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olive-oil-transformer-model/models/solarenergy/.ipynb_checkpoints/solarenergy_model_v2-checkpoint.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8adcbe0819b88578",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\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 To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\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[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[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[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[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[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[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[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",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n",
"Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n",
"Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n",
"Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"# from opt_einsum.paths import branch_1\n",
"!apt-get update\n",
"!apt-get install graphviz -y\n",
"\n",
"!pip install tensorflow\n",
"!pip install numpy\n",
"!pip install pandas\n",
"\n",
"!pip install keras\n",
"!pip install scikit-learn\n",
"!pip install matplotlib\n",
"!pip install joblib\n",
"!pip install pyarrow\n",
"!pip install fastparquet\n",
"!pip install scipy\n",
"!pip install seaborn\n",
"!pip install tqdm\n",
"!pip install pydot\n",
"!pip install tensorflow-io\n",
"!pip install tensorflow-addons"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e6fe6bb613168a8a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-27 21:08:46.612732: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-11-27 21:08:46.612772: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-11-27 21:08:46.612813: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-11-27 21:08:46.620849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
"\n",
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
"\n",
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
"\n",
" warnings.warn(\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.layers import (\n",
" Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n",
" LayerNormalization, Input, Activation, Lambda, Bidirectional, \n",
" Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n",
" GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n",
" Conv1D, Multiply\n",
")\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
"from tensorflow.keras.optimizers import AdamW\n",
"from tensorflow.keras.metrics import AUC\n",
"from tensorflow.keras.utils import plot_model\n",
"\n",
"# Data processing and analysis\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import RobustScaler\n",
"from sklearn.metrics import (\n",
" mean_absolute_error, mean_squared_error, r2_score, \n",
" confusion_matrix, classification_report, roc_auc_score\n",
")\n",
"\n",
"# Visualization\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Additional utilities\n",
"import tensorflow_addons as tfa\n",
"from scipy import stats\n",
"import json\n",
"from datetime import datetime\n",
"import os\n",
"import joblib\n",
"\n",
"folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
"\n",
"random_state_value = None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3da8b15c7eb9833f",
"metadata": {},
"outputs": [],
"source": [
"def get_season(date):\n",
" month = date.month\n",
" day = date.day\n",
" if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n",
" return 'Winter'\n",
" elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n",
" return 'Spring'\n",
" elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n",
" return 'Summer'\n",
" elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n",
" return 'Autumn'\n",
" else:\n",
" return 'Unknown'\n",
"\n",
"\n",
"def get_time_period(hour):\n",
" if 5 <= hour < 12:\n",
" return 'Morning'\n",
" elif 12 <= hour < 17:\n",
" return 'Afternoon'\n",
" elif 17 <= hour < 21:\n",
" return 'Evening'\n",
" else:\n",
" return 'Night'\n",
"\n",
"\n",
"def add_time_features(df):\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
" df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n",
" df['year'] = df['datetime'].dt.year\n",
" df['month'] = df['datetime'].dt.month\n",
" df['day'] = df['datetime'].dt.day\n",
" df['hour'] = df['datetime'].dt.hour\n",
" df['minute'] = df['datetime'].dt.minute\n",
" df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n",
" df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n",
" df['day_of_week'] = df['datetime'].dt.dayofweek\n",
" df['day_of_year'] = df['datetime'].dt.dayofyear\n",
" df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n",
" df['quarter'] = df['datetime'].dt.quarter\n",
" df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n",
" df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n",
" df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n",
" df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n",
" df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n",
" df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n",
" df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n",
" df['season'] = df['datetime'].apply(get_season)\n",
" df['time_period'] = df['hour'].apply(get_time_period)\n",
" return df\n",
"\n",
"\n",
"def add_solar_features(df):\n",
" # Features based only on radiation and other available variables\n",
" df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n",
"\n",
" # Energy-specific features\n",
" df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n",
"\n",
" # Temperature impact on theoretical efficiency\n",
" df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n",
"\n",
" # Combined features\n",
" df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n",
" df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n",
" df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n",
" df['temp_effect'] = df['temp'] - df['tempmin']\n",
"\n",
" return df\n",
"\n",
"def add_solar_specific_features(df):\n",
" \"\"\"\n",
" Aggiunge feature specifiche per la predizione della radiazione solare\n",
" combinando caratteristiche astronomiche e meteorologiche\n",
" \"\"\"\n",
" # Caratteristiche astronomiche\n",
" df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n",
" df['solar_noon'] = np.abs(12 - df['hour'])\n",
" df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n",
"\n",
" # Angolo solare teorico\n",
" df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n",
"\n",
" # Interazioni con condizioni atmosferiche\n",
" df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n",
" df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n",
" df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n",
"\n",
" # Indici di chiarezza e trasmissione\n",
" df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n",
" df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n",
"\n",
" # Radiazione teorica e attenuazione\n",
" df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n",
" df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n",
"\n",
" # Rolling features\n",
" df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n",
" df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n",
" df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n",
"\n",
" # Interazioni temperatura-radiazione\n",
" df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n",
"\n",
" return df\n",
"\n",
"def add_radiation_energy_features(df):\n",
" \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n",
"\n",
" # Solar energy to UV ratio (independent from solarradiation)\n",
" df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n",
"\n",
" # Time aggregations\n",
" # Moving averages\n",
" windows = [3, 6, 12, 24] # hours\n",
" for w in windows:\n",
" df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n",
" df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n",
"\n",
" # Daily aggregations utilizzando datetime\n",
" df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n",
" df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n",
"\n",
" # Changes\n",
" df['energy_change'] = df['solarenergy'].diff()\n",
" df['uv_change'] = df['uvindex'].diff()\n",
"\n",
" # Lag features\n",
" lags = [1, 2, 3, 6, 12, 24] # hours\n",
" for lag in lags:\n",
" df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n",
" df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n",
"\n",
" # Peak indicators\n",
" df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n",
" df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n",
"\n",
" # Aggiungiamo alcune metriche di volatilità\n",
" df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n",
" df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n",
"\n",
" # Indice di intensità solare composito\n",
" df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n",
"\n",
" # Interazioni\n",
" df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n",
" df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n",
"\n",
" return df\n",
"\n",
"def add_atmospheric_features(df):\n",
" # Indice di Massa d'Aria (Air Mass Index)\n",
" # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n",
" df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n",
" (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n",
"\n",
" # Indice di Stabilità Atmosferica\n",
" # Combina temperatura, umidità e pressione\n",
" df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n",
"\n",
" # Vapor Pressure Deficit (VPD)\n",
" # Importante per la radiazione diffusa\n",
" df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n",
" df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n",
" df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n",
"\n",
" return df\n",
"\n",
"def add_diffusion_features(df):\n",
" # Indice di Diffusione\n",
" df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n",
"\n",
" # Radiazione Diretta vs Diffusa\n",
" df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n",
" df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n",
"\n",
" # Fattore di Trasparenza Atmosferica\n",
" df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n",
"\n",
" return df\n",
"\n",
"def calculate_trend(x):\n",
" try:\n",
" return np.polyfit(np.arange(len(x)), x, 1)[0]\n",
" except:\n",
" return np.nan\n",
"\n",
"def add_persistence_features(df):\n",
" # Create a copy to avoid modifying the original dataframe\n",
" df = df.copy()\n",
"\n",
" # Calculate trends more efficiently\n",
" windows = [3, 6, 12, 24]\n",
" for w in windows:\n",
" # Use numba or vectorized operations if possible\n",
" df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n",
" window=w,\n",
" min_periods=w\n",
" ).apply(calculate_trend, raw=True)\n",
"\n",
" # Optimize volatility calculation by doing it in one pass\n",
" rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n",
" df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n",
"\n",
" return df\n",
"\n",
"def add_weather_pattern_features(df):\n",
" # Pattern giornalieri\n",
" df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n",
" lambda x: (x < 30).sum()\n",
" )\n",
"\n",
" # Stabilità delle condizioni\n",
" for col in ['temp', 'humidity', 'cloudcover']:\n",
" df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n",
"\n",
" # Indice di Variabilità Meteorologica\n",
" df['weather_variability_index'] = (df['temp_stability'] +\n",
" df['humidity_stability'] +\n",
" df['cloudcover_stability']) / 3\n",
"\n",
" return df\n",
"\n",
"def add_efficiency_features(df):\n",
" # Perdite per temperatura\n",
" df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n",
"\n",
" # Perdite per polvere/sporco (stima basata su umidità e pressione)\n",
" df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n",
"\n",
" # Efficienza complessiva stimata\n",
" df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n",
" df['atmospheric_transmittance']\n",
"\n",
" # Potenziale di produzione\n",
" df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n",
"\n",
" return df\n",
"\n",
"def add_advanced_seasonal_features(df):\n",
" # Differenza dalla durata media del giorno\n",
" avg_day_length = 12\n",
" df['day_length_deviation'] = df['day_length'] - avg_day_length\n",
"\n",
" # Intensità stagionale\n",
" df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n",
"\n",
" # Indice di Stagionalità\n",
" df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n",
"\n",
" # Correzione per alba/tramonto\n",
" df['daylight_correction'] = np.where(\n",
" (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n",
" 0,\n",
" 1\n",
" )\n",
"\n",
" return df\n",
"\n",
"def add_basic_interactions(df):\n",
" \"\"\"\n",
" Aggiunge le interazioni base tra variabili meteorologiche\n",
" \"\"\"\n",
" # Feature esistenti originali\n",
" df['temp_humidity'] = df['temp'] * df['humidity']\n",
" df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n",
" df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n",
" df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n",
"\n",
" # Clear sky e trasparenza atmosferica\n",
" df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n",
" df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n",
"\n",
" return df\n",
"\n",
"def add_rolling_and_lag_features(df):\n",
" \"\"\"\n",
" Aggiunge feature rolling e lag\n",
" \"\"\"\n",
" # Rolling means esistenti\n",
" df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n",
" df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n",
"\n",
" # Lag features esistenti\n",
" df['temp_1h_lag'] = df['temp'].shift(1)\n",
" df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n",
" df['humidity_1h_lag'] = df['humidity'].shift(1)\n",
"\n",
" return df\n",
"\n",
"def add_condition_indicators(df):\n",
" \"\"\"\n",
" Aggiunge indicatori di condizioni particolari\n",
" \"\"\"\n",
" # Extreme conditions indicator esistente\n",
" df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n",
" (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n",
"\n",
" return df\n",
"\n",
"def add_physics_based_conversion_features(df):\n",
" \"\"\"\n",
" Aggiunge feature specifiche per la conversione tra radiazione ed energia\n",
" \"\"\"\n",
" # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n",
" df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n",
"\n",
" # Efficienza di conversione reale vs teorica\n",
" df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n",
"\n",
" # Energia accumulata nel tempo (integrazione)\n",
" df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n",
"\n",
" # Differenza tra energia teorica e reale\n",
" df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n",
"\n",
" # Indice di performance del sistema\n",
" df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n",
"\n",
" return df\n",
"\n",
"def add_advanced_features(df):\n",
" \"\"\"\n",
" Add all advanced features to the DataFrame\n",
" \"\"\"\n",
" # Feature esistenti di base\n",
" # 1. Feature temporali di base\n",
" df = add_time_features(df)\n",
"\n",
" # 2. Feature solari e meteorologiche\n",
" df = add_solar_features(df)\n",
" df = add_solar_specific_features(df)\n",
" df = add_radiation_energy_features(df)\n",
"\n",
" # 3. Feature atmosferiche e di diffusione\n",
" df = add_atmospheric_features(df)\n",
" df = add_diffusion_features(df)\n",
"\n",
" # 4. Feature di persistenza e pattern\n",
" df = add_persistence_features(df)\n",
" df = add_weather_pattern_features(df)\n",
"\n",
" # 5. Feature di efficienza e stagionalità\n",
" df = add_efficiency_features(df)\n",
" df = add_advanced_seasonal_features(df)\n",
"\n",
" # 6. Interazioni e feature derivate\n",
" df = add_basic_interactions(df)\n",
" df = add_rolling_and_lag_features(df)\n",
" df = add_condition_indicators(df)\n",
"\n",
" # 7. Nuove feature di conversione fisica\n",
" df = add_physics_based_conversion_features(df)\n",
"\n",
" # 8. One-hot encoding delle feature categoriche\n",
" df = pd.get_dummies(df, columns=['season', 'time_period'])\n",
"\n",
" return df\n",
"\n",
"\n",
"def prepare_advanced_data(df):\n",
" \"\"\"\n",
" Prepare data for advanced modeling with proper datetime handling\n",
" \"\"\"\n",
" # Assicuriamoci che abbiamo una copia del DataFrame\n",
" df = df.copy()\n",
"\n",
" # Apply feature engineering functions\n",
" df = add_advanced_features(df)\n",
"\n",
" #all_columns = list(df.columns)\n",
" #print(all_columns)\n",
"\n",
" features = {\n",
" # Primary Features (strong direct correlation)\n",
" 'primary_features': [\n",
" 'uvindex',\n",
" 'cloudcover',\n",
" 'visibility',\n",
" 'temp',\n",
" 'pressure',\n",
" 'humidity',\n",
" 'solarradiation'\n",
" ],\n",
"\n",
" # Astronomical and Temporal Features\n",
" 'astronomical_features': [\n",
" 'solar_elevation',\n",
" 'solar_angle',\n",
" 'day_length',\n",
" 'hour_sin',\n",
" 'hour_cos',\n",
" 'day_of_year_sin',\n",
" 'day_of_year_cos',\n",
" 'month_sin',\n",
" 'month_cos',\n",
" 'solar_noon',\n",
" 'daylight_correction'\n",
" ],\n",
"\n",
" # Key Indices and Interactions\n",
" 'key_interactions': [\n",
" 'clear_sky_index',\n",
" 'atmospheric_attenuation',\n",
" 'theoretical_radiation',\n",
" 'expected_radiation',\n",
" 'cloud_elevation',\n",
" 'visibility_elevation',\n",
" 'uv_cloud_interaction',\n",
" 'temp_radiation_potential',\n",
" 'air_mass_index',\n",
" 'atmospheric_stability',\n",
" 'vapor_pressure_deficit',\n",
" 'diffusion_index',\n",
" 'atmospheric_transmittance',\n",
" 'temp_humidity_interaction',\n",
" 'clear_sky_factor'\n",
" ],\n",
"\n",
" # Rolling Features (temporal trends)\n",
" 'rolling_features': [\n",
" 'cloud_rolling_12h',\n",
" 'temp_rolling_12h',\n",
" 'uv_rolling_12h',\n",
" 'cloudcover_rolling_mean_6h',\n",
" 'temp_rolling_mean_6h',\n",
" 'energy_rolling_mean_6h',\n",
" 'uv_rolling_mean_6h',\n",
" 'energy_volatility',\n",
" 'uv_volatility'\n",
" ],\n",
"\n",
" # Lag Features\n",
" 'lag_features': [\n",
" 'temp_1h_lag',\n",
" 'cloudcover_1h_lag',\n",
" 'humidity_1h_lag',\n",
" 'energy_lag_1h',\n",
" 'uv_lag_1h'\n",
" ],\n",
"\n",
" # Efficiency and Performance Features\n",
" 'efficiency_features': [\n",
" 'temp_losses',\n",
" 'soiling_loss_factor',\n",
" 'estimated_efficiency',\n",
" 'production_potential',\n",
" 'system_performance_ratio',\n",
" 'conversion_efficiency_ratio'\n",
" ],\n",
"\n",
" # Weather Pattern Features\n",
" 'weather_pattern_features': [\n",
" 'clear_sky_duration',\n",
" 'weather_variability_index',\n",
" 'temp_stability',\n",
" 'humidity_stability',\n",
" 'cloudcover_stability'\n",
" ],\n",
"\n",
" # Categorical Features\n",
" 'categorical_features': [\n",
" 'season_Spring',\n",
" 'season_Summer',\n",
" 'season_Autumn',\n",
" 'season_Winter',\n",
" 'time_period_Morning',\n",
" 'time_period_Afternoon',\n",
" 'time_period_Evening',\n",
" 'time_period_Night'\n",
" ]\n",
" }\n",
"\n",
" final_features = [feature for group in features.values() for feature in group]\n",
"\n",
" if not isinstance(df.index, pd.DatetimeIndex):\n",
" if 'datetime' in df.columns:\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
" df.set_index('datetime', inplace=True)\n",
" else:\n",
" raise ValueError(\"No datetime column or index found in DataFrame\")\n",
"\n",
" # Ordiniamo il DataFrame per datetime\n",
" df = df.sort_index()\n",
"\n",
" # Handle missing values\n",
" target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n",
" for column in final_features + target_variables:\n",
" if column in df.columns:\n",
" if isinstance(df.index, pd.DatetimeIndex):\n",
" df[column] = df[column].interpolate(method='time')\n",
" else:\n",
" df[column] = df[column].interpolate(method='linear')\n",
"\n",
" df.fillna(0, inplace=True)\n",
"\n",
" # Temporal split\n",
" data_after_2010 = df[df['year'] >= 2010].copy()\n",
" data_before_2010 = df[df['year'] < 2010].copy()\n",
"\n",
" X = data_after_2010[final_features]\n",
" y = data_after_2010['solarenergy']\n",
" X_to_predict = data_before_2010[final_features]\n",
"\n",
" # Train-test split\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n",
" )\n",
"\n",
" # Scaling\n",
" scaler_X = RobustScaler()\n",
" X_train_scaled = scaler_X.fit_transform(X_train)\n",
" X_test_scaled = scaler_X.transform(X_test)\n",
" X_to_predict_scaled = scaler_X.transform(X_to_predict)\n",
"\n",
" scaler_y = RobustScaler()\n",
" y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n",
" y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n",
"\n",
" # Print info about selected features\n",
" print(\"\\nSelected features:\")\n",
" print(f\"Number of features: {len(final_features)}\")\n",
" print(\"Features list:\", final_features)\n",
"\n",
" return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n",
"\n",
"\n",
"def create_sequence_data(X, sequence_length=24):\n",
" \"\"\"\n",
" Converts data into sequences for LSTM input\n",
" sequence_length represents how many previous hours to consider\n",
" \"\"\"\n",
" sequences = []\n",
" for i in range(len(X) - sequence_length + 1):\n",
" sequences.append(X[i:i + sequence_length])\n",
" return np.array(sequences)\n",
"\n",
"\n",
"def prepare_hybrid_data(df):\n",
" X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n",
"\n",
" # Convert data into sequences\n",
" sequence_length = 24 # 24 hours of historical data\n",
"\n",
" X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n",
" X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n",
"\n",
" # Adjust y by removing the first (sequence_length-1) elements\n",
" y_train = y_train_scaled[sequence_length - 1:]\n",
" y_test = y_test_scaled[sequence_length - 1:]\n",
"\n",
" X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n",
"\n",
" return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "570b18f2caa3e0db",
"metadata": {},
"outputs": [],
"source": [
"def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n",
" from tensorflow import keras\n",
" from keras.models import Model\n",
" from keras.layers import (\n",
" Input, Dense, Conv1D, BatchNormalization, Dropout, \n",
" MultiHeadAttention, LayerNormalization, Lambda,\n",
" Concatenate, Activation, Bidirectional, LSTM, Add\n",
" )\n",
" from keras.regularizers import l2\n",
" from keras.optimizers import AdamW\n",
" import tensorflow as tf\n",
" import numpy as np\n",
" import tensorflow_addons as tfa\n",
" from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n",
" \n",
" # Input layer\n",
" inputs = Input(shape=input_shape)\n",
" \n",
" # Feature groups definition\n",
" feature_dims = {\n",
" 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n",
" 'weather': [0, 1, 2, 3, 4, 5],\n",
" 'temporal': [10, 11, 12, 13, 14, 15],\n",
" 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n",
" 'rolling': [33, 34, 35, 36, 37, 38, 39],\n",
" 'lag': [40, 41, 42, 43, 44],\n",
" 'performance': [45, 46, 47, 48, 49, 50]\n",
" }\n",
" \n",
" # Feature extraction\n",
" feature_tensors = {}\n",
" for name, indices in feature_dims.items():\n",
" valid_indices = [i for i in indices if i < input_shape[-1]]\n",
" if valid_indices:\n",
" feature_tensors[name] = Lambda(\n",
" lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n",
" )(inputs)\n",
" \n",
" # Feature processing with residual connections\n",
" def process_feature_group(tensor, units, name):\n",
" x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n",
" kernel_regularizer=l2(l2_lambda))(tensor)\n",
" x = BatchNormalization()(x)\n",
" x = Dropout(0.2)(x)\n",
" \n",
" residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n",
" x = Add()([x, residual])\n",
" x = LayerNormalization()(x)\n",
" \n",
" return x\n",
" \n",
" # Process each feature group\n",
" processed_features = {}\n",
" for name, tensor in feature_tensors.items():\n",
" units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n",
" processed_features[name] = process_feature_group(tensor, units, name)\n",
" \n",
" # Enhanced attention mechanism\n",
" def attention_block(x, num_heads=4):\n",
" attention_output = MultiHeadAttention(\n",
" num_heads=num_heads, \n",
" key_dim=x.shape[-1] // num_heads\n",
" )(x, x)\n",
" x = LayerNormalization()(x + attention_output)\n",
" \n",
" ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n",
" ffn = Dropout(0.1)(ffn)\n",
" ffn = Dense(x.shape[-1])(ffn)\n",
" \n",
" return LayerNormalization()(x + ffn)\n",
" \n",
" # Merge primary features with attention\n",
" primary_features = [\n",
" processed_features['solar'],\n",
" processed_features['weather'],\n",
" processed_features['performance']\n",
" ]\n",
" primary_context = Concatenate(axis=-1)(primary_features)\n",
" primary_context = attention_block(primary_context)\n",
" \n",
" # Merge secondary features\n",
" secondary_features = [\n",
" processed_features[name] for name in ['temporal', 'rolling', 'lag']\n",
" if name in processed_features\n",
" ]\n",
" if secondary_features:\n",
" secondary_context = Concatenate(axis=-1)(secondary_features)\n",
" secondary_context = attention_block(secondary_context)\n",
" else:\n",
" secondary_context = primary_context\n",
" \n",
" # Final feature merge\n",
" combined = Concatenate(axis=-1)([\n",
" primary_context, \n",
" secondary_context,\n",
" processed_features['derived']\n",
" ])\n",
" \n",
" # Sequential processing with residual LSTM\n",
" def residual_lstm_block(x, units):\n",
" lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n",
" residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n",
" x = Add()([lstm_out, residual])\n",
" x = LayerNormalization()(x)\n",
" return x\n",
" \n",
" x = residual_lstm_block(combined, 128)\n",
" x = residual_lstm_block(x, 64)\n",
" x = Bidirectional(LSTM(64))(x)\n",
" x = Dropout(0.2)(x)\n",
" \n",
" # Classification branch\n",
" class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" class_x = BatchNormalization()(class_x)\n",
" class_x = Dropout(0.2)(class_x)\n",
" class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n",
" class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n",
" \n",
" # Enhanced regression branch with multiple pathways\n",
" def create_regression_pathway(x, name):\n",
" x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" x = BatchNormalization()(x)\n",
" x = Dropout(0.2)(x)\n",
"\n",
" high_value_attention = Dense(128, activation='sigmoid')(x)\n",
" x = x * high_value_attention\n",
" \n",
" residual = x\n",
" x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" x = BatchNormalization()(x)\n",
" x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" x = Add()([x, residual])\n",
" \n",
" x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" return Dense(1, name=f'{name}_output')(x)\n",
" \n",
" # Create specialized regression pathways\n",
" low_range = create_regression_pathway(x, 'low_range')\n",
" mid_range = create_regression_pathway(x, 'mid_range')\n",
" high_range = create_regression_pathway(x, 'high_range')\n",
" \n",
" # Create context vector for attention\n",
" context = Dense(64, activation='swish')(x)\n",
" \n",
" # Calculate attention scores\n",
" attention_scores = Dense(3, activation='softmax', \n",
" bias_initializer=tf.keras.initializers.Constant([0.2, 0.3, 0.5]))(context)\n",
" \n",
" # Combine predictions using attention weights\n",
" reg_output = Lambda(\n",
" lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n",
" name='regression_output'\n",
" )([attention_scores, low_range, mid_range, high_range])\n",
"\n",
" # Final output processing remains the same...\n",
" final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n",
" final_x = BatchNormalization()(final_x)\n",
" final_x = Dropout(0.2)(final_x)\n",
" \n",
" residual = final_x\n",
" final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n",
" final_x = BatchNormalization()(final_x)\n",
" final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n",
" final_x = Add()([final_x, residual])\n",
" \n",
" final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n",
" final_x = Dense(1)(final_x)\n",
" final_output = Lambda(\n",
" lambda x: tf.clip_by_value(x, min_output, max_output),\n",
" name='final_output'\n",
" )(final_x)\n",
" \n",
" # Build model with all outputs\n",
" model = Model(\n",
" inputs=inputs,\n",
" outputs=[class_output, reg_output, final_output]\n",
" )\n",
" \n",
" # Enhanced loss functions\n",
" def enhanced_regression_loss(y_true, y_pred):\n",
" mae = tf.abs(y_true - y_pred)\n",
" mse = tf.square(y_true - y_pred)\n",
" \n",
" # Aumentiamo i pesi per i valori più alti\n",
" value_ranges = tf.cast(y_true > 2.0, tf.float32) * 2.0 + \\\n",
" tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.5 + \\\n",
" tf.cast(y_true <= 1.0, tf.float32)\n",
" \n",
" # Aggiungiamo un termine per penalizzare specificamente la sottostima\n",
" underestimation_penalty = tf.maximum(0.0, y_true - y_pred) * 0.3\n",
" \n",
" weighted_loss = (0.4 * mae + 0.4 * mse + 0.2 * underestimation_penalty) * value_ranges\n",
" return tf.reduce_mean(weighted_loss)\n",
" \n",
" def final_loss(y_true, y_pred):\n",
" y_true = tf.clip_by_value(y_true, min_output, max_output)\n",
" mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n",
" mse = tf.reduce_mean(tf.square(y_true - y_pred))\n",
" return 0.5 * mae + 0.5 * mse\n",
" \n",
" # Learning rate schedule\n",
" clr = CosineDecayRestarts(\n",
" initial_learning_rate=2e-4,\n",
" first_decay_steps=1000,\n",
" t_mul=2.0,\n",
" m_mul=0.9,\n",
" alpha=1e-7\n",
" )\n",
" \n",
" # Optimizer\n",
" optimizer = AdamW(\n",
" learning_rate=clr,\n",
" weight_decay=0.01,\n",
" clipnorm=1.0\n",
" )\n",
" \n",
" # Compile model\n",
" model.compile(\n",
" optimizer=optimizer,\n",
" loss={\n",
" 'classification_output': 'binary_crossentropy',\n",
" 'regression_output': enhanced_regression_loss,\n",
" 'final_output': final_loss\n",
" },\n",
" loss_weights={\n",
" 'classification_output': 0.2,\n",
" 'regression_output': 0.4,\n",
" 'final_output': 0.4\n",
" }\n",
" )\n",
"\n",
" # Plot model architecture\n",
" try:\n",
" plot_model(\n",
" model,\n",
" to_file=f'{folder_name}_model_architecture.png',\n",
" show_shapes=True,\n",
" show_layer_names=True,\n",
" dpi=150,\n",
" show_layer_activations=True\n",
" )\n",
" except Exception as e:\n",
" print(f\"Warning: Could not plot model architecture: {e}\")\n",
"\n",
" return model\n",
"\n",
"\n",
"def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n",
" \"\"\"\n",
" Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual solar energy values (kWh)\n",
" y_pred : array-like\n",
" Predicted solar energy values (kWh)\n",
" hour : array-like, optional\n",
" Array of hours corresponding to predictions, for temporal analysis\n",
" folder_name : str, optional\n",
" Directory to save analysis plots\n",
"\n",
" Returns:\n",
" --------\n",
" dict\n",
" Dictionary containing all calculated metrics\n",
" \"\"\"\n",
"\n",
" # Data preparation\n",
" y_true = np.array(y_true).ravel()\n",
" y_pred = np.array(y_pred).ravel()\n",
" errors = y_pred - y_true\n",
"\n",
" # Basic metrics calculation\n",
" mae_raw = mean_absolute_error(y_true, y_pred)\n",
" rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n",
" r2_raw = r2_score(y_true, y_pred)\n",
"\n",
" # Corrected MAPE calculation\n",
" mask = y_true > 10 # Consider only values above 10 kWh\n",
" if np.any(mask):\n",
" mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n",
" else:\n",
" mape = np.nan\n",
"\n",
" # Corrected error margin accuracy\n",
" within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n",
" within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n",
" within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n",
"\n",
" # Energy level classification\n",
" def get_energy_level(value):\n",
" if value <= 0.5:\n",
" return 'Very Low'\n",
" elif value <= 2.0:\n",
" return 'Low'\n",
" elif value <= 4.0:\n",
" return 'Moderate'\n",
" elif value <= 6.0:\n",
" return 'High'\n",
" elif value <= 8.0:\n",
" return 'Very High'\n",
" else:\n",
" return 'Extreme'\n",
"\n",
" # Calculate energy levels\n",
" y_true_levels = [get_energy_level(v) for v in y_true]\n",
" y_pred_levels = [get_energy_level(v) for v in y_pred]\n",
" level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n",
"\n",
" unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n",
"\n",
" # Print main metrics\n",
" print(\"\\nSolar Energy Prediction Metrics:\")\n",
" print(\"\\nAbsolute Metrics:\")\n",
" print(f\"MAE: {mae_raw:.2f} kWh\")\n",
" print(f\"RMSE: {rmse_raw:.2f} kWh\")\n",
" print(f\"R² Score: {r2_raw:.3f}\")\n",
" print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n",
"\n",
" print(\"\\nAccuracy Metrics:\")\n",
" print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n",
" print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n",
" print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n",
"\n",
" print(\"\\nLevel Accuracy:\")\n",
" print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n",
"\n",
" # Confusion matrix for energy levels\n",
" cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n",
" print(\"\\nConfusion Matrix for Energy Levels:\")\n",
" cm_df = pd.DataFrame(\n",
" cm,\n",
" columns=unique_levels,\n",
" index=unique_levels\n",
" )\n",
" print(cm_df)\n",
"\n",
" # Time period analysis\n",
" if hour is not None:\n",
" day_periods = {\n",
" 'Morning (5-11)': (5, 11),\n",
" 'Noon (11-13)': (11, 13),\n",
" 'Afternoon (13-17)': (13, 17),\n",
" 'Evening (17-21)': (17, 21),\n",
" 'Night (21-5)': (21, 5)\n",
" }\n",
"\n",
" print(\"\\nAnalysis by Time Period:\")\n",
" for period, (start, end) in day_periods.items():\n",
" if start < end:\n",
" mask = (hour >= start) & (hour < end)\n",
" else:\n",
" mask = (hour >= start) | (hour < end)\n",
"\n",
" if np.any(mask):\n",
" period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n",
"\n",
" # Corrected period MAPE calculation\n",
" period_mask = mask & (y_true > 10)\n",
" if np.any(period_mask):\n",
" period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n",
" print(f\"\\n{period}:\")\n",
" print(f\"MAE: {period_mae:.2f} kWh\")\n",
" print(f\"MAPE: {period_mape:.2f}%\")\n",
" else:\n",
" print(f\"\\n{period}:\")\n",
" print(f\"MAE: {period_mae:.2f} kWh\")\n",
" print(\"MAPE: N/A (insufficient data)\")\n",
"\n",
" # Visualizations\n",
" if folder_name is not None:\n",
" try:\n",
" # Figure 1: Main analysis plots\n",
" plt.figure(figsize=(20, 15))\n",
"\n",
" # Plot 1: Scatter plot of actual vs predicted values\n",
" plt.subplot(3, 2, 1)\n",
" plt.scatter(y_true, y_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.xlabel('Actual Energy (kWh)')\n",
" plt.ylabel('Predicted Energy (kWh)')\n",
" plt.title('Actual vs Predicted Values')\n",
" plt.grid(True)\n",
"\n",
" # Plot 2: Absolute error distribution\n",
" plt.subplot(3, 2, 2)\n",
" plt.hist(errors, bins=50, alpha=0.7)\n",
" plt.xlabel('Prediction Error (kWh)')\n",
" plt.ylabel('Frequency')\n",
" plt.title('Error Distribution')\n",
" plt.grid(True)\n",
"\n",
" # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n",
" plt.subplot(3, 2, 3)\n",
" mask = y_true > 0.5\n",
" if np.any(mask):\n",
" percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n",
" plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n",
" plt.xlabel('Percentage Error (%)')\n",
" plt.ylabel('Frequency')\n",
" plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n",
" plt.grid(True)\n",
"\n",
" # Plot 4: Errors vs actual values\n",
" plt.subplot(3, 2, 4)\n",
" plt.scatter(y_true, errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.xlabel('Actual Energy (kWh)')\n",
" plt.ylabel('Error (kWh)')\n",
" plt.title('Errors vs Actual Values')\n",
" plt.grid(True)\n",
"\n",
" # Plot 5: Error boxplot by Energy level\n",
" plt.subplot(3, 2, 5)\n",
" sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n",
" plt.xticks(rotation=45)\n",
" plt.xlabel('Energy Level')\n",
" plt.ylabel('Error (kWh)')\n",
" plt.title('Error Distribution by Level')\n",
"\n",
" # Plot 6: Confusion matrix heatmap\n",
" plt.subplot(3, 2, 6)\n",
" sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Confusion Matrix')\n",
" plt.xticks(rotation=45)\n",
" plt.yticks(rotation=45)\n",
"\n",
" plt.tight_layout()\n",
" filename = f'{folder_name}_energy_analysis.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" plt.close()\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError saving plots: {str(e)}\")\n",
"\n",
" # Additional error statistics\n",
" print(\"\\nError Statistics:\")\n",
" print(f\"Mean error: {np.mean(errors):.3f}\")\n",
" print(f\"Error standard deviation: {np.std(errors):.3f}\")\n",
" print(f\"Median error: {np.median(errors):.3f}\")\n",
" print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n",
"\n",
" # Return structured metrics\n",
" metrics = {\n",
" 'absolute': {\n",
" 'mae': mae_raw,\n",
" 'rmse': rmse_raw,\n",
" 'r2': r2_raw,\n",
" 'mape': float(mape) if not np.isnan(mape) else None\n",
" },\n",
" 'accuracy': {\n",
" 'within_5_wm2': float(within_5_percent),\n",
" 'within_10_wm2': float(within_10_percent),\n",
" 'within_20_wm2': float(within_20_percent)\n",
" },\n",
" 'categorical': {\n",
" 'level_accuracy': float(level_accuracy)\n",
" },\n",
" 'error_stats': {\n",
" 'mean': float(np.mean(errors)),\n",
" 'std': float(np.std(errors)),\n",
" 'median': float(np.median(errors)),\n",
" 'p95_abs': float(np.percentile(np.abs(errors), 95))\n",
" }\n",
" }\n",
"\n",
" return metrics\n",
"\n",
"\n",
"def plot_training_history(history, folder_name=None):\n",
" \"\"\"\n",
" Visualize and save training history for the hybrid model\n",
" \"\"\"\n",
" plt.figure(figsize=(15, 10))\n",
"\n",
" # Loss plots\n",
" plt.subplot(2, 2, 1)\n",
" plt.plot(history.history['classification_output_loss'], label='Class Loss')\n",
" plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n",
" plt.plot(history.history['final_output_loss'], label='Final Loss')\n",
" plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n",
" plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n",
" plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n",
" plt.title('Model Losses')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Classification metrics\n",
" plt.subplot(2, 2, 2)\n",
" plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n",
" plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n",
" plt.plot(history.history['classification_output_auc'], label='Class AUC')\n",
" plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n",
" plt.title('Classification Metrics')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Metric Value')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Regression metrics\n",
" plt.subplot(2, 2, 3)\n",
" plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n",
" plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n",
" plt.title('Regression MAE')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('MAE')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Final output metrics\n",
" plt.subplot(2, 2, 4)\n",
" plt.plot(history.history['final_output_mae'], label='Final MAE')\n",
" plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n",
" plt.title('Final Output MAE')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('MAE')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" plt.tight_layout()\n",
"\n",
" if folder_name is not None:\n",
" filename = f'{folder_name}_training_history.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nTraining history plot saved as: {filename}\")\n",
"\n",
" # Save history to JSON\n",
" history_dict = history.history\n",
" json_filename = f'{folder_name}_training_history.json'\n",
" with open(json_filename, 'w') as f:\n",
" json.dump(history_dict, f)\n",
" print(f\"Training history saved as: {json_filename}\")\n",
"\n",
" plt.show()\n",
"\n",
"def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n",
" \"\"\"\n",
" Calculates comprehensive metrics for the solar energy prediction model.\n",
" \n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Ground truth values\n",
" y_class : array-like\n",
" Classification predictions (probability of non-zero values)\n",
" y_reg : array-like\n",
" Regression predictions (unrestricted values)\n",
" y_final : array-like\n",
" Final clipped predictions\n",
" min_output : float\n",
" Minimum allowed output value\n",
" max_output : float\n",
" Maximum allowed output value\n",
" \n",
" Returns:\n",
" --------\n",
" dict\n",
" Dictionary containing all calculated metrics\n",
" \"\"\"\n",
" from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n",
" \n",
" # Ensure proper array formatting and dimensionality\n",
" y_true = np.array(y_true).flatten()\n",
" y_class = np.array(y_class).flatten()\n",
" y_reg = np.array(y_reg).flatten()\n",
" y_final = np.array(y_final).flatten()\n",
" \n",
" # Validate input dimensions\n",
" assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n",
" \"All input arrays must have the same length\"\n",
" \n",
" # Classification metrics with error handling\n",
" print(\"\\nClassification Metrics:\")\n",
" try:\n",
" y_true_binary = (y_true > 0).astype(int)\n",
" y_pred_binary = (y_class > 0.5).astype(int)\n",
" \n",
" accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n",
" auc_roc = roc_auc_score(y_true > 0, y_class)\n",
" print(f\"Accuracy: {accuracy:.2f}%\")\n",
" print(f\"AUC-ROC: {auc_roc:.4f}\")\n",
" \n",
" print(\"\\nConfusion Matrix:\")\n",
" conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n",
" print(conf_matrix)\n",
" \n",
" print(\"\\nClassification Report:\")\n",
" class_report = classification_report(\n",
" y_true_binary, \n",
" y_pred_binary,\n",
" target_names=['Zero', 'Non-Zero'],\n",
" digits=4\n",
" )\n",
" print(class_report)\n",
" except Exception as e:\n",
" print(f\"Error in classification metrics calculation: {str(e)}\")\n",
" \n",
" # Regression metrics with error handling\n",
" print(\"\\nRegression Metrics (non-zero values):\")\n",
" mask_nonzero = y_true > 0\n",
" if np.any(mask_nonzero):\n",
" try:\n",
" y_true_nonzero = y_true[mask_nonzero]\n",
" y_reg_nonzero = y_reg[mask_nonzero]\n",
" \n",
" # Range validation\n",
" out_of_range = np.sum(\n",
" (y_reg_nonzero < min_output) | \n",
" (y_reg_nonzero > max_output)\n",
" )\n",
" \n",
" # Error metrics with numerical stability\n",
" epsilon = 1e-7\n",
" diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n",
" (y_true_nonzero + epsilon))\n",
" diff = np.clip(diff, 0, 1)\n",
" \n",
" # Calculate metrics\n",
" mape = np.mean(diff) * 100\n",
" within_2_percent = np.mean(diff <= 0.02) * 100\n",
" within_5_percent = np.mean(diff <= 0.05) * 100\n",
" within_10_percent = np.mean(diff <= 0.10) * 100\n",
" within_20_percent = np.mean(diff <= 0.20) * 100\n",
" mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n",
" rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n",
" \n",
" print(f\"Out of range: {out_of_range} predictions\")\n",
" print(f\"MAPE: {mape:.2f}%\")\n",
" print(f\"Within ±2%: {within_2_percent:.2f}%\")\n",
" print(f\"Within ±5%: {within_5_percent:.2f}%\")\n",
" print(f\"Within ±10%: {within_10_percent:.2f}%\")\n",
" print(f\"Within ±20%: {within_20_percent:.2f}%\")\n",
" print(f\"MAE: {mae:.2f}\")\n",
" print(f\"RMSE: {rmse:.2f}\")\n",
" except Exception as e:\n",
" print(f\"Error in regression metrics calculation: {str(e)}\")\n",
" else:\n",
" print(\"No non-zero values in this batch\")\n",
" \n",
" # Final output metrics with error handling\n",
" print(\"\\nFinal Combined Output Metrics:\")\n",
" try:\n",
" # Ensure outputs are within bounds\n",
" out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n",
" \n",
" # Calculate metrics with numerical stability\n",
" epsilon = 1e-7\n",
" diff = np.abs((y_true - y_final) / (y_true + epsilon))\n",
" diff = np.clip(diff, 0, 1)\n",
" \n",
" mape = np.mean(diff) * 100\n",
" within_2_percent = np.mean(diff <= 0.02) * 100\n",
" within_5_percent = np.mean(diff <= 0.05) * 100\n",
" within_10_percent = np.mean(diff <= 0.10) * 100\n",
" within_20_percent = np.mean(diff <= 0.20) * 100\n",
" mae = np.mean(np.abs(y_true - y_final))\n",
" rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n",
" \n",
" print(f\"Out of range: {out_of_range} predictions\")\n",
" print(f\"MAPE: {mape:.2f}%\")\n",
" print(f\"Within ±2%: {within_2_percent:.2f}%\")\n",
" print(f\"Within ±5%: {within_5_percent:.2f}%\")\n",
" print(f\"Within ±10%: {within_10_percent:.2f}%\")\n",
" print(f\"Within ±20%: {within_20_percent:.2f}%\")\n",
" print(f\"MAE: {mae:.2f}\")\n",
" print(f\"RMSE: {rmse:.2f}\")\n",
" except Exception as e:\n",
" print(f\"Error in final output metrics calculation: {str(e)}\")\n",
"\n",
"def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n",
" \"\"\"\n",
" Advanced training function for the hybrid solar energy model\n",
" \"\"\" \n",
" # Prepare binary targets for classification\n",
" y_train_binary = (y_train > 0).astype(float)\n",
" y_test_binary = (y_test > 0).astype(float)\n",
"\n",
" # Training targets dictionary - usando i nomi esatti degli output del modello\n",
" train_targets = {\n",
" 'classification_output': y_train_binary,\n",
" 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n",
" 'final_output': y_train\n",
" }\n",
"\n",
" # Validation targets dictionary\n",
" test_targets = {\n",
" 'classification_output': y_test_binary,\n",
" 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n",
" 'final_output': y_test\n",
" }\n",
"\n",
" def evaluate_epoch(epoch, logs):\n",
" if epoch % 20 == 0:\n",
" print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n",
" predictions = model.predict(X_test, verbose=0)\n",
" calculate_metrics(y_test, *predictions, min_output, max_output)\n",
"\n",
" callbacks = [\n",
" tf.keras.callbacks.EarlyStopping(\n",
" monitor='val_final_output_loss',\n",
" patience=35,\n",
" restore_best_weights=True,\n",
" mode='min',\n",
" verbose=1,\n",
" min_delta=1e-5\n",
" ),\n",
" tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=f'{folder_name}_best_model.h5',\n",
" monitor='val_final_output_loss',\n",
" save_best_only=True,\n",
" mode='min',\n",
" save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n",
" ),\n",
" tf.keras.callbacks.TensorBoard(\n",
" log_dir=f'./{folder_name}_logs',\n",
" histogram_freq=1,\n",
" write_graph=True,\n",
" update_freq='epoch'\n",
" ),\n",
" tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n",
" tf.keras.callbacks.TerminateOnNaN()\n",
" ]\n",
"\n",
" '''\n",
" tf.keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_final_output_loss',\n",
" factor=0.8,\n",
" patience=10,\n",
" verbose=1,\n",
" mode='min',\n",
" min_delta=1e-4,\n",
" cooldown=2,\n",
" min_lr=1e-7\n",
" ),\n",
" '''\n",
" try:\n",
" history = model.fit(\n",
" X_train,\n",
" train_targets,\n",
" validation_data=(X_test, test_targets),\n",
" epochs=epochs,\n",
" batch_size=batch_size,\n",
" callbacks=callbacks,\n",
" verbose=1,\n",
" shuffle=False\n",
" )\n",
"\n",
" print(\"\\nTraining completed successfully!\")\n",
"\n",
" # Final evaluation\n",
" predictions = model.predict(X_test, verbose=0)\n",
" calculate_metrics(y_test, *predictions, min_output, max_output)\n",
"\n",
" return history\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError during training: {str(e)}\")\n",
" print(\"\\nModel output names:\", [output.name for output in model.outputs])\n",
" print(\"Training targets keys:\", train_targets.keys())\n",
" raise\n",
"\n",
" finally:\n",
" tf.keras.backend.clear_session()\n",
"\n",
"\n",
"def integrate_predictions(df, predictions, sequence_length=24):\n",
" \"\"\"\n",
" Integrates solar energy predictions into the original dataset for pre-2010 data.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pandas.DataFrame\n",
" Original dataset\n",
" predictions : tuple\n",
" Tuple containing (classification_pred, regression_pred, final_pred)\n",
" - classification_pred: probability of non-zero values\n",
" - regression_pred: predicted values (used for non-zero cases)\n",
" - final_pred: final combined predictions\n",
" sequence_length : int\n",
" Sequence length used for predictions\n",
"\n",
" Returns:\n",
" --------\n",
" pandas.DataFrame\n",
" Updated dataset with solar energy predictions and additional prediction details\n",
" \"\"\"\n",
" # Convert datetime to datetime format if not already\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
"\n",
" # Identify pre-2010 rows\n",
" mask_pre_2010 = df['datetime'].dt.year < 2010\n",
"\n",
" # Unpack predictions\n",
" classification_pred, regression_pred, final_pred = predictions\n",
"\n",
" # Create temporary DataFrame with all predictions\n",
" dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n",
" predictions_df = pd.DataFrame({\n",
" 'datetime': dates_pre_2010,\n",
" 'solarenergy_predicted': final_pred.flatten(),\n",
" 'solarenergy_classification': classification_pred.flatten(),\n",
" 'solarenergy_regression': regression_pred.flatten()\n",
" })\n",
"\n",
" # Merge with original dataset\n",
" df = df.merge(predictions_df, on='datetime', how='left')\n",
"\n",
" # Update solar energy column where missing\n",
" df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n",
"\n",
" # Print detailed statistics\n",
" print(\"\\nPrediction Integration Statistics:\")\n",
" print(f\"Added {len(final_pred)} predictions to dataset\")\n",
" print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n",
"\n",
" # Analyze prediction components for the filled values\n",
" mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n",
" if mask_filled.any():\n",
" filled_data = df[mask_filled]\n",
"\n",
" print(\"\\nFilled Values Analysis:\")\n",
" print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n",
" print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n",
"\n",
" # Distribution of predicted values\n",
" non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n",
" if len(non_zero_pred) > 0:\n",
" print(f\"\\nNon-zero predictions statistics:\")\n",
" print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n",
" print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n",
" print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n",
"\n",
" # Optionally, you can keep or remove the intermediate prediction columns\n",
" columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n",
" 'solarenergy_regression']\n",
" df = df.drop(columns_to_drop, axis=1)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b3b0c2e65ddf484",
"metadata": {},
"outputs": [],
"source": [
"def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n",
" \"\"\"\n",
" Analizza dettagliatamente la distribuzione della variabile solarenergy.\n",
"\n",
" Parameters:\n",
" -----------\n",
" data : pandas.DataFrame\n",
" DataFrame contenente la colonna solarenergy\n",
" solar_column : str, default='solarenergy'\n",
" Nome della colonna da analizzare\n",
"\n",
" Returns:\n",
" --------\n",
" dict\n",
" Dizionario contenente le statistiche principali\n",
" \"\"\"\n",
"\n",
" # Creiamo una figura con più subplot\n",
" fig = plt.figure(figsize=(20, 12))\n",
"\n",
" # 1. Statistiche di base\n",
" stats_dict = {\n",
" 'count': len(data[solar_column]),\n",
" 'missing': data[solar_column].isnull().sum(),\n",
" 'zeros': (data[solar_column] == 0).sum(),\n",
" 'mean': data[solar_column].mean(),\n",
" 'median': data[solar_column].median(),\n",
" 'std': data[solar_column].std(),\n",
" 'min': data[solar_column].min(),\n",
" 'max': data[solar_column].max(),\n",
" 'skewness': stats.skew(data[solar_column].dropna()),\n",
" 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n",
" }\n",
"\n",
" # Calcolo dei percentili\n",
" percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n",
" for p in percentiles:\n",
" stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n",
"\n",
" # 2. Visualizzazioni\n",
"\n",
" # 2.1 Distribuzione\n",
" plt.subplot(2, 2, 1)\n",
" sns.histplot(data=data, x=solar_column, kde=True)\n",
" plt.title(f'Distribuzione di {name}')\n",
" plt.xlabel(f'{name}')\n",
" plt.ylabel('Frequenza')\n",
"\n",
" # 2.2 Box Plot\n",
" plt.subplot(2, 2, 2)\n",
" sns.boxplot(y=data[solar_column])\n",
" plt.title(f'Box Plot di {name}')\n",
"\n",
" # 2.3 QQ Plot\n",
" plt.subplot(2, 2, 3)\n",
" stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n",
" plt.title(f'Q-Q Plot di {name}')\n",
"\n",
" # 2.4 Distribuzione Log-trasformata\n",
" plt.subplot(2, 2, 4)\n",
" sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n",
" plt.title(f'Distribuzione Log-trasformata di {name}')\n",
" plt.xlabel(f'Log({name} + 1)')\n",
" plt.ylabel('Frequenza')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # 3. Analisi temporale se disponibile\n",
" if 'timestamp' in data.columns or 'datetime' in data.columns:\n",
" time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n",
" if isinstance(data[time_col].iloc[0], (int, float)):\n",
" data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n",
" else:\n",
" data['temp_datetime'] = pd.to_datetime(data[time_col])\n",
"\n",
" # Plot temporale\n",
" plt.figure(figsize=(15, 6))\n",
" plt.plot(data['temp_datetime'], data[solar_column])\n",
" plt.title(f'Serie Temporale di {name}')\n",
" plt.xlabel('Data')\n",
" plt.ylabel(f'{name}')\n",
" plt.xticks(rotation=45)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # Analisi stagionale\n",
" data['month'] = data['temp_datetime'].dt.month\n",
" seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n",
"\n",
" plt.figure(figsize=(12, 6))\n",
" seasonal_stats['mean'].plot(kind='bar')\n",
" plt.title(f'Media Mensile di {name}')\n",
" plt.xlabel('Mese')\n",
" plt.ylabel(f'{name} Media')\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # 4. Stampa delle statistiche principali\n",
" print(f\"\\nStatistiche principali di {name}:\")\n",
" print(\"-\" * 50)\n",
" for key, value in stats_dict.items():\n",
" print(f\"{key:15}: {value:,.4f}\")\n",
"\n",
" # 5. Suggerimenti per la normalizzazione\n",
" print(\"\\nSuggerimenti per la normalizzazione:\")\n",
" print(\"-\" * 50)\n",
"\n",
" skewness = abs(stats_dict['skewness'])\n",
" if skewness > 1:\n",
" print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n",
" print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n",
"\n",
" range_ratio = stats_dict['max'] / stats_dict['std']\n",
" if range_ratio > 10:\n",
" print(\"- La variabile ha una scala molto ampia\")\n",
" print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n",
"\n",
" zero_ratio = stats_dict['zeros'] / stats_dict['count']\n",
" if zero_ratio > 0.1:\n",
" print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n",
" print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n",
"\n",
" return stats_dict"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1b1ee91d1573ec66",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing solar energy model training...\n",
"\n",
"1. Preparing data...\n",
"\n",
"Selected features:\n",
"Number of features: 66\n",
"Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n",
"Training data shape: (112882, 24, 66)\n",
"Test data shape: (16849, 24, 66)\n",
"Saving scaler X to: 2024-11-27_21-08_scale_X.joblib\n",
"Saving scaler X to: 2024-11-27_21-08_scale_y.joblib\n",
"Saving features to: 2024-11-27_21-08_features.json\n"
]
}
],
"source": [
"df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n",
"\n",
"print(\"Initializing solar energy model training...\")\n",
"\n",
"# Data preparation\n",
"print(\"\\n1. Preparing data...\")\n",
"X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n",
"\n",
"print(f\"Training data shape: {X_train_seq.shape}\")\n",
"print(f\"Test data shape: {X_test_seq.shape}\")\n",
"\n",
"# Save or load scaler and features\n",
"scaler_X_path = f'{folder_name}_scale_X.joblib'\n",
"scaler_y_path = f'{folder_name}_scale_y.joblib'\n",
"features_path = f'{folder_name}_features.json'\n",
"model_path = f'{folder_name}_best_model.h5'\n",
"history_path = f'{folder_name}_training_history.json'\n",
"\n",
"if os.path.exists(scaler_X_path):\n",
" print(f\"Loading existing scaler X from: {scaler_X_path}\")\n",
" scaler = joblib.load(scaler_X_path)\n",
"else:\n",
" print(f\"Saving scaler X to: {scaler_X_path}\")\n",
" joblib.dump(scaler_X, scaler_X_path)\n",
"\n",
"if os.path.exists(scaler_y_path):\n",
" print(f\"Loading existing scaler X from: {scaler_y_path}\")\n",
" scaler = joblib.load(scaler_y_path)\n",
"else:\n",
" print(f\"Saving scaler X to: {scaler_y_path}\")\n",
" joblib.dump(scaler_y, scaler_y_path)\n",
"\n",
"if os.path.exists(features_path):\n",
" print(f\"Loading existing features from: {features_path}\")\n",
" with open(features_path, 'r') as f:\n",
" features = json.load(f)\n",
"else:\n",
" print(f\"Saving features to: {features_path}\")\n",
" with open(features_path, 'w') as f:\n",
" json.dump(features, f)\n",
"\n",
"# Data quality verification\n",
"if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n",
" raise ValueError(\"Found NaN values in training data\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"2. Creating model...\n",
"\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n",
"\n",
"Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n",
"Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-27 21:10:01.813937: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:c1:00.0, compute capability: 8.9\n",
"2024-11-27 21:10:03.248774: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Class distribution in training set:\n",
"Zeros: 56899 (50.41%)\n",
"Non-zeros: 55983 (49.59%)\n",
"\n",
"Class distribution in test set:\n",
"Zeros: 8576 (50.90%)\n",
"Non-zeros: 8273 (49.10%)\n",
"\n",
"Model output names: ['classification_output', 'regression_output', 'final_output']\n",
"\n",
"4. Starting training...\n",
"Epoch 1/150\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-27 21:10:32.191900: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
"2024-11-27 21:10:32.298794: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
"2024-11-27 21:10:34.451815: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x788398f56dc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2024-11-27 21:10:34.451844: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
"2024-11-27 21:10:34.457783: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2024-11-27 21:10:34.617898: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"221/221 [==============================] - ETA: 0s - loss: 9.8910 - classification_output_loss: 0.2126 - regression_output_loss: 0.2540 - final_output_loss: 0.2462\n",
"Epoch 1 Detailed Metrics:\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 95.32%\n",
"AUC-ROC: 0.9922\n",
"\n",
"Confusion Matrix:\n",
"[[8143 433]\n",
" [ 356 7917]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9581 0.9495 0.9538 8576\n",
" Non-Zero 0.9481 0.9570 0.9525 8273\n",
"\n",
" accuracy 0.9532 16849\n",
" macro avg 0.9531 0.9532 0.9532 16849\n",
"weighted avg 0.9532 0.9532 0.9532 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 254 predictions\n",
"MAPE: 56.51%\n",
"Within ±10%: 2.84%\n",
"MAE: 0.66\n",
"RMSE: 0.87\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 26.05%\n",
"Within ±2%: 50.80%\n",
"Within ±5%: 51.52%\n",
"Within ±10%: 52.70%\n",
"Within ±20%: 55.44%\n",
"MAE: 0.26\n",
"RMSE: 0.50\n",
"221/221 [==============================] - 66s 118ms/step - loss: 9.8910 - classification_output_loss: 0.2126 - regression_output_loss: 0.2540 - final_output_loss: 0.2462 - val_loss: 7.4260 - val_classification_output_loss: 0.2758 - val_regression_output_loss: 0.5297 - val_final_output_loss: 0.2561\n",
"Epoch 2/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 5.5438 - classification_output_loss: 0.1039 - regression_output_loss: 0.1222 - final_output_loss: 0.1019 - val_loss: 4.4098 - val_classification_output_loss: 0.1265 - val_regression_output_loss: 0.2653 - val_final_output_loss: 0.1421\n",
"Epoch 3/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 3.6318 - classification_output_loss: 0.0805 - regression_output_loss: 0.0784 - final_output_loss: 0.0640 - val_loss: 3.1895 - val_classification_output_loss: 0.0942 - val_regression_output_loss: 0.1152 - val_final_output_loss: 0.0740\n",
"Epoch 4/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 2.9613 - classification_output_loss: 0.0749 - regression_output_loss: 0.0657 - final_output_loss: 0.0525 - val_loss: 2.8679 - val_classification_output_loss: 0.0888 - val_regression_output_loss: 0.0849 - val_final_output_loss: 0.0594\n",
"Epoch 5/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 2.6830 - classification_output_loss: 0.0980 - regression_output_loss: 0.1210 - final_output_loss: 0.1113 - val_loss: 2.0443 - val_classification_output_loss: 0.1070 - val_regression_output_loss: 0.0857 - val_final_output_loss: 0.0795\n",
"Epoch 6/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 1.5181 - classification_output_loss: 0.0801 - regression_output_loss: 0.0955 - final_output_loss: 0.0794 - val_loss: 1.0910 - val_classification_output_loss: 0.0828 - val_regression_output_loss: 0.0707 - val_final_output_loss: 0.0415\n",
"Epoch 7/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.8616 - classification_output_loss: 0.0660 - regression_output_loss: 0.0747 - final_output_loss: 0.0623 - val_loss: 0.6750 - val_classification_output_loss: 0.0770 - val_regression_output_loss: 0.0747 - val_final_output_loss: 0.0501\n",
"Epoch 8/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.5565 - classification_output_loss: 0.0590 - regression_output_loss: 0.0660 - final_output_loss: 0.0551 - val_loss: 0.4601 - val_classification_output_loss: 0.0789 - val_regression_output_loss: 0.0524 - val_final_output_loss: 0.0450\n",
"Epoch 9/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.3984 - classification_output_loss: 0.0550 - regression_output_loss: 0.0527 - final_output_loss: 0.0459 - val_loss: 0.3471 - val_classification_output_loss: 0.0811 - val_regression_output_loss: 0.0377 - val_final_output_loss: 0.0388\n",
"Epoch 10/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.3124 - classification_output_loss: 0.0515 - regression_output_loss: 0.0426 - final_output_loss: 0.0362 - val_loss: 0.2867 - val_classification_output_loss: 0.0779 - val_regression_output_loss: 0.0344 - val_final_output_loss: 0.0309\n",
"Epoch 11/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.2684 - classification_output_loss: 0.0505 - regression_output_loss: 0.0387 - final_output_loss: 0.0341 - val_loss: 0.2611 - val_classification_output_loss: 0.0774 - val_regression_output_loss: 0.0425 - val_final_output_loss: 0.0295\n",
"Epoch 12/150\n",
"221/221 [==============================] - 14s 64ms/step - loss: 0.2453 - classification_output_loss: 0.0511 - regression_output_loss: 0.0351 - final_output_loss: 0.0309 - val_loss: 0.2517 - val_classification_output_loss: 0.0762 - val_regression_output_loss: 0.0492 - val_final_output_loss: 0.0317\n",
"Epoch 13/150\n",
"221/221 [==============================] - 16s 72ms/step - loss: 0.2371 - classification_output_loss: 0.0527 - regression_output_loss: 0.0345 - final_output_loss: 0.0305 - val_loss: 0.2434 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0442 - val_final_output_loss: 0.0288\n",
"Epoch 14/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.2522 - classification_output_loss: 0.0646 - regression_output_loss: 0.0616 - final_output_loss: 0.0581 - val_loss: 0.2371 - val_classification_output_loss: 0.0925 - val_regression_output_loss: 0.0673 - val_final_output_loss: 0.0559\n",
"Epoch 15/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.2149 - classification_output_loss: 0.0641 - regression_output_loss: 0.0808 - final_output_loss: 0.0715 - val_loss: 0.1643 - val_classification_output_loss: 0.0859 - val_regression_output_loss: 0.0367 - val_final_output_loss: 0.0422\n",
"Epoch 16/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.1577 - classification_output_loss: 0.0550 - regression_output_loss: 0.0564 - final_output_loss: 0.0577 - val_loss: 0.1408 - val_classification_output_loss: 0.0782 - val_regression_output_loss: 0.0429 - val_final_output_loss: 0.0535\n",
"Epoch 17/150\n",
"221/221 [==============================] - 14s 65ms/step - loss: 0.1291 - classification_output_loss: 0.0543 - regression_output_loss: 0.0494 - final_output_loss: 0.0513 - val_loss: 0.1191 - val_classification_output_loss: 0.0814 - val_regression_output_loss: 0.0446 - val_final_output_loss: 0.0376\n",
"Epoch 18/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.1125 - classification_output_loss: 0.0515 - regression_output_loss: 0.0482 - final_output_loss: 0.0502 - val_loss: 0.0997 - val_classification_output_loss: 0.0746 - val_regression_output_loss: 0.0386 - val_final_output_loss: 0.0277\n",
"Epoch 19/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.1009 - classification_output_loss: 0.0477 - regression_output_loss: 0.0482 - final_output_loss: 0.0463 - val_loss: 0.0908 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0392 - val_final_output_loss: 0.0290\n",
"Epoch 20/150\n",
"221/221 [==============================] - 12s 56ms/step - loss: 0.0917 - classification_output_loss: 0.0479 - regression_output_loss: 0.0449 - final_output_loss: 0.0450 - val_loss: 0.0862 - val_classification_output_loss: 0.0749 - val_regression_output_loss: 0.0395 - val_final_output_loss: 0.0288\n",
"Epoch 21/150\n",
"221/221 [==============================] - ETA: 0s - loss: 0.0817 - classification_output_loss: 0.0455 - regression_output_loss: 0.0404 - final_output_loss: 0.0391\n",
"Epoch 21 Detailed Metrics:\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 97.19%\n",
"AUC-ROC: 0.9972\n",
"\n",
"Confusion Matrix:\n",
"[[8267 309]\n",
" [ 165 8108]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9804 0.9640 0.9721 8576\n",
" Non-Zero 0.9633 0.9801 0.9716 8273\n",
"\n",
" accuracy 0.9719 16849\n",
" macro avg 0.9719 0.9720 0.9719 16849\n",
"weighted avg 0.9720 0.9719 0.9719 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 0 predictions\n",
"MAPE: 15.37%\n",
"Within ±10%: 54.25%\n",
"MAE: 0.09\n",
"RMSE: 0.12\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 12.10%\n",
"Within ±2%: 56.40%\n",
"Within ±5%: 64.44%\n",
"Within ±10%: 74.69%\n",
"Within ±20%: 86.26%\n",
"MAE: 0.06\n",
"RMSE: 0.10\n",
"221/221 [==============================] - 18s 82ms/step - loss: 0.0817 - classification_output_loss: 0.0455 - regression_output_loss: 0.0404 - final_output_loss: 0.0391 - val_loss: 0.0788 - val_classification_output_loss: 0.0702 - val_regression_output_loss: 0.0328 - val_final_output_loss: 0.0330\n",
"Epoch 22/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0735 - classification_output_loss: 0.0416 - regression_output_loss: 0.0358 - final_output_loss: 0.0353 - val_loss: 0.0816 - val_classification_output_loss: 0.0700 - val_regression_output_loss: 0.0360 - val_final_output_loss: 0.0482\n",
"Epoch 23/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0671 - classification_output_loss: 0.0405 - regression_output_loss: 0.0324 - final_output_loss: 0.0304 - val_loss: 0.0769 - val_classification_output_loss: 0.0669 - val_regression_output_loss: 0.0366 - val_final_output_loss: 0.0434\n",
"Epoch 24/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0625 - classification_output_loss: 0.0384 - regression_output_loss: 0.0299 - final_output_loss: 0.0290 - val_loss: 0.0634 - val_classification_output_loss: 0.0635 - val_regression_output_loss: 0.0243 - val_final_output_loss: 0.0282\n",
"Epoch 25/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0598 - classification_output_loss: 0.0376 - regression_output_loss: 0.0286 - final_output_loss: 0.0286 - val_loss: 0.0611 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0279\n",
"Epoch 26/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0575 - classification_output_loss: 0.0372 - regression_output_loss: 0.0278 - final_output_loss: 0.0272 - val_loss: 0.0580 - val_classification_output_loss: 0.0639 - val_regression_output_loss: 0.0231 - val_final_output_loss: 0.0214\n",
"Epoch 27/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0550 - classification_output_loss: 0.0372 - regression_output_loss: 0.0259 - final_output_loss: 0.0253 - val_loss: 0.0571 - val_classification_output_loss: 0.0634 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0183\n",
"Epoch 28/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0537 - classification_output_loss: 0.0362 - regression_output_loss: 0.0255 - final_output_loss: 0.0243 - val_loss: 0.0586 - val_classification_output_loss: 0.0624 - val_regression_output_loss: 0.0291 - val_final_output_loss: 0.0193\n",
"Epoch 29/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0531 - classification_output_loss: 0.0366 - regression_output_loss: 0.0253 - final_output_loss: 0.0240 - val_loss: 0.0584 - val_classification_output_loss: 0.0612 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0207\n",
"Epoch 30/150\n",
"221/221 [==============================] - 14s 65ms/step - loss: 0.0531 - classification_output_loss: 0.0372 - regression_output_loss: 0.0255 - final_output_loss: 0.0242 - val_loss: 0.0560 - val_classification_output_loss: 0.0585 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0197\n",
"Epoch 31/150\n",
"221/221 [==============================] - 15s 66ms/step - loss: 0.0540 - classification_output_loss: 0.0383 - regression_output_loss: 0.0268 - final_output_loss: 0.0246 - val_loss: 0.0532 - val_classification_output_loss: 0.0567 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0191\n",
"Epoch 32/150\n",
"221/221 [==============================] - 15s 67ms/step - loss: 0.0749 - classification_output_loss: 0.0499 - regression_output_loss: 0.0496 - final_output_loss: 0.0445 - val_loss: 0.1607 - val_classification_output_loss: 0.1001 - val_regression_output_loss: 0.0736 - val_final_output_loss: 0.1997\n",
"Epoch 33/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0973 - classification_output_loss: 0.0590 - regression_output_loss: 0.0656 - final_output_loss: 0.0644 - val_loss: 0.0712 - val_classification_output_loss: 0.0623 - val_regression_output_loss: 0.0377 - val_final_output_loss: 0.0417\n",
"Epoch 34/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0738 - classification_output_loss: 0.0466 - regression_output_loss: 0.0489 - final_output_loss: 0.0476 - val_loss: 0.0797 - val_classification_output_loss: 0.0901 - val_regression_output_loss: 0.0561 - val_final_output_loss: 0.0314\n",
"Epoch 35/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0667 - classification_output_loss: 0.0457 - regression_output_loss: 0.0417 - final_output_loss: 0.0450 - val_loss: 0.0609 - val_classification_output_loss: 0.0789 - val_regression_output_loss: 0.0319 - val_final_output_loss: 0.0248\n",
"Epoch 36/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0641 - classification_output_loss: 0.0440 - regression_output_loss: 0.0410 - final_output_loss: 0.0432 - val_loss: 0.0593 - val_classification_output_loss: 0.0600 - val_regression_output_loss: 0.0364 - val_final_output_loss: 0.0304\n",
"Epoch 37/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0605 - classification_output_loss: 0.0402 - regression_output_loss: 0.0382 - final_output_loss: 0.0434 - val_loss: 0.0544 - val_classification_output_loss: 0.0662 - val_regression_output_loss: 0.0297 - val_final_output_loss: 0.0239\n",
"Epoch 38/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0568 - classification_output_loss: 0.0400 - regression_output_loss: 0.0357 - final_output_loss: 0.0397 - val_loss: 0.0514 - val_classification_output_loss: 0.0625 - val_regression_output_loss: 0.0275 - val_final_output_loss: 0.0244\n",
"Epoch 39/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0553 - classification_output_loss: 0.0391 - regression_output_loss: 0.0359 - final_output_loss: 0.0379 - val_loss: 0.0512 - val_classification_output_loss: 0.0554 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0281\n",
"Epoch 40/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0545 - classification_output_loss: 0.0368 - regression_output_loss: 0.0363 - final_output_loss: 0.0394 - val_loss: 0.0502 - val_classification_output_loss: 0.0571 - val_regression_output_loss: 0.0272 - val_final_output_loss: 0.0280\n",
"Epoch 41/150\n",
"221/221 [==============================] - ETA: 0s - loss: 0.0531 - classification_output_loss: 0.0383 - regression_output_loss: 0.0354 - final_output_loss: 0.0374\n",
"Epoch 41 Detailed Metrics:\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 97.16%\n",
"AUC-ROC: 0.9981\n",
"\n",
"Confusion Matrix:\n",
"[[8170 406]\n",
" [ 72 8201]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9913 0.9527 0.9716 8576\n",
" Non-Zero 0.9528 0.9913 0.9717 8273\n",
"\n",
" accuracy 0.9716 16849\n",
" macro avg 0.9720 0.9720 0.9716 16849\n",
"weighted avg 0.9724 0.9716 0.9716 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 0 predictions\n",
"MAPE: 15.53%\n",
"Within ±10%: 52.10%\n",
"MAE: 0.11\n",
"RMSE: 0.15\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 11.09%\n",
"Within ±2%: 54.76%\n",
"Within ±5%: 61.83%\n",
"Within ±10%: 73.46%\n",
"Within ±20%: 86.85%\n",
"MAE: 0.06\n",
"RMSE: 0.12\n",
"221/221 [==============================] - 20s 93ms/step - loss: 0.0531 - classification_output_loss: 0.0383 - regression_output_loss: 0.0354 - final_output_loss: 0.0374 - val_loss: 0.0628 - val_classification_output_loss: 0.0729 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0382\n",
"Epoch 42/150\n",
"221/221 [==============================] - 14s 64ms/step - loss: 0.0510 - classification_output_loss: 0.0360 - regression_output_loss: 0.0341 - final_output_loss: 0.0371 - val_loss: 0.0813 - val_classification_output_loss: 0.0869 - val_regression_output_loss: 0.0547 - val_final_output_loss: 0.0684\n",
"Epoch 43/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0471 - classification_output_loss: 0.0342 - regression_output_loss: 0.0303 - final_output_loss: 0.0331 - val_loss: 0.0712 - val_classification_output_loss: 0.0698 - val_regression_output_loss: 0.0475 - val_final_output_loss: 0.0620\n",
"Epoch 44/150\n",
"221/221 [==============================] - 12s 56ms/step - loss: 0.0450 - classification_output_loss: 0.0357 - regression_output_loss: 0.0286 - final_output_loss: 0.0312 - val_loss: 0.0759 - val_classification_output_loss: 0.0638 - val_regression_output_loss: 0.0598 - val_final_output_loss: 0.0638\n",
"Epoch 45/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0428 - classification_output_loss: 0.0342 - regression_output_loss: 0.0271 - final_output_loss: 0.0297 - val_loss: 0.0562 - val_classification_output_loss: 0.0535 - val_regression_output_loss: 0.0389 - val_final_output_loss: 0.0422\n",
"Epoch 46/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0433 - classification_output_loss: 0.0321 - regression_output_loss: 0.0281 - final_output_loss: 0.0324 - val_loss: 0.0522 - val_classification_output_loss: 0.0545 - val_regression_output_loss: 0.0308 - val_final_output_loss: 0.0420\n",
"Epoch 47/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0379 - classification_output_loss: 0.0302 - regression_output_loss: 0.0238 - final_output_loss: 0.0248 - val_loss: 0.0420 - val_classification_output_loss: 0.0546 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0272\n",
"Epoch 48/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0416 - classification_output_loss: 0.0301 - regression_output_loss: 0.0277 - final_output_loss: 0.0318 - val_loss: 0.0371 - val_classification_output_loss: 0.0525 - val_regression_output_loss: 0.0195 - val_final_output_loss: 0.0173\n",
"Epoch 49/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0393 - classification_output_loss: 0.0289 - regression_output_loss: 0.0266 - final_output_loss: 0.0279 - val_loss: 0.0393 - val_classification_output_loss: 0.0540 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0210\n",
"Epoch 50/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0358 - classification_output_loss: 0.0273 - regression_output_loss: 0.0230 - final_output_loss: 0.0251 - val_loss: 0.0379 - val_classification_output_loss: 0.0504 - val_regression_output_loss: 0.0200 - val_final_output_loss: 0.0230\n",
"Epoch 51/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0336 - classification_output_loss: 0.0270 - regression_output_loss: 0.0211 - final_output_loss: 0.0225 - val_loss: 0.0357 - val_classification_output_loss: 0.0516 - val_regression_output_loss: 0.0191 - val_final_output_loss: 0.0177\n",
"Epoch 52/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0346 - classification_output_loss: 0.0268 - regression_output_loss: 0.0224 - final_output_loss: 0.0249 - val_loss: 0.0362 - val_classification_output_loss: 0.0489 - val_regression_output_loss: 0.0206 - val_final_output_loss: 0.0191\n",
"Epoch 53/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0323 - classification_output_loss: 0.0252 - regression_output_loss: 0.0205 - final_output_loss: 0.0224 - val_loss: 0.0373 - val_classification_output_loss: 0.0466 - val_regression_output_loss: 0.0209 - val_final_output_loss: 0.0245\n",
"Epoch 54/150\n",
"221/221 [==============================] - 13s 60ms/step - loss: 0.0319 - classification_output_loss: 0.0243 - regression_output_loss: 0.0204 - final_output_loss: 0.0226 - val_loss: 0.0378 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0288\n",
"Epoch 55/150\n",
"221/221 [==============================] - 14s 65ms/step - loss: 0.0303 - classification_output_loss: 0.0241 - regression_output_loss: 0.0189 - final_output_loss: 0.0206 - val_loss: 0.0362 - val_classification_output_loss: 0.0470 - val_regression_output_loss: 0.0193 - val_final_output_loss: 0.0250\n",
"Epoch 56/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0299 - classification_output_loss: 0.0231 - regression_output_loss: 0.0191 - final_output_loss: 0.0203 - val_loss: 0.0341 - val_classification_output_loss: 0.0454 - val_regression_output_loss: 0.0180 - val_final_output_loss: 0.0220\n",
"Epoch 57/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0295 - classification_output_loss: 0.0230 - regression_output_loss: 0.0187 - final_output_loss: 0.0204 - val_loss: 0.0323 - val_classification_output_loss: 0.0449 - val_regression_output_loss: 0.0175 - val_final_output_loss: 0.0178\n",
"Epoch 58/150\n",
"221/221 [==============================] - 14s 61ms/step - loss: 0.0294 - classification_output_loss: 0.0228 - regression_output_loss: 0.0190 - final_output_loss: 0.0201 - val_loss: 0.0315 - val_classification_output_loss: 0.0443 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0148\n",
"Epoch 59/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0291 - classification_output_loss: 0.0223 - regression_output_loss: 0.0190 - final_output_loss: 0.0199 - val_loss: 0.0314 - val_classification_output_loss: 0.0421 - val_regression_output_loss: 0.0194 - val_final_output_loss: 0.0145\n",
"Epoch 60/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0289 - classification_output_loss: 0.0215 - regression_output_loss: 0.0190 - final_output_loss: 0.0198 - val_loss: 0.0314 - val_classification_output_loss: 0.0406 - val_regression_output_loss: 0.0198 - val_final_output_loss: 0.0150\n",
"Epoch 61/150\n",
"221/221 [==============================] - ETA: 0s - loss: 0.0286 - classification_output_loss: 0.0215 - regression_output_loss: 0.0188 - final_output_loss: 0.0195\n",
"Epoch 61 Detailed Metrics:\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 98.47%\n",
"AUC-ROC: 0.9991\n",
"\n",
"Confusion Matrix:\n",
"[[8422 154]\n",
" [ 104 8169]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9878 0.9820 0.9849 8576\n",
" Non-Zero 0.9815 0.9874 0.9845 8273\n",
"\n",
" accuracy 0.9847 16849\n",
" macro avg 0.9846 0.9847 0.9847 16849\n",
"weighted avg 0.9847 0.9847 0.9847 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 0 predictions\n",
"MAPE: 10.90%\n",
"Within ±10%: 75.56%\n",
"MAE: 0.06\n",
"RMSE: 0.08\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 7.64%\n",
"Within ±2%: 61.83%\n",
"Within ±5%: 75.90%\n",
"Within ±10%: 86.14%\n",
"Within ±20%: 91.10%\n",
"MAE: 0.03\n",
"RMSE: 0.06\n",
"221/221 [==============================] - 20s 89ms/step - loss: 0.0286 - classification_output_loss: 0.0215 - regression_output_loss: 0.0188 - final_output_loss: 0.0195 - val_loss: 0.0319 - val_classification_output_loss: 0.0408 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0160\n",
"Epoch 62/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0285 - classification_output_loss: 0.0218 - regression_output_loss: 0.0187 - final_output_loss: 0.0194 - val_loss: 0.0318 - val_classification_output_loss: 0.0398 - val_regression_output_loss: 0.0199 - val_final_output_loss: 0.0168\n",
"Epoch 63/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0283 - classification_output_loss: 0.0212 - regression_output_loss: 0.0187 - final_output_loss: 0.0193 - val_loss: 0.0311 - val_classification_output_loss: 0.0394 - val_regression_output_loss: 0.0189 - val_final_output_loss: 0.0166\n",
"Epoch 64/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0284 - classification_output_loss: 0.0216 - regression_output_loss: 0.0187 - final_output_loss: 0.0193 - val_loss: 0.0301 - val_classification_output_loss: 0.0386 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0156\n",
"Epoch 65/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0285 - classification_output_loss: 0.0215 - regression_output_loss: 0.0190 - final_output_loss: 0.0194 - val_loss: 0.0294 - val_classification_output_loss: 0.0384 - val_regression_output_loss: 0.0167 - val_final_output_loss: 0.0152\n",
"Epoch 66/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0291 - classification_output_loss: 0.0220 - regression_output_loss: 0.0198 - final_output_loss: 0.0198 - val_loss: 0.0287 - val_classification_output_loss: 0.0382 - val_regression_output_loss: 0.0157 - val_final_output_loss: 0.0147\n",
"Epoch 67/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0294 - classification_output_loss: 0.0210 - regression_output_loss: 0.0203 - final_output_loss: 0.0206 - val_loss: 0.0298 - val_classification_output_loss: 0.0383 - val_regression_output_loss: 0.0166 - val_final_output_loss: 0.0165\n",
"Epoch 68/150\n",
"221/221 [==============================] - 14s 65ms/step - loss: 0.0292 - classification_output_loss: 0.0215 - regression_output_loss: 0.0197 - final_output_loss: 0.0205 - val_loss: 0.0533 - val_classification_output_loss: 0.0485 - val_regression_output_loss: 0.0295 - val_final_output_loss: 0.0615\n",
"Epoch 69/150\n",
"221/221 [==============================] - 15s 69ms/step - loss: 0.0710 - classification_output_loss: 0.0471 - regression_output_loss: 0.0555 - final_output_loss: 0.0583 - val_loss: 0.0680 - val_classification_output_loss: 0.0545 - val_regression_output_loss: 0.0634 - val_final_output_loss: 0.0480\n",
"Epoch 70/150\n",
"221/221 [==============================] - 15s 66ms/step - loss: 0.0486 - classification_output_loss: 0.0344 - regression_output_loss: 0.0357 - final_output_loss: 0.0410 - val_loss: 0.0567 - val_classification_output_loss: 0.0625 - val_regression_output_loss: 0.0441 - val_final_output_loss: 0.0364\n",
"Epoch 71/150\n",
"221/221 [==============================] - 14s 64ms/step - loss: 0.0421 - classification_output_loss: 0.0306 - regression_output_loss: 0.0299 - final_output_loss: 0.0348 - val_loss: 0.0354 - val_classification_output_loss: 0.0437 - val_regression_output_loss: 0.0220 - val_final_output_loss: 0.0191\n",
"Epoch 72/150\n",
"221/221 [==============================] - 14s 64ms/step - loss: 0.0435 - classification_output_loss: 0.0292 - regression_output_loss: 0.0323 - final_output_loss: 0.0362 - val_loss: 0.0408 - val_classification_output_loss: 0.0599 - val_regression_output_loss: 0.0255 - val_final_output_loss: 0.0216\n",
"Epoch 73/150\n",
"221/221 [==============================] - 14s 61ms/step - loss: 0.0400 - classification_output_loss: 0.0282 - regression_output_loss: 0.0288 - final_output_loss: 0.0327 - val_loss: 0.0370 - val_classification_output_loss: 0.0472 - val_regression_output_loss: 0.0226 - val_final_output_loss: 0.0226\n",
"Epoch 74/150\n",
"221/221 [==============================] - 14s 61ms/step - loss: 0.0380 - classification_output_loss: 0.0259 - regression_output_loss: 0.0276 - final_output_loss: 0.0313 - val_loss: 0.0367 - val_classification_output_loss: 0.0419 - val_regression_output_loss: 0.0256 - val_final_output_loss: 0.0209\n",
"Epoch 75/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0402 - classification_output_loss: 0.0304 - regression_output_loss: 0.0280 - final_output_loss: 0.0354 - val_loss: 0.0743 - val_classification_output_loss: 0.0526 - val_regression_output_loss: 0.0704 - val_final_output_loss: 0.0586\n",
"Epoch 76/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0383 - classification_output_loss: 0.0291 - regression_output_loss: 0.0271 - final_output_loss: 0.0307 - val_loss: 0.0345 - val_classification_output_loss: 0.0395 - val_regression_output_loss: 0.0229 - val_final_output_loss: 0.0203\n",
"Epoch 77/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0376 - classification_output_loss: 0.0269 - regression_output_loss: 0.0269 - final_output_loss: 0.0311 - val_loss: 0.0397 - val_classification_output_loss: 0.0391 - val_regression_output_loss: 0.0284 - val_final_output_loss: 0.0288\n",
"Epoch 78/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0406 - classification_output_loss: 0.0314 - regression_output_loss: 0.0305 - final_output_loss: 0.0321 - val_loss: 0.0363 - val_classification_output_loss: 0.0440 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0245\n",
"Epoch 79/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0345 - classification_output_loss: 0.0234 - regression_output_loss: 0.0241 - final_output_loss: 0.0291 - val_loss: 0.0360 - val_classification_output_loss: 0.0415 - val_regression_output_loss: 0.0232 - val_final_output_loss: 0.0255\n",
"Epoch 80/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0384 - classification_output_loss: 0.0242 - regression_output_loss: 0.0280 - final_output_loss: 0.0336 - val_loss: 0.0311 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0170\n",
"Epoch 81/150\n",
"220/221 [============================>.] - ETA: 0s - loss: 0.0370 - classification_output_loss: 0.0254 - regression_output_loss: 0.0275 - final_output_loss: 0.0306\n",
"Epoch 81 Detailed Metrics:\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 98.21%\n",
"AUC-ROC: 0.9992\n",
"\n",
"Confusion Matrix:\n",
"[[8336 240]\n",
" [ 62 8211]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9926 0.9720 0.9822 8576\n",
" Non-Zero 0.9716 0.9925 0.9819 8273\n",
"\n",
" accuracy 0.9821 16849\n",
" macro avg 0.9821 0.9823 0.9821 16849\n",
"weighted avg 0.9823 0.9821 0.9821 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 0 predictions\n",
"MAPE: 13.51%\n",
"Within ±10%: 63.69%\n",
"MAE: 0.08\n",
"RMSE: 0.11\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 9.85%\n",
"Within ±2%: 55.76%\n",
"Within ±5%: 64.52%\n",
"Within ±10%: 78.28%\n",
"Within ±20%: 89.15%\n",
"MAE: 0.05\n",
"RMSE: 0.09\n",
"221/221 [==============================] - 19s 88ms/step - loss: 0.0370 - classification_output_loss: 0.0255 - regression_output_loss: 0.0275 - final_output_loss: 0.0306 - val_loss: 0.0402 - val_classification_output_loss: 0.0444 - val_regression_output_loss: 0.0279 - val_final_output_loss: 0.0278\n",
"Epoch 82/150\n",
"221/221 [==============================] - 14s 62ms/step - loss: 0.0354 - classification_output_loss: 0.0252 - regression_output_loss: 0.0253 - final_output_loss: 0.0298 - val_loss: 0.0540 - val_classification_output_loss: 0.0589 - val_regression_output_loss: 0.0413 - val_final_output_loss: 0.0413\n",
"Epoch 83/150\n",
"221/221 [==============================] - 13s 61ms/step - loss: 0.0334 - classification_output_loss: 0.0233 - regression_output_loss: 0.0236 - final_output_loss: 0.0282 - val_loss: 0.0580 - val_classification_output_loss: 0.0433 - val_regression_output_loss: 0.0434 - val_final_output_loss: 0.0611\n",
"Epoch 84/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0347 - classification_output_loss: 0.0248 - regression_output_loss: 0.0246 - final_output_loss: 0.0291 - val_loss: 0.0545 - val_classification_output_loss: 0.0436 - val_regression_output_loss: 0.0441 - val_final_output_loss: 0.0502\n",
"Epoch 85/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0343 - classification_output_loss: 0.0215 - regression_output_loss: 0.0257 - final_output_loss: 0.0286 - val_loss: 0.0355 - val_classification_output_loss: 0.0364 - val_regression_output_loss: 0.0230 - val_final_output_loss: 0.0287\n",
"Epoch 86/150\n",
"221/221 [==============================] - 12s 56ms/step - loss: 0.0329 - classification_output_loss: 0.0255 - regression_output_loss: 0.0238 - final_output_loss: 0.0262 - val_loss: 0.0344 - val_classification_output_loss: 0.0436 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0222\n",
"Epoch 87/150\n",
"221/221 [==============================] - 12s 56ms/step - loss: 0.0337 - classification_output_loss: 0.0200 - regression_output_loss: 0.0247 - final_output_loss: 0.0293 - val_loss: 0.0319 - val_classification_output_loss: 0.0355 - val_regression_output_loss: 0.0233 - val_final_output_loss: 0.0180\n",
"Epoch 88/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0324 - classification_output_loss: 0.0207 - regression_output_loss: 0.0244 - final_output_loss: 0.0264 - val_loss: 0.0315 - val_classification_output_loss: 0.0424 - val_regression_output_loss: 0.0198 - val_final_output_loss: 0.0185\n",
"Epoch 89/150\n",
"221/221 [==============================] - 12s 55ms/step - loss: 0.0318 - classification_output_loss: 0.0229 - regression_output_loss: 0.0221 - final_output_loss: 0.0272 - val_loss: 0.0302 - val_classification_output_loss: 0.0393 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0170\n",
"Epoch 90/150\n",
"221/221 [==============================] - 14s 63ms/step - loss: 0.0317 - classification_output_loss: 0.0227 - regression_output_loss: 0.0217 - final_output_loss: 0.0280 - val_loss: 0.0327 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0178 - val_final_output_loss: 0.0184\n",
"Epoch 91/150\n",
"221/221 [==============================] - 13s 58ms/step - loss: 0.0311 - classification_output_loss: 0.0193 - regression_output_loss: 0.0234 - final_output_loss: 0.0260 - val_loss: 0.0487 - val_classification_output_loss: 0.0357 - val_regression_output_loss: 0.0416 - val_final_output_loss: 0.0414\n",
"Epoch 92/150\n",
"221/221 [==============================] - 13s 57ms/step - loss: 0.0302 - classification_output_loss: 0.0207 - regression_output_loss: 0.0215 - final_output_loss: 0.0256 - val_loss: 0.0466 - val_classification_output_loss: 0.0446 - val_regression_output_loss: 0.0367 - val_final_output_loss: 0.0396\n",
"Epoch 93/150\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0300 - classification_output_loss: 0.0211 - regression_output_loss: 0.0223 - final_output_loss: 0.0239 - val_loss: 0.0289 - val_classification_output_loss: 0.0364 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0174\n",
"Epoch 94/150\n",
"220/221 [============================>.] - ETA: 0s - loss: 0.0281 - classification_output_loss: 0.0154 - regression_output_loss: 0.0205 - final_output_loss: 0.0246Restoring model weights from the end of the best epoch: 59.\n",
"221/221 [==============================] - 13s 59ms/step - loss: 0.0281 - classification_output_loss: 0.0154 - regression_output_loss: 0.0205 - final_output_loss: 0.0246 - val_loss: 0.0277 - val_classification_output_loss: 0.0366 - val_regression_output_loss: 0.0174 - val_final_output_loss: 0.0154\n",
"Epoch 94: early stopping\n",
"\n",
"Training completed successfully!\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 98.39%\n",
"AUC-ROC: 0.9990\n",
"\n",
"Confusion Matrix:\n",
"[[8434 142]\n",
" [ 129 8144]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9849 0.9834 0.9842 8576\n",
" Non-Zero 0.9829 0.9844 0.9836 8273\n",
"\n",
" accuracy 0.9839 16849\n",
" macro avg 0.9839 0.9839 0.9839 16849\n",
"weighted avg 0.9839 0.9839 0.9839 16849\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 1 predictions\n",
"MAPE: 11.01%\n",
"Within ±10%: 74.37%\n",
"MAE: 0.05\n",
"RMSE: 0.08\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 7.37%\n",
"Within ±2%: 63.64%\n",
"Within ±5%: 77.48%\n",
"Within ±10%: 86.83%\n",
"Within ±20%: 91.82%\n",
"MAE: 0.03\n",
"RMSE: 0.05\n"
]
}
],
"source": [
"#Model creation\n",
"print(\"\\n2. Creating model...\")\n",
"input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n",
"\n",
"min_val = df['solarenergy'].min()\n",
"min_val_scaled = scaler_y.transform([[0]])[0][0]\n",
"\n",
"max_val = df['solarenergy'].max()\n",
"max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n",
"\n",
"print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n",
"\n",
"print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n",
"\n",
"increase_percentage = 8\n",
"\n",
"max_val = max_val * (1 + increase_percentage / 100)\n",
"max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n",
"\n",
"print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n",
"\n",
"# Create the hybrid model\n",
"model = create_solarenergy_model(\n",
" input_shape=input_shape, \n",
" folder_name=folder_name, \n",
" min_output=min_val_scaled, \n",
" max_output=max_val_scaled\n",
")\n",
"\n",
"# Prepare binary targets for classification\n",
"y_train_binary = (y_train > 0).astype(float)\n",
"y_test_binary = (y_test > 0).astype(float)\n",
"\n",
"print(\"\\nClass distribution in training set:\")\n",
"print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n",
"print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n",
"\n",
"print(\"\\nClass distribution in test set:\")\n",
"print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n",
"print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n",
"\n",
"# Get the exact output names from the model\n",
"output_names = [output.name.split('/')[0] for output in model.outputs]\n",
"print(\"\\nModel output names:\", output_names)\n",
"\n",
"print(\"\\n4. Starting training...\")\n",
"history = train_hybrid_model(\n",
" model=model,\n",
" X_train=X_train_seq,\n",
" y_train=y_train,\n",
" X_test=X_test_seq,\n",
" y_test=y_test,\n",
" epochs=150,\n",
" batch_size=512,\n",
" folder_name=folder_name,\n",
" min_output=min_val_scaled,\n",
" max_output=max_val_scaled\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "958d78b99e8898d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"5. Generating predictions...\n",
"527/527 [==============================] - 6s 11ms/step\n",
"\n",
"6. Evaluating model...\n",
"\n",
"Solar Energy Prediction Metrics:\n",
"\n",
"Absolute Metrics:\n",
"MAE: 0.03 kWh\n",
"RMSE: 0.06 kWh\n",
"R² Score: 0.995\n",
"MAPE: N/A (insufficient data)\n",
"\n",
"Accuracy Metrics:\n",
"Within ±5 kWh: 100.0%\n",
"Within ±10 kWh: 100.0%\n",
"Within ±20 kWh: 100.0%\n",
"\n",
"Level Accuracy:\n",
"Level Accuracy: 98.1%\n",
"\n",
"Confusion Matrix for Energy Levels:\n",
" Low Moderate Very Low\n",
"Low 3540 132 1\n",
"Moderate 13 2095 0\n",
"Very Low 169 0 10899\n",
"\n",
"Plot saved as: 2024-11-27_21-08_energy_analysis.png\n",
"\n",
"Error Statistics:\n",
"Mean error: -0.006\n",
"Error standard deviation: 0.064\n",
"Median error: 0.000\n",
"95th percentile absolute error: 0.121\n"
]
}
],
"source": [
"print(\"\\n5. Generating predictions...\")\n",
"predictions = model.predict(X_test_seq)\n",
"classification_pred, regression_pred, final_pred = predictions\n",
"\n",
"# Clip solo le predizioni di regressione e finali\n",
"regression_pred = np.clip(regression_pred, min_val_scaled, max_val_scaled)\n",
"final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n",
"\n",
"# Inverse transform per tornare ai valori originali\n",
"regression_pred_original = scaler_y.inverse_transform(regression_pred)\n",
"final_pred_original = scaler_y.inverse_transform(final_pred)\n",
"y_test_original = scaler_y.inverse_transform(y_test)\n",
"\n",
"print(\"\\n6. Evaluating model...\")\n",
"# Valutazione delle predizioni finali\n",
"metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n",
"\n",
"# Create results dictionary con metriche aggiuntive per il modello ibrido\n",
"training_results = {\n",
" 'model_params': {\n",
" 'input_shape': input_shape,\n",
" 'n_features': len(features),\n",
" 'sequence_length': X_train_seq.shape[1]\n",
" },\n",
" 'training_params': {\n",
" 'batch_size': 192,\n",
" 'total_epochs': len(history.history['loss']),\n",
" 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n",
" },\n",
" 'performance_metrics': {\n",
" 'regression': {\n",
" 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n",
" 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n",
" },\n",
" 'final_output': {\n",
" 'final_loss': float(history.history['val_final_output_loss'][-1]),\n",
" 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n",
" 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n",
" }\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5c05d1d03336b1e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"7. Predicting missing data...\n",
"7122/7122 [==============================] - 77s 11ms/step\n",
"\n",
"8. Integrating predictions into original dataset...\n",
"\n",
"Prediction Integration Statistics:\n",
"Added 227879 predictions to dataset\n",
"Rows with solar energy after integration: 357615\n",
"\n",
"Filled Values Analysis:\n",
"Zero predictions (classification < 0.5): 119217\n",
"Non-zero predictions (classification >= 0.5): 108662\n",
"\n",
"Non-zero predictions statistics:\n",
"Mean: 1.25\n",
"Median: 1.12\n",
"Std: 0.88\n",
"\n",
"Prediction Statistics:\n",
"Total predictions added: 227879\n",
"\n",
"Classification Statistics:\n",
"Predicted zeros: 119217 (52.32%)\n",
"Predicted non-zeros: 108662 (47.68%)\n",
"Mean classification confidence: 0.4824\n",
"\n",
"Final Predictions Statistics:\n",
"Mean solar energy: 0.61\n",
"Min solar energy: 0.00\n",
"Max solar energy: 3.20\n",
"Zero predictions: 116719 (51.22%)\n",
"\n",
"Training completed successfully!\n"
]
}
],
"source": [
"print(\"\\n7. Predicting missing data...\")\n",
"to_predict_predictions = model.predict(X_to_predict_seq)\n",
"classification_pred, regression_pred, final_pred = to_predict_predictions\n",
"\n",
"# Clip solo le predizioni finali che useremo per l'integrazione\n",
"final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n",
"final_pred_original = scaler_y.inverse_transform(final_pred)\n",
"\n",
"print(\"\\n8. Integrating predictions into original dataset...\")\n",
"df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n",
"\n",
"df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n",
"\n",
"# Add prediction statistics to training_results\n",
"training_results['prediction_stats'] = {\n",
" 'n_predictions_added': len(final_pred_original),\n",
" 'classification_stats': {\n",
" 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n",
" 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n",
" 'mean_confidence': float(classification_pred.mean()),\n",
" },\n",
" 'regression_stats': {\n",
" 'mean_predicted_value': float(regression_pred.mean()),\n",
" 'min_predicted_value': float(regression_pred.min()),\n",
" 'max_predicted_value': float(regression_pred.max()),\n",
" },\n",
" 'final_predictions': {\n",
" 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n",
" 'min_predicted_solarenergy': float(final_pred_original.min()),\n",
" 'max_predicted_solarenergy': float(final_pred_original.max()),\n",
" 'zero_predictions': int(np.sum(final_pred_original == 0)),\n",
" 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n",
" }\n",
"}\n",
"\n",
"print(\"\\nPrediction Statistics:\")\n",
"print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n",
"print(\"\\nClassification Statistics:\")\n",
"print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n",
" f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n",
"print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n",
" f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n",
"print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n",
"\n",
"print(\"\\nFinal Predictions Statistics:\")\n",
"print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n",
"print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n",
"print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n",
"print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n",
" f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n",
"\n",
"print(\"\\nTraining completed successfully!\")\n",
"\n",
"tf.keras.backend.clear_session()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ef29b3ecdf12c6db",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"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 : 182,202.0000\n",
"mean : 0.6344\n",
"median : 0.0000\n",
"std : 0.9133\n",
"min : 0.0000\n",
"max : 4.0000\n",
"skewness : 1.3007\n",
"kurtosis : 0.4372\n",
"percentile_1 : 0.0000\n",
"percentile_5 : 0.0000\n",
"percentile_10 : 0.0000\n",
"percentile_25 : 0.0000\n",
"percentile_50 : 0.0000\n",
"percentile_75 : 1.1064\n",
"percentile_90 : 2.2000\n",
"percentile_95 : 2.6993\n",
"percentile_99 : 3.1000\n",
"\n",
"Suggerimenti per la normalizzazione:\n",
"--------------------------------------------------\n",
"- La distribuzione è fortemente asimmetrica (skewness > 1)\n",
"- Considerare una trasformazione logaritmica: np.log1p(x)\n",
"- Alta presenza di zeri (50.94%)\n",
"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n"
]
},
{
"data": {
"text/plain": [
"{'count': 357679,\n",
" 'missing': 64,\n",
" 'zeros': 182202,\n",
" 'mean': 0.6344184307798103,\n",
" 'median': 0.0,\n",
" 'std': 0.9132957616282624,\n",
" 'min': 0.0,\n",
" 'max': 4.0,\n",
" 'skewness': 1.3006834240564749,\n",
" 'kurtosis': 0.4371730534542304,\n",
" 'percentile_1': 0.0,\n",
" 'percentile_5': 0.0,\n",
" 'percentile_10': 0.0,\n",
" 'percentile_25': 0.0,\n",
" 'percentile_50': 0.0,\n",
" 'percentile_75': 1.106435239315033,\n",
" 'percentile_90': 2.2,\n",
" 'percentile_95': 2.699264335632324,\n",
" 'percentile_99': 3.1}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e884cc287364c4ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Plot saved as: 2024-11-27_21-08_error_analysis.png\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 2000x1500 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Classification Statistics:\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.98 0.98 0.98 8576\n",
" 1.0 0.98 0.98 0.98 8273\n",
"\n",
" accuracy 0.98 16849\n",
" macro avg 0.98 0.98 0.98 16849\n",
"weighted avg 0.98 0.98 0.98 16849\n",
"\n",
"AUC-ROC: 0.9990\n",
"\n",
"Regression Statistics (Non-zero values):\n",
"MAE: 0.0539\n",
"RMSE: 0.0752\n",
"Mean error: -0.0245\n",
"Error std: 0.0711\n",
"\n",
"Final Prediction Statistics:\n",
"MAE: 0.0261\n",
"RMSE: 0.0540\n",
"Mean error: -0.0052\n",
"Error std: 0.0537\n",
"\n",
"Error Thresholds (Final Predictions):\n",
"Predictions within ±0.5: 99.9%\n",
"Predictions within ±1.0: 100.0%\n",
"Predictions within ±1.5: 100.0%\n",
"Predictions within ±2.0: 100.0%\n"
]
}
],
"source": [
"def plot_error_analysis(y_true, predictions, folder_name=None):\n",
" \"\"\"\n",
" Function to visualize prediction error analysis for the hybrid model\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual values\n",
" predictions : tuple\n",
" Tuple containing (classification_pred, regression_pred, final_pred)\n",
" folder_name : str, optional\n",
" Directory to save plots. If None, plots are only displayed\n",
"\n",
" Generates:\n",
" ----------\n",
" - Classification analysis plots\n",
" - Regression error analysis plots\n",
" - Final prediction error analysis plots\n",
" \"\"\"\n",
" from sklearn.metrics import roc_curve\n",
"\n",
" # Unpack predictions\n",
" classification_pred, regression_pred, final_pred = predictions\n",
"\n",
" # Convert to 1D numpy arrays if needed\n",
" y_true = np.ravel(y_true)\n",
" classification_pred = np.ravel(classification_pred)\n",
" regression_pred = np.ravel(regression_pred)\n",
" final_pred = np.ravel(final_pred)\n",
"\n",
" # Create binary ground truth\n",
" y_true_binary = (y_true > 0).astype(float)\n",
"\n",
" # Calculate errors for regression and final predictions\n",
" regression_errors = regression_pred - y_true\n",
" final_errors = final_pred - y_true\n",
"\n",
" # Create main figure\n",
" plt.figure(figsize=(20, 15))\n",
"\n",
" # Classification Analysis (Top Row)\n",
" # Plot 1: Classification Distribution\n",
" plt.subplot(3, 3, 1)\n",
" plt.hist(classification_pred, bins=50, alpha=0.7)\n",
" plt.axvline(x=0.5, color='r', linestyle='--')\n",
" plt.title('Classification Probability Distribution')\n",
" plt.xlabel('Classification Probability')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 2: ROC Curve\n",
" plt.subplot(3, 3, 2)\n",
" fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n",
" plt.plot(fpr, tpr)\n",
" plt.plot([0, 1], [0, 1], 'r--')\n",
" plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
"\n",
" # Plot 3: Classification Confusion Matrix\n",
" plt.subplot(3, 3, 3)\n",
" cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Classification Confusion Matrix')\n",
" plt.xlabel('Predicted')\n",
" plt.ylabel('Actual')\n",
"\n",
" # Regression Analysis (Middle Row)\n",
" # Plot 4: Regression Error Distribution\n",
" plt.subplot(3, 3, 4)\n",
" plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n",
" plt.title('Regression Error Distribution (Non-zero Values)')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 5: Actual vs Predicted (Regression)\n",
" plt.subplot(3, 3, 5)\n",
" mask_nonzero = y_true > 0\n",
" plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n",
" plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n",
" [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 6: Regression Errors vs Actual Values\n",
" plt.subplot(3, 3, 6)\n",
" plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" # Final Predictions Analysis (Bottom Row)\n",
" # Plot 7: Final Error Distribution\n",
" plt.subplot(3, 3, 7)\n",
" plt.hist(final_errors, bins=50, alpha=0.7)\n",
" plt.title('Final Prediction Error Distribution')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 8: Actual vs Predicted (Final)\n",
" plt.subplot(3, 3, 8)\n",
" plt.scatter(y_true, final_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Final)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 9: Final Errors vs Actual Values\n",
" plt.subplot(3, 3, 9)\n",
" plt.scatter(y_true, final_errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Final Errors vs Actual Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" plt.tight_layout()\n",
"\n",
" # Save plot if directory is specified\n",
" if folder_name is not None:\n",
" try:\n",
" filename = f'{folder_name}_error_analysis.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" except Exception as e:\n",
" print(f\"\\nError saving plot: {str(e)}\")\n",
"\n",
" plt.show()\n",
"\n",
" # Print comprehensive statistics\n",
" print(\"\\nClassification Statistics:\")\n",
" print(classification_report(y_true_binary, classification_pred > 0.5))\n",
" print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n",
"\n",
" print(\"\\nRegression Statistics (Non-zero values):\")\n",
" mask_nonzero = y_true > 0\n",
" if np.any(mask_nonzero):\n",
" print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n",
" print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n",
"\n",
" print(\"\\nFinal Prediction Statistics:\")\n",
" print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(final_errors):.4f}\")\n",
" print(f\"Error std: {np.std(final_errors):.4f}\")\n",
"\n",
" # Calculate percentage of errors within thresholds\n",
" thresholds = [0.5, 1.0, 1.5, 2.0]\n",
" print(\"\\nError Thresholds (Final Predictions):\")\n",
" for threshold in thresholds:\n",
" within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n",
" print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n",
"\n",
"# Example usage\n",
"plot_error_analysis(y_test, predictions, folder_name=folder_name)"
]
}
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