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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8adcbe0819b88578",
"metadata": {
"ExecuteTime": {
"end_time": "2024-11-20T00:55:22.066729Z",
"start_time": "2024-11-20T00:54:13.878615Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[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[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[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",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"from opt_einsum.paths import branch_1\n",
"!apt-get update\n",
"!apt-get install graphviz -y\n",
"\n",
"!pip install tensorflow==2.13.0\n",
"!pip install numpy\n",
"!pip install pandas\n",
"!pip install keras==2.13.1\n",
"!pip install scikit-learn\n",
"!pip install matplotlib\n",
"!pip install joblib\n",
"!pip install pyarrow\n",
"!pip install fastparquet\n",
"!pip install scipy\n",
"!pip install seaborn\n",
"!pip install tqdm\n",
"!pip install pydot\n",
"!pip install tensorflow-io\n",
"!pip install tensorflow-addons"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7a813e3cbca057b7",
"metadata": {
"ExecuteTime": {
"end_time": "2024-11-20T00:55:22.782689Z",
"start_time": "2024-11-20T00:55:22.089165Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-21 08:23:10.586264: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
"\n",
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
"\n",
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
"\n",
" warnings.warn(\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, Conv1D, GlobalAveragePooling1D\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.models import Model\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import RobustScaler\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
"from tensorflow.keras.optimizers import AdamW\n",
"import json\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"from tensorflow.keras.utils import plot_model\n",
"import tensorflow_addons as tfa\n",
"import os\n",
"import joblib\n",
"import seaborn as sns\n",
"from sklearn.metrics import confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n",
"\n",
"folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
"random_state_value = None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b3f525e19f78a1da",
"metadata": {},
"outputs": [],
"source": [
"def get_season(date):\n",
" month = date.month\n",
" day = date.day\n",
" if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n",
" return 'Winter'\n",
" elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n",
" return 'Spring'\n",
" elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n",
" return 'Summer'\n",
" elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n",
" return 'Autumn'\n",
" else:\n",
" return 'Unknown'\n",
"\n",
"\n",
"def get_time_period(hour):\n",
" if 5 <= hour < 12:\n",
" return 'Morning'\n",
" elif 12 <= hour < 17:\n",
" return 'Afternoon'\n",
" elif 17 <= hour < 21:\n",
" return 'Evening'\n",
" else:\n",
" return 'Night'\n",
"\n",
"\n",
"def add_time_features(df):\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
" df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n",
" df['year'] = df['datetime'].dt.year\n",
" df['month'] = df['datetime'].dt.month\n",
" df['day'] = df['datetime'].dt.day\n",
" df['hour'] = df['datetime'].dt.hour\n",
" df['minute'] = df['datetime'].dt.minute\n",
" df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n",
" df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n",
" df['day_of_week'] = df['datetime'].dt.dayofweek\n",
" df['day_of_year'] = df['datetime'].dt.dayofyear\n",
" df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n",
" df['quarter'] = df['datetime'].dt.quarter\n",
" df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n",
" df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n",
" df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n",
" df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n",
" df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n",
" df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n",
" df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n",
" df['season'] = df['datetime'].apply(get_season)\n",
" df['time_period'] = df['hour'].apply(get_time_period)\n",
" return df\n",
"\n",
"\n",
"def add_solar_features(df):\n",
" # Calculate solar angle\n",
" df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n",
"\n",
" # Interactions between relevant features\n",
" df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n",
" df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n",
"\n",
" # Derived features\n",
" df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n",
" df['temp_gradient'] = df['temp'] - df['tempmin']\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_solar_specific_features(df):\n",
" # Solar angle and day length calculations\n",
" df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n",
" df['solar_noon'] = 12 - df['hour']\n",
" df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n",
"\n",
" # Feature interactions\n",
" df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n",
" df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n",
"\n",
" # Extended window rolling features\n",
" df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n",
" df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_uv_specific_features(df):\n",
" # Solar zenith angle calculation\n",
" lat = 41.9 # assuming constant latitude for the dataset - Rome's latitude\n",
" df['solar_zenith'] = 90 - np.degrees(\n",
" np.arcsin(\n",
" np.sin(np.radians(lat)) * np.sin(df['solar_elevation']) +\n",
" np.cos(np.radians(lat)) * np.cos(df['solar_elevation']) * np.cos(df['hour'] * 15)\n",
" )\n",
" )\n",
"\n",
" # UV peak hours indicator (10:00-16:00)\n",
" df['is_uv_peak_hours'] = ((df['hour'] >= 10) & (df['hour'] <= 16)).astype(int)\n",
"\n",
" # Atmospheric attenuation factor\n",
" df['atmospheric_attenuation'] = (100 - df['cloudcover']) * (df['visibility'] / 100) * (1 - df['humidity'] / 200)\n",
"\n",
" # Seasonal UV factor\n",
" df['uv_seasonal_factor'] = np.where(df['season_Summer'], 1.0,\n",
" np.where(df['season_Spring'], 0.7,\n",
" np.where(df['season_Autumn'], 0.5, 0.3)))\n",
"\n",
" # Solar elevation and atmospheric transparency interaction\n",
" df['solar_clarity_index'] = df['solar_elevation'] * df['atmospheric_attenuation'] / 100\n",
"\n",
" # UV-specific rolling features\n",
" df['clarity_rolling_3h'] = df['atmospheric_attenuation'].rolling(window=3).mean()\n",
" df['temp_uv_interaction'] = df['temp'] * df['solar_clarity_index']\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_advanced_features(df):\n",
" \"\"\"\n",
" Add all advanced features in the correct order\n",
" \"\"\"\n",
" # 1. First add basic time features\n",
" df = add_time_features(df)\n",
"\n",
" # 2. One-hot encoding for categorical features\n",
" df = pd.get_dummies(df, columns=['season', 'time_period'])\n",
"\n",
" # 3. Add solar and specific features\n",
" df = add_solar_features(df)\n",
" df = add_solar_specific_features(df)\n",
"\n",
" # 4. Ensure datetime index\n",
" if not isinstance(df.index, pd.DatetimeIndex):\n",
" df.index = pd.to_datetime(df.index)\n",
"\n",
" # 5. Add weather variable interactions\n",
" df['temp_humidity'] = df['temp'] * df['humidity']\n",
" df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n",
" df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n",
"\n",
" # 6. Add solar radiation derived features\n",
" df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n",
" df['day_length'] = np.sin(df['day_of_year_sin']) * 12 + 12\n",
"\n",
" # 7. Add lag features\n",
" df['temp_1h_lag'] = df['temp'].shift(1)\n",
" df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n",
" df['humidity_1h_lag'] = df['humidity'].shift(1)\n",
"\n",
" # 8. Add rolling means\n",
" df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n",
" df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n",
" df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n",
"\n",
" # 9. Add atmospheric stability\n",
" df['atmospheric_stability'] = df.groupby(df.index.date)['pressure'].transform(\n",
" lambda x: x.std()\n",
" ).fillna(0)\n",
"\n",
" # 10. Add extreme conditions indicator\n",
" df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n",
" (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n",
"\n",
" # 11. Add atmospheric transparency\n",
" df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n",
"\n",
" # 12. Add transitional seasons indicator\n",
" df['is_transition_season'] = ((df['season_Spring'] | df['season_Autumn'])).astype(int)\n",
"\n",
" # 13. Add solar cloud effect\n",
" if 'solar_elevation' in df.columns:\n",
" df['solar_cloud_effect'] = df['solar_elevation'] * (100 - df['cloudcover']) / 100\n",
"\n",
" # 14. Finally add UV specific features\n",
" df = add_uv_specific_features(df)\n",
"\n",
" return df\n",
"\n",
"\n",
"def prepare_advanced_data(df):\n",
" \"\"\"\n",
" Prepares data for UV index prediction model with advanced feature engineering\n",
" and optimized preprocessing.\n",
"\n",
" Args:\n",
" df: DataFrame with meteorological data\n",
"\n",
" Returns:\n",
" tuple: (X_train_scaled, X_test_scaled, y_train, y_test, scaler, final_features, X_to_predict_scaled)\n",
" \"\"\"\n",
" # Apply feature engineering functions\n",
" df = add_advanced_features(df)\n",
"\n",
" # Optimized feature selection for UV index\n",
" selected_features = {\n",
" # Primary meteorological features\n",
" 'atmospheric': [\n",
" 'temp', 'humidity', 'cloudcover', 'visibility',\n",
" 'clear_sky_index', 'atmospheric_transparency'\n",
" ],\n",
"\n",
" # Essential temporal features\n",
" 'temporal': [\n",
" 'hour_sin', 'hour_cos',\n",
" 'day_of_year_sin', 'day_of_year_cos'\n",
" ],\n",
"\n",
" # Solar features\n",
" 'solar': [\n",
" 'solar_angle', 'solar_elevation',\n",
" 'day_length', 'solar_noon',\n",
" 'solar_cloud_effect'\n",
" ],\n",
"\n",
" # Key interactions\n",
" 'interactions': [\n",
" 'cloud_temp_interaction',\n",
" 'visibility_cloud_interaction',\n",
" 'temp_humidity_interaction',\n",
" 'solar_clarity_index'\n",
" ],\n",
"\n",
" # Rolling features\n",
" 'rolling': [\n",
" 'cloud_rolling_12h',\n",
" 'temp_rolling_mean_6h'\n",
" ]\n",
" }\n",
"\n",
" # Flatten feature list\n",
" base_features = [item for sublist in selected_features.values() for item in sublist]\n",
"\n",
" # Add categorical features (one-hot encoded)\n",
" categorical_columns = [col for col in df.columns if col.startswith(('season_', 'time_period_'))]\n",
" final_features = base_features + categorical_columns\n",
"\n",
" # Temporal preprocessing\n",
" df = df.sort_values('datetime')\n",
" df.set_index('datetime', inplace=True)\n",
"\n",
" # Advanced interpolation for missing values\n",
" for column in final_features:\n",
" if column in df.columns:\n",
" if df[column].isnull().any():\n",
" if column in selected_features['rolling']:\n",
" df[column] = df[column].ffill().bfill()\n",
" else:\n",
" df[column] = df[column].interpolate(method='time', limit_direction='both')\n",
"\n",
" # Temporal data split\n",
" data_after_2010 = df[df.index.year >= 2010].copy()\n",
" data_before_2010 = df[df.index.year < 2010].copy()\n",
"\n",
" print(f\"\\nTemporal distribution of data:\")\n",
" print(f\"Records after 2010: {len(data_after_2010):,}\")\n",
" print(f\"Records before 2010: {len(data_before_2010):,}\")\n",
"\n",
" # Feature and target preparation\n",
" X = data_after_2010[final_features]\n",
" y = data_after_2010['uvindex']\n",
" X_to_predict = data_before_2010[final_features]\n",
"\n",
" # Data validation\n",
" if X.isnull().any().any() or y.isnull().any():\n",
" print(\"\\nWarning: Found missing values after preprocessing\")\n",
" print(\"Features with missing values:\", X.columns[X.isnull().any()].tolist())\n",
" X = X.fillna(X.mean())\n",
" y = y.fillna(y.mean())\n",
"\n",
" # Stratified data split\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y,\n",
" test_size=0.5,\n",
" random_state=random_state_value,\n",
" stratify=pd.qcut(y, q=5, duplicates='drop', labels=False)\n",
" )\n",
"\n",
" # Robust feature scaling\n",
" feature_scaler = RobustScaler()\n",
" X_train_scaled = feature_scaler.fit_transform(X_train)\n",
" X_test_scaled = feature_scaler.transform(X_test)\n",
" X_to_predict_scaled = feature_scaler.transform(X_to_predict)\n",
"\n",
" target_scaler = RobustScaler()\n",
" y_train_scaled = target_scaler.fit_transform(y_train.values.reshape(-1, 1)).ravel()\n",
" y_test_scaled = target_scaler.transform(y_test.values.reshape(-1, 1)).ravel()\n",
"\n",
" # Final validation\n",
" assert not np.isnan(X_train_scaled).any(), \"Found NaN in X_train_scaled\"\n",
" assert not np.isnan(X_test_scaled).any(), \"Found NaN in X_test_scaled\"\n",
" assert not np.isnan(X_to_predict_scaled).any(), \"Found NaN in X_to_predict_scaled\"\n",
"\n",
" # Print feature information\n",
" print(\"\\nNumber of features used:\", len(final_features))\n",
" print(\"\\nFeature categories:\")\n",
" for category, features in selected_features.items():\n",
" print(f\"{category}: {len(features)} features\")\n",
" print(f\"Categorical: {len(categorical_columns)} features\")\n",
"\n",
" return (X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled,\n",
" feature_scaler, target_scaler, final_features, X_to_predict_scaled)\n",
"\n",
"\n",
"def create_sequence_data(X, sequence_length=24):\n",
" \"\"\"\n",
" Converts data into sequences for LSTM input\n",
" sequence_length represents how many previous hours to consider\n",
" \"\"\"\n",
" sequences = []\n",
" for i in range(len(X) - sequence_length + 1):\n",
" sequences.append(X[i:i + sequence_length])\n",
" return np.array(sequences)\n",
"\n",
"\n",
"def prepare_hybrid_data(df):\n",
" # Use existing data preparation\n",
" X_train_scaled, X_test_scaled, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_scaled = prepare_advanced_data(df)\n",
"\n",
" # Convert data to sequences\n",
" sequence_length = 24 # 24 hours of historical data\n",
"\n",
" X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n",
" X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n",
"\n",
" # Adjust y by removing the first (sequence_length-1) elements\n",
" y_train = y_train[sequence_length - 1:]\n",
" y_test = y_test[sequence_length - 1:]\n",
"\n",
" X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n",
"\n",
" return X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9dff3259-b376-4cfc-89d8-ab2ea18aaa5e",
"metadata": {},
"outputs": [],
"source": [
"def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01,\n",
" survival_probability=0.8, return_sequences=True):\n",
" \"\"\"LSTM layer with stochastic depth\"\"\"\n",
" residual = x\n",
"\n",
" # Main path\n",
" x = Bidirectional(LSTM(units, return_sequences=return_sequences,\n",
" kernel_regularizer=regularizers.l2(l2_reg)))(x)\n",
" x = LayerNormalization()(x)\n",
" x = Dropout(dropout_rate)(x)\n",
"\n",
" # Adjust residual dimension if needed\n",
" if return_sequences:\n",
" # For Bidirectional LSTM, the output dimension is 2 * units\n",
" target_dim = 2 * units\n",
" if int(residual.shape[-1]) != target_dim:\n",
" # Use Dense layer instead of Conv1D for better dimension matching\n",
" residual = Dense(target_dim)(residual)\n",
"\n",
" # Apply stochastic depth only if dimensions match\n",
" if x.shape[-1] == residual.shape[-1]:\n",
" x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n",
" else:\n",
" print(f\"Warning: Dimension mismatch - x: {x.shape}, residual: {residual.shape}\")\n",
" # Skip residual connection if dimensions don't match\n",
" pass\n",
"\n",
" return x\n",
"\n",
"\n",
"def attention_block(x, units, num_heads=8, survival_probability=0.8):\n",
" \"\"\"\n",
" Attention block with stochastic depth.\n",
" \"\"\"\n",
" original_x = x\n",
"\n",
" # Compute self-attention\n",
" attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n",
"\n",
" # Ensure dimensions match before applying stochastic depth\n",
" if attention.shape[-1] != original_x.shape[-1]:\n",
" original_x = Dense(attention.shape[-1])(original_x)\n",
"\n",
" # Apply stochastic depth to the attention path\n",
" x = tfa.layers.StochasticDepth(survival_probability)([attention, original_x])\n",
" x = LayerNormalization()(x)\n",
"\n",
" # Store the input to the FFN\n",
" ffn_input = x\n",
"\n",
" # FFN block\n",
" x = Dense(units * 4, activation='swish')(x)\n",
" x = Dense(ffn_input.shape[-1])(x) # Match the input dimension\n",
"\n",
" # Apply stochastic depth to the FFN\n",
" x = tfa.layers.StochasticDepth(survival_probability)([x, ffn_input])\n",
" x = LayerNormalization()(x)\n",
"\n",
" return x\n",
"\n",
"\n",
"def create_uv_index_model(input_shape, folder_name, l2_lambda=0.005, max_output=11):\n",
" inputs = Input(shape=input_shape)\n",
"\n",
" # Further adjusted hyperparameters\n",
" survival_probs = [0.98, 0.95, 0.92] # Even higher survival probabilities\n",
" attention_survival_probs = [0.95, 0.92, 0.9]\n",
"\n",
" # First LSTM block\n",
" x = create_residual_lstm_layer(\n",
" inputs, 64, dropout_rate=0.2, # Further reduced dropout\n",
" l2_reg=l2_lambda,\n",
" survival_probability=survival_probs[0],\n",
" return_sequences=True\n",
" )\n",
" x = attention_block(x, 128, num_heads=2, # Reduced heads\n",
" survival_probability=attention_survival_probs[0])\n",
"\n",
" # Second LSTM block\n",
" x = create_residual_lstm_layer(\n",
" x, 32, dropout_rate=0.15,\n",
" l2_reg=l2_lambda,\n",
" survival_probability=survival_probs[1],\n",
" return_sequences=True\n",
" )\n",
" x = attention_block(x, 64, num_heads=2,\n",
" survival_probability=attention_survival_probs[1])\n",
"\n",
" # Third LSTM block\n",
" x = create_residual_lstm_layer(\n",
" x, 16, dropout_rate=0.1,\n",
" l2_reg=l2_lambda,\n",
" survival_probability=survival_probs[2],\n",
" return_sequences=True\n",
" )\n",
" x = attention_block(x, 32, num_heads=2,\n",
" survival_probability=attention_survival_probs[2])\n",
"\n",
" # Global attention with reduced complexity\n",
" x_input = x\n",
" x = MultiHeadAttention(num_heads=2, key_dim=32)(x, x)\n",
"\n",
" if x.shape[-1] != x_input.shape[-1]:\n",
" x_input = Dense(x.shape[-1])(x_input)\n",
"\n",
" x = tfa.layers.StochasticDepth(survival_probability=0.95)([x, x_input])\n",
" x = LayerNormalization()(x)\n",
"\n",
" # Simplified dense layers\n",
" x = GlobalAveragePooling1D()(x)\n",
"\n",
" # Gradual dimension reduction\n",
" x = Dense(32, activation='swish', kernel_regularizer=regularizers.l2(l2_lambda / 2), kernel_constraint=tf.keras.constraints.MaxNorm(3))(x)\n",
" x = BatchNormalization()(x)\n",
" x = Dropout(0.05)(x) # Minimal dropout\n",
"\n",
" x = Dense(16, activation='swish',\n",
" kernel_regularizer=regularizers.l2(l2_lambda / 2))(x)\n",
" x = BatchNormalization()(x)\n",
"\n",
" # Modified output layer\n",
" x = Dense(8, activation='swish')(x)\n",
" outputs = Dense(1, activation='sigmoid')(x) # Sigmoid activation\n",
" outputs = Lambda(lambda x: x * max_output)(outputs) # Scale to [0, 11] range\n",
"\n",
" model = Model(inputs=inputs, outputs=outputs, name=\"UvModel\")\n",
"\n",
" # More stable learning rate schedule\n",
" initial_learning_rate = 0.0001 # Further reduced\n",
" warmup_steps = 1000\n",
" decay_steps = 5000\n",
"\n",
" # Corretto learning rate schedule\n",
" class CustomLRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
" def __init__(self, initial_lr=0.0001, warmup_steps=1000, decay_steps=5000):\n",
" super().__init__()\n",
" self.initial_lr = initial_lr\n",
" self.warmup_steps = warmup_steps\n",
" self.decay_steps = decay_steps\n",
"\n",
" def __call__(self, step):\n",
" # Convert to float32\n",
" step_f = tf.cast(step, tf.float32)\n",
" warmup_steps_f = tf.cast(self.warmup_steps, tf.float32)\n",
" decay_steps_f = tf.cast(self.decay_steps, tf.float32)\n",
"\n",
" # Warmup phase\n",
" warmup_progress = step_f / warmup_steps_f\n",
" warmup_lr = self.initial_lr * warmup_progress\n",
"\n",
" # Decay phase\n",
" decay_progress = (step_f - warmup_steps_f) / decay_steps_f\n",
" decay_factor = 0.5 * (1.0 + tf.cos(tf.constant(np.pi) * decay_progress))\n",
" decay_lr = self.initial_lr * decay_factor\n",
"\n",
" # Combine phases\n",
" lr = tf.where(step_f < warmup_steps_f, warmup_lr, decay_lr)\n",
" return lr\n",
"\n",
" def get_config(self):\n",
" return {\n",
" \"initial_lr\": self.initial_lr,\n",
" \"warmup_steps\": self.warmup_steps,\n",
" \"decay_steps\": self.decay_steps\n",
" }\n",
"\n",
" # Utilizzo dello schedule corretto\n",
" lr_schedule = CustomLRSchedule(\n",
" initial_lr=initial_learning_rate,\n",
" warmup_steps=warmup_steps,\n",
" decay_steps=decay_steps\n",
" )\n",
"\n",
" optimizer = AdamW(\n",
" learning_rate=lr_schedule,\n",
" weight_decay=0.0005,\n",
" beta_1=0.9,\n",
" beta_2=0.999,\n",
" epsilon=1e-7\n",
" )\n",
"\n",
" # Improved loss function\n",
" def smooth_uv_loss(y_true, y_pred):\n",
" # Basic MSE with smoothing\n",
" mse = tf.square(y_true - y_pred)\n",
"\n",
" # Smooth L1 component for better stability\n",
" abs_diff = tf.abs(y_true - y_pred)\n",
" smooth_l1 = tf.where(abs_diff < 1.0,\n",
" 0.5 * tf.square(abs_diff),\n",
" abs_diff - 0.5)\n",
"\n",
" # Combined loss with dynamic weighting\n",
" combined_loss = 0.7 * mse + 0.3 * smooth_l1\n",
"\n",
" # Gentle weighting for high UV values\n",
" high_uv_weight = tf.where(y_true >= 8.0, 1.2, 1.0)\n",
"\n",
" # Smooth peak hours weight\n",
" time_of_day = tf.cast(tf.math.floormod(tf.range(tf.shape(y_true)[0]), 24),\n",
" tf.float32)\n",
" peak_weight = 1.0 + 0.2 * tf.math.sigmoid((time_of_day - 10.0) * 0.5) * \\\n",
" tf.math.sigmoid((16.0 - time_of_day) * 0.5)\n",
"\n",
" total_weight = high_uv_weight * peak_weight\n",
"\n",
" return tf.reduce_mean(combined_loss * total_weight)\n",
"\n",
" # Improved MAPE metric\n",
" def smooth_mape(y_true, y_pred):\n",
" epsilon = 1e-7\n",
" diff = tf.abs(y_true - y_pred)\n",
" scale = tf.maximum(tf.abs(y_true) + epsilon, 0.5) # Minimum scale of 0.5\n",
" return tf.reduce_mean(diff / scale) * 100\n",
"\n",
" model.compile(\n",
" optimizer=optimizer,\n",
" loss=smooth_uv_loss,\n",
" metrics=[\n",
" 'mae',\n",
" 'mse',\n",
" tf.keras.metrics.RootMeanSquaredError(),\n",
" smooth_mape\n",
" ]\n",
" )\n",
"\n",
" model.summary()\n",
"\n",
" plot_model(model,\n",
" to_file=f'{folder_name}_model_architecture.png',\n",
" show_shapes=True,\n",
" show_layer_names=True,\n",
" dpi=150,\n",
" show_layer_activations=True)\n",
"\n",
" return model\n",
"\n",
"\n",
"def evaluate_uv_predictions(y_true, y_pred, folder_name=None):\n",
" \"\"\"\n",
" Comprehensive evaluation of UV index predictions with detailed analysis and visualizations.\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual UV index values\n",
" y_pred : array-like\n",
" Predicted UV index values\n",
" folder_name : str, optional\n",
" Folder to save analysis plots\n",
"\n",
" Returns:\n",
" --------\n",
" dict\n",
" Dictionary containing all calculated metrics\n",
" \"\"\"\n",
"\n",
" # Initialize plot paths\n",
" main_plot_path = None\n",
" conf_matrix_path = None\n",
"\n",
" # Data preprocessing\n",
" y_true = np.array(y_true).ravel()\n",
" y_pred = np.array(y_pred).ravel()\n",
"\n",
" # Rounding and clipping predictions\n",
" y_pred_rounded = np.round(y_pred * 2) / 2 # Round to nearest 0.5\n",
" y_pred_clipped = np.clip(y_pred_rounded, 0, 11)\n",
"\n",
" # Calculate errors\n",
" errors = y_pred - y_true\n",
" errors_rounded = y_pred_clipped - y_true\n",
"\n",
" # Function to determine UV risk level\n",
" def get_uv_risk_level(values):\n",
" levels = np.full_like(values, 'Low', dtype=object)\n",
" levels[(values > 2) & (values <= 5)] = 'Moderate'\n",
" levels[(values > 5) & (values <= 7)] = 'High'\n",
" levels[(values > 7) & (values <= 10)] = 'Very High'\n",
" levels[values > 10] = 'Extreme'\n",
" return levels\n",
"\n",
" # Calculate basic metrics\n",
" metrics = {\n",
" 'raw': {\n",
" 'mae': mean_absolute_error(y_true, y_pred),\n",
" 'rmse': np.sqrt(mean_squared_error(y_true, y_pred)),\n",
" 'r2': r2_score(y_true, y_pred),\n",
" 'mean_error': np.mean(errors),\n",
" 'std_error': np.std(errors),\n",
" 'median_error': np.median(errors),\n",
" 'p95_abs_error': np.percentile(np.abs(errors), 95)\n",
" },\n",
" 'rounded': {\n",
" 'mae': mean_absolute_error(y_true, y_pred_clipped),\n",
" 'rmse': np.sqrt(mean_squared_error(y_true, y_pred_clipped)),\n",
" 'r2': r2_score(y_true, y_pred_clipped)\n",
" }\n",
" }\n",
"\n",
" # Calculate accuracies for different margins\n",
" for data_type, errors_data in [('raw', errors), ('rounded', errors_rounded)]:\n",
" metrics[data_type].update({\n",
" 'within_05': np.mean(np.abs(errors_data) <= 0.5) * 100,\n",
" 'within_1': np.mean(np.abs(errors_data) <= 1.0) * 100,\n",
" 'within_15': np.mean(np.abs(errors_data) <= 1.5) * 100,\n",
" 'within_2': np.mean(np.abs(errors_data) <= 2.0) * 100\n",
" })\n",
"\n",
" # Analysis by UV risk level\n",
" y_true_risk = get_uv_risk_level(y_true)\n",
" y_pred_risk = get_uv_risk_level(y_pred_clipped)\n",
"\n",
" # Calculate confusion matrix with handling for missing classes\n",
" risk_levels = ['Low', 'Moderate', 'High', 'Very High', 'Extreme']\n",
"\n",
" # Get unique labels present in the data\n",
" present_labels = np.unique(np.concatenate([y_true_risk, y_pred_risk]))\n",
"\n",
" # Calculate confusion matrix for present labels\n",
" cm = confusion_matrix(y_true_risk, y_pred_risk, labels=present_labels)\n",
"\n",
" # Create full confusion matrix with zeros\n",
" full_cm = np.zeros((len(risk_levels), len(risk_levels)))\n",
"\n",
" # Map present labels to their positions in the full matrix\n",
" label_positions = {label: i for i, label in enumerate(risk_levels)}\n",
" for i, true_label in enumerate(present_labels):\n",
" for j, pred_label in enumerate(present_labels):\n",
" full_cm[label_positions[true_label], label_positions[pred_label]] = cm[i, j]\n",
"\n",
" # Create DataFrame with all risk levels\n",
" cm_df = pd.DataFrame(full_cm, columns=risk_levels, index=risk_levels)\n",
"\n",
" # Analysis by UV range\n",
" uv_ranges = [\n",
" (0, 2, 'Low'),\n",
" (2, 5, 'Moderate'),\n",
" (5, 7, 'High'),\n",
" (7, 10, 'Very High'),\n",
" (10, 11, 'Extreme')\n",
" ]\n",
"\n",
" range_analysis = {}\n",
" for low, high, label in uv_ranges:\n",
" mask = (y_true >= low) & (y_true < high)\n",
" if mask.any():\n",
" range_analysis[label] = {\n",
" 'mae': mean_absolute_error(y_true[mask], y_pred[mask]),\n",
" 'count': np.sum(mask),\n",
" 'accuracy_within_05': np.mean(np.abs(errors[mask]) <= 0.5) * 100,\n",
" 'accuracy_within_1': np.mean(np.abs(errors[mask]) <= 1.0) * 100\n",
" }\n",
"\n",
" # Visualizations\n",
" if folder_name is not None:\n",
" try:\n",
" # Main figure with 4 subplots\n",
" fig = plt.figure(figsize=(20, 15))\n",
"\n",
" # 1. Error distribution\n",
" plt.subplot(2, 2, 1)\n",
" plt.hist(errors, bins=50, alpha=0.7)\n",
" plt.title('Prediction Error Distribution')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # 2. Actual vs Predicted scatter plot\n",
" plt.subplot(2, 2, 2)\n",
" plt.scatter(y_true, y_pred, alpha=0.5)\n",
" plt.plot([0, 11], [0, 11], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # 3. Errors vs Actual Values\n",
" plt.subplot(2, 2, 3)\n",
" plt.scatter(y_true, errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Errors vs Actual Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" # 4. Accuracy and MAE by range\n",
" ax = plt.subplot(2, 2, 4)\n",
" x_labels = [f\"{label}\\n({low}-{high})\" for low, high, label in uv_ranges]\n",
" accuracies = [range_analysis[label]['accuracy_within_05']\n",
" for _, _, label in uv_ranges if label in range_analysis]\n",
" mae_values = [range_analysis[label]['mae']\n",
" for _, _, label in uv_ranges if label in range_analysis]\n",
"\n",
" bars = plt.bar(x_labels, accuracies, alpha=0.6)\n",
" plt.ylabel('Accuracy within ±0.5 (%)')\n",
" plt.title('Accuracy and MAE by UV Range')\n",
"\n",
" # Add MAE as line\n",
" ax2 = ax.twinx()\n",
" ax2.plot(x_labels, mae_values, 'r-o', label='MAE')\n",
" ax2.set_ylabel('MAE', color='red')\n",
"\n",
" plt.tight_layout()\n",
"\n",
" # Save main figure\n",
" main_plot_path = f'{folder_name}_uv_analysis.png'\n",
" plt.savefig(main_plot_path, dpi=300, bbox_inches='tight')\n",
"\n",
" # Confusion matrix as separate plot\n",
" plt.figure(figsize=(10, 8))\n",
" sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Confusion Matrix for UV Risk Levels')\n",
"\n",
" conf_matrix_path = f'{folder_name}_confusion_matrix.png'\n",
" plt.savefig(conf_matrix_path, dpi=300, bbox_inches='tight')\n",
"\n",
" plt.close('all')\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError saving plots: {str(e)}\")\n",
" main_plot_path = None\n",
" conf_matrix_path = None\n",
"\n",
" # Print detailed report\n",
" print(\"\\nUV Index Prediction Analysis:\")\n",
" print(\"\\nRaw Metrics:\")\n",
" for key, value in metrics['raw'].items():\n",
" print(f\"{key}: {value:.3f}\")\n",
"\n",
" print(\"\\nRounded Metrics:\")\n",
" for key, value in metrics['rounded'].items():\n",
" print(f\"{key}: {value:.3f}\")\n",
"\n",
" print(\"\\nAnalysis by UV Range:\")\n",
" for label, stats in range_analysis.items():\n",
" print(f\"\\n{label}:\")\n",
" for key, value in stats.items():\n",
" print(f\" {key}: {value:.3f}\")\n",
"\n",
" print(\"\\nConfusion Matrix:\")\n",
" print(cm_df)\n",
"\n",
" # Add range analysis and confusion matrix to metrics dictionary\n",
" metrics.update({\n",
" 'range_analysis': range_analysis,\n",
" 'confusion_matrix': cm_df.to_dict(),\n",
" 'plot_paths': {\n",
" 'main_analysis': main_plot_path,\n",
" 'confusion_matrix': conf_matrix_path\n",
" }\n",
" })\n",
"\n",
" return metrics\n",
"\n",
"\n",
"def plot_training_history(history, folder_name=None):\n",
" \"\"\"\n",
" Visualize and save the loss and metrics plots during training\n",
"\n",
" Parameters:\n",
" -----------\n",
" history : tensorflow.keras.callbacks.History\n",
" The history object returned by model training\n",
" folder_name : str\n",
" Folder where to save the plot\n",
" \"\"\"\n",
"\n",
" try:\n",
" # Create the figure\n",
" plt.figure(figsize=(12, 4))\n",
"\n",
" # Loss Plot\n",
" plt.subplot(1, 2, 1)\n",
" plt.plot(history.history['loss'], label='Training Loss')\n",
" plt.plot(history.history['val_loss'], label='Validation Loss')\n",
" plt.title('Model Loss')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # MAE Plot\n",
" plt.subplot(1, 2, 2)\n",
" plt.plot(history.history['mae'], label='Training MAE')\n",
" plt.plot(history.history['val_mae'], label='Validation MAE')\n",
" plt.title('Model MAE')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('MAE')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" plt.tight_layout()\n",
"\n",
" if folder_name is not None:\n",
" os.makedirs(folder_name, exist_ok=True)\n",
" # Generate filename with timestamp\n",
" filename = os.path.join(folder_name, 'training_history.png')\n",
"\n",
" # Save the figure\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nTraining history plot saved as: {filename}\")\n",
"\n",
" # Also save numerical data in CSV format\n",
" history_df = pd.DataFrame({\n",
" 'epoch': range(1, len(history.history['loss']) + 1),\n",
" 'training_loss': history.history['loss'],\n",
" 'validation_loss': history.history['val_loss'],\n",
" 'training_mae': history.history['mae'],\n",
" 'validation_mae': history.history['val_mae']\n",
" })\n",
"\n",
" if folder_name is not None:\n",
" csv_filename = os.path.join(folder_name, 'training_history.csv')\n",
" history_df.to_csv(csv_filename, index=False)\n",
" print(f\"Training history data saved as: {csv_filename}\")\n",
"\n",
" # Calculate and save final statistics\n",
" final_stats = {\n",
" 'final_training_loss': history.history['loss'][-1],\n",
" 'final_validation_loss': history.history['val_loss'][-1],\n",
" 'final_training_mae': history.history['mae'][-1],\n",
" 'final_validation_mae': history.history['val_mae'][-1],\n",
" 'best_validation_loss': min(history.history['val_loss']),\n",
" 'best_validation_mae': min(history.history['val_mae']),\n",
" 'epochs': len(history.history['loss']),\n",
" }\n",
"\n",
" if folder_name is not None:\n",
" # Save statistics in JSON format\n",
" stats_filename = os.path.join(folder_name, 'training_stats.json')\n",
" with open(stats_filename, 'w') as f:\n",
" json.dump(final_stats, f, indent=4)\n",
" print(f\"Final statistics saved as: {stats_filename}\")\n",
"\n",
" # Print main statistics\n",
" print(\"\\nFinal training statistics:\")\n",
" print(f\"Final Loss (train/val): {final_stats['final_training_loss']:.4f}/{final_stats['final_validation_loss']:.4f}\")\n",
" print(f\"Final MAE (train/val): {final_stats['final_training_mae']:.4f}/{final_stats['final_validation_mae']:.4f}\")\n",
" print(f\"Best validation loss: {final_stats['best_validation_loss']:.4f}\")\n",
" print(f\"Best validation MAE: {final_stats['best_validation_mae']:.4f}\")\n",
"\n",
" plt.show()\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError during plot creation or saving: {str(e)}\")\n",
"\n",
"\n",
"def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='uv_index'):\n",
" \"\"\"\n",
" Advanced training function for the hybrid UV index model with detailed monitoring\n",
" and training management.\n",
"\n",
" Parameters:\n",
" -----------\n",
" model : keras.Model\n",
" The compiled hybrid model\n",
" X_train : numpy.ndarray\n",
" Training data\n",
" y_train : numpy.ndarray\n",
" Training targets\n",
" X_test : numpy.ndarray\n",
" Validation data\n",
" y_test : numpy.ndarray\n",
" Validation targets\n",
" epochs : int, optional\n",
" Maximum number of training epochs\n",
" batch_size : int, optional\n",
" Batch size\n",
"\n",
" Returns:\n",
" --------\n",
" history : keras.callbacks.History\n",
" Training history with all metrics\n",
" \"\"\"\n",
"\n",
" # Advanced callbacks for training\n",
" callbacks = [\n",
" # Advanced Early Stopping\n",
" EarlyStopping(\n",
" monitor='mae',\n",
" patience=15,\n",
" restore_best_weights=True,\n",
" mode='min',\n",
" verbose=1,\n",
" min_delta=1e-6\n",
" ),\n",
" ReduceLROnPlateau(\n",
" monitor='mae',\n",
" factor=0.05,\n",
" patience=3,\n",
" verbose=1,\n",
" mode='min',\n",
" min_delta=1e-6,\n",
" cooldown=2,\n",
" min_lr=1e-7\n",
" ),\n",
" ReduceLROnPlateau(\n",
" monitor='val_loss',\n",
" factor=0.2,\n",
" patience=2,\n",
" verbose=1,\n",
" mode='min',\n",
" min_delta=1e-6,\n",
" cooldown=1,\n",
" min_lr=1e-7\n",
" ),\n",
" tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=f'{folder_name}_best_uv_model.h5',\n",
" monitor='mae',\n",
" save_best_only=True,\n",
" mode='min'\n",
" ),\n",
" tf.keras.callbacks.TensorBoard(\n",
" log_dir=f'./{folder_name}_logs',\n",
" histogram_freq=1,\n",
" write_graph=True,\n",
" update_freq='epoch'\n",
" ),\n",
" tf.keras.callbacks.LambdaCallback(\n",
" on_epoch_end=lambda epoch, logs: print(\n",
" f\"\\nEpoch {epoch + 1}: Out of range predictions: \"\n",
" f\"{np.sum((model.predict(X_test) < 0) | (model.predict(X_test) > 11))}\"\n",
" ) if epoch % 20 == 0 else None\n",
" )\n",
" ]\n",
"\n",
" try:\n",
" history = model.fit(\n",
" X_train, y_train,\n",
" validation_data=(X_test, y_test),\n",
" epochs=epochs,\n",
" batch_size=batch_size,\n",
" callbacks=callbacks,\n",
" verbose=1,\n",
" shuffle=False,\n",
" validation_freq=1,\n",
" )\n",
"\n",
" # Post-training analysis\n",
" print(\"\\nTraining completed successfully!\")\n",
"\n",
" return history\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError during training: {str(e)}\")\n",
" raise\n",
"\n",
" finally:\n",
" # Memory cleanup\n",
" tf.keras.backend.clear_session()\n",
"\n",
"\n",
"def integrate_predictions(df, predictions, sequence_length=24):\n",
" \"\"\"\n",
" Integrate UV index predictions into the original dataset for pre-2010 data.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pandas.DataFrame\n",
" Original dataset\n",
" predictions : numpy.ndarray\n",
" Array of UV index predictions\n",
" sequence_length : int\n",
" Sequence length used for predictions\n",
"\n",
" Returns:\n",
" --------\n",
" pandas.DataFrame\n",
" Updated dataset with UV index predictions\n",
" \"\"\"\n",
" # Convert datetime to datetime format if not already\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
"\n",
" # Identify pre-2010 rows\n",
" mask_pre_2010 = df['datetime'].dt.year < 2010\n",
"\n",
" # Create temporary DataFrame with predictions\n",
" dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n",
" predictions_df = pd.DataFrame({\n",
" 'datetime': dates_pre_2010,\n",
" 'uvindex_predicted': predictions.flatten()\n",
" })\n",
"\n",
" # Merge with original dataset\n",
" df = df.merge(predictions_df, on='datetime', how='left')\n",
"\n",
" # Update uvindex column where missing\n",
" df['uvindex'] = df['uvindex'].fillna(df['uvindex_predicted'])\n",
"\n",
" # Remove temporary column\n",
" df = df.drop('uvindex_predicted', axis=1)\n",
"\n",
" print(f\"Added {len(predictions)} predictions to dataset\")\n",
" print(f\"Rows with UV index after integration: {df['uvindex'].notna().sum()}\")\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "initial_id",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing UV index model training...\n",
"\n",
"1. Preparing data...\n",
"\n",
"Temporal distribution of data:\n",
"Records after 2010: 129,777\n",
"Records before 2010: 227,902\n",
"\n",
"Warning: Found missing values after preprocessing\n",
"Features with missing values: []\n",
"\n",
"Number of features used: 30\n",
"\n",
"Feature categories:\n",
"atmospheric: 6 features\n",
"temporal: 4 features\n",
"solar: 5 features\n",
"interactions: 4 features\n",
"rolling: 2 features\n",
"Categorical: 9 features\n",
"Training data shape: (64865, 24, 30)\n",
"Test data shape: (64866, 24, 30)\n",
"Saving scaler to: 2024-11-21_08-23_feature_scaler.joblib\n",
"Saving scaler to: 2024-11-21_08-23_target_scaler.joblib\n",
"Saving features to: 2024-11-21_08-23_features.json\n"
]
}
],
"source": [
"df = pd.read_parquet('../../sources/weather_data.parquet')\n",
"\n",
"print(\"Initializing UV index model training...\")\n",
"\n",
"# Data preparation\n",
"print(\"\\n1. Preparing data...\")\n",
"X_train_seq, X_test_seq, y_train, y_test, feature_scaler, target_scaler, features, X_to_predict_seq = prepare_hybrid_data(df)\n",
"\n",
"print(f\"Training data shape: {X_train_seq.shape}\")\n",
"print(f\"Test data shape: {X_test_seq.shape}\")\n",
"\n",
"# Save or load scaler and features\n",
"feature_scaler_path = f'{folder_name}_feature_scaler.joblib'\n",
"target_scaler_path = f'{folder_name}_target_scaler.joblib'\n",
"features_path = f'{folder_name}_features.json'\n",
"model_path = f'{folder_name}_best_model.h5'\n",
"history_path = f'{folder_name}_training_history.json'\n",
"\n",
"if os.path.exists(feature_scaler_path):\n",
" print(f\"Loading existing scaler from: {feature_scaler_path}\")\n",
" scaler = joblib.load(feature_scaler_path)\n",
"else:\n",
" print(f\"Saving scaler to: {feature_scaler_path}\")\n",
" joblib.dump(feature_scaler, feature_scaler_path)\n",
"\n",
"if os.path.exists(target_scaler_path):\n",
" print(f\"Loading existing scaler from: {target_scaler_path}\")\n",
" scaler = joblib.load(target_scaler_path)\n",
"else:\n",
" print(f\"Saving scaler to: {target_scaler_path}\")\n",
" joblib.dump(target_scaler, target_scaler_path)\n",
"\n",
"if os.path.exists(features_path):\n",
" print(f\"Loading existing features from: {features_path}\")\n",
" with open(features_path, 'r') as f:\n",
" features = json.load(f)\n",
"else:\n",
" print(f\"Saving features to: {features_path}\")\n",
" with open(features_path, 'w') as f:\n",
" json.dump(features, f)\n",
"\n",
"# Data quality verification\n",
"if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n",
" raise ValueError(\"Found NaN values in training data\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "83771453-71db-4bb2-833d-7b81c022863d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"2. Model initialization...\n",
"Creating new model...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-21 08:26:35.683631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:25:00.0, compute capability: 8.9\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"UvModel\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_1 (InputLayer) [(None, 24, 30)] 0 [] \n",
" \n",
" bidirectional (Bidirection (None, 24, 128) 48640 ['input_1[0][0]'] \n",
" al) \n",
" \n",
" layer_normalization (Layer (None, 24, 128) 256 ['bidirectional[0][0]'] \n",
" Normalization) \n",
" \n",
" dropout (Dropout) (None, 24, 128) 0 ['layer_normalization[0][0]'] \n",
" \n",
" dense (Dense) (None, 24, 128) 3968 ['input_1[0][0]'] \n",
" \n",
" stochastic_depth (Stochast (None, 24, 128) 0 ['dropout[0][0]', \n",
" icDepth) 'dense[0][0]'] \n",
" \n",
" multi_head_attention (Mult (None, 24, 128) 131968 ['stochastic_depth[0][0]', \n",
" iHeadAttention) 'stochastic_depth[0][0]'] \n",
" \n",
" stochastic_depth_1 (Stocha (None, 24, 128) 0 ['multi_head_attention[0][0]',\n",
" sticDepth) 'stochastic_depth[0][0]'] \n",
" \n",
" layer_normalization_1 (Lay (None, 24, 128) 256 ['stochastic_depth_1[0][0]'] \n",
" erNormalization) \n",
" \n",
" dense_1 (Dense) (None, 24, 512) 66048 ['layer_normalization_1[0][0]'\n",
" ] \n",
" \n",
" dense_2 (Dense) (None, 24, 128) 65664 ['dense_1[0][0]'] \n",
" \n",
" stochastic_depth_2 (Stocha (None, 24, 128) 0 ['dense_2[0][0]', \n",
" sticDepth) 'layer_normalization_1[0][0]'\n",
" ] \n",
" \n",
" layer_normalization_2 (Lay (None, 24, 128) 256 ['stochastic_depth_2[0][0]'] \n",
" erNormalization) \n",
" \n",
" bidirectional_1 (Bidirecti (None, 24, 64) 41216 ['layer_normalization_2[0][0]'\n",
" onal) ] \n",
" \n",
" layer_normalization_3 (Lay (None, 24, 64) 128 ['bidirectional_1[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_1 (Dropout) (None, 24, 64) 0 ['layer_normalization_3[0][0]'\n",
" ] \n",
" \n",
" dense_3 (Dense) (None, 24, 64) 8256 ['layer_normalization_2[0][0]'\n",
" ] \n",
" \n",
" stochastic_depth_3 (Stocha (None, 24, 64) 0 ['dropout_1[0][0]', \n",
" sticDepth) 'dense_3[0][0]'] \n",
" \n",
" multi_head_attention_1 (Mu (None, 24, 64) 33216 ['stochastic_depth_3[0][0]', \n",
" ltiHeadAttention) 'stochastic_depth_3[0][0]'] \n",
" \n",
" stochastic_depth_4 (Stocha (None, 24, 64) 0 ['multi_head_attention_1[0][0]\n",
" sticDepth) ', \n",
" 'stochastic_depth_3[0][0]'] \n",
" \n",
" layer_normalization_4 (Lay (None, 24, 64) 128 ['stochastic_depth_4[0][0]'] \n",
" erNormalization) \n",
" \n",
" dense_4 (Dense) (None, 24, 256) 16640 ['layer_normalization_4[0][0]'\n",
" ] \n",
" \n",
" dense_5 (Dense) (None, 24, 64) 16448 ['dense_4[0][0]'] \n",
" \n",
" stochastic_depth_5 (Stocha (None, 24, 64) 0 ['dense_5[0][0]', \n",
" sticDepth) 'layer_normalization_4[0][0]'\n",
" ] \n",
" \n",
" layer_normalization_5 (Lay (None, 24, 64) 128 ['stochastic_depth_5[0][0]'] \n",
" erNormalization) \n",
" \n",
" bidirectional_2 (Bidirecti (None, 24, 32) 10368 ['layer_normalization_5[0][0]'\n",
" onal) ] \n",
" \n",
" layer_normalization_6 (Lay (None, 24, 32) 64 ['bidirectional_2[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_2 (Dropout) (None, 24, 32) 0 ['layer_normalization_6[0][0]'\n",
" ] \n",
" \n",
" dense_6 (Dense) (None, 24, 32) 2080 ['layer_normalization_5[0][0]'\n",
" ] \n",
" \n",
" stochastic_depth_6 (Stocha (None, 24, 32) 0 ['dropout_2[0][0]', \n",
" sticDepth) 'dense_6[0][0]'] \n",
" \n",
" multi_head_attention_2 (Mu (None, 24, 32) 8416 ['stochastic_depth_6[0][0]', \n",
" ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n",
" \n",
" stochastic_depth_7 (Stocha (None, 24, 32) 0 ['multi_head_attention_2[0][0]\n",
" sticDepth) ', \n",
" 'stochastic_depth_6[0][0]'] \n",
" \n",
" layer_normalization_7 (Lay (None, 24, 32) 64 ['stochastic_depth_7[0][0]'] \n",
" erNormalization) \n",
" \n",
" dense_7 (Dense) (None, 24, 128) 4224 ['layer_normalization_7[0][0]'\n",
" ] \n",
" \n",
" dense_8 (Dense) (None, 24, 32) 4128 ['dense_7[0][0]'] \n",
" \n",
" stochastic_depth_8 (Stocha (None, 24, 32) 0 ['dense_8[0][0]', \n",
" sticDepth) 'layer_normalization_7[0][0]'\n",
" ] \n",
" \n",
" layer_normalization_8 (Lay (None, 24, 32) 64 ['stochastic_depth_8[0][0]'] \n",
" erNormalization) \n",
" \n",
" multi_head_attention_3 (Mu (None, 24, 32) 8416 ['layer_normalization_8[0][0]'\n",
" ltiHeadAttention) , 'layer_normalization_8[0][0]\n",
" '] \n",
" \n",
" stochastic_depth_9 (Stocha (None, 24, 32) 0 ['multi_head_attention_3[0][0]\n",
" sticDepth) ', \n",
" 'layer_normalization_8[0][0]'\n",
" ] \n",
" \n",
" layer_normalization_9 (Lay (None, 24, 32) 64 ['stochastic_depth_9[0][0]'] \n",
" erNormalization) \n",
" \n",
" global_average_pooling1d ( (None, 32) 0 ['layer_normalization_9[0][0]'\n",
" GlobalAveragePooling1D) ] \n",
" \n",
" dense_9 (Dense) (None, 32) 1056 ['global_average_pooling1d[0][\n",
" 0]'] \n",
" \n",
" batch_normalization (Batch (None, 32) 128 ['dense_9[0][0]'] \n",
" Normalization) \n",
" \n",
" dropout_3 (Dropout) (None, 32) 0 ['batch_normalization[0][0]'] \n",
" \n",
" dense_10 (Dense) (None, 16) 528 ['dropout_3[0][0]'] \n",
" \n",
" batch_normalization_1 (Bat (None, 16) 64 ['dense_10[0][0]'] \n",
" chNormalization) \n",
" \n",
" dense_11 (Dense) (None, 8) 136 ['batch_normalization_1[0][0]'\n",
" ] \n",
" \n",
" dense_12 (Dense) (None, 1) 9 ['dense_11[0][0]'] \n",
" \n",
" lambda (Lambda) (None, 1) 0 ['dense_12[0][0]'] \n",
" \n",
"==================================================================================================\n",
"Total params: 473025 (1.80 MB)\n",
"Trainable params: 472929 (1.80 MB)\n",
"Non-trainable params: 96 (384.00 Byte)\n",
"__________________________________________________________________________________________________\n",
"\n",
"3. Starting training...\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-21 08:26:51.620818: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
"2024-11-21 08:26:51.695976: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8905\n",
"2024-11-21 08:26:51.911310: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xd713390 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2024-11-21 08:26:51.911349: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
"2024-11-21 08:26:51.921786: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2024-11-21 08:26:52.001781: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
"2024-11-21 08:26:52.063791: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"507/507 [==============================] - ETA: 0s - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
" saving_api.save_model(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2028/2028 [==============================] - 25s 11ms/step\n",
"2028/2028 [==============================] - 19s 9ms/step\n",
"\n",
"Epoch 1: Out of range predictions: 0\n",
"507/507 [==============================] - 95s 151ms/step - loss: 4.4444 - mae: 1.3032 - mse: 2.1820 - root_mean_squared_error: 1.4772 - smooth_mape: 226.3000 - val_loss: 3.3901 - val_mae: 0.7726 - val_mse: 1.0065 - val_root_mean_squared_error: 1.0033 - val_smooth_mape: 96.0347 - lr: 5.0600e-05\n",
"Epoch 2/100\n",
"507/507 [==============================] - 24s 48ms/step - loss: 3.4513 - mae: 0.9422 - mse: 1.2050 - root_mean_squared_error: 1.0977 - smooth_mape: 144.6453 - val_loss: 3.0292 - val_mae: 0.6905 - val_mse: 0.9136 - val_root_mean_squared_error: 0.9558 - val_smooth_mape: 72.3569 - lr: 9.9998e-05\n",
"Epoch 3/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 2.8616 - mae: 0.7835 - mse: 0.9255 - root_mean_squared_error: 0.9620 - smooth_mape: 105.5174 - val_loss: 2.6105 - val_mae: 0.6461 - val_mse: 0.8668 - val_root_mean_squared_error: 0.9310 - val_smooth_mape: 62.3054 - lr: 9.7355e-05\n",
"Epoch 4/100\n",
"507/507 [==============================] - 27s 52ms/step - loss: 2.2615 - mae: 0.6304 - mse: 0.6460 - root_mean_squared_error: 0.8038 - smooth_mape: 84.4009 - val_loss: 1.9317 - val_mae: 0.4135 - val_mse: 0.4540 - val_root_mean_squared_error: 0.6738 - val_smooth_mape: 35.5852 - lr: 8.9946e-05\n",
"Epoch 5/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 1.6904 - mae: 0.4345 - mse: 0.3313 - root_mean_squared_error: 0.5756 - smooth_mape: 59.1463 - val_loss: 1.4432 - val_mae: 0.2685 - val_mse: 0.1950 - val_root_mean_squared_error: 0.4416 - val_smooth_mape: 27.2014 - lr: 7.8518e-05\n",
"Epoch 6/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 1.3637 - mae: 0.3450 - mse: 0.2236 - root_mean_squared_error: 0.4729 - smooth_mape: 45.5589 - val_loss: 1.2321 - val_mae: 0.2588 - val_mse: 0.1828 - val_root_mean_squared_error: 0.4276 - val_smooth_mape: 25.4509 - lr: 6.4221e-05\n",
"Epoch 7/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 1.1519 - mae: 0.2973 - mse: 0.1789 - root_mean_squared_error: 0.4229 - smooth_mape: 38.0580 - val_loss: 1.0643 - val_mae: 0.2375 - val_mse: 0.1577 - val_root_mean_squared_error: 0.3971 - val_smooth_mape: 23.5643 - lr: 4.8492e-05\n",
"Epoch 8/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 1.0083 - mae: 0.2665 - mse: 0.1546 - root_mean_squared_error: 0.3932 - smooth_mape: 32.9333 - val_loss: 0.9257 - val_mae: 0.2038 - val_mse: 0.1143 - val_root_mean_squared_error: 0.3380 - val_smooth_mape: 21.4354 - lr: 3.2915e-05\n",
"Epoch 9/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.9148 - mae: 0.2470 - mse: 0.1407 - root_mean_squared_error: 0.3751 - smooth_mape: 29.7929 - val_loss: 0.8520 - val_mae: 0.1890 - val_mse: 0.1027 - val_root_mean_squared_error: 0.3204 - val_smooth_mape: 20.0954 - lr: 1.9058e-05\n",
"Epoch 10/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.8584 - mae: 0.2366 - mse: 0.1317 - root_mean_squared_error: 0.3628 - smooth_mape: 28.3349 - val_loss: 0.8144 - val_mae: 0.1853 - val_mse: 0.0991 - val_root_mean_squared_error: 0.3148 - val_smooth_mape: 19.7012 - lr: 8.3134e-06\n",
"Epoch 11/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.8331 - mae: 0.2331 - mse: 0.1292 - root_mean_squared_error: 0.3594 - smooth_mape: 27.8952 - val_loss: 0.7991 - val_mae: 0.1829 - val_mse: 0.0966 - val_root_mean_squared_error: 0.3108 - val_smooth_mape: 19.7143 - lr: 1.7639e-06\n",
"Epoch 12/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.8266 - mae: 0.2326 - mse: 0.1289 - root_mean_squared_error: 0.3591 - smooth_mape: 27.6276 - val_loss: 0.8002 - val_mae: 0.1851 - val_mse: 0.0996 - val_root_mean_squared_error: 0.3155 - val_smooth_mape: 19.6762 - lr: 6.7976e-08\n",
"Epoch 13/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.8256 - mae: 0.2332 - mse: 0.1297 - root_mean_squared_error: 0.3601 - smooth_mape: 27.8768 - val_loss: 0.7938 - val_mae: 0.1827 - val_mse: 0.0977 - val_root_mean_squared_error: 0.3126 - val_smooth_mape: 19.5952 - lr: 3.3964e-06\n",
"Epoch 14/100\n",
"507/507 [==============================] - 27s 52ms/step - loss: 0.8108 - mae: 0.2311 - mse: 0.1283 - root_mean_squared_error: 0.3582 - smooth_mape: 27.5029 - val_loss: 0.7689 - val_mae: 0.1841 - val_mse: 0.0983 - val_root_mean_squared_error: 0.3135 - val_smooth_mape: 19.2959 - lr: 1.1414e-05\n",
"Epoch 15/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.7666 - mae: 0.2260 - mse: 0.1253 - root_mean_squared_error: 0.3539 - smooth_mape: 26.7909 - val_loss: 0.7071 - val_mae: 0.1791 - val_mse: 0.0942 - val_root_mean_squared_error: 0.3069 - val_smooth_mape: 18.7677 - lr: 2.3315e-05\n",
"Epoch 16/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.6896 - mae: 0.2200 - mse: 0.1214 - root_mean_squared_error: 0.3484 - smooth_mape: 25.8531 - val_loss: 0.6141 - val_mae: 0.1740 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 18.6023 - lr: 3.7901e-05\n",
"Epoch 17/100\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.5905 - mae: 0.2116 - mse: 0.1160 - root_mean_squared_error: 0.3406 - smooth_mape: 24.5564 - val_loss: 0.5156 - val_mae: 0.1681 - val_mse: 0.0874 - val_root_mean_squared_error: 0.2956 - val_smooth_mape: 17.4665 - lr: 5.3704e-05\n",
"Epoch 18/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.4901 - mae: 0.2037 - mse: 0.1116 - root_mean_squared_error: 0.3341 - smooth_mape: 23.3802 - val_loss: 0.4216 - val_mae: 0.1617 - val_mse: 0.0852 - val_root_mean_squared_error: 0.2919 - val_smooth_mape: 17.2917 - lr: 6.9134e-05\n",
"Epoch 19/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.3984 - mae: 0.1930 - mse: 0.1038 - root_mean_squared_error: 0.3221 - smooth_mape: 21.8713 - val_loss: 0.3391 - val_mae: 0.1637 - val_mse: 0.0790 - val_root_mean_squared_error: 0.2810 - val_smooth_mape: 17.4952 - lr: 8.2639e-05\n",
"Epoch 20/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.3280 - mae: 0.1882 - mse: 0.1019 - root_mean_squared_error: 0.3192 - smooth_mape: 21.1186 - val_loss: 0.2774 - val_mae: 0.1532 - val_mse: 0.0766 - val_root_mean_squared_error: 0.2767 - val_smooth_mape: 16.5548 - lr: 9.2860e-05\n",
"Epoch 21/100\n",
"2028/2028 [==============================] - 22s 11ms/step\n",
"2028/2028 [==============================] - 23s 12ms/step\n",
"\n",
"Epoch 21: Out of range predictions: 0\n",
"507/507 [==============================] - 75s 148ms/step - loss: 0.2717 - mae: 0.1800 - mse: 0.0959 - root_mean_squared_error: 0.3097 - smooth_mape: 19.9770 - val_loss: 0.2327 - val_mae: 0.1514 - val_mse: 0.0756 - val_root_mean_squared_error: 0.2750 - val_smooth_mape: 16.9079 - lr: 9.8768e-05\n",
"Epoch 22/100\n",
"507/507 [==============================] - 27s 52ms/step - loss: 0.2290 - mae: 0.1732 - mse: 0.0907 - root_mean_squared_error: 0.3011 - smooth_mape: 19.0873 - val_loss: 0.1969 - val_mae: 0.1482 - val_mse: 0.0722 - val_root_mean_squared_error: 0.2687 - val_smooth_mape: 16.2890 - lr: 9.9769e-05\n",
"Epoch 23/100\n",
"507/507 [==============================] - 26s 50ms/step - loss: 0.1994 - mae: 0.1705 - mse: 0.0889 - root_mean_squared_error: 0.2982 - smooth_mape: 18.7545 - val_loss: 0.1750 - val_mae: 0.1452 - val_mse: 0.0739 - val_root_mean_squared_error: 0.2719 - val_smooth_mape: 15.6235 - lr: 9.5762e-05\n",
"Epoch 24/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.1768 - mae: 0.1661 - mse: 0.0861 - root_mean_squared_error: 0.2934 - smooth_mape: 18.1383 - val_loss: 0.1559 - val_mae: 0.1482 - val_mse: 0.0716 - val_root_mean_squared_error: 0.2676 - val_smooth_mape: 16.7181 - lr: 8.7150e-05\n",
"Epoch 25/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.1601 - mae: 0.1629 - mse: 0.0835 - root_mean_squared_error: 0.2890 - smooth_mape: 17.7203 - val_loss: 0.1425 - val_mae: 0.1434 - val_mse: 0.0702 - val_root_mean_squared_error: 0.2649 - val_smooth_mape: 15.5010 - lr: 7.4800e-05\n",
"Epoch 26/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1472 - mae: 0.1586 - mse: 0.0806 - root_mean_squared_error: 0.2839 - smooth_mape: 17.2387 - val_loss: 0.1347 - val_mae: 0.1454 - val_mse: 0.0710 - val_root_mean_squared_error: 0.2665 - val_smooth_mape: 16.1275 - lr: 5.9955e-05\n",
"Epoch 27/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1399 - mae: 0.1584 - mse: 0.0803 - root_mean_squared_error: 0.2834 - smooth_mape: 17.2506 - val_loss: 0.1270 - val_mae: 0.1401 - val_mse: 0.0687 - val_root_mean_squared_error: 0.2621 - val_smooth_mape: 15.1573 - lr: 4.4108e-05\n",
"Epoch 28/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.1344 - mae: 0.1563 - mse: 0.0792 - root_mean_squared_error: 0.2815 - smooth_mape: 16.9289 - val_loss: 0.1229 - val_mae: 0.1394 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 15.1774 - lr: 2.8853e-05\n",
"Epoch 29/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.1293 - mae: 0.1536 - mse: 0.0767 - root_mean_squared_error: 0.2770 - smooth_mape: 16.6836 - val_loss: 0.1206 - val_mae: 0.1383 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.8600 - lr: 1.5727e-05\n",
"Epoch 30/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1275 - mae: 0.1526 - mse: 0.0763 - root_mean_squared_error: 0.2763 - smooth_mape: 16.5544 - val_loss: 0.1198 - val_mae: 0.1375 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2613 - val_smooth_mape: 14.7848 - lr: 6.0491e-06\n",
"Epoch 31/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.1259 - mae: 0.1517 - mse: 0.0753 - root_mean_squared_error: 0.2744 - smooth_mape: 16.4806 - val_loss: 0.1192 - val_mae: 0.1370 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.5789 - lr: 7.9394e-07\n",
"Epoch 32/100\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.1263 - mae: 0.1522 - mse: 0.0759 - root_mean_squared_error: 0.2754 - smooth_mape: 16.5490 - val_loss: 0.1192 - val_mae: 0.1368 - val_mse: 0.0679 - val_root_mean_squared_error: 0.2606 - val_smooth_mape: 14.5403 - lr: 4.9000e-07\n",
"Epoch 33/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.1258 - mae: 0.1518 - mse: 0.0754 - root_mean_squared_error: 0.2745 - smooth_mape: 16.4660 - val_loss: 0.1189 - val_mae: 0.1376 - val_mse: 0.0678 - val_root_mean_squared_error: 0.2605 - val_smooth_mape: 14.7214 - lr: 5.1679e-06\n",
"Epoch 34/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4794\n",
"Epoch 34: ReduceLROnPlateau reducing learning rate to 7.178531632234808e-07.\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1255 - mae: 0.1520 - mse: 0.0756 - root_mean_squared_error: 0.2749 - smooth_mape: 16.4811 - val_loss: 0.1179 - val_mae: 0.1389 - val_mse: 0.0676 - val_root_mean_squared_error: 0.2601 - val_smooth_mape: 14.8385 - lr: 7.1785e-07\n",
"Epoch 35/100\n",
"507/507 [==============================] - 27s 52ms/step - loss: 0.1245 - mae: 0.1527 - mse: 0.0760 - root_mean_squared_error: 0.2756 - smooth_mape: 16.5704 - val_loss: 0.1169 - val_mae: 0.1410 - val_mse: 0.0685 - val_root_mean_squared_error: 0.2618 - val_smooth_mape: 15.6455 - lr: 2.7133e-05\n",
"Epoch 36/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1227 - mae: 0.1525 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.5028 - val_loss: 0.1139 - val_mae: 0.1381 - val_mse: 0.0683 - val_root_mean_squared_error: 0.2614 - val_smooth_mape: 14.6552 - lr: 4.2209e-05\n",
"Epoch 37/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.1198 - mae: 0.1535 - mse: 0.0769 - root_mean_squared_error: 0.2773 - smooth_mape: 16.6359 - val_loss: 0.1107 - val_mae: 0.1409 - val_mse: 0.0686 - val_root_mean_squared_error: 0.2619 - val_smooth_mape: 14.7102 - lr: 5.8070e-05\n",
"Epoch 38/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710\n",
"Epoch 38: ReduceLROnPlateau reducing learning rate to 3.6559198633767668e-06.\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.1160 - mae: 0.1532 - mse: 0.0769 - root_mean_squared_error: 0.2772 - smooth_mape: 16.5710 - val_loss: 0.1057 - val_mae: 0.1380 - val_mse: 0.0675 - val_root_mean_squared_error: 0.2599 - val_smooth_mape: 14.8251 - lr: 3.6559e-06\n",
"Epoch 39/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.1113 - mae: 0.1525 - mse: 0.0762 - root_mean_squared_error: 0.2761 - smooth_mape: 16.4623 - val_loss: 0.1040 - val_mae: 0.1391 - val_mse: 0.0703 - val_root_mean_squared_error: 0.2652 - val_smooth_mape: 14.3343 - lr: 8.5841e-05\n",
"Epoch 40/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.1080 - mae: 0.1531 - mse: 0.0770 - root_mean_squared_error: 0.2775 - smooth_mape: 16.5384 - val_loss: 0.0995 - val_mae: 0.1393 - val_mse: 0.0697 - val_root_mean_squared_error: 0.2640 - val_smooth_mape: 15.2621 - lr: 9.4957e-05\n",
"Epoch 41/100\n",
"2028/2028 [==============================] - 22s 11ms/step\n",
"2028/2028 [==============================] - 23s 11ms/step\n",
"\n",
"Epoch 41: Out of range predictions: 0\n",
"507/507 [==============================] - 73s 144ms/step - loss: 0.1038 - mae: 0.1523 - mse: 0.0766 - root_mean_squared_error: 0.2767 - smooth_mape: 16.4194 - val_loss: 0.0982 - val_mae: 0.1425 - val_mse: 0.0723 - val_root_mean_squared_error: 0.2689 - val_smooth_mape: 14.8658 - lr: 9.9549e-05\n",
"Epoch 42/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6916\n",
"Epoch 42: ReduceLROnPlateau reducing learning rate to 4.957754936185666e-06.\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.1026 - mae: 0.1539 - mse: 0.0783 - root_mean_squared_error: 0.2798 - smooth_mape: 16.6892 - val_loss: 0.0915 - val_mae: 0.1368 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 14.4180 - lr: 4.9578e-06\n",
"Epoch 43/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.0964 - mae: 0.1500 - mse: 0.0749 - root_mean_squared_error: 0.2736 - smooth_mape: 16.2060 - val_loss: 0.0881 - val_mae: 0.1362 - val_mse: 0.0671 - val_root_mean_squared_error: 0.2591 - val_smooth_mape: 14.6337 - lr: 9.3815e-05\n",
"Epoch 44/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0926 - mae: 0.1483 - mse: 0.0735 - root_mean_squared_error: 0.2711 - smooth_mape: 15.9664 - val_loss: 0.0856 - val_mae: 0.1355 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4813 - lr: 8.4067e-05\n",
"Epoch 45/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.0904 - mae: 0.1477 - mse: 0.0732 - root_mean_squared_error: 0.2706 - smooth_mape: 15.9141 - val_loss: 0.0839 - val_mae: 0.1354 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2587 - val_smooth_mape: 14.4705 - lr: 7.0890e-05\n",
"Epoch 46/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0876 - mae: 0.1461 - mse: 0.0718 - root_mean_squared_error: 0.2680 - smooth_mape: 15.7518 - val_loss: 0.0825 - val_mae: 0.1373 - val_mse: 0.0668 - val_root_mean_squared_error: 0.2585 - val_smooth_mape: 14.4117 - lr: 5.5612e-05\n",
"Epoch 47/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0853 - mae: 0.1446 - mse: 0.0706 - root_mean_squared_error: 0.2658 - smooth_mape: 15.5602 - val_loss: 0.0805 - val_mae: 0.1370 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2566 - val_smooth_mape: 14.8418 - lr: 3.9768e-05\n",
"Epoch 48/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.0851 - mae: 0.1449 - mse: 0.0713 - root_mean_squared_error: 0.2671 - smooth_mape: 15.6229 - val_loss: 0.0801 - val_mae: 0.1363 - val_mse: 0.0660 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.7760 - lr: 2.4955e-05\n",
"Epoch 49/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0833 - mae: 0.1432 - mse: 0.0699 - root_mean_squared_error: 0.2643 - smooth_mape: 15.4598 - val_loss: 0.0791 - val_mae: 0.1348 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2557 - val_smooth_mape: 14.3369 - lr: 1.2661e-05\n",
"Epoch 50/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0819 - mae: 0.1421 - mse: 0.0687 - root_mean_squared_error: 0.2620 - smooth_mape: 15.3369 - val_loss: 0.0790 - val_mae: 0.1342 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.2211 - lr: 4.1248e-06\n",
"Epoch 51/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.3209 - val_loss: 0.0791 - val_mae: 0.1332 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.1215 - lr: 2.0452e-07\n",
"Epoch 52/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575\n",
"Epoch 52: ReduceLROnPlateau reducing learning rate to 2.589756149973255e-07.\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.0820 - mae: 0.1420 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3575 - val_loss: 0.0791 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.1793 - lr: 1.2949e-06\n",
"Epoch 53/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.0818 - mae: 0.1417 - mse: 0.0687 - root_mean_squared_error: 0.2621 - smooth_mape: 15.2994 - val_loss: 0.0790 - val_mae: 0.1349 - val_mse: 0.0655 - val_root_mean_squared_error: 0.2560 - val_smooth_mape: 14.1680 - lr: 7.2861e-06\n",
"Epoch 54/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0818 - mae: 0.1421 - mse: 0.0689 - root_mean_squared_error: 0.2625 - smooth_mape: 15.3289 - val_loss: 0.0786 - val_mae: 0.1360 - val_mse: 0.0654 - val_root_mean_squared_error: 0.2558 - val_smooth_mape: 14.6983 - lr: 1.7575e-05\n",
"Epoch 55/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0826 - mae: 0.1438 - mse: 0.0701 - root_mean_squared_error: 0.2648 - smooth_mape: 15.5292 - val_loss: 0.0784 - val_mae: 0.1367 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.7914 - lr: 3.1127e-05\n",
"Epoch 56/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452\n",
"Epoch 56: ReduceLROnPlateau reducing learning rate to 2.3289143427973617e-06.\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0820 - mae: 0.1433 - mse: 0.0700 - root_mean_squared_error: 0.2647 - smooth_mape: 15.4452 - val_loss: 0.0777 - val_mae: 0.1360 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.4766 - lr: 2.3289e-06\n",
"Epoch 57/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0823 - mae: 0.1451 - mse: 0.0711 - root_mean_squared_error: 0.2667 - smooth_mape: 15.6261 - val_loss: 0.0785 - val_mae: 0.1375 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2596 - val_smooth_mape: 15.0211 - lr: 6.2374e-05\n",
"Epoch 58/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.0818 - mae: 0.1454 - mse: 0.0715 - root_mean_squared_error: 0.2675 - smooth_mape: 15.6269 - val_loss: 0.0772 - val_mae: 0.1366 - val_mse: 0.0669 - val_root_mean_squared_error: 0.2586 - val_smooth_mape: 14.1091 - lr: 7.6924e-05\n",
"Epoch 59/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0817 - mae: 0.1461 - mse: 0.0723 - root_mean_squared_error: 0.2689 - smooth_mape: 15.7262 - val_loss: 0.0761 - val_mae: 0.1370 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2575 - val_smooth_mape: 14.8032 - lr: 8.8765e-05\n",
"Epoch 60/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620\n",
"Epoch 60: ReduceLROnPlateau reducing learning rate to 4.835262006963604e-06.\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.0805 - mae: 0.1461 - mse: 0.0719 - root_mean_squared_error: 0.2681 - smooth_mape: 15.7620 - val_loss: 0.0759 - val_mae: 0.1358 - val_mse: 0.0674 - val_root_mean_squared_error: 0.2597 - val_smooth_mape: 14.4084 - lr: 4.8353e-06\n",
"Epoch 61/100\n",
"2028/2028 [==============================] - 22s 11ms/step\n",
"2028/2028 [==============================] - 23s 11ms/step\n",
"\n",
"Epoch 61: Out of range predictions: 0\n",
"507/507 [==============================] - 75s 147ms/step - loss: 0.0787 - mae: 0.1447 - mse: 0.0711 - root_mean_squared_error: 0.2666 - smooth_mape: 15.5565 - val_loss: 0.0737 - val_mae: 0.1352 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3196 - lr: 9.9946e-05\n",
"Epoch 62/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0781 - mae: 0.1451 - mse: 0.0715 - root_mean_squared_error: 0.2674 - smooth_mape: 15.5903 - val_loss: 0.0726 - val_mae: 0.1344 - val_mse: 0.0658 - val_root_mean_squared_error: 0.2565 - val_smooth_mape: 14.3699 - lr: 9.8161e-05\n",
"Epoch 63/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0771 - mae: 0.1446 - mse: 0.0713 - root_mean_squared_error: 0.2670 - smooth_mape: 15.5551 - val_loss: 0.0721 - val_mae: 0.1350 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.3152 - lr: 9.1530e-05\n",
"Epoch 64/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5067\n",
"Epoch 64: ReduceLROnPlateau reducing learning rate to 4.035990059492178e-06.\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0757 - mae: 0.1441 - mse: 0.0705 - root_mean_squared_error: 0.2655 - smooth_mape: 15.5081 - val_loss: 0.0716 - val_mae: 0.1347 - val_mse: 0.0663 - val_root_mean_squared_error: 0.2574 - val_smooth_mape: 14.7350 - lr: 4.0360e-06\n",
"Epoch 65/100\n",
"507/507 [==============================] - 26s 50ms/step - loss: 0.0745 - mae: 0.1431 - mse: 0.0698 - root_mean_squared_error: 0.2642 - smooth_mape: 15.3922 - val_loss: 0.0710 - val_mae: 0.1360 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2571 - val_smooth_mape: 14.1923 - lr: 6.6819e-05\n",
"Epoch 66/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0729 - mae: 0.1413 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.1677 - val_loss: 0.0697 - val_mae: 0.1349 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2538 - lr: 5.1225e-05\n",
"Epoch 67/100\n",
"507/507 [==============================] - 28s 55ms/step - loss: 0.0722 - mae: 0.1408 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.0982 - val_loss: 0.0691 - val_mae: 0.1346 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2549 - val_smooth_mape: 14.2971 - lr: 3.5508e-05\n",
"Epoch 68/100\n",
"507/507 [==============================] - 25s 48ms/step - loss: 0.0707 - mae: 0.1389 - mse: 0.0669 - root_mean_squared_error: 0.2587 - smooth_mape: 14.9200 - val_loss: 0.0686 - val_mae: 0.1339 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 14.3511 - lr: 2.1250e-05\n",
"Epoch 69/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0705 - mae: 0.1388 - mse: 0.0669 - root_mean_squared_error: 0.2586 - smooth_mape: 14.9215 - val_loss: 0.0684 - val_mae: 0.1325 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2540 - val_smooth_mape: 14.0225 - lr: 9.8841e-06\n",
"Epoch 70/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0698 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2573 - smooth_mape: 14.8352 - val_loss: 0.0684 - val_mae: 0.1320 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9647 - lr: 2.5551e-06\n",
"Epoch 71/100\n",
"507/507 [==============================] - 25s 49ms/step - loss: 0.0695 - mae: 0.1374 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7516 - val_loss: 0.0685 - val_mae: 0.1317 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2545 - val_smooth_mape: 13.8579 - lr: 1.5795e-10\n",
"Epoch 72/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7707\n",
"Epoch 72: ReduceLROnPlateau reducing learning rate to 4.95273479828029e-07.\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0696 - mae: 0.1376 - mse: 0.0660 - root_mean_squared_error: 0.2569 - smooth_mape: 14.7708 - val_loss: 0.0684 - val_mae: 0.1318 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2542 - val_smooth_mape: 13.9681 - lr: 2.4764e-06\n",
"Epoch 73/100\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0694 - mae: 0.1373 - mse: 0.0658 - root_mean_squared_error: 0.2565 - smooth_mape: 14.7647 - val_loss: 0.0683 - val_mae: 0.1327 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 13.9221 - lr: 9.7346e-06\n",
"Epoch 74/100\n",
"507/507 [==============================] - 24s 47ms/step - loss: 0.0698 - mae: 0.1380 - mse: 0.0663 - root_mean_squared_error: 0.2575 - smooth_mape: 14.8505 - val_loss: 0.0683 - val_mae: 0.1332 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2543 - val_smooth_mape: 14.4362 - lr: 2.1044e-05\n",
"Epoch 75/100\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0697 - mae: 0.1381 - mse: 0.0664 - root_mean_squared_error: 0.2577 - smooth_mape: 14.8281 - val_loss: 0.0680 - val_mae: 0.1335 - val_mse: 0.0646 - val_root_mean_squared_error: 0.2541 - val_smooth_mape: 14.2706 - lr: 3.5268e-05\n",
"Epoch 76/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0952\n",
"Epoch 76: ReduceLROnPlateau reducing learning rate to 2.548691554693505e-06.\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0713 - mae: 0.1407 - mse: 0.0685 - root_mean_squared_error: 0.2616 - smooth_mape: 15.0982 - val_loss: 0.0680 - val_mae: 0.1339 - val_mse: 0.0648 - val_root_mean_squared_error: 0.2546 - val_smooth_mape: 14.0539 - lr: 2.5487e-06\n",
"Epoch 77/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0332\n",
"Epoch 77: ReduceLROnPlateau reducing learning rate to 1.3316345575731248e-05.\n",
"507/507 [==============================] - 25s 49ms/step - loss: 0.0706 - mae: 0.1401 - mse: 0.0679 - root_mean_squared_error: 0.2606 - smooth_mape: 15.0357 - val_loss: 0.0682 - val_mae: 0.1346 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2556 - val_smooth_mape: 14.3903 - lr: 6.6582e-05\n",
"Epoch 78/100\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0707 - mae: 0.1408 - mse: 0.0683 - root_mean_squared_error: 0.2614 - smooth_mape: 15.1099 - val_loss: 0.0680 - val_mae: 0.1327 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.0229 - lr: 8.0521e-05\n",
"Epoch 79/100\n",
"507/507 [==============================] - 24s 47ms/step - loss: 0.0710 - mae: 0.1417 - mse: 0.0691 - root_mean_squared_error: 0.2629 - smooth_mape: 15.2021 - val_loss: 0.0677 - val_mae: 0.1354 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2561 - val_smooth_mape: 14.2230 - lr: 9.1389e-05\n",
"Epoch 80/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279\n",
"Epoch 80: ReduceLROnPlateau reducing learning rate to 4.904638990410604e-06.\n",
"507/507 [==============================] - 27s 54ms/step - loss: 0.0726 - mae: 0.1444 - mse: 0.0712 - root_mean_squared_error: 0.2668 - smooth_mape: 15.5279 - val_loss: 0.0682 - val_mae: 0.1343 - val_mse: 0.0661 - val_root_mean_squared_error: 0.2570 - val_smooth_mape: 14.2839 - lr: 4.9046e-06\n",
"Epoch 81/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0713 - mae: 0.1438 - mse: 0.0699 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4713\n",
"Epoch 81: ReduceLROnPlateau reducing learning rate to 1.9991402223240587e-05.\n",
"2028/2028 [==============================] - 23s 11ms/step\n",
"2028/2028 [==============================] - 23s 11ms/step\n",
"\n",
"Epoch 81: Out of range predictions: 0\n",
"507/507 [==============================] - 75s 148ms/step - loss: 0.0713 - mae: 0.1438 - mse: 0.0700 - root_mean_squared_error: 0.2645 - smooth_mape: 15.4736 - val_loss: 0.0690 - val_mae: 0.1379 - val_mse: 0.0677 - val_root_mean_squared_error: 0.2602 - val_smooth_mape: 15.5488 - lr: 9.9957e-05\n",
"Epoch 82/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0696 - mae: 0.1419 - mse: 0.0686 - root_mean_squared_error: 0.2619 - smooth_mape: 15.2732 - val_loss: 0.0667 - val_mae: 0.1334 - val_mse: 0.0656 - val_root_mean_squared_error: 0.2562 - val_smooth_mape: 14.2230 - lr: 9.6794e-05\n",
"Epoch 83/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0688 - mae: 0.1409 - mse: 0.0682 - root_mean_squared_error: 0.2612 - smooth_mape: 15.1230 - val_loss: 0.0659 - val_mae: 0.1343 - val_mse: 0.0652 - val_root_mean_squared_error: 0.2553 - val_smooth_mape: 14.2547 - lr: 8.8923e-05\n",
"Epoch 84/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9698\n",
"Epoch 84: ReduceLROnPlateau reducing learning rate to 3.8567635783692825e-06.\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0674 - mae: 0.1393 - mse: 0.0670 - root_mean_squared_error: 0.2589 - smooth_mape: 14.9716 - val_loss: 0.0655 - val_mae: 0.1338 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2066 - lr: 3.8568e-06\n",
"Epoch 85/100\n",
"507/507 [==============================] - 26s 52ms/step - loss: 0.0673 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9972 - val_loss: 0.0680 - val_mae: 0.1333 - val_mse: 0.0682 - val_root_mean_squared_error: 0.2611 - val_smooth_mape: 13.8129 - lr: 6.2617e-05\n",
"Epoch 86/100\n",
"507/507 [==============================] - 25s 49ms/step - loss: 0.0669 - mae: 0.1389 - mse: 0.0670 - root_mean_squared_error: 0.2588 - smooth_mape: 14.9195 - val_loss: 0.0647 - val_mae: 0.1343 - val_mse: 0.0645 - val_root_mean_squared_error: 0.2539 - val_smooth_mape: 14.2430 - lr: 4.6829e-05\n",
"Epoch 87/100\n",
"507/507 [==============================] - 23s 46ms/step - loss: 0.0656 - mae: 0.1372 - mse: 0.0657 - root_mean_squared_error: 0.2563 - smooth_mape: 14.7366 - val_loss: 0.0643 - val_mae: 0.1317 - val_mse: 0.0644 - val_root_mean_squared_error: 0.2537 - val_smooth_mape: 14.1979 - lr: 3.1360e-05\n",
"Epoch 88/100\n",
"507/507 [==============================] - 22s 44ms/step - loss: 0.0649 - mae: 0.1365 - mse: 0.0651 - root_mean_squared_error: 0.2552 - smooth_mape: 14.6326 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2528 - val_smooth_mape: 14.3781 - lr: 1.7767e-05\n",
"Epoch 89/100\n",
"507/507 [==============================] - 23s 45ms/step - loss: 0.0647 - mae: 0.1361 - mse: 0.0649 - root_mean_squared_error: 0.2548 - smooth_mape: 14.6184 - val_loss: 0.0637 - val_mae: 0.1317 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 13.9675 - lr: 7.4173e-06\n",
"Epoch 90/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0642 - mae: 0.1356 - mse: 0.0644 - root_mean_squared_error: 0.2538 - smooth_mape: 14.5656 - val_loss: 0.0638 - val_mae: 0.1309 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8811 - lr: 1.3523e-06\n",
"Epoch 91/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922\n",
"Epoch 91: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
"507/507 [==============================] - 24s 47ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0637 - root_mean_squared_error: 0.2524 - smooth_mape: 14.4922 - val_loss: 0.0638 - val_mae: 0.1311 - val_mse: 0.0640 - val_root_mean_squared_error: 0.2530 - val_smooth_mape: 13.8034 - lr: 1.8244e-07\n",
"Epoch 92/100\n",
"507/507 [==============================] - 25s 49ms/step - loss: 0.0634 - mae: 0.1342 - mse: 0.0635 - root_mean_squared_error: 0.2520 - smooth_mape: 14.4498 - val_loss: 0.0637 - val_mae: 0.1312 - val_mse: 0.0639 - val_root_mean_squared_error: 0.2527 - val_smooth_mape: 13.8449 - lr: 4.0254e-06\n",
"Epoch 93/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0636 - mae: 0.1346 - mse: 0.0638 - root_mean_squared_error: 0.2526 - smooth_mape: 14.4781 - val_loss: 0.0636 - val_mae: 0.1318 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.0036 - lr: 1.2494e-05\n",
"Epoch 94/100\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0639 - mae: 0.1350 - mse: 0.0642 - root_mean_squared_error: 0.2534 - smooth_mape: 14.4772 - val_loss: 0.0635 - val_mae: 0.1324 - val_mse: 0.0638 - val_root_mean_squared_error: 0.2526 - val_smooth_mape: 14.1743 - lr: 2.4737e-05\n",
"Epoch 95/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348\n",
"Epoch 95: ReduceLROnPlateau reducing learning rate to 1.976121529878583e-06.\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0648 - mae: 0.1366 - mse: 0.0653 - root_mean_squared_error: 0.2556 - smooth_mape: 14.6348 - val_loss: 0.0638 - val_mae: 0.1331 - val_mse: 0.0643 - val_root_mean_squared_error: 0.2535 - val_smooth_mape: 14.4393 - lr: 1.9761e-06\n",
"Epoch 96/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371\n",
"Epoch 96: ReduceLROnPlateau reducing learning rate to 1.1072350753238425e-05.\n",
"507/507 [==============================] - 25s 50ms/step - loss: 0.0652 - mae: 0.1374 - mse: 0.0659 - root_mean_squared_error: 0.2567 - smooth_mape: 14.7371 - val_loss: 0.0644 - val_mae: 0.1353 - val_mse: 0.0650 - val_root_mean_squared_error: 0.2550 - val_smooth_mape: 14.2866 - lr: 5.5362e-05\n",
"Epoch 97/100\n",
"507/507 [==============================] - 27s 53ms/step - loss: 0.0660 - mae: 0.1386 - mse: 0.0668 - root_mean_squared_error: 0.2585 - smooth_mape: 14.8711 - val_loss: 0.0640 - val_mae: 0.1324 - val_mse: 0.0647 - val_root_mean_squared_error: 0.2544 - val_smooth_mape: 14.0692 - lr: 7.0662e-05\n",
"Epoch 98/100\n",
"507/507 [==============================] - ETA: 0s - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726\n",
"Epoch 98: ReduceLROnPlateau reducing learning rate to 1.6776460688561202e-05.\n",
"507/507 [==============================] - 26s 51ms/step - loss: 0.0653 - mae: 0.1380 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7726 - val_loss: 0.0652 - val_mae: 0.1359 - val_mse: 0.0662 - val_root_mean_squared_error: 0.2572 - val_smooth_mape: 14.0106 - lr: 8.3882e-05\n",
"Epoch 99/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9081\n",
"Epoch 99: ReduceLROnPlateau reducing learning rate to 4.684684972744436e-06.\n",
"507/507 [==============================] - 24s 47ms/step - loss: 0.0660 - mae: 0.1395 - mse: 0.0672 - root_mean_squared_error: 0.2593 - smooth_mape: 14.9098 - val_loss: 0.0642 - val_mae: 0.1338 - val_mse: 0.0653 - val_root_mean_squared_error: 0.2555 - val_smooth_mape: 14.2021 - lr: 4.6847e-06\n",
"Epoch 100/100\n",
"506/507 [============================>.] - ETA: 0s - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7397\n",
"Epoch 100: ReduceLROnPlateau reducing learning rate to 1.9821693422272803e-05.\n",
"507/507 [==============================] - 24s 46ms/step - loss: 0.0649 - mae: 0.1379 - mse: 0.0662 - root_mean_squared_error: 0.2574 - smooth_mape: 14.7407 - val_loss: 0.0652 - val_mae: 0.1356 - val_mse: 0.0667 - val_root_mean_squared_error: 0.2582 - val_smooth_mape: 13.8309 - lr: 9.9108e-05\n",
"\n",
"Training completed successfully!\n"
]
}
],
"source": [
"# Model creation or loading\n",
"print(\"\\n2. Model initialization...\")\n",
"input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n",
"\n",
"MAX_UVINDEX = 11\n",
"\n",
"max_val_scaled = target_scaler.transform([[MAX_UVINDEX]])[0][0]\n",
"\n",
"if os.path.exists(model_path):\n",
" print(f\"Loading existing model from: {model_path}\")\n",
" model = tf.keras.models.load_model(model_path)\n",
"\n",
" # Load existing history if available\n",
" if os.path.exists(history_path):\n",
" print(f\"Loading existing training history from: {history_path}\")\n",
" with open(history_path, 'r') as f:\n",
" history_dict = json.load(f)\n",
" history = type('History', (), {'history': history_dict})()\n",
" else:\n",
" history = type('History', (), {'history': {}})()\n",
"else:\n",
" print(\"Creating new model...\")\n",
" model = create_uv_index_model(input_shape=input_shape, folder_name=folder_name, max_output=max_val_scaled)\n",
"\n",
" print(\"\\n3. Starting training...\")\n",
" history = train_hybrid_model(\n",
" model=model,\n",
" X_train=X_train_seq,\n",
" y_train=y_train,\n",
" X_test=X_test_seq,\n",
" y_test=y_test,\n",
" epochs=100,\n",
" batch_size=128,\n",
" folder_name=folder_name\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f0059ecf-7f4f-496f-bed8-85e2d990ff71",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"4. Generating predictions...\n",
"2028/2028 [==============================] - 18s 9ms/step\n",
"\n",
"5. Model evaluation...\n",
"\n",
"Error saving plots: Unknown format code 'd' for object of type 'float'\n",
"\n",
"UV Index Prediction Analysis:\n",
"\n",
"Raw Metrics:\n",
"mae: 0.407\n",
"rmse: 0.775\n",
"r2: 0.918\n",
"mean_error: -0.076\n",
"std_error: 0.771\n",
"median_error: 0.012\n",
"p95_abs_error: 1.745\n",
"within_05: 71.379\n",
"within_1: 86.040\n",
"within_15: 92.984\n",
"within_2: 96.562\n",
"\n",
"Rounded Metrics:\n",
"mae: 0.393\n",
"rmse: 0.782\n",
"r2: 0.916\n",
"within_05: 78.975\n",
"within_1: 90.160\n",
"within_15: 95.037\n",
"within_2: 97.478\n",
"\n",
"Analysis by UV Range:\n",
"\n",
"Low:\n",
" mae: 0.133\n",
" count: 41407.000\n",
" accuracy_within_05: 90.828\n",
" accuracy_within_1: 97.341\n",
"\n",
"Moderate:\n",
" mae: 0.874\n",
" count: 11467.000\n",
" accuracy_within_05: 36.418\n",
" accuracy_within_1: 66.713\n",
"\n",
"High:\n",
" mae: 0.871\n",
" count: 5415.000\n",
" accuracy_within_05: 37.876\n",
" accuracy_within_1: 65.614\n",
"\n",
"Very High:\n",
" mae: 0.905\n",
" count: 6343.000\n",
" accuracy_within_05: 38.862\n",
" accuracy_within_1: 66.404\n",
"\n",
"Extreme:\n",
" mae: 1.649\n",
" count: 234.000\n",
" accuracy_within_05: 0.000\n",
" accuracy_within_1: 38.462\n",
"\n",
"Confusion Matrix:\n",
" Low Moderate High Very High Extreme\n",
"Low 43040.0 2283.0 97.0 17.0 0.0\n",
"Moderate 1336.0 7625.0 1181.0 107.0 0.0\n",
"High 10.0 1155.0 3149.0 576.0 0.0\n",
"Very High 0.0 114.0 1110.0 3066.0 0.0\n",
"Extreme 0.0 0.0 0.0 0.0 0.0\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 2000x1500 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"\\n4. Generating predictions...\")\n",
"predictions = model.predict(X_test_seq)\n",
"predictions = np.clip(predictions, 0, max_val_scaled)\n",
"\n",
"predictions_original = target_scaler.inverse_transform(predictions.reshape(-1, 1))\n",
"y_test_original = target_scaler.inverse_transform(y_test.reshape(-1, 1))\n",
"\n",
"print(\"\\n5. Model evaluation...\")\n",
"metrics = evaluate_uv_predictions(y_test_original, predictions_original, folder_name=folder_name)\n",
"\n",
"# Save training results only if new training was performed\n",
"if not os.path.exists(model_path):\n",
" training_results = {\n",
" 'model_params': {\n",
" 'input_shape': input_shape,\n",
" 'n_features': len(features),\n",
" 'sequence_length': X_train_seq.shape[1]\n",
" },\n",
" 'training_params': {\n",
" 'batch_size': 128,\n",
" 'total_epochs': len(history.history['loss']),\n",
" 'best_epoch': np.argmin(history.history['val_loss']) + 1,\n",
" },\n",
" 'performance_metrics': {\n",
" 'final_loss': float(history.history['val_loss'][-1]),\n",
" 'final_mae': float(history.history['val_mae'][-1]),\n",
" 'best_val_loss': float(min(history.history['val_loss'])),\n",
" 'out_of_range_predictions': int(np.sum((predictions < 0) | (predictions > 11)))\n",
" }\n",
" }\n",
"\n",
" # Save training history\n",
" with open(history_path, 'w') as f:\n",
" history_dict = {key: [float(val) for val in values]\n",
" for key, values in history.history.items()}\n",
" json.dump(history_dict, f, indent=4)\n",
"else:\n",
" # Load existing training results if available\n",
" results_path = f'{folder_name}_training_results.json'\n",
" if os.path.exists(results_path):\n",
" with open(results_path, 'r') as f:\n",
" training_results = json.load(f)\n",
" else:\n",
" training_results = {}\n",
"\n",
"tf.keras.backend.clear_session()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4365d2bf-daf8-49e1-be13-cce222bbb5bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"6. Predicting missing data...\n",
"7122/7122 [==============================] - 74s 10ms/step\n",
"\n",
"7. Integrating predictions into dataset...\n",
"Added 227879 predictions to dataset\n",
"Rows with UV index after integration: 357615\n",
"Updated dataset saved to: ../../sources/weather_data_uvindex.parquet\n",
"\n",
"All files saved with prefix: 2024-11-21_08-23\n"
]
}
],
"source": [
"print(\"\\n6. Predicting missing data...\")\n",
"to_predict_predictions = model.predict(X_to_predict_seq)\n",
"to_predict_predictions = np.clip(to_predict_predictions, 0, max_val_scaled)\n",
"\n",
"to_predict_predictions_original = target_scaler.inverse_transform(to_predict_predictions.reshape(-1, 1))\n",
"\n",
"print(\"\\n7. Integrating predictions into dataset...\")\n",
"df_updated = integrate_predictions(df.copy(), to_predict_predictions_original)\n",
"\n",
"output_path = f'../../sources/weather_data_uvindex.parquet'\n",
"df_updated.to_parquet(output_path)\n",
"print(f\"Updated dataset saved to: {output_path}\")\n",
"\n",
"# Add prediction statistics\n",
"prediction_stats = {\n",
" 'n_predictions_added': len(to_predict_predictions),\n",
" 'mean_predicted_uv': float(to_predict_predictions.mean()),\n",
" 'min_predicted_uv': float(to_predict_predictions.min()),\n",
" 'max_predicted_uv': float(to_predict_predictions.max()),\n",
"}\n",
"\n",
"\n",
"def convert_to_serializable(obj):\n",
" \"\"\"Convert numpy types to Python standard types for JSON serialization\"\"\"\n",
" if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,\n",
" np.int16, np.int32, np.int64, np.uint8,\n",
" np.uint16, np.uint32, np.uint64)):\n",
" return int(obj)\n",
" elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):\n",
" return float(obj)\n",
" elif isinstance(obj, (np.ndarray,)):\n",
" return obj.tolist()\n",
" elif isinstance(obj, dict):\n",
" return {key: convert_to_serializable(value) for key, value in obj.items()}\n",
" elif isinstance(obj, list):\n",
" return [convert_to_serializable(item) for item in obj]\n",
" return obj\n",
"\n",
"\n",
"if not os.path.exists(model_path):\n",
" training_results['prediction_stats'] = prediction_stats\n",
"\n",
" training_results = convert_to_serializable(training_results)\n",
" # Save final results\n",
" results_path = f'{folder_name}_training_results.json'\n",
" with open(results_path, 'w') as f:\n",
" json.dump(training_results, f, indent=4)\n",
"\n",
"print(f\"\\nAll files saved with prefix: {folder_name}\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "08fd4208-0afb-4bf1-bdef-b10b4065fe55",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Plot saved as: 2024-11-21_08-23/error_analysis.png\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1500x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Error statistics:\n",
"MAE: 0.1356\n",
"MSE: 0.0667\n",
"RMSE: 0.2582\n",
"Mean errors: -0.0252\n",
"Std errors: 0.2569\n",
"Predictions within ±0.5: 93.0%\n",
"Predictions within ±1.0: 99.0%\n",
"Predictions within ±1.5: 99.9%\n",
"Predictions within ±2.0: 100.0%\n"
]
}
],
"source": [
"def plot_error_analysis(y_true, y_pred, folder_name=None):\n",
" \"\"\"\n",
" Function to visualize prediction error analysis\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual values\n",
" y_pred : array-like\n",
" Predicted values\n",
" folder_name : str, optional\n",
" Folder to save plots. If None, plots are not saved.\n",
" \"\"\"\n",
"\n",
" # Convert to 1D numpy array if necessary\n",
" if isinstance(y_true, pd.Series):\n",
" y_true = y_true.values\n",
" if isinstance(y_pred, pd.Series):\n",
" y_pred = y_pred.values\n",
"\n",
" y_true = y_true.ravel()\n",
" y_pred = y_pred.ravel()\n",
"\n",
" # Calculate errors\n",
" errors = y_pred - y_true\n",
"\n",
" # Create main figure\n",
" fig = plt.figure(figsize=(15, 5))\n",
"\n",
" # Plot 1: Error Distribution\n",
" plt.subplot(1, 3, 1)\n",
" plt.hist(errors, bins=50, alpha=0.7)\n",
" plt.title('Prediction Error Distribution')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 2: Actual vs Predicted\n",
" plt.subplot(1, 3, 2)\n",
" plt.scatter(y_true, y_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 3: Errors vs Actual Values\n",
" plt.subplot(1, 3, 3)\n",
" plt.scatter(y_true, errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Errors vs Actual Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" plt.tight_layout()\n",
"\n",
" # Save plot if folder is specified\n",
" if folder_name is not None:\n",
" try:\n",
" # Create folder if it doesn't exist\n",
" os.makedirs(folder_name, exist_ok=True)\n",
"\n",
" # Generate filename with timestamp\n",
" filename = os.path.join(folder_name, 'error_analysis.png')\n",
"\n",
" # Save figure\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" except Exception as e:\n",
" print(f\"\\nError saving plot: {str(e)}\")\n",
"\n",
" plt.show()\n",
"\n",
" # Print error statistics\n",
" print(\"\\nError statistics:\")\n",
" print(f\"MAE: {np.mean(np.abs(errors)):.4f}\")\n",
" print(f\"MSE: {np.mean(errors ** 2):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(errors ** 2)):.4f}\")\n",
" print(f\"Mean errors: {np.mean(errors):.4f}\")\n",
" print(f\"Std errors: {np.std(errors):.4f}\")\n",
"\n",
" # Calculate percentage of errors within thresholds\n",
" thresholds = [0.5, 1.0, 1.5, 2.0]\n",
" for threshold in thresholds:\n",
" within_threshold = np.mean(np.abs(errors) <= threshold) * 100\n",
" print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n",
"\n",
"\n",
"plot_error_analysis(y_test, predictions, folder_name=folder_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c57d6b2-00a6-4d31-935e-449a29dafd79",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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