{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fv9OoQsMFk5A"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans ce carnet, nous montrons comment :\n",
    "\n",
    "- préparer des données de séries temporelles 2D pour entraîner un modèle de régression SVM  \n",
    "- implémenter un SVR en utilisant le noyau RBF  \n",
    "- évaluer le modèle à l'aide de graphiques et de la MAPE  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation des modules\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "M687KNlQFp0-"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cj-kfVdMGjWP"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fywSjC6GsRz"
   },
   "source": [
    "### Charger les données\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "aBDkEB11Fumg",
    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2403.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       load\n",
       "2012-01-01 00:00:00  2698.0\n",
       "2012-01-01 01:00:00  2558.0\n",
       "2012-01-01 02:00:00  2444.0\n",
       "2012-01-01 03:00:00  2402.0\n",
       "2012-01-01 04:00:00  2403.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('../../data')[['load']]\n",
    "energy.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O0BWP13rGnh4"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "hGaNPKu_Gidk",
    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
   },
   "outputs": [],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IPuNor4eGwYY"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ysvsNyONGt0Q"
   },
   "outputs": [],
   "source": [
    "train_start_dt = '2014-11-01 00:00:00'\n",
    "test_start_dt = '2014-12-30 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "id": "SsfdLoPyGy9w",
    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
   },
   "outputs": [],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XbFTqBw6G1Ch"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maintenant, vous devez préparer les données pour l'entraînement en effectuant le filtrage et la mise à l'échelle de vos données.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cYivRdQpHDj3",
    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
   },
   "outputs": [],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mettez les données à l'échelle pour qu'elles soient dans la plage (0, 1).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "3DNntGQnZX8G",
    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "26Yht-rzZexe",
    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
   },
   "outputs": [],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x0n6jqxOQ41Z"
   },
   "source": [
    "### Création de données avec des pas de temps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fdmxTZtOQ8xs"
   },
   "source": [
    "Pour notre SVR, nous transformons les données d'entrée pour qu'elles soient de la forme `[batch, timesteps]`. Ainsi, nous remodelons les `train_data` et `test_data` existants de manière à ajouter une nouvelle dimension qui correspond aux timesteps. Pour notre exemple, nous prenons `timesteps = 5`. Ainsi, les entrées du modèle sont les données des 4 premiers timesteps, et la sortie sera les données du 5ème timestep.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rpju-Sc2HFm0"
   },
   "outputs": [],
   "source": [
    "# Converting to numpy arrays\n",
    "\n",
    "train_data = train.values\n",
    "test_data = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the timesteps\n",
    "\n",
    "timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O-JrsrsVJhUQ",
    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
   },
   "outputs": [],
   "source": [
    "# Converting data to 2D tensor\n",
    "\n",
    "train_data_timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "exJD8AI7KE4g",
    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
   },
   "outputs": [],
   "source": [
    "# Converting test data to 2D tensor\n",
    "\n",
    "test_data_timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2u0R2sIsLuq5"
   },
   "outputs": [],
   "source": [
    "x_train, y_train = None\n",
    "x_test, y_test = None\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8wIPOtAGLZlh"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EhA403BEPEiD"
   },
   "outputs": [],
   "source": [
    "# Create model using RBF kernel\n",
    "\n",
    "model = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GS0UA3csMbqp",
    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
   },
   "outputs": [],
   "source": [
    "# Fit model on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rz_x8S3UrlcF"
   },
   "source": [
    "### Faire une prédiction de modèle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XR0gnt3MnuYS",
    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
   },
   "outputs": [],
   "source": [
    "# Making predictions\n",
    "\n",
    "y_train_pred = None\n",
    "y_test_pred = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_2epncg-SGzr"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the predictions\n",
    "\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xmm_YLXhq7gV",
    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
   },
   "outputs": [],
   "source": [
    "# Scaling the original values\n",
    "\n",
    "y_train = scaler.inverse_transform(y_train)\n",
    "y_test = scaler.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u3LBj93coHEi",
    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
   },
   "outputs": [],
   "source": [
    "# Extract the timesteps for x-axis\n",
    "\n",
    "train_timestamps = None\n",
    "test_timestamps = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(25,6))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.title(\"Training data prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LnhzcnYtXHCm",
    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
   },
   "outputs": [],
   "source": [
    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "53Q02FoqQH4V",
    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "clOAUH-SXCJG",
    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
   },
   "outputs": [],
   "source": [
    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DHlKvVCId5ue"
   },
   "source": [
    "## Prédiction de l'ensemble des données\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cOFJ45vreO0N",
    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
   },
   "outputs": [],
   "source": [
    "# Extracting load values as numpy array\n",
    "data = None\n",
    "\n",
    "# Scaling\n",
    "data = None\n",
    "\n",
    "# Transforming to 2D tensor as per model input requirement\n",
    "data_timesteps=None\n",
    "\n",
    "# Selecting inputs and outputs from data\n",
    "X, Y = None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ESSAdQgwexIi"
   },
   "outputs": [],
   "source": [
    "# Make model predictions\n",
    "\n",
    "# Inverse scale and reshape\n",
    "Y_pred = None\n",
    "Y = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "M_qhihN0RVVX",
    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(30,8))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AcN7pMYXVGTK",
    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
   },
   "outputs": [],
   "source": [
    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de faire appel à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
   ]
  }
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