SEKPONA_Kokou_Sitsopé_3_notebook_prediction_112022.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMtaXhiuHhTi"
      },
      "source": [
        "# Importation des librairies etde la base de données"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "kXyYwd7SGlft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from scipy import stats as sts\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "#Models librairies\n",
        "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.dummy import DummyRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.pipeline import make_pipeline, Pipeline\n",
        "#Metrics Librairies\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score, roc_curve, auc\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "#Normalize values\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "\n",
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=FutureWarning)"
      ],
      "metadata": {
        "id": "bIfM2VXhqna0"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.read_csv(\"/content/drive/MyDrive/energy_batiment.csv\")\n",
        "df1=df1.iloc[:, 1:]"
      ],
      "metadata": {
        "id": "Jk7F8tvVqqt0"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l=list(df1.columns)\n",
        "l=[i for i in l if i!='ENERGYSTARScore']\n",
        "df=df1[l]\n",
        "df.shape, df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUKQSHMyubfE",
        "outputId": "4092130f-dbef-4996-e10d-512157c9701c"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((978, 18), (978, 19))"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5Cyanjd1oaK"
      },
      "source": [
        "# Encodage des variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqjEZzZq1r-7",
        "outputId": "02222925-1027-436c-fdb7-2a83a7f35105"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 5, (978, 19))"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ],
      "source": [
        "#Les targets\n",
        "targets = [\"SiteEnergyUse(kBtu)\", \"TotalGHGEmissions\"]\n",
        "\n",
        "quan1=[col for col in df1.columns if df1[col].dtypes in ['float64', 'int64']]\n",
        "quan1=[col for col in quan1 if col not in targets]\n",
        "qual=[col for col in df1.columns if df1[col].dtypes=='object']\n",
        "len(quan1), len(qual), df1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "idmubs83ijoG"
      },
      "outputs": [],
      "source": [
        "numeric_transformer = Pipeline(\n",
        "    steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]\n",
        ")\n",
        "\n",
        "categorical_transformer = OneHotEncoder(handle_unknown=\"ignore\")\n",
        "\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, quan1),\n",
        "        (\"cat\", categorical_transformer, qual),\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "CB8zQuJvo7YU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de7125a5-ba19-4293-a17f-fcdf8cacfd97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ],
      "source": [
        "quan=[i for i in quan1 if i!=\"ENERGYSTARScore\"]\n",
        "preprocessor1 = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, quan),\n",
        "        (\"cat\", categorical_transformer, qual),\n",
        "    ]\n",
        ")\n",
        "len(quan)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "id": "Emh-PVdXZ9Y5"
      },
      "outputs": [],
      "source": [
        "#Liste total erreur dans laquelle on va ajouter les erreurs totales et en faire un graphe finale\n",
        "dic_tot={}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azUt_HposmFK"
      },
      "source": [
        "# Normalisation par log"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "vaybFTy2tvJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f4f7dd0-7f35-4aa1-89a1-b77b5bf82115"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ],
      "source": [
        "for i in quan:\n",
        "  df[i]=df[i].replace([np.inf, -np.inf], 0)\n",
        "  df[i]=df[i].apply(lambda x: round(x, 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "kxHqIsfNzQd7"
      },
      "outputs": [],
      "source": [
        "for j in quan1:\n",
        "  df1[j]=df1[j].replace([np.inf, -np.inf], 0)\n",
        "  df1[j]=df1[j].apply(lambda x: round(x, 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "Rk_48kzjnht7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be8c5164-a460-4f71-9250-f7b4c3e6dcde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ],
      "source": [
        "for i in quan:\n",
        "  df[i]=df[i].apply(lambda x: x if x>0 else 1)\n",
        "  df[i]=df[i].apply(lambda x: np.log(x))\n",
        "for j in quan1:\n",
        "  df1[j]=df1[j].apply(lambda x: x if x>0 else 1)\n",
        "  df1[j]=df1[j].apply(lambda x: np.log(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PaE_9vFRvrDm",
        "outputId": "2ef3811d-ceca-41b0-9f31-506abcc1b9c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return func(*args, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[k1] = value[k2]\n"
          ]
        }
      ],
      "source": [
        "def clean_dataset(dfi):\n",
        "    assert isinstance(dfi, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
        "    dfi.dropna(inplace=True)\n",
        "    indices_to_keep = ~dfi.isin([np.nan, np.inf, -np.inf]).any(1)\n",
        "    return dfi[indices_to_keep].astype(np.float64)\n",
        "df[quan]=clean_dataset(df[quan])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ujspnn4ks6lg",
        "outputId": "bb3bbee2-4603-4014-ffd6-70ea8470a5b7"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ZipCode    Latitude  Longitude   YearBuilt  NumberofFloors  \\\n",
              "count  978.000000  978.000000      978.0  978.000000      978.000000   \n",
              "mean    11.493912    3.863180        0.0    3.708743        1.106676   \n",
              "std      0.000187    0.000982        0.0    0.839066        0.938432   \n",
              "min     11.492927    3.860940        0.0    0.000000        0.000000   \n",
              "25%     11.493783    3.862670        0.0    3.332205        0.000000   \n",
              "50%     11.493834    3.863106        0.0    3.881511        1.098612   \n",
              "75%     11.493967    3.863862        0.0    4.369448        1.609438   \n",
              "max     11.494751    3.865644        0.0    4.753590        4.595120   \n",
              "\n",
              "       PropertyGFATotal  ENERGYSTARScore  SourceEUI(kBtu/sf)  \\\n",
              "count        978.000000       978.000000          978.000000   \n",
              "mean          11.156180         3.910883            4.838483   \n",
              "std            0.992278         0.930999            0.798267   \n",
              "min            9.698920         0.000000            0.693147   \n",
              "25%           10.360176         3.784190            4.395991   \n",
              "50%           10.895395         4.248495            4.924351   \n",
              "75%           11.756303         4.477337            5.292928   \n",
              "max           14.484478         4.605170            7.870930   \n",
              "\n",
              "       SiteEnergyUse(kBtu)  SteamUse(kBtu)  Electricity(kBtu)  \\\n",
              "count         9.780000e+02      978.000000         978.000000   \n",
              "mean          8.514099e+06       -0.388801          -2.485188   \n",
              "std           2.193710e+07        1.442816           3.158439   \n",
              "min           7.971180e+04       -6.907755          -6.907755   \n",
              "25%           1.267853e+06        0.000000          -6.214608   \n",
              "50%           2.829813e+06        0.000000           0.000000   \n",
              "75%           7.725746e+06        0.000000           0.000000   \n",
              "max           2.930908e+08        0.000000           0.000000   \n",
              "\n",
              "       NaturalGas(kBtu)  ComplianceStatus  TotalGHGEmissions  \n",
              "count        978.000000        978.000000         978.000000  \n",
              "mean          -2.244773          0.693147         179.638865  \n",
              "std            3.075290          0.000000         686.832009  \n",
              "min           -6.907755          0.693147          -0.800000  \n",
              "25%           -6.214608          0.693147          20.207500  \n",
              "50%            0.000000          0.693147          49.735000  \n",
              "75%            0.000000          0.693147         142.280000  \n",
              "max            0.000000          0.693147       12307.160000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7af053b1-a7ef-4997-a9b3-b31e9175da4f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ZipCode</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>NumberofFloors</th>\n",
              "      <th>PropertyGFATotal</th>\n",
              "      <th>ENERGYSTARScore</th>\n",
              "      <th>SourceEUI(kBtu/sf)</th>\n",
              "      <th>SiteEnergyUse(kBtu)</th>\n",
              "      <th>SteamUse(kBtu)</th>\n",
              "      <th>Electricity(kBtu)</th>\n",
              "      <th>NaturalGas(kBtu)</th>\n",
              "      <th>ComplianceStatus</th>\n",
              "      <th>TotalGHGEmissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.0</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>9.780000e+02</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.493912</td>\n",
              "      <td>3.863180</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.708743</td>\n",
              "      <td>1.106676</td>\n",
              "      <td>11.156180</td>\n",
              "      <td>3.910883</td>\n",
              "      <td>4.838483</td>\n",
              "      <td>8.514099e+06</td>\n",
              "      <td>-0.388801</td>\n",
              "      <td>-2.485188</td>\n",
              "      <td>-2.244773</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>179.638865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.000187</td>\n",
              "      <td>0.000982</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.839066</td>\n",
              "      <td>0.938432</td>\n",
              "      <td>0.992278</td>\n",
              "      <td>0.930999</td>\n",
              "      <td>0.798267</td>\n",
              "      <td>2.193710e+07</td>\n",
              "      <td>1.442816</td>\n",
              "      <td>3.158439</td>\n",
              "      <td>3.075290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>686.832009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.492927</td>\n",
              "      <td>3.860940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.698920</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>7.971180e+04</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>-0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.493783</td>\n",
              "      <td>3.862670</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.332205</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.360176</td>\n",
              "      <td>3.784190</td>\n",
              "      <td>4.395991</td>\n",
              "      <td>1.267853e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.214608</td>\n",
              "      <td>-6.214608</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>20.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.493834</td>\n",
              "      <td>3.863106</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.881511</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>10.895395</td>\n",
              "      <td>4.248495</td>\n",
              "      <td>4.924351</td>\n",
              "      <td>2.829813e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>49.735000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.493967</td>\n",
              "      <td>3.863862</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.369448</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>11.756303</td>\n",
              "      <td>4.477337</td>\n",
              "      <td>5.292928</td>\n",
              "      <td>7.725746e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>142.280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>11.494751</td>\n",
              "      <td>3.865644</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.753590</td>\n",
              "      <td>4.595120</td>\n",
              "      <td>14.484478</td>\n",
              "      <td>4.605170</td>\n",
              "      <td>7.870930</td>\n",
              "      <td>2.930908e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>12307.160000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7af053b1-a7ef-4997-a9b3-b31e9175da4f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7af053b1-a7ef-4997-a9b3-b31e9175da4f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7af053b1-a7ef-4997-a9b3-b31e9175da4f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "id": "qNkDQkQDkV0R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc0120e3-d257-4cfe-da83-96021b44c488"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return func(*args, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "def clean_dataset(dfi):\n",
        "    assert isinstance(dfi, pd.DataFrame)\n",
        "    dfi.dropna(inplace=True)\n",
        "    indices_to_keep = ~dfi.isin([np.nan, np.inf, -np.inf]).any(1)\n",
        "    return dfi[indices_to_keep].astype(np.float64)\n",
        "    \n",
        "df1[quan1]=clean_dataset(df1[quan1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "s5v8RKgWZqt6"
      },
      "outputs": [],
      "source": [
        "\n",
        "X1=df[[i for i in df.columns if i not in targets]]#.apply(lambda x: str(x)).values\n",
        "y1=df[\"SiteEnergyUse(kBtu)\"].values\n",
        "X_train1, X_test1, y_train1, y_test1=train_test_split(X1, y1, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "zaFPAvjukyPt"
      },
      "outputs": [],
      "source": [
        "colonnes=[i for i in df1.columns if i not in targets]\n",
        "X=df1[colonnes]#.apply(lambda x: str(x)).values\n",
        "y=df1[\"SiteEnergyUse(kBtu)\"].values\n",
        "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "xzlNMv_RjTXn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "27ebb979-7f36-4a10-b56b-8d24b05cf0ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ZipCode    Latitude  Longitude   YearBuilt  NumberofFloors  \\\n",
              "count  978.000000  978.000000      978.0  978.000000      978.000000   \n",
              "mean    11.493912    3.863180        0.0    3.708743        1.106676   \n",
              "std      0.000187    0.000982        0.0    0.839066        0.938432   \n",
              "min     11.492927    3.860940        0.0    0.000000        0.000000   \n",
              "25%     11.493783    3.862670        0.0    3.332205        0.000000   \n",
              "50%     11.493834    3.863106        0.0    3.881511        1.098612   \n",
              "75%     11.493967    3.863862        0.0    4.369448        1.609438   \n",
              "max     11.494751    3.865644        0.0    4.753590        4.595120   \n",
              "\n",
              "       PropertyGFATotal  SourceEUI(kBtu/sf)  SiteEnergyUse(kBtu)  \\\n",
              "count        978.000000          978.000000         9.780000e+02   \n",
              "mean          11.156180            4.838483         8.514099e+06   \n",
              "std            0.992278            0.798267         2.193710e+07   \n",
              "min            9.698920            0.693147         7.971180e+04   \n",
              "25%           10.360176            4.395991         1.267853e+06   \n",
              "50%           10.895395            4.924351         2.829813e+06   \n",
              "75%           11.756303            5.292928         7.725746e+06   \n",
              "max           14.484478            7.870930         2.930908e+08   \n",
              "\n",
              "       SteamUse(kBtu)  Electricity(kBtu)  NaturalGas(kBtu)  ComplianceStatus  \\\n",
              "count      978.000000         978.000000        978.000000        978.000000   \n",
              "mean        -0.388801          -2.485188         -2.244773          0.693147   \n",
              "std          1.442816           3.158439          3.075290          0.000000   \n",
              "min         -6.907755          -6.907755         -6.907755          0.693147   \n",
              "25%          0.000000          -6.214608         -6.214608          0.693147   \n",
              "50%          0.000000           0.000000          0.000000          0.693147   \n",
              "75%          0.000000           0.000000          0.000000          0.693147   \n",
              "max          0.000000           0.000000          0.000000          0.693147   \n",
              "\n",
              "       TotalGHGEmissions  \n",
              "count         978.000000  \n",
              "mean          179.638865  \n",
              "std           686.832009  \n",
              "min            -0.800000  \n",
              "25%            20.207500  \n",
              "50%            49.735000  \n",
              "75%           142.280000  \n",
              "max         12307.160000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6ea0e462-7b79-40b2-8931-accfc71c969e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ZipCode</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>NumberofFloors</th>\n",
              "      <th>PropertyGFATotal</th>\n",
              "      <th>SourceEUI(kBtu/sf)</th>\n",
              "      <th>SiteEnergyUse(kBtu)</th>\n",
              "      <th>SteamUse(kBtu)</th>\n",
              "      <th>Electricity(kBtu)</th>\n",
              "      <th>NaturalGas(kBtu)</th>\n",
              "      <th>ComplianceStatus</th>\n",
              "      <th>TotalGHGEmissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.0</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>9.780000e+02</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>978.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>11.493912</td>\n",
              "      <td>3.863180</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.708743</td>\n",
              "      <td>1.106676</td>\n",
              "      <td>11.156180</td>\n",
              "      <td>4.838483</td>\n",
              "      <td>8.514099e+06</td>\n",
              "      <td>-0.388801</td>\n",
              "      <td>-2.485188</td>\n",
              "      <td>-2.244773</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>179.638865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.000187</td>\n",
              "      <td>0.000982</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.839066</td>\n",
              "      <td>0.938432</td>\n",
              "      <td>0.992278</td>\n",
              "      <td>0.798267</td>\n",
              "      <td>2.193710e+07</td>\n",
              "      <td>1.442816</td>\n",
              "      <td>3.158439</td>\n",
              "      <td>3.075290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>686.832009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.492927</td>\n",
              "      <td>3.860940</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.698920</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>7.971180e+04</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>-6.907755</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>-0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.493783</td>\n",
              "      <td>3.862670</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.332205</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.360176</td>\n",
              "      <td>4.395991</td>\n",
              "      <td>1.267853e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.214608</td>\n",
              "      <td>-6.214608</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>20.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>11.493834</td>\n",
              "      <td>3.863106</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.881511</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>10.895395</td>\n",
              "      <td>4.924351</td>\n",
              "      <td>2.829813e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>49.735000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.493967</td>\n",
              "      <td>3.863862</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.369448</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>11.756303</td>\n",
              "      <td>5.292928</td>\n",
              "      <td>7.725746e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>142.280000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>11.494751</td>\n",
              "      <td>3.865644</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.753590</td>\n",
              "      <td>4.595120</td>\n",
              "      <td>14.484478</td>\n",
              "      <td>7.870930</td>\n",
              "      <td>2.930908e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.693147</td>\n",
              "      <td>12307.160000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ea0e462-7b79-40b2-8931-accfc71c969e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ea0e462-7b79-40b2-8931-accfc71c969e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ea0e462-7b79-40b2-8931-accfc71c969e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VtnKX5522tw"
      },
      "source": [
        "# Modeles avec Grid SearchCv pour choix des meilleiurs parametres"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MA4-UWzH3QkQ"
      },
      "source": [
        "Nous allons trouver les meilleurs parametres d'abord apres refaire la prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "id": "XmIUpnmAE3ja"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics\n",
        "error_dic={'explained_variance':metrics.explained_variance_score,\n",
        "'max_error':metrics.max_error, \n",
        "\n",
        "'neg_mean_absolute_error':metrics.mean_absolute_error,\n",
        "\n",
        "'neg_mean_squared_error': metrics.mean_squared_error, \n",
        "\n",
        "'neg_root_mean_squared_error': metrics.mean_squared_error, \n",
        "\n",
        "#'neg_mean_squared_log_error':metrics.mean_squared_log_error, \n",
        "\n",
        "'neg_median_absolute_error':metrics.median_absolute_error, \n",
        "\n",
        "'r2': metrics.r2_score, \n",
        "\n",
        "#'neg_mean_poisson_deviance':metrics.mean_poisson_deviance,\n",
        "\n",
        "#'neg_mean_gamma_deviance': metrics.mean_gamma_deviance, \n",
        "\n",
        "'neg_mean_absolute_percentage_error': metrics.mean_absolute_percentage_error, \n",
        "\n",
        "#'d2_absolute_error_score': metrics.d2_absolute_error_score,\n",
        "\n",
        "#'d2_pinball_score': metrics.d2_pinball_score, \n",
        "\n",
        "#'d2_tweedie_score': metrics.d2_tweedie_score\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "wPigKYCf24BK"
      },
      "outputs": [],
      "source": [
        "def regression_model(pre, model, params):\n",
        "  model1=make_pipeline(pre, model)\n",
        "  model1=GridSearchCV(model1, param_grid=params, cv=5)#, scoring=score)\n",
        "  model1.fit(X_train, y_train)\n",
        "  y_pred=model1.predict(X_test)\n",
        "  erreur1=np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "  erreur2=mean_absolute_error(y_test, y_pred)\n",
        "  score=model1.score(X_test,y_test)\n",
        "  \n",
        "  print(f\"RMSE: {erreur1}, \\n MAE: {erreur2}\")\n",
        "  print(f\"Moyenne des erreur: \", (erreur1+erreur2)/2)\n",
        "  print(f\"Score: {score}\\n\")\n",
        "\n",
        "  liste=[err(y_test, y_pred)for err in error_dic.values()]\n",
        "  print('erreur totale moyenne: ',np.log(np.mean(liste)), \"\\n\")\n",
        "  '''accuracy=accuracy_score(y_test, y_pred)\n",
        "  print(f'Accuracy: {accuracy}')'''\n",
        "  sns.displot(np.abs(y_test-y_pred), kde=True, bins=40)\n",
        "  plt.show()\n",
        "  print('\\n')\n",
        "  return model1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "id": "QpFmMjpfloqG"
      },
      "outputs": [],
      "source": [
        "def regression_model1(pre, model, params):\n",
        "  model1=make_pipeline(pre, model)\n",
        "  model1=GridSearchCV(model1, param_grid=params, cv=5)#, scoring=score)\n",
        "  model1.fit(X_train1, y_train1)\n",
        "  y_pred=model1.predict(X_test1)\n",
        "  erreur1=np.sqrt(mean_squared_error(y_test1, y_pred))\n",
        "  erreur2=mean_absolute_error(y_test1, y_pred)\n",
        "  score=model1.score(X_test1,y_test1)\n",
        "  \n",
        "  print(f\"RMSE: {erreur1}, \\n MAE: {erreur2}\")\n",
        "  print(f\"Moyenne des erreur: \", (erreur1+erreur2)/2)\n",
        "  print(f\"Score: {score}\\n\")\n",
        "\n",
        "  liste=[err(y_test1, y_pred) for err in error_dic.values()]\n",
        "  print('erreur totale moyenne: ',np.log(np.mean(liste)), \"\\n\")\n",
        "  '''accuracy=accuracy_score(y_test, y_pred)\n",
        "  print(f'Accuracy: {accuracy}')'''\n",
        "  sns.displot(np.abs(y_test1-y_pred), kde=True, bins=40)\n",
        "  plt.show()\n",
        "  print('\\n')\n",
        "  return model1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Suru_Z5qVo0T"
      },
      "source": [
        "## Models naifs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvIZlXWV4xkE"
      },
      "source": [
        "* Avec EnergyStarScore"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ohL8GaWXl8bj"
      },
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "dNOQHUJmVrXc",
        "outputId": "5692f437-5e69-4bd1-a38d-f25c1a2ef172"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "5 fits failed out of a total of 15.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "5 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 394, in fit\n",
            "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/dummy.py\", line 605, in fit\n",
            "    \"Constant target value has to be specified \"\n",
            "TypeError: Constant target value has to be specified when the constant strategy is used.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [-0.00276269 -0.07718003         nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 11509012.477145627, \n",
            " MAE: 7658472.22567137\n",
            "Moyenne des erreur:  9583742.351408498\n",
            "Score: -0.00821346855752525\n",
            "\n",
            "erreur totale moyenne:  31.130987903954004 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'dummyregressor__strategy': 'mean'},\n",
            " meilleur score: -0.0027626874111213785\n"
          ]
        }
      ],
      "source": [
        "params={'dummyregressor__strategy':['mean', 'median', 'constant']}\n",
        "pre=preprocessor\n",
        "model0=regression_model(pre=pre, model=DummyRegressor(), params=params)\n",
        "print(f\"les meilleurs parametres du model: {model0.best_params_},\\n meilleur score: {model0.best_score_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkQLOLipbEgS",
        "outputId": "169fea56-1c71-4bd0-e27e-b669a03747f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['constant', 'quantile', 'strategy'])"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "DummyRegressor().get_params().keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNSLFeLslQK-"
      },
      "source": [
        "* Sans EnergyScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "GZFN4SzglYFZ",
        "outputId": "9f66a33e-8c2d-4fbb-90f2-8a2834bef067"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "5 fits failed out of a total of 15.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "5 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py\", line 394, in fit\n",
            "    self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/dummy.py\", line 605, in fit\n",
            "    \"Constant target value has to be specified \"\n",
            "TypeError: Constant target value has to be specified when the constant strategy is used.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [-0.00371563 -0.07805515         nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 23167427.38346557, \n",
            " MAE: 9101801.907462534\n",
            "Moyenne des erreur:  16134614.645464052\n",
            "Score: -6.373863543118752e-05\n",
            "\n",
            "erreur totale moyenne:  32.53022163351088 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'dummyregressor__strategy': 'mean'},\n",
            " meilleur score: -0.00371562912243979\n"
          ]
        }
      ],
      "source": [
        "params={'dummyregressor__strategy':['mean', 'median', 'constant']}\n",
        "pre=preprocessor1\n",
        "model0=regression_model1(pre=pre, model=DummyRegressor(), params=params)\n",
        "print(f\"les meilleurs parametres du model: {model0.best_params_},\\n meilleur score: {model0.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vtzI9iP4iTG"
      },
      "source": [
        "* Le model DummyRegressor obtient de meilleurs resultats avec la variable 'EnergyStarScore'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gakBc_Cq8wpL"
      },
      "source": [
        "## Modeles linéaires"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-VsKmZl3a-x"
      },
      "source": [
        "### 1. Model Ridge"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHV3kzm7pqEr"
      },
      "source": [
        "* Avec EnergyStarScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "9N3nQzUB3Zh7",
        "outputId": "7947c801-1254-42c5-f4f9-a9e3dcb91c83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 8032594.143868182, \n",
            " MAE: 6143870.137821731\n",
            "Moyenne des erreur:  7088232.140844956\n",
            "Score: 0.508879546274608\n",
            "\n",
            "erreur totale moyenne:  30.41174216801627 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'ridge__alpha': 13.257113655901108},\n",
            " meilleur score: 0.5593230153279489\n"
          ]
        }
      ],
      "source": [
        "params={'ridge__alpha': np.logspace(-5, 5, 50)}\n",
        "scaler=preprocessor\n",
        "model1=regression_model(pre=scaler, model=Ridge(), params=params)\n",
        "print(f\"les meilleurs parametres du model: {model1.best_params_},\\n meilleur score: {model1.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hi6kUa64p2O3"
      },
      "source": [
        "* Sans EnergyStarScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "C3X5zlN2p1tC",
        "outputId": "23d86b33-8f86-48fe-a848-5b8061541ed4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 13888090.958239287, \n",
            " MAE: 6279919.0419676015\n",
            "Moyenne des erreur:  10084005.000103444\n",
            "Score: 0.6406173026323243\n",
            "\n",
            "erreur totale moyenne:  31.506790617763233 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'ridge__alpha': 5.1794746792312125},\n",
            " meilleur score: 0.5413281032776024\n"
          ]
        }
      ],
      "source": [
        "params={'ridge__alpha': np.logspace(-5, 5, 50)}\n",
        "scaler=preprocessor1\n",
        "model1=regression_model1(pre=scaler, model=Ridge(), params=params)\n",
        "print(f\"les meilleurs parametres du model: {model1.best_params_},\\n meilleur score: {model1.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fe5i0MGY5DeR"
      },
      "source": [
        "* Ridge Obtient aussi de meilleurs resultat avec EnergyStarScore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSzZQJU33gEc"
      },
      "source": [
        "### 2.Model Lasso"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "X85PF_h33kqg",
        "outputId": "62ae40d9-378d-459b-d5ec-54bc8c37edf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31528683314454.805, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45300167715155.48, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39211142183713.664, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39986738638445.84, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 38675605705277.87, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 43154959829961.65, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54982465208693.984, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47549005989186.79, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49006814711160.27, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47224797153463.35, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52826021004211.66, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66223403529412.766, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57389029899914.39, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59901540455653.055, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57306616429969.88, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64438647481294.51, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79462779757478.75, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69157887740167.02, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 72896093696767.47, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69275369692971.86, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 78061502307544.58, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95094955091135.56, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83276998708290.97, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88290276023378.23, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 83331637772240.58, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 93918352445966.1, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113641434374338.34, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100782814100317.89, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 107159666448404.12, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100584302446744.92, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 112759073351791.86, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 137336726555031.23, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123258578561815.3, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 130501692510785.83, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121976470373884.72, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 136911500975701.44, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 166800227218056.06, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150896056114311.84, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 158928066288266.28, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148625151415484.66, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167009800743710.56, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202872552427261.06, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180723134204114.5, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194136393534956.94, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180735006573799.53, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 202032463610072.56, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 241067859352022.22, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200338235298156.28, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226777617998504.56, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 212189724832677.53, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 242822328032713.47, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 206400132117097.28, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173388257936072.97, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 218668771726243.4, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190743602971238.9, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 217895872849935.72, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 197613439096630.44, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 153398749202769.6, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 261522033475476.03, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 184714677650628.12, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 219110601441626.5, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 283912037316112.06, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 139414077049516.58, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 312673725276767.4, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 167948500543932.7, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 102044971854906.94, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131762071555190.64, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 95228483640732.94, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63436057855440.14, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99243376081017.95, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 111700953008453.1, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 75775187321360.69, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 140920344214835.1, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 135067045019736.81, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 115548732137499.1, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50725811597105.09, tolerance: 39973644024199.5\n",
            "  positive,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 7010750.9725071, \n",
            " MAE: 5029537.430323296\n",
            "Moyenne des erreur:  6020144.201415198\n",
            "Score: 0.6258847127965748\n",
            "\n",
            "erreur totale moyenne:  30.139616885815833 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'lasso__alpha': 117681.19524349991}\n",
            ", meilleur score: 0.5849851635383713\n"
          ]
        }
      ],
      "source": [
        "n_alphas = 100\n",
        "alpha = np.logspace(-2, 8, n_alphas)\n",
        "params={'lasso__alpha': alpha}\n",
        "pre=preprocessor\n",
        "model2=regression_model(pre=pre, model=Lasso(), params=params)\n",
        "#model2.best_params_, model2.best_score_\n",
        "print(f\"les meilleurs parametres du model: {model2.best_params_}\\n, meilleur score: {model2.best_score_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aDxYHN7htKQd",
        "outputId": "a966022a-d482-4244-ab01-0861a21edcf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30349110094136.89, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 29928562046708.934, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 30496138555607.965, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 33203065823514.434, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 39917509840848.69, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37165640222352.99, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 36615945367549.805, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 37262743253731.25, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 40534867260472.805, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48668634029846.4, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45384791528275.72, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44631508071025.44, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 45476521768477.44, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49362050130707.47, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59264093657281.88, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55298184258763.36, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54304580064591.53, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55422523363035.06, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59878938419557.94, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 71820275205732.11, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67463037137724.08, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 65862205574650.19, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67282422798413.734, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 72677570201666.75, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86881773009569.83, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81927178606612.81, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 79620588760244.39, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 81365471752014.33, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88483060122322.48, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 105105606233764.86, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99538136971381.69, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 96651117388469.66, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 98577334791255.86, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 107615995201211.27, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127549125782192.31, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121277854080090.19, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 117807053876868.27, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 119409384595762.55, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131181079143988.95, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 155809229248003.9, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148318338790134.03, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 143702666716332.5, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 144765415530774.0, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160429601775728.75, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190222651414492.53, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 180920520004500.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172878319510380.78, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 172962578807143.47, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 196648001746857.06, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 226556296403608.78, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 200960058646780.66, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 157548491484744.28, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194978304195913.16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 234424957202957.2, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 235462559379431.28, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 233292034910091.66, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 159854975226822.84, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145110227440815.9, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 220757730236937.8, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 203941433578171.7, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 115619999679102.53, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 68748247391360.79, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211454282771623.2, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 164611893149459.2, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 80852983181048.73, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 156456208203693.75, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86631493984506.9, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 66392195725792.06, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118610089541470.12, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94434115126158.66, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 94057503621724.55, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125775862183924.94, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99745972004580.64, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 190598201917972.25, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116513733460359.47, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 31026050843792.477, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49288149146281.14, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 142245525822988.6, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 171840497425076.78, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 34409091980425.47, tolerance: 26977772617744.49\n",
            "  positive,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 14034806.892536398, \n",
            " MAE: 5783991.353461563\n",
            "Moyenne des erreur:  9909399.12299898\n",
            "Score: 0.6329840465798651\n",
            "\n",
            "erreur totale moyenne:  31.527808112498395 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "les meilleurs parametres du model: {'lasso__alpha': 148496.82622544636}\n",
            ", meilleur score: 0.5614055204392224\n"
          ]
        }
      ],
      "source": [
        "n_alphas = 100\n",
        "alpha = np.logspace(-2, 8, n_alphas)\n",
        "params={'lasso__alpha': alpha}\n",
        "pre=preprocessor1\n",
        "model2=regression_model1(pre=pre, model=Lasso(), params=params)\n",
        "#model2.best_params_, model2.best_score_\n",
        "print(f\"les meilleurs parametres du model: {model2.best_params_}\\n, meilleur score: {model2.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lr_eOZV65eVe"
      },
      "source": [
        "* Lasso Aussi obtient de bons resultats avec EnergyStarScore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyi3m7WL3oaD"
      },
      "source": [
        "### 3. Model ElasticNet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nvaSopM3rLa",
        "outputId": "bb9c68e7-8523-4202-da7b-44bf2dc097fb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('onehotencoder', OneHotEncoder(handle_unknown='ignore')),\n",
              "                ('elasticnet', ElasticNet())])"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ],
      "source": [
        "model_3=make_pipeline( OneHotEncoder(handle_unknown='ignore'), ElasticNet())\n",
        "model_3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WSAJp44a3s6w",
        "outputId": "6b8b3118-8ae8-4535-ead5-45be6d51a874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 428639738701408.94, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 329522351645728.1, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 406704713305592.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 297454674991500.4, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 414563553214980.7, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 369971789616930.2, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 284628276965877.0, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 350707096633406.44, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 256495852331062.6, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 358312384262387.75, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 311164833906925.1, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 239591643591560.3, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 294549739385801.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 215449479085070.78, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 301792572476444.56, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 252097349416393.25, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 194302480702498.0, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 238105119434858.94, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 174257026626305.88, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 244850225534822.53, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 192490855651288.56, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 148522573013773.44, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 181135303960223.38, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132764263669943.45, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 187177420335883.53, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 131841983337706.33, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 101799769073262.31, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123291009635065.5, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 90676859532624.02, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128243710166523.72, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 69386587542170.84, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53297189211587.06, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64226001187186.24, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 47642812577448.71, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 67387886213897.945, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4927036031729121.0, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3763314324336360.0, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4750041673207106.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3447134165253307.5, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4707836078520280.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4271863811400646.5, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3263855512806127.5, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4115415246083973.5, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2987565822850438.5, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4080971902562898.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3603242827412706.0, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2753922014195009.5, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3467879793759031.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2518720836644378.0, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3441875983589431.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2920479996149738.0, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2232971353469037.5, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2806956456902972.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2040179082084225.8, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2789932541371046.5, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2222621436172551.8, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1700260503433466.2, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2132063232078038.8, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1551379319408954.5, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2124284099523855.5, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1508075991799000.5, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1154544067594175.8, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1442346527327241.5, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1051379732426721.6, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1443432690091778.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 773286375937660.8, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 593019778012438.5, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 736066657599515.4, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 538005613412836.5, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 743614846869181.6, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.5526318348121984e+16, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.6834753785728836e+16, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.3999456615678784e+16, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.49353227825825e+16, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.470375829543672e+16, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 364236582902056.0, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 42233079412664.0, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51942935792266.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 250578990493500.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9185617083137704e+16, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2384720720114108e+16, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7468431300519752e+16, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7201300627975827.0, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8233980290778016e+16, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.524105938204916e+16, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.158287648829457e+16, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4701525225456422e+16, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0642327667826966e+16, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4657341490938206e+16, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553521584761668.0, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6523824780203336.0, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8260909906166703.0, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5992160163574904.0, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188841433569567.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.747555561390842e+16, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.715690709196069e+16, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.847856263862824e+16, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.87515400876711e+16, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.623487158239869e+16, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4098008587463398e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1105617570938802e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2273914798133525e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.936322452649667e+16, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3722252048208531e+17, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.847927784285497e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4731606038019168e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6182088288240506e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1903349129033707e+17, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.791611910464862e+17, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0340449194432717e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6337631941831846e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.7919534441503677e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.332931773982926e+17, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9731729824653014e+17, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 100191747857650.72, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88150032159267.62, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 93531165976555.83, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 88016727059294.98, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99201544240286.36, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0579308528298534e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6542863509287136e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.814365254160498e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3514915459564326e+17, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.996605077439873e+17, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 405886798776976.1, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150484894349964.0, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.0598824868533466e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.65596135762754e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.816196777962389e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3530092837716314e+17, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.9985207271259248e+17, tolerance: 39973644024199.5\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.060034246013976e+17, tolerance: 41200939731534.79\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6560915938765117e+17, tolerance: 33122050549107.324\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.8163391989777693e+17, tolerance: 36327023111804.9\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3531273104890467e+17, tolerance: 27062744383526.05\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.998669694942943e+17, tolerance: 39973644024199.5\n",
            "  positive,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 6967526.685534875, \n",
            " MAE: 4947134.088543512\n",
            "Moyenne des erreur:  5957330.387039194\n",
            "Score: 0.6304836541749512\n",
            "\n",
            "erreur totale moyenne:  30.127247872039824 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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45/4M+BZwdvrxFLDxOPv+Z+CaGZZ/1Tm3Lv2YM8QXQnQqwVgskXc9IKYrDwVY0VjB9s4REuoTLFIyjncF/PekPv8N59wm59wdzrk7gAfS62blnHsc6F+QKk/CwPgUAHV5fAUMsHZxNRNTCfb1jXldiojkyPECuMU59/KxC9PLls/zmJ8ys5fSTRR189xHxgbTPSDq8vgKGGB5fQXlIT/bXlMzhEipOF63gLluy5fN43jfAL5EalTdl4C/Az4y04ZmdhtwG8DSpUvncaiUgYkpDKguy78eENP5fMbprdX8/sAAY9E4FeEAO3Zs59IrZ2rF0Sg5kWJwvFTabGYfd859e/pCM/sYqUnZT4hzrmvaPr4N/GyObTeSbmfesGHDvBtGB8djVEUCBHz532ljbWs1z+0fYMfhEc5bVsdU0nH9nV+bcVuNkhMpfMcL4M8CD5jZB3g9cDcAIeD9J3owM2t1znWmv30/sGWu7RfC4PgUtXne/HBEfUWI1poI2zqHWb9UfYJFit2cAZy+Yn2LmV0OnJle/HPn3HE/kt7M7gXeDjSaWQfweeDtZraOVBPEPuAT8y/9+BwwODHFadWRbB5mQa1treaRHd10DatPsEixy6hh1Dn3KPDoiezYOXfLDIu/eyL7OFnJQIRYPElteTCXhz0pq1sqeWxnD1s7h7wuRUSyLP8bRk9CPJL6M75QmiAAwgE/q5or2dk1Cr78vnEoIienuAM4fCSAC+cKGOC0RVXE4kmC7Wd4XYqIZFFxB3CkFjOojhRWAC+pK6cs6Ce44nyvSxGRLCrqAJ6K1FIdCeL35d8kPHPx+Yw1LVUEl5xNNK55gkWKVVEHcDxcW3DND0esWVSF+YPs6dbQZJFiVbQB7JwjHqmhtqwwA7ilOkxiuFsTtYsUsaIN4J6RKM4fyvs5IGZjZky9+jsO9o8zFo17XY6IZEHRBvCrvak/3Qu1CQIg9upmHLBTV8EiRaloA/jItI6F1Af4WMmhwzRVhtnVPep1KSKSBUUbwK/2jkMyQVUefg7ciVjVXEnn0CSjk2qGECk2RRvA+3rHCESH8RVYF7RjrWquBGBPj66CRYpN8QZw3xiB6KDXZZy0+ooQ9RUhdqsZQqToFGUAJ5MuFcCThR/AAKuaKjk0OMF4TM0QIsWkKAMY4Ie3XUxl95s+TakgrWquxAF7ezQoQ6SYFPYdqln4fMY5S2oJRvNjSse5Plpox86dx319Y2WImrIgu7tHObOtZqHLExGPFGUA55u5PlroCx+66rivNzNWNVfy/IEBJqcSRIL+hS5RRDxQtE0QxWZVUyVJ9/oAExEpfArgAtFSHaYi7FcAixQRBXCBMDOWN1Swv2+cRHLeHxItInlEAVxAVjRWEEskeW1wwutSRGQBKIALyJK6cvw+49U+NUOIFAMFcAEJBXy015apHVikSCiAC8yKxgoGx6eYCqs/sEihUwAXmOWNFQBM1i73thAROWkK4AJTUxakviLERM0Kr0sRkZOkAC5AKxoriFYtZmRyyutSROQkKIAL0IqGCvD5+c/dfV6XIiInQQFcgBbVRLBEjN/u7vG6FBE5CZqMpwD5fUZ4uIMndtV6XYqInARdAReoyPBB9veNs1+DMkQKlgK4QEWGDwDwxK5ejysRkflSABeowOQgbbVlPLFL7cAihUoBXKAMuHR1I0/u6SOeSHpdjojMg27CFagdO7Yzfu9GRla9k7e8/yOExw4fXdfUUMem++71sDoRyYQCuEBNJR03fOSTbHx8Lyve/QkuWtlwdN2mu273sDIRyZSaIApYWdBPS3WYA/3jXpciIvOgAC5wS+vLOTw8STSe8LoUETlBCuACt6SuHOfgtcFJr0sRkROkAC5wrTUR/GYcHFAzhEihyVoAm9ndZtZtZlumLas3s4fNbFf6a122jl8qAn4frTUROgb0OXEihSabV8D/DFxzzLI7gUecc6uBR9Lfy0lqryujZyTK5JTagUUKSdYC2Dn3ONB/zOLrgHvSz+8B3pet45eS9rpyAF0FixSYXLcBtzjnOtPPDwMts21oZreZ2WYz29zTo+G2c1lUEyHgMzrUDixSUDy7Ceecc4CbY/1G59wG59yGpqamHFZWePw+Y3Ftma6ARQpMrgO4y8xaAdJfu3N8/KLVXldG31iM8Vjc61JEJEO5DuAHgVvTz28FfpLj4xetJWoHFik42eyGdi/wFLDGzDrM7KPAXcA7zGwXcFX6e1kAzVVhQn6f+gOLFJCsTcbjnLtlllVXZuuYpcznMxbXpvoDV3ldjIhkRCPhisiS+nIGx6eIByu8LkVEMqAALiLtdWUARKvbPa5ERDKhAC4iTZVhwgEf0ao2r0sRkQwogIuImdFeV8akroBFCoICuMi015WTCNdwUJO0i+Q9BXCRWZJuB35qT5/HlYjI8SiAi0x9RQjf1DhP7VUAi+Q7BXCRMTPCI4d4ak8fqek2RCRfKYCLUGT4IIeHJ3m1d8zrUkRkDgrgIhQePgSgZgiRPKcALkKB6CCLqiM8qRtxInlNAVyEDLj4lAae2at2YJF8pgAuUhevbKB3NMau7lGvSxGRWWRtNjTxzo4d2/nqX9wB53yYmz77Jaq6Xzq6rqmhjk333ethdSJyhAK4CE0lHTfd8WX+6T9fpWHDu3jP2R87um7TXbd7WJmITKcmiCLWXldOx8CE2oFF8pQCuIgtqSsjGk/SMxr1uhQRmYECuIi163PiRPKaAriIVUYC1JYHNTOaSJ5SABe59royXhucJJlUO7BIvlEAF7kldeXEEkm6R9QOLJJvFMBF7sjnxOnj6kXyjwK4yJWHAjRUhHQjTiQPKYBLQKodeIKE2oFF8ooCuAS015UTTzoOD096XYqITKMALgFH2oE71B1NJK8ogEtAJOinqSrMQbUDi+QVBXCJWFpfTufQBElf0OtSRCRNAVwiltWXk3QQrW73uhQRSVMAl4jFtWUE/cZEzVKvSxGRNAVwifD7jPa6ciarl3ldioikKYBLyLL6chKRGvbp4+pF8oICuIQsa0hNT/nYzh6PKxERUACXlNryEP7JIR5XAIvkBQVwiYkM7+epvX1E4wmvSxEpeQrgElM2dIDxWILn9g14XYpIyVMAl5jwcAdBv/EfaoYQ8ZwCuMT4klNctLKBh7d16dOSRTzmSQCb2T4ze9nMXjCzzV7UUMquXtvCq71j7OkZ9boUkZLm5RXw5c65dc65DR7WUJKuWtsCwK+2dnlciUhpUxNECWqtKeOc9hp+vU0BLOIlrwLYAb82s+fM7LaZNjCz28xss5lt7unRDaOF9o61Lbx4cJAuTdIu4hmvAvitzrn1wDuBT5rZZcdu4Jzb6Jzb4Jzb0NTUlPsKi9zVZywC4GFdBYt4xpMAds4dSn/tBh4ALvCijlK2urmS5Q3laoYQ8VDOA9jMKsys6shz4GpgS67rKHVmxtVnLOKpPb2MTE55XY5ISfLiCrgF+K2ZvQg8C/zcOfeQB3WUvKvXtjCVcDyyvdvrUkRKUiDXB3TO7QXOyfVx5c3WL62jrbaMB54/xPvObfO6HJGSo25oJcznM65bt5gndvXQPaLeECK5pgAucdevbyPp4KcvdnpdikjJUQCXuFXNVZzVVsMDz3d4XYpIyVEAC+8/t40th4bZ1TXidSkiJUUBLLz3nMX4fcam5w95XYpISVEAC01VYS5d3chPnj9EMqkpKkVyRQEsANywvp3XhiZ5fJfm3RDJFQWwAPAHZyyisTLEvz293+tSREqGAlgACAV83Hz+Uh7Z0c3B/nGvyxEpCQpgOeqWC5dioKtgkRxRAMtRbbVlvOusVr7/zAFN0COSAwpgeYNPXHYKI9E49z57wOtSRIqeAlje4Kz2Gi5Z1cB3nniVyamE1+WIFLWcz4Ym+e/TV6zm5o1P829P7+djl64E4PqbbqGnb2DG7Zsa6th03725LFGkKCiA5U0uWtnAW1c18o3/2MMtFyylIhygp2+A6+/82ozbb7rr9hxXKFIc1AQhM7rj6lPpG4vxrcf3el2KSNFSAMuM1i+t49pzFvOtx/bQMaB+wSLZoACWWd35ztMwgy/+dBuaIUJk4SmAZVaLa8v4zJWn8uttXUzUrfK6HJGiowCWOX380hWc1VbDwLK3MRaNe12OSFFRAMucAn4ff3fTOTh/iF9tO4xzaowQWSgKYDmuU1uqqN3/GAf7J3h6b7/X5YgUDQWwZKSidxtrW6t5dl8/OzqHvS5HpChoIEaJ2bFjO5deec2M6+Ya0WbAFac1MzwxxcPbuwgFfKxsqjzu8TSCTmR2CuASM5V08x7R5vcZ7zmnlQeeP8QvXj7M1We0cGpL1Zyv0Qg6kdmpCUJOSDjg533r2miuDvPLLYf53b5+9REWmScFsJywSNDP9ee2saaliif39NG//ErGY+qiJnKiFMAyLwG/jz84o4ULV9Qz3ng67/yHJ/jdPvWQEDkRCmCZNzPjopUNNO3YRNI5bvrWU3z+J1sYGIt5XZpIQVAAy0mLjL7GQ5+5jA9etIx/fXo/b/ubR/n243uJxjWhu8hcFMCyICrCAb543Zn84jOXcu7SOr78i+1c9tePMtxyLrF40uvyRPKSAlgW1GmLqrnnIxfwvY9dyClNlQwtfSt3/+erPLWnTzfqRI6hfsBy1FyDNHbs3HlC+7pkVSOXrGrkgmtvpeptf8yz+/p5bv8Aq1oqObuthtaayEKU/Ab5NOhjrlr27t3NypUzzy5X6INT8unfYL5y+TMogOWouQZpfOFDV81rn+GxLt5z9mL6x2K81DHI9s4RXjk8QmNliETTmQyOx6gtD51M2Ufl06CPuWr5woeuyps6F1o+/RvMVy5/BgWw5ER9RYi3r2nmLac08krXCC93DDGw/HIu+PIjXHFaM+9f38bla5oJBdQqJqVDASw5FQr4OKuthjMXV/PDr3+Jd9/2OR588TUe2nqYmrIgb1/TxJWnt/C2U5uoKQt6Xa5IVimAxRNmRmi8l79471o+967TeGJ3Lz9/qZNHd3Tzkxdew+8zNiyr4+JTGtiwrJ51S2upDBfef9dYPMlYNE40kWQqniThHIFFp9I5NEHI76MyEiDk92FmXpcqHii8/9FSdAJ+H5evaebyNc0kko4XDg7yyPYu/uOVHv7hkV0cmQO+va6MU1uqWN1cySlNlbTWRlhUHaG5OkJ1xLv/ylOJJPv7xtndPcKurlF2dY9y+Iyb+cZje2bsglf5B/+d+zZ3HP0+6DdqyoI0VoYZXnQuj+3s4fTWKpqrFv5GpeQXBbDkFb/POG9ZHectq+PPrzmN4ckpnj8wyIsHB9nVPcqurhF+u6uXWOKNwRYJ+pg6+1a+/8wBIkEfkaA//Ug9H2s4jUe2d1FbHqS2PERdeYjqSICAP7M253giSe9ojK7hSQ4OjLOra5Td3aPs6h7h1d4xphKvT0m0pL4Mf2yMNYuqqIwEqAwHCAV8BH0+/D7jn758Bx/4H39LNJ5kNBpndDLOwESMgwPjjC15K7fe/SwALdXhVHNNWw1npR/N1fkdyg5jIpbAHTNFU9DvK7hJm5xzJB0kko5E0hFPJkn6wwt6DE8C2MyuAf4B8APfcc7d5UUdkv+qI0HedmoTbzu16eiyeCLJocEJDg9N0jUSpWtokq7hSb7/71upbGtlcipBz2iU6FSSyalE6o2/8h189J7Nb9p/VSRARShAeSgV2GUhP8454klHLJ5kKpFkaCJO31iU6Z/G5DNYWl/O6pYqrjq9hVXNlZzaUsXKpgrKQwEuvfKvefsN75jxZ4p37WJ5Y8WM6370N3/OV775Xba+NsyWQ0O8fGiIR3Z0Hz12c9XroXx2ew2rm6torY0QzPAXyXxMTiXoGYnSNxajdyRK31iU3tHYjMv6N3ySjU/snXlH5/0J537x11RFgjRUhmipitBcHaalOkJzVZjm6ggt1WGaqyLUlQez0izjnGMkGqd3JErPSJSe0fTXkSi96eeH1/4R3/ntXiZiCZLH/NaobjlnQevJeQCbmR/4P8A7gA7gd2b2oHNuW65rkcIU8PtY1lDBsoY3htivvnoH1/7RtW9Y5pwjGk/y4D9+nm98ayMD4zGGxqcYGI8xMD7F0HiMiakE47EEk1MJJs2VX0IAAAeISURBVKYS+MwI+Iyg30fQ76O6LEBzOiyaqyK01ZaxsqmCSNC/4D+bPzHJRSsbuGhlw9FlY9E42zqHebljiC2Hhnjp0BC/eeX1UPb7jNaaCEvry1lUE6G2LERdeZDa8iBVkSBBv4+A3wj6Db/PRzyR+sUylUj9khmenGJo4vXH8MQU/WOxo+E6Fpt5SHlVOEBDZYjGyjArGis4f3k9P910P+de/m7ekJ0OYokkL//2V7z7LdcznP6FtqdnlCf39DI8+eYBOgGfUVcRoqEiRH360VARojISoCz4+i9Lf/pADnAu1Rw0FoszHk0wFoszMhmnbzT1y6FvNErvWGzGZqGAz2isDNNUFcY/NcbyhgrKgn6C/tRfLQGf4fcZW7bO8stlnry4Ar4A2O2c2wtgZj8ArgMUwLLgzIxI0E8gOsy6JbVelzMvFeEA5y+v5/zl9UeXHQnlV3vGODgwzoH+1OOZvf0MTUwxOo9PsK4KB6guC1JTlgrvc9praawM01AZoin9tbEyTGNVmIaK0Iy/gB7/xz9j3ZIPzLj/gz94ir963+fftHxyKkH3cJSukcnU1+FJekaj9I+mfgn0j0XZcmiIvrEY47EEiWMvS2fgs9R5qwwHjv4Mp7ZU0VgZSv08VWGaKiOpr1VhasuC+HypML/0yr/iqhuunnG/O8d7j3vsE2G5/pRbM7sRuMY597H09x8ELnTOfeqY7W4Dbkt/uwZ45QQP1Qgs7Nk6OflUj2qZWT7VAvlVj2qZXSb19Drn3jTMNG9vwjnnNgIb5/t6M9vsnNuwgCWdlHyqR7XMLJ9qgfyqR7XM7mTq8WLY0SFgybTv29PLRERKihcB/DtgtZmtMLMQcDPwoAd1iIh4KudNEM65uJl9CvgVqW5odzvntmbhUPNuvsiSfKpHtcwsn2qB/KpHtcxu/k2lub4JJyIiKZp6SkTEIwpgERGPFHwAm9k1ZvaKme02sztnWB82sx+m1z9jZss9rOXDZtZjZi+kHx/LYi13m1m3mW2ZZb2Z2dfStb5kZus9rOXtZjY07bz8RRZrWWJmj5rZNjPbamafmWGbnJybDGvJ5bmJmNmzZvZiup4vzLBNTt5PGdaSs/dT+nh+M3vezH42w7r5nRfnXME+SN3E2wOsBELAi8DaY7b5E+Cb6ec3Az/0sJYPA/+Yo3NzGbAe2DLL+ncBvwQMuAh4xsNa3g78LEfnpRVYn35eBeyc4d8pJ+cmw1pyeW4MqEw/DwLPABcds02u3k+Z1JKz91P6eHcA35/p32O+56XQr4CPDmt2zsWAI8Oap7sOuCf9/H7gSsvO5KuZ1JIzzrnHgf45NrkO+BeX8jRQa2atHtWSM865Tufc79PPR4DtQNsxm+Xk3GRYS86kf97R9LfB9OPYu/Q5eT9lWEvOmFk78G7gO7NsMq/zUugB3AYcnPZ9B2/+D3x0G+dcHBgCGlh4mdQCcEP6z9r7zWzJDOtzJdN6c+Xi9J+bvzSzM3JxwPSfieeSurqaLufnZo5aIIfnJv1n9gtAN/Cwc27Wc5Pl91MmtUDu3k9/D/w58OaZfFLmdV4KPYALzU+B5c65s4GHef03Zqn7PbDMOXcO8HXg37N9QDOrBH4MfNY5N5zt451ELTk9N865hHNuHakRqheY2ZnZPN5J1pKT95OZvQfods49t9D7LvQAzmRY89FtzCwA1AB9XtTinOtzzkXT334HOC8LdWQqb4aEO+eGj/y56Zz7BRA0s8ZsHc/MgqQC73vOuU0zbJKzc3O8WnJ9bqYddxB4FDh2AplcvZ+OW0sO30+XANea2T5STYtXmNm/HbPNvM5LoQdwJsOaHwRuTT+/EfiNS7eU57qWY9oRryXV5ueVB4EPpe/4XwQMOec6vSjEzBYdaS8zswtI/b/Myps6fZzvAtudc1+ZZbOcnJtMasnxuWkys9r08zJSc3bvOGaznLyfMqklV+8n59z/dM61O+eWk3pf/8Y591+O2Wxe5yVvZ0PLhJtlWLOZfRHY7Jx7kNR/8H81s92kbgTd7GEtt5vZtUA8XcuHs1ELgJndS+oOeqOZdQCfJ3UjA+fcN4FfkLrbvxsYB/7Yw1puBP6bmcWBCeDmLP2ShNTVzAeBl9PtiwCfA5ZOqydX5yaTWnJ5blqBeyz1oQk+4D7n3M+8eD9lWEvO3k8zWYjzoqHIIiIeKfQmCBGRgqUAFhHxiAJYRMQjCmAREY8ogEVEZmHHmUjqmG2/Om1ioJ1mNnjc16gXhIjIzMzsMmCU1NwgGY8KNLNPA+c65z4y13a6AhYRmcVME0mZ2Slm9pCZPWdmT5jZaTO89Bbg3uPtv6AHYoiIeGAj8F+dc7vM7ELg/wJXHFlpZsuAFcBvjrcjBbCISIbSEye9BfjRtNkmw8dsdjNwv3Mucbz9KYBFRDLnAwbTs7TN5mbgk5nuTEREMpCeLvRVM/tDOPrxVeccWZ9uD64DnspkfwpgEZFZpCeSegpYY2YdZvZR4APAR83sRWArb/zkm5uBH2Q6YZK6oYmIeERXwCIiHlEAi4h4RAEsIuIRBbCIiEcUwCIiHlEAi4h4RAEsIuKR/w9W330dOggnwgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            " les meilleurs parametres du model: {'elasticnet__alpha': 100000.0, 'elasticnet__l1_ratio': 1.0},\n",
            " meilleur score: 0.5830634935583221\n"
          ]
        }
      ],
      "source": [
        "params={'elasticnet__alpha':np.logspace(-5, 5, 10), \n",
        "'elasticnet__l1_ratio':np.linspace(0,1, 8)\n",
        "        }\n",
        "process=preprocessor\n",
        "model3=regression_model(pre=process,  model=ElasticNet(), params=params)\n",
        "print(f\"\\n les meilleurs parametres du model: {model3.best_params_},\\n meilleur score: {model3.best_score_}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U0EzWEbhvTeq",
        "outputId": "664a59e1-cca6-4954-951b-205f6ccaf9e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 362344048084421.6, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 298197007853216.5, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 279105246197780.22, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 328376135945927.7, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 388844176952392.7, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 312265276526199.7, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 257380215461741.53, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 240408107429726.16, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 282634781577024.9, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 335152619217971.9, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 262070426092764.9, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 216446224516772.75, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 201723935974124.94, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 236919241654159.1, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 281333185919365.66, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 211683506025993.56, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 175312854708048.6, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 163043235427992.03, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 191197524227593.97, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 227362532190530.9, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 160936149264991.38, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 133815926494255.1, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124266510463032.69, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 145347156779020.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 173124555043139.5, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 109539966928145.44, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91635026433362.02, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 85103791913561.19, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 99092382142897.28, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 118279575021890.92, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57182929962442.3, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 48094612835759.72, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 44958804851427.49, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52021303176420.16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62041258783267.55, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4243390512615651.5, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3456615941872014.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3291541351887864.5, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3926116341509385.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4499515938370253.5, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3676205882298079.0, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2996511034582203.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2851163035170860.5, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3399121571340303.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3901402343109384.5, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3097608865823970.5, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2526689593927334.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2401920725426268.0, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2861574129247862.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3290668417607609.0, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2507148783708501.0, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2046716001892677.2, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1943475606312687.8, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2313148370725419.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2666705067325880.5, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1904256018452877.0, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1556021644192777.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1475410437655815.8, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1753489692154583.2, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2028679895339980.2, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1288057185288326.5, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1053723989113976.5, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 997102723818875.4, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1182181890310215.5, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1375231272861148.8, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 656828994735275.5, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 538360104529090.9, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 507337626102067.9, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 598838138904231.8, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 703660311787759.5, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.0658572875585212e+16, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.439537284384004e+16, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.381546132307782e+16, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2.8273955776343304e+16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3.1809623396178864e+16, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61263750122520.0, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4401820313939131.0, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4260152447011557.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8523068244161975.0, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7841448514337549.0, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246312840766478.0, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.2910308194785924e+16, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0390895065723266e+16, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0110581019239394e+16, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1966516001591232e+16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3614351686278982e+16, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383540532134876.0, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5996371136280296.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5729645840684405.0, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6843145544527770.0, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7802299200541471.0, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.332434704443342e+16, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.71136291395995e+16, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5.7193732026034584e+16, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6.358070723360334e+16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7.227630235823392e+16, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1449074407093358e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.032999985182035e+16, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8.798323373260022e+16, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9.163495342684118e+16, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.0957788469878709e+17, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4957052723590352e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1917467174203418e+17, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1754360276981616e+17, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.1828191085138242e+17, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.4310263965966926e+17, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.651767031672839e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3291702735987115e+17, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3167553918550389e+17, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3163647405262328e+17, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.582250221129846e+17, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 91951886547776.47, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86758081184102.86, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 84336534518812.8, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 86176126894000.12, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 93864589696630.25, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6719205668343498e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3472798842995413e+17, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3350792665279246e+17, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3340423242725878e+17, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6018872917417178e+17, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 150713720912767.0, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6735678707068006e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3487635484365246e+17, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3365769831631586e+17, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3354908284767614e+17, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.6034936028875018e+17, tolerance: 32072580166786.844\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.673695968747113e+17, tolerance: 33474134458457.46\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3488789430562866e+17, tolerance: 26977772617744.49\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.33669344830519e+17, tolerance: 26734064517264.605\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.3356034896114133e+17, tolerance: 26712258959257.387\n",
            "  positive,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:622: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 1.603618521069787e+17, tolerance: 32072580166786.844\n",
            "  positive,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 14381289.12324392, \n",
            " MAE: 5704219.182509814\n",
            "Moyenne des erreur:  10042754.152876867\n",
            "Score: 0.6146390583298234\n",
            "\n",
            "erreur totale moyenne:  31.576583194839227 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            " les meilleurs parametres du model: {'elasticnet__alpha': 100000.0, 'elasticnet__l1_ratio': 1.0},\n",
            " meilleur score: 0.5562483191140639\n"
          ]
        }
      ],
      "source": [
        "params={'elasticnet__alpha':np.logspace(-5, 5, 10), \n",
        "'elasticnet__l1_ratio':np.linspace(0,1, 8)\n",
        "        }\n",
        "process=preprocessor1\n",
        "model3=regression_model1(pre=process,  model=ElasticNet(), params=params)\n",
        "print(f\"\\n les meilleurs parametres du model: {model3.best_params_},\\n meilleur score: {model3.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_VDaiU15zEn"
      },
      "source": [
        "* ElasticNet obtient aussi en moyenne de bons resultats avec EnergyStarScore, difference de precision de pres de 0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFgYSUXW6GYp"
      },
      "source": [
        "**Les models linéaires obtiennent un meilleur score avec EnergyStarScore**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBESWhRq94Ej"
      },
      "source": [
        "## Les models non linéaires"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIvVXHwS3wAE"
      },
      "source": [
        "### 4. SVR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "bJvp1G0XcXNl",
        "outputId": "38e100dc-c57a-448c-9819-bba5b90a169e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 12433740.669606233, \n",
            " MAE: 6175200.042864243\n",
            "Moyenne des erreur:  9304470.356235238\n",
            "Score: -0.17673855525547677\n",
            "\n",
            "erreur totale moyenne:  31.285554613280443 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "`\n",
            " les meilleurs parametres du model: {'svr__C': 1000.0, 'svr__gamma': 0.1},\n",
            " meilleur score: -0.0757306303829917\n"
          ]
        }
      ],
      "source": [
        "params={'svr__C':np.logspace(-2, 3, 6),\n",
        "        'svr__gamma': np.logspace(-2, 1, 4)}\n",
        "pre=preprocessor\n",
        "model4=regression_model(pre=pre, model=SVR(), params=params)\n",
        "print(f\"`\\n les meilleurs parametres du model: {model4.best_params_},\\n meilleur score: {model4.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y6Tsr35awBue"
      },
      "source": [
        " * Sans EnergyStarScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "qfXS6orXwA9z",
        "outputId": "95cecf24-9a08-4a21-b06c-268d8aeeaf2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 23786192.36708056, \n",
            " MAE: 7147860.58394109\n",
            "Moyenne des erreur:  15467026.475510826\n",
            "Score: -0.05419733330502807\n",
            "\n",
            "erreur totale moyenne:  32.582937537385824 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "`\n",
            " les meilleurs parametres du model: {'svr__C': 1000.0, 'svr__gamma': 0.1},\n",
            " meilleur score: -0.07604229463953885\n"
          ]
        }
      ],
      "source": [
        "params={'svr__C':np.logspace(-2, 3, 6),\n",
        "        'svr__gamma': np.logspace(-2, 1, 4)}\n",
        "pre=preprocessor1\n",
        "model4=regression_model1(pre=pre, model=SVR(), params=params)\n",
        "print(f\"`\\n les meilleurs parametres du model: {model4.best_params_},\\n meilleur score: {model4.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NiHBiXy6fCf"
      },
      "source": [
        "* SVR obtient de meme un meilleur resultat en moyenne avec EnergyStarScore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "462wT8AK36xv"
      },
      "source": [
        "### 5. Ramdom Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZACD41FbBIbv",
        "outputId": "c8f12526-805a-40d5-b1d7-c995bcc0489a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['bootstrap', 'ccp_alpha', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ],
      "source": [
        "RandomForestRegressor().get_params().keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "0BFpg-hj39Eg",
        "outputId": "9801b950-9008-490c-e54c-32b53e76ff35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 3529336.9645682927, \n",
            " MAE: 1415262.0173671239\n",
            "Moyenne des erreur:  2472299.490967708\n",
            "Score: 0.9051881496161317\n",
            "\n",
            "erreur totale moyenne:  28.766948220627576 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAc2ElEQVR4nO3de5SddX3v8fd37rnOhGQIySSBBBAriQgG5FI9KPaYWisUEXF5NLZUltUWrV1WbNepPafHHno53qs9LKXSLkQQYwVFLALqoWo0IJeEIIlcE3KZ3CYDSSaZmd/5Yz+BIUySPZm9929n5v1aa6/Zz2XzfHiS/ckzv/08z46UEpKk2mvIHUCSxisLWJIysYAlKRMLWJIysYAlKZOm3AFGY8mSJen222/PHUOSAGKkLziqj4C3bNmSO4IkHbGjuoAl6WhmAUtSJhawJGViAUtSJhawJGViAUtSJhawJGViAUtSJhawJGViAUtSJhawJGViAUtSJhawJGVyVN+O8khcfOk76d66fdhlndOnseymG2qcSNJ4Ne4KuHvrdi6+6nPDLlt29ZU1TiNpPHMIQpIysYAlKRMLWJIysYAlKRMLWJIysYAlKRMLWJIysYAlKRMLWJIysYAlKRMLWJIysYAlKRMLWJIyqVoBR8S1EbE5IlYOmfcPEfFIRDwYEd+KiI4hyz4eEWsj4lcR8aZq5ZKkelHNI+CvAksOmHcHsDCl9ErgUeDjABHxCuAy4NTiNV+MiMYqZpOk7KpWwCmlHwPbDpj3Hyml/mLyZ8Cc4vmFwNdTSn0ppceBtcBZ1comSfUg5xjwHwDfK553AU8PWbaumPcSEXFFRKyIiBXd3d1VjihJ1ZOlgCPiL4F+4PqRvjaldE1KaXFKaXFnZ2flw0lSjdT8K4ki4r3AW4ALUkqpmL0emDtktTnFPEkas2p6BBwRS4A/B96aUto1ZNEtwGUR0RoR84GTgZ/XMpsk1VrVjoAj4gbgfGBGRKwDPkHprIdW4I6IAPhZSun9KaVVEXET8DCloYkPppQGqpVNkupB1Qo4pfTOYWZ/5RDrfxL4ZLXySFK98Uo4ScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScqkagUcEddGxOaIWDlk3jERcUdErCl+TivmR0R8LiLWRsSDEXFGtXJJUr2o5hHwV4ElB8y7CrgzpXQycGcxDfDbwMnF4wrgS1XMJUl1oWoFnFL6MbDtgNkXAtcVz68DLhoy/19Tyc+AjoiYVa1sklQPaj0GPDOltKF4vhGYWTzvAp4est66Yt5LRMQVEbEiIlZ0d3dXL6kkVVm2D+FSSglIR/C6a1JKi1NKizs7O6uQTJJqo9YFvGn/0ELxc3Mxfz0wd8h6c4p5kjRm1bqAbwGWFs+XAt8eMv89xdkQZwM9Q4YqJGlMaqrWfzgibgDOB2ZExDrgE8DVwE0RcTnwJHBpsfptwJuBtcAu4PerlUuS6kXVCjil9M6DLLpgmHUT8MFqZZGkeuSVcJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUiQUsSZlYwJKUSZYCjog/jYhVEbEyIm6IiLaImB8RyyNibUTcGBEtObJJUq3UvIAjogu4ElicUloINAKXAX8HfDqldBKwHbi81tkkqZZyDUE0ARMiogmYCGwA3gDcXCy/DrgoUzZJqomaF3BKaT3wj8BTlIq3B7gX2JFS6i9WWwd0Dff6iLgiIlZExIru7u5aRJakqsgxBDENuBCYD8wGJgFLyn19SumalNLilNLizs7OKqWUpOrLMQTxRuDxlFJ3SmkfsAw4D+gohiQA5gDrM2STpJrJUcBPAWdHxMSICOAC4GHgbuCSYp2lwLczZJOkmskxBryc0odt9wEPFRmuAT4GfCQi1gLTga/UOpsk1VLT4VepvJTSJ4BPHDD7MeCsDHEkKQuvhJOkTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTCxgScrEApakTMoq4Ig4r5x5kqTylXsE/Pky50mSynTIu6FFxDnAuUBnRHxkyKKplL5MU5J0hA53O8oWYHKx3pQh83fyws3TJUlH4JAFnFL6EfCjiPhqSunJGmWSpHGh3Buyt0bENcAJQ1+TUnpDNUJJ0nhQbgF/A/hn4MvAQPXiSNL4UW4B96eUvlTVJJI0zpR7GtqtEfGBiJgVEcfsf1Q1mSSNceUeAS8tfn50yLwELKhsHEkaP8oq4JTS/GoHkaTxpqwCjoj3DDc/pfSvlY0jSeNHuUMQZw553gZcANwHWMCSdITKHYL4k6HTEdEBfL0qiSRpnDjS21E+BzguLEmjUO4Y8K2UznqA0k14fgO4qVqhJGk8KHcM+B+HPO8HnkwpratCHkkaN8oagihuyvMIpTuiTQP2VjOUJI0H5X4jxqXAz4G3A5cCyyPC21FK0iiUOwTxl8CZKaXNABHRCfwAuLlawSRprCv3LIiG/eVb2DqC10qShlHuEfDtEfF94IZi+h3AbdWJJEnjw+G+E+4kYGZK6aMRcTHwm8WinwLXVzucJI1lhzsC/gzwcYCU0jJgGUBELCqW/W5V00nSGHa4cdyZKaWHDpxZzDuhKokkaZw4XAF3HGLZhEoGkaTx5nAFvCIi3nfgzIj4Q+DeI91oRHRExM0R8UhErI6Ic4pv2bgjItYUP6cd6X9fko4GhxsD/jDwrYh4Fy8U7mKgBfi9UWz3s8DtKaVLIqIFmAj8BXBnSunqiLgKuAr42Ci2IUl17ZAFnFLaBJwbEa8HFhazv5tSuutINxgR7cDrgPcW29gL7I2IC4Hzi9WuA36IBSxpDCv3fsB3A3dXaJvzgW7gXyLiNEpH1h+i9IHfhmKdjcDM4V4cEVcAVwDMmzevQpEkqfZyXM3WBJwBfCmldDqlewtfNXSFlFLihdtfcsCya1JKi1NKizs7O6seVpKqJUcBrwPWpZSWF9M3UyrkTRExC6D4ufkgr5ekMaHmBZxS2gg8HRGnFLMuAB4GbgGWFvOWAt+udTZJqqVy7wVRaX8CXF+cAfEY8PuU/jG4KSIuB56kdNtLSRqzshRwSul+SqezHeiCWmeRpFy8paQkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1ImFrAkZWIBS1Im47aAU0r8amMvtz7wDN29fbnjSBqHmnIHyOXHj27h/nU7aAh4ctsu3rJoVu5IksaZcXkEvH3XXh5Yv4NTZ0/lD86bT3tbMz9a000ickeTNI6MywJe/tg2GiM4Z8F0JrU2cfaJx7Bj1z52HXNy7miSxpFxV8ADTW08uqmXRXPamdRaGoE5qXMy0ye30Dvr1ZnTSRpPxl0B72k/gQScMnPK8/MigkWz29k3cQZrNz+bL5ykcWXcFfDuaQuY3NrEsVNaXzR/QeckAL6/amOOWJLGoXFVwLv3DrBn6jwWdE4i4sUfuE1pa6bl2Y3cvtICllQb46qA71m7hdTYzIIZk4ZdPmH7Wh5a38P6HbtrnEzSeJStgCOiMSJ+GRHfKabnR8TyiFgbETdGREult/nKOe10PPVj5kybOOzytp4nAfjJ2i2V3rQkvUTOI+APAauHTP8d8OmU0knAduDySm9w5tQ2pmx6gMaG4c/3bd69jWMmtfDTx7ZWetOS9BJZCjgi5gC/A3y5mA7gDcDNxSrXARfVPBfwmvnHsPyxbaSUar15SeNMriPgzwB/DgwW09OBHSml/mJ6HdA13Asj4oqIWBERK7q7uyse7OwF01m/YzfrtjsOLKm6al7AEfEWYHNK6d4jeX1K6ZqU0uKU0uLOzs4KpysVMMBPf+0whKTqynEEfB7w1oh4Avg6paGHzwIdEbH/5kBzgPUZsnHysZPpmNjMiie35di8pHGk5gWcUvp4SmlOSukE4DLgrpTSu4C7gUuK1ZYC3651NoCGhuC0OR3c//SOHJuXNI7U03nAHwM+EhFrKY0JfyVXkNPndbBm87P07tmXK4KkcSDr/YBTSj8Eflg8fww4K2ee/V41t4OU4KF1PZx70ozccSSNUfV0BFw3XjW3A4BfOgwhqYos4GF0TGxh/oxJjgNLqioL+CBeNbeDB9dZwJKqxwI+iIVd7Wza2cfm3j25o0gaoyzgg1g4eyoAq9bvzJxE0lhlAR/EqV3tADy0vidzEkljlQV8EJNbm1gwYxIrLWBJVWIBH8LCrnYLWFLVWMCHsKirnWd69rD12b7cUSSNQRbwIZzaVfogbuUzfhAnqfIs4EM4dXbpgziHISRVgwV8CO0Tmjl++kQLWFJVWMCHsbCr3VPRJFWFBXwYi7raWbd9N9uf25s7iqQxxgI+jIXFOPAqP4iTVGEW8GEsLM6EcBhCUqVZwIfRMbGFOdMmsPIZC1hSZVnAZVjkFXGSqsACLsPCrnae3LqLnt1+R5ykyrGAy7Cwa/8HcR4FS6ocC7gM++8N7DCEpEqygMswfXIrs9vbWOnN2SVVkAVcJm9NKanSLOAyLexq57Etz9G7xw/iJFWGBVymRcUHcQ97RZykCrGAy3SqV8RJqjALuEzHTmlj5tRW7wkhqWIs4BFY5K0pJVWQBTwCp85u59fdz7Jrb3/uKJLGAAt4BBZ1tZOSH8RJqgwLeAT2X5LsMISkSmjKHaCePPLIal57wZJhl3VOn8Y3b/waMya3ekWcpIqwgIfYN5i4+KrPDbts2dVXEhEs7JrqFXGSKsIhiBFa1NXOms297N47kDuKpKOcBTxCC7vaGUyweqPDEJJGxwIeof0fxDkMIWm0LOARmt3exozJrfzyqR25o0g6ytW8gCNibkTcHREPR8SqiPhQMf+YiLgjItYUP6fVOls5IoJXH9/BvU9uzx1F0lEuxxFwP/BnKaVXAGcDH4yIVwBXAXemlE4G7iym69Li44/hqW272Ny7J3cUSUexmhdwSmlDSum+4nkvsBroAi4EritWuw64qNbZyvXqE0oH5/c+4VGwpCOXdQw4Ik4ATgeWAzNTShuKRRuBmQd5zRURsSIiVnR3d9ck54EWzm6ntamBFQ5DSBqFbAUcEZOBbwIfTim96JyulFIC0nCvSyldk1JanFJa3NnZWYOkL9XS1MBpczosYEmjkqWAI6KZUvlen1JaVszeFBGziuWzgM05spXrrPnHsHJ9D8/2eWc0SUcmx1kQAXwFWJ1S+tSQRbcAS4vnS4Fv1zrbSJxz4nQGBhO/eHxb7iiSjlI5joDPA94NvCEi7i8ebwauBn4rItYAbyym69arj59GS2MDP/n1ltxRJB2lan4znpTSPUAcZPEFtcwyGm3NjZw+r4OfPrY1dxRJRymvhBuFc06czqpndtKzy6+qlzRy3o6yTMPdK7hv8izSb1zCPWu38DuvnJUpmaSjlQVcpuHuFTw4mPjCHSv54a82W8CSRswCHoWGhqBt59P88NF2BgcTDQ0HG9qWpJdyDHiUJux4gu7ePh7e4P2BJY2MBTxKbT1PAXDXI3V93YikOmQBj1Jj/y7OmNfB7Ss35o4i6ShjAVfAmxfN4uENO3ly63O5o0g6iljAFfCmU48D4HseBUsaAQu4AuYeM5FXzmnntoc2HH5lSSpYwBXy1tNm8+C6HtZs6s0dRdJRwgKukItO76KpIbj5vnW5o0g6SljAFTJjcivnn3Isy+5bT//AYO44ko4CFnAFvX3xHLp7+/jB6k25o0g6CljAFXTBy4+lq2MC//KfT+SOIukoYAFXUFNjA0vPPZ7lj29j1TM9ueNIqnMWcIW9Y/E8JrY08n9/9FjuKJLqnAVcYe0Tm1l67gnc+uAznpIm6ZAs4Cp432sXMLG5kc/cuSZ3FEl1zAKugmMmtXD5b87nuw9uYMUTfmuypOFZwFXy/vNPZFZ7G3996yoGBlPuOJLqkN+IMUrDfVfcfq3zXsXKma/l2nse532vW1DjZJLqnQU8SsN9V9x+37z6St54/iX8w3/8ite/vJOTjp1S43SS6plDEFUUwN9evJDJrU184Pr72LW3P3ckSXXEAq6yY6e08bnLTmft5mf56DceZNDxYEkFC7gGfvPkGXxsycv57kMb+NvbVpOSJSzJMeCaueJ1C3hmx26+fM/jtDU38mf/9WVE+DX20nhmAddIRPCJ3z2Vvv5BvnD3Wrbt2stf/+6ptDT5S4g0XlnAVTTcKWoJmDLnHL62HNZs6uWL73o1nVNa8wSUlJUFXEWHOkXt3675PA81v5m3fuEern7bK/kvL+uscTpJufn7byaTtq3h5vefy8SWRpZe+3M+cuP9bH9ub+5YkmrII+BMHnlkNX/0nneQopGps89k2eAA//6LX3Ncz8Pc+U9/wYSWxtwRJVWZBZzJgcMTW57t4z/XbuGJpjM4/x/v5n2vXcA7zpzLlLbmjCklVZMFXCdmTG7lwld1cfWf/xE7zn0H/+u7fXzylgeY1L2KKZsepGnvTjqnT2PZTTfkjiqpQizgOtO3cQ3vv+h8Nu3cwy+f2sGaptN57rjTmTd9Ik/8fBm79w44PCGNERZwnZo5tY0lC4/jvD3TeWh9D49s7KX3xCWc+ckf8NsLj+O3Fx3HuSfOoK258mV88aXvpHvr9mGXHeoo/GCv88hdGp4FXOemtDVz7okzOGfBdG744t/z+v92Jbc9tJFv3LuO1qYGzjtpBq9/+bGcs2A6J3ZOqsjVdd1bt7/k9LmUEgODiW996mNs6NnNnn2DpJRobmygtamB5sYGNm/r4W3DnHa37OorR51JGovqroAjYgnwWaAR+HJK6erMkepCRNDWu56/v+Q0/uaihfz88W3cuXozdz1SegB0TGzm9LkdnHLcVF42czInzJjEzKltHDullebG4c843LNvgO7ePjb39tHd20d37x56Zr+GO1dvYtfeAZ7b28+uvQPs6htgICU44wrO+d93DR9y8Qf50o9+zYTmRiY0NzKptZFJLU3snLWYb6x4muPa25g5tfSY2tbkpdga9+qqgCOiEfgn4LeAdcAvIuKWlNLDeZPVh4NdWfeK4+bx3g//d1Y8uY0Hnu7hnrVb2Dfw4hv+TGhuZEJLI82Nwd7+QfqKx7Df1jH7TB7b8hwTW0oFeszEFia2NtHa1MDDdy3jY3/6x7Q1N9AQQV//IPsGBtnbP8inv3Qt889+E7v3DbB77wA7du1j3fbd9M05h4/e/OCLNtHW3PB8Gc+c2sbMKa0c197GscXzSa1NtDU30tbcwITmRtqaG2lubKAhoCGCCCpW4CklUirty5RS8RMSpfkU03sHBunrH6BvX+nnnn2D7Nk3QF9/6edLpot1+op5t95+B7v79hEkSINEKn4yyKTWZt516dtobW6grbmR1qYGWptK//+tTcV0Mf/5ec0vrNPS2FD3/6Dt389Q2tf75714urTf9z9/8etfumzofycB/QOp+LMo7fvd+wboK/4sdu994c+lb98gX77uep7ds4/U0EhqaCo9ooG21lZ+8pkPVGV470B1VcDAWcDalNJjABHxdeBCwALm4FfWLbv6Si49cy6Xnjm3tN7AIE9seY6nt+9i084+Nu3cw3N9/ezeN8De/sEXvbEntDTSOaW19JjcyrFTW7n4ot/jbVd9dtgMT29+kHeeNW/YZdf+1S84/5R3v2T+zX//Eb729RvZ2LOHTb19bN655/nnm3bu4cF1O9jYs4e+/sER7Y8oyrihKOOGgCCeL84EMKRIDyzYWimVZiO9bbOY2NnxfJ6BlBhMMDiY6B0Y4P/c8eiottPYUCrg/TW8v4+D558ccvn+6RfKbZiyO9QyXti/Q5fVremnA6X91tQQNDUGjRH09Gylv0a3jY16ujViRFwCLEkp/WEx/W7gNSmlPx6yzhXAFcXkKcCvRriZGcCWCsStFvMduXrOBvWdr56zwdGR75GU0vDfT3YQ9XYEfFgppWuAa4709RGxIqW0uIKRKsp8R66es0F956vnbHDU5BtR+UL93QtiPTB3yPScYp4kjTn1VsC/AE6OiPkR0QJcBtySOZMkVUVdDUGklPoj4o+B71M6De3alNKqCm/miIcvasR8R66es0F956vnbDBG89XVh3CSNJ7U2xCEJI0bFrAkZTJmCzgilkTEryJibURcNczy1oi4sVi+PCJOqLN8742I7oi4v3j8YQ2zXRsRmyNi5UGWR0R8rsj+YEScUUfZzo+IniH77a9qla3Y/tyIuDsiHo6IVRHxoWHWybL/ysyWbf9FRFtE/DwiHijy/Y9h1sn2vi0z38jet6XLA8fWg9IHeL8GFgAtwAPAKw5Y5wPAPxfPLwNurLN87wW+kGn/vQ44A1h5kOVvBr5H6aKqs4HldZTtfOA7Gf/uzQLOKJ5PAR4d5s82y/4rM1u2/Vfsj8nF82ZgOXD2AevkfN+Wk29E79uxegT8/CXNKaW9wP5Lmoe6ELiueH4zcEHU7mL6cvJlk1L6MbDtEKtcCPxrKvkZ0BERs+okW1YppQ0ppfuK573AaqDrgNWy7L8ys2VT7I9ni8nm4nHgWQLZ3rdl5huRsVrAXcDTQ6bX8dK/aM+vk1LqB3qA6TVJV14+gLcVv6LeHBFzh1meS7n5czmn+DXxexFxaq4Qxa/Hp1M6Uhoq+/47RDbIuP8iojEi7gc2A3eklA667zK8b8vJByN4347VAh4LbgVOSCm9EriDF/7V16HdBxyfUjoN+Dzw7zlCRMRk4JvAh1NKO3NkOJjDZMu6/1JKAymlV1G6CvasiFhYy+0fThn5RvS+HasFXM4lzc+vExFNQDuwtSbpysiXUtqaUuorJr8MvLpG2cpRt5eMp5R27v81MaV0G9AcETNqmSEimikV3PUppWXDrJJt/x0uWz3sv2LbO4C7gQPvr5Dzffu8g+Ub6ft2rBZwOZc03wIsLZ5fAtyVilH0esh3wJjgWymN19WLW4D3FJ/mnw30pJQ25A4FEBHH7R8TjIizKP0dr9kbtNj2V4DVKaVPHWS1LPuvnGw5919EdEZER/F8AqX7gj9ywGrZ3rfl5Bvp+7auLkWulHSQS5oj4n8CK1JKt1D6i/hvEbGW0oc6l9VZvisj4q1Af5HvvbXKFxE3UPo0fEZErAM+QekDB1JK/wzcRumT/LXALuD36yjbJcAfRUQ/sBu4rIb/sAKcB7wbeKgYKwT4C2DekIy59l852XLuv1nAdVH6YoYG4KaU0nfq5X1bZr4RvW+9FFmSMhmrQxCSVPcsYEnKxAKWpEwsYEnKxAKWNK7FYW7wdMC6nx5yo51HI2LHqLbtWRCSxrOIeB3wLKX7c5R95V1E/AlwekrpD4502x4BSxrXhrvBU0ScGBG3R8S9EfH/IuLlw7z0ncANo9n2mLwQQ5JG6Rrg/SmlNRHxGuCLwBv2L4yI44H5wF2j2YgFLElDFDcrOhf4xpA7XbYesNplwM0ppYHRbMsClqQXawB2FHc9O5jLgA9WYkOSpEJxi87HI+Lt8PxXSJ22f3kxHjwN+Olot2UBSxrXihs8/RQ4JSLWRcTlwLuAyyPiAWAVL/7GmsuAr1fiJkWehiZJmXgELEmZWMCSlIkFLEmZWMCSlIkFLEmZWMCSlIkFLEmZ/H8UfmfMYAlXdwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "`\n",
            " les meilleurs parametres du model: {'randomforestregressor__criterion': 'squared_error', 'randomforestregressor__n_estimators': 300},\n",
            " meilleur score: 0.7739650646758411\n"
          ]
        }
      ],
      "source": [
        "#max_depth = [5,15,30,45]\n",
        "#max_depth = [None]\n",
        "#min_samples_leaf = [1,3,5,10]\n",
        "#max_features = ['auto', 'sqrt']\n",
        "n_estimators = [100,200,300]\n",
        "criterion=[\"squared_error\", \"absolute_error\", \"poisson\"]\n",
        "dic={}\n",
        "dic['randomforestregressor__n_estimators']=n_estimators\n",
        "dic[\"randomforestregressor__criterion\"]=criterion\n",
        "#dic['randomforestregressor__min_samples_leaf']=min_samples_leaf\n",
        "#dic['randomforestregressor__max_features']=max_features\n",
        "#dic['randomforestregressor__max_depth']=max_depth\n",
        "#params = dict(n_estimators=n_estimators,criterion=criterion, min_samples_leaf=min_samples_leaf , max_features=max_features , max_depth=max_depth)\n",
        "\n",
        "pre=preprocessor\n",
        "model5=regression_model(pre=pre, model=RandomForestRegressor(), params=dic)\n",
        "print(f\"`\\n les meilleurs parametres du model: {model5.best_params_},\\n meilleur score: {model5.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_xhxgWLwVSb"
      },
      "source": [
        "* Sans EnergyStarScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "id": "oIjgGwpowaY7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "outputId": "4b3fe4f6-1dfe-482c-cdc9-07a0dc657b93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE: 6080703.555285042, \n",
            " MAE: 1614771.324976956\n",
            "Moyenne des erreur:  3847737.4401309993\n",
            "Score: 0.931106266261433\n",
            "\n",
            "erreur totale moyenne:  29.85496865556118 \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcbklEQVR4nO3df5TddX3n8ed7fmUy+TlJhhgykcQSsIhYcVSU1lXQGltXXNe6sK2ipWW7UvzVakXPHk971nN0deuPdqvNAgVPLRQRK60u1gr+2kUwgvIbEvk5MWQmgRCSQJJJ3vvH/YZOh/mVydz7uZl5Ps6Zk3s/3++93xc5yYtvPvfz/d7ITCRJjddSOoAkzVYWsCQVYgFLUiEWsCQVYgFLUiFtpQMciXXr1uV1111XOoYkTSRGGzyqz4C3bdtWOoIkTdlRXcCSdDSzgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgo5qm9HOVVveds5DG5/fNRtPUu7ueaqKxqcSNJsNCsLeHD747zlw58fdds1n3hPg9NImq2cgpCkQixgSSrEApakQixgSSqkbgUcEZdGxEBE3DFi/MKIuCci7oyI/zFs/KKI2BQR90bE6+uVS5KaRT1XQVwG/CXwpUMDEfEa4CzgRZm5NyKOqcZPAs4GXgAcC/xLRJyQmQfqmE+SiqrbGXBmfh94bMTwfwU+kZl7q30GqvGzgCszc29mPgBsAl5Wr2yS1AwaPQd8AvBrEXFTRHwvIl5aja8EHhm2X3819iwRcX5EbIiIDYODg3WOK0n10+gCbgOWAKcBHwSuiog4nDfIzPWZ2ZeZfT09PfXIKEkN0egC7geuyZqbgYPAMmAzsGrYfr3VmCTNWI0u4H8AXgMQEScAHcA24Frg7IiYExFrgLXAzQ3OJkkNVbdVEBFxBfBqYFlE9AMfAy4FLq2Wpu0Dzs3MBO6MiKuAu4Ah4AJXQEia6epWwJl5zhibfmeM/T8OfLxeeSSp2XglnCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiF1K+CIuDQiBiLijlG2/VFEZEQsq55HRHw+IjZFxG0RcWq9cklSs6jnGfBlwLqRgxGxCvh14OFhw28A1lY/5wNfqGMuSWoKdSvgzPw+8Ngomz4DfAjIYWNnAV/Kmh8BiyNiRb2ySVIzaOgccEScBWzOzJ+N2LQSeGTY8/5qTJJmrLZGHSgiuoCPUJt+OJL3OZ/aNAXPfe5zpyGZJJXRyDPgXwLWAD+LiAeBXuCWiHgOsBlYNWzf3mrsWTJzfWb2ZWZfT09PnSNLUv00rIAz8/bMPCYzV2fmamrTDKdm5qPAtcA7qtUQpwFPZOaWRmWTpBLquQztCuBG4MSI6I+I88bZ/ZvA/cAm4H8D765XLklqFnWbA87McybYvnrY4wQuqFcWSWpGXgknSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYVYwJJUiAUsSYXUrYAj4tKIGIiIO4aNfSoi7omI2yLiaxGxeNi2iyJiU0TcGxGvr1cuSWoW9TwDvgxYN2Ls28DJmXkKcB9wEUBEnAScDbyges1fRURrHbNJUnF1K+DM/D7w2Iixf87Moerpj4De6vFZwJWZuTczHwA2AS+rVzZJagYl54B/F/g/1eOVwCPDtvVXY88SEedHxIaI2DA4OFjniJJUP0UKOCI+CgwBXz7c12bm+szsy8y+np6e6Q8nSQ3S1ugDRsQ7gTcCZ2ZmVsObgVXDduutxiRpxmroGXBErAM+BLwpM/cM23QtcHZEzImINcBa4OZGZpOkRqvbGXBEXAG8GlgWEf3Ax6itepgDfDsiAH6UmX+QmXdGxFXAXdSmJi7IzAP1yiZJzaBuBZyZ54wyfMk4+38c+Hi98khSs/FKOEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqxAKWpEIsYEkqpG4FHBGXRsRARNwxbGxJRHw7IjZWv3ZX4xERn4+ITRFxW0ScWq9cktQs6nkGfBmwbsTYh4HvZOZa4DvVc4A3AGurn/OBL9QxlyQ1hboVcGZ+H3hsxPBZwOXV48uBNw8b/1LW/AhYHBEr6pVNkppBo+eAl2fmlurxo8Dy6vFK4JFh+/VXY88SEedHxIaI2DA4OFi/pJJUZ8U+hMvMBHIKr1ufmX2Z2dfT01OHZJLUGI0u4K2HphaqXweq8c3AqmH79VZjkjRjNbqArwXOrR6fC3x92Pg7qtUQpwFPDJuqkKQZqa1ebxwRVwCvBpZFRD/wMeATwFURcR7wEPC2avdvAr8BbAL2AO+qVy5JahZ1K+DMPGeMTWeOsm8CF9QriyQ1I6+Ek6RCLGBJKsQClqRCLGBJKsQClqRCJlXAEXH6ZMYkSZM32TPgv5jkmCRpksZdBxwRrwBeCfRExAeGbVoItNYzmCTNdBNdiNEBzK/2WzBsfCfw1nqFkqTZYNwCzszvAd+LiMsy86EGZZKkWWGylyLPiYj1wOrhr8nMM+oRSpJmg8kW8FeALwIXAwfqF0eSZo/JFvBQZvo9bZI0jSa7DO0fI+LdEbGi+mbjJRGxpK7JJGmGm+wZ8KGbqH9w2FgCz5veOJI0e0yqgDNzTb2DSNJsM6kCjoh3jDaemV+a3jiSNHtMdgripcMed1L7VotbAAtYkqZoslMQFw5/HhGLgSvrkkiSZomp3o5yN+C8sCQdgcnOAf8jtVUPULsJzy8DV9UrlCTNBpOdA/70sMdDwEOZ2V+HPJI0a0xqCqK6Kc891O6I1g3sq2coSZoNJvuNGG8DbgZ+C3gbcFNEeDtKSToCk52C+Cjw0swcAIiIHuBfgKvrFUySZrrJroJoOVS+le2H8VpJ0igmW6LXRcS3IuKdEfFO4BvAN6d60Ih4f0TcGRF3RMQVEdEZEWsi4qaI2BQRfx8RHVN9f0k6GoxbwBFxfEScnpkfBP4aOKX6uRFYP5UDRsRK4D1AX2aeTG1Z29nAJ4HPZObxwOPAeVN5f0k6Wkx0BvxZat//RmZek5kfyMwPAF+rtk1VGzA3ItqALmALcAb/Oqd8OfDmI3h/SWp6ExXw8sy8feRgNbZ6KgfMzM3U1hU/TK14nwB+AuzIzKFqt35g5Wivj4jzI2JDRGwYHBycSgRJagoTFfDicbbNncoBI6IbOIvapczHAvOAdZN9fWauz8y+zOzr6emZSgRJagoTFfCGiPj9kYMR8XvUzlqn4rXAA5k5mJn7gWuA04HF1ZQEQC+weYrvL0lHhYnWAb8P+FpE/Db/Wrh9QAfwH6Z4zIeB0yKiC3iK2q0tNwA3AG+ldpe1c4GvT/H9JemoMG4BZ+ZW4JUR8Rrg5Gr4G5l5/VQPmJk3RcTV1O4nPATcSm1FxTeAKyPiv1djl0z1GJJ0NJjs/YBvoHaGOi0y82PAx0YM3w+8bLqOIUnNzqvZJKkQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJakQC1iSCrGAJamQWVnAQx0L+N59g3zj9i3s2TdUOo6kWWpSX0s/0zy25kwG+p/gQCZL53Vw2vOWlo4kaRaadWfAAzufZu+CXvpWd3Pcki7u/MVODh7M0rEkzUKzroC/cfsWiOCE5Qt4Ye8idu0d4oHtu0vHkjQLzboC/qfbttC+ZxtL5nWwZuk85ra3snHrrtKxJM1Cs6qAdz69n00Du+h67D4AWlqCYxd3snXn04WTSZqNihRwRCyOiKsj4p6IuDsiXhERSyLi2xGxsfq1e7qPu7CznR9/9LXM33rbM2PLF3ay46n9PL3/wHQfTpLGVeoM+HPAdZn5fOBFwN3Ah4HvZOZa4DvV82nX0dZCy8H9zzxfvrATwLNgSQ3X8AKOiEXAq4BLADJzX2buAM4CLq92uxx4cyPyLF8wB4CtT+5txOEk6RklzoDXAIPA30TErRFxcUTMA5Zn5pZqn0eB5aO9OCLOj4gNEbFhcHDwiMPMaW9l8dx2BjwDltRgJQq4DTgV+EJmvhjYzYjphsxMYNTFuZm5PjP7MrOvp6dnWgItX9jJ1p2eAUtqrBIF3A/0Z+ZN1fOrqRXy1ohYAVD9OtCoQMsWdLBr7xB7h/wgTlLjNLyAM/NR4JGIOLEaOhO4C7gWOLcaOxf4eqMydXd1APD4nv0T7ClJ06fUvSAuBL4cER3A/cC7qP3P4KqIOA94CHhbo8IcKuAdu/c16pCSVKaAM/OnQN8om85sdBaARXPbCTwDltRYs+pKuLG0tgQL57bz+B7PgCU1jgVc6e6ygCU1lgVcWdzVwY49+0df+yZJdWABV7q72hk6mBzomF86iqRZwgKuHFoJMdQ57fcAkqRRWcCVRV3tAAzNWVg4iaTZwgKuzO9oI6L2hZ2S1AgWcKWlJVgwp40DcyxgSY1hAQ+zoLOdoQ6nICQ1hgU8zMK5ngFLahwLeJgFne0caJ/PvqGDpaNImgUs4GEWdrZBBI8+4c3ZJdWfBTzMws7aUrT+x/cUTiJpNrCAh1nQWbs5XP+OpwonkTQbWMDDLOhsh0z6H7eAJdWfBTxMa0vQun83my1gSQ1gAY/Qunenc8CSGsICHqFt35Nsdg5YUgNYwCO07t3Jo088zdAB1wJLqi8LeIS2fU8ydDDZ+uTe0lEkzXAW8Ahte3cC+EGcpLqzgEdo3fck4MUYkurPAh6hdW+tgD0DllRvFvAILXmAZfPneDGGpLqzgEfR2z3XpWiS6s4CHsXK7rnOAUuqu2IFHBGtEXFrRPxT9XxNRNwUEZsi4u8joqNUtt7uufxix9McPJilIkiaBUqeAb8XuHvY808Cn8nM44HHgfOKpAJ6F89l34GDDO5yLbCk+ilSwBHRC/wmcHH1PIAzgKurXS4H3lwiG0BvdxeAH8RJqqtSZ8CfBT4EHLredymwIzOHquf9wMoSwaA2BwyuBZZUXw0v4Ih4IzCQmT+Z4uvPj4gNEbFhcHBwmtPVrFxcK2BXQkiqpxJnwKcDb4qIB4ErqU09fA5YHBFt1T69wObRXpyZ6zOzLzP7enp66hJw3pw2urvanYKQVFcNL+DMvCgzezNzNXA2cH1m/jZwA/DWardzga83Ottwvd1dXg0nqa6aaR3wnwAfiIhN1OaELykZpte1wJLqrG3iXeonM78LfLd6fD/wspJ5hlu5eC433DtAZlJbpCFJ06uZzoCbSm/3XJ7ef5Dtu/eVjiJphrKAx7DStcCS6swCHkNvtRbYD+Ik1YsFPAYvxpBUbxbwGBZ2trOws82LMSTVjQU8jt7uLueAJdWNBTwO7wssqZ4s4HH0ds9l8+NPkel9gSVNPwt4HCsXz2X3vgPs2LO/dBRJM5AFPI5D9wX2gzhJ9WABj6PXpWiS6sgCHse/FrBnwJKmnwU8jkVz25nX0WoBS6oLC3gcEeFaYEl1YwFPYNUS1wJLqg8LeALHLZ3Hg9t3uxZY0rSzgCewemkXT+8/yNade0tHkTTDWMATWL1sHgAPbt9dOImkmcYCnsDqpVUBb7OAJU0vC3gCKxZ10t4aPLjdD+IkTS8LeAJtrS2sWtLlGbCkaWcBT8LqaiWEJE0nC3gSVi+dx0Pb97gUTdK0soAnYfWyLp7af8ClaJKmlQU8Cb/UMx+Anw/uKpxE0kxiAU/C2uW1Ar5v65OFk0iaSSzgSeiZP4dFc9u5b6tnwJKmT8MLOCJWRcQNEXFXRNwZEe+txpdExLcjYmP1a3ejs40lIjhh+Xw2DXgGLGn6lDgDHgL+KDNPAk4DLoiIk4APA9/JzLXAd6rnTWPt8gXct3WXKyEkTZuGF3BmbsnMW6rHTwJ3AyuBs4DLq90uB97c6GzjWXvMfJ54aj+DT7oSQtL0KDoHHBGrgRcDNwHLM3NLtelRYPkYrzk/IjZExIbBwcGG5AQ4YfkCADYOOA8saXoUK+CImA98FXhfZu4cvi1r/84f9d/6mbk+M/sys6+np6cBSWtcCSFpuhUp4Ihop1a+X87Ma6rhrRGxotq+AhgokW0sPfPnsHReB3f9YufEO0vSJJRYBRHAJcDdmfnnwzZdC5xbPT4X+Hqjs40nIjh55SJu3/xE6SiSZogSZ8CnA28HzoiIn1Y/vwF8AnhdRGwEXls9byqn9C5i48Auntp3oHQUSTNAW6MPmJk/BGKMzWc2MsvheuHKRRw4mNy1ZScvOa5plilLOkp5JdxheGHvIgDucBpC0jSwgA/DcxZ2smz+HG7rt4AlHTkL+DBEBKf0LuK2/h2lo0iaARo+B9zs7rnnbn7tzHXPGu9Z2s01V13BS47r5vp7Bti+ay9L588pkFDSTGEBj7D/YPKWD3/+WePXfOI9AJz2vKUA3PzAY7zhhSsamk3SzOIUxGE6pXcRXR2t3Hj/9tJRJB3lLODD1N7aQt/qJdz4cwtY0pGxgKfgFc9bysaBXd4ZTdIRsYCn4FePXwbA9+5r3N3YJM08FvAUnLxyIccu6uS6O7ZMvLMkjcECnoKI4PUnP4fvb9zGrr1DpeNIOkpZwFP0hpNXsG/oIDfc01R3zZR0FLGAp+glx3XTs2AO/3Dr5tJRJB2lLOApam0JznnpKq6/d4CHt+8pHUfSUcgCPgL/+eXH0RLB3970UOkoko5CFvAReM6iTta94DlccfPD7Nizr3QcSUcZC/gIXXjm8ezeO8RfXL+pdBRJRxkL+Ag9/zkL+U8vXcWXbnyQTX5lvaTDYAFPg/e/7gTmzWnjwitu5en9fl+cpMnxdpSTNNZ9gqF2r+DP/NnneNdlP+ZPvnobf/62X6G1ZayvvZOkGgt4ksa6TzDU7hX8mucfwwdffyKf+ta9AHzyP55CZ3trIyNKOspYwNPogtccD8CnvnUv923dxafeegonr1xUOJWkZuUc8DS74DXHc+k7+xjY+TT//i9/yAV/dws3/nw7mVk6mqQm4xlwHZzx/OVc/8ev5gvf/Tl/d9NDfOO2LTyvZx5vetGxvPaXl/OCYxcS4RyxNNtZwNNgvA/otj/0EMtPfT2bnzyJzw7s4rP/spHWfbvo3PEAT9zz/ziu6wAt+W9XThz6AlBJM5sFPA3G+4DuT9/xWt7+++8GYM++IR7cvof7B+fx8NwFtB3zQra2BKuWdLFm6TxWL+tiQWf7M18AKmlms4AbqKujjZNWLOSkFQsZOniQT37kvZz+rv/GA9t288C23XAvLJvfwd6Vr2DDg4/x4ud2u5xNmsGaroAjYh3wOaAVuDgzP1E4Ul20tbQw9Iu7efWJx/DvTkge37P/mSLetuJU3vrFG1nc1c6r1vbw0jVL6DuumxOWL7CQpRmkqQo4IlqB/wW8DugHfhwR12bmXWWT1VdEsGReB0vmdfCS47q5+lN/zEWf/iLX3zPADzZu49qf/QKAue2tHH/MfNYun8+q7i6OWTiHYxZ0csyCOSya286c9hbmtLUyp62FOW0ttLW6yKVZjbcqZqxN462jGff9xn3dONvGeeVUF/WM/d82tWNN9ffk4EHYs3+I3XsPsHvvELv3DbFn7wF27xvif/7FF9n51H4OtrSRre1kSzvZ0kZGCwtb9/Ojv75onKMenqYqYOBlwKbMvB8gIq4EzgJmdAGP1HJgH2885VjeeMqxZCaPPPYUGx56jDs272TjwJP8303b2Lpz4m9kbolauR86Z46AZ54Fo46PtzijHn9Zx/17PM3Ha3SB6SjVc9ozD1sC2ltbaGsNWiN4Ysu903qoaKb1qRHxVmBdZv5e9fztwMsz8w+H7XM+cH719ERgKr8jy4BtRxh3OjRLDmieLM2SA8wymmbJAc2TZTI5tmXms5ZKNdsZ8IQycz2w/kjeIyI2ZGbfNEU66nNA82RplhxglmbOAc2T5UhyNNsk4WZg1bDnvdWYJM04zVbAPwbWRsSaiOgAzgauLZxJkuqiqaYgMnMoIv4Q+Ba1ZWiXZuaddTjUEU1hTKNmyQHNk6VZcoBZRtMsOaB5skw5R1N9CCdJs0mzTUFI0qxhAUtSIbOqgCNiXUTcGxGbIuLDBXNcGhEDEXFHqQxVjlURcUNE3BURd0bEewtm6YyImyPiZ1WWPy2VpcrTGhG3RsQ/Fc7xYETcHhE/jYgNhbMsjoirI+KeiLg7Il5RIMOJ1e/FoZ+dEfG+RucYluf91Z/XOyLiiojoPKzXz5Y54Ooy5/sYdpkzcE6Jy5wj4lXALuBLmXlyo48/LMcKYEVm3hIRC4CfAG8u9HsSwLzM3BUR7cAPgfdm5o8anaXK8wGgD1iYmW8skaHK8SDQl5nFLziIiMuBH2TmxdUqpa7M3FEwTyu1Zaovz8yHChx/JbU/pydl5lMRcRXwzcy8bLLvMZvOgJ+5zDkz9wGHLnNuuMz8PvBYiWOPyLElM2+pHj8J3A2sLJQlM3NX9bS9+ilydhARvcBvAheXOH4ziohFwKuASwAyc1/J8q2cCfy8RPkO0wbMjYg2oAv4xeG8eDYV8ErgkWHP+ylUNs0oIlYDLwZuKpihNSJ+CgwA387MUlk+C3wIOFjo+MMl8M8R8ZPqMvxS1gCDwN9UUzMXR8S8gnmgdp1AsW8uyMzNwKeBh4EtwBOZ+c+H8x6zqYA1hoiYD3wVeF9m7iyVIzMPZOavULsC8mUR0fDpmYh4IzCQmT9p9LHH8KuZeSrwBuCCavqqhDbgVOALmfliYDdQ8nOUDuBNwFcKZuim9q/oNcCxwLyI+J3DeY/ZVMBe5jyKar71q8CXM/Oa0nkAqn/a3gCM/j1P9XU68KZq7vVK4IyI+NsCOYBnzrLIzAHga9Sm0kroB/qH/avkamqFXMobgFsyc2vBDK8FHsjMwczcD1wDvPJw3mA2FbCXOY9QffB1CXB3Zv554Sw9EbG4ejyX2oel9zQ6R2ZelJm9mbma2p+R6zPzsM5qpktEzKs+HKX65/6vA0VWzmTmo8AjEXFiNXQmZW8Tew4Fpx8qDwOnRURX9XfpTGqfo0xaU12KXE8NvMx5QhFxBfBqYFlE9AMfy8xLCkQ5HXg7cHs19wrwkcz8ZoEsK4DLq0+2W4CrMrPoErAmsBz4WvUN2m3A32XmdQXzXAh8uTqBuR94V4kQ1f+MXgf8lxLHPyQzb4qIq4FbgCHgVg7zsuRZswxNkprNbJqCkKSmYgFLUiEWsCQVYgFLUiEWsCSN4XBunBURnxl2k6D7ImLCS7VdBSFJY5jqjbMi4kLgxZn5u+Pt5xmwJI1htBtnRcQvRcR11f05fhARzx/lpZO6UGTWXIghSdNkPfAHmbkxIl4O/BVwxqGNEXEctftDXD/RG1nAkjRJ1Y2rXgl8pbpCEWDOiN3OBq7OzAMTvZ8FLEmT1wLsqO7aN5azgQsm+2aSpEmobtf6QET8FtRuaBURLzq0vZoP7gZunMz7WcCSNIbqxlk3AidGRH9EnAf8NnBeRPwMuJN/+806ZwNX5iSXl7kMTZIK8QxYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgr5/1INCGpTqAvRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "`\n",
            " les meilleurs parametres du model: {'randomforestregressor__criterion': 'absolute_error', 'randomforestregressor__n_estimators': 100},\n",
            " meilleur score: 0.8512323110519923\n"
          ]
        }
      ],
      "source": [
        "pre=preprocessor1\n",
        "\n",
        "model5=regression_model1(pre=pre, model=RandomForestRegressor(), params=dic)\n",
        "print(f\"`\\n les meilleurs parametres du model: {model5.best_params_},\\n meilleur score: {model5.best_score_}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_AvidAr67vb"
      },
      "source": [
        "* RandomForest obtient le meilleur score sur le jeu de donnée et aussi fonctionne mieux sans EnergyStarScore. Comme son calcul est couteux, nous allons donc choisir ce modele pour reduire le cout de collecte des données"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "id": "ZixMsdq2SWwU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4ac19fc-1535-4b8f-b48b-2b006a7ca0bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            " 3.41921391e-04 1.61489035e-04 4.36505310e-03 4.25990457e-03\n",
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            " 5.22192940e-05 1.39996368e-05 2.48440807e-06 4.00837663e-05\n",
            " 2.72677935e-05 2.47895700e-05 7.09411966e-06 5.64883512e-07\n",
            " 2.74237585e-05 1.74358217e-06 3.25364800e-06 4.03852438e-06\n",
            " 1.36895057e-04 3.24081902e-06 2.27873445e-06 1.82410090e-04\n",
            " 1.06070727e-05 4.13834705e-04 1.68948520e-04 2.91062655e-06\n",
            " 8.35355092e-05 1.96561151e-07 3.63711090e-05 4.39009251e-05\n",
            " 2.98658310e-06 1.48527403e-06 1.00295346e-05 2.60807503e-05\n",
            " 2.18625737e-06 5.91106700e-06 3.72955951e-05 5.97221859e-06\n",
            " 6.69490488e-05 6.48228780e-05 3.84031690e-06 1.81276131e-05\n",
            " 4.17405450e-07 3.54408542e-05 3.74330710e-05 5.31168597e-06\n",
            " 2.00450089e-06 1.72874863e-05 5.79851906e-06 5.65183451e-06\n",
            " 4.32268069e-05 1.20164957e-06 1.31320795e-06 5.29600542e-05\n",
            " 2.49786865e-03 5.63958186e-05 8.11367222e-07 2.77479413e-05\n",
            " 2.82306785e-06 4.44449978e-06 5.40977656e-06 2.40436134e-05\n",
            " 4.33724378e-06 5.56436083e-05 9.85897689e-07 3.89922692e-05\n",
            " 9.50557439e-05 2.00722683e-06 2.40989378e-05 3.93663173e-06\n",
            " 1.19906743e-05 3.64931889e-05 1.07469554e-04 3.85382041e-05\n",
            " 8.01387589e-04 5.17880448e-04 6.75015606e-06 3.66612997e-03\n",
            " 1.39911709e-05 1.72253995e-04 2.21628306e-03 3.85860859e-04\n",
            " 2.99983611e-03 6.81754661e-04 1.33613077e-03 8.74098924e-04\n",
            " 2.98506549e-04 2.12976392e-03 1.14175111e-03 1.57933820e-05\n",
            " 1.37721193e-05 3.08656159e-04 3.05542942e-04 8.92005790e-05\n",
            " 3.14576958e-04 1.59531420e-03 2.41149279e-04 3.64933830e-05\n",
            " 2.47364806e-03 4.72707967e-03 1.80472066e-04 4.96929075e-04\n",
            " 9.83386313e-05 8.81996173e-04 4.10558120e-03 2.49359472e-04\n",
            " 1.74737181e-05 1.25922540e-04 5.35705740e-04 9.78905292e-04\n",
            " 1.41219269e-04 1.25175915e-04]\n"
          ]
        }
      ],
      "source": [
        "rf=model5.best_estimator_[-1]\n",
        "print(rf.feature_importances_)\n",
        "importances = rf.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "col_liste=[]\n",
        "for i in df.columns:\n",
        "\n",
        "  if i in df[quan]:\n",
        "    col_liste.append(list(df.columns).index(i))\n",
        "col_liste\n",
        "\n",
        "indices=importances[col_liste]\n",
        "indices\n",
        "\n",
        "len(indices)\n",
        "indices=[float(i) for i in indices]\n",
        "\n",
        "features = list(X_train[quan].columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "id": "NjzGZSS2S2Is",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "633dc581-9192-41bb-f79f-93798efc7316"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), indices)\n",
        "plt.yticks(range(len(indices)), [features[i] for i in range(len(indices))])\n",
        "plt.xlabel('Importance relative')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explication de l'importance des features"
      ],
      "metadata": {
        "id": "jNnhLSheMf9m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "id": "yu7uIRNvjRpF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1107615c-43a4-4505-ba2c-e6f9752419a4"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (569 kB)\n",
            "\u001b[K     |████████████████████████████████| 569 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.64.1)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.56.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (1.0.2)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.5)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.7.3)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap) (3.0.9)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba->shap) (4.13.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap) (3.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2022.6)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.2.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.41.0 slicer-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model5.best_params_"
      ],
      "metadata": {
        "id": "ZZSaSkUTlSK4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46743114-9d6c-43dd-eeee-091569ab1432"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'randomforestregressor__criterion': 'absolute_error',\n",
              " 'randomforestregressor__n_estimators': 300}"
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.model_selection import train_test_split\n",
        "import shap\n",
        "'''explainer= shap.TreeExplainer(model5.best_estimator_[-1])\n",
        "shap_values = explainer.shap_values(preprocessor.fit_transform(X_train))'''\n",
        "# Fits the explainer\n",
        "pro=preprocessor.fit(X_train)\n",
        "model_i=RandomForestRegressor(criterion=\"squared_error\",max_depth= 45, n_estimators=100 )\n",
        "model_i.fit(X_train[quan], y_train)\n",
        "explainer = shap.KernelExplainer(model_i.predict, X_test[quan])\n",
        "\n",
        "#shap_values = explainer(X_test)"
      ],
      "metadata": {
        "id": "IOj6v9BlMf99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "272a668d-86e6-4316-d5b4-c0ffb94c6c2a"
      },
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "WARNING:shap:Using 196 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importance Globale"
      ],
      "metadata": {
        "id": "RG-NqTQ9MDrb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shap_values = explainer.shap_values(X_test[quan])\n",
        "\n",
        "# visualize the model's dependence on the first feature\n",
        "shap.summary_plot(\n",
        "    shap_values, X_test[quan], \n",
        "    feature_names=quan, plot_type=\"bar\"\n",
        ")"
      ],
      "metadata": {
        "id": "HuXvzRrLp1W2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "9829af232af843e0ae68a1970ab1d078",
            "8f212992a7514b2996abbfbec6a7ed73",
            "a7dc8427b02f46a6917477d5c5a74a6c",
            "4181e2aea5f04cbd8f4f2d82df7e7317",
            "ba015984e5bd45c4a739115210340cf5",
            "ecf14ae06b24469eaa3077b664726f53",
            "4f39f8b531394a76b77f3a5474896e21",
            "4c6cacaf8e804b58bca92405ba3dbaf1",
            "1cae556e23104836a2d2e45e960f1cd5",
            "c2aa0b8247ad45b39bb31d07b50bb10d",
            "7caac4404b824904ae3c4689e0c81aeb"
          ]
        },
        "outputId": "088d907a-dff6-4a87-d340-cac4bce3838f"
      },
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9829af232af843e0ae68a1970ab1d078"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x424.8 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importance Locale"
      ],
      "metadata": {
        "id": "mCrnGBgmIIkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "shap_values = explainer.shap_values(X_test[quan].iloc[0])\n",
        "#print(len(shap_values), df0.iloc[0,:].shape)\n",
        "shap.force_plot(explainer.expected_value, shap_values,X_test[quan].iloc[0])"
      ],
      "metadata": {
        "id": "rFFuCB1iILe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b2995cbf-5c25-4894-f8e0-d61461dcb0ce"
      },
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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n.childContextTypes?u(\"107\",this.getName()||\"ReactCompositeComponent\"):void 0;for(var i in e)i in n.childContextTypes?void 0:u(\"108\",this.getName()||\"ReactCompositeComponent\",i);return c({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?v.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o?u(\"136\",this.getName()||\"ReactCompositeComponent\"):void 0;var a,c=!1;this._context===i?a=o.context:(a=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,a);var f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of \"+t._currentElement._owner.getName()+\".\":\"\"):void 0),null!=e.dangerouslySetInnerHTML&&(null!=e.children?v(\"60\"):void 0,\"object\"==typeof e.dangerouslySetInnerHTML&&V in e.dangerouslySetInnerHTML?void 0:v(\"61\")),null!=e.style&&\"object\"!=typeof e.style?v(\"62\",r(t)):void 0)}function o(t,e,n,r){if(!(r instanceof I)){var i=t._hostContainerInfo,o=i._node&&i._node.nodeType===H,u=o?i._node:i._ownerDocument;F(e,u),r.getReactMountReady().enqueue(a,{inst:t,registrationName:e,listener:n})}}function a(){var t=this;C.putListener(t.inst,t.registrationName,t.listener)}function u(){var t=this;S.postMountWrapper(t)}function c(){var t=this;A.postMountWrapper(t)}function s(){var t=this;P.postMountWrapper(t)}function l(){var t=this;t._rootNodeID?void 0:v(\"63\");var e=U(t);switch(e?void 0:v(\"64\"),t._tag){case\"iframe\":case\"object\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"video\":case\"audio\":t._wrapperState.listeners=[];for(var n in q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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              " <script>\n",
              "   if (window.SHAP) SHAP.ReactDom.render(\n",
              "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 8097443.530876496, \"outValue\": 598675.7253200002, \"link\": \"identity\", \"featureNames\": [\"ZipCode\", \"Latitude\", \"Longitude\", \"YearBuilt\", \"NumberofFloors\", \"PropertyGFATotal\", \"SourceEUI(kBtu/sf)\", \"SteamUse(kBtu)\", \"Electricity(kBtu)\", \"NaturalGas(kBtu)\", \"ComplianceStatus\"], \"features\": {\"0\": {\"effect\": -3229.638108209707, \"value\": 11.493773226125619}, \"1\": {\"effect\": 93336.23192965426, \"value\": 3.8639036162388676}, \"3\": {\"effect\": 401619.8608500692, \"value\": 4.653960350157523}, \"4\": {\"effect\": 49205.38524548896, \"value\": 0.6931471805599453}, \"5\": {\"effect\": -8073011.408172013, \"value\": 9.92896034850916}, \"6\": {\"effect\": -1014459.4907071972, \"value\": 4.4953553199808844}, \"7\": {\"effect\": -17786.807048276067, \"value\": 0.0}, \"8\": {\"effect\": 1827262.901902774, \"value\": 0.0}, \"9\": {\"effect\": -761704.8414487867, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
              "    document.getElementById('iV2SOG1N37WL2F5U2I6WE')\n",
              "  );\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf=model5.best_estimator_[-1]\n",
        "print(rf.feature_importances_)\n",
        "importances = rf.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "col_liste=[]\n",
        "for i in df.columns:\n",
        "\n",
        "  if i in df[quan]:\n",
        "    col_liste.append(list(df.columns).index(i))\n",
        "col_liste\n",
        "\n",
        "indices=importances[col_liste]\n",
        "indices\n",
        "\n",
        "len(indices)\n",
        "indices=[float(i) for i in indices]\n",
        "\n",
        "features = list(X_train[quan].columns)\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), indices)\n",
        "plt.yticks(range(len(indices)), [features[i] for i in range(len(indices))])\n",
        "plt.xlabel('Importance relative')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iRf-X9fWipYI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "dd0b209d-4e52-4160-ced1-543c4ff677a1"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9.67181718e-03 1.90323714e-02 0.00000000e+00 ... 9.30834241e-05\n",
            " 1.61188266e-04 9.45512126e-05]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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      "source": [
        "# Conclusion"
      ]
    },
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      "source": [
        "* Le meilleur modele à retenir pour la prediction de la consommation en energie est le RandomForest qui obtient un score de pres de 95%  sur ce jeu de données\n",
        "* En général, la presence de la variable EnergyStarScore ne fait pas varier trop la precision du model de plus de 5% ce qui fait qu'elle n'est pas indispensable.\n",
        " Aussi, le RandomForest qui a le meilleur score obtient plus de précision sans l'EnergyStarScore"
      ]
    }
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