giuseppenuc/olive-oil-transformer-model
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
olive-oil-transformer-model/models/solarradiation/.ipynb_checkpoints/solarradiation_model-checkpoint.ipynb

3024 lines
993 KiB
Plaintext
Raw Blame History

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8adcbe0819b88578",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
"Hit:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease \n",
"Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n",
"Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease \n",
"Hit:5 http://security.ubuntu.com/ubuntu jammy-security InRelease\n",
"Reading package lists... Done\n",
"Reading package lists... Done\n",
"Building dependency tree... Done\n",
"Reading state information... Done\n",
"graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 121 not upgraded.\n",
"Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
"Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.0.0)\n",
"Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.6.3)\n",
"Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.5.26)\n",
"Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.5.4)\n",
"Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
"Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.9.0)\n",
"Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (16.0.6)\n",
"Requirement already satisfied: ml-dtypes==0.2.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.2.0)\n",
"Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.26.0)\n",
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (3.3.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow) (23.1)\n",
"Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.24.3)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from tensorflow) (68.2.2)\n",
"Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)\n",
"Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.3.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (4.8.0)\n",
"Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.14.1)\n",
"Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (0.37.1)\n",
"Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.58.0)\n",
"Requirement already satisfied: tensorboard<2.15,>=2.14 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
"Requirement already satisfied: tensorflow-estimator<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
"Requirement already satisfied: keras<2.15,>=2.14.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (2.14.0)\n",
"Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from astunparse>=1.6.0->tensorflow) (0.41.2)\n",
"Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.23.1)\n",
"Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (1.0.0)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (3.4.4)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.31.0)\n",
"Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (0.7.1)\n",
"Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from tensorboard<2.15,>=2.14->tensorflow) (2.3.7)\n",
"Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (5.3.1)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.3.0)\n",
"Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (4.9)\n",
"Requirement already satisfied: urllib3>=2.0.5 in /usr/local/lib/python3.11/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (2.0.5)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.11/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (1.3.1)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (3.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow) (2023.7.22)\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from werkzeug>=1.0.1->tensorboard<2.15,>=2.14->tensorflow) (2.1.3)\n",
"Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.15,>=2.14->tensorflow) (0.5.0)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/lib/python3/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow) (3.2.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (1.26.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.3)\n",
"Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (1.26.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2024.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: keras in /usr/local/lib/python3.11/dist-packages (2.14.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.5.2)\n",
"Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.0)\n",
"Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.14.1)\n",
"Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (3.8.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.1.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (0.11.0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (4.42.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.4.5)\n",
"Requirement already satisfied: numpy<2,>=1.21 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (1.26.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (23.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (10.0.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (3.2.0)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (1.4.2)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: fastparquet in /usr/local/lib/python3.11/dist-packages (2024.11.0)\n",
"Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.3)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from fastparquet) (1.26.0)\n",
"Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.0)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (23.1)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2024.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.16.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (1.14.1)\n",
"Requirement already satisfied: numpy<2.3,>=1.23.5 in /usr/local/lib/python3.11/dist-packages (from scipy) (1.26.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (0.13.2)\n",
"Requirement already satisfied: numpy!=1.24.0,>=1.20 in /usr/local/lib/python3.11/dist-packages (from seaborn) (1.26.0)\n",
"Requirement already satisfied: pandas>=1.2 in /usr/local/lib/python3.11/dist-packages (from seaborn) (2.2.3)\n",
"Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /usr/local/lib/python3.11/dist-packages (from seaborn) (3.8.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.1.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.11.0)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.42.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.0.1)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.0)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2->seaborn) (2024.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: pydot in /usr/local/lib/python3.11/dist-packages (3.0.2)\n",
"Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.11/dist-packages (from pydot) (3.2.0)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tensorflow-io in /usr/local/lib/python3.11/dist-packages (0.37.1)\n",
"Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.11/dist-packages (from tensorflow-io) (0.37.1)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n",
"Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.11/dist-packages (0.23.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (23.1)\n",
"Requirement already satisfied: typeguard<3.0.0,>=2.7 in /usr/local/lib/python3.11/dist-packages (from tensorflow-addons) (2.13.3)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"from opt_einsum.paths import branch_1\n",
"!apt-get update\n",
"!apt-get install graphviz -y\n",
"\n",
"!pip install tensorflow\n",
"!pip install numpy\n",
"!pip install pandas\n",
"\n",
"!pip install keras\n",
"!pip install scikit-learn\n",
"!pip install matplotlib\n",
"!pip install joblib\n",
"!pip install pyarrow\n",
"!pip install fastparquet\n",
"!pip install scipy\n",
"!pip install seaborn\n",
"!pip install tqdm\n",
"!pip install pydot\n",
"!pip install tensorflow-io\n",
"!pip install tensorflow-addons"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e6fe6bb613168a8a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-26 05:41:43.497052: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-11-26 05:41:43.497104: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-11-26 05:41:43.497156: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-11-26 05:41:43.506575: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
"\n",
"TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
"TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
"Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
"\n",
"For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
"\n",
" warnings.warn(\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.layers import Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, LayerNormalization, Input, Activation, Lambda, Bidirectional, Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D, \\\n",
" GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.models import Model\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import RobustScaler\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
"from tensorflow.keras.optimizers import AdamW\n",
"import json\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"from tensorflow.keras.utils import plot_model\n",
"import tensorflow_addons as tfa\n",
"import os\n",
"import joblib\n",
"import seaborn as sns\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, confusion_matrix, classification_report, roc_auc_score\n",
"from tensorflow.keras.metrics import AUC\n",
"from scipy import stats\n",
"\n",
"folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n",
"\n",
"random_state_value = None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3da8b15c7eb9833f",
"metadata": {},
"outputs": [],
"source": [
"def get_season(date):\n",
" month = date.month\n",
" day = date.day\n",
" if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n",
" return 'Winter'\n",
" elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n",
" return 'Spring'\n",
" elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n",
" return 'Summer'\n",
" elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n",
" return 'Autumn'\n",
" else:\n",
" return 'Unknown'\n",
"\n",
"\n",
"def get_time_period(hour):\n",
" if 5 <= hour < 12:\n",
" return 'Morning'\n",
" elif 12 <= hour < 17:\n",
" return 'Afternoon'\n",
" elif 17 <= hour < 21:\n",
" return 'Evening'\n",
" else:\n",
" return 'Night'\n",
"\n",
"\n",
"def add_time_features(df):\n",
" \"\"\"\n",
" Add time-based features to the DataFrame.\n",
" Works with both 'datetime' as column or index.\n",
" \"\"\"\n",
" # Se datetime è l'indice, lo usiamo direttamente\n",
" if isinstance(df.index, pd.DatetimeIndex):\n",
" datetime_col = df.index\n",
" else:\n",
" # Se datetime è una colonna, la convertiamo\n",
" if 'datetime' in df.columns:\n",
" datetime_col = pd.to_datetime(df['datetime'])\n",
" else:\n",
" raise ValueError(\"No datetime column or index found in DataFrame\")\n",
"\n",
" # Creazione delle feature temporali\n",
" df['timestamp'] = datetime_col.astype(np.int64) // 10 ** 9\n",
" df['year'] = datetime_col.year\n",
" df['month'] = datetime_col.month\n",
" df['day'] = datetime_col.day\n",
" df['hour'] = datetime_col.hour\n",
" df['minute'] = datetime_col.minute\n",
" df['hour_sin'] = np.sin(datetime_col.hour * (2 * np.pi / 24))\n",
" df['hour_cos'] = np.cos(datetime_col.hour * (2 * np.pi / 24))\n",
" df['day_of_week'] = datetime_col.dayofweek\n",
" df['day_of_year'] = datetime_col.dayofyear\n",
" df['week_of_year'] = datetime_col.isocalendar().week.astype(int)\n",
" df['quarter'] = datetime_col.quarter\n",
" df['is_month_end'] = datetime_col.is_month_end.astype(int)\n",
" df['is_quarter_end'] = datetime_col.is_quarter_end.astype(int)\n",
" df['is_year_end'] = datetime_col.is_year_end.astype(int)\n",
" df['month_sin'] = np.sin(datetime_col.month * (2 * np.pi / 12))\n",
" df['month_cos'] = np.cos(datetime_col.month * (2 * np.pi / 12))\n",
" df['day_of_year_sin'] = np.sin(datetime_col.dayofyear * (2 * np.pi / 365.25))\n",
" df['day_of_year_cos'] = np.cos(datetime_col.dayofyear * (2 * np.pi / 365.25))\n",
" df['season'] = datetime_col.map(get_season)\n",
" df['time_period'] = datetime_col.hour.map(get_time_period)\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_solar_features(df):\n",
" # Solar angle calculation\n",
" df['solar_angle'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n",
"\n",
" # Interactions between relevant features\n",
" df['cloud_temp_interaction'] = df['cloudcover'] * df['temp']\n",
" df['visibility_cloud_interaction'] = df['visibility'] * (100 - df['cloudcover'])\n",
"\n",
" # Derived features\n",
" df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n",
" df['temp_gradient'] = df['temp'] - df['tempmin']\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_solar_specific_features(df):\n",
" \"\"\"\n",
" Aggiunge feature specifiche per la predizione della radiazione solare\n",
" combinando caratteristiche astronomiche e meteorologiche\n",
" \"\"\"\n",
" # Caratteristiche astronomiche\n",
" df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n",
" df['solar_noon'] = 12 - df['hour']\n",
" df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n",
"\n",
" # Angolo solare teorico\n",
" df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n",
"\n",
" # Interazioni con condizioni atmosferiche\n",
" df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n",
" df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n",
" df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n",
"\n",
" # Indici di chiarezza e trasmissione\n",
" df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n",
" df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n",
"\n",
" # Radiazione teorica e attenuazione\n",
" df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n",
" df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n",
"\n",
" # Rolling features\n",
" df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n",
" df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n",
" df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n",
"\n",
" # Interazioni temperatura-radiazione\n",
" df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_radiation_energy_features(df):\n",
" \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n",
"\n",
" # Assicuriamoci che l'indice sia di tipo datetime\n",
" if not isinstance(df.index, pd.DatetimeIndex):\n",
" df.index = pd.to_datetime(df['datetime'])\n",
"\n",
" # Solar energy to UV ratio (independent from solarradiation)\n",
" df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n",
"\n",
" # Time aggregations\n",
" # Moving averages\n",
" windows = [3, 6, 12, 24] # hours\n",
" for w in windows:\n",
" df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n",
" df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n",
"\n",
" # Daily aggregations utilizzando datetime\n",
" df['energy_daily_sum'] = df.groupby(df.index.date)['solarenergy'].transform('sum')\n",
" df['uv_daily_max'] = df.groupby(df.index.date)['uvindex'].transform('max')\n",
"\n",
" # Changes\n",
" df['energy_change'] = df['solarenergy'].diff()\n",
" df['uv_change'] = df['uvindex'].diff()\n",
"\n",
" # Lag features\n",
" lags = [1, 2, 3, 6, 12, 24] # hours\n",
" for lag in lags:\n",
" df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n",
" df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n",
"\n",
" # Peak indicators\n",
" df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n",
" df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n",
"\n",
" # Aggiungiamo alcune metriche di volatilità\n",
" df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n",
" df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n",
"\n",
" # Indice di intensità solare composito\n",
" df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n",
"\n",
" # Interazioni\n",
" df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n",
" df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n",
"\n",
" return df\n",
"\n",
"\n",
"def add_advanced_features(df):\n",
" \"\"\"\n",
" Add all advanced features to the DataFrame\n",
" Assumes df has a DatetimeIndex\n",
" \"\"\"\n",
" # Verifichiamo che abbiamo un DatetimeIndex\n",
" if not isinstance(df.index, pd.DatetimeIndex):\n",
" raise ValueError(\"DataFrame must have a DatetimeIndex\")\n",
"\n",
" # Existing features\n",
" df = add_time_features(df)\n",
" df = add_solar_features(df)\n",
" df = add_solar_specific_features(df)\n",
" df = add_radiation_energy_features(df)\n",
"\n",
" # Weather variable interactions\n",
" df['temp_humidity'] = df['temp'] * df['humidity']\n",
" df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n",
" df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n",
"\n",
" # Derived features\n",
" df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n",
" df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n",
" df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n",
"\n",
" # Rolling means\n",
" df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n",
" df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n",
"\n",
" # Lag features\n",
" df['temp_1h_lag'] = df['temp'].shift(1)\n",
" df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n",
" df['humidity_1h_lag'] = df['humidity'].shift(1)\n",
"\n",
" # Extreme conditions indicator\n",
" df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n",
" (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n",
"\n",
" # One-hot encoding for categorical features\n",
" df = pd.get_dummies(df, columns=['season', 'time_period'])\n",
"\n",
" return df\n",
"\n",
"\n",
"def prepare_advanced_data(df):\n",
" \"\"\"\n",
" Prepare data for advanced modeling with proper datetime handling\n",
" \"\"\"\n",
" # Assicuriamoci che abbiamo una copia del DataFrame\n",
" df = df.copy()\n",
"\n",
" # Verifichiamo se datetime è già l'indice\n",
" if not isinstance(df.index, pd.DatetimeIndex):\n",
" if 'datetime' in df.columns:\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
" df.set_index('datetime', inplace=True)\n",
" else:\n",
" raise ValueError(\"No datetime column or index found in DataFrame\")\n",
"\n",
" # Ordiniamo il DataFrame per datetime\n",
" df = df.sort_index()\n",
"\n",
" # Apply feature engineering functions\n",
" df = add_advanced_features(df)\n",
"\n",
" #all_columns = list(df.columns)\n",
" #print(all_columns)\n",
"\n",
" features = {\n",
" # Primary Features (strong direct correlation)\n",
" 'primary_features': [\n",
" 'uvindex', # Direct radiation indicator\n",
" 'cloudcover', # Cloud coverage\n",
" 'visibility', # Atmospheric transparency\n",
" 'temp', # Temperature\n",
" 'pressure', # Atmospheric pressure\n",
" 'humidity', # Humidity\n",
" ],\n",
"\n",
" # Astronomical and Temporal Features\n",
" 'astronomical_features': [\n",
" 'solar_elevation', # Solar elevation\n",
" 'solar_angle', # Solar angle\n",
" 'day_length', # Day length\n",
" 'hour_sin', # Daily cycle\n",
" 'hour_cos',\n",
" 'day_of_year_sin', # Annual cycle\n",
" 'day_of_year_cos',\n",
" 'month_sin', # Monthly cycle\n",
" 'month_cos',\n",
" ],\n",
"\n",
" # Key Indices and Interactions\n",
" 'key_interactions': [\n",
" 'clear_sky_index', # Clear sky index\n",
" 'atmospheric_attenuation', # Atmospheric attenuation\n",
" 'theoretical_radiation', # Theoretical radiation\n",
" 'expected_radiation', # Expected radiation\n",
" 'cloud_elevation', # Cloud-elevation interaction\n",
" 'visibility_elevation', # Visibility-elevation interaction\n",
" 'uv_cloud_interaction', # UV-cloud interaction\n",
" 'temp_radiation_potential', # Temperature-radiation potential\n",
" ],\n",
"\n",
" # Rolling Features (temporal trends)\n",
" 'rolling_features': [\n",
" 'cloud_rolling_12h', # Cloud coverage moving average\n",
" 'temp_rolling_12h', # Temperature moving average\n",
" 'uv_rolling_12h', # UV moving average\n",
" 'cloudcover_rolling_mean_6h',\n",
" 'temp_rolling_mean_6h',\n",
" ],\n",
"\n",
" # Lag Features (most recent)\n",
" 'lag_features': [\n",
" 'temp_1h_lag', # 1-hour temperature lag\n",
" 'cloudcover_1h_lag', # 1-hour cloud coverage lag\n",
" 'humidity_1h_lag', # 1-hour humidity lag\n",
" 'uv_lag_1h', # 1-hour UV lag\n",
" ],\n",
"\n",
" # Categorical Features\n",
" 'categorical_features': [\n",
" 'season_Spring', # Seasons\n",
" 'season_Summer',\n",
" 'season_Autumn',\n",
" 'season_Winter',\n",
" 'time_period_Morning', # Time periods\n",
" 'time_period_Afternoon',\n",
" 'time_period_Evening',\n",
" 'time_period_Night',\n",
" ]\n",
" }\n",
"\n",
" final_features = [feature for group in features.values() for feature in group]\n",
"\n",
" # Handle missing values\n",
" target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n",
" for column in final_features + target_variables:\n",
" if column in df.columns:\n",
" df[column] = df[column].interpolate(method='time')\n",
"\n",
" df.fillna(0, inplace=True)\n",
"\n",
" # Temporal split\n",
" data_after_2010 = df[df['year'] >= 2010].copy()\n",
" data_before_2010 = df[df['year'] < 2010].copy()\n",
"\n",
" X = data_after_2010[final_features]\n",
" y = data_after_2010['solarradiation']\n",
" X_to_predict = data_before_2010[final_features]\n",
"\n",
" # Train-test split\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=random_state_value, shuffle=False\n",
" )\n",
"\n",
" # Scaling\n",
" scaler_X = RobustScaler()\n",
" X_train_scaled = scaler_X.fit_transform(X_train)\n",
" X_test_scaled = scaler_X.transform(X_test)\n",
" X_to_predict_scaled = scaler_X.transform(X_to_predict)\n",
"\n",
" scaler_y = RobustScaler()\n",
" y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n",
" y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n",
"\n",
" # Print info about selected features\n",
" print(\"\\nSelected features:\")\n",
" print(f\"Number of features: {len(final_features)}\")\n",
" print(\"Features list:\", final_features)\n",
"\n",
" return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n",
"\n",
"\n",
"def create_sequence_data(X, sequence_length=24):\n",
" \"\"\"\n",
" Converts data into sequences for LSTM input\n",
" sequence_length represents how many previous hours to consider\n",
" \"\"\"\n",
" sequences = []\n",
" for i in range(len(X) - sequence_length + 1):\n",
" sequences.append(X[i:i + sequence_length])\n",
" return np.array(sequences)\n",
"\n",
"\n",
"def prepare_hybrid_data(df):\n",
" X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n",
"\n",
" # Convert data into sequences\n",
" sequence_length = 24 # 24 hours of historical data\n",
"\n",
" X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n",
" X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n",
"\n",
" # Adjust y by removing the first (sequence_length-1) elements\n",
" y_train = y_train_scaled[sequence_length - 1:]\n",
" y_test = y_test_scaled[sequence_length - 1:]\n",
"\n",
" X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n",
"\n",
" return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "570b18f2caa3e0db",
"metadata": {},
"outputs": [],
"source": [
"def create_residual_lstm_layer(x, units, dropout_rate, l2_reg=0.01, return_sequences=True, survival_probability=0.8):\n",
" \"\"\"\n",
" Creates a bidirectional LSTM layer with residual connections and regularization.\n",
"\n",
" Parameters:\n",
" x: Input tensor\n",
" units: Number of LSTM units\n",
" dropout_rate: Dropout rate for regularization\n",
" l2_reg: L2 regularization factor\n",
" return_sequences: Whether to return sequences or just the last output\n",
" survival_probability: Probability of layer survival for stochastic depth\n",
" \"\"\"\n",
" residual = x\n",
" x = Bidirectional(LSTM(units, return_sequences=return_sequences, kernel_regularizer=regularizers.l2(l2_reg)))(x)\n",
" x = LayerNormalization()(x)\n",
" x = Dropout(dropout_rate)(x)\n",
"\n",
" if return_sequences:\n",
" if int(residual.shape[-1]) != 2 * units:\n",
" residual = Dense(2 * units, activation='linear')(residual)\n",
" x = tfa.layers.StochasticDepth(survival_probability)([x, residual])\n",
" return x\n",
"\n",
"\n",
"def attention_block(x, units, num_heads=8, survival_probability=0.8):\n",
" \"\"\"\n",
" Creates a multi-head attention block with residual connections.\n",
"\n",
" Parameters:\n",
" x: Input tensor\n",
" units: Dimension of the key space\n",
" num_heads: Number of attention heads\n",
" survival_probability: Probability of layer survival for stochastic depth\n",
" \"\"\"\n",
" attention = MultiHeadAttention(num_heads=num_heads, key_dim=units)(x, x)\n",
" x = tfa.layers.StochasticDepth(survival_probability)([x, attention])\n",
" x = LayerNormalization()(x)\n",
" return x\n",
"\n",
"\n",
"def asymmetric_loss(y_true, y_pred):\n",
" \"\"\"\n",
" Loss function che penalizza maggiormente la sottostima dei valori alti\n",
" \"\"\"\n",
" diff = y_true - y_pred\n",
" abs_diff = tf.abs(diff)\n",
"\n",
" # Calcola il peso basato sul valore reale\n",
" value_weight = tf.exp(y_true / tf.reduce_max(y_true)) - 1\n",
"\n",
" # Penalizza maggiormente la sottostima (quando y_pred < y_true)\n",
" underestimation_penalty = tf.where(diff > 0, 2.0, 1.0)\n",
"\n",
" # Combina i pesi\n",
" total_weight = value_weight * underestimation_penalty\n",
"\n",
" # Calcola la loss pesata\n",
" weighted_loss = total_weight * abs_diff\n",
"\n",
" return tf.reduce_mean(weighted_loss)\n",
"\n",
"def add_peak_features(x):\n",
" \"\"\"\n",
" Aggiunge feature specifiche per identificare potenziali picchi\n",
" \"\"\"\n",
" # Moving average delle ultime n osservazioni\n",
" ma = tf.keras.layers.Conv1D(1, kernel_size=5, padding='same')(x)\n",
"\n",
" # Differenza dal moving average (identifica anomalie)\n",
" diff_ma = Lambda(lambda x: x[0] - x[1])([x, ma])\n",
"\n",
" # Rate of change\n",
" roc = Lambda(lambda x: x[:, 1:] - x[:, :-1])(x)\n",
" roc = tf.pad(roc, [[0, 0], [1, 0], [0, 0]])\n",
"\n",
" # Concatena tutte le feature\n",
" enhanced_x = Concatenate()([x, diff_ma, roc])\n",
"\n",
" return enhanced_x\n",
"\n",
"def create_regression_branch(shared_features, l2_lambda=0.005, name_suffix=''):\n",
" \"\"\"\n",
" Branch di regressione migliorato per valori alti\n",
" \"\"\"\n",
" # Branch principale\n",
" main_branch = shared_features\n",
" dense_units = [512, 256, 128, 64] # Unità aumentate\n",
"\n",
" for units in dense_units:\n",
" main_branch = Dense(\n",
" units,\n",
" kernel_regularizer=regularizers.l2(l2_lambda)\n",
" )(main_branch)\n",
" main_branch = BatchNormalization()(main_branch)\n",
" main_branch = Activation('swish')(main_branch)\n",
"\n",
" # Branch specializzato per valori alti\n",
" high_values_branch = shared_features\n",
" for units in [256, 128, 64]:\n",
" high_values_branch = Dense(\n",
" units,\n",
" kernel_regularizer=regularizers.l2(l2_lambda),\n",
" activation='relu' # Usa ReLU per preservare valori alti\n",
" )(high_values_branch)\n",
"\n",
" # Gate per decidere quanto pesare il branch dei valori alti\n",
" gate = Dense(1, activation='sigmoid')(shared_features)\n",
"\n",
" # Combina i branch\n",
" main_output = Dense(1)(main_branch)\n",
" high_values_output = Dense(1)(high_values_branch)\n",
"\n",
" # Output finale pesato\n",
" final_output = Lambda(lambda x: x[0] * (1 - x[2]) + x[1] * x[2])(\n",
" [main_output, high_values_output, gate]\n",
" )\n",
"\n",
" return final_output\n",
"\n",
"def create_peak_specialized_ensemble(shared_features, l2_lambda=0.005):\n",
" \"\"\"\n",
" Ensemble di modelli specializzati per diverse fasce di valori\n",
" \"\"\"\n",
" # Modello generale\n",
" general_model = create_regression_branch(shared_features, name_suffix='general')\n",
"\n",
" # Modello specializzato per valori alti\n",
" high_values_features = Dense(256, activation='relu')(shared_features)\n",
" high_values_model = create_regression_branch(high_values_features, name_suffix='high')\n",
"\n",
" # Modello specializzato per picchi estremi\n",
" peak_features = Dense(512, activation='relu')(shared_features)\n",
" peak_model = create_regression_branch(peak_features, name_suffix='peak')\n",
"\n",
" # Gate network per pesare i modelli\n",
" gate_features = Concatenate()([shared_features,\n",
" Dense(32)(shared_features),\n",
" Dense(32)(high_values_features),\n",
" Dense(32)(peak_features)])\n",
"\n",
" gates = Dense(3, activation='softmax')(gate_features)\n",
"\n",
" # Combina le predizioni\n",
" final_output = Lambda(lambda x: (x[0] * x[3][:, 0:1] +\n",
" x[1] * x[3][:, 1:2] +\n",
" x[2] * x[3][:, 2:3]))([general_model,\n",
" high_values_model,\n",
" peak_model,\n",
" gates])\n",
"\n",
" return final_output\n",
"\n",
"def create_solarradiation_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=1):\n",
" \"\"\"\n",
" Creates a hybrid model with enhanced peak prediction capabilities\n",
" \"\"\"\n",
" inputs = Input(shape=input_shape)\n",
"\n",
" # Backbone comune\n",
" survival_probs = [0.9, 0.8, 0.7, 0.6]\n",
" attention_survival_probs = [0.85, 0.75, 0.65, 0.55]\n",
" lstm_units = [256, 128, 64, 32]\n",
" dropout_rates = [0.4, 0.3, 0.2, 0.2]\n",
" attention_heads = [32, 24, 16, 8]\n",
"\n",
" x = inputs\n",
" lstm_blocks = 4\n",
" for i in range(lstm_blocks):\n",
" x = create_residual_lstm_layer(\n",
" x,\n",
" units=lstm_units[i],\n",
" dropout_rate=dropout_rates[i],\n",
" l2_reg=l2_lambda,\n",
" return_sequences=True,\n",
" survival_probability=survival_probs[i]\n",
" )\n",
" x = attention_block(\n",
" x,\n",
" units=lstm_units[i],\n",
" num_heads=attention_heads[i],\n",
" survival_probability=attention_survival_probs[i]\n",
" )\n",
" if i < lstm_blocks - 1:\n",
" x = MaxPooling1D()(x)\n",
"\n",
" # Final shared LSTM layer\n",
" shared_features = create_residual_lstm_layer(\n",
" x,\n",
" units=32,\n",
" dropout_rate=0.1,\n",
" l2_reg=l2_lambda,\n",
" return_sequences=False,\n",
" survival_probability=0.6\n",
" )\n",
"\n",
" # Enhance features for peak detection\n",
" enhanced_features = add_peak_features(x)\n",
" enhanced_shared_features = create_residual_lstm_layer(\n",
" enhanced_features,\n",
" units=64, # Increased units for enhanced features\n",
" dropout_rate=0.1,\n",
" l2_reg=l2_lambda,\n",
" return_sequences=False,\n",
" survival_probability=0.6\n",
" )\n",
"\n",
" # Classification branch\n",
" classification_x = Dense(64, kernel_regularizer=regularizers.l2(l2_lambda))(shared_features)\n",
" classification_x = BatchNormalization()(classification_x)\n",
" classification_x = Activation('swish')(classification_x)\n",
" classification_x = Dropout(0.2)(classification_x)\n",
" classification_x = Dense(32, kernel_regularizer=regularizers.l2(l2_lambda))(classification_x)\n",
" classification_x = BatchNormalization()(classification_x)\n",
" classification_x = Activation('swish')(classification_x)\n",
" classification_output = Dense(1, activation='sigmoid', name='classification_output')(classification_x)\n",
"\n",
" # Combined features for regression\n",
" regression_features = Concatenate()([shared_features, enhanced_shared_features])\n",
"\n",
" # Create specialized ensemble for regression\n",
" regression_output = create_peak_specialized_ensemble(regression_features, l2_lambda)\n",
"\n",
" # Clip regression values\n",
" regression_output = Lambda(\n",
" lambda x: tf.clip_by_value(x, min_output, max_output),\n",
" name='regression_output'\n",
" )(regression_output)\n",
"\n",
" # Combine outputs using threshold activation\n",
" thresholded_classification = ThresholdedReLU(theta=0.5)(classification_output)\n",
" normalized_classification = Lambda(lambda x: tf.cast(x > 0, tf.float32))(thresholded_classification)\n",
" final_output = Lambda(\n",
" lambda inputs: inputs[0] * inputs[1],\n",
" name='final_output'\n",
" )([regression_output, normalized_classification])\n",
"\n",
" # Create model\n",
" model = Model(\n",
" inputs=inputs,\n",
" outputs=[\n",
" classification_output,\n",
" regression_output,\n",
" final_output\n",
" ],\n",
" name=\"SolarRadiationModel\"\n",
" )\n",
"\n",
" # Custom loss functions\n",
" def hybrid_focal_loss(y_true, y_pred):\n",
" mse = tf.square(y_true - y_pred)\n",
" error_ratio = tf.abs(y_true - y_pred) / (tf.abs(y_true) + 1.0)\n",
" focal_weight = tf.pow(error_ratio, 2)\n",
" weighted_mse = focal_weight * mse\n",
" mae = tf.abs(y_true - y_pred)\n",
" return tf.reduce_mean(0.7 * weighted_mse + 0.3 * mae)\n",
"\n",
" def masked_regression_loss(y_true, y_pred):\n",
" mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)\n",
" return asymmetric_loss(y_true * mask, y_pred * mask)\n",
"\n",
" # Metrics\n",
" def rmse(y_true, y_pred):\n",
" return tf.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))\n",
"\n",
" def custom_mape(y_true, y_pred):\n",
" epsilon = 1e-7\n",
" diff = tf.abs((y_true - y_pred) / (y_true + epsilon))\n",
" diff = tf.clip_by_value(diff, 0, 1)\n",
" return tf.reduce_mean(diff) * 100\n",
"\n",
" # Optimizer with reduced initial learning rate\n",
" optimizer = AdamW(\n",
" learning_rate=0.0002, # Reduced from 0.0003\n",
" beta_1=0.9,\n",
" beta_2=0.999,\n",
" epsilon=1e-7,\n",
" weight_decay=0.001,\n",
" amsgrad=True\n",
" )\n",
"\n",
" # Compile model\n",
" model.compile(\n",
" optimizer=optimizer,\n",
" loss={\n",
" 'classification_output': 'binary_crossentropy',\n",
" 'regression_output': masked_regression_loss,\n",
" 'final_output': hybrid_focal_loss\n",
" },\n",
" loss_weights={\n",
" 'classification_output': 0.2,\n",
" 'regression_output': 0.5,\n",
" 'final_output': 0.3\n",
" },\n",
" metrics={\n",
" 'classification_output': ['accuracy', AUC()],\n",
" 'regression_output': ['mse', 'mae', rmse, custom_mape],\n",
" 'final_output': ['mse', 'mae', rmse, custom_mape]\n",
" }\n",
" )\n",
"\n",
" model.summary()\n",
"\n",
" # Save model architecture visualization\n",
" plot_model(\n",
" model,\n",
" to_file=f'{folder_name}_model_architecture.png',\n",
" show_shapes=True,\n",
" show_layer_names=True,\n",
" dpi=150,\n",
" show_layer_activations=True\n",
" )\n",
"\n",
" return model\n",
"\n",
"\n",
"def evaluate_solarradiation_predictions(y_true, y_pred, hour=None, folder_name=None):\n",
" \"\"\"\n",
" Comprehensive evaluation of solar radiation predictions with detailed analysis and visualizations.\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual solar radiation values (W/m²)\n",
" y_pred : array-like\n",
" Predicted solar radiation values (W/m²)\n",
" hour : array-like, optional\n",
" Array of hours corresponding to predictions, for temporal analysis\n",
" folder_name : str, optional\n",
" Directory to save analysis plots\n",
"\n",
" Returns:\n",
" --------\n",
" dict\n",
" Dictionary containing all calculated metrics\n",
" \"\"\"\n",
"\n",
" # Data preparation\n",
" y_true = np.array(y_true).ravel()\n",
" y_pred = np.array(y_pred).ravel()\n",
" errors = y_pred - y_true\n",
"\n",
" # Basic metrics calculation\n",
" mae_raw = mean_absolute_error(y_true, y_pred)\n",
" rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n",
" r2_raw = r2_score(y_true, y_pred)\n",
"\n",
" # Corrected MAPE calculation\n",
" mask = y_true > 10 # Consider only values above 10 W/m²\n",
" if np.any(mask):\n",
" mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n",
" else:\n",
" mape = np.nan\n",
"\n",
" # Corrected error margin accuracy\n",
" within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 W/m²\n",
" within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 W/m²\n",
" within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 W/m²\n",
"\n",
" # Radiation level classification\n",
" def get_radiation_level(value):\n",
" if value <= 200:\n",
" return 'Very Low'\n",
" elif value <= 400:\n",
" return 'Low'\n",
" elif value <= 600:\n",
" return 'Moderate'\n",
" elif value <= 800:\n",
" return 'High'\n",
" elif value <= 1000:\n",
" return 'Very High'\n",
" else:\n",
" return 'Extreme'\n",
"\n",
" # Calculate radiation levels\n",
" y_true_levels = [get_radiation_level(v) for v in y_true]\n",
" y_pred_levels = [get_radiation_level(v) for v in y_pred]\n",
" level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n",
"\n",
" # Print main metrics\n",
" print(\"\\nSolar Radiation Prediction Metrics:\")\n",
" print(\"\\nAbsolute Metrics:\")\n",
" print(f\"MAE: {mae_raw:.2f} W/m²\")\n",
" print(f\"RMSE: {rmse_raw:.2f} W/m²\")\n",
" print(f\"R² Score: {r2_raw:.3f}\")\n",
" print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n",
"\n",
" print(\"\\nAccuracy Metrics:\")\n",
" print(f\"Within ±5 W/m²: {within_5_percent:.1f}%\")\n",
" print(f\"Within ±10 W/m²: {within_10_percent:.1f}%\")\n",
" print(f\"Within ±20 W/m²: {within_20_percent:.1f}%\")\n",
"\n",
" print(\"\\nLevel Accuracy:\")\n",
" print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n",
"\n",
" # Confusion matrix for radiation levels\n",
" cm = confusion_matrix(y_true_levels, y_pred_levels)\n",
" print(\"\\nConfusion Matrix for Radiation Levels:\")\n",
" cm_df = pd.DataFrame(\n",
" cm,\n",
" columns=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme'],\n",
" index=['Very Low', 'Low', 'Moderate', 'High', 'Very High', 'Extreme']\n",
" )\n",
" print(cm_df)\n",
"\n",
" # Time period analysis\n",
" if hour is not None:\n",
" day_periods = {\n",
" 'Morning (5-11)': (5, 11),\n",
" 'Noon (11-13)': (11, 13),\n",
" 'Afternoon (13-17)': (13, 17),\n",
" 'Evening (17-21)': (17, 21),\n",
" 'Night (21-5)': (21, 5)\n",
" }\n",
"\n",
" print(\"\\nAnalysis by Time Period:\")\n",
" for period, (start, end) in day_periods.items():\n",
" if start < end:\n",
" mask = (hour >= start) & (hour < end)\n",
" else:\n",
" mask = (hour >= start) | (hour < end)\n",
"\n",
" if np.any(mask):\n",
" period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n",
"\n",
" # Corrected period MAPE calculation\n",
" period_mask = mask & (y_true > 10)\n",
" if np.any(period_mask):\n",
" period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n",
" print(f\"\\n{period}:\")\n",
" print(f\"MAE: {period_mae:.2f} W/m²\")\n",
" print(f\"MAPE: {period_mape:.2f}%\")\n",
" else:\n",
" print(f\"\\n{period}:\")\n",
" print(f\"MAE: {period_mae:.2f} W/m²\")\n",
" print(\"MAPE: N/A (insufficient data)\")\n",
"\n",
" # Visualizations\n",
" if folder_name is not None:\n",
" try:\n",
" # Figure 1: Main analysis plots\n",
" plt.figure(figsize=(20, 15))\n",
"\n",
" # Plot 1: Scatter plot of actual vs predicted values\n",
" plt.subplot(3, 2, 1)\n",
" plt.scatter(y_true, y_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.xlabel('Actual Radiation (W/m²)')\n",
" plt.ylabel('Predicted Radiation (W/m²)')\n",
" plt.title('Actual vs Predicted Values')\n",
" plt.grid(True)\n",
"\n",
" # Plot 2: Absolute error distribution\n",
" plt.subplot(3, 2, 2)\n",
" plt.hist(errors, bins=50, alpha=0.7)\n",
" plt.xlabel('Prediction Error (W/m²)')\n",
" plt.ylabel('Frequency')\n",
" plt.title('Error Distribution')\n",
" plt.grid(True)\n",
"\n",
" # Plot 3: Percentage error distribution (only for values > 10 W/m²)\n",
" plt.subplot(3, 2, 3)\n",
" mask = y_true > 10\n",
" if np.any(mask):\n",
" percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n",
" plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n",
" plt.xlabel('Percentage Error (%)')\n",
" plt.ylabel('Frequency')\n",
" plt.title('Percentage Error Distribution (for values > 10 W/m²)')\n",
" plt.grid(True)\n",
"\n",
" # Plot 4: Errors vs actual values\n",
" plt.subplot(3, 2, 4)\n",
" plt.scatter(y_true, errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.xlabel('Actual Radiation (W/m²)')\n",
" plt.ylabel('Error (W/m²)')\n",
" plt.title('Errors vs Actual Values')\n",
" plt.grid(True)\n",
"\n",
" # Plot 5: Error boxplot by radiation level\n",
" plt.subplot(3, 2, 5)\n",
" sns.boxplot(x=[get_radiation_level(v) for v in y_true], y=errors)\n",
" plt.xticks(rotation=45)\n",
" plt.xlabel('Radiation Level')\n",
" plt.ylabel('Error (W/m²)')\n",
" plt.title('Error Distribution by Level')\n",
"\n",
" # Plot 6: Confusion matrix heatmap\n",
" plt.subplot(3, 2, 6)\n",
" sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Confusion Matrix')\n",
" plt.xticks(rotation=45)\n",
" plt.yticks(rotation=45)\n",
"\n",
" plt.tight_layout()\n",
" filename = f'{folder_name}_radiation_analysis.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" plt.close()\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError saving plots: {str(e)}\")\n",
"\n",
" # Additional error statistics\n",
" print(\"\\nError Statistics:\")\n",
" print(f\"Mean error: {np.mean(errors):.3f}\")\n",
" print(f\"Error standard deviation: {np.std(errors):.3f}\")\n",
" print(f\"Median error: {np.median(errors):.3f}\")\n",
" print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n",
"\n",
" # Return structured metrics\n",
" metrics = {\n",
" 'absolute': {\n",
" 'mae': mae_raw,\n",
" 'rmse': rmse_raw,\n",
" 'r2': r2_raw,\n",
" 'mape': float(mape) if not np.isnan(mape) else None\n",
" },\n",
" 'accuracy': {\n",
" 'within_5_wm2': float(within_5_percent),\n",
" 'within_10_wm2': float(within_10_percent),\n",
" 'within_20_wm2': float(within_20_percent)\n",
" },\n",
" 'categorical': {\n",
" 'level_accuracy': float(level_accuracy)\n",
" },\n",
" 'error_stats': {\n",
" 'mean': float(np.mean(errors)),\n",
" 'std': float(np.std(errors)),\n",
" 'median': float(np.median(errors)),\n",
" 'p95_abs': float(np.percentile(np.abs(errors), 95))\n",
" }\n",
" }\n",
"\n",
" return metrics\n",
"\n",
"\n",
"def plot_training_history(history, folder_name=None):\n",
" \"\"\"\n",
" Visualize and save training history for the hybrid model\n",
" \"\"\"\n",
" plt.figure(figsize=(15, 10))\n",
"\n",
" # Loss plots\n",
" plt.subplot(2, 2, 1)\n",
" plt.plot(history.history['classification_output_loss'], label='Class Loss')\n",
" plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n",
" plt.plot(history.history['final_output_loss'], label='Final Loss')\n",
" plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n",
" plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n",
" plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n",
" plt.title('Model Losses')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Loss')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Classification metrics\n",
" plt.subplot(2, 2, 2)\n",
" plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n",
" plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n",
" plt.plot(history.history['classification_output_auc'], label='Class AUC')\n",
" plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n",
" plt.title('Classification Metrics')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('Metric Value')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Regression metrics\n",
" plt.subplot(2, 2, 3)\n",
" plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n",
" plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n",
" plt.title('Regression MAE')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('MAE')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" # Final output metrics\n",
" plt.subplot(2, 2, 4)\n",
" plt.plot(history.history['final_output_mae'], label='Final MAE')\n",
" plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n",
" plt.title('Final Output MAE')\n",
" plt.xlabel('Epoch')\n",
" plt.ylabel('MAE')\n",
" plt.legend()\n",
" plt.grid(True)\n",
"\n",
" plt.tight_layout()\n",
"\n",
" if folder_name is not None:\n",
" filename = f'{folder_name}_training_history.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nTraining history plot saved as: {filename}\")\n",
"\n",
" # Save history to JSON\n",
" history_dict = history.history\n",
" json_filename = f'{folder_name}_training_history.json'\n",
" with open(json_filename, 'w') as f:\n",
" json.dump(history_dict, f)\n",
" print(f\"Training history saved as: {json_filename}\")\n",
"\n",
" plt.show()\n",
"\n",
"def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n",
" \"\"\"\n",
" Helper function to calculate and print metrics for all outputs\n",
" \n",
" Parameters:\n",
" - y_true: true values\n",
" - y_class: classification predictions\n",
" - y_reg: regression predictions\n",
" - y_final: final combined predictions\n",
" \"\"\"\n",
" from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n",
" \n",
" y_true = np.array(y_true).flatten()\n",
" y_class = np.array(y_class).flatten()\n",
" y_reg = np.array(y_reg).flatten()\n",
" y_final = np.array(y_final).flatten()\n",
" \n",
" # Classification metrics\n",
" print(\"\\nClassification Metrics:\")\n",
" y_true_binary = (y_true > 0).astype(int)\n",
" y_pred_binary = (y_class > 0.5).astype(int)\n",
" \n",
" accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n",
" auc_roc = roc_auc_score(y_true > 0, y_class)\n",
" print(f\"Accuracy: {accuracy:.2f}%\")\n",
" print(f\"AUC-ROC: {auc_roc:.4f}\")\n",
" \n",
" print(\"\\nConfusion Matrix:\")\n",
" print(confusion_matrix(y_true_binary, y_pred_binary))\n",
" \n",
" print(\"\\nClassification Report:\")\n",
" print(classification_report(y_true_binary, y_pred_binary, \n",
" target_names=['Zero', 'Non-Zero'],\n",
" digits=4))\n",
" \n",
" # Regression metrics (non-zero values)\n",
" print(\"\\nRegression Metrics (non-zero values):\")\n",
" mask_nonzero = y_true > 0\n",
" if np.any(mask_nonzero): # verifichiamo che ci siano valori non-zero\n",
" y_true_nonzero = y_true[mask_nonzero]\n",
" y_reg_nonzero = y_reg[mask_nonzero]\n",
" \n",
" out_of_range = np.sum((y_reg_nonzero < min_output) | (y_reg_nonzero > max_output))\n",
" diff = np.abs((y_true_nonzero - y_reg_nonzero) / (y_true_nonzero + 1e-7))\n",
" diff = np.clip(diff, 0, 1)\n",
" mape = np.mean(diff) * 100\n",
" within_10_percent = np.mean(diff <= 0.10) * 100\n",
" mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n",
" rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n",
" \n",
" print(f\"Out of range: {out_of_range} predictions\")\n",
" print(f\"MAPE: {mape:.2f}%\")\n",
" print(f\"Within ±10%: {within_10_percent:.2f}%\")\n",
" print(f\"MAE: {mae:.2f}\")\n",
" print(f\"RMSE: {rmse:.2f}\")\n",
" else:\n",
" print(\"No non-zero values in this batch\")\n",
" \n",
" # Final combined output metrics\n",
" print(\"\\nFinal Combined Output Metrics:\")\n",
" out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n",
" diff = np.abs((y_true - y_final) / (y_true + 1e-7))\n",
" diff = np.clip(diff, 0, 1)\n",
" mape = np.mean(diff) * 100\n",
" within_10_percent = np.mean(diff <= 0.10) * 100\n",
" mae = np.mean(np.abs(y_true - y_final))\n",
" rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n",
" \n",
" print(f\"Out of range: {out_of_range} predictions\")\n",
" print(f\"MAPE: {mape:.2f}%\")\n",
" print(f\"Within ±10%: {within_10_percent:.2f}%\")\n",
" print(f\"MAE: {mae:.2f}\")\n",
" print(f\"RMSE: {rmse:.2f}\")\n",
"\n",
"def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarradiation', min_output=0, max_output=1):\n",
" \"\"\"\n",
" Advanced training function for the hybrid solar radiation model\n",
" \"\"\" \n",
" # Prepare binary targets for classification\n",
" y_train_binary = (y_train > 0).astype(float)\n",
" y_test_binary = (y_test > 0).astype(float)\n",
"\n",
" # Training targets dictionary - usando i nomi esatti degli output del modello\n",
" train_targets = {\n",
" 'classification_output': y_train_binary,\n",
" 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n",
" 'final_output': y_train\n",
" }\n",
"\n",
" # Validation targets dictionary\n",
" test_targets = {\n",
" 'classification_output': y_test_binary,\n",
" 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n",
" 'final_output': y_test\n",
" }\n",
"\n",
" callbacks = [\n",
" EarlyStopping(\n",
" monitor='val_final_output_loss',\n",
" patience=15,\n",
" restore_best_weights=True,\n",
" mode='min',\n",
" verbose=1,\n",
" min_delta=1e-4\n",
" ),\n",
" ReduceLROnPlateau(\n",
" monitor='val_final_output_loss',\n",
" factor=0.5,\n",
" patience=7,\n",
" verbose=1,\n",
" mode='min',\n",
" min_delta=1e-4,\n",
" cooldown=2,\n",
" min_lr=1e-7\n",
" ),\n",
" tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=f'{folder_name}_best_model.h5',\n",
" monitor='val_final_output_loss',\n",
" save_best_only=True,\n",
" mode='min',\n",
" save_weights_only=False\n",
" ),\n",
" tf.keras.callbacks.TensorBoard(\n",
" log_dir=f'./{folder_name}_logs',\n",
" histogram_freq=1,\n",
" write_graph=True,\n",
" update_freq='epoch'\n",
" ),\n",
" tf.keras.callbacks.LambdaCallback(\n",
" on_epoch_end=lambda epoch, logs: (\n",
" print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\") and\n",
" calculate_metrics(y_test, *model.predict(X_test, verbose=0), min_output, max_output)\n",
" ) if epoch % 10 == 0 else None\n",
" )\n",
" ]\n",
"\n",
" try:\n",
" history = model.fit(\n",
" X_train,\n",
" train_targets,\n",
" validation_data=(X_test, test_targets),\n",
" epochs=epochs,\n",
" batch_size=batch_size,\n",
" callbacks=callbacks,\n",
" verbose=1,\n",
" shuffle=False\n",
" )\n",
"\n",
" print(\"\\nTraining completed successfully!\")\n",
"\n",
" # Final evaluation\n",
" predictions = model.predict(X_test, verbose=0)\n",
" calculate_metrics(y_test, *predictions, min_output, max_output)\n",
"\n",
" return history\n",
"\n",
" except Exception as e:\n",
" print(f\"\\nError during training: {str(e)}\")\n",
" print(\"\\nModel output names:\", [output.name for output in model.outputs])\n",
" print(\"Training targets keys:\", train_targets.keys())\n",
" raise\n",
"\n",
" finally:\n",
" tf.keras.backend.clear_session()\n",
"\n",
"\n",
"def integrate_predictions(df, predictions, sequence_length=24):\n",
" \"\"\"\n",
" Integrates solar radiation predictions into the original dataset for pre-2010 data.\n",
"\n",
" Parameters:\n",
" -----------\n",
" df : pandas.DataFrame\n",
" Original dataset\n",
" predictions : tuple\n",
" Tuple containing (classification_pred, regression_pred, final_pred)\n",
" - classification_pred: probability of non-zero values\n",
" - regression_pred: predicted values (used for non-zero cases)\n",
" - final_pred: final combined predictions\n",
" sequence_length : int\n",
" Sequence length used for predictions\n",
"\n",
" Returns:\n",
" --------\n",
" pandas.DataFrame\n",
" Updated dataset with solar radiation predictions and additional prediction details\n",
" \"\"\"\n",
" # Convert datetime to datetime format if not already\n",
" df['datetime'] = pd.to_datetime(df['datetime'])\n",
"\n",
" # Identify pre-2010 rows\n",
" mask_pre_2010 = df['datetime'].dt.year < 2010\n",
"\n",
" # Unpack predictions\n",
" classification_pred, regression_pred, final_pred = predictions\n",
"\n",
" # Create temporary DataFrame with all predictions\n",
" dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n",
" predictions_df = pd.DataFrame({\n",
" 'datetime': dates_pre_2010,\n",
" 'solarradiation_predicted': final_pred.flatten(),\n",
" 'solarradiation_classification': classification_pred.flatten(),\n",
" 'solarradiation_regression': regression_pred.flatten()\n",
" })\n",
"\n",
" # Merge with original dataset\n",
" df = df.merge(predictions_df, on='datetime', how='left')\n",
"\n",
" # Update solar radiation column where missing\n",
" df['solarradiation'] = df['solarradiation'].fillna(df['solarradiation_predicted'])\n",
"\n",
" # Print detailed statistics\n",
" print(\"\\nPrediction Integration Statistics:\")\n",
" print(f\"Added {len(final_pred)} predictions to dataset\")\n",
" print(f\"Rows with solar radiation after integration: {df['solarradiation'].notna().sum()}\")\n",
"\n",
" # Analyze prediction components for the filled values\n",
" mask_filled = df['solarradiation'] == df['solarradiation_predicted']\n",
" if mask_filled.any():\n",
" filled_data = df[mask_filled]\n",
"\n",
" print(\"\\nFilled Values Analysis:\")\n",
" print(f\"Zero predictions (classification < 0.5): {(filled_data['solarradiation_classification'] < 0.5).sum()}\")\n",
" print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarradiation_classification'] >= 0.5).sum()}\")\n",
"\n",
" # Distribution of predicted values\n",
" non_zero_pred = filled_data[filled_data['solarradiation_predicted'] > 0]\n",
" if len(non_zero_pred) > 0:\n",
" print(f\"\\nNon-zero predictions statistics:\")\n",
" print(f\"Mean: {non_zero_pred['solarradiation_predicted'].mean():.2f}\")\n",
" print(f\"Median: {non_zero_pred['solarradiation_predicted'].median():.2f}\")\n",
" print(f\"Std: {non_zero_pred['solarradiation_predicted'].std():.2f}\")\n",
"\n",
" # Optionally, you can keep or remove the intermediate prediction columns\n",
" columns_to_drop = ['solarradiation_predicted', 'solarradiation_classification',\n",
" 'solarradiation_regression']\n",
" df = df.drop(columns_to_drop, axis=1)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b3b0c2e65ddf484",
"metadata": {},
"outputs": [],
"source": [
"def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n",
" \"\"\"\n",
" Analizza dettagliatamente la distribuzione della variabile solarenergy.\n",
"\n",
" Parameters:\n",
" -----------\n",
" data : pandas.DataFrame\n",
" DataFrame contenente la colonna solarenergy\n",
" solar_column : str, default='solarenergy'\n",
" Nome della colonna da analizzare\n",
"\n",
" Returns:\n",
" --------\n",
" dict\n",
" Dizionario contenente le statistiche principali\n",
" \"\"\"\n",
"\n",
" # Creiamo una figura con più subplot\n",
" fig = plt.figure(figsize=(20, 12))\n",
"\n",
" # 1. Statistiche di base\n",
" stats_dict = {\n",
" 'count': len(data[solar_column]),\n",
" 'missing': data[solar_column].isnull().sum(),\n",
" 'zeros': (data[solar_column] == 0).sum(),\n",
" 'mean': data[solar_column].mean(),\n",
" 'median': data[solar_column].median(),\n",
" 'std': data[solar_column].std(),\n",
" 'min': data[solar_column].min(),\n",
" 'max': data[solar_column].max(),\n",
" 'skewness': stats.skew(data[solar_column].dropna()),\n",
" 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n",
" }\n",
"\n",
" # Calcolo dei percentili\n",
" percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n",
" for p in percentiles:\n",
" stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n",
"\n",
" # 2. Visualizzazioni\n",
"\n",
" # 2.1 Distribuzione\n",
" plt.subplot(2, 2, 1)\n",
" sns.histplot(data=data, x=solar_column, kde=True)\n",
" plt.title(f'Distribuzione di {name}')\n",
" plt.xlabel(f'{name}')\n",
" plt.ylabel('Frequenza')\n",
"\n",
" # 2.2 Box Plot\n",
" plt.subplot(2, 2, 2)\n",
" sns.boxplot(y=data[solar_column])\n",
" plt.title(f'Box Plot di {name}')\n",
"\n",
" # 2.3 QQ Plot\n",
" plt.subplot(2, 2, 3)\n",
" stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n",
" plt.title(f'Q-Q Plot di {name}')\n",
"\n",
" # 2.4 Distribuzione Log-trasformata\n",
" plt.subplot(2, 2, 4)\n",
" sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n",
" plt.title(f'Distribuzione Log-trasformata di {name}')\n",
" plt.xlabel(f'Log({name} + 1)')\n",
" plt.ylabel('Frequenza')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # 3. Analisi temporale se disponibile\n",
" if 'timestamp' in data.columns or 'datetime' in data.columns:\n",
" time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n",
" if isinstance(data[time_col].iloc[0], (int, float)):\n",
" data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n",
" else:\n",
" data['temp_datetime'] = pd.to_datetime(data[time_col])\n",
"\n",
" # Plot temporale\n",
" plt.figure(figsize=(15, 6))\n",
" plt.plot(data['temp_datetime'], data[solar_column])\n",
" plt.title(f'Serie Temporale di {name}')\n",
" plt.xlabel('Data')\n",
" plt.ylabel(f'{name}')\n",
" plt.xticks(rotation=45)\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # Analisi stagionale\n",
" data['month'] = data['temp_datetime'].dt.month\n",
" seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n",
"\n",
" plt.figure(figsize=(12, 6))\n",
" seasonal_stats['mean'].plot(kind='bar')\n",
" plt.title(f'Media Mensile di {name}')\n",
" plt.xlabel('Mese')\n",
" plt.ylabel(f'{name} Media')\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" # 4. Stampa delle statistiche principali\n",
" print(f\"\\nStatistiche principali di {name}:\")\n",
" print(\"-\" * 50)\n",
" for key, value in stats_dict.items():\n",
" print(f\"{key:15}: {value:,.4f}\")\n",
"\n",
" # 5. Suggerimenti per la normalizzazione\n",
" print(\"\\nSuggerimenti per la normalizzazione:\")\n",
" print(\"-\" * 50)\n",
"\n",
" skewness = abs(stats_dict['skewness'])\n",
" if skewness > 1:\n",
" print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n",
" print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n",
"\n",
" range_ratio = stats_dict['max'] / stats_dict['std']\n",
" if range_ratio > 10:\n",
" print(\"- La variabile ha una scala molto ampia\")\n",
" print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n",
"\n",
" zero_ratio = stats_dict['zeros'] / stats_dict['count']\n",
" if zero_ratio > 0.1:\n",
" print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n",
" print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n",
"\n",
" return stats_dict"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1b1ee91d1573ec66",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initializing solar radiation model training...\n",
"\n",
"1. Preparing data...\n",
"\n",
"Selected features:\n",
"Number of features: 40\n",
"Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'uv_lag_1h', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n",
"Training data shape: (103798, 24, 40)\n",
"Test data shape: (25933, 24, 40)\n",
"Saving scaler X to: 2024-11-26_05-41_scale_X.joblib\n",
"Saving scaler X to: 2024-11-26_05-41_scale_y.joblib\n",
"Saving features to: 2024-11-26_05-41_features.json\n"
]
}
],
"source": [
"df = pd.read_parquet('../../sources/weather_data_uvindex.parquet')\n",
"\n",
"print(\"Initializing solar radiation model training...\")\n",
"\n",
"# Data preparation\n",
"print(\"\\n1. Preparing data...\")\n",
"X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n",
"\n",
"print(f\"Training data shape: {X_train_seq.shape}\")\n",
"print(f\"Test data shape: {X_test_seq.shape}\")\n",
"\n",
"# Save or load scaler and features\n",
"scaler_X_path = f'{folder_name}_scale_X.joblib'\n",
"scaler_y_path = f'{folder_name}_scale_y.joblib'\n",
"features_path = f'{folder_name}_features.json'\n",
"model_path = f'{folder_name}_best_model.h5'\n",
"history_path = f'{folder_name}_training_history.json'\n",
"\n",
"if os.path.exists(scaler_X_path):\n",
" print(f\"Loading existing scaler X from: {scaler_X_path}\")\n",
" scaler = joblib.load(scaler_X_path)\n",
"else:\n",
" print(f\"Saving scaler X to: {scaler_X_path}\")\n",
" joblib.dump(scaler_X, scaler_X_path)\n",
"\n",
"if os.path.exists(scaler_y_path):\n",
" print(f\"Loading existing scaler X from: {scaler_y_path}\")\n",
" scaler = joblib.load(scaler_y_path)\n",
"else:\n",
" print(f\"Saving scaler X to: {scaler_y_path}\")\n",
" joblib.dump(scaler_y, scaler_y_path)\n",
"\n",
"if os.path.exists(features_path):\n",
" print(f\"Loading existing features from: {features_path}\")\n",
" with open(features_path, 'r') as f:\n",
" features = json.load(f)\n",
"else:\n",
" print(f\"Saving features to: {features_path}\")\n",
" with open(features_path, 'w') as f:\n",
" json.dump(features, f)\n",
"\n",
"# Data quality verification\n",
"if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n",
" raise ValueError(\"Found NaN values in training data\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "76deb4deb84dc4c5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"2. Creating model...\n",
"\n",
"Max dataset solar radiation : 1113.0 - Scaled Version : 3.2535460992907805\n",
"Max dataset solar radiation increased by 15% : 1279.9499999999998 - Scaled Version : 3.7415780141843973\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-26 05:41:50.507143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1886] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43404 MB memory: -> device: 0, name: NVIDIA L40, pci bus id: 0000:01:00.0, compute capability: 8.9\n",
"2024-11-26 05:41:51.386109: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"SolarRadiationModel\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_1 (InputLayer) [(None, 24, 40)] 0 [] \n",
" \n",
" bidirectional (Bidirection (None, 24, 512) 608256 ['input_1[0][0]'] \n",
" al) \n",
" \n",
" layer_normalization (Layer (None, 24, 512) 1024 ['bidirectional[0][0]'] \n",
" Normalization) \n",
" \n",
" dropout (Dropout) (None, 24, 512) 0 ['layer_normalization[0][0]'] \n",
" \n",
" dense (Dense) (None, 24, 512) 20992 ['input_1[0][0]'] \n",
" \n",
" stochastic_depth (Stochast (None, 24, 512) 0 ['dropout[0][0]', \n",
" icDepth) 'dense[0][0]'] \n",
" \n",
" multi_head_attention (Mult (None, 24, 512) 1680230 ['stochastic_depth[0][0]', \n",
" iHeadAttention) 4 'stochastic_depth[0][0]'] \n",
" \n",
" stochastic_depth_1 (Stocha (None, 24, 512) 0 ['stochastic_depth[0][0]', \n",
" sticDepth) 'multi_head_attention[0][0]']\n",
" \n",
" layer_normalization_1 (Lay (None, 24, 512) 1024 ['stochastic_depth_1[0][0]'] \n",
" erNormalization) \n",
" \n",
" max_pooling1d (MaxPooling1 (None, 12, 512) 0 ['layer_normalization_1[0][0]'\n",
" D) ] \n",
" \n",
" bidirectional_1 (Bidirecti (None, 12, 256) 656384 ['max_pooling1d[0][0]'] \n",
" onal) \n",
" \n",
" layer_normalization_2 (Lay (None, 12, 256) 512 ['bidirectional_1[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_1 (Dropout) (None, 12, 256) 0 ['layer_normalization_2[0][0]'\n",
" ] \n",
" \n",
" dense_1 (Dense) (None, 12, 256) 131328 ['max_pooling1d[0][0]'] \n",
" \n",
" stochastic_depth_2 (Stocha (None, 12, 256) 0 ['dropout_1[0][0]', \n",
" sticDepth) 'dense_1[0][0]'] \n",
" \n",
" multi_head_attention_1 (Mu (None, 12, 256) 3155200 ['stochastic_depth_2[0][0]', \n",
" ltiHeadAttention) 'stochastic_depth_2[0][0]'] \n",
" \n",
" stochastic_depth_3 (Stocha (None, 12, 256) 0 ['stochastic_depth_2[0][0]', \n",
" sticDepth) 'multi_head_attention_1[0][0]\n",
" '] \n",
" \n",
" layer_normalization_3 (Lay (None, 12, 256) 512 ['stochastic_depth_3[0][0]'] \n",
" erNormalization) \n",
" \n",
" max_pooling1d_1 (MaxPoolin (None, 6, 256) 0 ['layer_normalization_3[0][0]'\n",
" g1D) ] \n",
" \n",
" bidirectional_2 (Bidirecti (None, 6, 128) 164352 ['max_pooling1d_1[0][0]'] \n",
" onal) \n",
" \n",
" layer_normalization_4 (Lay (None, 6, 128) 256 ['bidirectional_2[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_2 (Dropout) (None, 6, 128) 0 ['layer_normalization_4[0][0]'\n",
" ] \n",
" \n",
" dense_2 (Dense) (None, 6, 128) 32896 ['max_pooling1d_1[0][0]'] \n",
" \n",
" stochastic_depth_4 (Stocha (None, 6, 128) 0 ['dropout_2[0][0]', \n",
" sticDepth) 'dense_2[0][0]'] \n",
" \n",
" multi_head_attention_2 (Mu (None, 6, 128) 527488 ['stochastic_depth_4[0][0]', \n",
" ltiHeadAttention) 'stochastic_depth_4[0][0]'] \n",
" \n",
" stochastic_depth_5 (Stocha (None, 6, 128) 0 ['stochastic_depth_4[0][0]', \n",
" sticDepth) 'multi_head_attention_2[0][0]\n",
" '] \n",
" \n",
" layer_normalization_5 (Lay (None, 6, 128) 256 ['stochastic_depth_5[0][0]'] \n",
" erNormalization) \n",
" \n",
" max_pooling1d_2 (MaxPoolin (None, 3, 128) 0 ['layer_normalization_5[0][0]'\n",
" g1D) ] \n",
" \n",
" bidirectional_3 (Bidirecti (None, 3, 64) 41216 ['max_pooling1d_2[0][0]'] \n",
" onal) \n",
" \n",
" layer_normalization_6 (Lay (None, 3, 64) 128 ['bidirectional_3[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_3 (Dropout) (None, 3, 64) 0 ['layer_normalization_6[0][0]'\n",
" ] \n",
" \n",
" dense_3 (Dense) (None, 3, 64) 8256 ['max_pooling1d_2[0][0]'] \n",
" \n",
" stochastic_depth_6 (Stocha (None, 3, 64) 0 ['dropout_3[0][0]', \n",
" sticDepth) 'dense_3[0][0]'] \n",
" \n",
" multi_head_attention_3 (Mu (None, 3, 64) 66368 ['stochastic_depth_6[0][0]', \n",
" ltiHeadAttention) 'stochastic_depth_6[0][0]'] \n",
" \n",
" stochastic_depth_7 (Stocha (None, 3, 64) 0 ['stochastic_depth_6[0][0]', \n",
" sticDepth) 'multi_head_attention_3[0][0]\n",
" '] \n",
" \n",
" layer_normalization_7 (Lay (None, 3, 64) 128 ['stochastic_depth_7[0][0]'] \n",
" erNormalization) \n",
" \n",
" conv1d (Conv1D) (None, 3, 1) 321 ['layer_normalization_7[0][0]'\n",
" ] \n",
" \n",
" lambda_1 (Lambda) (None, 2, 64) 0 ['layer_normalization_7[0][0]'\n",
" ] \n",
" \n",
" lambda (Lambda) (None, 3, 64) 0 ['layer_normalization_7[0][0]'\n",
" , 'conv1d[0][0]'] \n",
" \n",
" tf.compat.v1.pad (TFOpLamb (None, 3, 64) 0 ['lambda_1[0][0]'] \n",
" da) \n",
" \n",
" concatenate (Concatenate) (None, 3, 192) 0 ['layer_normalization_7[0][0]'\n",
" , 'lambda[0][0]', \n",
" 'tf.compat.v1.pad[0][0]'] \n",
" \n",
" bidirectional_4 (Bidirecti (None, 64) 24832 ['layer_normalization_7[0][0]'\n",
" onal) ] \n",
" \n",
" bidirectional_5 (Bidirecti (None, 128) 131584 ['concatenate[0][0]'] \n",
" onal) \n",
" \n",
" layer_normalization_8 (Lay (None, 64) 128 ['bidirectional_4[0][0]'] \n",
" erNormalization) \n",
" \n",
" layer_normalization_9 (Lay (None, 128) 256 ['bidirectional_5[0][0]'] \n",
" erNormalization) \n",
" \n",
" dropout_4 (Dropout) (None, 64) 0 ['layer_normalization_8[0][0]'\n",
" ] \n",
" \n",
" dropout_5 (Dropout) (None, 128) 0 ['layer_normalization_9[0][0]'\n",
" ] \n",
" \n",
" concatenate_1 (Concatenate (None, 192) 0 ['dropout_4[0][0]', \n",
" ) 'dropout_5[0][0]'] \n",
" \n",
" dense_16 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n",
" \n",
" dense_27 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n",
" \n",
" dense_6 (Dense) (None, 512) 98816 ['concatenate_1[0][0]'] \n",
" \n",
" dense_17 (Dense) (None, 512) 131584 ['dense_16[0][0]'] \n",
" \n",
" dense_28 (Dense) (None, 512) 262656 ['dense_27[0][0]'] \n",
" \n",
" batch_normalization_2 (Bat (None, 512) 2048 ['dense_6[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_6 (Bat (None, 512) 2048 ['dense_17[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_10 (Ba (None, 512) 2048 ['dense_28[0][0]'] \n",
" tchNormalization) \n",
" \n",
" activation_2 (Activation) (None, 512) 0 ['batch_normalization_2[0][0]'\n",
" ] \n",
" \n",
" activation_6 (Activation) (None, 512) 0 ['batch_normalization_6[0][0]'\n",
" ] \n",
" \n",
" activation_10 (Activation) (None, 512) 0 ['batch_normalization_10[0][0]\n",
" '] \n",
" \n",
" dense_7 (Dense) (None, 256) 131328 ['activation_2[0][0]'] \n",
" \n",
" dense_18 (Dense) (None, 256) 131328 ['activation_6[0][0]'] \n",
" \n",
" dense_29 (Dense) (None, 256) 131328 ['activation_10[0][0]'] \n",
" \n",
" batch_normalization_3 (Bat (None, 256) 1024 ['dense_7[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_7 (Bat (None, 256) 1024 ['dense_18[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_11 (Ba (None, 256) 1024 ['dense_29[0][0]'] \n",
" tchNormalization) \n",
" \n",
" activation_3 (Activation) (None, 256) 0 ['batch_normalization_3[0][0]'\n",
" ] \n",
" \n",
" activation_7 (Activation) (None, 256) 0 ['batch_normalization_7[0][0]'\n",
" ] \n",
" \n",
" activation_11 (Activation) (None, 256) 0 ['batch_normalization_11[0][0]\n",
" '] \n",
" \n",
" dense_4 (Dense) (None, 64) 4160 ['dropout_4[0][0]'] \n",
" \n",
" dense_8 (Dense) (None, 128) 32896 ['activation_3[0][0]'] \n",
" \n",
" dense_19 (Dense) (None, 128) 32896 ['activation_7[0][0]'] \n",
" \n",
" dense_30 (Dense) (None, 128) 32896 ['activation_11[0][0]'] \n",
" \n",
" batch_normalization (Batch (None, 64) 256 ['dense_4[0][0]'] \n",
" Normalization) \n",
" \n",
" batch_normalization_4 (Bat (None, 128) 512 ['dense_8[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_8 (Bat (None, 128) 512 ['dense_19[0][0]'] \n",
" chNormalization) \n",
" \n",
" batch_normalization_12 (Ba (None, 128) 512 ['dense_30[0][0]'] \n",
" tchNormalization) \n",
" \n",
" activation (Activation) (None, 64) 0 ['batch_normalization[0][0]'] \n",
" \n",
" activation_4 (Activation) (None, 128) 0 ['batch_normalization_4[0][0]'\n",
" ] \n",
" \n",
" activation_8 (Activation) (None, 128) 0 ['batch_normalization_8[0][0]'\n",
" ] \n",
" \n",
" activation_12 (Activation) (None, 128) 0 ['batch_normalization_12[0][0]\n",
" '] \n",
" \n",
" dropout_6 (Dropout) (None, 64) 0 ['activation[0][0]'] \n",
" \n",
" dense_9 (Dense) (None, 64) 8256 ['activation_4[0][0]'] \n",
" \n",
" dense_10 (Dense) (None, 256) 49408 ['concatenate_1[0][0]'] \n",
" \n",
" dense_20 (Dense) (None, 64) 8256 ['activation_8[0][0]'] \n",
" \n",
" dense_21 (Dense) (None, 256) 65792 ['dense_16[0][0]'] \n",
" \n",
" dense_31 (Dense) (None, 64) 8256 ['activation_12[0][0]'] \n",
" \n",
" dense_32 (Dense) (None, 256) 131328 ['dense_27[0][0]'] \n",
" \n",
" dense_5 (Dense) (None, 32) 2080 ['dropout_6[0][0]'] \n",
" \n",
" batch_normalization_5 (Bat (None, 64) 256 ['dense_9[0][0]'] \n",
" chNormalization) \n",
" \n",
" dense_11 (Dense) (None, 128) 32896 ['dense_10[0][0]'] \n",
" \n",
" batch_normalization_9 (Bat (None, 64) 256 ['dense_20[0][0]'] \n",
" chNormalization) \n",
" \n",
" dense_22 (Dense) (None, 128) 32896 ['dense_21[0][0]'] \n",
" \n",
" batch_normalization_13 (Ba (None, 64) 256 ['dense_31[0][0]'] \n",
" tchNormalization) \n",
" \n",
" dense_33 (Dense) (None, 128) 32896 ['dense_32[0][0]'] \n",
" \n",
" batch_normalization_1 (Bat (None, 32) 128 ['dense_5[0][0]'] \n",
" chNormalization) \n",
" \n",
" activation_5 (Activation) (None, 64) 0 ['batch_normalization_5[0][0]'\n",
" ] \n",
" \n",
" dense_12 (Dense) (None, 64) 8256 ['dense_11[0][0]'] \n",
" \n",
" activation_9 (Activation) (None, 64) 0 ['batch_normalization_9[0][0]'\n",
" ] \n",
" \n",
" dense_23 (Dense) (None, 64) 8256 ['dense_22[0][0]'] \n",
" \n",
" activation_13 (Activation) (None, 64) 0 ['batch_normalization_13[0][0]\n",
" '] \n",
" \n",
" dense_34 (Dense) (None, 64) 8256 ['dense_33[0][0]'] \n",
" \n",
" dense_38 (Dense) (None, 32) 6176 ['concatenate_1[0][0]'] \n",
" \n",
" dense_39 (Dense) (None, 32) 8224 ['dense_16[0][0]'] \n",
" \n",
" dense_40 (Dense) (None, 32) 16416 ['dense_27[0][0]'] \n",
" \n",
" activation_1 (Activation) (None, 32) 0 ['batch_normalization_1[0][0]'\n",
" ] \n",
" \n",
" dense_14 (Dense) (None, 1) 65 ['activation_5[0][0]'] \n",
" \n",
" dense_15 (Dense) (None, 1) 65 ['dense_12[0][0]'] \n",
" \n",
" dense_13 (Dense) (None, 1) 193 ['concatenate_1[0][0]'] \n",
" \n",
" dense_25 (Dense) (None, 1) 65 ['activation_9[0][0]'] \n",
" \n",
" dense_26 (Dense) (None, 1) 65 ['dense_23[0][0]'] \n",
" \n",
" dense_24 (Dense) (None, 1) 257 ['dense_16[0][0]'] \n",
" \n",
" dense_36 (Dense) (None, 1) 65 ['activation_13[0][0]'] \n",
" \n",
" dense_37 (Dense) (None, 1) 65 ['dense_34[0][0]'] \n",
" \n",
" dense_35 (Dense) (None, 1) 513 ['dense_27[0][0]'] \n",
" \n",
" concatenate_2 (Concatenate (None, 288) 0 ['concatenate_1[0][0]', \n",
" ) 'dense_38[0][0]', \n",
" 'dense_39[0][0]', \n",
" 'dense_40[0][0]'] \n",
" \n",
" classification_output (Den (None, 1) 33 ['activation_1[0][0]'] \n",
" se) \n",
" \n",
" lambda_2 (Lambda) (None, 1) 0 ['dense_14[0][0]', \n",
" 'dense_15[0][0]', \n",
" 'dense_13[0][0]'] \n",
" \n",
" lambda_3 (Lambda) (None, 1) 0 ['dense_25[0][0]', \n",
" 'dense_26[0][0]', \n",
" 'dense_24[0][0]'] \n",
" \n",
" lambda_4 (Lambda) (None, 1) 0 ['dense_36[0][0]', \n",
" 'dense_37[0][0]', \n",
" 'dense_35[0][0]'] \n",
" \n",
" dense_41 (Dense) (None, 3) 867 ['concatenate_2[0][0]'] \n",
" \n",
" lambda_5 (Lambda) (None, 1) 0 ['lambda_2[0][0]', \n",
" 'lambda_3[0][0]', \n",
" 'lambda_4[0][0]', \n",
" 'dense_41[0][0]'] \n",
" \n",
" thresholded_re_lu (Thresho (None, 1) 0 ['classification_output[0][0]'\n",
" ldedReLU) ] \n",
" \n",
" regression_output (Lambda) (None, 1) 0 ['lambda_5[0][0]'] \n",
" \n",
" lambda_6 (Lambda) (None, 1) 0 ['thresholded_re_lu[0][0]'] \n",
" \n",
" final_output (Lambda) (None, 1) 0 ['regression_output[0][0]', \n",
" 'lambda_6[0][0]'] \n",
" \n",
"==================================================================================================\n",
"Total params: 23955918 (91.38 MB)\n",
"Trainable params: 23949966 (91.36 MB)\n",
"Non-trainable params: 5952 (23.25 KB)\n",
"__________________________________________________________________________________________________\n",
"\n",
"Class distribution in training set:\n",
"Zeros: 52022 (50.12%)\n",
"Non-zeros: 51776 (49.88%)\n",
"\n",
"Class distribution in test set:\n",
"Zeros: 13007 (50.16%)\n",
"Non-zeros: 12926 (49.84%)\n",
"\n",
"Model output names: ['classification_output', 'regression_output', 'final_output']\n",
"\n",
"4. Starting training...\n",
"Epoch 1/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-11-26 05:42:25.841427: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:442] Loaded cuDNN version 8905\n",
"2024-11-26 05:42:26.758143: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
"2024-11-26 05:42:28.319667: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x74802ce90ad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2024-11-26 05:42:28.319705: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA L40, Compute Capability 8.9\n",
"2024-11-26 05:42:28.325479: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2024-11-26 05:42:28.469866: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"541/541 [==============================] - ETA: 0s - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.11/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
" saving_api.save_model(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch 1 Detailed Metrics:\n",
"541/541 [==============================] - 106s 111ms/step - loss: 14.9229 - classification_output_loss: 0.2997 - regression_output_loss: 0.2514 - final_output_loss: 0.1790 - classification_output_accuracy: 0.8674 - classification_output_auc: 0.9483 - regression_output_mse: 0.3870 - regression_output_mae: 0.4665 - regression_output_rmse: 0.5816 - regression_output_custom_mape: 68.0181 - final_output_mse: 0.2366 - final_output_mae: 0.2930 - final_output_rmse: 0.4493 - final_output_custom_mape: 76.9850 - val_loss: 4.8619 - val_classification_output_loss: 0.2702 - val_regression_output_loss: 0.1998 - val_final_output_loss: 0.1203 - val_classification_output_accuracy: 0.8850 - val_classification_output_auc: 0.9648 - val_regression_output_mse: 2.6679 - val_regression_output_mae: 1.1926 - val_regression_output_rmse: 1.4470 - val_regression_output_custom_mape: 76.3626 - val_final_output_mse: 0.1619 - val_final_output_mae: 0.2603 - val_final_output_rmse: 0.3866 - val_final_output_custom_mape: 77.3426 - lr: 2.0000e-04\n",
"Epoch 2/100\n",
"541/541 [==============================] - 53s 97ms/step - loss: 2.5852 - classification_output_loss: 0.1672 - regression_output_loss: 0.1392 - final_output_loss: 0.0880 - classification_output_accuracy: 0.9350 - classification_output_auc: 0.9830 - regression_output_mse: 5.1786 - regression_output_mae: 1.6349 - regression_output_rmse: 2.2598 - regression_output_custom_mape: 71.9229 - final_output_mse: 0.1133 - final_output_mae: 0.2027 - final_output_rmse: 0.3209 - final_output_custom_mape: 72.9816 - val_loss: 1.4136 - val_classification_output_loss: 0.2138 - val_regression_output_loss: 0.1684 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9236 - val_classification_output_auc: 0.9842 - val_regression_output_mse: 1.8603 - val_regression_output_mae: 1.0648 - val_regression_output_rmse: 1.3566 - val_regression_output_custom_mape: 75.8244 - val_final_output_mse: 0.1512 - val_final_output_mae: 0.2497 - val_final_output_rmse: 0.3643 - val_final_output_custom_mape: 75.8084 - lr: 2.0000e-04\n",
"Epoch 3/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.9558 - classification_output_loss: 0.1719 - regression_output_loss: 0.1366 - final_output_loss: 0.0895 - classification_output_accuracy: 0.9322 - classification_output_auc: 0.9815 - regression_output_mse: 5.6874 - regression_output_mae: 1.7310 - regression_output_rmse: 2.3695 - regression_output_custom_mape: 71.9037 - final_output_mse: 0.1126 - final_output_mae: 0.2038 - final_output_rmse: 0.3184 - final_output_custom_mape: 72.9206 - val_loss: 0.9508 - val_classification_output_loss: 0.4633 - val_regression_output_loss: 0.3961 - val_final_output_loss: 0.3536 - val_classification_output_accuracy: 0.8129 - val_classification_output_auc: 0.9060 - val_regression_output_mse: 1.6205 - val_regression_output_mae: 0.8607 - val_regression_output_rmse: 1.2559 - val_regression_output_custom_mape: 66.2296 - val_final_output_mse: 0.4082 - val_final_output_mae: 0.4125 - val_final_output_rmse: 0.6197 - val_final_output_custom_mape: 78.8389 - lr: 2.0000e-04\n",
"Epoch 4/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.5850 - classification_output_loss: 0.1792 - regression_output_loss: 0.2007 - final_output_loss: 0.1027 - classification_output_accuracy: 0.9293 - classification_output_auc: 0.9798 - regression_output_mse: 2.2127 - regression_output_mae: 0.9460 - regression_output_rmse: 1.2398 - regression_output_custom_mape: 73.1762 - final_output_mse: 0.1435 - final_output_mae: 0.2295 - final_output_rmse: 0.3588 - final_output_custom_mape: 74.5266 - val_loss: 0.4332 - val_classification_output_loss: 0.1561 - val_regression_output_loss: 0.1114 - val_final_output_loss: 0.1238 - val_classification_output_accuracy: 0.9345 - val_classification_output_auc: 0.9856 - val_regression_output_mse: 4.7324 - val_regression_output_mae: 1.5163 - val_regression_output_rmse: 2.1370 - val_regression_output_custom_mape: 76.3284 - val_final_output_mse: 0.1509 - val_final_output_mae: 0.2353 - val_final_output_rmse: 0.3733 - val_final_output_custom_mape: 76.9648 - lr: 2.0000e-04\n",
"Epoch 5/100\n",
"541/541 [==============================] - 58s 106ms/step - loss: 0.3949 - classification_output_loss: 0.1662 - regression_output_loss: 0.1463 - final_output_loss: 0.0885 - classification_output_accuracy: 0.9320 - classification_output_auc: 0.9831 - regression_output_mse: 2.7376 - regression_output_mae: 0.9965 - regression_output_rmse: 1.2987 - regression_output_custom_mape: 71.5179 - final_output_mse: 0.1125 - final_output_mae: 0.2059 - final_output_rmse: 0.3178 - final_output_custom_mape: 73.1178 - val_loss: 0.3969 - val_classification_output_loss: 0.2723 - val_regression_output_loss: 0.2098 - val_final_output_loss: 0.0787 - val_classification_output_accuracy: 0.8948 - val_classification_output_auc: 0.9729 - val_regression_output_mse: 0.1621 - val_regression_output_mae: 0.3438 - val_regression_output_rmse: 0.3980 - val_regression_output_custom_mape: 72.4170 - val_final_output_mse: 0.1050 - val_final_output_mae: 0.1904 - val_final_output_rmse: 0.3098 - val_final_output_custom_mape: 74.6832 - lr: 2.0000e-04\n",
"Epoch 6/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.2983 - classification_output_loss: 0.1354 - regression_output_loss: 0.1470 - final_output_loss: 0.0704 - classification_output_accuracy: 0.9464 - classification_output_auc: 0.9883 - regression_output_mse: 0.2598 - regression_output_mae: 0.4423 - regression_output_rmse: 0.5024 - regression_output_custom_mape: 71.7011 - final_output_mse: 0.0909 - final_output_mae: 0.1841 - final_output_rmse: 0.2871 - final_output_custom_mape: 71.9341 - val_loss: 0.2936 - val_classification_output_loss: 0.1702 - val_regression_output_loss: 0.1509 - val_final_output_loss: 0.1213 - val_classification_output_accuracy: 0.9348 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 0.2456 - val_regression_output_mae: 0.4147 - val_regression_output_rmse: 0.4831 - val_regression_output_custom_mape: 74.9922 - val_final_output_mse: 0.1488 - val_final_output_mae: 0.2237 - val_final_output_rmse: 0.3632 - val_final_output_custom_mape: 74.9167 - lr: 2.0000e-04\n",
"Epoch 7/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.2505 - classification_output_loss: 0.1442 - regression_output_loss: 0.1384 - final_output_loss: 0.0775 - classification_output_accuracy: 0.9443 - classification_output_auc: 0.9866 - regression_output_mse: 1.4617 - regression_output_mae: 0.7991 - regression_output_rmse: 1.0508 - regression_output_custom_mape: 71.1954 - final_output_mse: 0.0988 - final_output_mae: 0.1902 - final_output_rmse: 0.2984 - final_output_custom_mape: 72.2324 - val_loss: 0.3214 - val_classification_output_loss: 0.3465 - val_regression_output_loss: 0.1456 - val_final_output_loss: 0.2147 - val_classification_output_accuracy: 0.8648 - val_classification_output_auc: 0.9798 - val_regression_output_mse: 0.8371 - val_regression_output_mae: 0.7323 - val_regression_output_rmse: 0.9107 - val_regression_output_custom_mape: 73.4417 - val_final_output_mse: 0.2713 - val_final_output_mae: 0.3340 - val_final_output_rmse: 0.4815 - val_final_output_custom_mape: 76.5578 - lr: 2.0000e-04\n",
"Epoch 8/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.2237 - classification_output_loss: 0.1397 - regression_output_loss: 0.1364 - final_output_loss: 0.0796 - classification_output_accuracy: 0.9446 - classification_output_auc: 0.9878 - regression_output_mse: 5.1974 - regression_output_mae: 1.6560 - regression_output_rmse: 2.2692 - regression_output_custom_mape: 71.2507 - final_output_mse: 0.1033 - final_output_mae: 0.1957 - final_output_rmse: 0.3040 - final_output_custom_mape: 72.5075 - val_loss: 0.2685 - val_classification_output_loss: 0.3914 - val_regression_output_loss: 0.1217 - val_final_output_loss: 0.1222 - val_classification_output_accuracy: 0.8613 - val_classification_output_auc: 0.9606 - val_regression_output_mse: 3.7820 - val_regression_output_mae: 1.2866 - val_regression_output_rmse: 1.8121 - val_regression_output_custom_mape: 68.2919 - val_final_output_mse: 0.1599 - val_final_output_mae: 0.2535 - val_final_output_rmse: 0.3599 - val_final_output_custom_mape: 71.1078 - lr: 2.0000e-04\n",
"Epoch 9/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.1754 - classification_output_loss: 0.1188 - regression_output_loss: 0.0985 - final_output_loss: 0.0632 - classification_output_accuracy: 0.9523 - classification_output_auc: 0.9911 - regression_output_mse: 5.4764 - regression_output_mae: 1.6783 - regression_output_rmse: 2.3144 - regression_output_custom_mape: 69.8070 - final_output_mse: 0.0766 - final_output_mae: 0.1685 - final_output_rmse: 0.2609 - final_output_custom_mape: 70.8609 - val_loss: 0.1706 - val_classification_output_loss: 0.1096 - val_regression_output_loss: 0.1077 - val_final_output_loss: 0.0658 - val_classification_output_accuracy: 0.9538 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 5.1203 - val_regression_output_mae: 1.6260 - val_regression_output_rmse: 2.2470 - val_regression_output_custom_mape: 70.2256 - val_final_output_mse: 0.0762 - val_final_output_mae: 0.1588 - val_final_output_rmse: 0.2636 - val_final_output_custom_mape: 72.1157 - lr: 2.0000e-04\n",
"Epoch 10/100\n",
"541/541 [==============================] - 53s 98ms/step - loss: 0.1727 - classification_output_loss: 0.1290 - regression_output_loss: 0.1097 - final_output_loss: 0.0692 - classification_output_accuracy: 0.9479 - classification_output_auc: 0.9894 - regression_output_mse: 5.5291 - regression_output_mae: 1.6998 - regression_output_rmse: 2.3152 - regression_output_custom_mape: 70.2356 - final_output_mse: 0.0878 - final_output_mae: 0.1771 - final_output_rmse: 0.2731 - final_output_custom_mape: 71.4182 - val_loss: 0.1912 - val_classification_output_loss: 0.1751 - val_regression_output_loss: 0.0882 - val_final_output_loss: 0.0951 - val_classification_output_accuracy: 0.9463 - val_classification_output_auc: 0.9833 - val_regression_output_mse: 6.5049 - val_regression_output_mae: 1.9127 - val_regression_output_rmse: 2.5318 - val_regression_output_custom_mape: 75.4162 - val_final_output_mse: 0.1245 - val_final_output_mae: 0.2154 - val_final_output_rmse: 0.3377 - val_final_output_custom_mape: 75.3060 - lr: 2.0000e-04\n",
"Epoch 11/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452\n",
"Epoch 11 Detailed Metrics:\n",
"541/541 [==============================] - 54s 101ms/step - loss: 0.1454 - classification_output_loss: 0.0976 - regression_output_loss: 0.0864 - final_output_loss: 0.0573 - classification_output_accuracy: 0.9615 - classification_output_auc: 0.9941 - regression_output_mse: 5.8449 - regression_output_mae: 1.7543 - regression_output_rmse: 2.4040 - regression_output_custom_mape: 69.6693 - final_output_mse: 0.0682 - final_output_mae: 0.1573 - final_output_rmse: 0.2424 - final_output_custom_mape: 70.2452 - val_loss: 0.2093 - val_classification_output_loss: 0.2277 - val_regression_output_loss: 0.1437 - val_final_output_loss: 0.1177 - val_classification_output_accuracy: 0.9283 - val_classification_output_auc: 0.9779 - val_regression_output_mse: 6.0570 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.4544 - val_regression_output_custom_mape: 72.7221 - val_final_output_mse: 0.1364 - val_final_output_mae: 0.2321 - val_final_output_rmse: 0.3485 - val_final_output_custom_mape: 73.0335 - lr: 2.0000e-04\n",
"Epoch 12/100\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.1464 - classification_output_loss: 0.1072 - regression_output_loss: 0.1053 - final_output_loss: 0.0601 - classification_output_accuracy: 0.9561 - classification_output_auc: 0.9927 - regression_output_mse: 6.1092 - regression_output_mae: 1.8112 - regression_output_rmse: 2.4620 - regression_output_custom_mape: 69.8113 - final_output_mse: 0.0740 - final_output_mae: 0.1632 - final_output_rmse: 0.2523 - final_output_custom_mape: 70.3597 - val_loss: 0.1542 - val_classification_output_loss: 0.1503 - val_regression_output_loss: 0.0985 - val_final_output_loss: 0.0841 - val_classification_output_accuracy: 0.9489 - val_classification_output_auc: 0.9879 - val_regression_output_mse: 5.9678 - val_regression_output_mae: 1.7956 - val_regression_output_rmse: 2.4203 - val_regression_output_custom_mape: 72.0743 - val_final_output_mse: 0.1016 - val_final_output_mae: 0.1921 - val_final_output_rmse: 0.2866 - val_final_output_custom_mape: 72.4481 - lr: 2.0000e-04\n",
"Epoch 13/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.1351 - classification_output_loss: 0.0971 - regression_output_loss: 0.0999 - final_output_loss: 0.0592 - classification_output_accuracy: 0.9601 - classification_output_auc: 0.9939 - regression_output_mse: 6.1488 - regression_output_mae: 1.8152 - regression_output_rmse: 2.4696 - regression_output_custom_mape: 69.9159 - final_output_mse: 0.0739 - final_output_mae: 0.1643 - final_output_rmse: 0.2517 - final_output_custom_mape: 70.7436 - val_loss: 0.2396 - val_classification_output_loss: 0.3289 - val_regression_output_loss: 0.2191 - val_final_output_loss: 0.0616 - val_classification_output_accuracy: 0.8966 - val_classification_output_auc: 0.9705 - val_regression_output_mse: 0.7583 - val_regression_output_mae: 0.6739 - val_regression_output_rmse: 0.8690 - val_regression_output_custom_mape: 73.5880 - val_final_output_mse: 0.0818 - val_final_output_mae: 0.1722 - val_final_output_rmse: 0.2787 - val_final_output_custom_mape: 73.8794 - lr: 2.0000e-04\n",
"Epoch 14/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.1342 - classification_output_loss: 0.1155 - regression_output_loss: 0.0989 - final_output_loss: 0.0567 - classification_output_accuracy: 0.9515 - classification_output_auc: 0.9914 - regression_output_mse: 6.1207 - regression_output_mae: 1.8088 - regression_output_rmse: 2.4631 - regression_output_custom_mape: 69.6780 - final_output_mse: 0.0681 - final_output_mae: 0.1613 - final_output_rmse: 0.2447 - final_output_custom_mape: 70.4782 - val_loss: 0.1483 - val_classification_output_loss: 0.1125 - val_regression_output_loss: 0.1075 - val_final_output_loss: 0.0949 - val_classification_output_accuracy: 0.9545 - val_classification_output_auc: 0.9921 - val_regression_output_mse: 3.9937 - val_regression_output_mae: 1.5182 - val_regression_output_rmse: 1.9883 - val_regression_output_custom_mape: 74.0972 - val_final_output_mse: 0.1191 - val_final_output_mae: 0.2071 - val_final_output_rmse: 0.3226 - val_final_output_custom_mape: 73.9669 - lr: 2.0000e-04\n",
"Epoch 15/100\n",
"541/541 [==============================] - 57s 105ms/step - loss: 0.1104 - classification_output_loss: 0.0837 - regression_output_loss: 0.0778 - final_output_loss: 0.0495 - classification_output_accuracy: 0.9664 - classification_output_auc: 0.9953 - regression_output_mse: 6.4510 - regression_output_mae: 1.8607 - regression_output_rmse: 2.5321 - regression_output_custom_mape: 68.8861 - final_output_mse: 0.0558 - final_output_mae: 0.1437 - final_output_rmse: 0.2201 - final_output_custom_mape: 69.3373 - val_loss: 0.1422 - val_classification_output_loss: 0.1152 - val_regression_output_loss: 0.1280 - val_final_output_loss: 0.0574 - val_classification_output_accuracy: 0.9537 - val_classification_output_auc: 0.9933 - val_regression_output_mse: 5.8958 - val_regression_output_mae: 1.7291 - val_regression_output_rmse: 2.4158 - val_regression_output_custom_mape: 68.3024 - val_final_output_mse: 0.0661 - val_final_output_mae: 0.1556 - val_final_output_rmse: 0.2529 - val_final_output_custom_mape: 69.4068 - lr: 2.0000e-04\n",
"Epoch 16/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.1238 - classification_output_loss: 0.1192 - regression_output_loss: 0.0907 - final_output_loss: 0.0547 - classification_output_accuracy: 0.9500 - classification_output_auc: 0.9910 - regression_output_mse: 6.1427 - regression_output_mae: 1.7993 - regression_output_rmse: 2.4683 - regression_output_custom_mape: 68.9479 - final_output_mse: 0.0637 - final_output_mae: 0.1549 - final_output_rmse: 0.2359 - final_output_custom_mape: 69.7157 - val_loss: 0.1268 - val_classification_output_loss: 0.1301 - val_regression_output_loss: 0.0858 - val_final_output_loss: 0.0656 - val_classification_output_accuracy: 0.9430 - val_classification_output_auc: 0.9895 - val_regression_output_mse: 6.4156 - val_regression_output_mae: 1.8944 - val_regression_output_rmse: 2.5145 - val_regression_output_custom_mape: 71.3515 - val_final_output_mse: 0.0826 - val_final_output_mae: 0.1624 - val_final_output_rmse: 0.2670 - val_final_output_custom_mape: 71.2686 - lr: 2.0000e-04\n",
"Epoch 17/100\n",
"541/541 [==============================] - 58s 108ms/step - loss: 0.0942 - classification_output_loss: 0.0838 - regression_output_loss: 0.0617 - final_output_loss: 0.0405 - classification_output_accuracy: 0.9650 - classification_output_auc: 0.9955 - regression_output_mse: 6.3983 - regression_output_mae: 1.8268 - regression_output_rmse: 2.5217 - regression_output_custom_mape: 67.0560 - final_output_mse: 0.0399 - final_output_mae: 0.1244 - final_output_rmse: 0.1906 - final_output_custom_mape: 67.8066 - val_loss: 0.0979 - val_classification_output_loss: 0.1114 - val_regression_output_loss: 0.0596 - val_final_output_loss: 0.0473 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9919 - val_regression_output_mse: 5.9015 - val_regression_output_mae: 1.7669 - val_regression_output_rmse: 2.4102 - val_regression_output_custom_mape: 67.6186 - val_final_output_mse: 0.0482 - val_final_output_mae: 0.1296 - val_final_output_rmse: 0.2055 - val_final_output_custom_mape: 67.8666 - lr: 2.0000e-04\n",
"Epoch 18/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0781 - classification_output_loss: 0.0719 - regression_output_loss: 0.0469 - final_output_loss: 0.0356 - classification_output_accuracy: 0.9714 - classification_output_auc: 0.9967 - regression_output_mse: 6.5024 - regression_output_mae: 1.8395 - regression_output_rmse: 2.5405 - regression_output_custom_mape: 65.9929 - final_output_mse: 0.0320 - final_output_mae: 0.1099 - final_output_rmse: 0.1657 - final_output_custom_mape: 66.5092 - val_loss: 0.1399 - val_classification_output_loss: 0.2071 - val_regression_output_loss: 0.1092 - val_final_output_loss: 0.0458 - val_classification_output_accuracy: 0.9400 - val_classification_output_auc: 0.9852 - val_regression_output_mse: 6.7711 - val_regression_output_mae: 1.9289 - val_regression_output_rmse: 2.5859 - val_regression_output_custom_mape: 69.2916 - val_final_output_mse: 0.0527 - val_final_output_mae: 0.1335 - val_final_output_rmse: 0.2186 - val_final_output_custom_mape: 69.7183 - lr: 2.0000e-04\n",
"Epoch 19/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.1056 - classification_output_loss: 0.1087 - regression_output_loss: 0.0770 - final_output_loss: 0.0488 - classification_output_accuracy: 0.9540 - classification_output_auc: 0.9925 - regression_output_mse: 6.1650 - regression_output_mae: 1.7928 - regression_output_rmse: 2.4682 - regression_output_custom_mape: 68.5078 - final_output_mse: 0.0533 - final_output_mae: 0.1445 - final_output_rmse: 0.2165 - final_output_custom_mape: 69.2497 - val_loss: 0.1351 - val_classification_output_loss: 0.2118 - val_regression_output_loss: 0.0784 - val_final_output_loss: 0.0744 - val_classification_output_accuracy: 0.9309 - val_classification_output_auc: 0.9809 - val_regression_output_mse: 6.2044 - val_regression_output_mae: 1.8057 - val_regression_output_rmse: 2.4610 - val_regression_output_custom_mape: 71.1880 - val_final_output_mse: 0.0989 - val_final_output_mae: 0.1831 - val_final_output_rmse: 0.2888 - val_final_output_custom_mape: 71.7069 - lr: 2.0000e-04\n",
"Epoch 20/100\n",
"541/541 [==============================] - 52s 97ms/step - loss: 0.0826 - classification_output_loss: 0.0899 - regression_output_loss: 0.0503 - final_output_loss: 0.0377 - classification_output_accuracy: 0.9641 - classification_output_auc: 0.9946 - regression_output_mse: 6.4599 - regression_output_mae: 1.8346 - regression_output_rmse: 2.5319 - regression_output_custom_mape: 66.5618 - final_output_mse: 0.0347 - final_output_mae: 0.1170 - final_output_rmse: 0.1767 - final_output_custom_mape: 67.1488 - val_loss: 0.1313 - val_classification_output_loss: 0.1914 - val_regression_output_loss: 0.0979 - val_final_output_loss: 0.0590 - val_classification_output_accuracy: 0.9393 - val_classification_output_auc: 0.9835 - val_regression_output_mse: 7.0486 - val_regression_output_mae: 2.0071 - val_regression_output_rmse: 2.6313 - val_regression_output_custom_mape: 72.4229 - val_final_output_mse: 0.0695 - val_final_output_mae: 0.1603 - val_final_output_rmse: 0.2545 - val_final_output_custom_mape: 72.8319 - lr: 2.0000e-04\n",
"Epoch 21/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971\n",
"Epoch 21 Detailed Metrics:\n",
"541/541 [==============================] - 54s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0855 - regression_output_loss: 0.0795 - final_output_loss: 0.0458 - classification_output_accuracy: 0.9647 - classification_output_auc: 0.9953 - regression_output_mse: 6.5122 - regression_output_mae: 1.8662 - regression_output_rmse: 2.5424 - regression_output_custom_mape: 68.3928 - final_output_mse: 0.0492 - final_output_mae: 0.1379 - final_output_rmse: 0.2084 - final_output_custom_mape: 68.8971 - val_loss: 0.1567 - val_classification_output_loss: 0.1925 - val_regression_output_loss: 0.1182 - val_final_output_loss: 0.1052 - val_classification_output_accuracy: 0.9335 - val_classification_output_auc: 0.9888 - val_regression_output_mse: 6.3370 - val_regression_output_mae: 1.8864 - val_regression_output_rmse: 2.4981 - val_regression_output_custom_mape: 76.4414 - val_final_output_mse: 0.1555 - val_final_output_mae: 0.2558 - val_final_output_rmse: 0.3667 - val_final_output_custom_mape: 76.3954 - lr: 2.0000e-04\n",
"Epoch 22/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.0895 - classification_output_loss: 0.0784 - regression_output_loss: 0.0675 - final_output_loss: 0.0448 - classification_output_accuracy: 0.9675 - classification_output_auc: 0.9960 - regression_output_mse: 6.3547 - regression_output_mae: 1.8204 - regression_output_rmse: 2.5032 - regression_output_custom_mape: 67.3088 - final_output_mse: 0.0469 - final_output_mae: 0.1335 - final_output_rmse: 0.2007 - final_output_custom_mape: 68.4431 - val_loss: 0.1160 - val_classification_output_loss: 0.1100 - val_regression_output_loss: 0.1048 - val_final_output_loss: 0.0471 - val_classification_output_accuracy: 0.9584 - val_classification_output_auc: 0.9936 - val_regression_output_mse: 6.0390 - val_regression_output_mae: 1.7293 - val_regression_output_rmse: 2.4455 - val_regression_output_custom_mape: 66.8834 - val_final_output_mse: 0.0490 - val_final_output_mae: 0.1419 - val_final_output_rmse: 0.2174 - val_final_output_custom_mape: 69.0459 - lr: 2.0000e-04\n",
"Epoch 23/100\n",
"541/541 [==============================] - 53s 98ms/step - loss: 0.0919 - classification_output_loss: 0.0994 - regression_output_loss: 0.0648 - final_output_loss: 0.0429 - classification_output_accuracy: 0.9578 - classification_output_auc: 0.9935 - regression_output_mse: 6.2936 - regression_output_mae: 1.8038 - regression_output_rmse: 2.4971 - regression_output_custom_mape: 67.2289 - final_output_mse: 0.0428 - final_output_mae: 0.1306 - final_output_rmse: 0.1954 - final_output_custom_mape: 68.1846 - val_loss: 0.1030 - val_classification_output_loss: 0.1001 - val_regression_output_loss: 0.0829 - val_final_output_loss: 0.0521 - val_classification_output_accuracy: 0.9620 - val_classification_output_auc: 0.9930 - val_regression_output_mse: 4.9840 - val_regression_output_mae: 1.6330 - val_regression_output_rmse: 2.2237 - val_regression_output_custom_mape: 67.5453 - val_final_output_mse: 0.0686 - val_final_output_mae: 0.1495 - val_final_output_rmse: 0.2307 - val_final_output_custom_mape: 68.2594 - lr: 2.0000e-04\n",
"Epoch 24/100\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.0805 - classification_output_loss: 0.0740 - regression_output_loss: 0.0572 - final_output_loss: 0.0406 - classification_output_accuracy: 0.9696 - classification_output_auc: 0.9964 - regression_output_mse: 6.5178 - regression_output_mae: 1.8487 - regression_output_rmse: 2.5431 - regression_output_custom_mape: 66.7225 - final_output_mse: 0.0413 - final_output_mae: 0.1227 - final_output_rmse: 0.1848 - final_output_custom_mape: 67.3500 - val_loss: 0.1503 - val_classification_output_loss: 0.1089 - val_regression_output_loss: 0.1781 - val_final_output_loss: 0.0504 - val_classification_output_accuracy: 0.9567 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 7.3781 - val_regression_output_mae: 2.0860 - val_regression_output_rmse: 2.7043 - val_regression_output_custom_mape: 71.4390 - val_final_output_mse: 0.0652 - val_final_output_mae: 0.1535 - val_final_output_rmse: 0.2412 - val_final_output_custom_mape: 71.1141 - lr: 2.0000e-04\n",
"Epoch 25/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826\n",
"Epoch 25: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-05.\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.0978 - classification_output_loss: 0.0906 - regression_output_loss: 0.0818 - final_output_loss: 0.0454 - classification_output_accuracy: 0.9611 - classification_output_auc: 0.9948 - regression_output_mse: 6.4449 - regression_output_mae: 1.8471 - regression_output_rmse: 2.5298 - regression_output_custom_mape: 68.0068 - final_output_mse: 0.0497 - final_output_mae: 0.1380 - final_output_rmse: 0.2085 - final_output_custom_mape: 68.7826 - val_loss: 0.0920 - val_classification_output_loss: 0.1283 - val_regression_output_loss: 0.0552 - val_final_output_loss: 0.0492 - val_classification_output_accuracy: 0.9526 - val_classification_output_auc: 0.9923 - val_regression_output_mse: 6.4349 - val_regression_output_mae: 1.8355 - val_regression_output_rmse: 2.5185 - val_regression_output_custom_mape: 69.3148 - val_final_output_mse: 0.0507 - val_final_output_mae: 0.1446 - val_final_output_rmse: 0.2100 - val_final_output_custom_mape: 69.2727 - lr: 2.0000e-04\n",
"Epoch 26/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.0650 - classification_output_loss: 0.0610 - regression_output_loss: 0.0419 - final_output_loss: 0.0313 - classification_output_accuracy: 0.9754 - classification_output_auc: 0.9975 - regression_output_mse: 6.6869 - regression_output_mae: 1.8694 - regression_output_rmse: 2.5765 - regression_output_custom_mape: 65.2689 - final_output_mse: 0.0247 - final_output_mae: 0.0999 - final_output_rmse: 0.1486 - final_output_custom_mape: 65.4804 - val_loss: 0.0663 - val_classification_output_loss: 0.0825 - val_regression_output_loss: 0.0378 - val_final_output_loss: 0.0327 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9952 - val_regression_output_mse: 6.7468 - val_regression_output_mae: 1.8843 - val_regression_output_rmse: 2.5794 - val_regression_output_custom_mape: 64.9190 - val_final_output_mse: 0.0274 - val_final_output_mae: 0.1009 - val_final_output_rmse: 0.1557 - val_final_output_custom_mape: 64.8372 - lr: 1.0000e-04\n",
"Epoch 27/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0520 - classification_output_loss: 0.0582 - regression_output_loss: 0.0251 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9761 - classification_output_auc: 0.9978 - regression_output_mse: 6.7296 - regression_output_mae: 1.8683 - regression_output_rmse: 2.5843 - regression_output_custom_mape: 64.1190 - final_output_mse: 0.0169 - final_output_mae: 0.0854 - final_output_rmse: 0.1247 - final_output_custom_mape: 64.2327 - val_loss: 0.0596 - val_classification_output_loss: 0.0822 - val_regression_output_loss: 0.0315 - val_final_output_loss: 0.0279 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9952 - val_regression_output_mse: 6.7299 - val_regression_output_mae: 1.8644 - val_regression_output_rmse: 2.5772 - val_regression_output_custom_mape: 64.0526 - val_final_output_mse: 0.0205 - val_final_output_mae: 0.0887 - val_final_output_rmse: 0.1379 - val_final_output_custom_mape: 64.1891 - lr: 1.0000e-04\n",
"Epoch 28/100\n",
"541/541 [==============================] - 54s 101ms/step - loss: 0.0545 - classification_output_loss: 0.0621 - regression_output_loss: 0.0298 - final_output_loss: 0.0277 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9975 - regression_output_mse: 6.5792 - regression_output_mae: 1.8345 - regression_output_rmse: 2.5551 - regression_output_custom_mape: 63.7945 - final_output_mse: 0.0191 - final_output_mae: 0.0891 - final_output_rmse: 0.1301 - final_output_custom_mape: 64.3093 - val_loss: 0.0707 - val_classification_output_loss: 0.0879 - val_regression_output_loss: 0.0470 - val_final_output_loss: 0.0366 - val_classification_output_accuracy: 0.9646 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6182 - val_regression_output_mae: 1.8513 - val_regression_output_rmse: 2.5551 - val_regression_output_custom_mape: 65.1533 - val_final_output_mse: 0.0342 - val_final_output_mae: 0.1136 - val_final_output_rmse: 0.1726 - val_final_output_custom_mape: 65.5781 - lr: 1.0000e-04\n",
"Epoch 29/100\n",
"541/541 [==============================] - 53s 98ms/step - loss: 0.0504 - classification_output_loss: 0.0586 - regression_output_loss: 0.0257 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9756 - classification_output_auc: 0.9978 - regression_output_mse: 6.7302 - regression_output_mae: 1.8661 - regression_output_rmse: 2.5837 - regression_output_custom_mape: 63.8101 - final_output_mse: 0.0171 - final_output_mae: 0.0858 - final_output_rmse: 0.1252 - final_output_custom_mape: 64.0432 - val_loss: 0.0755 - val_classification_output_loss: 0.0734 - val_regression_output_loss: 0.0692 - val_final_output_loss: 0.0298 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5720 - val_regression_output_mae: 1.8334 - val_regression_output_rmse: 2.5485 - val_regression_output_custom_mape: 65.7695 - val_final_output_mse: 0.0219 - val_final_output_mae: 0.0957 - val_final_output_rmse: 0.1415 - val_final_output_custom_mape: 65.9068 - lr: 1.0000e-04\n",
"Epoch 30/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0618 - classification_output_loss: 0.0646 - regression_output_loss: 0.0430 - final_output_loss: 0.0325 - classification_output_accuracy: 0.9726 - classification_output_auc: 0.9974 - regression_output_mse: 6.6295 - regression_output_mae: 1.8588 - regression_output_rmse: 2.5643 - regression_output_custom_mape: 65.4595 - final_output_mse: 0.0267 - final_output_mae: 0.1036 - final_output_rmse: 0.1522 - final_output_custom_mape: 65.8207 - val_loss: 0.0563 - val_classification_output_loss: 0.0878 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0269 - val_classification_output_accuracy: 0.9639 - val_classification_output_auc: 0.9949 - val_regression_output_mse: 6.6181 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5541 - val_regression_output_custom_mape: 63.1844 - val_final_output_mse: 0.0184 - val_final_output_mae: 0.0849 - val_final_output_rmse: 0.1292 - val_final_output_custom_mape: 63.5460 - lr: 1.0000e-04\n",
"Epoch 31/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0539 - classification_output_loss: 0.0608 - regression_output_loss: 0.0321 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9746 - classification_output_auc: 0.9975 - regression_output_mse: 6.6719 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5729 - regression_output_custom_mape: 64.1531 - final_output_mse: 0.0197 - final_output_mae: 0.0908 - final_output_rmse: 0.1326 - final_output_custom_mape: 64.4701\n",
"Epoch 31 Detailed Metrics:\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0539 - classification_output_loss: 0.0608 - regression_output_loss: 0.0321 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9746 - classification_output_auc: 0.9975 - regression_output_mse: 6.6719 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5729 - regression_output_custom_mape: 64.1531 - final_output_mse: 0.0197 - final_output_mae: 0.0908 - final_output_rmse: 0.1326 - final_output_custom_mape: 64.4701 - val_loss: 0.0803 - val_classification_output_loss: 0.1105 - val_regression_output_loss: 0.0560 - val_final_output_loss: 0.0436 - val_classification_output_accuracy: 0.9555 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 6.3193 - val_regression_output_mae: 1.7972 - val_regression_output_rmse: 2.4978 - val_regression_output_custom_mape: 67.5192 - val_final_output_mse: 0.0457 - val_final_output_mae: 0.1347 - val_final_output_rmse: 0.2015 - val_final_output_custom_mape: 67.7615 - lr: 1.0000e-04\n",
"Epoch 32/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0588 - classification_output_loss: 0.0668 - regression_output_loss: 0.0386 - final_output_loss: 0.0303 - classification_output_accuracy: 0.9719 - classification_output_auc: 0.9971 - regression_output_mse: 6.6258 - regression_output_mae: 1.8496 - regression_output_rmse: 2.5637 - regression_output_custom_mape: 64.4690 - final_output_mse: 0.0235 - final_output_mae: 0.0968 - final_output_rmse: 0.1423 - final_output_custom_mape: 64.9170 - val_loss: 0.0554 - val_classification_output_loss: 0.0832 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0247 - val_classification_output_accuracy: 0.9658 - val_classification_output_auc: 0.9956 - val_regression_output_mse: 6.7171 - val_regression_output_mae: 1.8475 - val_regression_output_rmse: 2.5747 - val_regression_output_custom_mape: 63.5893 - val_final_output_mse: 0.0151 - val_final_output_mae: 0.0793 - val_final_output_rmse: 0.1190 - val_final_output_custom_mape: 63.5201 - lr: 1.0000e-04\n",
"Epoch 33/100\n",
"541/541 [==============================] - 53s 97ms/step - loss: 0.0562 - classification_output_loss: 0.0654 - regression_output_loss: 0.0351 - final_output_loss: 0.0293 - classification_output_accuracy: 0.9728 - classification_output_auc: 0.9973 - regression_output_mse: 6.6587 - regression_output_mae: 1.8556 - regression_output_rmse: 2.5699 - regression_output_custom_mape: 64.4570 - final_output_mse: 0.0214 - final_output_mae: 0.0943 - final_output_rmse: 0.1371 - final_output_custom_mape: 64.8190 - val_loss: 0.0688 - val_classification_output_loss: 0.0870 - val_regression_output_loss: 0.0540 - val_final_output_loss: 0.0265 - val_classification_output_accuracy: 0.9638 - val_classification_output_auc: 0.9946 - val_regression_output_mse: 6.6598 - val_regression_output_mae: 1.8470 - val_regression_output_rmse: 2.5624 - val_regression_output_custom_mape: 63.3716 - val_final_output_mse: 0.0182 - val_final_output_mae: 0.0846 - val_final_output_rmse: 0.1293 - val_final_output_custom_mape: 63.4470 - lr: 1.0000e-04\n",
"Epoch 34/100\n",
"541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0589 - regression_output_loss: 0.0381 - final_output_loss: 0.0284 - classification_output_accuracy: 0.9753 - classification_output_auc: 0.9977 - regression_output_mse: 6.7001 - regression_output_mae: 1.8630 - regression_output_rmse: 2.5779 - regression_output_custom_mape: 63.9678 - final_output_mse: 0.0203 - final_output_mae: 0.0921 - final_output_rmse: 0.1348 - final_output_custom_mape: 64.2577 - val_loss: 0.0620 - val_classification_output_loss: 0.0928 - val_regression_output_loss: 0.0371 - val_final_output_loss: 0.0294 - val_classification_output_accuracy: 0.9603 - val_classification_output_auc: 0.9940 - val_regression_output_mse: 6.7330 - val_regression_output_mae: 1.8643 - val_regression_output_rmse: 2.5765 - val_regression_output_custom_mape: 64.8509 - val_final_output_mse: 0.0217 - val_final_output_mae: 0.0925 - val_final_output_rmse: 0.1422 - val_final_output_custom_mape: 65.1600 - lr: 1.0000e-04\n",
"Epoch 35/100\n",
"541/541 [==============================] - 52s 97ms/step - loss: 0.0557 - classification_output_loss: 0.0614 - regression_output_loss: 0.0367 - final_output_loss: 0.0297 - classification_output_accuracy: 0.9745 - classification_output_auc: 0.9976 - regression_output_mse: 6.6579 - regression_output_mae: 1.8582 - regression_output_rmse: 2.5696 - regression_output_custom_mape: 64.6971 - final_output_mse: 0.0221 - final_output_mae: 0.0956 - final_output_rmse: 0.1398 - final_output_custom_mape: 65.0271 - val_loss: 0.0594 - val_classification_output_loss: 0.0939 - val_regression_output_loss: 0.0300 - val_final_output_loss: 0.0317 - val_classification_output_accuracy: 0.9612 - val_classification_output_auc: 0.9941 - val_regression_output_mse: 6.8011 - val_regression_output_mae: 1.8931 - val_regression_output_rmse: 2.5893 - val_regression_output_custom_mape: 65.7507 - val_final_output_mse: 0.0254 - val_final_output_mae: 0.1008 - val_final_output_rmse: 0.1520 - val_final_output_custom_mape: 65.6841 - lr: 1.0000e-04\n",
"Epoch 36/100\n",
"541/541 [==============================] - 57s 106ms/step - loss: 0.0461 - classification_output_loss: 0.0654 - regression_output_loss: 0.0198 - final_output_loss: 0.0250 - classification_output_accuracy: 0.9728 - classification_output_auc: 0.9971 - regression_output_mse: 6.6669 - regression_output_mae: 1.8474 - regression_output_rmse: 2.5716 - regression_output_custom_mape: 63.3631 - final_output_mse: 0.0152 - final_output_mae: 0.0813 - final_output_rmse: 0.1173 - final_output_custom_mape: 63.6640 - val_loss: 0.0649 - val_classification_output_loss: 0.1065 - val_regression_output_loss: 0.0418 - val_final_output_loss: 0.0242 - val_classification_output_accuracy: 0.9594 - val_classification_output_auc: 0.9931 - val_regression_output_mse: 6.5540 - val_regression_output_mae: 1.8143 - val_regression_output_rmse: 2.5381 - val_regression_output_custom_mape: 62.4616 - val_final_output_mse: 0.0149 - val_final_output_mae: 0.0784 - val_final_output_rmse: 0.1183 - val_final_output_custom_mape: 62.9478 - lr: 1.0000e-04\n",
"Epoch 37/100\n",
"541/541 [==============================] - 58s 107ms/step - loss: 0.0585 - classification_output_loss: 0.0619 - regression_output_loss: 0.0427 - final_output_loss: 0.0304 - classification_output_accuracy: 0.9744 - classification_output_auc: 0.9975 - regression_output_mse: 6.6088 - regression_output_mae: 1.8483 - regression_output_rmse: 2.5604 - regression_output_custom_mape: 64.4680 - final_output_mse: 0.0237 - final_output_mae: 0.0974 - final_output_rmse: 0.1426 - final_output_custom_mape: 64.9015 - val_loss: 0.0691 - val_classification_output_loss: 0.1106 - val_regression_output_loss: 0.0482 - val_final_output_loss: 0.0240 - val_classification_output_accuracy: 0.9576 - val_classification_output_auc: 0.9934 - val_regression_output_mse: 6.5947 - val_regression_output_mae: 1.8196 - val_regression_output_rmse: 2.5455 - val_regression_output_custom_mape: 62.2371 - val_final_output_mse: 0.0145 - val_final_output_mae: 0.0779 - val_final_output_rmse: 0.1159 - val_final_output_custom_mape: 62.1254 - lr: 1.0000e-04\n",
"Epoch 38/100\n",
"541/541 [==============================] - 55s 103ms/step - loss: 0.0531 - classification_output_loss: 0.0608 - regression_output_loss: 0.0336 - final_output_loss: 0.0279 - classification_output_accuracy: 0.9748 - classification_output_auc: 0.9977 - regression_output_mse: 6.5831 - regression_output_mae: 1.8327 - regression_output_rmse: 2.5548 - regression_output_custom_mape: 63.5282 - final_output_mse: 0.0194 - final_output_mae: 0.0900 - final_output_rmse: 0.1307 - final_output_custom_mape: 64.1170 - val_loss: 0.0563 - val_classification_output_loss: 0.0809 - val_regression_output_loss: 0.0317 - val_final_output_loss: 0.0292 - val_classification_output_accuracy: 0.9680 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 6.5566 - val_regression_output_mae: 1.8266 - val_regression_output_rmse: 2.5428 - val_regression_output_custom_mape: 63.8044 - val_final_output_mse: 0.0222 - val_final_output_mae: 0.0928 - val_final_output_rmse: 0.1405 - val_final_output_custom_mape: 64.0602 - lr: 1.0000e-04\n",
"Epoch 39/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0515 - classification_output_loss: 0.0622 - regression_output_loss: 0.0313 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6419 - regression_output_mae: 1.8481 - regression_output_rmse: 2.5667 - regression_output_custom_mape: 63.7719 - final_output_mse: 0.0181 - final_output_mae: 0.0884 - final_output_rmse: 0.1283 - final_output_custom_mape: 64.1751 - val_loss: 0.0704 - val_classification_output_loss: 0.1092 - val_regression_output_loss: 0.0453 - val_final_output_loss: 0.0350 - val_classification_output_accuracy: 0.9609 - val_classification_output_auc: 0.9938 - val_regression_output_mse: 6.1988 - val_regression_output_mae: 1.7405 - val_regression_output_rmse: 2.4718 - val_regression_output_custom_mape: 63.3963 - val_final_output_mse: 0.0315 - val_final_output_mae: 0.1112 - val_final_output_rmse: 0.1671 - val_final_output_custom_mape: 65.2804 - lr: 1.0000e-04\n",
"Epoch 40/100\n",
"541/541 [==============================] - 54s 101ms/step - loss: 0.0517 - classification_output_loss: 0.0625 - regression_output_loss: 0.0314 - final_output_loss: 0.0282 - classification_output_accuracy: 0.9742 - classification_output_auc: 0.9975 - regression_output_mse: 6.6585 - regression_output_mae: 1.8533 - regression_output_rmse: 2.5700 - regression_output_custom_mape: 64.0952 - final_output_mse: 0.0199 - final_output_mae: 0.0913 - final_output_rmse: 0.1320 - final_output_custom_mape: 64.4904 - val_loss: 0.0712 - val_classification_output_loss: 0.0779 - val_regression_output_loss: 0.0654 - val_final_output_loss: 0.0267 - val_classification_output_accuracy: 0.9666 - val_classification_output_auc: 0.9957 - val_regression_output_mse: 6.7348 - val_regression_output_mae: 1.8600 - val_regression_output_rmse: 2.5770 - val_regression_output_custom_mape: 63.5495 - val_final_output_mse: 0.0176 - val_final_output_mae: 0.0874 - val_final_output_rmse: 0.1274 - val_final_output_custom_mape: 63.5438 - lr: 1.0000e-04\n",
"Epoch 41/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0537 - classification_output_loss: 0.0611 - regression_output_loss: 0.0359 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9976 - regression_output_mse: 6.7033 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5791 - regression_output_custom_mape: 64.1775 - final_output_mse: 0.0202 - final_output_mae: 0.0923 - final_output_rmse: 0.1340 - final_output_custom_mape: 64.4763\n",
"Epoch 41 Detailed Metrics:\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.0537 - classification_output_loss: 0.0611 - regression_output_loss: 0.0359 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9743 - classification_output_auc: 0.9976 - regression_output_mse: 6.7033 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5791 - regression_output_custom_mape: 64.1775 - final_output_mse: 0.0202 - final_output_mae: 0.0923 - final_output_rmse: 0.1340 - final_output_custom_mape: 64.4763 - val_loss: 0.0600 - val_classification_output_loss: 0.0930 - val_regression_output_loss: 0.0397 - val_final_output_loss: 0.0232 - val_classification_output_accuracy: 0.9621 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 6.4476 - val_regression_output_mae: 1.7785 - val_regression_output_rmse: 2.5232 - val_regression_output_custom_mape: 62.2171 - val_final_output_mse: 0.0129 - val_final_output_mae: 0.0759 - val_final_output_rmse: 0.1099 - val_final_output_custom_mape: 62.3951 - lr: 1.0000e-04\n",
"Epoch 42/100\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0544 - classification_output_loss: 0.0687 - regression_output_loss: 0.0347 - final_output_loss: 0.0285 - classification_output_accuracy: 0.9719 - classification_output_auc: 0.9968 - regression_output_mse: 6.6631 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5712 - regression_output_custom_mape: 64.0519 - final_output_mse: 0.0203 - final_output_mae: 0.0917 - final_output_rmse: 0.1332 - final_output_custom_mape: 64.3852 - val_loss: 0.0628 - val_classification_output_loss: 0.1047 - val_regression_output_loss: 0.0382 - val_final_output_loss: 0.0251 - val_classification_output_accuracy: 0.9576 - val_classification_output_auc: 0.9943 - val_regression_output_mse: 6.5854 - val_regression_output_mae: 1.8202 - val_regression_output_rmse: 2.5484 - val_regression_output_custom_mape: 62.9115 - val_final_output_mse: 0.0159 - val_final_output_mae: 0.0809 - val_final_output_rmse: 0.1218 - val_final_output_custom_mape: 62.8272 - lr: 1.0000e-04\n",
"Epoch 43/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.0545 - classification_output_loss: 0.0614 - regression_output_loss: 0.0368 - final_output_loss: 0.0291 - classification_output_accuracy: 0.9748 - classification_output_auc: 0.9975 - regression_output_mse: 6.7100 - regression_output_mae: 1.8688 - regression_output_rmse: 2.5810 - regression_output_custom_mape: 64.6285 - final_output_mse: 0.0208 - final_output_mae: 0.0943 - final_output_rmse: 0.1371 - final_output_custom_mape: 64.8706 - val_loss: 0.0639 - val_classification_output_loss: 0.1024 - val_regression_output_loss: 0.0432 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9622 - val_classification_output_auc: 0.9938 - val_regression_output_mse: 6.9339 - val_regression_output_mae: 1.9011 - val_regression_output_rmse: 2.6127 - val_regression_output_custom_mape: 62.8598 - val_final_output_mse: 0.0143 - val_final_output_mae: 0.0768 - val_final_output_rmse: 0.1158 - val_final_output_custom_mape: 62.6952 - lr: 1.0000e-04\n",
"Epoch 44/100\n",
"541/541 [==============================] - 57s 106ms/step - loss: 0.0503 - classification_output_loss: 0.0579 - regression_output_loss: 0.0320 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9979 - regression_output_mse: 6.7157 - regression_output_mae: 1.8642 - regression_output_rmse: 2.5809 - regression_output_custom_mape: 63.8468 - final_output_mse: 0.0185 - final_output_mae: 0.0884 - final_output_rmse: 0.1284 - final_output_custom_mape: 64.1197 - val_loss: 0.0535 - val_classification_output_loss: 0.0933 - val_regression_output_loss: 0.0278 - val_final_output_loss: 0.0222 - val_classification_output_accuracy: 0.9624 - val_classification_output_auc: 0.9953 - val_regression_output_mse: 6.5462 - val_regression_output_mae: 1.7951 - val_regression_output_rmse: 2.5414 - val_regression_output_custom_mape: 61.7450 - val_final_output_mse: 0.0123 - val_final_output_mae: 0.0720 - val_final_output_rmse: 0.1075 - val_final_output_custom_mape: 61.9004 - lr: 1.0000e-04\n",
"Epoch 45/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0461 - classification_output_loss: 0.0569 - regression_output_loss: 0.0258 - final_output_loss: 0.0260 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9980 - regression_output_mse: 6.7333 - regression_output_mae: 1.8649 - regression_output_rmse: 2.5850 - regression_output_custom_mape: 63.6520 - final_output_mse: 0.0163 - final_output_mae: 0.0845 - final_output_rmse: 0.1212 - final_output_custom_mape: 63.8765 - val_loss: 0.0649 - val_classification_output_loss: 0.1188 - val_regression_output_loss: 0.0354 - val_final_output_loss: 0.0316 - val_classification_output_accuracy: 0.9471 - val_classification_output_auc: 0.9966 - val_regression_output_mse: 6.0672 - val_regression_output_mae: 1.7044 - val_regression_output_rmse: 2.4462 - val_regression_output_custom_mape: 62.7387 - val_final_output_mse: 0.0251 - val_final_output_mae: 0.1018 - val_final_output_rmse: 0.1497 - val_final_output_custom_mape: 63.6189 - lr: 1.0000e-04\n",
"Epoch 46/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.0510 - classification_output_loss: 0.0628 - regression_output_loss: 0.0325 - final_output_loss: 0.0277 - classification_output_accuracy: 0.9741 - classification_output_auc: 0.9974 - regression_output_mse: 6.6063 - regression_output_mae: 1.8383 - regression_output_rmse: 2.5597 - regression_output_custom_mape: 63.7547 - final_output_mse: 0.0190 - final_output_mae: 0.0901 - final_output_rmse: 0.1300 - final_output_custom_mape: 64.2525 - val_loss: 0.0523 - val_classification_output_loss: 0.0814 - val_regression_output_loss: 0.0291 - val_final_output_loss: 0.0254 - val_classification_output_accuracy: 0.9658 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 6.7808 - val_regression_output_mae: 1.8736 - val_regression_output_rmse: 2.5860 - val_regression_output_custom_mape: 65.4947 - val_final_output_mse: 0.0155 - val_final_output_mae: 0.0831 - val_final_output_rmse: 0.1224 - val_final_output_custom_mape: 65.5416 - lr: 1.0000e-04\n",
"Epoch 47/100\n",
"541/541 [==============================] - 51s 94ms/step - loss: 0.0509 - classification_output_loss: 0.0589 - regression_output_loss: 0.0343 - final_output_loss: 0.0272 - classification_output_accuracy: 0.9755 - classification_output_auc: 0.9977 - regression_output_mse: 6.7076 - regression_output_mae: 1.8620 - regression_output_rmse: 2.5796 - regression_output_custom_mape: 63.6718 - final_output_mse: 0.0182 - final_output_mae: 0.0885 - final_output_rmse: 0.1278 - final_output_custom_mape: 63.9957 - val_loss: 0.0655 - val_classification_output_loss: 0.1041 - val_regression_output_loss: 0.0332 - val_final_output_loss: 0.0474 - val_classification_output_accuracy: 0.9581 - val_classification_output_auc: 0.9955 - val_regression_output_mse: 5.7525 - val_regression_output_mae: 1.6878 - val_regression_output_rmse: 2.3943 - val_regression_output_custom_mape: 62.4874 - val_final_output_mse: 0.0233 - val_final_output_mae: 0.0817 - val_final_output_rmse: 0.1449 - val_final_output_custom_mape: 62.4108 - lr: 1.0000e-04\n",
"Epoch 48/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.0480 - classification_output_loss: 0.0625 - regression_output_loss: 0.0275 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9739 - classification_output_auc: 0.9975 - regression_output_mse: 6.6081 - regression_output_mae: 1.8378 - regression_output_rmse: 2.5612 - regression_output_custom_mape: 63.5768 - final_output_mse: 0.0170 - final_output_mae: 0.0853 - final_output_rmse: 0.1224 - final_output_custom_mape: 63.9522 - val_loss: 0.0500 - val_classification_output_loss: 0.0769 - val_regression_output_loss: 0.0292 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9676 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5384 - val_regression_output_mae: 1.7942 - val_regression_output_rmse: 2.5409 - val_regression_output_custom_mape: 61.9763 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0710 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 62.2404 - lr: 1.0000e-04\n",
"Epoch 49/100\n",
"541/541 [==============================] - 51s 95ms/step - loss: 0.0474 - classification_output_loss: 0.0564 - regression_output_loss: 0.0296 - final_output_loss: 0.0263 - classification_output_accuracy: 0.9760 - classification_output_auc: 0.9980 - regression_output_mse: 6.7164 - regression_output_mae: 1.8615 - regression_output_rmse: 2.5813 - regression_output_custom_mape: 63.5453 - final_output_mse: 0.0171 - final_output_mae: 0.0857 - final_output_rmse: 0.1235 - final_output_custom_mape: 63.7933 - val_loss: 0.0498 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0283 - val_final_output_loss: 0.0260 - val_classification_output_accuracy: 0.9693 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.5693 - val_regression_output_mae: 1.8158 - val_regression_output_rmse: 2.5473 - val_regression_output_custom_mape: 64.0865 - val_final_output_mse: 0.0165 - val_final_output_mae: 0.0844 - val_final_output_rmse: 0.1243 - val_final_output_custom_mape: 64.5164 - lr: 1.0000e-04\n",
"Epoch 50/100\n",
"541/541 [==============================] - 49s 91ms/step - loss: 0.0395 - classification_output_loss: 0.0549 - regression_output_loss: 0.0169 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9766 - classification_output_auc: 0.9981 - regression_output_mse: 6.7595 - regression_output_mae: 1.8651 - regression_output_rmse: 2.5900 - regression_output_custom_mape: 63.1470 - final_output_mse: 0.0132 - final_output_mae: 0.0776 - final_output_rmse: 0.1109 - final_output_custom_mape: 63.3583 - val_loss: 0.0451 - val_classification_output_loss: 0.0768 - val_regression_output_loss: 0.0201 - val_final_output_loss: 0.0230 - val_classification_output_accuracy: 0.9683 - val_classification_output_auc: 0.9956 - val_regression_output_mse: 6.4914 - val_regression_output_mae: 1.8122 - val_regression_output_rmse: 2.5338 - val_regression_output_custom_mape: 62.2374 - val_final_output_mse: 0.0132 - val_final_output_mae: 0.0753 - val_final_output_rmse: 0.1099 - val_final_output_custom_mape: 62.2972 - lr: 1.0000e-04\n",
"Epoch 51/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354\n",
"Epoch 51 Detailed Metrics:\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0401 - classification_output_loss: 0.0564 - regression_output_loss: 0.0186 - final_output_loss: 0.0232 - classification_output_accuracy: 0.9757 - classification_output_auc: 0.9979 - regression_output_mse: 6.6958 - regression_output_mae: 1.8472 - regression_output_rmse: 2.5775 - regression_output_custom_mape: 62.6248 - final_output_mse: 0.0125 - final_output_mae: 0.0761 - final_output_rmse: 0.1077 - final_output_custom_mape: 62.9354 - val_loss: 0.0727 - val_classification_output_loss: 0.0944 - val_regression_output_loss: 0.0562 - val_final_output_loss: 0.0432 - val_classification_output_accuracy: 0.9653 - val_classification_output_auc: 0.9951 - val_regression_output_mse: 4.2029 - val_regression_output_mae: 1.5086 - val_regression_output_rmse: 2.0452 - val_regression_output_custom_mape: 67.4825 - val_final_output_mse: 0.0431 - val_final_output_mae: 0.1288 - val_final_output_rmse: 0.1947 - val_final_output_custom_mape: 67.4020 - lr: 1.0000e-04\n",
"Epoch 52/100\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.0518 - classification_output_loss: 0.0650 - regression_output_loss: 0.0344 - final_output_loss: 0.0286 - classification_output_accuracy: 0.9733 - classification_output_auc: 0.9972 - regression_output_mse: 6.6534 - regression_output_mae: 1.8507 - regression_output_rmse: 2.5694 - regression_output_custom_mape: 64.0731 - final_output_mse: 0.0204 - final_output_mae: 0.0922 - final_output_rmse: 0.1333 - final_output_custom_mape: 64.4775 - val_loss: 0.0449 - val_classification_output_loss: 0.0869 - val_regression_output_loss: 0.0155 - val_final_output_loss: 0.0226 - val_classification_output_accuracy: 0.9648 - val_classification_output_auc: 0.9948 - val_regression_output_mse: 6.4651 - val_regression_output_mae: 1.8048 - val_regression_output_rmse: 2.5262 - val_regression_output_custom_mape: 62.5471 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0736 - val_final_output_rmse: 0.1087 - val_final_output_custom_mape: 62.6503 - lr: 1.0000e-04\n",
"Epoch 53/100\n",
"541/541 [==============================] - 53s 99ms/step - loss: 0.0411 - classification_output_loss: 0.0548 - regression_output_loss: 0.0207 - final_output_loss: 0.0237 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9980 - regression_output_mse: 6.7604 - regression_output_mae: 1.8647 - regression_output_rmse: 2.5895 - regression_output_custom_mape: 62.7709 - final_output_mse: 0.0133 - final_output_mae: 0.0777 - final_output_rmse: 0.1099 - final_output_custom_mape: 62.9869 - val_loss: 0.0771 - val_classification_output_loss: 0.0905 - val_regression_output_loss: 0.0766 - val_final_output_loss: 0.0281 - val_classification_output_accuracy: 0.9636 - val_classification_output_auc: 0.9944 - val_regression_output_mse: 6.3203 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5019 - val_regression_output_custom_mape: 64.9538 - val_final_output_mse: 0.0197 - val_final_output_mae: 0.0919 - val_final_output_rmse: 0.1340 - val_final_output_custom_mape: 64.7748 - lr: 1.0000e-04\n",
"Epoch 54/100\n",
"541/541 [==============================] - 52s 96ms/step - loss: 0.0483 - classification_output_loss: 0.0616 - regression_output_loss: 0.0307 - final_output_loss: 0.0264 - classification_output_accuracy: 0.9738 - classification_output_auc: 0.9975 - regression_output_mse: 6.6227 - regression_output_mae: 1.8377 - regression_output_rmse: 2.5629 - regression_output_custom_mape: 63.1048 - final_output_mse: 0.0169 - final_output_mae: 0.0860 - final_output_rmse: 0.1239 - final_output_custom_mape: 63.6682 - val_loss: 0.0536 - val_classification_output_loss: 0.0971 - val_regression_output_loss: 0.0289 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9632 - val_classification_output_auc: 0.9942 - val_regression_output_mse: 6.6649 - val_regression_output_mae: 1.8270 - val_regression_output_rmse: 2.5626 - val_regression_output_custom_mape: 62.0142 - val_final_output_mse: 0.0125 - val_final_output_mae: 0.0733 - val_final_output_rmse: 0.1081 - val_final_output_custom_mape: 62.4347 - lr: 1.0000e-04\n",
"Epoch 55/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - final_output_custom_mape: 63.4037\n",
"Epoch 55: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0466 - classification_output_loss: 0.0573 - regression_output_loss: 0.0293 - final_output_loss: 0.0257 - classification_output_accuracy: 0.9759 - classification_output_auc: 0.9979 - regression_output_mse: 6.7103 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5801 - regression_output_custom_mape: 63.0620 - final_output_mse: 0.0164 - final_output_mae: 0.0839 - final_output_rmse: 0.1207 - final_output_custom_mape: 63.4037 - val_loss: 0.0547 - val_classification_output_loss: 0.1083 - val_regression_output_loss: 0.0260 - val_final_output_loss: 0.0251 - val_classification_output_accuracy: 0.9587 - val_classification_output_auc: 0.9929 - val_regression_output_mse: 6.7626 - val_regression_output_mae: 1.8696 - val_regression_output_rmse: 2.5790 - val_regression_output_custom_mape: 64.1653 - val_final_output_mse: 0.0161 - val_final_output_mae: 0.0809 - val_final_output_rmse: 0.1229 - val_final_output_custom_mape: 64.2964 - lr: 1.0000e-04\n",
"Epoch 56/100\n",
"541/541 [==============================] - 57s 106ms/step - loss: 0.0357 - classification_output_loss: 0.0520 - regression_output_loss: 0.0131 - final_output_loss: 0.0217 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9983 - regression_output_mse: 6.7730 - regression_output_mae: 1.8622 - regression_output_rmse: 2.5912 - regression_output_custom_mape: 62.1226 - final_output_mse: 0.0110 - final_output_mae: 0.0715 - final_output_rmse: 0.1009 - final_output_custom_mape: 62.2862 - val_loss: 0.0406 - val_classification_output_loss: 0.0722 - val_regression_output_loss: 0.0164 - val_final_output_loss: 0.0202 - val_classification_output_accuracy: 0.9707 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5865 - val_regression_output_mae: 1.8079 - val_regression_output_rmse: 2.5506 - val_regression_output_custom_mape: 62.1180 - val_final_output_mse: 0.0098 - val_final_output_mae: 0.0662 - val_final_output_rmse: 0.0959 - val_final_output_custom_mape: 62.3044 - lr: 5.0000e-05\n",
"Epoch 57/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.0331 - classification_output_loss: 0.0515 - regression_output_loss: 0.0100 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9983 - regression_output_mse: 6.7417 - regression_output_mae: 1.8522 - regression_output_rmse: 2.5856 - regression_output_custom_mape: 61.5541 - final_output_mse: 0.0094 - final_output_mae: 0.0674 - final_output_rmse: 0.0938 - final_output_custom_mape: 61.7115 - val_loss: 0.0406 - val_classification_output_loss: 0.0763 - val_regression_output_loss: 0.0160 - val_final_output_loss: 0.0196 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.5545 - val_regression_output_mae: 1.7924 - val_regression_output_rmse: 2.5441 - val_regression_output_custom_mape: 61.5195 - val_final_output_mse: 0.0089 - val_final_output_mae: 0.0644 - val_final_output_rmse: 0.0920 - val_final_output_custom_mape: 61.8914 - lr: 5.0000e-05\n",
"Epoch 58/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0325 - classification_output_loss: 0.0506 - regression_output_loss: 0.0099 - final_output_loss: 0.0203 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9983 - regression_output_mse: 6.7571 - regression_output_mae: 1.8542 - regression_output_rmse: 2.5887 - regression_output_custom_mape: 61.4581 - final_output_mse: 0.0094 - final_output_mae: 0.0670 - final_output_rmse: 0.0932 - final_output_custom_mape: 61.6506 - val_loss: 0.0405 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0185 - val_final_output_loss: 0.0197 - val_classification_output_accuracy: 0.9710 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6865 - val_regression_output_mae: 1.8298 - val_regression_output_rmse: 2.5699 - val_regression_output_custom_mape: 62.0339 - val_final_output_mse: 0.0091 - val_final_output_mae: 0.0648 - val_final_output_rmse: 0.0928 - val_final_output_custom_mape: 62.0572 - lr: 5.0000e-05\n",
"Epoch 59/100\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0322 - classification_output_loss: 0.0514 - regression_output_loss: 0.0097 - final_output_loss: 0.0201 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7708 - regression_output_mae: 1.8569 - regression_output_rmse: 2.5913 - regression_output_custom_mape: 61.4620 - final_output_mse: 0.0090 - final_output_mae: 0.0665 - final_output_rmse: 0.0918 - final_output_custom_mape: 61.6222 - val_loss: 0.0479 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0320 - val_final_output_loss: 0.0218 - val_classification_output_accuracy: 0.9702 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6692 - val_regression_output_mae: 1.8295 - val_regression_output_rmse: 2.5661 - val_regression_output_custom_mape: 63.2262 - val_final_output_mse: 0.0109 - val_final_output_mae: 0.0716 - val_final_output_rmse: 0.1014 - val_final_output_custom_mape: 63.3540 - lr: 5.0000e-05\n",
"Epoch 60/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0336 - classification_output_loss: 0.0512 - regression_output_loss: 0.0125 - final_output_loss: 0.0210 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7842 - regression_output_mae: 1.8627 - regression_output_rmse: 2.5937 - regression_output_custom_mape: 61.7020 - final_output_mse: 0.0100 - final_output_mae: 0.0691 - final_output_rmse: 0.0961 - final_output_custom_mape: 61.8323 - val_loss: 0.0496 - val_classification_output_loss: 0.0797 - val_regression_output_loss: 0.0308 - val_final_output_loss: 0.0249 - val_classification_output_accuracy: 0.9691 - val_classification_output_auc: 0.9953 - val_regression_output_mse: 6.8019 - val_regression_output_mae: 1.8752 - val_regression_output_rmse: 2.5909 - val_regression_output_custom_mape: 66.9446 - val_final_output_mse: 0.0148 - val_final_output_mae: 0.0816 - val_final_output_rmse: 0.1192 - val_final_output_custom_mape: 67.0260 - lr: 5.0000e-05\n",
"Epoch 61/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795\n",
"Epoch 61 Detailed Metrics:\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.0320 - classification_output_loss: 0.0501 - regression_output_loss: 0.0103 - final_output_loss: 0.0206 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.7789 - regression_output_mae: 1.8593 - regression_output_rmse: 2.5925 - regression_output_custom_mape: 61.5656 - final_output_mse: 0.0095 - final_output_mae: 0.0677 - final_output_rmse: 0.0939 - final_output_custom_mape: 61.7795 - val_loss: 0.0505 - val_classification_output_loss: 0.0742 - val_regression_output_loss: 0.0356 - val_final_output_loss: 0.0244 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9958 - val_regression_output_mse: 6.6354 - val_regression_output_mae: 1.8309 - val_regression_output_rmse: 2.5603 - val_regression_output_custom_mape: 65.7531 - val_final_output_mse: 0.0137 - val_final_output_mae: 0.0801 - val_final_output_rmse: 0.1142 - val_final_output_custom_mape: 65.9202 - lr: 5.0000e-05\n",
"Epoch 62/100\n",
"541/541 [==============================] - 53s 98ms/step - loss: 0.0332 - classification_output_loss: 0.0518 - regression_output_loss: 0.0119 - final_output_loss: 0.0209 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9983 - regression_output_mse: 6.7770 - regression_output_mae: 1.8601 - regression_output_rmse: 2.5926 - regression_output_custom_mape: 61.7351 - final_output_mse: 0.0097 - final_output_mae: 0.0689 - final_output_rmse: 0.0954 - final_output_custom_mape: 61.8624 - val_loss: 0.0481 - val_classification_output_loss: 0.0746 - val_regression_output_loss: 0.0310 - val_final_output_loss: 0.0238 - val_classification_output_accuracy: 0.9690 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7120 - val_regression_output_mae: 1.8451 - val_regression_output_rmse: 2.5737 - val_regression_output_custom_mape: 65.7528 - val_final_output_mse: 0.0131 - val_final_output_mae: 0.0782 - val_final_output_rmse: 0.1123 - val_final_output_custom_mape: 65.8633 - lr: 5.0000e-05\n",
"Epoch 63/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0304 - classification_output_loss: 0.0504 - regression_output_loss: 0.0079 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7759 - regression_output_mae: 1.8572 - regression_output_rmse: 2.5922 - regression_output_custom_mape: 61.3674 - final_output_mse: 0.0087 - final_output_mae: 0.0655 - final_output_rmse: 0.0902 - final_output_custom_mape: 61.5143 - val_loss: 0.0364 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0121 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9703 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6819 - val_regression_output_mae: 1.8267 - val_regression_output_rmse: 2.5687 - val_regression_output_custom_mape: 62.2443 - val_final_output_mse: 0.0086 - val_final_output_mae: 0.0635 - val_final_output_rmse: 0.0902 - val_final_output_custom_mape: 62.3224 - lr: 5.0000e-05\n",
"Epoch 64/100\n",
"541/541 [==============================] - 56s 103ms/step - loss: 0.0309 - classification_output_loss: 0.0507 - regression_output_loss: 0.0090 - final_output_loss: 0.0198 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.7723 - regression_output_mae: 1.8552 - regression_output_rmse: 2.5911 - regression_output_custom_mape: 61.1519 - final_output_mse: 0.0086 - final_output_mae: 0.0653 - final_output_rmse: 0.0898 - final_output_custom_mape: 61.3432 - val_loss: 0.0393 - val_classification_output_loss: 0.0725 - val_regression_output_loss: 0.0162 - val_final_output_loss: 0.0217 - val_classification_output_accuracy: 0.9709 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7377 - val_regression_output_mae: 1.8477 - val_regression_output_rmse: 2.5799 - val_regression_output_custom_mape: 64.4454 - val_final_output_mse: 0.0108 - val_final_output_mae: 0.0712 - val_final_output_rmse: 0.1021 - val_final_output_custom_mape: 64.4477 - lr: 5.0000e-05\n",
"Epoch 65/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0312 - classification_output_loss: 0.0508 - regression_output_loss: 0.0096 - final_output_loss: 0.0202 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9983 - regression_output_mse: 6.7588 - regression_output_mae: 1.8526 - regression_output_rmse: 2.5891 - regression_output_custom_mape: 61.3250 - final_output_mse: 0.0091 - final_output_mae: 0.0665 - final_output_rmse: 0.0922 - final_output_custom_mape: 61.5369 - val_loss: 0.0398 - val_classification_output_loss: 0.0764 - val_regression_output_loss: 0.0174 - val_final_output_loss: 0.0194 - val_classification_output_accuracy: 0.9673 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.6505 - val_regression_output_mae: 1.8149 - val_regression_output_rmse: 2.5623 - val_regression_output_custom_mape: 61.7415 - val_final_output_mse: 0.0087 - val_final_output_mae: 0.0638 - val_final_output_rmse: 0.0908 - val_final_output_custom_mape: 61.9581 - lr: 5.0000e-05\n",
"Epoch 66/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0352 - classification_output_loss: 0.0543 - regression_output_loss: 0.0094 - final_output_loss: 0.0300 - classification_output_accuracy: 0.9775 - classification_output_auc: 0.9981 - regression_output_mse: 6.6501 - regression_output_mae: 1.8334 - regression_output_rmse: 2.5658 - regression_output_custom_mape: 61.4441 - final_output_mse: 0.0123 - final_output_mae: 0.0678 - final_output_rmse: 0.0945 - final_output_custom_mape: 61.6992 - val_loss: 0.0378 - val_classification_output_loss: 0.0773 - val_regression_output_loss: 0.0096 - val_final_output_loss: 0.0197 - val_classification_output_accuracy: 0.9685 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.5211 - val_regression_output_mae: 1.7843 - val_regression_output_rmse: 2.5370 - val_regression_output_custom_mape: 61.5626 - val_final_output_mse: 0.0090 - val_final_output_mae: 0.0649 - val_final_output_rmse: 0.0924 - val_final_output_custom_mape: 62.0307 - lr: 5.0000e-05\n",
"Epoch 67/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0315 - classification_output_loss: 0.0499 - regression_output_loss: 0.0088 - final_output_loss: 0.0201 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.7018 - regression_output_mae: 1.8423 - regression_output_rmse: 2.5784 - regression_output_custom_mape: 61.3784 - final_output_mse: 0.0090 - final_output_mae: 0.0662 - final_output_rmse: 0.0914 - final_output_custom_mape: 61.5973 - val_loss: 0.0422 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0222 - val_final_output_loss: 0.0203 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6187 - val_regression_output_mae: 1.8151 - val_regression_output_rmse: 2.5569 - val_regression_output_custom_mape: 62.3132 - val_final_output_mse: 0.0093 - val_final_output_mae: 0.0670 - val_final_output_rmse: 0.0939 - val_final_output_custom_mape: 62.4799 - lr: 5.0000e-05\n",
"Epoch 68/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0337 - classification_output_loss: 0.0514 - regression_output_loss: 0.0130 - final_output_loss: 0.0211 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9983 - regression_output_mse: 6.7455 - regression_output_mae: 1.8556 - regression_output_rmse: 2.5868 - regression_output_custom_mape: 61.6488 - final_output_mse: 0.0101 - final_output_mae: 0.0693 - final_output_rmse: 0.0959 - final_output_custom_mape: 61.7737 - val_loss: 0.0471 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0315 - val_final_output_loss: 0.0225 - val_classification_output_accuracy: 0.9718 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6541 - val_regression_output_mae: 1.8264 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 63.9875 - val_final_output_mse: 0.0115 - val_final_output_mae: 0.0740 - val_final_output_rmse: 0.1040 - val_final_output_custom_mape: 64.1717 - lr: 5.0000e-05\n",
"Epoch 69/100\n",
"462/541 [========================>.....] - ETA: 7s - loss: 0.0330 - classification_output_loss: 0.0495 - regression_output_loss: 0.0129 - final_output_loss: 0.0208 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9984 - regression_output_mse: 6.8607 - regression_output_mae: 1.8819 - regression_output_rmse: 2.6091 - regression_output_custom_mape: 61.7354 - final_output_mse: 0.0096 - final_output_mae: 0.0686 - final_output_rmse: 0.0946 - final_output_custom_mape: 61.9095"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"541/541 [==============================] - 56s 104ms/step - loss: 0.0270 - classification_output_loss: 0.0498 - regression_output_loss: 0.0037 - final_output_loss: 0.0185 - classification_output_accuracy: 0.9783 - classification_output_auc: 0.9984 - regression_output_mse: 6.8101 - regression_output_mae: 1.8609 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.8799 - final_output_mse: 0.0074 - final_output_mae: 0.0611 - final_output_rmse: 0.0833 - final_output_custom_mape: 60.9907 - val_loss: 0.0326 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0193 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7669 - val_regression_output_mae: 1.8484 - val_regression_output_rmse: 2.5858 - val_regression_output_custom_mape: 62.3941 - val_final_output_mse: 0.0084 - val_final_output_mae: 0.0634 - val_final_output_rmse: 0.0894 - val_final_output_custom_mape: 62.3456 - lr: 2.5000e-05\n",
"Epoch 74/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0266 - classification_output_loss: 0.0496 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9984 - regression_output_mse: 6.8160 - regression_output_mae: 1.8619 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.7902 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0824 - final_output_custom_mape: 60.9197 - val_loss: 0.0329 - val_classification_output_loss: 0.0714 - val_regression_output_loss: 0.0070 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7283 - val_regression_output_mae: 1.8351 - val_regression_output_rmse: 2.5782 - val_regression_output_custom_mape: 61.8653 - val_final_output_mse: 0.0081 - val_final_output_mae: 0.0621 - val_final_output_rmse: 0.0877 - val_final_output_custom_mape: 61.8723 - lr: 2.5000e-05\n",
"Epoch 75/100\n",
"541/541 [==============================] - 58s 108ms/step - loss: 0.0266 - classification_output_loss: 0.0498 - regression_output_loss: 0.0035 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9984 - regression_output_mse: 6.8235 - regression_output_mae: 1.8647 - regression_output_rmse: 2.6014 - regression_output_custom_mape: 60.8557 - final_output_mse: 0.0072 - final_output_mae: 0.0609 - final_output_rmse: 0.0826 - final_output_custom_mape: 60.9257 - val_loss: 0.0314 - val_classification_output_loss: 0.0692 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7276 - val_regression_output_mae: 1.8331 - val_regression_output_rmse: 2.5784 - val_regression_output_custom_mape: 61.4127 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0603 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.4123 - lr: 2.5000e-05\n",
"Epoch 76/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0259 - classification_output_loss: 0.0485 - regression_output_loss: 0.0029 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8060 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6619 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7422 - val_loss: 0.0325 - val_classification_output_loss: 0.0705 - val_regression_output_loss: 0.0072 - val_final_output_loss: 0.0185 - val_classification_output_accuracy: 0.9713 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6720 - val_regression_output_mae: 1.8182 - val_regression_output_rmse: 2.5674 - val_regression_output_custom_mape: 61.5169 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0608 - val_final_output_rmse: 0.0849 - val_final_output_custom_mape: 61.6409 - lr: 2.5000e-05\n",
"Epoch 77/100\n",
"541/541 [==============================] - 57s 105ms/step - loss: 0.0261 - classification_output_loss: 0.0495 - regression_output_loss: 0.0031 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9984 - regression_output_mse: 6.7979 - regression_output_mae: 1.8569 - regression_output_rmse: 2.5965 - regression_output_custom_mape: 60.6774 - final_output_mse: 0.0070 - final_output_mae: 0.0603 - final_output_rmse: 0.0815 - final_output_custom_mape: 60.7930 - val_loss: 0.0312 - val_classification_output_loss: 0.0681 - val_regression_output_loss: 0.0057 - val_final_output_loss: 0.0188 - val_classification_output_accuracy: 0.9739 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.6716 - val_regression_output_mae: 1.8195 - val_regression_output_rmse: 2.5681 - val_regression_output_custom_mape: 61.4893 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0619 - val_final_output_rmse: 0.0865 - val_final_output_custom_mape: 61.6521 - lr: 2.5000e-05\n",
"Epoch 78/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0258 - classification_output_loss: 0.0489 - regression_output_loss: 0.0030 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8116 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5993 - regression_output_custom_mape: 60.6759 - final_output_mse: 0.0071 - final_output_mae: 0.0601 - final_output_rmse: 0.0814 - final_output_custom_mape: 60.7954 - val_loss: 0.0317 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0069 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9725 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7023 - val_regression_output_mae: 1.8251 - val_regression_output_rmse: 2.5736 - val_regression_output_custom_mape: 61.2798 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0596 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.3668 - lr: 2.5000e-05\n",
"Epoch 79/100\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0256 - classification_output_loss: 0.0491 - regression_output_loss: 0.0027 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9790 - classification_output_auc: 0.9984 - regression_output_mse: 6.8148 - regression_output_mae: 1.8608 - regression_output_rmse: 2.6000 - regression_output_custom_mape: 60.6601 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0806 - final_output_custom_mape: 60.7576 - val_loss: 0.0317 - val_classification_output_loss: 0.0696 - val_regression_output_loss: 0.0068 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.7091 - val_regression_output_mae: 1.8271 - val_regression_output_rmse: 2.5748 - val_regression_output_custom_mape: 61.2307 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0830 - val_final_output_custom_mape: 61.2448 - lr: 2.5000e-05\n",
"Epoch 80/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0259 - classification_output_loss: 0.0489 - regression_output_loss: 0.0035 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9795 - classification_output_auc: 0.9985 - regression_output_mse: 6.7880 - regression_output_mae: 1.8545 - regression_output_rmse: 2.5947 - regression_output_custom_mape: 60.6354 - final_output_mse: 0.0069 - final_output_mae: 0.0603 - final_output_rmse: 0.0812 - final_output_custom_mape: 60.7862 - val_loss: 0.0316 - val_classification_output_loss: 0.0719 - val_regression_output_loss: 0.0061 - val_final_output_loss: 0.0177 - val_classification_output_accuracy: 0.9700 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6953 - val_regression_output_mae: 1.8237 - val_regression_output_rmse: 2.5712 - val_regression_output_custom_mape: 60.8627 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0583 - val_final_output_rmse: 0.0812 - val_final_output_custom_mape: 60.9105 - lr: 2.5000e-05\n",
"Epoch 81/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657\n",
"Epoch 81 Detailed Metrics:\n",
"541/541 [==============================] - 52s 95ms/step - loss: 0.0262 - classification_output_loss: 0.0505 - regression_output_loss: 0.0034 - final_output_loss: 0.0184 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9984 - regression_output_mse: 6.7970 - regression_output_mae: 1.8573 - regression_output_rmse: 2.5964 - regression_output_custom_mape: 60.7878 - final_output_mse: 0.0072 - final_output_mae: 0.0608 - final_output_rmse: 0.0823 - final_output_custom_mape: 60.8657 - val_loss: 0.0309 - val_classification_output_loss: 0.0678 - val_regression_output_loss: 0.0059 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9965 - val_regression_output_mse: 6.7524 - val_regression_output_mae: 1.8408 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.7875 - val_final_output_mse: 0.0076 - val_final_output_mae: 0.0607 - val_final_output_rmse: 0.0848 - val_final_output_custom_mape: 61.7851 - lr: 2.5000e-05\n",
"Epoch 82/100\n",
"541/541 [==============================] - 52s 97ms/step - loss: 0.0256 - classification_output_loss: 0.0487 - regression_output_loss: 0.0031 - final_output_loss: 0.0183 - classification_output_accuracy: 0.9789 - classification_output_auc: 0.9985 - regression_output_mse: 6.8378 - regression_output_mae: 1.8668 - regression_output_rmse: 2.6042 - regression_output_custom_mape: 60.8146 - final_output_mse: 0.0070 - final_output_mae: 0.0605 - final_output_rmse: 0.0816 - final_output_custom_mape: 60.8914 - val_loss: 0.0354 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0140 - val_final_output_loss: 0.0189 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7485 - val_regression_output_mae: 1.8401 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.0076 - val_final_output_mse: 0.0079 - val_final_output_mae: 0.0622 - val_final_output_rmse: 0.0868 - val_final_output_custom_mape: 61.9773 - lr: 2.5000e-05\n",
"Epoch 83/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0263 - classification_output_loss: 0.0488 - regression_output_loss: 0.0043 - final_output_loss: 0.0187 - classification_output_accuracy: 0.9794 - classification_output_auc: 0.9985 - regression_output_mse: 6.8124 - regression_output_mae: 1.8617 - regression_output_rmse: 2.5990 - regression_output_custom_mape: 60.8479 - final_output_mse: 0.0075 - final_output_mae: 0.0616 - final_output_rmse: 0.0838 - final_output_custom_mape: 60.9637 - val_loss: 0.0335 - val_classification_output_loss: 0.0685 - val_regression_output_loss: 0.0107 - val_final_output_loss: 0.0191 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9963 - val_regression_output_mse: 6.6785 - val_regression_output_mae: 1.8221 - val_regression_output_rmse: 2.5690 - val_regression_output_custom_mape: 62.2821 - val_final_output_mse: 0.0082 - val_final_output_mae: 0.0628 - val_final_output_rmse: 0.0883 - val_final_output_custom_mape: 62.4273 - lr: 2.5000e-05\n",
"Epoch 84/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0253 - classification_output_loss: 0.0486 - regression_output_loss: 0.0028 - final_output_loss: 0.0182 - classification_output_accuracy: 0.9786 - classification_output_auc: 0.9985 - regression_output_mse: 6.7787 - regression_output_mae: 1.8528 - regression_output_rmse: 2.5927 - regression_output_custom_mape: 60.6893 - final_output_mse: 0.0070 - final_output_mae: 0.0601 - final_output_rmse: 0.0811 - final_output_custom_mape: 60.8322 - val_loss: 0.0320 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0076 - val_final_output_loss: 0.0184 - val_classification_output_accuracy: 0.9719 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.6612 - val_regression_output_mae: 1.8178 - val_regression_output_rmse: 2.5656 - val_regression_output_custom_mape: 61.7606 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0606 - val_final_output_rmse: 0.0845 - val_final_output_custom_mape: 61.8305 - lr: 2.5000e-05\n",
"Epoch 85/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0481 - regression_output_loss: 0.0023 - final_output_loss: 0.0181 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8113 - regression_output_mae: 1.8599 - regression_output_rmse: 2.5989 - regression_output_custom_mape: 60.7074 - final_output_mse: 0.0069 - final_output_mae: 0.0598 - final_output_rmse: 0.0808 - final_output_custom_mape: 60.7955 - val_loss: 0.0304 - val_classification_output_loss: 0.0693 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9726 - val_classification_output_auc: 0.9964 - val_regression_output_mse: 6.7028 - val_regression_output_mae: 1.8257 - val_regression_output_rmse: 2.5734 - val_regression_output_custom_mape: 60.9782 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0592 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.0249 - lr: 2.5000e-05\n",
"Epoch 86/100\n",
"541/541 [==============================] - 54s 100ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9985 - regression_output_mse: 6.8052 - regression_output_mae: 1.8587 - regression_output_rmse: 2.5978 - regression_output_custom_mape: 60.6683 - final_output_mse: 0.0067 - final_output_mae: 0.0597 - final_output_rmse: 0.0800 - final_output_custom_mape: 60.7617 - val_loss: 0.0318 - val_classification_output_loss: 0.0694 - val_regression_output_loss: 0.0074 - val_final_output_loss: 0.0187 - val_classification_output_accuracy: 0.9717 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7475 - val_regression_output_mae: 1.8414 - val_regression_output_rmse: 2.5822 - val_regression_output_custom_mape: 62.4116 - val_final_output_mse: 0.0077 - val_final_output_mae: 0.0615 - val_final_output_rmse: 0.0857 - val_final_output_custom_mape: 62.3664 - lr: 2.5000e-05\n",
"Epoch 87/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929\n",
"Epoch 87: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0249 - classification_output_loss: 0.0487 - regression_output_loss: 0.0023 - final_output_loss: 0.0180 - classification_output_accuracy: 0.9785 - classification_output_auc: 0.9985 - regression_output_mse: 6.8167 - regression_output_mae: 1.8610 - regression_output_rmse: 2.6001 - regression_output_custom_mape: 60.7153 - final_output_mse: 0.0068 - final_output_mae: 0.0596 - final_output_rmse: 0.0803 - final_output_custom_mape: 60.7929 - val_loss: 0.0304 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0049 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7331 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5791 - val_regression_output_custom_mape: 61.3001 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0831 - val_final_output_custom_mape: 61.3393 - lr: 2.5000e-05\n",
"Epoch 88/100\n",
"541/541 [==============================] - 54s 99ms/step - loss: 0.0246 - classification_output_loss: 0.0491 - regression_output_loss: 0.0018 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9784 - classification_output_auc: 0.9984 - regression_output_mse: 6.8066 - regression_output_mae: 1.8585 - regression_output_rmse: 2.5979 - regression_output_custom_mape: 60.5242 - final_output_mse: 0.0065 - final_output_mae: 0.0587 - final_output_rmse: 0.0789 - final_output_custom_mape: 60.5975 - val_loss: 0.0309 - val_classification_output_loss: 0.0703 - val_regression_output_loss: 0.0056 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9724 - val_classification_output_auc: 0.9960 - val_regression_output_mse: 6.7678 - val_regression_output_mae: 1.8449 - val_regression_output_rmse: 2.5861 - val_regression_output_custom_mape: 61.8515 - val_final_output_mse: 0.0075 - val_final_output_mae: 0.0604 - val_final_output_rmse: 0.0843 - val_final_output_custom_mape: 61.8132 - lr: 1.2500e-05\n",
"Epoch 89/100\n",
"541/541 [==============================] - 55s 101ms/step - loss: 0.0243 - classification_output_loss: 0.0484 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9788 - classification_output_auc: 0.9985 - regression_output_mse: 6.8169 - regression_output_mae: 1.8608 - regression_output_rmse: 2.5999 - regression_output_custom_mape: 60.5160 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5783 - val_loss: 0.0308 - val_classification_output_loss: 0.0727 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9712 - val_classification_output_auc: 0.9959 - val_regression_output_mse: 6.7522 - val_regression_output_mae: 1.8404 - val_regression_output_rmse: 2.5829 - val_regression_output_custom_mape: 61.3068 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0594 - val_final_output_rmse: 0.0827 - val_final_output_custom_mape: 61.2601 - lr: 1.2500e-05\n",
"Epoch 90/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0244 - classification_output_loss: 0.0490 - regression_output_loss: 0.0016 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9787 - classification_output_auc: 0.9985 - regression_output_mse: 6.8088 - regression_output_mae: 1.8586 - regression_output_rmse: 2.5983 - regression_output_custom_mape: 60.4771 - final_output_mse: 0.0065 - final_output_mae: 0.0586 - final_output_rmse: 0.0786 - final_output_custom_mape: 60.5605 - val_loss: 0.0306 - val_classification_output_loss: 0.0704 - val_regression_output_loss: 0.0052 - val_final_output_loss: 0.0182 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7138 - val_regression_output_mae: 1.8292 - val_regression_output_rmse: 2.5759 - val_regression_output_custom_mape: 61.5655 - val_final_output_mse: 0.0073 - val_final_output_mae: 0.0599 - val_final_output_rmse: 0.0833 - val_final_output_custom_mape: 61.6074 - lr: 1.2500e-05\n",
"Epoch 91/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501\n",
"Epoch 91 Detailed Metrics:\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0240 - classification_output_loss: 0.0485 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8007 - regression_output_mae: 1.8559 - regression_output_rmse: 2.5968 - regression_output_custom_mape: 60.4504 - final_output_mse: 0.0064 - final_output_mae: 0.0583 - final_output_rmse: 0.0780 - final_output_custom_mape: 60.5501 - val_loss: 0.0300 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0046 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9721 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7305 - val_regression_output_mae: 1.8337 - val_regression_output_rmse: 2.5790 - val_regression_output_custom_mape: 61.3197 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0590 - val_final_output_rmse: 0.0820 - val_final_output_custom_mape: 61.3129 - lr: 1.2500e-05\n",
"Epoch 92/100\n",
"541/541 [==============================] - 56s 104ms/step - loss: 0.0241 - classification_output_loss: 0.0482 - regression_output_loss: 0.0015 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9986 - regression_output_mse: 6.8190 - regression_output_mae: 1.8612 - regression_output_rmse: 2.6005 - regression_output_custom_mape: 60.5316 - final_output_mse: 0.0064 - final_output_mae: 0.0585 - final_output_rmse: 0.0783 - final_output_custom_mape: 60.5853 - val_loss: 0.0303 - val_classification_output_loss: 0.0688 - val_regression_output_loss: 0.0054 - val_final_output_loss: 0.0183 - val_classification_output_accuracy: 0.9722 - val_classification_output_auc: 0.9962 - val_regression_output_mse: 6.7579 - val_regression_output_mae: 1.8427 - val_regression_output_rmse: 2.5845 - val_regression_output_custom_mape: 61.9738 - val_final_output_mse: 0.0074 - val_final_output_mae: 0.0605 - val_final_output_rmse: 0.0841 - val_final_output_custom_mape: 61.9213 - lr: 1.2500e-05\n",
"Epoch 93/100\n",
"541/541 [==============================] - 58s 106ms/step - loss: 0.0240 - classification_output_loss: 0.0480 - regression_output_loss: 0.0015 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8070 - regression_output_mae: 1.8578 - regression_output_rmse: 2.5980 - regression_output_custom_mape: 60.4706 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0784 - final_output_custom_mape: 60.5622 - val_loss: 0.0301 - val_classification_output_loss: 0.0695 - val_regression_output_loss: 0.0050 - val_final_output_loss: 0.0181 - val_classification_output_accuracy: 0.9728 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7989 - val_regression_output_mae: 1.8531 - val_regression_output_rmse: 2.5925 - val_regression_output_custom_mape: 61.7481 - val_final_output_mse: 0.0072 - val_final_output_mae: 0.0595 - val_final_output_rmse: 0.0828 - val_final_output_custom_mape: 61.6569 - lr: 1.2500e-05\n",
"Epoch 94/100\n",
"541/541 [==============================] - 55s 102ms/step - loss: 0.0239 - classification_output_loss: 0.0480 - regression_output_loss: 0.0013 - final_output_loss: 0.0177 - classification_output_accuracy: 0.9792 - classification_output_auc: 0.9985 - regression_output_mse: 6.8109 - regression_output_mae: 1.8589 - regression_output_rmse: 2.5988 - regression_output_custom_mape: 60.4838 - final_output_mse: 0.0065 - final_output_mae: 0.0585 - final_output_rmse: 0.0785 - final_output_custom_mape: 60.5632 - val_loss: 0.0301 - val_classification_output_loss: 0.0701 - val_regression_output_loss: 0.0047 - val_final_output_loss: 0.0180 - val_classification_output_accuracy: 0.9723 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.7354 - val_regression_output_mae: 1.8344 - val_regression_output_rmse: 2.5800 - val_regression_output_custom_mape: 61.3804 - val_final_output_mse: 0.0071 - val_final_output_mae: 0.0593 - val_final_output_rmse: 0.0824 - val_final_output_custom_mape: 61.3907 - lr: 1.2500e-05\n",
"Epoch 95/100\n",
"541/541 [==============================] - ETA: 0s - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216Restoring model weights from the end of the best epoch: 80.\n",
"\n",
"Epoch 95: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
"541/541 [==============================] - 52s 96ms/step - loss: 0.0236 - classification_output_loss: 0.0476 - regression_output_loss: 0.0012 - final_output_loss: 0.0176 - classification_output_accuracy: 0.9793 - classification_output_auc: 0.9986 - regression_output_mse: 6.8029 - regression_output_mae: 1.8564 - regression_output_rmse: 2.5971 - regression_output_custom_mape: 60.4213 - final_output_mse: 0.0063 - final_output_mae: 0.0582 - final_output_rmse: 0.0778 - final_output_custom_mape: 60.5216 - val_loss: 0.0304 - val_classification_output_loss: 0.0712 - val_regression_output_loss: 0.0051 - val_final_output_loss: 0.0179 - val_classification_output_accuracy: 0.9720 - val_classification_output_auc: 0.9961 - val_regression_output_mse: 6.6979 - val_regression_output_mae: 1.8236 - val_regression_output_rmse: 2.5727 - val_regression_output_custom_mape: 61.3934 - val_final_output_mse: 0.0070 - val_final_output_mae: 0.0591 - val_final_output_rmse: 0.0818 - val_final_output_custom_mape: 61.4749 - lr: 1.2500e-05\n",
"Epoch 95: early stopping\n",
"\n",
"Training completed successfully!\n",
"\n",
"Classification Metrics:\n",
"Accuracy: 97.00%\n",
"AUC-ROC: 0.9968\n",
"\n",
"Confusion Matrix:\n",
"[[12503 504]\n",
" [ 275 12651]]\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Zero 0.9785 0.9613 0.9698 13007\n",
" Non-Zero 0.9617 0.9787 0.9701 12926\n",
"\n",
" accuracy 0.9700 25933\n",
" macro avg 0.9701 0.9700 0.9700 25933\n",
"weighted avg 0.9701 0.9700 0.9700 25933\n",
"\n",
"\n",
"Regression Metrics (non-zero values):\n",
"Out of range: 0 predictions\n",
"MAPE: 23.39%\n",
"Within ±10%: 54.66%\n",
"MAE: 0.11\n",
"RMSE: 0.29\n",
"\n",
"Final Combined Output Metrics:\n",
"Out of range: 0 predictions\n",
"MAPE: 60.97%\n",
"Within ±10%: 27.97%\n",
"MAE: 0.06\n",
"RMSE: 0.08\n"
]
}
],
"source": [
"# Model creation\n",
"print(\"\\n2. Creating model...\")\n",
"input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n",
"\n",
"max_val = df['solarradiation'].max()\n",
"min_val_scaled = scaler_y.transform([[0]])[0][0]\n",
"max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n",
"\n",
"print(f\"\\nMax dataset solar radiation : {max_val} - Scaled Version : {max_val_scaled}\")\n",
"\n",
"increase_percentage = 15\n",
"\n",
"max_val = max_val * (1 + increase_percentage / 100)\n",
"max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n",
"\n",
"print(f\"Max dataset solar radiation increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n",
"\n",
"# Create the hybrid model\n",
"model = create_solarradiation_model(\n",
" input_shape=input_shape, \n",
" folder_name=folder_name, \n",
" min_output=min_val_scaled, \n",
" max_output=max_val_scaled\n",
")\n",
"\n",
"# Prepare binary targets for classification\n",
"y_train_binary = (y_train > 0).astype(float)\n",
"y_test_binary = (y_test > 0).astype(float)\n",
"\n",
"print(\"\\nClass distribution in training set:\")\n",
"print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n",
"print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n",
"\n",
"print(\"\\nClass distribution in test set:\")\n",
"print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n",
"print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n",
"\n",
"# Get the exact output names from the model\n",
"output_names = [output.name.split('/')[0] for output in model.outputs]\n",
"print(\"\\nModel output names:\", output_names)\n",
"\n",
"print(\"\\n4. Starting training...\")\n",
"history = train_hybrid_model(\n",
" model=model,\n",
" X_train=X_train_seq,\n",
" y_train=y_train,\n",
" X_test=X_test_seq,\n",
" y_test=y_test,\n",
" epochs=100,\n",
" batch_size=192,\n",
" folder_name=folder_name,\n",
" min_output=min_val_scaled,\n",
" max_output=max_val_scaled\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "958d78b99e8898d6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"5. Generating predictions...\n",
"811/811 [==============================] - 13s 15ms/step\n",
"\n",
"6. Evaluating model...\n",
"\n",
"Solar Radiation Prediction Metrics:\n",
"\n",
"Absolute Metrics:\n",
"MAE: 19.32 W/m²\n",
"RMSE: 27.95 W/m²\n",
"R² Score: 0.989\n",
"MAPE: 16.92%\n",
"\n",
"Accuracy Metrics:\n",
"Within ±5 W/m²: 8.8%\n",
"Within ±10 W/m²: 16.3%\n",
"Within ±20 W/m²: 74.5%\n",
"\n",
"Level Accuracy:\n",
"Level Accuracy: 92.6%\n",
"\n",
"Confusion Matrix for Radiation Levels:\n",
" Very Low Low Moderate High Very High Extreme\n",
"Very Low 0 0 0 0 10 0\n",
"Low 0 1494 0 174 153 0\n",
"Moderate 0 0 2041 413 0 407\n",
"High 0 215 156 1925 0 0\n",
"Very High 0 99 0 0 1038 0\n",
"Extreme 0 0 298 0 0 17510\n",
"\n",
"Plot saved as: 2024-11-26_05-41_radiation_analysis.png\n",
"\n",
"Error Statistics:\n",
"Mean error: 4.431\n",
"Error standard deviation: 27.596\n",
"Median error: 12.000\n",
"95th percentile absolute error: 57.806\n"
]
}
],
"source": [
"print(\"\\n5. Generating predictions...\")\n",
"predictions = model.predict(X_test_seq)\n",
"classification_pred, regression_pred, final_pred = predictions\n",
"\n",
"# Clip solo le predizioni di regressione e finali\n",
"regression_pred = np.clip(regression_pred, 0, 11)\n",
"final_pred = np.clip(final_pred, 0, 11)\n",
"\n",
"# Inverse transform per tornare ai valori originali\n",
"regression_pred_original = scaler_y.inverse_transform(regression_pred)\n",
"final_pred_original = scaler_y.inverse_transform(final_pred)\n",
"y_test_original = scaler_y.inverse_transform(y_test)\n",
"\n",
"print(\"\\n6. Evaluating model...\")\n",
"# Valutazione delle predizioni finali\n",
"metrics = evaluate_solarradiation_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n",
"\n",
"# Create results dictionary con metriche aggiuntive per il modello ibrido\n",
"training_results = {\n",
" 'model_params': {\n",
" 'input_shape': input_shape,\n",
" 'n_features': len(features),\n",
" 'sequence_length': X_train_seq.shape[1]\n",
" },\n",
" 'training_params': {\n",
" 'batch_size': 192,\n",
" 'total_epochs': len(history.history['loss']),\n",
" 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n",
" },\n",
" 'performance_metrics': {\n",
" 'classification': {\n",
" 'final_loss': float(history.history['val_classification_output_loss'][-1]),\n",
" 'final_accuracy': float(history.history['val_classification_output_accuracy'][-1]),\n",
" 'final_auc': float(history.history['val_classification_output_auc'][-1])\n",
" },\n",
" 'regression': {\n",
" 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n",
" 'final_mae': float(history.history['val_regression_output_mae'][-1]),\n",
" 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > 11)))\n",
" },\n",
" 'final_output': {\n",
" 'final_loss': float(history.history['val_final_output_loss'][-1]),\n",
" 'final_mae': float(history.history['val_final_output_mae'][-1]),\n",
" 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n",
" 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > 11)))\n",
" }\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5c05d1d03336b1e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"7. Predicting missing data...\n",
"7122/7122 [==============================] - 112s 16ms/step\n",
"\n",
"8. Integrating predictions into original dataset...\n",
"\n",
"Prediction Integration Statistics:\n",
"Added 227879 predictions to dataset\n",
"Rows with solar radiation after integration: 357615\n",
"\n",
"Filled Values Analysis:\n",
"Zero predictions (classification < 0.5): 113630\n",
"Non-zero predictions (classification >= 0.5): 114249\n",
"\n",
"Non-zero predictions statistics:\n",
"Mean: 181.31\n",
"Median: 12.00\n",
"Std: 254.32\n",
"\n",
"Prediction Statistics:\n",
"Total predictions added: 227879\n",
"\n",
"Classification Statistics:\n",
"Predicted zeros: 113630 (49.86%)\n",
"Predicted non-zeros: 114249 (50.14%)\n",
"Mean classification confidence: 0.4989\n",
"\n",
"Final Predictions Statistics:\n",
"Mean solar radiation: 181.31\n",
"Min solar radiation: 12.00\n",
"Max solar radiation: 966.98\n",
"Zero predictions: 0 (0.00%)\n",
"\n",
"Training completed successfully!\n"
]
}
],
"source": [
"print(\"\\n7. Predicting missing data...\")\n",
"to_predict_predictions = model.predict(X_to_predict_seq)\n",
"classification_pred, regression_pred, final_pred = to_predict_predictions\n",
"\n",
"# Clip solo le predizioni finali che useremo per l'integrazione\n",
"final_pred = np.clip(final_pred, 0, 11)\n",
"final_pred_original = scaler_y.inverse_transform(final_pred)\n",
"\n",
"print(\"\\n8. Integrating predictions into original dataset...\")\n",
"df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n",
"\n",
"df_updated.to_parquet('../../sources/weather_data_solarradiation.parquet')\n",
"\n",
"# Add prediction statistics to training_results\n",
"training_results['prediction_stats'] = {\n",
" 'n_predictions_added': len(final_pred_original),\n",
" 'classification_stats': {\n",
" 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n",
" 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n",
" 'mean_confidence': float(classification_pred.mean()),\n",
" },\n",
" 'regression_stats': {\n",
" 'mean_predicted_value': float(regression_pred.mean()),\n",
" 'min_predicted_value': float(regression_pred.min()),\n",
" 'max_predicted_value': float(regression_pred.max()),\n",
" },\n",
" 'final_predictions': {\n",
" 'mean_predicted_solarradiation': float(final_pred_original.mean()),\n",
" 'min_predicted_solarradiation': float(final_pred_original.min()),\n",
" 'max_predicted_solarradiation': float(final_pred_original.max()),\n",
" 'zero_predictions': int(np.sum(final_pred_original == 0)),\n",
" 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n",
" }\n",
"}\n",
"\n",
"print(\"\\nPrediction Statistics:\")\n",
"print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n",
"print(\"\\nClassification Statistics:\")\n",
"print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n",
" f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n",
"print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n",
" f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n",
"print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n",
"\n",
"print(\"\\nFinal Predictions Statistics:\")\n",
"print(f\"Mean solar radiation: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarradiation']:.2f}\")\n",
"print(f\"Min solar radiation: {training_results['prediction_stats']['final_predictions']['min_predicted_solarradiation']:.2f}\")\n",
"print(f\"Max solar radiation: {training_results['prediction_stats']['final_predictions']['max_predicted_solarradiation']:.2f}\")\n",
"print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n",
" f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n",
"\n",
"print(\"\\nTraining completed successfully!\")\n",
"\n",
"tf.keras.backend.clear_session()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ef29b3ecdf12c6db",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 2000x1200 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1500x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Statistiche principali di Solar Radiation:\n",
"--------------------------------------------------\n",
"count : 357,679.0000\n",
"missing : 64.0000\n",
"zeros : 59,357.0000\n",
"mean : 183.8441\n",
"median : 12.0000\n",
"std : 259.8156\n",
"min : 0.0000\n",
"max : 1,113.0000\n",
"skewness : 1.3491\n",
"kurtosis : 0.5914\n",
"percentile_1 : 0.0000\n",
"percentile_5 : 0.0000\n",
"percentile_10 : 0.0000\n",
"percentile_25 : 12.0000\n",
"percentile_50 : 12.0000\n",
"percentile_75 : 321.3083\n",
"percentile_90 : 624.6504\n",
"percentile_95 : 776.0000\n",
"percentile_99 : 907.6779\n",
"\n",
"Suggerimenti per la normalizzazione:\n",
"--------------------------------------------------\n",
"- La distribuzione è fortemente asimmetrica (skewness > 1)\n",
"- Considerare una trasformazione logaritmica: np.log1p(x)\n",
"- Alta presenza di zeri (16.60%)\n",
"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\n"
]
},
{
"data": {
"text/plain": [
"{'count': 357679,\n",
" 'missing': 64,\n",
" 'zeros': 59357,\n",
" 'mean': 183.84409789852336,\n",
" 'median': 12.0,\n",
" 'std': 259.8156425752193,\n",
" 'min': 0.0,\n",
" 'max': 1113.0,\n",
" 'skewness': 1.3490904735404219,\n",
" 'kurtosis': 0.5914208419781612,\n",
" 'percentile_1': 0.0,\n",
" 'percentile_5': 0.0,\n",
" 'percentile_10': 0.0,\n",
" 'percentile_25': 12.0,\n",
" 'percentile_50': 12.0,\n",
" 'percentile_75': 321.3082580566406,\n",
" 'percentile_90': 624.6503662109386,\n",
" 'percentile_95': 776.0,\n",
" 'percentile_99': 907.677912597656}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyze_distribution(df_updated, 'solarradiation', 'Solar Radiation')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "e884cc287364c4ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Error saving plot: [Errno 2] No such file or directory: '2024-11-26_05-41/error_analysis.png'\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 2000x1500 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Classification Statistics:\n",
" precision recall f1-score support\n",
"\n",
" 0.0 0.98 0.96 0.97 13007\n",
" 1.0 0.96 0.98 0.97 12926\n",
"\n",
" accuracy 0.97 25933\n",
" macro avg 0.97 0.97 0.97 25933\n",
"weighted avg 0.97 0.97 0.97 25933\n",
"\n",
"AUC-ROC: 0.9968\n",
"\n",
"Regression Statistics (Non-zero values):\n",
"MAE: 0.1056\n",
"RMSE: 0.2896\n",
"Mean error: 0.0143\n",
"Error std: 0.2892\n",
"\n",
"Final Prediction Statistics:\n",
"MAE: 0.0583\n",
"RMSE: 0.0835\n",
"Mean error: 0.0113\n",
"Error std: 0.0827\n",
"\n",
"Error Thresholds (Final Predictions):\n",
"Predictions within ±0.5: 99.9%\n",
"Predictions within ±1.0: 100.0%\n",
"Predictions within ±1.5: 100.0%\n",
"Predictions within ±2.0: 100.0%\n"
]
}
],
"source": [
"def plot_error_analysis(y_true, predictions, folder_name=None):\n",
" \"\"\"\n",
" Function to visualize prediction error analysis for the hybrid model\n",
"\n",
" Parameters:\n",
" -----------\n",
" y_true : array-like\n",
" Actual values\n",
" predictions : tuple\n",
" Tuple containing (classification_pred, regression_pred, final_pred)\n",
" folder_name : str, optional\n",
" Directory to save plots. If None, plots are only displayed\n",
"\n",
" Generates:\n",
" ----------\n",
" - Classification analysis plots\n",
" - Regression error analysis plots\n",
" - Final prediction error analysis plots\n",
" \"\"\"\n",
" from sklearn.metrics import roc_curve\n",
"\n",
" # Unpack predictions\n",
" classification_pred, regression_pred, final_pred = predictions\n",
"\n",
" # Convert to 1D numpy arrays if needed\n",
" y_true = np.ravel(y_true)\n",
" classification_pred = np.ravel(classification_pred)\n",
" regression_pred = np.ravel(regression_pred)\n",
" final_pred = np.ravel(final_pred)\n",
"\n",
" # Create binary ground truth\n",
" y_true_binary = (y_true > 0).astype(float)\n",
"\n",
" # Calculate errors for regression and final predictions\n",
" regression_errors = regression_pred - y_true\n",
" final_errors = final_pred - y_true\n",
"\n",
" # Create main figure\n",
" plt.figure(figsize=(20, 15))\n",
"\n",
" # Classification Analysis (Top Row)\n",
" # Plot 1: Classification Distribution\n",
" plt.subplot(3, 3, 1)\n",
" plt.hist(classification_pred, bins=50, alpha=0.7)\n",
" plt.axvline(x=0.5, color='r', linestyle='--')\n",
" plt.title('Classification Probability Distribution')\n",
" plt.xlabel('Classification Probability')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 2: ROC Curve\n",
" plt.subplot(3, 3, 2)\n",
" fpr, tpr, _ = roc_curve(y_true_binary, classification_pred)\n",
" plt.plot(fpr, tpr)\n",
" plt.plot([0, 1], [0, 1], 'r--')\n",
" plt.title(f'ROC Curve (AUC = {roc_auc_score(y_true_binary, classification_pred):.4f})')\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
"\n",
" # Plot 3: Classification Confusion Matrix\n",
" plt.subplot(3, 3, 3)\n",
" cm = confusion_matrix(y_true_binary, classification_pred > 0.5)\n",
" sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
" plt.title('Classification Confusion Matrix')\n",
" plt.xlabel('Predicted')\n",
" plt.ylabel('Actual')\n",
"\n",
" # Regression Analysis (Middle Row)\n",
" # Plot 4: Regression Error Distribution\n",
" plt.subplot(3, 3, 4)\n",
" plt.hist(regression_errors[y_true > 0], bins=50, alpha=0.7)\n",
" plt.title('Regression Error Distribution (Non-zero Values)')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 5: Actual vs Predicted (Regression)\n",
" plt.subplot(3, 3, 5)\n",
" mask_nonzero = y_true > 0\n",
" plt.scatter(y_true[mask_nonzero], regression_pred[mask_nonzero], alpha=0.5)\n",
" plt.plot([y_true[mask_nonzero].min(), y_true[mask_nonzero].max()],\n",
" [y_true[mask_nonzero].min(), y_true[mask_nonzero].max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Regression, Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 6: Regression Errors vs Actual Values\n",
" plt.subplot(3, 3, 6)\n",
" plt.scatter(y_true[mask_nonzero], regression_errors[mask_nonzero], alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Regression Errors vs Actual Values (Non-zero Values)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" # Final Predictions Analysis (Bottom Row)\n",
" # Plot 7: Final Error Distribution\n",
" plt.subplot(3, 3, 7)\n",
" plt.hist(final_errors, bins=50, alpha=0.7)\n",
" plt.title('Final Prediction Error Distribution')\n",
" plt.xlabel('Error')\n",
" plt.ylabel('Frequency')\n",
"\n",
" # Plot 8: Actual vs Predicted (Final)\n",
" plt.subplot(3, 3, 8)\n",
" plt.scatter(y_true, final_pred, alpha=0.5)\n",
" plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n",
" plt.title('Actual vs Predicted (Final)')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Predicted Values')\n",
"\n",
" # Plot 9: Final Errors vs Actual Values\n",
" plt.subplot(3, 3, 9)\n",
" plt.scatter(y_true, final_errors, alpha=0.5)\n",
" plt.axhline(y=0, color='r', linestyle='--')\n",
" plt.title('Final Errors vs Actual Values')\n",
" plt.xlabel('Actual Values')\n",
" plt.ylabel('Error')\n",
"\n",
" plt.tight_layout()\n",
"\n",
" # Save plot if directory is specified\n",
" if folder_name is not None:\n",
" try:\n",
" filename = f'{folder_name}_error_analysis.png'\n",
" plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
" print(f\"\\nPlot saved as: {filename}\")\n",
" except Exception as e:\n",
" print(f\"\\nError saving plot: {str(e)}\")\n",
"\n",
" plt.show()\n",
"\n",
" # Print comprehensive statistics\n",
" print(\"\\nClassification Statistics:\")\n",
" print(classification_report(y_true_binary, classification_pred > 0.5))\n",
" print(f\"AUC-ROC: {roc_auc_score(y_true_binary, classification_pred):.4f}\")\n",
"\n",
" print(\"\\nRegression Statistics (Non-zero values):\")\n",
" mask_nonzero = y_true > 0\n",
" if np.any(mask_nonzero):\n",
" print(f\"MAE: {np.mean(np.abs(regression_errors[mask_nonzero])):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(regression_errors[mask_nonzero] ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(regression_errors[mask_nonzero]):.4f}\")\n",
" print(f\"Error std: {np.std(regression_errors[mask_nonzero]):.4f}\")\n",
"\n",
" print(\"\\nFinal Prediction Statistics:\")\n",
" print(f\"MAE: {np.mean(np.abs(final_errors)):.4f}\")\n",
" print(f\"RMSE: {np.sqrt(np.mean(final_errors ** 2)):.4f}\")\n",
" print(f\"Mean error: {np.mean(final_errors):.4f}\")\n",
" print(f\"Error std: {np.std(final_errors):.4f}\")\n",
"\n",
" # Calculate percentage of errors within thresholds\n",
" thresholds = [0.5, 1.0, 1.5, 2.0]\n",
" print(\"\\nError Thresholds (Final Predictions):\")\n",
" for threshold in thresholds:\n",
" within_threshold = np.mean(np.abs(final_errors) <= threshold) * 100\n",
" print(f\"Predictions within ±{threshold}: {within_threshold:.1f}%\")\n",
"\n",
"# Example usage\n",
"plot_error_analysis(y_test, predictions, folder_name=folder_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd5197ea71becfc6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f982c92c-ba99-4df6-b3c8-df92426679db",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0rc1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue View Git Blame Copy Permalink