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olive-oil-transformer-model/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v1-checkpoint.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8adcbe0819b88578",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease\n",
"Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
"Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n",
"Hit:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n",
"Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
"Reading package lists... Done\n",
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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[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",
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"\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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",
"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": 24,
"id": "e884cc287364c4ed",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "too many values to unpack (expected 3)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[24], line 157\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredictions within ±\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mthreshold\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwithin_threshold\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;66;03m# Example usage\u001b[39;00m\n\u001b[0;32m--> 157\u001b[0m \u001b[43mplot_error_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_test_original\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfinal_pred_original\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfolder_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfolder_name\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[24], line 23\u001b[0m, in \u001b[0;36mplot_error_analysis\u001b[0;34m(y_true, predictions, folder_name)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m roc_curve\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Unpack predictions\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m classification_pred, regression_pred, final_pred \u001b[38;5;241m=\u001b[39m predictions\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# Convert to 1D numpy arrays if needed\u001b[39;00m\n\u001b[1;32m 26\u001b[0m y_true \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mravel(y_true)\n",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
]
}
],
"source": [
"def plot_error_analysis(y_true, predictions, folder_name=None):\n",
" \"\"\"\n",
" Function to visualize prediction error analysis for the hybrid model\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual values\n",
" predictions : tuple\n",
" Tuple containing (classification_pred, regression_pred, final_pred)\n",
" folder_name : str, optional\n",
" Directory to save plots. If None, plots are only displayed\n",
"\n",
" Generates:\n",
" ----------\n",
" - Classification analysis plots\n",
" - Regression error analysis plots\n",
" - Final prediction error analysis plots\n",
" \"\"\"\n",
" from sklearn.metrics import roc_curve\n",
"\n",
" # Unpack predictions\n",
" classification_pred, regression_pred, final_pred = predictions\n",
"\n",
" # Convert to 1D numpy arrays if needed\n",
" y_true = np.ravel(y_true)\n",
" classification_pred = np.ravel(classification_pred)\n",
" regression_pred = np.ravel(regression_pred)\n",
" final_pred = np.ravel(final_pred)\n",
"\n",
" # Create binary ground truth\n",
" y_true_binary = (y_true > 0).astype(float)\n",
"\n",
" # Calculate errors for regression and final predictions\n",
" regression_errors = regression_pred - y_true\n",
" final_errors = final_pred - y_true\n",
"\n",
" # Create main figure\n",
" plt.figure(figsize=(20, 15))\n",
"\n",
" # Classification Analysis (Top Row)\n",
" # Plot 1: Classification Distribution\n",
" plt.subplot(3, 3, 1)\n",
" plt.hist(classification_pred, bins=50, alpha=0.7)\n",
" plt.axvline(x=0.5, color='r', linestyle='--')\n",
" plt.title('Classification Probability Distribution')\n",
" plt.xlabel('Classification Probability')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 2: ROC Curve\n",
" plt.subplot(3, 3, 2)\n",
" fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n",
" plt.plot(fpr, tpr)\n",
" plt.plot([0, 1], [0, 1], 'r--')\n",
" plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
"\n",
" # Plot 3: Classification Confusion Matrix\n",
" plt.subplot(3, 3, 3)\n",
" cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Classification Confusion Matrix')\n",
" plt.xlabel('Predicted')\n",
" plt.ylabel('Actual')\n",
"\n",
" # Regression Analysis (Middle Row)\n",
" # Plot 4: Regression Error Distribution\n",
" plt.subplot(3, 3, 4)\n",
" plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n",
" plt.title('Regression Error Distribution (Non-zero Values)')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 5: Actual vs Predicted (Regression)\n",
" plt.subplot(3, 3, 5)\n",
" mask_nonzero = y_true > 0\n",
" plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n",
" plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n",
" [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 6: Regression Errors vs Actual Values\n",
" plt.subplot(3, 3, 6)\n",
" plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" # Final Predictions Analysis (Bottom Row)\n",
" # Plot 7: Final Error Distribution\n",
" plt.subplot(3, 3, 7)\n",
" plt.hist(final_errors, bins=50, alpha=0.7)\n",
" plt.title('Final Prediction Error Distribution')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 8: Actual vs Predicted (Final)\n",
" plt.subplot(3, 3, 8)\n",
" plt.scatter(y_true, final_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Final)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 9: Final Errors vs Actual Values\n",
" plt.subplot(3, 3, 9)\n",
" plt.scatter(y_true, final_errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Final Errors vs Actual Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" plt.tight_layout()\n",
"\n",
" # Save plot if directory is specified\n",
" if folder_name is not None:\n",
" try:\n",
" filename = f'{folder_name}_error_analysis.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" except Exception as e:\n",
" print(f\"\\nError saving plot: {str(e)}\")\n",
"\n",
" plt.show()\n",
"\n",
" # Print comprehensive statistics\n",
" print(\"\\nClassification Statistics:\")\n",
" print(classification_report(y_true_binary, classification_pred > 0.5))\n",
" print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n",
"\n",
" print(\"\\nRegression Statistics (Non-zero values):\")\n",
" mask_nonzero = y_true > 0\n",
" if np.any(mask_nonzero):\n",
" print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n",
" print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n",
"\n",
" print(\"\\nFinal Prediction Statistics:\")\n",
" print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(final_errors):.4f}\")\n",
" print(f\"Error std: {np.std(final_errors):.4f}\")\n",
"\n",
" # Calculate percentage of errors within thresholds\n",
" thresholds = [0.5, 1.0, 1.5, 2.0]\n",
" print(\"\\nError Thresholds (Final Predictions):\")\n",
" for threshold in thresholds:\n",
" within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n",
" print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n",
"\n",
"# Example usage\n",
"plot_error_analysis(y_test, predictions, folder_name=folder_name)"
]
},
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