRevXohODgYsbGxOr8/S5cuxbp167BmzRqcPHkSSUlJ2LZtm75vM2QyGb766iv8/fffWL9+PY4cOYKpU6dq1cnMzMSSJUvw888/4/jx44iNjZVG90+ePBkDBgyQkuVxcXHo2LHjU5/3xo0b6NWrF9566y38+eef2Lx5M06ePCntCDh//jzGjx+P+fPnIyoqCvv27UOXLl30fn1ERERERESkce/ePfTu3Rtt27ZFZGQkvv32W/z444/45JNPpDqhoaEIDw/Hzp07cfDgQZw4cQIXL17UaufEiRMICAjQKnN1dcXp06fLHYH95ZdfYunSpViyZAn+/PNP9OzZE6+//jr++eefMuur1Wp4enrit99+w5UrVzBnzhzMnDkTv/76q1a9w4cPIyoqCgcPHsSuXbv0fVu0rF+/HpaWljhz5gwWL16M+fPn4+DBg9Ly8vYZFCq5r0GlUqFfv36wsLDAmTNn8N1332HWrFlazzty5Ehs2LABOTk5Utl///tfeHh4oHv37s/0mp5Vu3btcPfuXdy6dcugcRARUTUTiYiIqNKsXbtWtLW1LVX+v//9TwQgHjp0SCrbvXu3CEDMysoSRVEUX375ZXHBggVaj/v5559FNzc3URRF8cCBA6JcLhdjY2Ol5X///bcIQDx79qwoiqIYFhYmmpiYiA8ePJDqnDhxQrSxsRGzs7O12m7UqJG4evVq6XF+fn6l4gYgbtu2TRRFUVy9erVobW0tPnr0SMd3QxSbNWsmfv3119J9Hx8f8YsvvnhifTc3N3Hx4sXS/by8PNHT01Ps27dvuW34+fmJYWFhT2z3t99+Ex0dHaX7a9euFQGI0dHRUtnKlStFFxcX6X5ISIjW84qiKN68eVMEIF66dEkUxaJ+ffz4sSiKojhixAjx3//+t9ZjTpw4IcpkMjErK0v8/fffRRsbGzE1NfWJsRIREREREZG2srbPCs2cOVNs0qSJqFarpbKVK1eKVlZWokqlElNTU0UTExPxt99+k5YnJyeLFhYW4oQJE6QyPz8/cf78+Vpt379/X+zQoYMIQPT19RVDQkLEzZs3iyqVSqrj7u4ufvrpp1qPa9u2rTh69GhRFEtvR5ZlzJgx4ltvvaX1el1cXMScnJwnPkYUi7ZtLS0ttS69evWS6nTt2lV88cUXS8U3bdo0URR132dQcl/D3r17RYVCIcbFxUllBw8e1NqPkJWVJdrb24ubN2+W6rRs2VKcO3duua+ruLCwMDEkJETn+sV17dpVq4+LS0lJEQGIR48erVDbRERUM/EEmURERNWoZcuW0m03NzcAwIMHD+Dt7Y3IyEiEh4drTcmmUqmQnZ2NzMxMXL16FV5eXvDy8pKWv/DCC7Czs8PVq1fRtm1bAICPjw+cnJykOpGRkUhPT4ejo6NWLFlZWbhx44bOsUdERKBVq1ZwcHAoc3l6ejrmzp2L3bt3Iy4uDvn5+cjKytJ5xHhKSgri4uLQvn17qUyhUCAgIACiKOocJwAcOnQICxcuxLVr15Camor8/HzpfbSwsAAAWFhYoFGjRtJj3Nzc8ODBA72ep6TIyEj8+eef+OWXX6QyURShVqtx8+ZN9OjRAz4+PmjYsCF69eqFXr164Y033pBiIiIiIiIiIv1cvXoVgYGBEARBKuvUqRPS09Nx9+5dPH78GHl5eWjXrp203NbWttT05FlZWdIU4YXc3Nxw6tQpXL58GcePH8cff/yBkJAQ/PDDD9i3bx/S09Nx//59dOrUSetxnTp10pqKvKSVK1dizZo1iI2NRVZWFnJzc+Hv769Vp0WLFlAqlU99/dbW1qVGv5ubm2vdL74vovB1FW7/6rrPoOS+hqioKHh5ecHV1VUqK/4eA4CZmRneffddrFmzBgMGDMDFixdx+fJl7Ny584mv58SJE3j11Vel+7m5uRBFEVu2bJHKVq9ejSFDhjyxDV0UvkeZmZnP1A4REdUsTIwTERFVIxMTE+l24UZ78anI582bhzfffLPU40punJfH0tJS6356ejrc3Nxw9OjRUnXt7Ox0brfkhnVJkydPxsGDB7FkyRI899xzMDc3R//+/ZGbm6vzc+hCJpOVSpTn5eVJt2/duoU+ffrgP//5Dz799FM4ODjg5MmTGDFiBHJzc6UkdPG+ADT9oW8CvqT09HR88MEHGD9+fKll3t7eUCqVuHjxIo4ePYoDBw5gzpw5mDt3Ls6dO6dXXxAREREREVHlqlevHh4/flzmsubNm6N58+YYPXo0Ro0ahc6dO+PYsWNo06aN3s+zadMmTJ48GUuXLkVgYCCsra3x+eef48yZM1r1Sm7bP4lMJsNzzz1Xbp2ytn+L74vQZZ+BrvGUNHLkSPj7++Pu3btYu3YtunfvDh8fnyfWDwgIkE4LBwBfffUV7t27h88++0wqK34O9YoqnCa+eLKfiIhqPybGiYiIjETr1q0RFRX1xA3apk2b4s6dO7hz5440avzKlStITk7GCy+8UG678fHxUCgUqF+/fpl1lEolVCpVufG1bNkSP/zwA5KSksocNR4eHo5hw4bhjTfeAKDZuNbnXF22trZwc3PDmTNnpPNu5+fnS+c3K+Tk5IS4uDjpfmpqKm7evCndv3DhAtRqNZYuXQqZTAYApc7Vpgtd3pOSWrdujStXrpS7U0KhUCAoKAhBQUEICwuDnZ0djhw5UuYBEURERERERFS+pk2b4vfff4coitIB6OHh4bC2toanpyfs7e1hYmKCc+fOwdvbG4BmxrLr169L254A0KpVK1y5cuWpz1e4/Z2RkQEbGxu4u7sjPDwcXbt2leqEh4eXGj1dfFnHjh0xevRoqUyf2dwqmy77DMrSpEkT3LlzBwkJCVKi+ty5c6XqtWjRAgEBAfj++++xYcMGrFixotx2zc3NtbapHRwckJqa+tTkv74uX74MExMTNGvWrFLbJSIi48bEOBERkZGYM2cO+vTpA29vb/Tv3x8ymQyRkZG4fPkyPvnkEwQFBaFFixYYMmQIli9fjvz8fIwePRpdu3ZFQEDAE9sNCgpCYGAg+vXrh8WLF8PX1xf379/H7t278cYbbyAgIAD169fHzZs3ERERAU9PT1hbW8PU1FSrnUGDBmHBggXo168fFi5cCDc3N1y6dAnu7u4IDAxE48aNsXXrVgQHB0MQBHz00UfSEei6mjBhAhYtWoTGjRvj+eefx7Jly5CcnKxVp3v37li3bh2Cg4NhZ2eHOXPmQC6XS8ufe+455OXl4euvv0ZwcDDCw8OxatUqveIAgPr162P//v2IioqCo6MjbG1tn/qYadOmoUOHDhg7dixGjhwJS0tLXLlyBQcPHsSKFSuwa9cuxMTEoEuXLrC3t8eePXugVqtLTeFHRERERERE2lJSUrRGEgOAo6MjRo8ejeXLl2PcuHEYO3YsoqKiEBYWhtDQUMhkMlhbWyMkJARTpkyBg4MDnJ2dERYWBplMpjX9es+ePTFy5EioVCppG/M///kP3N3d0b17d3h6eiIuLg6ffPIJnJycEBgYCACYMmUKwsLC0KhRI/j7+2Pt2rWIiIjQOsVWcY0bN8ZPP/2E/fv3o0GDBvj5559x7tw5NGjQoELviyiKiI+PL1Xu7OwsHSxeHl32GZSlR48eaNSoEUJCQrB48WKkpaVh9uzZAKD1vgKaUeNjx46FpaWldDB9VSpcT9LT05GYmIiIiAgolUqtQQUnTpxA586dnzo7HhER1S5P/2YkIiKiatGzZ0/s2rULBw4cQNu2bdGhQwd88cUX0hRjgiBgx44dsLe3R5cuXRAUFISGDRti8+bN5bYrCAL27NmDLl26YPjw4fD19cU777yD27dvS0d1v/XWW+jVqxe6desGJycnbNy4sVQ7SqUSBw4cgLOzM3r37o0WLVpg0aJF0g6DZcuWwd7eHh07dkRwcDB69uypNdJbF5MmTcK7776LkJAQaUq5khvNM2bMQNeuXdGnTx+89tpr6Nevn9a5wv38/LBs2TJ89tlnaN68OX755RcsXLhQrzgA4P3330eTJk0QEBAAJycnhIeHP/UxLVu2xLFjx3D9+nV07twZrVq1wpw5c+Du7g5AMw3d1q1b0b17dzRt2hSrVq3Cxo0beYQ6ERERERHRUxw9ehStWrXSusybNw8eHh7Ys2cPzp49Cz8/P4waNQojRoyQkrSAZns1MDAQffr0QVBQEDp16oSmTZtqnbbs1VdfhUKhwKFDh6SyoKAgnD59Gm+//TZ8fX3x1ltvwczMDIcPH5bOyT1+/HiEhoZi0qRJaNGiBfbt24edO3eicePGZb6ODz74AG+++SYGDhyI9u3b49GjR1qjx/WVmpoKNze3UpfCc4g/jS77DMoil8uxfft2pKeno23bthg5ciRmzZoFoPTp4AYNGgSFQoFBgwbpdaq4iipcPy5cuIANGzagVatW6N27t1adTZs24f3336/yWIiIyLgI4rOeTJOIiIioCg0bNgzJycnYvn27oUMhIiIiIiKiWiAjIwMeHh5YunQpRowYIZWvXLkSO3fuxP79+w0YXc0VHh6OF198EdHR0VoHsN+6dQuNGjXCuXPn9D6Avirs3bsXkyZNwp9//gmFgpPqEhHVJfyvT0RERERERERERES11qVLl3Dt2jW0a9cOKSkpmD9/PgCgb9++WvU++OADJCcnIy0tDdbW1oYItUbZtm0brKys0LhxY0RHR2PChAno1KmTlBTPy8vDo0ePMHv2bHTo0MEokuKA5sCItWvXMilORFQH8T8/EREREREREREREdVqS5YsQVRUFJRKJdq0aYMTJ06gXr16WnUUCoU0HTg9XVpaGqZNm4bY2FjUq1cPQUFBWLp0qbQ8PDwc3bp1g6+vL7Zs2WLASLX179/f0CEQEZGBcCp1IiIiIiIiIiIiIiIiIiKq1WSGDoCIiIiIiIiIiIiIiIiIiKgqMTFORERERERERERERERERES1GhPjRERERERERERERERERERUqzExTkREREREREREREREREREtRoT40REREREREREREREREREVKsxMU5ERERERERERERERERERLUaE+NERERERERERERERERERFSrMTFORERERERERERERERERES12v8DbQsKqe9tsaoAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 2000x1200 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1500x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"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": [
"<Figure size 2000x1500 with 10 Axes>"
]
},
"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)"
]
},
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