{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "8adcbe0819b88578", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", "Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\n", "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease \n", "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", "Hit:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", "Get:6 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2738 kB]\n", "Get:7 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1513 kB]\n", "Fetched 4508 kB in 2s (2961 kB/s) \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": [ "2024-11-27 13:56:39.957016: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-11-27 13:56:39.957067: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-11-27 13:56:39.957117: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2024-11-27 13:56:39.966205: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "/usr/local/lib/python3.11/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n", "\n", "TensorFlow Addons (TFA) has ended development and introduction of new features.\n", "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n", "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n", "\n", "For more information see: https://github.com/tensorflow/addons/issues/2807 \n", "\n", " warnings.warn(\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers import (\n", " Dense, LSTM, MultiHeadAttention, Dropout, BatchNormalization, \n", " LayerNormalization, Input, Activation, Lambda, Bidirectional, \n", " Add, MaxPooling1D, SpatialDropout1D, GlobalAveragePooling1D,\n", " GlobalMaxPooling1D, Concatenate, ThresholdedReLU, Average,\n", " Conv1D, Multiply\n", ")\n", "from tensorflow.keras import regularizers\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", "from tensorflow.keras.optimizers import AdamW\n", "from tensorflow.keras.metrics import AUC\n", "from tensorflow.keras.utils import plot_model\n", "\n", "# Data processing and analysis\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import RobustScaler\n", "from sklearn.metrics import (\n", " mean_absolute_error, mean_squared_error, r2_score, \n", " confusion_matrix, classification_report, roc_auc_score\n", ")\n", "\n", "# Visualization\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Additional utilities\n", "import tensorflow_addons as tfa\n", "from scipy import stats\n", "import json\n", "from datetime import datetime\n", "import os\n", "import joblib\n", "\n", "folder_name = datetime.now().strftime(\"%Y-%m-%d_%H-%M\")\n", "\n", "random_state_value = None" ] }, { "cell_type": "code", "execution_count": 3, "id": "3da8b15c7eb9833f", "metadata": {}, "outputs": [], "source": [ "def get_season(date):\n", " month = date.month\n", " day = date.day\n", " if (month == 12 and day >= 21) or (month <= 3 and day < 20):\n", " return 'Winter'\n", " elif (month == 3 and day >= 20) or (month <= 6 and day < 21):\n", " return 'Spring'\n", " elif (month == 6 and day >= 21) or (month <= 9 and day < 23):\n", " return 'Summer'\n", " elif (month == 9 and day >= 23) or (month <= 12 and day < 21):\n", " return 'Autumn'\n", " else:\n", " return 'Unknown'\n", "\n", "\n", "def get_time_period(hour):\n", " if 5 <= hour < 12:\n", " return 'Morning'\n", " elif 12 <= hour < 17:\n", " return 'Afternoon'\n", " elif 17 <= hour < 21:\n", " return 'Evening'\n", " else:\n", " return 'Night'\n", "\n", "\n", "def add_time_features(df):\n", " df['datetime'] = pd.to_datetime(df['datetime'])\n", " df['timestamp'] = df['datetime'].astype(np.int64) // 10 ** 9\n", " df['year'] = df['datetime'].dt.year\n", " df['month'] = df['datetime'].dt.month\n", " df['day'] = df['datetime'].dt.day\n", " df['hour'] = df['datetime'].dt.hour\n", " df['minute'] = df['datetime'].dt.minute\n", " df['hour_sin'] = np.sin(df['hour'] * (2 * np.pi / 24))\n", " df['hour_cos'] = np.cos(df['hour'] * (2 * np.pi / 24))\n", " df['day_of_week'] = df['datetime'].dt.dayofweek\n", " df['day_of_year'] = df['datetime'].dt.dayofyear\n", " df['week_of_year'] = df['datetime'].dt.isocalendar().week.astype(int)\n", " df['quarter'] = df['datetime'].dt.quarter\n", " df['is_month_end'] = df['datetime'].dt.is_month_end.astype(int)\n", " df['is_quarter_end'] = df['datetime'].dt.is_quarter_end.astype(int)\n", " df['is_year_end'] = df['datetime'].dt.is_year_end.astype(int)\n", " df['month_sin'] = np.sin(df['month'] * (2 * np.pi / 12))\n", " df['month_cos'] = np.cos(df['month'] * (2 * np.pi / 12))\n", " df['day_of_year_sin'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25))\n", " df['day_of_year_cos'] = np.cos(df['day_of_year'] * (2 * np.pi / 365.25))\n", " df['season'] = df['datetime'].apply(get_season)\n", " df['time_period'] = df['hour'].apply(get_time_period)\n", " return df\n", "\n", "\n", "def add_solar_features(df):\n", " # Features based only on radiation and other available variables\n", " df['solar_elevation'] = np.sin(df['day_of_year'] * (2 * np.pi / 365.25)) * np.sin(df['hour'] * (2 * np.pi / 24))\n", "\n", " # Energy-specific features\n", " df['radiation_clearsky'] = df['solarradiation'] * (100 - df['cloudcover']) / 100\n", "\n", " # Temperature impact on theoretical efficiency\n", " df['temp_efficiency_factor'] = 1 - 0.004 * (df['temp'] - 25) # Typical temperature coefficient\n", "\n", " # Combined features\n", " df['cloud_impact'] = df['cloudcover'] * df['solarradiation']\n", " df['visibility_radiation'] = df['visibility'] * df['solarradiation']\n", " df['clear_sky_index'] = (100 - df['cloudcover']) / 100\n", " df['temp_effect'] = df['temp'] - df['tempmin']\n", "\n", " return df\n", "\n", "def add_solar_specific_features(df):\n", " \"\"\"\n", " Aggiunge feature specifiche per la predizione della radiazione solare\n", " combinando caratteristiche astronomiche e meteorologiche\n", " \"\"\"\n", " # Caratteristiche astronomiche\n", " df['day_length'] = 12 + 3 * np.sin(2 * np.pi * (df['day_of_year'] - 81) / 365.25)\n", " df['solar_noon'] = np.abs(12 - df['hour'])\n", " df['solar_elevation'] = np.sin(2 * np.pi * df['day_of_year'] / 365.25) * np.cos(2 * np.pi * df['solar_noon'] / 24)\n", "\n", " # Angolo solare teorico\n", " df['solar_angle'] = np.sin(df['hour_sin']) * np.sin(df['day_of_year_sin'])\n", "\n", " # Interazioni con condizioni atmosferiche\n", " df['cloud_elevation'] = df['cloudcover'] * df['solar_elevation']\n", " df['visibility_elevation'] = df['visibility'] * df['solar_elevation']\n", " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", "\n", " # Indici di chiarezza e trasmissione\n", " df['clearness_index'] = (100 - df['cloudcover']) * df['visibility'] / 10000\n", " df['atmospheric_attenuation'] = (df['pressure'] / 1013.25) * (1 - (df['humidity'] / 100) * 0.6)\n", "\n", " # Radiazione teorica e attenuazione\n", " df['theoretical_radiation'] = df['solar_angle'].clip(0, 1) * 1000\n", " df['expected_radiation'] = df['theoretical_radiation'] * df['clearness_index']\n", "\n", " # Rolling features\n", " df['cloud_rolling_12h'] = df['cloudcover'].rolling(window=12).mean()\n", " df['temp_rolling_12h'] = df['temp'].rolling(window=12).mean()\n", " df['uv_rolling_12h'] = df['uvindex'].rolling(window=12).mean()\n", "\n", " # Interazioni temperatura-radiazione\n", " df['temp_radiation_potential'] = df['temp'] * df['solar_elevation']\n", "\n", " return df\n", "\n", "def add_radiation_energy_features(df):\n", " \"\"\"Adds specific features based on solarenergy and uvindex\"\"\"\n", "\n", " # Solar energy to UV ratio (independent from solarradiation)\n", " df['energy_uv_ratio'] = df['solarenergy'] / (df['uvindex'] + 1e-6)\n", "\n", " # Time aggregations\n", " # Moving averages\n", " windows = [3, 6, 12, 24] # hours\n", " for w in windows:\n", " df[f'energy_rolling_mean_{w}h'] = df['solarenergy'].rolling(window=w).mean()\n", " df[f'uv_rolling_mean_{w}h'] = df['uvindex'].rolling(window=w).mean()\n", "\n", " # Daily aggregations utilizzando datetime\n", " df['energy_daily_sum'] = df.groupby(df['datetime'].dt.date)['solarenergy'].transform('sum')\n", " df['uv_daily_max'] = df.groupby(df['datetime'].dt.date)['uvindex'].transform('max')\n", "\n", " # Changes\n", " df['energy_change'] = df['solarenergy'].diff()\n", " df['uv_change'] = df['uvindex'].diff()\n", "\n", " # Lag features\n", " lags = [1, 2, 3, 6, 12, 24] # hours\n", " for lag in lags:\n", " df[f'energy_lag_{lag}h'] = df['solarenergy'].shift(lag)\n", " df[f'uv_lag_{lag}h'] = df['uvindex'].shift(lag)\n", "\n", " # Peak indicators\n", " df['is_energy_peak'] = (df['solarenergy'] > df['energy_rolling_mean_6h'] * 1.2).astype(int)\n", " df['is_uv_peak'] = (df['uvindex'] > df['uv_rolling_mean_6h'] * 1.2).astype(int)\n", "\n", " # Aggiungiamo alcune metriche di volatilità\n", " df['energy_volatility'] = df['energy_change'].rolling(window=24).std()\n", " df['uv_volatility'] = df['uv_change'].rolling(window=24).std()\n", "\n", " # Indice di intensità solare composito\n", " df['solar_intensity_index'] = (df['solarenergy'] * df['uvindex']) / (df['cloudcover'] + 1e-6)\n", "\n", " # Interazioni\n", " df['uv_cloud_interaction'] = df['uvindex'] * (100 - df['cloudcover']) / 100\n", " df['energy_temp_interaction'] = df['solarenergy'] * df['temp']\n", "\n", " return df\n", "\n", "def add_atmospheric_features(df):\n", " # Indice di Massa d'Aria (Air Mass Index)\n", " # Rappresenta il percorso ottico relativo dei raggi solari attraverso l'atmosfera\n", " df['air_mass_index'] = 1 / (np.cos(np.radians(90 - df['solar_elevation'])) + 0.50572 *\n", " (96.07995 - (90 - df['solar_elevation']))**-1.6364)\n", "\n", " # Indice di Stabilità Atmosferica\n", " # Combina temperatura, umidità e pressione\n", " df['atmospheric_stability'] = (df['temp'] * (100 - df['humidity'])) / df['pressure']\n", "\n", " # Vapor Pressure Deficit (VPD)\n", " # Importante per la radiazione diffusa\n", " df['saturation_vapor_pressure'] = 0.6108 * np.exp(17.27 * df['temp'] / (df['temp'] + 237.3))\n", " df['actual_vapor_pressure'] = df['saturation_vapor_pressure'] * (df['humidity'] / 100)\n", " df['vapor_pressure_deficit'] = df['saturation_vapor_pressure'] - df['actual_vapor_pressure']\n", "\n", " return df\n", "\n", "def add_diffusion_features(df):\n", " # Indice di Diffusione\n", " df['diffusion_index'] = (df['cloudcover'] * df['humidity']) / 10000\n", "\n", " # Radiazione Diretta vs Diffusa\n", " df['direct_radiation'] = df['solarradiation'] * (1 - df['diffusion_index'])\n", " df['diffuse_radiation'] = df['solarradiation'] * df['diffusion_index']\n", "\n", " # Fattore di Trasparenza Atmosferica\n", " df['atmospheric_transmittance'] = (1 - df['cloudcover']/100) * (df['visibility']/10) * (1 - df['humidity']/200)\n", "\n", " return df\n", "\n", "def calculate_trend(x):\n", " try:\n", " return np.polyfit(np.arange(len(x)), x, 1)[0]\n", " except:\n", " return np.nan\n", "\n", "def add_persistence_features(df):\n", " # Create a copy to avoid modifying the original dataframe\n", " df = df.copy()\n", "\n", " # Calculate trends more efficiently\n", " windows = [3, 6, 12, 24]\n", " for w in windows:\n", " # Use numba or vectorized operations if possible\n", " df[f'radiation_trend_{w}h'] = df['solarradiation'].rolling(\n", " window=w,\n", " min_periods=w\n", " ).apply(calculate_trend, raw=True)\n", "\n", " # Optimize volatility calculation by doing it in one pass\n", " rolling_24 = df['solarradiation'].rolling(24, min_periods=1)\n", " df['radiation_volatility'] = rolling_24.std() / rolling_24.mean().clip(lower=1e-10)\n", "\n", " return df\n", "\n", "def add_weather_pattern_features(df):\n", " # Pattern giornalieri\n", " df['clear_sky_duration'] = df.groupby(df['datetime'].dt.date)['cloudcover'].transform(\n", " lambda x: (x < 30).sum()\n", " )\n", "\n", " # Stabilità delle condizioni\n", " for col in ['temp', 'humidity', 'cloudcover']:\n", " df[f'{col}_stability'] = df[col].rolling(12).std() / df[col].rolling(12).mean()\n", "\n", " # Indice di Variabilità Meteorologica\n", " df['weather_variability_index'] = (df['temp_stability'] +\n", " df['humidity_stability'] +\n", " df['cloudcover_stability']) / 3\n", "\n", " return df\n", "\n", "def add_efficiency_features(df):\n", " # Perdite per temperatura\n", " df['temp_losses'] = 0.004 * (df['temp'] - 25).clip(lower=0) # 0.4% per grado sopra 25°C\n", "\n", " # Perdite per polvere/sporco (stima basata su umidità e pressione)\n", " df['soiling_loss_factor'] = 0.002 * (df['humidity']/100) * (df['pressure']/1013.25)\n", "\n", " # Efficienza complessiva stimata\n", " df['estimated_efficiency'] = (1 - df['temp_losses']) * (1 - df['soiling_loss_factor']) * \\\n", " df['atmospheric_transmittance']\n", "\n", " # Potenziale di produzione\n", " df['production_potential'] = df['solarradiation'] * df['estimated_efficiency']\n", "\n", " return df\n", "\n", "def add_advanced_seasonal_features(df):\n", " # Differenza dalla durata media del giorno\n", " avg_day_length = 12\n", " df['day_length_deviation'] = df['day_length'] - avg_day_length\n", "\n", " # Intensità stagionale\n", " df['seasonal_intensity'] = np.sin(2 * np.pi * (df['day_of_year'] - 172) / 365.25)\n", "\n", " # Indice di Stagionalità\n", " df['seasonality_index'] = df['seasonal_intensity'] * df['solar_elevation']\n", "\n", " # Correzione per alba/tramonto\n", " df['daylight_correction'] = np.where(\n", " (df['hour'] >= df['day_length']) | (df['hour'] <= 24-df['day_length']),\n", " 0,\n", " 1\n", " )\n", "\n", " return df\n", "\n", "def add_basic_interactions(df):\n", " \"\"\"\n", " Aggiunge le interazioni base tra variabili meteorologiche\n", " \"\"\"\n", " # Feature esistenti originali\n", " df['temp_humidity'] = df['temp'] * df['humidity']\n", " df['temp_cloudcover'] = df['temp'] * df['cloudcover']\n", " df['visibility_cloudcover'] = df['visibility'] * df['cloudcover']\n", " df['temp_humidity_interaction'] = df['temp'] * df['humidity'] / 100\n", "\n", " # Clear sky e trasparenza atmosferica\n", " df['clear_sky_factor'] = (100 - df['cloudcover']) / 100\n", " df['atmospheric_transparency'] = (100 - df['cloudcover']) * (df['visibility'] / 10)\n", "\n", " return df\n", "\n", "def add_rolling_and_lag_features(df):\n", " \"\"\"\n", " Aggiunge feature rolling e lag\n", " \"\"\"\n", " # Rolling means esistenti\n", " df['temp_rolling_mean_6h'] = df['temp'].rolling(window=6).mean()\n", " df['cloudcover_rolling_mean_6h'] = df['cloudcover'].rolling(window=6).mean()\n", "\n", " # Lag features esistenti\n", " df['temp_1h_lag'] = df['temp'].shift(1)\n", " df['cloudcover_1h_lag'] = df['cloudcover'].shift(1)\n", " df['humidity_1h_lag'] = df['humidity'].shift(1)\n", "\n", " return df\n", "\n", "def add_condition_indicators(df):\n", " \"\"\"\n", " Aggiunge indicatori di condizioni particolari\n", " \"\"\"\n", " # Extreme conditions indicator esistente\n", " df['extreme_conditions'] = ((df['temp'] > df['temp'].quantile(0.75)) &\n", " (df['humidity'] < df['humidity'].quantile(0.25))).astype(int)\n", "\n", " return df\n", "\n", "def add_physics_based_conversion_features(df):\n", " \"\"\"\n", " Aggiunge feature specifiche per la conversione tra radiazione ed energia\n", " \"\"\"\n", " # Conversione da kWh a MJ/m²/h (1 W = 1 J/s = 0.0036 MJ/h)\n", " df['radiation_to_energy'] = df['solarradiation'] * 0.0036\n", "\n", " # Efficienza di conversione reale vs teorica\n", " df['conversion_efficiency_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", "\n", " # Energia accumulata nel tempo (integrazione)\n", " df['energy_integral'] = df['radiation_to_energy'].rolling(window=24).sum()\n", "\n", " # Differenza tra energia teorica e reale\n", " df['energy_conversion_gap'] = df['radiation_to_energy'] - df['solarenergy']\n", "\n", " # Indice di performance del sistema\n", " df['system_performance_ratio'] = df['solarenergy'] / df['radiation_to_energy'].clip(lower=1e-6)\n", "\n", " return df\n", "\n", "def add_advanced_features(df):\n", " \"\"\"\n", " Add all advanced features to the DataFrame\n", " \"\"\"\n", " # Feature esistenti di base\n", " # 1. Feature temporali di base\n", " df = add_time_features(df)\n", "\n", " # 2. Feature solari e meteorologiche\n", " df = add_solar_features(df)\n", " df = add_solar_specific_features(df)\n", " df = add_radiation_energy_features(df)\n", "\n", " # 3. Feature atmosferiche e di diffusione\n", " df = add_atmospheric_features(df)\n", " df = add_diffusion_features(df)\n", "\n", " # 4. Feature di persistenza e pattern\n", " df = add_persistence_features(df)\n", " df = add_weather_pattern_features(df)\n", "\n", " # 5. Feature di efficienza e stagionalità\n", " df = add_efficiency_features(df)\n", " df = add_advanced_seasonal_features(df)\n", "\n", " # 6. Interazioni e feature derivate\n", " df = add_basic_interactions(df)\n", " df = add_rolling_and_lag_features(df)\n", " df = add_condition_indicators(df)\n", "\n", " # 7. Nuove feature di conversione fisica\n", " df = add_physics_based_conversion_features(df)\n", "\n", " # 8. One-hot encoding delle feature categoriche\n", " df = pd.get_dummies(df, columns=['season', 'time_period'])\n", "\n", " return df\n", "\n", "\n", "def prepare_advanced_data(df):\n", " \"\"\"\n", " Prepare data for advanced modeling with proper datetime handling\n", " \"\"\"\n", " # Assicuriamoci che abbiamo una copia del DataFrame\n", " df = df.copy()\n", "\n", " # Apply feature engineering functions\n", " df = add_advanced_features(df)\n", "\n", " #all_columns = list(df.columns)\n", " #print(all_columns)\n", "\n", " features = {\n", " # Primary Features (strong direct correlation)\n", " 'primary_features': [\n", " 'uvindex',\n", " 'cloudcover',\n", " 'visibility',\n", " 'temp',\n", " 'pressure',\n", " 'humidity',\n", " 'solarradiation'\n", " ],\n", "\n", " # Astronomical and Temporal Features\n", " 'astronomical_features': [\n", " 'solar_elevation',\n", " 'solar_angle',\n", " 'day_length',\n", " 'hour_sin',\n", " 'hour_cos',\n", " 'day_of_year_sin',\n", " 'day_of_year_cos',\n", " 'month_sin',\n", " 'month_cos',\n", " 'solar_noon',\n", " 'daylight_correction'\n", " ],\n", "\n", " # Key Indices and Interactions\n", " 'key_interactions': [\n", " 'clear_sky_index',\n", " 'atmospheric_attenuation',\n", " 'theoretical_radiation',\n", " 'expected_radiation',\n", " 'cloud_elevation',\n", " 'visibility_elevation',\n", " 'uv_cloud_interaction',\n", " 'temp_radiation_potential',\n", " 'air_mass_index',\n", " 'atmospheric_stability',\n", " 'vapor_pressure_deficit',\n", " 'diffusion_index',\n", " 'atmospheric_transmittance',\n", " 'temp_humidity_interaction',\n", " 'clear_sky_factor'\n", " ],\n", "\n", " # Rolling Features (temporal trends)\n", " 'rolling_features': [\n", " 'cloud_rolling_12h',\n", " 'temp_rolling_12h',\n", " 'uv_rolling_12h',\n", " 'cloudcover_rolling_mean_6h',\n", " 'temp_rolling_mean_6h',\n", " 'energy_rolling_mean_6h',\n", " 'uv_rolling_mean_6h',\n", " 'energy_volatility',\n", " 'uv_volatility'\n", " ],\n", "\n", " # Lag Features\n", " 'lag_features': [\n", " 'temp_1h_lag',\n", " 'cloudcover_1h_lag',\n", " 'humidity_1h_lag',\n", " 'energy_lag_1h',\n", " 'uv_lag_1h'\n", " ],\n", "\n", " # Efficiency and Performance Features\n", " 'efficiency_features': [\n", " 'temp_losses',\n", " 'soiling_loss_factor',\n", " 'estimated_efficiency',\n", " 'production_potential',\n", " 'system_performance_ratio',\n", " 'conversion_efficiency_ratio'\n", " ],\n", "\n", " # Weather Pattern Features\n", " 'weather_pattern_features': [\n", " 'clear_sky_duration',\n", " 'weather_variability_index',\n", " 'temp_stability',\n", " 'humidity_stability',\n", " 'cloudcover_stability'\n", " ],\n", "\n", " # Categorical Features\n", " 'categorical_features': [\n", " 'season_Spring',\n", " 'season_Summer',\n", " 'season_Autumn',\n", " 'season_Winter',\n", " 'time_period_Morning',\n", " 'time_period_Afternoon',\n", " 'time_period_Evening',\n", " 'time_period_Night'\n", " ]\n", " }\n", "\n", " final_features = [feature for group in features.values() for feature in group]\n", "\n", " if not isinstance(df.index, pd.DatetimeIndex):\n", " if 'datetime' in df.columns:\n", " df['datetime'] = pd.to_datetime(df['datetime'])\n", " df.set_index('datetime', inplace=True)\n", " else:\n", " raise ValueError(\"No datetime column or index found in DataFrame\")\n", "\n", " # Ordiniamo il DataFrame per datetime\n", " df = df.sort_index()\n", "\n", " # Handle missing values\n", " target_variables = ['solarradiation', 'solarenergy', 'uvindex']\n", " for column in final_features + target_variables:\n", " if column in df.columns:\n", " if isinstance(df.index, pd.DatetimeIndex):\n", " df[column] = df[column].interpolate(method='time')\n", " else:\n", " df[column] = df[column].interpolate(method='linear')\n", "\n", " df.fillna(0, inplace=True)\n", "\n", " # Temporal split\n", " data_after_2010 = df[df['year'] >= 2010].copy()\n", " data_before_2010 = df[df['year'] < 2010].copy()\n", "\n", " X = data_after_2010[final_features]\n", " y = data_after_2010['solarenergy']\n", " X_to_predict = data_before_2010[final_features]\n", "\n", " # Train-test split\n", " X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.13, random_state=random_state_value, shuffle=False\n", " )\n", "\n", " # Scaling\n", " scaler_X = RobustScaler()\n", " X_train_scaled = scaler_X.fit_transform(X_train)\n", " X_test_scaled = scaler_X.transform(X_test)\n", " X_to_predict_scaled = scaler_X.transform(X_to_predict)\n", "\n", " scaler_y = RobustScaler()\n", " y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1))\n", " y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1))\n", "\n", " # Print info about selected features\n", " print(\"\\nSelected features:\")\n", " print(f\"Number of features: {len(final_features)}\")\n", " print(\"Features list:\", final_features)\n", "\n", " return X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, final_features, X_to_predict_scaled\n", "\n", "\n", "def create_sequence_data(X, sequence_length=24):\n", " \"\"\"\n", " Converts data into sequences for LSTM input\n", " sequence_length represents how many previous hours to consider\n", " \"\"\"\n", " sequences = []\n", " for i in range(len(X) - sequence_length + 1):\n", " sequences.append(X[i:i + sequence_length])\n", " return np.array(sequences)\n", "\n", "\n", "def prepare_hybrid_data(df):\n", " X_train_scaled, X_test_scaled, y_train_scaled, y_test_scaled, scaler_X, scaler_y, features, X_to_predict_scaled = prepare_advanced_data(df)\n", "\n", " # Convert data into sequences\n", " sequence_length = 24 # 24 hours of historical data\n", "\n", " X_train_seq = create_sequence_data(X_train_scaled, sequence_length)\n", " X_test_seq = create_sequence_data(X_test_scaled, sequence_length)\n", "\n", " # Adjust y by removing the first (sequence_length-1) elements\n", " y_train = y_train_scaled[sequence_length - 1:]\n", " y_test = y_test_scaled[sequence_length - 1:]\n", "\n", " X_to_predict_seq = create_sequence_data(X_to_predict_scaled, sequence_length)\n", "\n", " return X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq" ] }, { "cell_type": "code", "execution_count": 20, "id": "570b18f2caa3e0db", "metadata": {}, "outputs": [], "source": [ "def create_solarenergy_model(input_shape, folder_name, l2_lambda=0.005, min_output=0, max_output=4.0):\n", " from tensorflow import keras\n", " from keras.models import Model\n", " from keras.layers import (\n", " Input, Dense, Conv1D, BatchNormalization, Dropout, \n", " MultiHeadAttention, LayerNormalization, Lambda,\n", " Concatenate, Activation, Bidirectional, LSTM, Add\n", " )\n", " from keras.regularizers import l2\n", " from keras.optimizers import AdamW\n", " import tensorflow as tf\n", " import numpy as np\n", " import tensorflow_addons as tfa\n", " from tensorflow.keras.optimizers.schedules import CosineDecayRestarts\n", " \n", " # Input layer\n", " inputs = Input(shape=input_shape)\n", " \n", " # Feature groups definition\n", " feature_dims = {\n", " 'solar': [6, 7, 8, 9, 16, 18, 19, 20, 21],\n", " 'weather': [0, 1, 2, 3, 4, 5],\n", " 'temporal': [10, 11, 12, 13, 14, 15],\n", " 'derived': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31],\n", " 'rolling': [33, 34, 35, 36, 37, 38, 39],\n", " 'lag': [40, 41, 42, 43, 44],\n", " 'performance': [45, 46, 47, 48, 49, 50]\n", " }\n", " \n", " # Feature extraction\n", " feature_tensors = {}\n", " for name, indices in feature_dims.items():\n", " valid_indices = [i for i in indices if i < input_shape[-1]]\n", " if valid_indices:\n", " feature_tensors[name] = Lambda(\n", " lambda x, idx=valid_indices: tf.gather(x, idx, axis=-1)\n", " )(inputs)\n", " \n", " # Feature processing with residual connections\n", " def process_feature_group(tensor, units, name):\n", " x = Conv1D(units, kernel_size=3, padding='same', activation='swish',\n", " kernel_regularizer=l2(l2_lambda))(tensor)\n", " x = BatchNormalization()(x)\n", " x = Dropout(0.2)(x)\n", " \n", " residual = Conv1D(units, kernel_size=1, padding='same')(tensor)\n", " x = Add()([x, residual])\n", " x = LayerNormalization()(x)\n", " \n", " return x\n", " \n", " # Process each feature group\n", " processed_features = {}\n", " for name, tensor in feature_tensors.items():\n", " units = 64 if name == 'solar' else 32 if name == 'weather' else 16\n", " processed_features[name] = process_feature_group(tensor, units, name)\n", " \n", " # Enhanced attention mechanism\n", " def attention_block(x, num_heads=4):\n", " attention_output = MultiHeadAttention(\n", " num_heads=num_heads, \n", " key_dim=x.shape[-1] // num_heads\n", " )(x, x)\n", " x = LayerNormalization()(x + attention_output)\n", " \n", " ffn = Dense(x.shape[-1] * 2, activation='swish')(x)\n", " ffn = Dropout(0.1)(ffn)\n", " ffn = Dense(x.shape[-1])(ffn)\n", " \n", " return LayerNormalization()(x + ffn)\n", " \n", " # Merge primary features with attention\n", " primary_features = [\n", " processed_features['solar'],\n", " processed_features['weather'],\n", " processed_features['performance']\n", " ]\n", " primary_context = Concatenate(axis=-1)(primary_features)\n", " primary_context = attention_block(primary_context)\n", " \n", " # Merge secondary features\n", " secondary_features = [\n", " processed_features[name] for name in ['temporal', 'rolling', 'lag']\n", " if name in processed_features\n", " ]\n", " if secondary_features:\n", " secondary_context = Concatenate(axis=-1)(secondary_features)\n", " secondary_context = attention_block(secondary_context)\n", " else:\n", " secondary_context = primary_context\n", " \n", " # Final feature merge\n", " combined = Concatenate(axis=-1)([\n", " primary_context, \n", " secondary_context,\n", " processed_features['derived']\n", " ])\n", " \n", " # Sequential processing with residual LSTM\n", " def residual_lstm_block(x, units):\n", " lstm_out = Bidirectional(LSTM(units, return_sequences=True))(x)\n", " residual = Conv1D(units * 2, kernel_size=1, padding='same')(x)\n", " x = Add()([lstm_out, residual])\n", " x = LayerNormalization()(x)\n", " return x\n", " \n", " x = residual_lstm_block(combined, 128)\n", " x = residual_lstm_block(x, 64)\n", " x = Bidirectional(LSTM(64))(x)\n", " x = Dropout(0.2)(x)\n", " \n", " # Classification branch\n", " class_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " class_x = BatchNormalization()(class_x)\n", " class_x = Dropout(0.2)(class_x)\n", " class_x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(class_x)\n", " class_output = Dense(1, activation='sigmoid', name='classification_output')(class_x)\n", " \n", " # Enhanced regression branch with multiple pathways\n", " def create_regression_pathway(x, name):\n", " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " x = BatchNormalization()(x)\n", " x = Dropout(0.2)(x)\n", " \n", " residual = x\n", " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " x = BatchNormalization()(x)\n", " x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " x = Add()([x, residual])\n", " \n", " x = Dense(64, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " return Dense(1, name=f'{name}_output')(x)\n", " \n", " # Create specialized regression pathways\n", " low_range = create_regression_pathway(x, 'low_range')\n", " mid_range = create_regression_pathway(x, 'mid_range')\n", " high_range = create_regression_pathway(x, 'high_range')\n", " \n", " # Create context vector for attention\n", " context = Dense(64, activation='swish')(x)\n", " \n", " # Calculate attention scores\n", " attention_scores = Dense(3, activation='softmax')(context)\n", " \n", " # Combine predictions using attention weights\n", " reg_output = Lambda(\n", " lambda x: x[0][:, 0:1] * x[1] + x[0][:, 1:2] * x[2] + x[0][:, 2:3] * x[3],\n", " name='regression_output'\n", " )([attention_scores, low_range, mid_range, high_range])\n", "\n", " # Final output processing remains the same...\n", " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(x)\n", " final_x = BatchNormalization()(final_x)\n", " final_x = Dropout(0.2)(final_x)\n", " \n", " residual = final_x\n", " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", " final_x = BatchNormalization()(final_x)\n", " final_x = Dense(256, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", " final_x = Add()([final_x, residual])\n", " \n", " final_x = Dense(128, activation='swish', kernel_regularizer=l2(l2_lambda))(final_x)\n", " final_x = Dense(1)(final_x)\n", " final_output = Lambda(\n", " lambda x: tf.clip_by_value(x, min_output, max_output),\n", " name='final_output'\n", " )(final_x)\n", " \n", " # Build model with all outputs\n", " model = Model(\n", " inputs=inputs,\n", " outputs=[class_output, reg_output, final_output]\n", " )\n", " \n", " # Enhanced loss functions\n", " def enhanced_regression_loss(y_true, y_pred):\n", " mae = tf.abs(y_true - y_pred)\n", " mse = tf.square(y_true - y_pred)\n", " \n", " value_ranges = tf.cast(y_true > 2.0, tf.float32) * 1.5 + \\\n", " tf.cast(tf.logical_and(y_true <= 2.0, y_true > 1.0), tf.float32) * 1.2 + \\\n", " tf.cast(y_true <= 1.0, tf.float32)\n", " \n", " weighted_loss = (0.5 * mae + 0.5 * mse) * value_ranges\n", " return tf.reduce_mean(weighted_loss)\n", " \n", " def final_loss(y_true, y_pred):\n", " y_true = tf.clip_by_value(y_true, min_output, max_output)\n", " mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n", " mse = tf.reduce_mean(tf.square(y_true - y_pred))\n", " return 0.5 * mae + 0.5 * mse\n", " \n", " # Learning rate schedule\n", " clr = CosineDecayRestarts(\n", " initial_learning_rate=2e-4,\n", " first_decay_steps=1000,\n", " t_mul=2.0,\n", " m_mul=0.9,\n", " alpha=1e-7\n", " )\n", " \n", " # Optimizer\n", " optimizer = AdamW(\n", " learning_rate=clr,\n", " weight_decay=0.01,\n", " clipnorm=1.0\n", " )\n", " \n", " # Compile model\n", " model.compile(\n", " optimizer=optimizer,\n", " loss={\n", " 'classification_output': 'binary_crossentropy',\n", " 'regression_output': enhanced_regression_loss,\n", " 'final_output': final_loss\n", " }\n", " )\n", "\n", " # Plot model architecture\n", " try:\n", " plot_model(\n", " model,\n", " to_file=f'{folder_name}_model_architecture.png',\n", " show_shapes=True,\n", " show_layer_names=True,\n", " dpi=150,\n", " show_layer_activations=True\n", " )\n", " except Exception as e:\n", " print(f\"Warning: Could not plot model architecture: {e}\")\n", "\n", " return model\n", "\n", "\n", "def evaluate_solarenergy_predictions(y_true, y_pred, hour=None, folder_name=None):\n", " \"\"\"\n", " Comprehensive evaluation of solar energy predictions with detailed analysis and visualizations.\n", "\n", " Parameters:\n", " -----------\n", " y_true : array-like\n", " Actual solar energy values (kWh)\n", " y_pred : array-like\n", " Predicted solar energy values (kWh)\n", " hour : array-like, optional\n", " Array of hours corresponding to predictions, for temporal analysis\n", " folder_name : str, optional\n", " Directory to save analysis plots\n", "\n", " Returns:\n", " --------\n", " dict\n", " Dictionary containing all calculated metrics\n", " \"\"\"\n", "\n", " # Data preparation\n", " y_true = np.array(y_true).ravel()\n", " y_pred = np.array(y_pred).ravel()\n", " errors = y_pred - y_true\n", "\n", " # Basic metrics calculation\n", " mae_raw = mean_absolute_error(y_true, y_pred)\n", " rmse_raw = np.sqrt(mean_squared_error(y_true, y_pred))\n", " r2_raw = r2_score(y_true, y_pred)\n", "\n", " # Corrected MAPE calculation\n", " mask = y_true > 10 # Consider only values above 10 kWh\n", " if np.any(mask):\n", " mape = np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100\n", " else:\n", " mape = np.nan\n", "\n", " # Corrected error margin accuracy\n", " within_5_percent = np.mean(np.abs(errors) <= 5) * 100 # Within 5 kWh\n", " within_10_percent = np.mean(np.abs(errors) <= 10) * 100 # Within 10 kWh\n", " within_20_percent = np.mean(np.abs(errors) <= 20) * 100 # Within 20 kWh\n", "\n", " # Energy level classification\n", " def get_energy_level(value):\n", " if value <= 0.5:\n", " return 'Very Low'\n", " elif value <= 2.0:\n", " return 'Low'\n", " elif value <= 4.0:\n", " return 'Moderate'\n", " elif value <= 6.0:\n", " return 'High'\n", " elif value <= 8.0:\n", " return 'Very High'\n", " else:\n", " return 'Extreme'\n", "\n", " # Calculate energy levels\n", " y_true_levels = [get_energy_level(v) for v in y_true]\n", " y_pred_levels = [get_energy_level(v) for v in y_pred]\n", " level_accuracy = np.mean([t == p for t, p in zip(y_true_levels, y_pred_levels)])\n", "\n", " unique_levels = sorted(list(set(y_true_levels + y_pred_levels)))\n", "\n", " # Print main metrics\n", " print(\"\\nSolar Energy Prediction Metrics:\")\n", " print(\"\\nAbsolute Metrics:\")\n", " print(f\"MAE: {mae_raw:.2f} kWh\")\n", " print(f\"RMSE: {rmse_raw:.2f} kWh\")\n", " print(f\"R² Score: {r2_raw:.3f}\")\n", " print(f\"MAPE: {mape:.2f}%\" if not np.isnan(mape) else \"MAPE: N/A (insufficient data)\")\n", "\n", " print(\"\\nAccuracy Metrics:\")\n", " print(f\"Within ±5 kWh: {within_5_percent:.1f}%\")\n", " print(f\"Within ±10 kWh: {within_10_percent:.1f}%\")\n", " print(f\"Within ±20 kWh: {within_20_percent:.1f}%\")\n", "\n", " print(\"\\nLevel Accuracy:\")\n", " print(f\"Level Accuracy: {level_accuracy * 100:.1f}%\")\n", "\n", " # Confusion matrix for energy levels\n", " cm = confusion_matrix(y_true_levels, y_pred_levels, labels=unique_levels)\n", " print(\"\\nConfusion Matrix for Energy Levels:\")\n", " cm_df = pd.DataFrame(\n", " cm,\n", " columns=unique_levels,\n", " index=unique_levels\n", " )\n", " print(cm_df)\n", "\n", " # Time period analysis\n", " if hour is not None:\n", " day_periods = {\n", " 'Morning (5-11)': (5, 11),\n", " 'Noon (11-13)': (11, 13),\n", " 'Afternoon (13-17)': (13, 17),\n", " 'Evening (17-21)': (17, 21),\n", " 'Night (21-5)': (21, 5)\n", " }\n", "\n", " print(\"\\nAnalysis by Time Period:\")\n", " for period, (start, end) in day_periods.items():\n", " if start < end:\n", " mask = (hour >= start) & (hour < end)\n", " else:\n", " mask = (hour >= start) | (hour < end)\n", "\n", " if np.any(mask):\n", " period_mae = mean_absolute_error(y_true[mask], y_pred[mask])\n", "\n", " # Corrected period MAPE calculation\n", " period_mask = mask & (y_true > 10)\n", " if np.any(period_mask):\n", " period_mape = np.mean(np.abs((y_true[period_mask] - y_pred[period_mask]) / y_true[period_mask])) * 100\n", " print(f\"\\n{period}:\")\n", " print(f\"MAE: {period_mae:.2f} kWh\")\n", " print(f\"MAPE: {period_mape:.2f}%\")\n", " else:\n", " print(f\"\\n{period}:\")\n", " print(f\"MAE: {period_mae:.2f} kWh\")\n", " print(\"MAPE: N/A (insufficient data)\")\n", "\n", " # Visualizations\n", " if folder_name is not None:\n", " try:\n", " # Figure 1: Main analysis plots\n", " plt.figure(figsize=(20, 15))\n", "\n", " # Plot 1: Scatter plot of actual vs predicted values\n", " plt.subplot(3, 2, 1)\n", " plt.scatter(y_true, y_pred, alpha=0.5)\n", " plt.plot([y_true.min(), y_true.max()], [y_true.min(), y_true.max()], 'r--', lw=2)\n", " plt.xlabel('Actual Energy (kWh)')\n", " plt.ylabel('Predicted Energy (kWh)')\n", " plt.title('Actual vs Predicted Values')\n", " plt.grid(True)\n", "\n", " # Plot 2: Absolute error distribution\n", " plt.subplot(3, 2, 2)\n", " plt.hist(errors, bins=50, alpha=0.7)\n", " plt.xlabel('Prediction Error (kWh)')\n", " plt.ylabel('Frequency')\n", " plt.title('Error Distribution')\n", " plt.grid(True)\n", "\n", " # Plot 3: Percentage error distribution (only for values > 0.5 kWh)\n", " plt.subplot(3, 2, 3)\n", " mask = y_true > 0.5\n", " if np.any(mask):\n", " percentage_errors = ((y_pred[mask] - y_true[mask]) / y_true[mask]) * 100\n", " plt.hist(np.clip(percentage_errors, -100, 100), bins=50, alpha=0.7)\n", " plt.xlabel('Percentage Error (%)')\n", " plt.ylabel('Frequency')\n", " plt.title('Percentage Error Distribution (for values > 0.5 kWh)')\n", " plt.grid(True)\n", "\n", " # Plot 4: Errors vs actual values\n", " plt.subplot(3, 2, 4)\n", " plt.scatter(y_true, errors, alpha=0.5)\n", " plt.axhline(y=0, color='r', linestyle='--')\n", " plt.xlabel('Actual Energy (kWh)')\n", " plt.ylabel('Error (kWh)')\n", " plt.title('Errors vs Actual Values')\n", " plt.grid(True)\n", "\n", " # Plot 5: Error boxplot by Energy level\n", " plt.subplot(3, 2, 5)\n", " sns.boxplot(x=[get_energy_level(v) for v in y_true], y=errors)\n", " plt.xticks(rotation=45)\n", " plt.xlabel('Energy Level')\n", " plt.ylabel('Error (kWh)')\n", " plt.title('Error Distribution by Level')\n", "\n", " # Plot 6: Confusion matrix heatmap\n", " plt.subplot(3, 2, 6)\n", " sns.heatmap(cm_df, annot=True, fmt='d', cmap='Blues')\n", " plt.title('Confusion Matrix')\n", " plt.xticks(rotation=45)\n", " plt.yticks(rotation=45)\n", "\n", " plt.tight_layout()\n", " filename = f'{folder_name}_energy_analysis.png'\n", " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", " print(f\"\\nPlot saved as: {filename}\")\n", " plt.close()\n", "\n", " except Exception as e:\n", " print(f\"\\nError saving plots: {str(e)}\")\n", "\n", " # Additional error statistics\n", " print(\"\\nError Statistics:\")\n", " print(f\"Mean error: {np.mean(errors):.3f}\")\n", " print(f\"Error standard deviation: {np.std(errors):.3f}\")\n", " print(f\"Median error: {np.median(errors):.3f}\")\n", " print(f\"95th percentile absolute error: {np.percentile(np.abs(errors), 95):.3f}\")\n", "\n", " # Return structured metrics\n", " metrics = {\n", " 'absolute': {\n", " 'mae': mae_raw,\n", " 'rmse': rmse_raw,\n", " 'r2': r2_raw,\n", " 'mape': float(mape) if not np.isnan(mape) else None\n", " },\n", " 'accuracy': {\n", " 'within_5_wm2': float(within_5_percent),\n", " 'within_10_wm2': float(within_10_percent),\n", " 'within_20_wm2': float(within_20_percent)\n", " },\n", " 'categorical': {\n", " 'level_accuracy': float(level_accuracy)\n", " },\n", " 'error_stats': {\n", " 'mean': float(np.mean(errors)),\n", " 'std': float(np.std(errors)),\n", " 'median': float(np.median(errors)),\n", " 'p95_abs': float(np.percentile(np.abs(errors), 95))\n", " }\n", " }\n", "\n", " return metrics\n", "\n", "\n", "def plot_training_history(history, folder_name=None):\n", " \"\"\"\n", " Visualize and save training history for the hybrid model\n", " \"\"\"\n", " plt.figure(figsize=(15, 10))\n", "\n", " # Loss plots\n", " plt.subplot(2, 2, 1)\n", " plt.plot(history.history['classification_output_loss'], label='Class Loss')\n", " plt.plot(history.history['regression_output_loss'], label='Reg Loss')\n", " plt.plot(history.history['final_output_loss'], label='Final Loss')\n", " plt.plot(history.history['val_classification_output_loss'], label='Val Class Loss')\n", " plt.plot(history.history['val_regression_output_loss'], label='Val Reg Loss')\n", " plt.plot(history.history['val_final_output_loss'], label='Val Final Loss')\n", " plt.title('Model Losses')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # Classification metrics\n", " plt.subplot(2, 2, 2)\n", " plt.plot(history.history['classification_output_accuracy'], label='Class Acc')\n", " plt.plot(history.history['val_classification_output_accuracy'], label='Val Class Acc')\n", " plt.plot(history.history['classification_output_auc'], label='Class AUC')\n", " plt.plot(history.history['val_classification_output_auc'], label='Val Class AUC')\n", " plt.title('Classification Metrics')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Metric Value')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # Regression metrics\n", " plt.subplot(2, 2, 3)\n", " plt.plot(history.history['regression_output_mae'], label='Reg MAE')\n", " plt.plot(history.history['val_regression_output_mae'], label='Val Reg MAE')\n", " plt.title('Regression MAE')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('MAE')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " # Final output metrics\n", " plt.subplot(2, 2, 4)\n", " plt.plot(history.history['final_output_mae'], label='Final MAE')\n", " plt.plot(history.history['val_final_output_mae'], label='Val Final MAE')\n", " plt.title('Final Output MAE')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('MAE')\n", " plt.legend()\n", " plt.grid(True)\n", "\n", " plt.tight_layout()\n", "\n", " if folder_name is not None:\n", " filename = f'{folder_name}_training_history.png'\n", " plt.savefig(filename, dpi=300, bbox_inches='tight')\n", " print(f\"\\nTraining history plot saved as: {filename}\")\n", "\n", " # Save history to JSON\n", " history_dict = history.history\n", " json_filename = f'{folder_name}_training_history.json'\n", " with open(json_filename, 'w') as f:\n", " json.dump(history_dict, f)\n", " print(f\"Training history saved as: {json_filename}\")\n", "\n", " plt.show()\n", "\n", "def calculate_metrics(y_true, y_class, y_reg, y_final, min_output, max_output):\n", " \"\"\"\n", " Calculates comprehensive metrics for the solar energy prediction model.\n", " \n", " Parameters:\n", " -----------\n", " y_true : array-like\n", " Ground truth values\n", " y_class : array-like\n", " Classification predictions (probability of non-zero values)\n", " y_reg : array-like\n", " Regression predictions (unrestricted values)\n", " y_final : array-like\n", " Final clipped predictions\n", " min_output : float\n", " Minimum allowed output value\n", " max_output : float\n", " Maximum allowed output value\n", " \n", " Returns:\n", " --------\n", " dict\n", " Dictionary containing all calculated metrics\n", " \"\"\"\n", " from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix\n", " \n", " # Ensure proper array formatting and dimensionality\n", " y_true = np.array(y_true).flatten()\n", " y_class = np.array(y_class).flatten()\n", " y_reg = np.array(y_reg).flatten()\n", " y_final = np.array(y_final).flatten()\n", " \n", " # Validate input dimensions\n", " assert len(y_true) == len(y_class) == len(y_reg) == len(y_final), \\\n", " \"All input arrays must have the same length\"\n", " \n", " # Classification metrics with error handling\n", " print(\"\\nClassification Metrics:\")\n", " try:\n", " y_true_binary = (y_true > 0).astype(int)\n", " y_pred_binary = (y_class > 0.5).astype(int)\n", " \n", " accuracy = np.mean((y_class > 0.5) == (y_true > 0)) * 100\n", " auc_roc = roc_auc_score(y_true > 0, y_class)\n", " print(f\"Accuracy: {accuracy:.2f}%\")\n", " print(f\"AUC-ROC: {auc_roc:.4f}\")\n", " \n", " print(\"\\nConfusion Matrix:\")\n", " conf_matrix = confusion_matrix(y_true_binary, y_pred_binary)\n", " print(conf_matrix)\n", " \n", " print(\"\\nClassification Report:\")\n", " class_report = classification_report(\n", " y_true_binary, \n", " y_pred_binary,\n", " target_names=['Zero', 'Non-Zero'],\n", " digits=4\n", " )\n", " print(class_report)\n", " except Exception as e:\n", " print(f\"Error in classification metrics calculation: {str(e)}\")\n", " \n", " # Regression metrics with error handling\n", " print(\"\\nRegression Metrics (non-zero values):\")\n", " mask_nonzero = y_true > 0\n", " if np.any(mask_nonzero):\n", " try:\n", " y_true_nonzero = y_true[mask_nonzero]\n", " y_reg_nonzero = y_reg[mask_nonzero]\n", " \n", " # Range validation\n", " out_of_range = np.sum(\n", " (y_reg_nonzero < min_output) | \n", " (y_reg_nonzero > max_output)\n", " )\n", " \n", " # Error metrics with numerical stability\n", " epsilon = 1e-7\n", " diff = np.abs((y_true_nonzero - y_reg_nonzero) / \n", " (y_true_nonzero + epsilon))\n", " diff = np.clip(diff, 0, 1)\n", " \n", " # Calculate metrics\n", " mape = np.mean(diff) * 100\n", " within_10_percent = np.mean(diff <= 0.10) * 100\n", " mae = np.mean(np.abs(y_true_nonzero - y_reg_nonzero))\n", " rmse = np.sqrt(np.mean(np.square(y_true_nonzero - y_reg_nonzero)))\n", " \n", " print(f\"Out of range: {out_of_range} predictions\")\n", " print(f\"MAPE: {mape:.2f}%\")\n", " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", " print(f\"MAE: {mae:.2f}\")\n", " print(f\"RMSE: {rmse:.2f}\")\n", " except Exception as e:\n", " print(f\"Error in regression metrics calculation: {str(e)}\")\n", " else:\n", " print(\"No non-zero values in this batch\")\n", " \n", " # Final output metrics with error handling\n", " print(\"\\nFinal Combined Output Metrics:\")\n", " try:\n", " # Ensure outputs are within bounds\n", " out_of_range = np.sum((y_final < min_output) | (y_final > max_output))\n", " \n", " # Calculate metrics with numerical stability\n", " epsilon = 1e-7\n", " diff = np.abs((y_true - y_final) / (y_true + epsilon))\n", " diff = np.clip(diff, 0, 1)\n", " \n", " mape = np.mean(diff) * 100\n", " within_2_percent = np.mean(diff <= 0.02) * 100\n", " within_5_percent = np.mean(diff <= 0.05) * 100\n", " within_10_percent = np.mean(diff <= 0.10) * 100\n", " within_20_percent = np.mean(diff <= 0.20) * 100\n", " mae = np.mean(np.abs(y_true - y_final))\n", " rmse = np.sqrt(np.mean(np.square(y_true - y_final)))\n", " \n", " print(f\"Out of range: {out_of_range} predictions\")\n", " print(f\"MAPE: {mape:.2f}%\")\n", " print(f\"Within ±2%: {within_2_percent:.2f}%\")\n", " print(f\"Within ±5%: {within_5_percent:.2f}%\")\n", " print(f\"Within ±10%: {within_10_percent:.2f}%\")\n", " print(f\"Within ±20%: {within_20_percent:.2f}%\")\n", " print(f\"MAE: {mae:.2f}\")\n", " print(f\"RMSE: {rmse:.2f}\")\n", " except Exception as e:\n", " print(f\"Error in final output metrics calculation: {str(e)}\")\n", "\n", "def train_hybrid_model(model, X_train, y_train, X_test, y_test, epochs=100, batch_size=32, folder_name='solarenergy', min_output=0, max_output=1):\n", " \"\"\"\n", " Advanced training function for the hybrid solar energy model\n", " \"\"\" \n", " # Prepare binary targets for classification\n", " y_train_binary = (y_train > 0).astype(float)\n", " y_test_binary = (y_test > 0).astype(float)\n", "\n", " # Training targets dictionary - usando i nomi esatti degli output del modello\n", " train_targets = {\n", " 'classification_output': y_train_binary,\n", " 'regression_output': y_train, # Questo nome corrisponde a quello nel modello\n", " 'final_output': y_train\n", " }\n", "\n", " # Validation targets dictionary\n", " test_targets = {\n", " 'classification_output': y_test_binary,\n", " 'regression_output': y_test, # Questo nome corrisponde a quello nel modello\n", " 'final_output': y_test\n", " }\n", "\n", " def evaluate_epoch(epoch, logs):\n", " if epoch % 20 == 0:\n", " print(f\"\\nEpoch {epoch + 1} Detailed Metrics:\")\n", " predictions = model.predict(X_test, verbose=0)\n", " calculate_metrics(y_test, *predictions, min_output, max_output)\n", "\n", " callbacks = [\n", " tf.keras.callbacks.EarlyStopping(\n", " monitor='val_final_output_loss',\n", " patience=35,\n", " restore_best_weights=True,\n", " mode='min',\n", " verbose=1,\n", " min_delta=1e-5\n", " ),\n", " tf.keras.callbacks.ModelCheckpoint(\n", " filepath=f'{folder_name}_best_model.h5',\n", " monitor='val_final_output_loss',\n", " save_best_only=True,\n", " mode='min',\n", " save_weights_only=True # Modificato a True per evitare problemi di serializzazione\n", " ),\n", " tf.keras.callbacks.TensorBoard(\n", " log_dir=f'./{folder_name}_logs',\n", " histogram_freq=1,\n", " write_graph=True,\n", " update_freq='epoch'\n", " ),\n", " tf.keras.callbacks.LambdaCallback(on_epoch_end=evaluate_epoch),\n", " tf.keras.callbacks.TerminateOnNaN()\n", " ]\n", "\n", " '''\n", " tf.keras.callbacks.ReduceLROnPlateau(\n", " monitor='val_final_output_loss',\n", " factor=0.8,\n", " patience=10,\n", " verbose=1,\n", " mode='min',\n", " min_delta=1e-4,\n", " cooldown=2,\n", " min_lr=1e-7\n", " ),\n", " '''\n", " try:\n", " history = model.fit(\n", " X_train,\n", " train_targets,\n", " validation_data=(X_test, test_targets),\n", " epochs=epochs,\n", " batch_size=batch_size,\n", " callbacks=callbacks,\n", " verbose=1,\n", " shuffle=False\n", " )\n", "\n", " print(\"\\nTraining completed successfully!\")\n", "\n", " # Final evaluation\n", " predictions = model.predict(X_test, verbose=0)\n", " calculate_metrics(y_test, *predictions, min_output, max_output)\n", "\n", " return history\n", "\n", " except Exception as e:\n", " print(f\"\\nError during training: {str(e)}\")\n", " print(\"\\nModel output names:\", [output.name for output in model.outputs])\n", " print(\"Training targets keys:\", train_targets.keys())\n", " raise\n", "\n", " finally:\n", " tf.keras.backend.clear_session()\n", "\n", "\n", "def integrate_predictions(df, predictions, sequence_length=24):\n", " \"\"\"\n", " Integrates solar energy predictions into the original dataset for pre-2010 data.\n", "\n", " Parameters:\n", " -----------\n", " df : pandas.DataFrame\n", " Original dataset\n", " predictions : tuple\n", " Tuple containing (classification_pred, regression_pred, final_pred)\n", " - classification_pred: probability of non-zero values\n", " - regression_pred: predicted values (used for non-zero cases)\n", " - final_pred: final combined predictions\n", " sequence_length : int\n", " Sequence length used for predictions\n", "\n", " Returns:\n", " --------\n", " pandas.DataFrame\n", " Updated dataset with solar energy predictions and additional prediction details\n", " \"\"\"\n", " # Convert datetime to datetime format if not already\n", " df['datetime'] = pd.to_datetime(df['datetime'])\n", "\n", " # Identify pre-2010 rows\n", " mask_pre_2010 = df['datetime'].dt.year < 2010\n", "\n", " # Unpack predictions\n", " classification_pred, regression_pred, final_pred = predictions\n", "\n", " # Create temporary DataFrame with all predictions\n", " dates_pre_2010 = df[mask_pre_2010]['datetime'].iloc[sequence_length - 1:]\n", " predictions_df = pd.DataFrame({\n", " 'datetime': dates_pre_2010,\n", " 'solarenergy_predicted': final_pred.flatten(),\n", " 'solarenergy_classification': classification_pred.flatten(),\n", " 'solarenergy_regression': regression_pred.flatten()\n", " })\n", "\n", " # Merge with original dataset\n", " df = df.merge(predictions_df, on='datetime', how='left')\n", "\n", " # Update solar energy column where missing\n", " df['solarenergy'] = df['solarenergy'].fillna(df['solarenergy_predicted'])\n", "\n", " # Print detailed statistics\n", " print(\"\\nPrediction Integration Statistics:\")\n", " print(f\"Added {len(final_pred)} predictions to dataset\")\n", " print(f\"Rows with solar energy after integration: {df['solarenergy'].notna().sum()}\")\n", "\n", " # Analyze prediction components for the filled values\n", " mask_filled = df['solarenergy'] == df['solarenergy_predicted']\n", " if mask_filled.any():\n", " filled_data = df[mask_filled]\n", "\n", " print(\"\\nFilled Values Analysis:\")\n", " print(f\"Zero predictions (classification < 0.5): {(filled_data['solarenergy_classification'] < 0.5).sum()}\")\n", " print(f\"Non-zero predictions (classification >= 0.5): {(filled_data['solarenergy_classification'] >= 0.5).sum()}\")\n", "\n", " # Distribution of predicted values\n", " non_zero_pred = filled_data[filled_data['solarenergy_predicted'] > 0]\n", " if len(non_zero_pred) > 0:\n", " print(f\"\\nNon-zero predictions statistics:\")\n", " print(f\"Mean: {non_zero_pred['solarenergy_predicted'].mean():.2f}\")\n", " print(f\"Median: {non_zero_pred['solarenergy_predicted'].median():.2f}\")\n", " print(f\"Std: {non_zero_pred['solarenergy_predicted'].std():.2f}\")\n", "\n", " # Optionally, you can keep or remove the intermediate prediction columns\n", " columns_to_drop = ['solarenergy_predicted', 'solarenergy_classification',\n", " 'solarenergy_regression']\n", " df = df.drop(columns_to_drop, axis=1)\n", "\n", " return df" ] }, { "cell_type": "code", "execution_count": 21, "id": "b3b0c2e65ddf484", "metadata": {}, "outputs": [], "source": [ "def analyze_distribution(data, solar_column='solarenergy', name = 'Solar Energy'):\n", " \"\"\"\n", " Analizza dettagliatamente la distribuzione della variabile solarenergy.\n", "\n", " Parameters:\n", " -----------\n", " data : pandas.DataFrame\n", " DataFrame contenente la colonna solarenergy\n", " solar_column : str, default='solarenergy'\n", " Nome della colonna da analizzare\n", "\n", " Returns:\n", " --------\n", " dict\n", " Dizionario contenente le statistiche principali\n", " \"\"\"\n", "\n", " # Creiamo una figura con più subplot\n", " fig = plt.figure(figsize=(20, 12))\n", "\n", " # 1. Statistiche di base\n", " stats_dict = {\n", " 'count': len(data[solar_column]),\n", " 'missing': data[solar_column].isnull().sum(),\n", " 'zeros': (data[solar_column] == 0).sum(),\n", " 'mean': data[solar_column].mean(),\n", " 'median': data[solar_column].median(),\n", " 'std': data[solar_column].std(),\n", " 'min': data[solar_column].min(),\n", " 'max': data[solar_column].max(),\n", " 'skewness': stats.skew(data[solar_column].dropna()),\n", " 'kurtosis': stats.kurtosis(data[solar_column].dropna())\n", " }\n", "\n", " # Calcolo dei percentili\n", " percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]\n", " for p in percentiles:\n", " stats_dict[f'percentile_{p}'] = np.percentile(data[solar_column].dropna(), p)\n", "\n", " # 2. Visualizzazioni\n", "\n", " # 2.1 Distribuzione\n", " plt.subplot(2, 2, 1)\n", " sns.histplot(data=data, x=solar_column, kde=True)\n", " plt.title(f'Distribuzione di {name}')\n", " plt.xlabel(f'{name}')\n", " plt.ylabel('Frequenza')\n", "\n", " # 2.2 Box Plot\n", " plt.subplot(2, 2, 2)\n", " sns.boxplot(y=data[solar_column])\n", " plt.title(f'Box Plot di {name}')\n", "\n", " # 2.3 QQ Plot\n", " plt.subplot(2, 2, 3)\n", " stats.probplot(data[solar_column].dropna(), dist=\"norm\", plot=plt)\n", " plt.title(f'Q-Q Plot di {name}')\n", "\n", " # 2.4 Distribuzione Log-trasformata\n", " plt.subplot(2, 2, 4)\n", " sns.histplot(data=np.log1p(data[solar_column]), kde=True)\n", " plt.title(f'Distribuzione Log-trasformata di {name}')\n", " plt.xlabel(f'Log({name} + 1)')\n", " plt.ylabel('Frequenza')\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # 3. Analisi temporale se disponibile\n", " if 'timestamp' in data.columns or 'datetime' in data.columns:\n", " time_col = 'timestamp' if 'timestamp' in data.columns else 'datetime'\n", " if isinstance(data[time_col].iloc[0], (int, float)):\n", " data['temp_datetime'] = pd.to_datetime(data[time_col], unit='s')\n", " else:\n", " data['temp_datetime'] = pd.to_datetime(data[time_col])\n", "\n", " # Plot temporale\n", " plt.figure(figsize=(15, 6))\n", " plt.plot(data['temp_datetime'], data[solar_column])\n", " plt.title(f'Serie Temporale di {name}')\n", " plt.xlabel('Data')\n", " plt.ylabel(f'{name}')\n", " plt.xticks(rotation=45)\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # Analisi stagionale\n", " data['month'] = data['temp_datetime'].dt.month\n", " seasonal_stats = data.groupby('month')[solar_column].agg(['mean', 'std', 'median'])\n", "\n", " plt.figure(figsize=(12, 6))\n", " seasonal_stats['mean'].plot(kind='bar')\n", " plt.title(f'Media Mensile di {name}')\n", " plt.xlabel('Mese')\n", " plt.ylabel(f'{name} Media')\n", " plt.tight_layout()\n", " plt.show()\n", "\n", " # 4. Stampa delle statistiche principali\n", " print(f\"\\nStatistiche principali di {name}:\")\n", " print(\"-\" * 50)\n", " for key, value in stats_dict.items():\n", " print(f\"{key:15}: {value:,.4f}\")\n", "\n", " # 5. Suggerimenti per la normalizzazione\n", " print(\"\\nSuggerimenti per la normalizzazione:\")\n", " print(\"-\" * 50)\n", "\n", " skewness = abs(stats_dict['skewness'])\n", " if skewness > 1:\n", " print(\"- La distribuzione è fortemente asimmetrica (skewness > 1)\")\n", " print(\"- Considerare una trasformazione logaritmica: np.log1p(x)\")\n", "\n", " range_ratio = stats_dict['max'] / stats_dict['std']\n", " if range_ratio > 10:\n", " print(\"- La variabile ha una scala molto ampia\")\n", " print(\"- Considerare RobustScaler o StandardScaler per la normalizzazione\")\n", "\n", " zero_ratio = stats_dict['zeros'] / stats_dict['count']\n", " if zero_ratio > 0.1:\n", " print(f\"- Alta presenza di zeri ({zero_ratio:.2%})\")\n", " print(\"- Considerare un modello in due parti: classificazione degli zeri + regressione sui valori non-zero\")\n", "\n", " return stats_dict" ] }, { "cell_type": "code", "execution_count": 6, "id": "1b1ee91d1573ec66", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing solar energy model training...\n", "\n", "1. Preparing data...\n", "\n", "Selected features:\n", "Number of features: 66\n", "Features list: ['uvindex', 'cloudcover', 'visibility', 'temp', 'pressure', 'humidity', 'solarradiation', 'solar_elevation', 'solar_angle', 'day_length', 'hour_sin', 'hour_cos', 'day_of_year_sin', 'day_of_year_cos', 'month_sin', 'month_cos', 'solar_noon', 'daylight_correction', 'clear_sky_index', 'atmospheric_attenuation', 'theoretical_radiation', 'expected_radiation', 'cloud_elevation', 'visibility_elevation', 'uv_cloud_interaction', 'temp_radiation_potential', 'air_mass_index', 'atmospheric_stability', 'vapor_pressure_deficit', 'diffusion_index', 'atmospheric_transmittance', 'temp_humidity_interaction', 'clear_sky_factor', 'cloud_rolling_12h', 'temp_rolling_12h', 'uv_rolling_12h', 'cloudcover_rolling_mean_6h', 'temp_rolling_mean_6h', 'energy_rolling_mean_6h', 'uv_rolling_mean_6h', 'energy_volatility', 'uv_volatility', 'temp_1h_lag', 'cloudcover_1h_lag', 'humidity_1h_lag', 'energy_lag_1h', 'uv_lag_1h', 'temp_losses', 'soiling_loss_factor', 'estimated_efficiency', 'production_potential', 'system_performance_ratio', 'conversion_efficiency_ratio', 'clear_sky_duration', 'weather_variability_index', 'temp_stability', 'humidity_stability', 'cloudcover_stability', 'season_Spring', 'season_Summer', 'season_Autumn', 'season_Winter', 'time_period_Morning', 'time_period_Afternoon', 'time_period_Evening', 'time_period_Night']\n", "Training data shape: (112882, 24, 66)\n", "Test data shape: (16849, 24, 66)\n", "Saving scaler X to: 2024-11-27_13-56_scale_X.joblib\n", "Saving scaler X to: 2024-11-27_13-56_scale_y.joblib\n", "Saving features to: 2024-11-27_13-56_features.json\n" ] } ], "source": [ "df = pd.read_parquet('../../sources/weather_data_solarradiation.parquet')\n", "\n", "print(\"Initializing solar energy model training...\")\n", "\n", "# Data preparation\n", "print(\"\\n1. Preparing data...\")\n", "X_train_seq, X_test_seq, y_train, y_test, scaler_X, scaler_y, features, X_to_predict_seq = prepare_hybrid_data(df)\n", "\n", "print(f\"Training data shape: {X_train_seq.shape}\")\n", "print(f\"Test data shape: {X_test_seq.shape}\")\n", "\n", "# Save or load scaler and features\n", "scaler_X_path = f'{folder_name}_scale_X.joblib'\n", "scaler_y_path = f'{folder_name}_scale_y.joblib'\n", "features_path = f'{folder_name}_features.json'\n", "model_path = f'{folder_name}_best_model.h5'\n", "history_path = f'{folder_name}_training_history.json'\n", "\n", "if os.path.exists(scaler_X_path):\n", " print(f\"Loading existing scaler X from: {scaler_X_path}\")\n", " scaler = joblib.load(scaler_X_path)\n", "else:\n", " print(f\"Saving scaler X to: {scaler_X_path}\")\n", " joblib.dump(scaler_X, scaler_X_path)\n", "\n", "if os.path.exists(scaler_y_path):\n", " print(f\"Loading existing scaler X from: {scaler_y_path}\")\n", " scaler = joblib.load(scaler_y_path)\n", "else:\n", " print(f\"Saving scaler X to: {scaler_y_path}\")\n", " joblib.dump(scaler_y, scaler_y_path)\n", "\n", "if os.path.exists(features_path):\n", " print(f\"Loading existing features from: {features_path}\")\n", " with open(features_path, 'r') as f:\n", " features = json.load(f)\n", "else:\n", " print(f\"Saving features to: {features_path}\")\n", " with open(features_path, 'w') as f:\n", " json.dump(features, f)\n", "\n", "# Data quality verification\n", "if np.isnan(X_train_seq).any() or np.isnan(y_train).any():\n", " raise ValueError(\"Found NaN values in training data\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "096e79e3-7a3d-4e17-9a30-4d0747ee2d40", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "2. Creating model...\n", "\\Min dataset solar energy : 0.0 - Scaled Version : 0.0\n", "\n", "Max dataset solar energy : 4.0 - Scaled Version : 3.3333333333333335\n", "Max dataset solar energy increased by 8% : 4.32 - Scaled Version : 3.6000000000000005\n", "\n", "Class distribution in training set:\n", "Zeros: 56899 (50.41%)\n", "Non-zeros: 55983 (49.59%)\n", "\n", "Class distribution in test set:\n", "Zeros: 8576 (50.90%)\n", "Non-zeros: 8273 (49.10%)\n", "\n", "Model output names: ['classification_output', 'regression_output', 'final_output']\n", "\n", "4. Starting training...\n", "Epoch 1/150\n", "221/221 [==============================] - ETA: 0s - loss: 10.1910 - classification_output_loss: 0.2183 - regression_output_loss: 0.3452 - final_output_loss: 0.2500\n", "Epoch 1 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 95.14%\n", "AUC-ROC: 0.9914\n", "\n", "Confusion Matrix:\n", "[[8046 530]\n", " [ 289 7984]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9653 0.9382 0.9516 8576\n", " Non-Zero 0.9377 0.9651 0.9512 8273\n", "\n", " accuracy 0.9514 16849\n", " macro avg 0.9515 0.9516 0.9514 16849\n", "weighted avg 0.9518 0.9514 0.9514 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 148 predictions\n", "MAPE: 51.77%\n", "Within ±10%: 4.40%\n", "MAE: 0.63\n", "RMSE: 0.84\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 26.69%\n", "Within ±2%: 51.07%\n", "Within ±5%: 51.72%\n", "Within ±10%: 52.69%\n", "Within ±20%: 55.31%\n", "MAE: 0.28\n", "RMSE: 0.52\n", "221/221 [==============================] - 58s 112ms/step - loss: 10.1910 - classification_output_loss: 0.2183 - regression_output_loss: 0.3452 - final_output_loss: 0.2500 - val_loss: 7.7224 - val_classification_output_loss: 0.2687 - val_regression_output_loss: 0.4593 - val_final_output_loss: 0.2756\n", "Epoch 2/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 5.8851 - classification_output_loss: 0.1001 - regression_output_loss: 0.1639 - final_output_loss: 0.0979 - val_loss: 4.6694 - val_classification_output_loss: 0.1285 - val_regression_output_loss: 0.2137 - val_final_output_loss: 0.1128\n", "Epoch 3/150\n", "221/221 [==============================] - 16s 71ms/step - loss: 3.9307 - classification_output_loss: 0.0793 - regression_output_loss: 0.1165 - final_output_loss: 0.0672 - val_loss: 3.4524 - val_classification_output_loss: 0.0937 - val_regression_output_loss: 0.1159 - val_final_output_loss: 0.0695\n", "Epoch 4/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 3.2319 - classification_output_loss: 0.0735 - regression_output_loss: 0.0949 - final_output_loss: 0.0527 - val_loss: 3.1207 - val_classification_output_loss: 0.0850 - val_regression_output_loss: 0.0849 - val_final_output_loss: 0.0601\n", "Epoch 5/150\n", "221/221 [==============================] - 15s 69ms/step - loss: 2.9473 - classification_output_loss: 0.0913 - regression_output_loss: 0.1650 - final_output_loss: 0.1204 - val_loss: 2.3847 - val_classification_output_loss: 0.1023 - val_regression_output_loss: 0.2639 - val_final_output_loss: 0.2111\n", "Epoch 6/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 1.7403 - classification_output_loss: 0.0797 - regression_output_loss: 0.1275 - final_output_loss: 0.1103 - val_loss: 1.2609 - val_classification_output_loss: 0.0809 - val_regression_output_loss: 0.0645 - val_final_output_loss: 0.0449\n", "Epoch 7/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 1.0165 - classification_output_loss: 0.0666 - regression_output_loss: 0.0859 - final_output_loss: 0.0577 - val_loss: 0.7915 - val_classification_output_loss: 0.0723 - val_regression_output_loss: 0.0517 - val_final_output_loss: 0.0379\n", "Epoch 8/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.6764 - classification_output_loss: 0.0585 - regression_output_loss: 0.0728 - final_output_loss: 0.0537 - val_loss: 0.5565 - val_classification_output_loss: 0.0699 - val_regression_output_loss: 0.0461 - val_final_output_loss: 0.0349\n", "Epoch 9/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.4936 - classification_output_loss: 0.0550 - regression_output_loss: 0.0576 - final_output_loss: 0.0426 - val_loss: 0.4275 - val_classification_output_loss: 0.0706 - val_regression_output_loss: 0.0355 - val_final_output_loss: 0.0321\n", "Epoch 10/150\n", "221/221 [==============================] - 15s 68ms/step - loss: 0.3914 - classification_output_loss: 0.0525 - regression_output_loss: 0.0459 - final_output_loss: 0.0336 - val_loss: 0.3597 - val_classification_output_loss: 0.0706 - val_regression_output_loss: 0.0372 - val_final_output_loss: 0.0319\n", "Epoch 11/150\n", "221/221 [==============================] - 14s 61ms/step - loss: 0.3393 - classification_output_loss: 0.0518 - regression_output_loss: 0.0439 - final_output_loss: 0.0333 - val_loss: 0.3203 - val_classification_output_loss: 0.0724 - val_regression_output_loss: 0.0320 - val_final_output_loss: 0.0283\n", "Epoch 12/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.3109 - classification_output_loss: 0.0509 - regression_output_loss: 0.0403 - final_output_loss: 0.0305 - val_loss: 0.3037 - val_classification_output_loss: 0.0705 - val_regression_output_loss: 0.0329 - val_final_output_loss: 0.0285\n", "Epoch 13/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.3006 - classification_output_loss: 0.0529 - regression_output_loss: 0.0396 - final_output_loss: 0.0300 - val_loss: 0.2963 - val_classification_output_loss: 0.0673 - val_regression_output_loss: 0.0301 - val_final_output_loss: 0.0262\n", "Epoch 14/150\n", "221/221 [==============================] - 14s 65ms/step - loss: 0.3137 - classification_output_loss: 0.0644 - regression_output_loss: 0.0694 - final_output_loss: 0.0570 - val_loss: 0.4100 - val_classification_output_loss: 0.0884 - val_regression_output_loss: 0.2605 - val_final_output_loss: 0.1666\n", "Epoch 15/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.2755 - classification_output_loss: 0.0625 - regression_output_loss: 0.1108 - final_output_loss: 0.0794 - val_loss: 0.2491 - val_classification_output_loss: 0.0744 - val_regression_output_loss: 0.1431 - val_final_output_loss: 0.0542\n", "Epoch 16/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.1950 - classification_output_loss: 0.0579 - regression_output_loss: 0.0713 - final_output_loss: 0.0523 - val_loss: 0.1741 - val_classification_output_loss: 0.0664 - val_regression_output_loss: 0.0509 - val_final_output_loss: 0.0638\n", "Epoch 17/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.1556 - classification_output_loss: 0.0523 - regression_output_loss: 0.0559 - final_output_loss: 0.0525 - val_loss: 0.1413 - val_classification_output_loss: 0.0684 - val_regression_output_loss: 0.0566 - val_final_output_loss: 0.0392\n", "Epoch 18/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.1328 - classification_output_loss: 0.0533 - regression_output_loss: 0.0550 - final_output_loss: 0.0497 - val_loss: 0.1157 - val_classification_output_loss: 0.0687 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0333\n", "Epoch 19/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.1164 - classification_output_loss: 0.0504 - regression_output_loss: 0.0539 - final_output_loss: 0.0463 - val_loss: 0.1044 - val_classification_output_loss: 0.0741 - val_regression_output_loss: 0.0402 - val_final_output_loss: 0.0340\n", "Epoch 20/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.1014 - classification_output_loss: 0.0468 - regression_output_loss: 0.0480 - final_output_loss: 0.0427 - val_loss: 0.0948 - val_classification_output_loss: 0.0719 - val_regression_output_loss: 0.0393 - val_final_output_loss: 0.0335\n", "Epoch 21/150\n", "221/221 [==============================] - ETA: 0s - loss: 0.0903 - classification_output_loss: 0.0442 - regression_output_loss: 0.0435 - final_output_loss: 0.0394\n", "Epoch 21 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 97.44%\n", "AUC-ROC: 0.9967\n", "\n", "Confusion Matrix:\n", "[[8334 242]\n", " [ 189 8084]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9778 0.9718 0.9748 8576\n", " Non-Zero 0.9709 0.9772 0.9740 8273\n", "\n", " accuracy 0.9744 16849\n", " macro avg 0.9744 0.9745 0.9744 16849\n", "weighted avg 0.9744 0.9744 0.9744 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 0 predictions\n", "MAPE: 15.37%\n", "Within ±10%: 52.36%\n", "MAE: 0.09\n", "RMSE: 0.12\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 12.08%\n", "Within ±2%: 56.62%\n", "Within ±5%: 65.80%\n", "Within ±10%: 77.45%\n", "Within ±20%: 86.37%\n", "MAE: 0.05\n", "RMSE: 0.09\n", "221/221 [==============================] - 21s 93ms/step - loss: 0.0903 - classification_output_loss: 0.0442 - regression_output_loss: 0.0435 - final_output_loss: 0.0394 - val_loss: 0.0834 - val_classification_output_loss: 0.0671 - val_regression_output_loss: 0.0350 - val_final_output_loss: 0.0276\n", "Epoch 22/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0806 - classification_output_loss: 0.0424 - regression_output_loss: 0.0390 - final_output_loss: 0.0346 - val_loss: 0.0752 - val_classification_output_loss: 0.0653 - val_regression_output_loss: 0.0304 - val_final_output_loss: 0.0250\n", "Epoch 23/150\n", "221/221 [==============================] - 14s 64ms/step - loss: 0.0738 - classification_output_loss: 0.0397 - regression_output_loss: 0.0367 - final_output_loss: 0.0320 - val_loss: 0.0805 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0418 - val_final_output_loss: 0.0347\n", "Epoch 24/150\n", "221/221 [==============================] - 12s 55ms/step - loss: 0.0691 - classification_output_loss: 0.0393 - regression_output_loss: 0.0349 - final_output_loss: 0.0304 - val_loss: 0.0790 - val_classification_output_loss: 0.0668 - val_regression_output_loss: 0.0393 - val_final_output_loss: 0.0401\n", "Epoch 25/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0635 - classification_output_loss: 0.0381 - regression_output_loss: 0.0313 - final_output_loss: 0.0264 - val_loss: 0.0660 - val_classification_output_loss: 0.0640 - val_regression_output_loss: 0.0269 - val_final_output_loss: 0.0273\n", "Epoch 26/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0606 - classification_output_loss: 0.0377 - regression_output_loss: 0.0300 - final_output_loss: 0.0254 - val_loss: 0.0620 - val_classification_output_loss: 0.0636 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0247\n", "Epoch 27/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0586 - classification_output_loss: 0.0375 - regression_output_loss: 0.0292 - final_output_loss: 0.0247 - val_loss: 0.0586 - val_classification_output_loss: 0.0626 - val_regression_output_loss: 0.0229 - val_final_output_loss: 0.0202\n", "Epoch 28/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0568 - classification_output_loss: 0.0368 - regression_output_loss: 0.0286 - final_output_loss: 0.0235 - val_loss: 0.0576 - val_classification_output_loss: 0.0613 - val_regression_output_loss: 0.0241 - val_final_output_loss: 0.0192\n", "Epoch 29/150\n", "221/221 [==============================] - 15s 66ms/step - loss: 0.0561 - classification_output_loss: 0.0376 - regression_output_loss: 0.0283 - final_output_loss: 0.0231 - val_loss: 0.0575 - val_classification_output_loss: 0.0607 - val_regression_output_loss: 0.0244 - val_final_output_loss: 0.0198\n", "Epoch 30/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0559 - classification_output_loss: 0.0386 - regression_output_loss: 0.0283 - final_output_loss: 0.0230 - val_loss: 0.0560 - val_classification_output_loss: 0.0580 - val_regression_output_loss: 0.0228 - val_final_output_loss: 0.0196\n", "Epoch 31/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0564 - classification_output_loss: 0.0383 - regression_output_loss: 0.0294 - final_output_loss: 0.0235 - val_loss: 0.0549 - val_classification_output_loss: 0.0565 - val_regression_output_loss: 0.0209 - val_final_output_loss: 0.0194\n", "Epoch 32/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0808 - classification_output_loss: 0.0454 - regression_output_loss: 0.0574 - final_output_loss: 0.0500 - val_loss: 0.2867 - val_classification_output_loss: 0.1518 - val_regression_output_loss: 0.2633 - val_final_output_loss: 0.2595\n", "Epoch 33/150\n", "221/221 [==============================] - 16s 73ms/step - loss: 0.1274 - classification_output_loss: 0.0714 - regression_output_loss: 0.0997 - final_output_loss: 0.0752 - val_loss: 0.0907 - val_classification_output_loss: 0.0657 - val_regression_output_loss: 0.0706 - val_final_output_loss: 0.0496\n", "Epoch 34/150\n", "221/221 [==============================] - 14s 64ms/step - loss: 0.0842 - classification_output_loss: 0.0474 - regression_output_loss: 0.0614 - final_output_loss: 0.0538 - val_loss: 0.0677 - val_classification_output_loss: 0.0667 - val_regression_output_loss: 0.0386 - val_final_output_loss: 0.0308\n", "Epoch 35/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0717 - classification_output_loss: 0.0459 - regression_output_loss: 0.0460 - final_output_loss: 0.0472 - val_loss: 0.0621 - val_classification_output_loss: 0.0637 - val_regression_output_loss: 0.0334 - val_final_output_loss: 0.0299\n", "Epoch 36/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0646 - classification_output_loss: 0.0419 - regression_output_loss: 0.0418 - final_output_loss: 0.0416 - val_loss: 0.0593 - val_classification_output_loss: 0.0620 - val_regression_output_loss: 0.0338 - val_final_output_loss: 0.0294\n", "Epoch 37/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0596 - classification_output_loss: 0.0426 - regression_output_loss: 0.0384 - final_output_loss: 0.0366 - val_loss: 0.0512 - val_classification_output_loss: 0.0627 - val_regression_output_loss: 0.0245 - val_final_output_loss: 0.0231\n", "Epoch 38/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0604 - classification_output_loss: 0.0406 - regression_output_loss: 0.0406 - final_output_loss: 0.0407 - val_loss: 0.0608 - val_classification_output_loss: 0.0703 - val_regression_output_loss: 0.0375 - val_final_output_loss: 0.0331\n", "Epoch 39/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0584 - classification_output_loss: 0.0401 - regression_output_loss: 0.0394 - final_output_loss: 0.0397 - val_loss: 0.0669 - val_classification_output_loss: 0.0657 - val_regression_output_loss: 0.0483 - val_final_output_loss: 0.0424\n", "Epoch 40/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0594 - classification_output_loss: 0.0389 - regression_output_loss: 0.0415 - final_output_loss: 0.0420 - val_loss: 0.0562 - val_classification_output_loss: 0.0665 - val_regression_output_loss: 0.0356 - val_final_output_loss: 0.0276\n", "Epoch 41/150\n", "221/221 [==============================] - ETA: 0s - loss: 0.0554 - classification_output_loss: 0.0376 - regression_output_loss: 0.0377 - final_output_loss: 0.0388\n", "Epoch 41 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 97.17%\n", "AUC-ROC: 0.9972\n", "\n", "Confusion Matrix:\n", "[[8195 381]\n", " [ 96 8177]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9884 0.9556 0.9717 8576\n", " Non-Zero 0.9555 0.9884 0.9717 8273\n", "\n", " accuracy 0.9717 16849\n", " macro avg 0.9720 0.9720 0.9717 16849\n", "weighted avg 0.9722 0.9717 0.9717 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 36 predictions\n", "MAPE: 13.32%\n", "Within ±10%: 65.24%\n", "MAE: 0.07\n", "RMSE: 0.10\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 9.74%\n", "Within ±2%: 57.72%\n", "Within ±5%: 67.29%\n", "Within ±10%: 80.49%\n", "Within ±20%: 89.54%\n", "MAE: 0.04\n", "RMSE: 0.09\n", "221/221 [==============================] - 19s 85ms/step - loss: 0.0554 - classification_output_loss: 0.0376 - regression_output_loss: 0.0377 - final_output_loss: 0.0388 - val_loss: 0.0519 - val_classification_output_loss: 0.0735 - val_regression_output_loss: 0.0259 - val_final_output_loss: 0.0259\n", "Epoch 42/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0509 - classification_output_loss: 0.0371 - regression_output_loss: 0.0339 - final_output_loss: 0.0344 - val_loss: 0.0480 - val_classification_output_loss: 0.0602 - val_regression_output_loss: 0.0278 - val_final_output_loss: 0.0235\n", "Epoch 43/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0487 - classification_output_loss: 0.0352 - regression_output_loss: 0.0327 - final_output_loss: 0.0329 - val_loss: 0.0547 - val_classification_output_loss: 0.0679 - val_regression_output_loss: 0.0422 - val_final_output_loss: 0.0236\n", "Epoch 44/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0519 - classification_output_loss: 0.0353 - regression_output_loss: 0.0379 - final_output_loss: 0.0365 - val_loss: 0.0542 - val_classification_output_loss: 0.0592 - val_regression_output_loss: 0.0421 - val_final_output_loss: 0.0267\n", "Epoch 45/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0480 - classification_output_loss: 0.0316 - regression_output_loss: 0.0347 - final_output_loss: 0.0335 - val_loss: 0.0737 - val_classification_output_loss: 0.0704 - val_regression_output_loss: 0.0508 - val_final_output_loss: 0.0603\n", "Epoch 46/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0424 - classification_output_loss: 0.0313 - regression_output_loss: 0.0283 - final_output_loss: 0.0270 - val_loss: 0.0604 - val_classification_output_loss: 0.0554 - val_regression_output_loss: 0.0507 - val_final_output_loss: 0.0393\n", "Epoch 47/150\n", "221/221 [==============================] - 12s 54ms/step - loss: 0.0466 - classification_output_loss: 0.0329 - regression_output_loss: 0.0325 - final_output_loss: 0.0346 - val_loss: 0.0596 - val_classification_output_loss: 0.0603 - val_regression_output_loss: 0.0387 - val_final_output_loss: 0.0460\n", "Epoch 48/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0439 - classification_output_loss: 0.0302 - regression_output_loss: 0.0305 - final_output_loss: 0.0312 - val_loss: 0.0587 - val_classification_output_loss: 0.0572 - val_regression_output_loss: 0.0415 - val_final_output_loss: 0.0438\n", "Epoch 49/150\n", "221/221 [==============================] - 12s 54ms/step - loss: 0.0405 - classification_output_loss: 0.0296 - regression_output_loss: 0.0272 - final_output_loss: 0.0277 - val_loss: 0.0537 - val_classification_output_loss: 0.0566 - val_regression_output_loss: 0.0406 - val_final_output_loss: 0.0345\n", "Epoch 50/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0379 - classification_output_loss: 0.0294 - regression_output_loss: 0.0256 - final_output_loss: 0.0246 - val_loss: 0.0520 - val_classification_output_loss: 0.0551 - val_regression_output_loss: 0.0376 - val_final_output_loss: 0.0356\n", "Epoch 51/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0376 - classification_output_loss: 0.0280 - regression_output_loss: 0.0258 - final_output_loss: 0.0256 - val_loss: 0.0502 - val_classification_output_loss: 0.0509 - val_regression_output_loss: 0.0355 - val_final_output_loss: 0.0359\n", "Epoch 52/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0353 - classification_output_loss: 0.0265 - regression_output_loss: 0.0240 - final_output_loss: 0.0231 - val_loss: 0.0491 - val_classification_output_loss: 0.0519 - val_regression_output_loss: 0.0348 - val_final_output_loss: 0.0345\n", "Epoch 53/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0343 - classification_output_loss: 0.0259 - regression_output_loss: 0.0232 - final_output_loss: 0.0226 - val_loss: 0.0422 - val_classification_output_loss: 0.0486 - val_regression_output_loss: 0.0270 - val_final_output_loss: 0.0280\n", "Epoch 54/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0336 - classification_output_loss: 0.0255 - regression_output_loss: 0.0231 - final_output_loss: 0.0220 - val_loss: 0.0381 - val_classification_output_loss: 0.0474 - val_regression_output_loss: 0.0225 - val_final_output_loss: 0.0235\n", "Epoch 55/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0331 - classification_output_loss: 0.0244 - regression_output_loss: 0.0228 - final_output_loss: 0.0222 - val_loss: 0.0339 - val_classification_output_loss: 0.0464 - val_regression_output_loss: 0.0192 - val_final_output_loss: 0.0175\n", "Epoch 56/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0322 - classification_output_loss: 0.0240 - regression_output_loss: 0.0224 - final_output_loss: 0.0211 - val_loss: 0.0334 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0190 - val_final_output_loss: 0.0175\n", "Epoch 57/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0311 - classification_output_loss: 0.0228 - regression_output_loss: 0.0217 - final_output_loss: 0.0202 - val_loss: 0.0330 - val_classification_output_loss: 0.0446 - val_regression_output_loss: 0.0184 - val_final_output_loss: 0.0180\n", "Epoch 58/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0307 - classification_output_loss: 0.0227 - regression_output_loss: 0.0216 - final_output_loss: 0.0198 - val_loss: 0.0320 - val_classification_output_loss: 0.0437 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0164\n", "Epoch 59/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0306 - classification_output_loss: 0.0229 - regression_output_loss: 0.0216 - final_output_loss: 0.0198 - val_loss: 0.0312 - val_classification_output_loss: 0.0420 - val_regression_output_loss: 0.0188 - val_final_output_loss: 0.0153\n", "Epoch 60/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0303 - classification_output_loss: 0.0226 - regression_output_loss: 0.0215 - final_output_loss: 0.0196 - val_loss: 0.0315 - val_classification_output_loss: 0.0412 - val_regression_output_loss: 0.0203 - val_final_output_loss: 0.0151\n", "Epoch 61/150\n", "221/221 [==============================] - ETA: 0s - loss: 0.0302 - classification_output_loss: 0.0224 - regression_output_loss: 0.0215 - final_output_loss: 0.0195\n", "Epoch 61 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 98.47%\n", "AUC-ROC: 0.9989\n", "\n", "Confusion Matrix:\n", "[[8426 150]\n", " [ 108 8165]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9873 0.9825 0.9849 8576\n", " Non-Zero 0.9820 0.9869 0.9844 8273\n", "\n", " accuracy 0.9847 16849\n", " macro avg 0.9847 0.9847 0.9847 16849\n", "weighted avg 0.9847 0.9847 0.9847 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 3 predictions\n", "MAPE: 11.46%\n", "Within ±10%: 73.71%\n", "MAE: 0.06\n", "RMSE: 0.09\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 7.33%\n", "Within ±2%: 61.98%\n", "Within ±5%: 76.04%\n", "Within ±10%: 87.28%\n", "Within ±20%: 91.97%\n", "MAE: 0.03\n", "RMSE: 0.06\n", "221/221 [==============================] - 18s 80ms/step - loss: 0.0302 - classification_output_loss: 0.0224 - regression_output_loss: 0.0215 - final_output_loss: 0.0195 - val_loss: 0.0322 - val_classification_output_loss: 0.0401 - val_regression_output_loss: 0.0219 - val_final_output_loss: 0.0160\n", "Epoch 62/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0300 - classification_output_loss: 0.0223 - regression_output_loss: 0.0214 - final_output_loss: 0.0194 - val_loss: 0.0326 - val_classification_output_loss: 0.0397 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0172\n", "Epoch 63/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0300 - classification_output_loss: 0.0224 - regression_output_loss: 0.0216 - final_output_loss: 0.0193 - val_loss: 0.0316 - val_classification_output_loss: 0.0394 - val_regression_output_loss: 0.0202 - val_final_output_loss: 0.0167\n", "Epoch 64/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0300 - classification_output_loss: 0.0223 - regression_output_loss: 0.0216 - final_output_loss: 0.0193 - val_loss: 0.0307 - val_classification_output_loss: 0.0389 - val_regression_output_loss: 0.0188 - val_final_output_loss: 0.0160\n", "Epoch 65/150\n", "221/221 [==============================] - 14s 61ms/step - loss: 0.0301 - classification_output_loss: 0.0228 - regression_output_loss: 0.0216 - final_output_loss: 0.0194 - val_loss: 0.0297 - val_classification_output_loss: 0.0381 - val_regression_output_loss: 0.0173 - val_final_output_loss: 0.0153\n", "Epoch 66/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0304 - classification_output_loss: 0.0229 - regression_output_loss: 0.0223 - final_output_loss: 0.0196 - val_loss: 0.0290 - val_classification_output_loss: 0.0379 - val_regression_output_loss: 0.0161 - val_final_output_loss: 0.0149\n", "Epoch 67/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0310 - classification_output_loss: 0.0223 - regression_output_loss: 0.0230 - final_output_loss: 0.0206 - val_loss: 0.0295 - val_classification_output_loss: 0.0380 - val_regression_output_loss: 0.0163 - val_final_output_loss: 0.0159\n", "Epoch 68/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0309 - classification_output_loss: 0.0224 - regression_output_loss: 0.0226 - final_output_loss: 0.0207 - val_loss: 0.0684 - val_classification_output_loss: 0.0568 - val_regression_output_loss: 0.0484 - val_final_output_loss: 0.0650\n", "Epoch 69/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0856 - classification_output_loss: 0.0495 - regression_output_loss: 0.0722 - final_output_loss: 0.0719 - val_loss: 0.0708 - val_classification_output_loss: 0.0585 - val_regression_output_loss: 0.0718 - val_final_output_loss: 0.0365\n", "Epoch 70/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0494 - classification_output_loss: 0.0324 - regression_output_loss: 0.0353 - final_output_loss: 0.0392 - val_loss: 0.0511 - val_classification_output_loss: 0.0511 - val_regression_output_loss: 0.0411 - val_final_output_loss: 0.0326\n", "Epoch 71/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0468 - classification_output_loss: 0.0323 - regression_output_loss: 0.0350 - final_output_loss: 0.0360 - val_loss: 0.0500 - val_classification_output_loss: 0.0791 - val_regression_output_loss: 0.0313 - val_final_output_loss: 0.0274\n", "Epoch 72/150\n", "221/221 [==============================] - 14s 65ms/step - loss: 0.0464 - classification_output_loss: 0.0292 - regression_output_loss: 0.0353 - final_output_loss: 0.0372 - val_loss: 0.0456 - val_classification_output_loss: 0.0707 - val_regression_output_loss: 0.0265 - val_final_output_loss: 0.0248\n", "Epoch 73/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0434 - classification_output_loss: 0.0299 - regression_output_loss: 0.0321 - final_output_loss: 0.0346 - val_loss: 0.0395 - val_classification_output_loss: 0.0458 - val_regression_output_loss: 0.0251 - val_final_output_loss: 0.0250\n", "Epoch 74/150\n", "221/221 [==============================] - 12s 55ms/step - loss: 0.0417 - classification_output_loss: 0.0296 - regression_output_loss: 0.0302 - final_output_loss: 0.0321 - val_loss: 0.0424 - val_classification_output_loss: 0.0670 - val_regression_output_loss: 0.0280 - val_final_output_loss: 0.0196\n", "Epoch 75/150\n", "221/221 [==============================] - 15s 67ms/step - loss: 0.0413 - classification_output_loss: 0.0321 - regression_output_loss: 0.0302 - final_output_loss: 0.0313 - val_loss: 0.0469 - val_classification_output_loss: 0.0514 - val_regression_output_loss: 0.0394 - val_final_output_loss: 0.0278\n", "Epoch 76/150\n", "221/221 [==============================] - 14s 63ms/step - loss: 0.0441 - classification_output_loss: 0.0293 - regression_output_loss: 0.0320 - final_output_loss: 0.0370 - val_loss: 0.0386 - val_classification_output_loss: 0.0517 - val_regression_output_loss: 0.0234 - val_final_output_loss: 0.0223\n", "Epoch 77/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0398 - classification_output_loss: 0.0254 - regression_output_loss: 0.0291 - final_output_loss: 0.0324 - val_loss: 0.0391 - val_classification_output_loss: 0.0423 - val_regression_output_loss: 0.0290 - val_final_output_loss: 0.0239\n", "Epoch 78/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0394 - classification_output_loss: 0.0269 - regression_output_loss: 0.0292 - final_output_loss: 0.0311 - val_loss: 0.0409 - val_classification_output_loss: 0.0598 - val_regression_output_loss: 0.0259 - val_final_output_loss: 0.0228\n", "Epoch 79/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0391 - classification_output_loss: 0.0290 - regression_output_loss: 0.0278 - final_output_loss: 0.0307 - val_loss: 0.0429 - val_classification_output_loss: 0.0595 - val_regression_output_loss: 0.0274 - val_final_output_loss: 0.0275\n", "Epoch 80/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0377 - classification_output_loss: 0.0264 - regression_output_loss: 0.0277 - final_output_loss: 0.0304 - val_loss: 0.0384 - val_classification_output_loss: 0.0522 - val_regression_output_loss: 0.0217 - val_final_output_loss: 0.0247\n", "Epoch 81/150\n", "220/221 [============================>.] - ETA: 0s - loss: 0.0388 - classification_output_loss: 0.0237 - regression_output_loss: 0.0278 - final_output_loss: 0.0333\n", "Epoch 81 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 98.00%\n", "AUC-ROC: 0.9985\n", "\n", "Confusion Matrix:\n", "[[8307 269]\n", " [ 68 8205]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9919 0.9686 0.9801 8576\n", " Non-Zero 0.9683 0.9918 0.9799 8273\n", "\n", " accuracy 0.9800 16849\n", " macro avg 0.9801 0.9802 0.9800 16849\n", "weighted avg 0.9803 0.9800 0.9800 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 0 predictions\n", "MAPE: 12.72%\n", "Within ±10%: 71.45%\n", "MAE: 0.07\n", "RMSE: 0.09\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 8.60%\n", "Within ±2%: 60.42%\n", "Within ±5%: 72.54%\n", "Within ±10%: 85.01%\n", "Within ±20%: 90.31%\n", "MAE: 0.03\n", "RMSE: 0.07\n", "221/221 [==============================] - 18s 81ms/step - loss: 0.0388 - classification_output_loss: 0.0238 - regression_output_loss: 0.0278 - final_output_loss: 0.0333 - val_loss: 0.0374 - val_classification_output_loss: 0.0522 - val_regression_output_loss: 0.0261 - val_final_output_loss: 0.0189\n", "Epoch 82/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0362 - classification_output_loss: 0.0243 - regression_output_loss: 0.0261 - final_output_loss: 0.0289 - val_loss: 0.0404 - val_classification_output_loss: 0.0759 - val_regression_output_loss: 0.0210 - val_final_output_loss: 0.0202\n", "Epoch 83/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0381 - classification_output_loss: 0.0257 - regression_output_loss: 0.0282 - final_output_loss: 0.0311 - val_loss: 0.0443 - val_classification_output_loss: 0.0467 - val_regression_output_loss: 0.0348 - val_final_output_loss: 0.0287\n", "Epoch 84/150\n", "221/221 [==============================] - 12s 55ms/step - loss: 0.0395 - classification_output_loss: 0.0270 - regression_output_loss: 0.0296 - final_output_loss: 0.0321 - val_loss: 0.0554 - val_classification_output_loss: 0.0404 - val_regression_output_loss: 0.0469 - val_final_output_loss: 0.0469\n", "Epoch 85/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0371 - classification_output_loss: 0.0265 - regression_output_loss: 0.0273 - final_output_loss: 0.0296 - val_loss: 0.0588 - val_classification_output_loss: 0.0505 - val_regression_output_loss: 0.0473 - val_final_output_loss: 0.0508\n", "Epoch 86/150\n", "221/221 [==============================] - 12s 54ms/step - loss: 0.0354 - classification_output_loss: 0.0232 - regression_output_loss: 0.0255 - final_output_loss: 0.0294 - val_loss: 0.0574 - val_classification_output_loss: 0.0465 - val_regression_output_loss: 0.0445 - val_final_output_loss: 0.0525\n", "Epoch 87/150\n", "221/221 [==============================] - 15s 66ms/step - loss: 0.0342 - classification_output_loss: 0.0247 - regression_output_loss: 0.0241 - final_output_loss: 0.0274 - val_loss: 0.0581 - val_classification_output_loss: 0.0452 - val_regression_output_loss: 0.0468 - val_final_output_loss: 0.0525\n", "Epoch 88/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0332 - classification_output_loss: 0.0225 - regression_output_loss: 0.0243 - final_output_loss: 0.0265 - val_loss: 0.0604 - val_classification_output_loss: 0.0435 - val_regression_output_loss: 0.0500 - val_final_output_loss: 0.0577\n", "Epoch 89/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0340 - classification_output_loss: 0.0237 - regression_output_loss: 0.0252 - final_output_loss: 0.0271 - val_loss: 0.0487 - val_classification_output_loss: 0.0424 - val_regression_output_loss: 0.0385 - val_final_output_loss: 0.0406\n", "Epoch 90/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0324 - classification_output_loss: 0.0201 - regression_output_loss: 0.0233 - final_output_loss: 0.0268 - val_loss: 0.0375 - val_classification_output_loss: 0.0365 - val_regression_output_loss: 0.0240 - val_final_output_loss: 0.0314\n", "Epoch 91/150\n", "221/221 [==============================] - 13s 58ms/step - loss: 0.0308 - classification_output_loss: 0.0210 - regression_output_loss: 0.0222 - final_output_loss: 0.0248 - val_loss: 0.0620 - val_classification_output_loss: 0.0410 - val_regression_output_loss: 0.0594 - val_final_output_loss: 0.0555\n", "Epoch 92/150\n", "221/221 [==============================] - 13s 59ms/step - loss: 0.0320 - classification_output_loss: 0.0218 - regression_output_loss: 0.0241 - final_output_loss: 0.0256 - val_loss: 0.0486 - val_classification_output_loss: 0.0387 - val_regression_output_loss: 0.0335 - val_final_output_loss: 0.0487\n", "Epoch 93/150\n", "221/221 [==============================] - 12s 56ms/step - loss: 0.0302 - classification_output_loss: 0.0189 - regression_output_loss: 0.0222 - final_output_loss: 0.0246 - val_loss: 0.0351 - val_classification_output_loss: 0.0392 - val_regression_output_loss: 0.0237 - val_final_output_loss: 0.0264\n", "Epoch 94/150\n", "221/221 [==============================] - 13s 60ms/step - loss: 0.0318 - classification_output_loss: 0.0213 - regression_output_loss: 0.0241 - final_output_loss: 0.0261 - val_loss: 0.0389 - val_classification_output_loss: 0.0408 - val_regression_output_loss: 0.0253 - val_final_output_loss: 0.0318\n", "Epoch 95/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0289 - classification_output_loss: 0.0183 - regression_output_loss: 0.0209 - final_output_loss: 0.0234 - val_loss: 0.0330 - val_classification_output_loss: 0.0442 - val_regression_output_loss: 0.0201 - val_final_output_loss: 0.0224\n", "Epoch 96/150\n", "221/221 [==============================] - 14s 62ms/step - loss: 0.0309 - classification_output_loss: 0.0187 - regression_output_loss: 0.0233 - final_output_loss: 0.0260 - val_loss: 0.0332 - val_classification_output_loss: 0.0377 - val_regression_output_loss: 0.0280 - val_final_output_loss: 0.0180\n", "Epoch 97/150\n", "221/221 [==============================] - 14s 64ms/step - loss: 0.0315 - classification_output_loss: 0.0188 - regression_output_loss: 0.0240 - final_output_loss: 0.0263 - val_loss: 0.0290 - val_classification_output_loss: 0.0359 - val_regression_output_loss: 0.0204 - val_final_output_loss: 0.0160\n", "Epoch 98/150\n", "221/221 [==============================] - 13s 61ms/step - loss: 0.0284 - classification_output_loss: 0.0178 - regression_output_loss: 0.0207 - final_output_loss: 0.0234 - val_loss: 0.0283 - val_classification_output_loss: 0.0347 - val_regression_output_loss: 0.0183 - val_final_output_loss: 0.0180\n", "Epoch 99/150\n", "221/221 [==============================] - 12s 54ms/step - loss: 0.0282 - classification_output_loss: 0.0188 - regression_output_loss: 0.0204 - final_output_loss: 0.0232 - val_loss: 0.0331 - val_classification_output_loss: 0.0527 - val_regression_output_loss: 0.0196 - val_final_output_loss: 0.0194\n", "Epoch 100/150\n", "221/221 [==============================] - 13s 57ms/step - loss: 0.0271 - classification_output_loss: 0.0170 - regression_output_loss: 0.0202 - final_output_loss: 0.0216 - val_loss: 0.0318 - val_classification_output_loss: 0.0343 - val_regression_output_loss: 0.0214 - val_final_output_loss: 0.0241\n", "Epoch 101/150\n", "220/221 [============================>.] - ETA: 0s - loss: 0.0269 - classification_output_loss: 0.0168 - regression_output_loss: 0.0206 - final_output_loss: 0.0216Restoring model weights from the end of the best epoch: 66.\n", "\n", "Epoch 101 Detailed Metrics:\n", "\n", "Classification Metrics:\n", "Accuracy: 98.55%\n", "AUC-ROC: 0.9991\n", "\n", "Confusion Matrix:\n", "[[8442 134]\n", " [ 110 8163]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9871 0.9844 0.9858 8576\n", " Non-Zero 0.9838 0.9867 0.9853 8273\n", "\n", " accuracy 0.9855 16849\n", " macro avg 0.9855 0.9855 0.9855 16849\n", "weighted avg 0.9855 0.9855 0.9855 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 4 predictions\n", "MAPE: 10.69%\n", "Within ±10%: 75.69%\n", "MAE: 0.05\n", "RMSE: 0.07\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 7.46%\n", "Within ±2%: 62.80%\n", "Within ±5%: 77.28%\n", "Within ±10%: 86.92%\n", "Within ±20%: 91.37%\n", "MAE: 0.03\n", "RMSE: 0.05\n", "221/221 [==============================] - 19s 87ms/step - loss: 0.0269 - classification_output_loss: 0.0169 - regression_output_loss: 0.0206 - final_output_loss: 0.0216 - val_loss: 0.0341 - val_classification_output_loss: 0.0528 - val_regression_output_loss: 0.0221 - val_final_output_loss: 0.0203\n", "Epoch 101: early stopping\n", "\n", "Training completed successfully!\n", "\n", "Classification Metrics:\n", "Accuracy: 98.55%\n", "AUC-ROC: 0.9991\n", "\n", "Confusion Matrix:\n", "[[8442 134]\n", " [ 110 8163]]\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " Zero 0.9871 0.9844 0.9858 8576\n", " Non-Zero 0.9838 0.9867 0.9853 8273\n", "\n", " accuracy 0.9855 16849\n", " macro avg 0.9855 0.9855 0.9855 16849\n", "weighted avg 0.9855 0.9855 0.9855 16849\n", "\n", "\n", "Regression Metrics (non-zero values):\n", "Out of range: 4 predictions\n", "MAPE: 10.69%\n", "Within ±10%: 75.69%\n", "MAE: 0.05\n", "RMSE: 0.07\n", "\n", "Final Combined Output Metrics:\n", "Out of range: 0 predictions\n", "MAPE: 7.46%\n", "Within ±2%: 62.80%\n", "Within ±5%: 77.28%\n", "Within ±10%: 86.92%\n", "Within ±20%: 91.37%\n", "MAE: 0.03\n", "RMSE: 0.05\n" ] } ], "source": [ "#Model creation\n", "print(\"\\n2. Creating model...\")\n", "input_shape = (X_train_seq.shape[1], X_train_seq.shape[2])\n", "\n", "min_val = df['solarenergy'].min()\n", "min_val_scaled = scaler_y.transform([[0]])[0][0]\n", "\n", "max_val = df['solarenergy'].max()\n", "max_val_scaled = scaler_y.transform([[max_val]])[0][0]\n", "\n", "print(f\"\\Min dataset solar energy : {min_val} - Scaled Version : {min_val_scaled}\")\n", "\n", "print(f\"\\nMax dataset solar energy : {max_val} - Scaled Version : {max_val_scaled}\")\n", "\n", "increase_percentage = 8\n", "\n", "max_val = max_val * (1 + increase_percentage / 100)\n", "max_val_scaled = max_val_scaled * (1 + increase_percentage / 100)\n", "\n", "print(f\"Max dataset solar energy increased by {increase_percentage}% : {max_val} - Scaled Version : {max_val_scaled}\")\n", "\n", "# Create the hybrid model\n", "model = create_solarenergy_model(\n", " input_shape=input_shape, \n", " folder_name=folder_name, \n", " min_output=min_val_scaled, \n", " max_output=max_val_scaled\n", ")\n", "\n", "# Prepare binary targets for classification\n", "y_train_binary = (y_train > 0).astype(float)\n", "y_test_binary = (y_test > 0).astype(float)\n", "\n", "print(\"\\nClass distribution in training set:\")\n", "print(f\"Zeros: {np.sum(y_train_binary == 0)} ({np.mean(y_train_binary == 0)*100:.2f}%)\")\n", "print(f\"Non-zeros: {np.sum(y_train_binary == 1)} ({np.mean(y_train_binary == 1)*100:.2f}%)\")\n", "\n", "print(\"\\nClass distribution in test set:\")\n", "print(f\"Zeros: {np.sum(y_test_binary == 0)} ({np.mean(y_test_binary == 0)*100:.2f}%)\")\n", "print(f\"Non-zeros: {np.sum(y_test_binary == 1)} ({np.mean(y_test_binary == 1)*100:.2f}%)\")\n", "\n", "# Get the exact output names from the model\n", "output_names = [output.name.split('/')[0] for output in model.outputs]\n", "print(\"\\nModel output names:\", output_names)\n", "\n", "print(\"\\n4. Starting training...\")\n", "history = train_hybrid_model(\n", " model=model,\n", " X_train=X_train_seq,\n", " y_train=y_train,\n", " X_test=X_test_seq,\n", " y_test=y_test,\n", " epochs=150,\n", " batch_size=512,\n", " folder_name=folder_name,\n", " min_output=min_val_scaled,\n", " max_output=max_val_scaled\n", ")" ] }, { "cell_type": "code", "execution_count": 26, "id": "958d78b99e8898d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "5. Generating predictions...\n", "527/527 [==============================] - 6s 11ms/step\n", "\n", "6. Evaluating model...\n", "\n", "Solar Energy Prediction Metrics:\n", "\n", "Absolute Metrics:\n", "MAE: 0.03 kWh\n", "RMSE: 0.06 kWh\n", "R² Score: 0.995\n", "MAPE: N/A (insufficient data)\n", "\n", "Accuracy Metrics:\n", "Within ±5 kWh: 100.0%\n", "Within ±10 kWh: 100.0%\n", "Within ±20 kWh: 100.0%\n", "\n", "Level Accuracy:\n", "Level Accuracy: 97.7%\n", "\n", "Confusion Matrix for Energy Levels:\n", " Low Moderate Very Low\n", "Low 3537 135 1\n", "Moderate 8 2100 0\n", "Very Low 250 0 10818\n", "\n", "Plot saved as: 2024-11-27_13-56_energy_analysis.png\n", "\n", "Error Statistics:\n", "Mean error: 0.000\n", "Error standard deviation: 0.065\n", "Median error: 0.000\n", "95th percentile absolute error: 0.126\n" ] } ], "source": [ "print(\"\\n5. Generating predictions...\")\n", "predictions = model.predict(X_test_seq)\n", "classification_pred, regression_pred, final_pred = predictions\n", "\n", "# Clip solo le predizioni di regressione e finali\n", "regression_pred = np.clip(regression_pred, min_val_scaled, max_val_scaled)\n", "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", "\n", "# Inverse transform per tornare ai valori originali\n", "regression_pred_original = scaler_y.inverse_transform(regression_pred)\n", "final_pred_original = scaler_y.inverse_transform(final_pred)\n", "y_test_original = scaler_y.inverse_transform(y_test)\n", "\n", "print(\"\\n6. Evaluating model...\")\n", "# Valutazione delle predizioni finali\n", "metrics = evaluate_solarenergy_predictions(y_test_original, final_pred_original, folder_name=folder_name)\n", "\n", "# Create results dictionary con metriche aggiuntive per il modello ibrido\n", "training_results = {\n", " 'model_params': {\n", " 'input_shape': input_shape,\n", " 'n_features': len(features),\n", " 'sequence_length': X_train_seq.shape[1]\n", " },\n", " 'training_params': {\n", " 'batch_size': 192,\n", " 'total_epochs': len(history.history['loss']),\n", " 'best_epoch': np.argmin(history.history['val_final_output_loss']) + 1\n", " },\n", " 'performance_metrics': {\n", " 'regression': {\n", " 'final_loss': float(history.history['val_regression_output_loss'][-1]),\n", " 'out_of_range_predictions': int(np.sum((regression_pred < 0) | (regression_pred > max_val_scaled)))\n", " },\n", " 'final_output': {\n", " 'final_loss': float(history.history['val_final_output_loss'][-1]),\n", " 'best_val_loss': float(min(history.history['val_final_output_loss'])),\n", " 'out_of_range_predictions': int(np.sum((final_pred < 0) | (final_pred > max_val_scaled)))\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 27, "id": "5c05d1d03336b1e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "7. Predicting missing data...\n", "7122/7122 [==============================] - 81s 11ms/step\n", "\n", "8. Integrating predictions into original dataset...\n", "\n", "Prediction Integration Statistics:\n", "Added 227879 predictions to dataset\n", "Rows with solar energy after integration: 357615\n", "\n", "Filled Values Analysis:\n", "Zero predictions (classification < 0.5): 121515\n", "Non-zero predictions (classification >= 0.5): 106364\n", "\n", "Non-zero predictions statistics:\n", "Mean: 1.32\n", "Median: 1.21\n", "Std: 0.96\n", "\n", "Prediction Statistics:\n", "Total predictions added: 227879\n", "\n", "Classification Statistics:\n", "Predicted zeros: 121515 (53.32%)\n", "Predicted non-zeros: 106364 (46.68%)\n", "Mean classification confidence: 0.4731\n", "\n", "Final Predictions Statistics:\n", "Mean solar energy: 0.65\n", "Min solar energy: 0.00\n", "Max solar energy: 3.38\n", "Zero predictions: 115218 (50.56%)\n", "\n", "Training completed successfully!\n" ] } ], "source": [ "print(\"\\n7. Predicting missing data...\")\n", "to_predict_predictions = model.predict(X_to_predict_seq)\n", "classification_pred, regression_pred, final_pred = to_predict_predictions\n", "\n", "# Clip solo le predizioni finali che useremo per l'integrazione\n", "final_pred = np.clip(final_pred, min_val_scaled, max_val_scaled)\n", "final_pred_original = scaler_y.inverse_transform(final_pred)\n", "\n", "print(\"\\n8. Integrating predictions into original dataset...\")\n", "df_updated = integrate_predictions(df.copy(), predictions=(classification_pred, regression_pred, final_pred_original))\n", "\n", "df_updated.to_parquet('../../sources/weather_data_solarenergy.parquet')\n", "\n", "# Add prediction statistics to training_results\n", "training_results['prediction_stats'] = {\n", " 'n_predictions_added': len(final_pred_original),\n", " 'classification_stats': {\n", " 'predicted_zeros': int(np.sum(classification_pred < 0.5)),\n", " 'predicted_non_zeros': int(np.sum(classification_pred >= 0.5)),\n", " 'mean_confidence': float(classification_pred.mean()),\n", " },\n", " 'regression_stats': {\n", " 'mean_predicted_value': float(regression_pred.mean()),\n", " 'min_predicted_value': float(regression_pred.min()),\n", " 'max_predicted_value': float(regression_pred.max()),\n", " },\n", " 'final_predictions': {\n", " 'mean_predicted_solarenergy': float(final_pred_original.mean()),\n", " 'min_predicted_solarenergy': float(final_pred_original.min()),\n", " 'max_predicted_solarenergy': float(final_pred_original.max()),\n", " 'zero_predictions': int(np.sum(final_pred_original == 0)),\n", " 'non_zero_predictions': int(np.sum(final_pred_original > 0)),\n", " }\n", "}\n", "\n", "print(\"\\nPrediction Statistics:\")\n", "print(f\"Total predictions added: {training_results['prediction_stats']['n_predictions_added']}\")\n", "print(\"\\nClassification Statistics:\")\n", "print(f\"Predicted zeros: {training_results['prediction_stats']['classification_stats']['predicted_zeros']} \"\n", " f\"({training_results['prediction_stats']['classification_stats']['predicted_zeros']/len(final_pred_original)*100:.2f}%)\")\n", "print(f\"Predicted non-zeros: {training_results['prediction_stats']['classification_stats']['predicted_non_zeros']} \"\n", " f\"({training_results['prediction_stats']['classification_stats']['predicted_non_zeros']/len(final_pred_original)*100:.2f}%)\")\n", "print(f\"Mean classification confidence: {training_results['prediction_stats']['classification_stats']['mean_confidence']:.4f}\")\n", "\n", "print(\"\\nFinal Predictions Statistics:\")\n", "print(f\"Mean solar energy: {training_results['prediction_stats']['final_predictions']['mean_predicted_solarenergy']:.2f}\")\n", "print(f\"Min solar energy: {training_results['prediction_stats']['final_predictions']['min_predicted_solarenergy']:.2f}\")\n", "print(f\"Max solar energy: {training_results['prediction_stats']['final_predictions']['max_predicted_solarenergy']:.2f}\")\n", "print(f\"Zero predictions: {training_results['prediction_stats']['final_predictions']['zero_predictions']} \"\n", " f\"({training_results['prediction_stats']['final_predictions']['zero_predictions']/len(final_pred_original)*100:.2f}%)\")\n", "\n", "print(\"\\nTraining completed successfully!\")\n", "\n", "tf.keras.backend.clear_session()" ] }, { "cell_type": "code", "execution_count": 28, "id": "ef29b3ecdf12c6db", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Statistiche principali di Solar Energy:\n", "--------------------------------------------------\n", "count : 357,679.0000\n", "missing : 64.0000\n", "zeros : 180,701.0000\n", "mean : 0.6626\n", "median : 0.0000\n", "std : 0.9546\n", "min : 0.0000\n", "max : 4.0000\n", "skewness : 1.2789\n", "kurtosis : 0.3378\n", "percentile_1 : 0.0000\n", "percentile_5 : 0.0000\n", "percentile_10 : 0.0000\n", "percentile_25 : 0.0000\n", "percentile_50 : 0.0000\n", "percentile_75 : 1.2000\n", "percentile_90 : 2.3082\n", "percentile_95 : 2.8033\n", "percentile_99 : 3.2000\n", "\n", "Suggerimenti per la normalizzazione:\n", "--------------------------------------------------\n", "- La distribuzione è fortemente asimmetrica (skewness > 1)\n", "- Considerare una trasformazione logaritmica: np.log1p(x)\n", "- Alta presenza di zeri (50.52%)\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': 180701,\n", " 'mean': 0.6626335375253618,\n", " 'median': 0.0,\n", " 'std': 0.9546401546018566,\n", " 'min': 0.0,\n", " 'max': 4.0,\n", " 'skewness': 1.2788578488075855,\n", " 'kurtosis': 0.33780217102281096,\n", " 'percentile_1': 0.0,\n", " 'percentile_5': 0.0,\n", " 'percentile_10': 0.0,\n", " 'percentile_25': 0.0,\n", " 'percentile_50': 0.0,\n", " 'percentile_75': 1.2,\n", " 'percentile_90': 2.3082294940948502,\n", " 'percentile_95': 2.8033479690551752,\n", " 'percentile_99': 3.2}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analyze_distribution(df_updated, 'solarenergy', 'Solar Energy')" ] }, { "cell_type": "code", "execution_count": 29, "id": "e884cc287364c4ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Plot saved as: 2024-11-27_13-56_error_analysis.png\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Classification Statistics:\n", " precision recall f1-score support\n", "\n", " 0.0 0.99 0.98 0.99 8576\n", " 1.0 0.98 0.99 0.99 8273\n", "\n", " accuracy 0.99 16849\n", " macro avg 0.99 0.99 0.99 16849\n", "weighted avg 0.99 0.99 0.99 16849\n", "\n", "AUC-ROC: 0.9991\n", "\n", "Regression Statistics (Non-zero values):\n", "MAE: 0.0480\n", "RMSE: 0.0668\n", "Mean error: -0.0089\n", "Error std: 0.0662\n", "\n", "Final Prediction Statistics:\n", "MAE: 0.0268\n", "RMSE: 0.0540\n", "Mean error: 0.0004\n", "Error std: 0.0540\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)" ] } ], "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 }