training/sk_regressors.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entrainement des modèles de régression `sklearn`\n",
    "\n",
    "**Objectif**\n",
    "\n",
    "Entrainer un modèle de regréssion à predire les prix d'un type de bien choisi (appartements ou maison) pour une zone géographique donnée (Paris, Marseille, etc.).\n",
    "\n",
    "**Données**\n",
    "\n",
    "`DVF+` est une base de données construite à partir des bases de données `DVF` (Demandes de Valeurs Foncières) & `BNB` (Base Nationale des Bâtiments). Cette base a été construite en utilisant les méthodes du module `preprocessing` de la librairie `lib`. \n",
    "\n",
    "**Méthode** \n",
    "\n",
    "1. Choix d'une zone géographique (`geo_area`) et d'une type de bien (`property_type`)\n",
    "\n",
    "<br>\n",
    "\n",
    "2. Préparation du jeu de donneés pour la modélisation \n",
    "    - Filtrage des données (nombre de pièces, surface habitable, surface terrain, prix/m²)\n",
    "    - Application du logarithme à la variable `l_valeur_fonciere`\n",
    "    - Ajout des variables de tendance des prix du marché: moyenne mobile avec lag de 1 jour appliquée à la variable `valeur_fonciere`\n",
    "    - Création des dummies \n",
    "    - Suppression des variables ayant une proportion de donnnées manquantes trop élevée\n",
    "\n",
    "<br>\n",
    "\n",
    "3. Séparation des jeux de données d'entrainement & test\n",
    "    - Ordonner les transactions par ordre chronologique\n",
    "    - Les 80% premières transactions sont dans le jeu de données train\n",
    "    - Les 20% suivantes sont dans le jeu de données test \n",
    "\n",
    "<br>\n",
    "\n",
    "4. Sélection des variables \n",
    "    - Calcul de l'information mutuelle (MI) entre les variables explicatives et la cible (`valeur_fonciere` ou `l_valeur_fonciere`)\n",
    "    - `RandomForestRegressor` pour récupérer l'importance des variables mesurée par la *Mean Decrease Gini* (MDG)\n",
    "    - Sélection des variables dont l'importance (MI et/ou MDG) est supérieure à seuil prédéfini (ex: quantile d'ordre 75%) \n",
    "\n",
    "<br>\n",
    "\n",
    "5. Entrainement de plusieurs régresseurs\n",
    "    - A partir des features sélectionnées\n",
    "    - `LinearRegression`, `DecisionTreeRegressor`, `XGBRegressor`, ... \n",
    "    - Diagnostic\n",
    "\n",
    "<br>\n",
    "\n",
    "6. Optimisation du type de modèle le + adapté à la prédiction\n",
    "    - Via [`optuna`](https://optuna.org/)\n",
    "    - Identification de la combinaison d'hyperparamètres optimales\n",
    "    - Entrainement du modèle optimal sur le train set \n",
    "\n",
    "<br>\n",
    "\n",
    "7. Ré-entrainement & Sauvegarde"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.enums import IDF\n",
    "\n",
    "from lib.dataset import (\n",
    "    load_dvfplus, \n",
    "    prepare_dataset, \n",
    "    prepare_dummies, \n",
    "    temporal_train_test_split, \n",
    "    get_feature_vector, \n",
    "    get_target_vector, \n",
    "    compute_mutual_info, \n",
    "    compute_rf_importances, \n",
    "    select_important_features,\n",
    "    summarize_dataset,\n",
    ")\n",
    "\n",
    "from lib.dataset.build import (\n",
    "    add_distance_to_transportation, \n",
    "    add_public_facilities, \n",
    "    add_distance_to_parks, \n",
    ")\n",
    "\n",
    "from lib.dataset.utils import (\n",
    "    get_categorical_vars, \n",
    "    get_most_frequent_levels,\n",
    "    scale_features, \n",
    "    is_numeric\n",
    ")\n",
    "\n",
    "from lib.model import (\n",
    "    CustomRegressor, \n",
    "    compute_metrics, \n",
    "    display_regression_report, \n",
    "    plot_predictions, \n",
    "    get_predictions, \n",
    ")\n",
    "from lib.model.optimize import optuna_objective, OptimPruner\n",
    "from lib.model.loader import save_model, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import rich \n",
    "from tqdm import tqdm\n",
    "\n",
    "from multiprocessing import cpu_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# estimators\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "# optimization \n",
    "import optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# garbage collector\n",
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = \"../data/\"\n",
    "BACKUP_DIR = \"../backup/\"\n",
    "\n",
    "CPU_COUNT = cpu_count()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_args = {\n",
    "    \"geo_area\": \"urban_areas\", \n",
    "    \"region\": None, \n",
    "    \"departement\": \"Seine-et-Marne\",\n",
    "    \"property_type\": \"flats\"\n",
    "}\n",
    "\n",
    "df = load_dvfplus(\n",
    "    zip_dir=DATA_DIR, \n",
    "    zip_name=\"dvf+\",\n",
    "    property_type=df_args[\"property_type\"], \n",
    "    geo_area=df_args[\"geo_area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add features related to transportation and public facilities \n",
    "\n",
    "if df_args[\"geo_area\"] == \"Paris\":\n",
    "\n",
    "    transportation = pd.read_csv(f\"{DATA_DIR}other/transportation.csv\")\n",
    "    parks = pd.read_csv(f\"{DATA_DIR}other/parks.csv\")\n",
    "    facilities = pd.read_csv(f\"{DATA_DIR}other/facilities.csv\") \n",
    "\n",
    "    df = add_distance_to_transportation(df, transportation)\n",
    "    del transportation\n",
    "\n",
    "    df = add_distance_to_parks(df, parks)\n",
    "    del parks \n",
    "\n",
    "    df = add_public_facilities(df, facilities)\n",
    "    del facilities\n",
    "\n",
    "elif df_args[\"geo_area\"] == \"urban_areas\":\n",
    "\n",
    "    if df_args[\"region\"] is not None:\n",
    "        df = df.loc[df.nom_region==df_args[\"region\"], :]\n",
    "        \n",
    "    if df_args[\"departement\"] is not None:\n",
    "        df = df.loc[df.nom_departement==df_args[\"departement\"], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id_mutation</th>\n",
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       "      <th>lot2_numero</th>\n",
       "      <th>lot2_surface_carrez</th>\n",
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       "      <th>lot5_numero</th>\n",
       "      <th>lot5_surface_carrez</th>\n",
       "      <th>nombre_lots</th>\n",
       "      <th>code_type_local</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>code_nature_culture</th>\n",
       "      <th>nature_culture</th>\n",
       "      <th>code_nature_culture_speciale</th>\n",
       "      <th>nature_culture_speciale</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>dependance</th>\n",
       "      <th>code_region</th>\n",
       "      <th>nom_departement</th>\n",
       "      <th>nom_region</th>\n",
       "      <th>degre_densite</th>\n",
       "      <th>pop</th>\n",
       "      <th>annee</th>\n",
       "      <th>trimestre</th>\n",
       "      <th>mois</th>\n",
       "      <th>jour</th>\n",
       "      <th>parcelle_id</th>\n",
       "      <th>code_iris</th>\n",
       "      <th>periode_construction</th>\n",
       "      <th>periode_construction_max</th>\n",
       "      <th>hauteur_mean</th>\n",
       "      <th>altitude_sol_mean</th>\n",
       "      <th>conso_ener_mean</th>\n",
       "      <th>estim_ges_mean</th>\n",
       "      <th>conso_ener_std</th>\n",
       "      <th>estim_ges_std</th>\n",
       "      <th>conso_ener_min</th>\n",
       "      <th>estim_ges_min</th>\n",
       "      <th>conso_ener_max</th>\n",
       "      <th>estim_ges_max</th>\n",
       "      <th>ratio_ges_conso</th>\n",
       "      <th>enr_solaire_photovoltaique</th>\n",
       "      <th>enr_solaire_thermique_(chauffage)solaire_thermique_(ecs)</th>\n",
       "      <th>enr_solaire_thermique_(ecs+chauffage)</th>\n",
       "      <th>baie_u</th>\n",
       "      <th>mur_u_ext</th>\n",
       "      <th>pb_u</th>\n",
       "      <th>ph_u</th>\n",
       "      <th>mur_pos_isol_ext</th>\n",
       "      <th>prc_s_vitree_ext</th>\n",
       "      <th>presence_balcon</th>\n",
       "      <th>presence_climatisation</th>\n",
       "      <th>baie_orientation_indetermine</th>\n",
       "      <th>baie_orientation_nord</th>\n",
       "      <th>baie_orientation_ouest</th>\n",
       "      <th>baie_orientation_est</th>\n",
       "      <th>baie_orientation_horizontale</th>\n",
       "      <th>baie_orientation_est_ou_ouest</th>\n",
       "      <th>baie_orientation_sud</th>\n",
       "      <th>distance_batiment_historique_plus_proche</th>\n",
       "      <th>qpv</th>\n",
       "      <th>nb_lot_garpark</th>\n",
       "      <th>nb_lot_tot</th>\n",
       "      <th>nb_log</th>\n",
       "      <th>nb_lot_tertiaire</th>\n",
       "      <th>alea_argiles</th>\n",
       "      <th>alea_radon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70621</th>\n",
       "      <td>2017-554686</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>1</td>\n",
       "      <td>Vente</td>\n",
       "      <td>47500.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AV DE BIR HAKEIM</td>\n",
       "      <td>0040</td>\n",
       "      <td>77350.0</td>\n",
       "      <td>77285</td>\n",
       "      <td>Le Mée-sur-Seine</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77285000BR0122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.645366</td>\n",
       "      <td>48.542418</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>Île-de-France</td>\n",
       "      <td>Grands centres urbains</td>\n",
       "      <td>20917.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>77285000BR0122</td>\n",
       "      <td>772850204.0</td>\n",
       "      <td>&lt;1948</td>\n",
       "      <td>DE_1975_A_1993</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>56.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>70.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>238.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ITI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fort</td>\n",
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       "    <tr>\n",
       "      <th>70622</th>\n",
       "      <td>2017-554687</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>1</td>\n",
       "      <td>Vente</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>41.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>Île-de-France</td>\n",
       "      <td>Centres urbains intermédiaires</td>\n",
       "      <td>18812.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70623</th>\n",
       "      <td>2017-554690</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>1</td>\n",
       "      <td>Vente</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RUE DU GRAND NOYER</td>\n",
       "      <td>0250</td>\n",
       "      <td>77170.0</td>\n",
       "      <td>77053</td>\n",
       "      <td>Brie-Comte-Robert</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77053000AW0266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>Île-de-France</td>\n",
       "      <td>Centres urbains intermédiaires</td>\n",
       "      <td>18812.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70624</th>\n",
       "      <td>2017-554698</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>1</td>\n",
       "      <td>Vente</td>\n",
       "      <td>127000.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALL DE LA TRAMONTANE</td>\n",
       "      <td>1675</td>\n",
       "      <td>77176.0</td>\n",
       "      <td>77445</td>\n",
       "      <td>Savigny-le-Temple</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77445000AD0002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>74.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>Île-de-France</td>\n",
       "      <td>Centres urbains intermédiaires</td>\n",
       "      <td>29987.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70625</th>\n",
       "      <td>2017-554702</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>1</td>\n",
       "      <td>Vente</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RUE URBAIN LE VERRIER</td>\n",
       "      <td>1872</td>\n",
       "      <td>77127.0</td>\n",
       "      <td>77251</td>\n",
       "      <td>Lieusaint</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77251000ZK0391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.554792</td>\n",
       "      <td>48.633341</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>Île-de-France</td>\n",
       "      <td>Centres urbains intermédiaires</td>\n",
       "      <td>13853.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>77251000ZK0391</td>\n",
       "      <td>772510106.0</td>\n",
       "      <td>2006-2012</td>\n",
       "      <td>DE_2001_A_2010</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>213.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>171.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Faible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_mutation date_mutation  numero_disposition nature_mutation  \\\n",
       "70621  2017-554686    2017-07-04                   1           Vente   \n",
       "70622  2017-554687    2017-07-04                   1           Vente   \n",
       "70623  2017-554690    2017-07-05                   1           Vente   \n",
       "70624  2017-554698    2017-07-03                   1           Vente   \n",
       "70625  2017-554702    2017-07-06                   1           Vente   \n",
       "\n",
       "       valeur_fonciere  adresse_numero adresse_suffixe       adresse_nom_voie  \\\n",
       "70621          47500.0           678.0             NaN       AV DE BIR HAKEIM   \n",
       "70622         145000.0             7.0             NaN     RUE DU GRAND NOYER   \n",
       "70623         145000.0             7.0             NaN     RUE DU GRAND NOYER   \n",
       "70624         127000.0             9.0             NaN   ALL DE LA TRAMONTANE   \n",
       "70625         159000.0            10.0             NaN  RUE URBAIN LE VERRIER   \n",
       "\n",
       "      adresse_code_voie  code_postal code_commune        nom_commune  \\\n",
       "70621              0040      77350.0        77285   Le Mée-sur-Seine   \n",
       "70622              0250      77170.0        77053  Brie-Comte-Robert   \n",
       "70623              0250      77170.0        77053  Brie-Comte-Robert   \n",
       "70624              1675      77176.0        77445  Savigny-le-Temple   \n",
       "70625              1872      77127.0        77251          Lieusaint   \n",
       "\n",
       "      code_departement  ancien_code_commune ancien_nom_commune  \\\n",
       "70621               77                  NaN                NaN   \n",
       "70622               77                  NaN                NaN   \n",
       "70623               77                  NaN                NaN   \n",
       "70624               77                  NaN                NaN   \n",
       "70625               77                  NaN                NaN   \n",
       "\n",
       "          id_parcelle ancien_id_parcelle  numero_volume lot1_numero  \\\n",
       "70621  77285000BR0122                NaN            NaN       148.0   \n",
       "70622  77053000AW0266                NaN            NaN        39.0   \n",
       "70623  77053000AW0266                NaN            NaN        37.0   \n",
       "70624  77445000AD0002                NaN            NaN       114.0   \n",
       "70625  77251000ZK0391                NaN            NaN        72.0   \n",
       "\n",
       "       lot1_surface_carrez lot2_numero  lot2_surface_carrez lot3_numero  \\\n",
       "70621                  NaN         NaN                  NaN         NaN   \n",
       "70622                41.00         NaN                  NaN         NaN   \n",
       "70623                43.08         NaN                  NaN         NaN   \n",
       "70624                74.30         NaN                  NaN         NaN   \n",
       "70625                  NaN        87.0                  NaN         NaN   \n",
       "\n",
       "       lot3_surface_carrez lot4_numero  lot4_surface_carrez  lot5_numero  \\\n",
       "70621                  NaN         NaN                  NaN          NaN   \n",
       "70622                  NaN         NaN                  NaN          NaN   \n",
       "70623                  NaN         NaN                  NaN          NaN   \n",
       "70624                  NaN         NaN                  NaN          NaN   \n",
       "70625                  NaN         NaN                  NaN          NaN   \n",
       "\n",
       "       lot5_surface_carrez  nombre_lots  code_type_local   type_local  \\\n",
       "70621                  NaN            1              2.0  Appartement   \n",
       "70622                  NaN            1              2.0  Appartement   \n",
       "70623                  NaN            1              2.0  Appartement   \n",
       "70624                  NaN            1              2.0  Appartement   \n",
       "70625                  NaN            2              2.0  Appartement   \n",
       "\n",
       "       surface_reelle_bati  nombre_pieces_principales code_nature_culture  \\\n",
       "70621                 33.0                        1.0                 NaN   \n",
       "70622                 41.0                        2.0                 NaN   \n",
       "70623                 42.0                        2.0                 NaN   \n",
       "70624                 77.0                        4.0                 NaN   \n",
       "70625                 51.0                        2.0                 NaN   \n",
       "\n",
       "      nature_culture code_nature_culture_speciale nature_culture_speciale  \\\n",
       "70621            NaN                          NaN                     NaN   \n",
       "70622            NaN                          NaN                     NaN   \n",
       "70623            NaN                          NaN                     NaN   \n",
       "70624            NaN                          NaN                     NaN   \n",
       "70625            NaN                          NaN                     NaN   \n",
       "\n",
       "       surface_terrain  longitude   latitude  dependance  code_region  \\\n",
       "70621              NaN   2.645366  48.542418           1           11   \n",
       "70622              NaN        NaN        NaN           1           11   \n",
       "70623              NaN        NaN        NaN           1           11   \n",
       "70624              NaN        NaN        NaN           1           11   \n",
       "70625              NaN   2.554792  48.633341           1           11   \n",
       "\n",
       "      nom_departement     nom_region                   degre_densite      pop  \\\n",
       "70621  Seine-et-Marne  Île-de-France          Grands centres urbains  20917.0   \n",
       "70622  Seine-et-Marne  Île-de-France  Centres urbains intermédiaires  18812.0   \n",
       "70623  Seine-et-Marne  Île-de-France  Centres urbains intermédiaires  18812.0   \n",
       "70624  Seine-et-Marne  Île-de-France  Centres urbains intermédiaires  29987.0   \n",
       "70625  Seine-et-Marne  Île-de-France  Centres urbains intermédiaires  13853.0   \n",
       "\n",
       "       annee  trimestre  mois  jour     parcelle_id    code_iris  \\\n",
       "70621   2017          3     7     4  77285000BR0122  772850204.0   \n",
       "70622   2017          3     7     4             NaN          NaN   \n",
       "70623   2017          3     7     5             NaN          NaN   \n",
       "70624   2017          3     7     3             NaN          NaN   \n",
       "70625   2017          3     7     6  77251000ZK0391  772510106.0   \n",
       "\n",
       "      periode_construction periode_construction_max  hauteur_mean  \\\n",
       "70621                <1948           DE_1975_A_1993          33.0   \n",
       "70622                  NaN                      NaN           NaN   \n",
       "70623                  NaN                      NaN           NaN   \n",
       "70624                  NaN                      NaN           NaN   \n",
       "70625            2006-2012           DE_2001_A_2010           6.0   \n",
       "\n",
       "       altitude_sol_mean  conso_ener_mean  estim_ges_mean  conso_ener_std  \\\n",
       "70621               67.0            138.2            23.4            56.9   \n",
       "70622                NaN              NaN             NaN             NaN   \n",
       "70623                NaN              NaN             NaN             NaN   \n",
       "70624                NaN              NaN             NaN             NaN   \n",
       "70625               85.0            213.7            10.3            34.6   \n",
       "\n",
       "       estim_ges_std  conso_ener_min  estim_ges_min  conso_ener_max  \\\n",
       "70621           19.0            70.3            2.0           238.1   \n",
       "70622            NaN             NaN            NaN             NaN   \n",
       "70623            NaN             NaN            NaN             NaN   \n",
       "70624            NaN             NaN            NaN             NaN   \n",
       "70625            2.2           171.0            8.0           274.0   \n",
       "\n",
       "       estim_ges_max  ratio_ges_conso  enr_solaire_photovoltaique  \\\n",
       "70621           51.0             0.02                         0.0   \n",
       "70622            NaN              NaN                         NaN   \n",
       "70623            NaN              NaN                         NaN   \n",
       "70624            NaN              NaN                         NaN   \n",
       "70625           15.0             0.05                         0.0   \n",
       "\n",
       "       enr_solaire_thermique_(chauffage)solaire_thermique_(ecs)  \\\n",
       "70621                                                0.0          \n",
       "70622                                                NaN          \n",
       "70623                                                NaN          \n",
       "70624                                                NaN          \n",
       "70625                                                0.0          \n",
       "\n",
       "       enr_solaire_thermique_(ecs+chauffage)  baie_u  mur_u_ext  pb_u  ph_u  \\\n",
       "70621                                    0.0     NaN        NaN   NaN   NaN   \n",
       "70622                                    NaN     NaN        NaN   NaN   NaN   \n",
       "70623                                    NaN     NaN        NaN   NaN   NaN   \n",
       "70624                                    NaN     NaN        NaN   NaN   NaN   \n",
       "70625                                    0.0     2.2       0.36  0.27   0.2   \n",
       "\n",
       "      mur_pos_isol_ext  prc_s_vitree_ext  presence_balcon  \\\n",
       "70621              ITI               NaN              NaN   \n",
       "70622              NaN               NaN              NaN   \n",
       "70623              NaN               NaN              NaN   \n",
       "70624              NaN               NaN              NaN   \n",
       "70625              ITI             0.174              0.0   \n",
       "\n",
       "       presence_climatisation  baie_orientation_indetermine  \\\n",
       "70621                     0.0                           0.0   \n",
       "70622                     NaN                           NaN   \n",
       "70623                     NaN                           NaN   \n",
       "70624                     NaN                           NaN   \n",
       "70625                     0.0                           0.0   \n",
       "\n",
       "       baie_orientation_nord  baie_orientation_ouest  baie_orientation_est  \\\n",
       "70621                    1.0                     0.0                   0.0   \n",
       "70622                    NaN                     NaN                   NaN   \n",
       "70623                    NaN                     NaN                   NaN   \n",
       "70624                    NaN                     NaN                   NaN   \n",
       "70625                    0.0                     1.0                   0.0   \n",
       "\n",
       "       baie_orientation_horizontale  baie_orientation_est_ou_ouest  \\\n",
       "70621                           0.0                            0.0   \n",
       "70622                           NaN                            NaN   \n",
       "70623                           NaN                            NaN   \n",
       "70624                           NaN                            NaN   \n",
       "70625                           0.0                            0.0   \n",
       "\n",
       "       baie_orientation_sud  distance_batiment_historique_plus_proche  qpv  \\\n",
       "70621                   0.0                                       NaN  0.0   \n",
       "70622                   NaN                                       NaN  NaN   \n",
       "70623                   NaN                                       NaN  NaN   \n",
       "70624                   NaN                                       NaN  NaN   \n",
       "70625                   0.0                                       NaN  0.0   \n",
       "\n",
       "       nb_lot_garpark  nb_lot_tot  nb_log  nb_lot_tertiaire alea_argiles  \\\n",
       "70621            90.0       269.0   179.0               0.0         Fort   \n",
       "70622             NaN         NaN     NaN               NaN          NaN   \n",
       "70623             NaN         NaN     NaN               NaN          NaN   \n",
       "70624             NaN         NaN     NaN               NaN          NaN   \n",
       "70625           108.0       213.0    81.0               0.0          NaN   \n",
       "\n",
       "      alea_radon  \n",
       "70621     Faible  \n",
       "70622        NaN  \n",
       "70623        NaN  \n",
       "70624        NaN  \n",
       "70625     Faible  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33696, 91)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration \n",
    "\n",
    "L'objectif est de représenter les variables `valeur_fonciere`, `nombre_pieces_principales`, `surface_reelle_bati` et `surface_terrain` pour déterminer ensuite des filtres à appliquer aux données. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quelques statistiques descriptives relatives aux variables quantitatives qui décrivent les biens. Ces résultats sont utiles pour identifier les valeurs aberrantes et déterminer des seuils. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset summary for 33,696 observations\n",
      "********************************************************************************\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  Missing values                  </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Variable                  </span>┃<span style=\"font-weight: bold\"> Missing values (%) </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ nombre_pieces_principales │ 0.0%               │\n",
       "│ surface_reelle_bati       │ 0.0%               │\n",
       "│ surface_terrain           │ 99.1%              │\n",
       "│ valeur_fonciere_m2        │ 0.01%              │\n",
       "└───────────────────────────┴────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                  Missing values                  \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mVariable                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMissing values (%)\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ nombre_pieces_principales │ 0.0%               │\n",
       "│ surface_reelle_bati       │ 0.0%               │\n",
       "│ surface_terrain           │ 99.1%              │\n",
       "│ valeur_fonciere_m2        │ 0.01%              │\n",
       "└───────────────────────────┴────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "Numeric variables\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">       valeur_fonciere_m2  surface_reelle_bati  surface_terrain\n",
       "count        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33691.000000</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33696.000000</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">302.000000</span>\n",
       "mean          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3191.550257</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">57.046741</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1339.927152</span>\n",
       "std           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2380.865958</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21.512491</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4752.687050</span>\n",
       "min              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.005556</span>             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.000000</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>%            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1017.911851</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">18.000000</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>%            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1548.780488</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25.000000</span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">28.000000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1847.222222</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30.000000</span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">36.200000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2421.875000</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.000000</span>        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">59.250000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3097.826087</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">57.000000</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">153.000000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">75</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3783.333333</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">70.000000</span>       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">490.250000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4392.857143</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">84.000000</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3210.900000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">95</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4821.428571</span>            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">93.000000</span>      <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5661.200000</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">99</span>%           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6071.428571</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">115.000000</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">17313.470000</span>\n",
       "max         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">157352.941176</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">265.000000</span>     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">61470.000000</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "       valeur_fonciere_m2  surface_reelle_bati  surface_terrain\n",
       "count        \u001b[1;36m33691.000000\u001b[0m         \u001b[1;36m33696.000000\u001b[0m       \u001b[1;36m302.000000\u001b[0m\n",
       "mean          \u001b[1;36m3191.550257\u001b[0m            \u001b[1;36m57.046741\u001b[0m      \u001b[1;36m1339.927152\u001b[0m\n",
       "std           \u001b[1;36m2380.865958\u001b[0m            \u001b[1;36m21.512491\u001b[0m      \u001b[1;36m4752.687050\u001b[0m\n",
       "min              \u001b[1;36m0.005556\u001b[0m             \u001b[1;36m4.000000\u001b[0m         \u001b[1;36m1.000000\u001b[0m\n",
       "\u001b[1;36m1\u001b[0m%            \u001b[1;36m1017.911851\u001b[0m            \u001b[1;36m18.000000\u001b[0m         \u001b[1;36m1.000000\u001b[0m\n",
       "\u001b[1;36m5\u001b[0m%            \u001b[1;36m1548.780488\u001b[0m            \u001b[1;36m25.000000\u001b[0m        \u001b[1;36m28.000000\u001b[0m\n",
       "\u001b[1;36m10\u001b[0m%           \u001b[1;36m1847.222222\u001b[0m            \u001b[1;36m30.000000\u001b[0m        \u001b[1;36m36.200000\u001b[0m\n",
       "\u001b[1;36m25\u001b[0m%           \u001b[1;36m2421.875000\u001b[0m            \u001b[1;36m41.000000\u001b[0m        \u001b[1;36m59.250000\u001b[0m\n",
       "\u001b[1;36m50\u001b[0m%           \u001b[1;36m3097.826087\u001b[0m            \u001b[1;36m57.000000\u001b[0m       \u001b[1;36m153.000000\u001b[0m\n",
       "\u001b[1;36m75\u001b[0m%           \u001b[1;36m3783.333333\u001b[0m            \u001b[1;36m70.000000\u001b[0m       \u001b[1;36m490.250000\u001b[0m\n",
       "\u001b[1;36m90\u001b[0m%           \u001b[1;36m4392.857143\u001b[0m            \u001b[1;36m84.000000\u001b[0m      \u001b[1;36m3210.900000\u001b[0m\n",
       "\u001b[1;36m95\u001b[0m%           \u001b[1;36m4821.428571\u001b[0m            \u001b[1;36m93.000000\u001b[0m      \u001b[1;36m5661.200000\u001b[0m\n",
       "\u001b[1;36m99\u001b[0m%           \u001b[1;36m6071.428571\u001b[0m           \u001b[1;36m115.000000\u001b[0m     \u001b[1;36m17313.470000\u001b[0m\n",
       "max         \u001b[1;36m157352.941176\u001b[0m           \u001b[1;36m265.000000\u001b[0m     \u001b[1;36m61470.000000\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************************************************************\n",
      "Number of rooms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">    nombre_pieces_principales  proportion\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.343987</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.296712</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.168863</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.145210</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.039085</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.004778</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000505</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000326</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>                         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000267</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000059</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000059</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>                        <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000059</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">66.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000030</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span>                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000030</span>\n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22.0</span>    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.000030</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "    nombre_pieces_principales  proportion\n",
       "\u001b[1;36m0\u001b[0m                         \u001b[1;36m3.0\u001b[0m    \u001b[1;36m0.343987\u001b[0m\n",
       "\u001b[1;36m1\u001b[0m                         \u001b[1;36m2.0\u001b[0m    \u001b[1;36m0.296712\u001b[0m\n",
       "\u001b[1;36m2\u001b[0m                         \u001b[1;36m4.0\u001b[0m    \u001b[1;36m0.168863\u001b[0m\n",
       "\u001b[1;36m3\u001b[0m                         \u001b[1;36m1.0\u001b[0m    \u001b[1;36m0.145210\u001b[0m\n",
       "\u001b[1;36m4\u001b[0m                         \u001b[1;36m5.0\u001b[0m    \u001b[1;36m0.039085\u001b[0m\n",
       "\u001b[1;36m5\u001b[0m                         \u001b[1;36m6.0\u001b[0m    \u001b[1;36m0.004778\u001b[0m\n",
       "\u001b[1;36m6\u001b[0m                         \u001b[1;36m7.0\u001b[0m    \u001b[1;36m0.000505\u001b[0m\n",
       "\u001b[1;36m7\u001b[0m                         \u001b[1;36m0.0\u001b[0m    \u001b[1;36m0.000326\u001b[0m\n",
       "\u001b[1;36m8\u001b[0m                         \u001b[1;36m8.0\u001b[0m    \u001b[1;36m0.000267\u001b[0m\n",
       "\u001b[1;36m9\u001b[0m                        \u001b[1;36m11.0\u001b[0m    \u001b[1;36m0.000059\u001b[0m\n",
       "\u001b[1;36m10\u001b[0m                       \u001b[1;36m10.0\u001b[0m    \u001b[1;36m0.000059\u001b[0m\n",
       "\u001b[1;36m11\u001b[0m                        \u001b[1;36m9.0\u001b[0m    \u001b[1;36m0.000059\u001b[0m\n",
       "\u001b[1;36m12\u001b[0m                       \u001b[1;36m66.0\u001b[0m    \u001b[1;36m0.000030\u001b[0m\n",
       "\u001b[1;36m13\u001b[0m                       \u001b[1;36m12.0\u001b[0m    \u001b[1;36m0.000030\u001b[0m\n",
       "\u001b[1;36m14\u001b[0m                       \u001b[1;36m22.0\u001b[0m    \u001b[1;36m0.000030\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summarize_dataset(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Préparation du dataset\n",
    "\n",
    "Application des filtres statiques sur les variables qui décrivent le bien via le dictionnaire `numeric_filters` : \n",
    "- nombre de pièces principales\n",
    "- surface du bien\n",
    "- valeur foncière/m² (cf: [meilleursagents](https://www.meilleursagents.com/prix-immobilier/)) \n",
    "- surface du terrain (pour les maisons)\n",
    "\n",
    "Le paramètre `na_thresold` indique la proportion maximale de données manquantes que l'on autorise pour appliquer les techniques d'imputation (par la médiane pour les valeurs numériques, par le mode pour les catégorielles). Les colonnes ayant une proportion de donnnées manquantes supérieures à `na_thresold` sont supprimées. \n",
    "\n",
    "Les paramètres de `mov_av_windows` sont utilisées pour calculer les moyennes mobiles du prix moyen journalier ($\\approx$ prix du marché à court/moyen/long terme). Notons que l'on applique un lag de 1 jour puisqu'il est nécessaire d'utiliser le prix du marché à la date $t-1$ pour expliquer le prix du bien vendu à la date $t$. Lorsque le paramètre `neighborhood_var` est renseignée, on calcule le prix du marché à court/moyen/long terme par quartier dans la base étudiée. Un quartier peut être un arrondissement, une commune ou un département. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Preprocessing summary:\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Preprocessing summary:\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'created'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_surface_reelle_bati'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_hauteur_mean'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_altitude_sol_mean'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_mean'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_mean'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_std'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_std'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_min'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_min'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_max'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_max'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_ratio_ges_conso'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_baie_u'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_mur_u_ext'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_pb_u'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_ph_u'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_prc_s_vitree_ext'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'valeur_fonciere_m2'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma7'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma14'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma30'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma90'</span>\n",
       "    <span style=\"font-weight: bold\">]</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'removed'</span>: <span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'surface_terrain'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'distance_batiment_historique_plus_proche'</span><span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
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       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'created'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "        \u001b[32m'l_valeur_fonciere'\u001b[0m,\n",
       "        \u001b[32m'l_surface_reelle_bati'\u001b[0m,\n",
       "        \u001b[32m'l_hauteur_mean'\u001b[0m,\n",
       "        \u001b[32m'l_altitude_sol_mean'\u001b[0m,\n",
       "        \u001b[32m'l_conso_ener_mean'\u001b[0m,\n",
       "        \u001b[32m'l_estim_ges_mean'\u001b[0m,\n",
       "        \u001b[32m'l_conso_ener_std'\u001b[0m,\n",
       "        \u001b[32m'l_estim_ges_std'\u001b[0m,\n",
       "        \u001b[32m'l_conso_ener_min'\u001b[0m,\n",
       "        \u001b[32m'l_estim_ges_min'\u001b[0m,\n",
       "        \u001b[32m'l_conso_ener_max'\u001b[0m,\n",
       "        \u001b[32m'l_estim_ges_max'\u001b[0m,\n",
       "        \u001b[32m'l_ratio_ges_conso'\u001b[0m,\n",
       "        \u001b[32m'l_baie_u'\u001b[0m,\n",
       "        \u001b[32m'l_mur_u_ext'\u001b[0m,\n",
       "        \u001b[32m'l_pb_u'\u001b[0m,\n",
       "        \u001b[32m'l_ph_u'\u001b[0m,\n",
       "        \u001b[32m'l_prc_s_vitree_ext'\u001b[0m,\n",
       "        \u001b[32m'valeur_fonciere_m2'\u001b[0m,\n",
       "        \u001b[32m'l_valeur_fonciere_ma7'\u001b[0m,\n",
       "        \u001b[32m'l_valeur_fonciere_ma14'\u001b[0m,\n",
       "        \u001b[32m'l_valeur_fonciere_ma30'\u001b[0m,\n",
       "        \u001b[32m'l_valeur_fonciere_ma90'\u001b[0m\n",
       "    \u001b[1m]\u001b[0m,\n",
       "    \u001b[32m'removed'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'surface_terrain'\u001b[0m, \u001b[32m'distance_batiment_historique_plus_proche'\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "preproc_args = {\n",
    "\n",
    "    \"numeric_filters\": {\n",
    "        \"nombre_pieces_principales\": (1, 6),\n",
    "        \"surface_reelle_bati\": (18, 120), \n",
    "        # \"surface_terrain\": (40, 1200), \n",
    "        \"valeur_fonciere_m2\": (1000, 7000)\n",
    "    },  \n",
    "\n",
    "    \"na_threshold\": 0.5,\n",
    "\n",
    "    \"mov_av_windows\": [7, 14, 30, 90], \n",
    "\n",
    "    \"neighborhood_var\": \"nom_commune\"\n",
    "    \n",
    "}\n",
    "\n",
    "df_preproc, dvf_vars, bnb_vars, other_vars = prepare_dataset(df, target_var=\"l_valeur_fonciere\", **preproc_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_mutation</th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>dependance</th>\n",
       "      <th>nom_departement</th>\n",
       "      <th>trimestre</th>\n",
       "      <th>mois</th>\n",
       "      <th>periode_construction</th>\n",
       "      <th>periode_construction_max</th>\n",
       "      <th>hauteur_mean</th>\n",
       "      <th>altitude_sol_mean</th>\n",
       "      <th>conso_ener_mean</th>\n",
       "      <th>estim_ges_mean</th>\n",
       "      <th>conso_ener_std</th>\n",
       "      <th>estim_ges_std</th>\n",
       "      <th>conso_ener_min</th>\n",
       "      <th>estim_ges_min</th>\n",
       "      <th>conso_ener_max</th>\n",
       "      <th>estim_ges_max</th>\n",
       "      <th>ratio_ges_conso</th>\n",
       "      <th>enr_solaire_photovoltaique</th>\n",
       "      <th>enr_solaire_thermique_(chauffage)solaire_thermique_(ecs)</th>\n",
       "      <th>enr_solaire_thermique_(ecs+chauffage)</th>\n",
       "      <th>baie_u</th>\n",
       "      <th>mur_u_ext</th>\n",
       "      <th>pb_u</th>\n",
       "      <th>ph_u</th>\n",
       "      <th>mur_pos_isol_ext</th>\n",
       "      <th>prc_s_vitree_ext</th>\n",
       "      <th>presence_balcon</th>\n",
       "      <th>presence_climatisation</th>\n",
       "      <th>baie_orientation_indetermine</th>\n",
       "      <th>baie_orientation_nord</th>\n",
       "      <th>baie_orientation_ouest</th>\n",
       "      <th>baie_orientation_est</th>\n",
       "      <th>baie_orientation_horizontale</th>\n",
       "      <th>baie_orientation_est_ou_ouest</th>\n",
       "      <th>baie_orientation_sud</th>\n",
       "      <th>qpv</th>\n",
       "      <th>nb_lot_garpark</th>\n",
       "      <th>nb_lot_tot</th>\n",
       "      <th>nb_log</th>\n",
       "      <th>nb_lot_tertiaire</th>\n",
       "      <th>alea_argiles</th>\n",
       "      <th>alea_radon</th>\n",
       "      <th>l_valeur_fonciere</th>\n",
       "      <th>l_surface_reelle_bati</th>\n",
       "      <th>l_hauteur_mean</th>\n",
       "      <th>l_altitude_sol_mean</th>\n",
       "      <th>l_conso_ener_mean</th>\n",
       "      <th>l_estim_ges_mean</th>\n",
       "      <th>l_conso_ener_std</th>\n",
       "      <th>l_estim_ges_std</th>\n",
       "      <th>l_conso_ener_min</th>\n",
       "      <th>l_estim_ges_min</th>\n",
       "      <th>l_conso_ener_max</th>\n",
       "      <th>l_estim_ges_max</th>\n",
       "      <th>l_ratio_ges_conso</th>\n",
       "      <th>l_baie_u</th>\n",
       "      <th>l_mur_u_ext</th>\n",
       "      <th>l_pb_u</th>\n",
       "      <th>l_ph_u</th>\n",
       "      <th>l_prc_s_vitree_ext</th>\n",
       "      <th>valeur_fonciere_m2</th>\n",
       "      <th>l_valeur_fonciere_ma7</th>\n",
       "      <th>l_valeur_fonciere_ma14</th>\n",
       "      <th>l_valeur_fonciere_ma30</th>\n",
       "      <th>l_valeur_fonciere_ma90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-554686</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>47500.0</td>\n",
       "      <td>Le Mée-sur-Seine</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>&lt;1948</td>\n",
       "      <td>DE_1975_A_1993</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>56.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>70.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>238.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fort</td>\n",
       "      <td>Faible</td>\n",
       "      <td>10.768485</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>4.928702</td>\n",
       "      <td>3.152736</td>\n",
       "      <td>4.041295</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>4.252772</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.472691</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>-3.912023</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>1439.393939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-554687</td>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>Brie-Comte-Robert</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>&lt;1948</td>\n",
       "      <td>DE_1975_A_1993</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Moyen</td>\n",
       "      <td>Faible</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>3536.585366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-554690</td>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>Brie-Comte-Robert</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>&lt;1948</td>\n",
       "      <td>DE_1975_A_1993</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Moyen</td>\n",
       "      <td>Faible</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>3452.380952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-554698</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>127000.0</td>\n",
       "      <td>Savigny-le-Temple</td>\n",
       "      <td>77.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>&lt;1948</td>\n",
       "      <td>DE_1975_A_1993</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Moyen</td>\n",
       "      <td>Faible</td>\n",
       "      <td>11.751942</td>\n",
       "      <td>4.343805</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>1649.350649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-554702</td>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>Lieusaint</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Seine-et-Marne</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2006-2012</td>\n",
       "      <td>DE_2001_A_2010</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>213.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>171.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.2</td>\n",
       "      <td>ITI</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Moyen</td>\n",
       "      <td>Faible</td>\n",
       "      <td>11.976659</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>5.364573</td>\n",
       "      <td>2.332144</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>5.141664</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>5.613128</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>-1.021651</td>\n",
       "      <td>-1.309333</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>-1.748700</td>\n",
       "      <td>3117.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_mutation date_mutation  valeur_fonciere        nom_commune  \\\n",
       "0  2017-554686    2017-07-04          47500.0   Le Mée-sur-Seine   \n",
       "1  2017-554687    2017-07-04         145000.0  Brie-Comte-Robert   \n",
       "2  2017-554690    2017-07-05         145000.0  Brie-Comte-Robert   \n",
       "3  2017-554698    2017-07-03         127000.0  Savigny-le-Temple   \n",
       "4  2017-554702    2017-07-06         159000.0          Lieusaint   \n",
       "\n",
       "   surface_reelle_bati nombre_pieces_principales  dependance nom_departement  \\\n",
       "0                 33.0                         1           1  Seine-et-Marne   \n",
       "1                 41.0                         2           1  Seine-et-Marne   \n",
       "2                 42.0                         2           1  Seine-et-Marne   \n",
       "3                 77.0                         4           1  Seine-et-Marne   \n",
       "4                 51.0                         2           1  Seine-et-Marne   \n",
       "\n",
       "  trimestre mois periode_construction periode_construction_max  hauteur_mean  \\\n",
       "0         3    7                <1948           DE_1975_A_1993          33.0   \n",
       "1         3    7                <1948           DE_1975_A_1993          10.0   \n",
       "2         3    7                <1948           DE_1975_A_1993          10.0   \n",
       "3         3    7                <1948           DE_1975_A_1993          10.0   \n",
       "4         3    7            2006-2012           DE_2001_A_2010           6.0   \n",
       "\n",
       "   altitude_sol_mean  conso_ener_mean  estim_ges_mean  conso_ener_std  \\\n",
       "0               67.0            138.2            23.4            56.9   \n",
       "1               79.0            235.8            16.8            52.7   \n",
       "2               79.0            235.8            16.8            52.7   \n",
       "3               79.0            235.8            16.8            52.7   \n",
       "4               85.0            213.7            10.3            34.6   \n",
       "\n",
       "   estim_ges_std  conso_ener_min  estim_ges_min  conso_ener_max  \\\n",
       "0           19.0            70.3            2.0           238.1   \n",
       "1            5.8           162.1            9.4           322.5   \n",
       "2            5.8           162.1            9.4           322.5   \n",
       "3            5.8           162.1            9.4           322.5   \n",
       "4            2.2           171.0            8.0           274.0   \n",
       "\n",
       "   estim_ges_max  ratio_ges_conso  enr_solaire_photovoltaique  \\\n",
       "0           51.0             0.02                         0.0   \n",
       "1           28.0             0.05                         0.0   \n",
       "2           28.0             0.05                         0.0   \n",
       "3           28.0             0.05                         0.0   \n",
       "4           15.0             0.05                         0.0   \n",
       "\n",
       "   enr_solaire_thermique_(chauffage)solaire_thermique_(ecs)  \\\n",
       "0                                                0.0          \n",
       "1                                                0.0          \n",
       "2                                                0.0          \n",
       "3                                                0.0          \n",
       "4                                                0.0          \n",
       "\n",
       "   enr_solaire_thermique_(ecs+chauffage)  baie_u  mur_u_ext  pb_u  ph_u  \\\n",
       "0                                    0.0     2.3       0.43  1.45   1.2   \n",
       "1                                    0.0     2.3       0.43  1.45   1.2   \n",
       "2                                    0.0     2.3       0.43  1.45   1.2   \n",
       "3                                    0.0     2.3       0.43  1.45   1.2   \n",
       "4                                    0.0     2.2       0.36  0.27   0.2   \n",
       "\n",
       "  mur_pos_isol_ext  prc_s_vitree_ext  presence_balcon  presence_climatisation  \\\n",
       "0              ITI             0.254              0.0                     0.0   \n",
       "1              ITI             0.254              0.0                     0.0   \n",
       "2              ITI             0.254              0.0                     0.0   \n",
       "3              ITI             0.254              0.0                     0.0   \n",
       "4              ITI             0.174              0.0                     0.0   \n",
       "\n",
       "   baie_orientation_indetermine  baie_orientation_nord  \\\n",
       "0                           0.0                    1.0   \n",
       "1                           0.0                    0.0   \n",
       "2                           0.0                    0.0   \n",
       "3                           0.0                    0.0   \n",
       "4                           0.0                    0.0   \n",
       "\n",
       "   baie_orientation_ouest  baie_orientation_est  baie_orientation_horizontale  \\\n",
       "0                     0.0                   0.0                           0.0   \n",
       "1                     0.0                   0.0                           0.0   \n",
       "2                     0.0                   0.0                           0.0   \n",
       "3                     0.0                   0.0                           0.0   \n",
       "4                     1.0                   0.0                           0.0   \n",
       "\n",
       "   baie_orientation_est_ou_ouest  baie_orientation_sud  qpv  nb_lot_garpark  \\\n",
       "0                            0.0                   0.0  0.0            90.0   \n",
       "1                            0.0                   0.0  0.0            35.0   \n",
       "2                            0.0                   0.0  0.0            35.0   \n",
       "3                            0.0                   0.0  0.0            35.0   \n",
       "4                            0.0                   0.0  0.0           108.0   \n",
       "\n",
       "   nb_lot_tot  nb_log  nb_lot_tertiaire alea_argiles alea_radon  \\\n",
       "0       269.0   179.0               0.0         Fort     Faible   \n",
       "1        89.0    36.5               0.0        Moyen     Faible   \n",
       "2        89.0    36.5               0.0        Moyen     Faible   \n",
       "3        89.0    36.5               0.0        Moyen     Faible   \n",
       "4       213.0    81.0               0.0        Moyen     Faible   \n",
       "\n",
       "   l_valeur_fonciere  l_surface_reelle_bati  l_hauteur_mean  \\\n",
       "0          10.768485               3.496508        3.496508   \n",
       "1          11.884489               3.713572        2.302585   \n",
       "2          11.884489               3.737670        2.302585   \n",
       "3          11.751942               4.343805        2.302585   \n",
       "4          11.976659               3.931826        1.791759   \n",
       "\n",
       "   l_altitude_sol_mean  l_conso_ener_mean  l_estim_ges_mean  l_conso_ener_std  \\\n",
       "0             4.204693           4.928702          3.152736          4.041295   \n",
       "1             4.369448           5.462984          2.821379          3.964615   \n",
       "2             4.369448           5.462984          2.821379          3.964615   \n",
       "3             4.369448           5.462984          2.821379          3.964615   \n",
       "4             4.442651           5.364573          2.332144          3.543854   \n",
       "\n",
       "   l_estim_ges_std  l_conso_ener_min  l_estim_ges_min  l_conso_ener_max  \\\n",
       "0         2.944439          4.252772         0.693147          5.472691   \n",
       "1         1.757858          5.088213         2.240710          5.776103   \n",
       "2         1.757858          5.088213         2.240710          5.776103   \n",
       "3         1.757858          5.088213         2.240710          5.776103   \n",
       "4         0.788457          5.141664         2.079442          5.613128   \n",
       "\n",
       "   l_estim_ges_max  l_ratio_ges_conso  l_baie_u  l_mur_u_ext    l_pb_u  \\\n",
       "0         3.931826          -3.912023  0.832909    -0.843970  0.371564   \n",
       "1         3.332205          -2.995732  0.832909    -0.843970  0.371564   \n",
       "2         3.332205          -2.995732  0.832909    -0.843970  0.371564   \n",
       "3         3.332205          -2.995732  0.832909    -0.843970  0.371564   \n",
       "4         2.708050          -2.995732  0.788457    -1.021651 -1.309333   \n",
       "\n",
       "     l_ph_u  l_prc_s_vitree_ext  valeur_fonciere_m2  l_valeur_fonciere_ma7  \\\n",
       "0  0.182322           -1.370421         1439.393939                    NaN   \n",
       "1  0.182322           -1.370421         3536.585366                    NaN   \n",
       "2  0.182322           -1.370421         3452.380952                    NaN   \n",
       "3  0.182322           -1.370421         1649.350649                    NaN   \n",
       "4 -1.609438           -1.748700         3117.647059                    NaN   \n",
       "\n",
       "   l_valeur_fonciere_ma14  l_valeur_fonciere_ma30  l_valeur_fonciere_ma90  \n",
       "0                     NaN                     NaN                     NaN  \n",
       "1                     NaN                     NaN                     NaN  \n",
       "2                     NaN                     NaN                     NaN  \n",
       "3                     NaN                     NaN                     NaN  \n",
       "4                     NaN                     NaN                     NaN  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preproc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32769, 71)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preproc.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encodage des dummies\n",
    "\n",
    "Cette section permet de transformer les variables catégorielles en variables binaires. Pour chaque variable, la catégorie de référence correspond à la plus fréquente dans le jeu de données. Cette catégorie de référence est ensuite supprimée afin d'éviter tout problème de multicolinéarité dans la matrice des variables explicatives. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nombre_pieces_principales',\n",
       " 'nom_departement',\n",
       " 'trimestre',\n",
       " 'mois',\n",
       " 'periode_construction',\n",
       " 'periode_construction_max',\n",
       " 'mur_pos_isol_ext',\n",
       " 'alea_argiles',\n",
       " 'alea_radon',\n",
       " 'baie_orientation']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical variable = pandas column with <= 30 unique values\n",
    "\n",
    "categorical_vars = get_categorical_vars(df_preproc, n_levels_max=30)   \n",
    "categorical_vars.append(\"baie_orientation\")\n",
    "\n",
    "categorical_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'baie_orientation': 'nord',\n",
       " 'nombre_pieces_principales': '3',\n",
       " 'nom_departement': 'Seine-et-Marne',\n",
       " 'trimestre': '3',\n",
       " 'mois': '6',\n",
       " 'periode_construction': '<1948',\n",
       " 'periode_construction_max': 'DE_1975_A_1993',\n",
       " 'mur_pos_isol_ext': 'ITI',\n",
       " 'alea_argiles': 'Moyen',\n",
       " 'alea_radon': 'Faible'}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select most representative levels for each categorical variable\n",
    "dummy_ref_levels = get_most_frequent_levels(df_preproc, categorical_vars)\n",
    "dummy_ref_levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Preprocessing summary:\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Preprocessing summary:\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'created'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITE'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad sup'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1994_A_2000'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'alea_argiles_Fort'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_non isole'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_isole'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1975_A_1993'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1970-1988'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE+ITR'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1989-1999'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_2001_A_2010'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITR'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'alea_argiles_Moyen'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1961_A_1974'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_&gt;2012'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1949_A_1960'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_non renseigné'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_&lt;1948'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2000-2005'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1949-1970'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'alea_radon_Faible'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_APRES_2011'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_AVANT_1949'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad inf'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITR'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2006-2012'</span>\n",
       "    <span style=\"font-weight: bold\">]</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'removed'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_nord'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_3'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'nom_departement_Seine-et-Marne'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_3'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mois_6'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_&lt;1948'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1975_A_1993'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'alea_argiles_Moyen'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'alea_radon_Faible'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'enr_solaire_thermique_(chauffage)solaire_thermique_(ecs)'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'enr_solaire_thermique_(ecs+chauffage)'</span>\n",
       "    <span style=\"font-weight: bold\">]</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'created'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "        \u001b[32m'mur_pos_isol_ext_ITI+ITE'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_bad sup'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITI'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_1994_A_2000'\u001b[0m,\n",
       "        \u001b[32m'alea_argiles_Fort'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_non isole'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_isole'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_1975_A_1993'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_1970-1988'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITE'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITE+ITR'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_1989-1999'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_2001_A_2010'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITR'\u001b[0m,\n",
       "        \u001b[32m'alea_argiles_Moyen'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_1961_A_1974'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_>2012'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_1949_A_1960'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_non renseigné'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_<1948'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_2000-2005'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_1949-1970'\u001b[0m,\n",
       "        \u001b[32m'alea_radon_Faible'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_APRES_2011'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_AVANT_1949'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_bad inf'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITI+ITR'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_2006-2012'\u001b[0m\n",
       "    \u001b[1m]\u001b[0m,\n",
       "    \u001b[32m'removed'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "        \u001b[32m'baie_orientation_nord'\u001b[0m,\n",
       "        \u001b[32m'nombre_pieces_principales_3'\u001b[0m,\n",
       "        \u001b[32m'nom_departement_Seine-et-Marne'\u001b[0m,\n",
       "        \u001b[32m'trimestre_3'\u001b[0m,\n",
       "        \u001b[32m'mois_6'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_<1948'\u001b[0m,\n",
       "        \u001b[32m'periode_construction_max_DE_1975_A_1993'\u001b[0m,\n",
       "        \u001b[32m'mur_pos_isol_ext_ITI'\u001b[0m,\n",
       "        \u001b[32m'alea_argiles_Moyen'\u001b[0m,\n",
       "        \u001b[32m'alea_radon_Faible'\u001b[0m,\n",
       "        \u001b[32m'enr_solaire_thermique_\u001b[0m\u001b[32m(\u001b[0m\u001b[32mchauffage\u001b[0m\u001b[32m)\u001b[0m\u001b[32msolaire_thermique_\u001b[0m\u001b[32m(\u001b[0m\u001b[32mecs\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m,\n",
       "        \u001b[32m'enr_solaire_thermique_\u001b[0m\u001b[32m(\u001b[0m\u001b[32mecs+chauffage\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\n",
       "    \u001b[1m]\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preproc, dvf_vars, bnb_vars = prepare_dummies(\n",
    "    df_preproc, \n",
    "    categorical_vars, \n",
    "    dummy_ref_levels, \n",
    "    dvf_vars, \n",
    "    bnb_vars, \n",
    "    other_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set id_mutation as index \n",
    "\n",
    "df_preproc.set_index(\"id_mutation\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>date_mutation</th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>dependance</th>\n",
       "      <th>hauteur_mean</th>\n",
       "      <th>altitude_sol_mean</th>\n",
       "      <th>conso_ener_mean</th>\n",
       "      <th>estim_ges_mean</th>\n",
       "      <th>conso_ener_std</th>\n",
       "      <th>estim_ges_std</th>\n",
       "      <th>conso_ener_min</th>\n",
       "      <th>estim_ges_min</th>\n",
       "      <th>conso_ener_max</th>\n",
       "      <th>estim_ges_max</th>\n",
       "      <th>ratio_ges_conso</th>\n",
       "      <th>enr_solaire_photovoltaique</th>\n",
       "      <th>baie_u</th>\n",
       "      <th>mur_u_ext</th>\n",
       "      <th>pb_u</th>\n",
       "      <th>ph_u</th>\n",
       "      <th>prc_s_vitree_ext</th>\n",
       "      <th>presence_balcon</th>\n",
       "      <th>presence_climatisation</th>\n",
       "      <th>baie_orientation_indetermine</th>\n",
       "      <th>baie_orientation_ouest</th>\n",
       "      <th>baie_orientation_est</th>\n",
       "      <th>baie_orientation_horizontale</th>\n",
       "      <th>baie_orientation_est_ou_ouest</th>\n",
       "      <th>baie_orientation_sud</th>\n",
       "      <th>qpv</th>\n",
       "      <th>nb_lot_garpark</th>\n",
       "      <th>nb_lot_tot</th>\n",
       "      <th>nb_log</th>\n",
       "      <th>nb_lot_tertiaire</th>\n",
       "      <th>l_valeur_fonciere</th>\n",
       "      <th>l_surface_reelle_bati</th>\n",
       "      <th>l_hauteur_mean</th>\n",
       "      <th>l_altitude_sol_mean</th>\n",
       "      <th>l_conso_ener_mean</th>\n",
       "      <th>l_estim_ges_mean</th>\n",
       "      <th>l_conso_ener_std</th>\n",
       "      <th>l_estim_ges_std</th>\n",
       "      <th>l_conso_ener_min</th>\n",
       "      <th>l_estim_ges_min</th>\n",
       "      <th>l_conso_ener_max</th>\n",
       "      <th>l_estim_ges_max</th>\n",
       "      <th>l_ratio_ges_conso</th>\n",
       "      <th>l_baie_u</th>\n",
       "      <th>l_mur_u_ext</th>\n",
       "      <th>l_pb_u</th>\n",
       "      <th>l_ph_u</th>\n",
       "      <th>l_prc_s_vitree_ext</th>\n",
       "      <th>valeur_fonciere_m2</th>\n",
       "      <th>l_valeur_fonciere_ma7</th>\n",
       "      <th>l_valeur_fonciere_ma14</th>\n",
       "      <th>l_valeur_fonciere_ma30</th>\n",
       "      <th>l_valeur_fonciere_ma90</th>\n",
       "      <th>nombre_pieces_principales_1</th>\n",
       "      <th>nombre_pieces_principales_2</th>\n",
       "      <th>nombre_pieces_principales_4</th>\n",
       "      <th>nombre_pieces_principales_5</th>\n",
       "      <th>nombre_pieces_principales_6</th>\n",
       "      <th>trimestre_1</th>\n",
       "      <th>trimestre_2</th>\n",
       "      <th>trimestre_4</th>\n",
       "      <th>mois_1</th>\n",
       "      <th>mois_10</th>\n",
       "      <th>mois_11</th>\n",
       "      <th>mois_12</th>\n",
       "      <th>mois_2</th>\n",
       "      <th>mois_3</th>\n",
       "      <th>mois_4</th>\n",
       "      <th>mois_5</th>\n",
       "      <th>mois_7</th>\n",
       "      <th>mois_8</th>\n",
       "      <th>mois_9</th>\n",
       "      <th>periode_construction_1949-1970</th>\n",
       "      <th>periode_construction_1970-1988</th>\n",
       "      <th>periode_construction_1989-1999</th>\n",
       "      <th>periode_construction_2000-2005</th>\n",
       "      <th>periode_construction_2006-2012</th>\n",
       "      <th>periode_construction_&gt;2012</th>\n",
       "      <th>periode_construction_bad inf</th>\n",
       "      <th>periode_construction_bad sup</th>\n",
       "      <th>periode_construction_max_APRES_2011</th>\n",
       "      <th>periode_construction_max_AVANT_1949</th>\n",
       "      <th>periode_construction_max_DE_1949_A_1960</th>\n",
       "      <th>periode_construction_max_DE_1961_A_1974</th>\n",
       "      <th>periode_construction_max_DE_1994_A_2000</th>\n",
       "      <th>periode_construction_max_DE_2001_A_2010</th>\n",
       "      <th>periode_construction_max_non renseigné</th>\n",
       "      <th>mur_pos_isol_ext_ITE</th>\n",
       "      <th>mur_pos_isol_ext_ITE+ITR</th>\n",
       "      <th>mur_pos_isol_ext_ITI+ITE</th>\n",
       "      <th>mur_pos_isol_ext_ITI+ITR</th>\n",
       "      <th>mur_pos_isol_ext_ITR</th>\n",
       "      <th>mur_pos_isol_ext_isole</th>\n",
       "      <th>mur_pos_isol_ext_non isole</th>\n",
       "      <th>alea_argiles_Fort</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-554686</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>47500.0</td>\n",
       "      <td>Le Mée-sur-Seine</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>56.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>70.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>238.1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.768485</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>4.928702</td>\n",
       "      <td>3.152736</td>\n",
       "      <td>4.041295</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>4.252772</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.472691</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>-3.912023</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>1439.393939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554687</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>Brie-Comte-Robert</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>3536.585366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554690</th>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>Brie-Comte-Robert</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>3452.380952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554698</th>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>127000.0</td>\n",
       "      <td>Savigny-le-Temple</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>235.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>52.7</td>\n",
       "      <td>5.8</td>\n",
       "      <td>162.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>322.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.751942</td>\n",
       "      <td>4.343805</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>1649.350649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554702</th>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>Lieusaint</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>213.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>171.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.976659</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>5.364573</td>\n",
       "      <td>2.332144</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>5.141664</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>5.613128</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>-1.021651</td>\n",
       "      <td>-1.309333</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>-1.748700</td>\n",
       "      <td>3117.647059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_mutation  valeur_fonciere        nom_commune  \\\n",
       "id_mutation                                                     \n",
       "2017-554686    2017-07-04          47500.0   Le Mée-sur-Seine   \n",
       "2017-554687    2017-07-04         145000.0  Brie-Comte-Robert   \n",
       "2017-554690    2017-07-05         145000.0  Brie-Comte-Robert   \n",
       "2017-554698    2017-07-03         127000.0  Savigny-le-Temple   \n",
       "2017-554702    2017-07-06         159000.0          Lieusaint   \n",
       "\n",
       "             surface_reelle_bati  dependance  hauteur_mean  altitude_sol_mean  \\\n",
       "id_mutation                                                                     \n",
       "2017-554686                 33.0           1          33.0               67.0   \n",
       "2017-554687                 41.0           1          10.0               79.0   \n",
       "2017-554690                 42.0           1          10.0               79.0   \n",
       "2017-554698                 77.0           1          10.0               79.0   \n",
       "2017-554702                 51.0           1           6.0               85.0   \n",
       "\n",
       "             conso_ener_mean  estim_ges_mean  conso_ener_std  estim_ges_std  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686            138.2            23.4            56.9           19.0   \n",
       "2017-554687            235.8            16.8            52.7            5.8   \n",
       "2017-554690            235.8            16.8            52.7            5.8   \n",
       "2017-554698            235.8            16.8            52.7            5.8   \n",
       "2017-554702            213.7            10.3            34.6            2.2   \n",
       "\n",
       "             conso_ener_min  estim_ges_min  conso_ener_max  estim_ges_max  \\\n",
       "id_mutation                                                                 \n",
       "2017-554686            70.3            2.0           238.1           51.0   \n",
       "2017-554687           162.1            9.4           322.5           28.0   \n",
       "2017-554690           162.1            9.4           322.5           28.0   \n",
       "2017-554698           162.1            9.4           322.5           28.0   \n",
       "2017-554702           171.0            8.0           274.0           15.0   \n",
       "\n",
       "             ratio_ges_conso  enr_solaire_photovoltaique  baie_u  mur_u_ext  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686             0.02                         0.0     2.3       0.43   \n",
       "2017-554687             0.05                         0.0     2.3       0.43   \n",
       "2017-554690             0.05                         0.0     2.3       0.43   \n",
       "2017-554698             0.05                         0.0     2.3       0.43   \n",
       "2017-554702             0.05                         0.0     2.2       0.36   \n",
       "\n",
       "             pb_u  ph_u  prc_s_vitree_ext  presence_balcon  \\\n",
       "id_mutation                                                  \n",
       "2017-554686  1.45   1.2             0.254              0.0   \n",
       "2017-554687  1.45   1.2             0.254              0.0   \n",
       "2017-554690  1.45   1.2             0.254              0.0   \n",
       "2017-554698  1.45   1.2             0.254              0.0   \n",
       "2017-554702  0.27   0.2             0.174              0.0   \n",
       "\n",
       "             presence_climatisation  baie_orientation_indetermine  \\\n",
       "id_mutation                                                         \n",
       "2017-554686                     0.0                           0.0   \n",
       "2017-554687                     0.0                           0.0   \n",
       "2017-554690                     0.0                           0.0   \n",
       "2017-554698                     0.0                           0.0   \n",
       "2017-554702                     0.0                           0.0   \n",
       "\n",
       "             baie_orientation_ouest  baie_orientation_est  \\\n",
       "id_mutation                                                 \n",
       "2017-554686                     0.0                   0.0   \n",
       "2017-554687                     0.0                   0.0   \n",
       "2017-554690                     0.0                   0.0   \n",
       "2017-554698                     0.0                   0.0   \n",
       "2017-554702                     1.0                   0.0   \n",
       "\n",
       "             baie_orientation_horizontale  baie_orientation_est_ou_ouest  \\\n",
       "id_mutation                                                                \n",
       "2017-554686                           0.0                            0.0   \n",
       "2017-554687                           0.0                            0.0   \n",
       "2017-554690                           0.0                            0.0   \n",
       "2017-554698                           0.0                            0.0   \n",
       "2017-554702                           0.0                            0.0   \n",
       "\n",
       "             baie_orientation_sud  qpv  nb_lot_garpark  nb_lot_tot  nb_log  \\\n",
       "id_mutation                                                                  \n",
       "2017-554686                   0.0  0.0            90.0       269.0   179.0   \n",
       "2017-554687                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554690                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554698                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554702                   0.0  0.0           108.0       213.0    81.0   \n",
       "\n",
       "             nb_lot_tertiaire  l_valeur_fonciere  l_surface_reelle_bati  \\\n",
       "id_mutation                                                               \n",
       "2017-554686               0.0          10.768485               3.496508   \n",
       "2017-554687               0.0          11.884489               3.713572   \n",
       "2017-554690               0.0          11.884489               3.737670   \n",
       "2017-554698               0.0          11.751942               4.343805   \n",
       "2017-554702               0.0          11.976659               3.931826   \n",
       "\n",
       "             l_hauteur_mean  l_altitude_sol_mean  l_conso_ener_mean  \\\n",
       "id_mutation                                                           \n",
       "2017-554686        3.496508             4.204693           4.928702   \n",
       "2017-554687        2.302585             4.369448           5.462984   \n",
       "2017-554690        2.302585             4.369448           5.462984   \n",
       "2017-554698        2.302585             4.369448           5.462984   \n",
       "2017-554702        1.791759             4.442651           5.364573   \n",
       "\n",
       "             l_estim_ges_mean  l_conso_ener_std  l_estim_ges_std  \\\n",
       "id_mutation                                                        \n",
       "2017-554686          3.152736          4.041295         2.944439   \n",
       "2017-554687          2.821379          3.964615         1.757858   \n",
       "2017-554690          2.821379          3.964615         1.757858   \n",
       "2017-554698          2.821379          3.964615         1.757858   \n",
       "2017-554702          2.332144          3.543854         0.788457   \n",
       "\n",
       "             l_conso_ener_min  l_estim_ges_min  l_conso_ener_max  \\\n",
       "id_mutation                                                        \n",
       "2017-554686          4.252772         0.693147          5.472691   \n",
       "2017-554687          5.088213         2.240710          5.776103   \n",
       "2017-554690          5.088213         2.240710          5.776103   \n",
       "2017-554698          5.088213         2.240710          5.776103   \n",
       "2017-554702          5.141664         2.079442          5.613128   \n",
       "\n",
       "             l_estim_ges_max  l_ratio_ges_conso  l_baie_u  l_mur_u_ext  \\\n",
       "id_mutation                                                              \n",
       "2017-554686         3.931826          -3.912023  0.832909    -0.843970   \n",
       "2017-554687         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554690         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554698         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554702         2.708050          -2.995732  0.788457    -1.021651   \n",
       "\n",
       "               l_pb_u    l_ph_u  l_prc_s_vitree_ext  valeur_fonciere_m2  \\\n",
       "id_mutation                                                               \n",
       "2017-554686  0.371564  0.182322           -1.370421         1439.393939   \n",
       "2017-554687  0.371564  0.182322           -1.370421         3536.585366   \n",
       "2017-554690  0.371564  0.182322           -1.370421         3452.380952   \n",
       "2017-554698  0.371564  0.182322           -1.370421         1649.350649   \n",
       "2017-554702 -1.309333 -1.609438           -1.748700         3117.647059   \n",
       "\n",
       "             l_valeur_fonciere_ma7  l_valeur_fonciere_ma14  \\\n",
       "id_mutation                                                  \n",
       "2017-554686                    NaN                     NaN   \n",
       "2017-554687                    NaN                     NaN   \n",
       "2017-554690                    NaN                     NaN   \n",
       "2017-554698                    NaN                     NaN   \n",
       "2017-554702                    NaN                     NaN   \n",
       "\n",
       "             l_valeur_fonciere_ma30  l_valeur_fonciere_ma90  \\\n",
       "id_mutation                                                   \n",
       "2017-554686                     NaN                     NaN   \n",
       "2017-554687                     NaN                     NaN   \n",
       "2017-554690                     NaN                     NaN   \n",
       "2017-554698                     NaN                     NaN   \n",
       "2017-554702                     NaN                     NaN   \n",
       "\n",
       "             nombre_pieces_principales_1  nombre_pieces_principales_2  \\\n",
       "id_mutation                                                             \n",
       "2017-554686                            1                            0   \n",
       "2017-554687                            0                            1   \n",
       "2017-554690                            0                            1   \n",
       "2017-554698                            0                            0   \n",
       "2017-554702                            0                            1   \n",
       "\n",
       "             nombre_pieces_principales_4  nombre_pieces_principales_5  \\\n",
       "id_mutation                                                             \n",
       "2017-554686                            0                            0   \n",
       "2017-554687                            0                            0   \n",
       "2017-554690                            0                            0   \n",
       "2017-554698                            1                            0   \n",
       "2017-554702                            0                            0   \n",
       "\n",
       "             nombre_pieces_principales_6  trimestre_1  trimestre_2  \\\n",
       "id_mutation                                                          \n",
       "2017-554686                            0            0            0   \n",
       "2017-554687                            0            0            0   \n",
       "2017-554690                            0            0            0   \n",
       "2017-554698                            0            0            0   \n",
       "2017-554702                            0            0            0   \n",
       "\n",
       "             trimestre_4  mois_1  mois_10  mois_11  mois_12  mois_2  mois_3  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686            0       0        0        0        0       0       0   \n",
       "2017-554687            0       0        0        0        0       0       0   \n",
       "2017-554690            0       0        0        0        0       0       0   \n",
       "2017-554698            0       0        0        0        0       0       0   \n",
       "2017-554702            0       0        0        0        0       0       0   \n",
       "\n",
       "             mois_4  mois_5  mois_7  mois_8  mois_9  \\\n",
       "id_mutation                                           \n",
       "2017-554686       0       0       1       0       0   \n",
       "2017-554687       0       0       1       0       0   \n",
       "2017-554690       0       0       1       0       0   \n",
       "2017-554698       0       0       1       0       0   \n",
       "2017-554702       0       0       1       0       0   \n",
       "\n",
       "             periode_construction_1949-1970  periode_construction_1970-1988  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686                               0                               0   \n",
       "2017-554687                               0                               0   \n",
       "2017-554690                               0                               0   \n",
       "2017-554698                               0                               0   \n",
       "2017-554702                               0                               0   \n",
       "\n",
       "             periode_construction_1989-1999  periode_construction_2000-2005  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686                               0                               0   \n",
       "2017-554687                               0                               0   \n",
       "2017-554690                               0                               0   \n",
       "2017-554698                               0                               0   \n",
       "2017-554702                               0                               0   \n",
       "\n",
       "             periode_construction_2006-2012  periode_construction_>2012  \\\n",
       "id_mutation                                                               \n",
       "2017-554686                               0                           0   \n",
       "2017-554687                               0                           0   \n",
       "2017-554690                               0                           0   \n",
       "2017-554698                               0                           0   \n",
       "2017-554702                               1                           0   \n",
       "\n",
       "             periode_construction_bad inf  periode_construction_bad sup  \\\n",
       "id_mutation                                                               \n",
       "2017-554686                             0                             0   \n",
       "2017-554687                             0                             0   \n",
       "2017-554690                             0                             0   \n",
       "2017-554698                             0                             0   \n",
       "2017-554702                             0                             0   \n",
       "\n",
       "             periode_construction_max_APRES_2011  \\\n",
       "id_mutation                                        \n",
       "2017-554686                                    0   \n",
       "2017-554687                                    0   \n",
       "2017-554690                                    0   \n",
       "2017-554698                                    0   \n",
       "2017-554702                                    0   \n",
       "\n",
       "             periode_construction_max_AVANT_1949  \\\n",
       "id_mutation                                        \n",
       "2017-554686                                    0   \n",
       "2017-554687                                    0   \n",
       "2017-554690                                    0   \n",
       "2017-554698                                    0   \n",
       "2017-554702                                    0   \n",
       "\n",
       "             periode_construction_max_DE_1949_A_1960  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_1961_A_1974  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_1994_A_2000  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_2001_A_2010  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        1   \n",
       "\n",
       "             periode_construction_max_non renseigné  mur_pos_isol_ext_ITE  \\\n",
       "id_mutation                                                                 \n",
       "2017-554686                                       0                     0   \n",
       "2017-554687                                       0                     0   \n",
       "2017-554690                                       0                     0   \n",
       "2017-554698                                       0                     0   \n",
       "2017-554702                                       0                     0   \n",
       "\n",
       "             mur_pos_isol_ext_ITE+ITR  mur_pos_isol_ext_ITI+ITE  \\\n",
       "id_mutation                                                       \n",
       "2017-554686                         0                         0   \n",
       "2017-554687                         0                         0   \n",
       "2017-554690                         0                         0   \n",
       "2017-554698                         0                         0   \n",
       "2017-554702                         0                         0   \n",
       "\n",
       "             mur_pos_isol_ext_ITI+ITR  mur_pos_isol_ext_ITR  \\\n",
       "id_mutation                                                   \n",
       "2017-554686                         0                     0   \n",
       "2017-554687                         0                     0   \n",
       "2017-554690                         0                     0   \n",
       "2017-554698                         0                     0   \n",
       "2017-554702                         0                     0   \n",
       "\n",
       "             mur_pos_isol_ext_isole  mur_pos_isol_ext_non isole  \\\n",
       "id_mutation                                                       \n",
       "2017-554686                       0                           0   \n",
       "2017-554687                       0                           0   \n",
       "2017-554690                       0                           0   \n",
       "2017-554698                       0                           0   \n",
       "2017-554702                       0                           0   \n",
       "\n",
       "             alea_argiles_Fort  \n",
       "id_mutation                     \n",
       "2017-554686                  1  \n",
       "2017-554687                  0  \n",
       "2017-554690                  0  \n",
       "2017-554698                  0  \n",
       "2017-554702                  0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preproc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'date_mutation'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'valeur_fonciere'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nom_commune'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'surface_reelle_bati'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'dependance'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'hauteur_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'altitude_sol_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'conso_ener_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'estim_ges_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'conso_ener_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'estim_ges_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'conso_ener_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'estim_ges_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'conso_ener_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'estim_ges_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'ratio_ges_conso'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'enr_solaire_photovoltaique'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'pb_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'ph_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'prc_s_vitree_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'presence_balcon'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'presence_climatisation'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_indetermine'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_ouest'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_est'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_horizontale'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_est_ou_ouest'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_sud'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'qpv'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_garpark'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tot'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_log'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tertiaire'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_surface_reelle_bati'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_hauteur_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_altitude_sol_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_ratio_ges_conso'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_baie_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_pb_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_ph_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_prc_s_vitree_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'valeur_fonciere_m2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma7'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma14'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma30'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma90'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_5'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_6'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_10'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_11'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_12'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_5'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_7'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_8'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_9'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1949-1970'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1970-1988'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1989-1999'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2000-2005'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2006-2012'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_&gt;2012'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad inf'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad sup'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_APRES_2011'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_AVANT_1949'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1949_A_1960'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1961_A_1974'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1994_A_2000'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_2001_A_2010'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_non renseigné'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE+ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITE'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_isole'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_non isole'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'alea_argiles_Fort'</span>\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[32m'date_mutation'\u001b[0m,\n",
       "    \u001b[32m'valeur_fonciere'\u001b[0m,\n",
       "    \u001b[32m'nom_commune'\u001b[0m,\n",
       "    \u001b[32m'surface_reelle_bati'\u001b[0m,\n",
       "    \u001b[32m'dependance'\u001b[0m,\n",
       "    \u001b[32m'hauteur_mean'\u001b[0m,\n",
       "    \u001b[32m'altitude_sol_mean'\u001b[0m,\n",
       "    \u001b[32m'conso_ener_mean'\u001b[0m,\n",
       "    \u001b[32m'estim_ges_mean'\u001b[0m,\n",
       "    \u001b[32m'conso_ener_std'\u001b[0m,\n",
       "    \u001b[32m'estim_ges_std'\u001b[0m,\n",
       "    \u001b[32m'conso_ener_min'\u001b[0m,\n",
       "    \u001b[32m'estim_ges_min'\u001b[0m,\n",
       "    \u001b[32m'conso_ener_max'\u001b[0m,\n",
       "    \u001b[32m'estim_ges_max'\u001b[0m,\n",
       "    \u001b[32m'ratio_ges_conso'\u001b[0m,\n",
       "    \u001b[32m'enr_solaire_photovoltaique'\u001b[0m,\n",
       "    \u001b[32m'baie_u'\u001b[0m,\n",
       "    \u001b[32m'mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'pb_u'\u001b[0m,\n",
       "    \u001b[32m'ph_u'\u001b[0m,\n",
       "    \u001b[32m'prc_s_vitree_ext'\u001b[0m,\n",
       "    \u001b[32m'presence_balcon'\u001b[0m,\n",
       "    \u001b[32m'presence_climatisation'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_indetermine'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_ouest'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_est'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_horizontale'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_est_ou_ouest'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_sud'\u001b[0m,\n",
       "    \u001b[32m'qpv'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_garpark'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_tot'\u001b[0m,\n",
       "    \u001b[32m'nb_log'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_tertiaire'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere'\u001b[0m,\n",
       "    \u001b[32m'l_surface_reelle_bati'\u001b[0m,\n",
       "    \u001b[32m'l_hauteur_mean'\u001b[0m,\n",
       "    \u001b[32m'l_altitude_sol_mean'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_mean'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_mean'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_std'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_std'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_min'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_min'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_max'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_max'\u001b[0m,\n",
       "    \u001b[32m'l_ratio_ges_conso'\u001b[0m,\n",
       "    \u001b[32m'l_baie_u'\u001b[0m,\n",
       "    \u001b[32m'l_mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'l_pb_u'\u001b[0m,\n",
       "    \u001b[32m'l_ph_u'\u001b[0m,\n",
       "    \u001b[32m'l_prc_s_vitree_ext'\u001b[0m,\n",
       "    \u001b[32m'valeur_fonciere_m2'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma7'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma14'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma30'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma90'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_1'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_2'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_4'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_5'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_6'\u001b[0m,\n",
       "    \u001b[32m'trimestre_1'\u001b[0m,\n",
       "    \u001b[32m'trimestre_2'\u001b[0m,\n",
       "    \u001b[32m'trimestre_4'\u001b[0m,\n",
       "    \u001b[32m'mois_1'\u001b[0m,\n",
       "    \u001b[32m'mois_10'\u001b[0m,\n",
       "    \u001b[32m'mois_11'\u001b[0m,\n",
       "    \u001b[32m'mois_12'\u001b[0m,\n",
       "    \u001b[32m'mois_2'\u001b[0m,\n",
       "    \u001b[32m'mois_3'\u001b[0m,\n",
       "    \u001b[32m'mois_4'\u001b[0m,\n",
       "    \u001b[32m'mois_5'\u001b[0m,\n",
       "    \u001b[32m'mois_7'\u001b[0m,\n",
       "    \u001b[32m'mois_8'\u001b[0m,\n",
       "    \u001b[32m'mois_9'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1949-1970'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1970-1988'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1989-1999'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_2000-2005'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_2006-2012'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_>2012'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_bad inf'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_bad sup'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_APRES_2011'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_AVANT_1949'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1949_A_1960'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1961_A_1974'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1994_A_2000'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_2001_A_2010'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_non renseigné'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITE'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITE+ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITI+ITE'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITI+ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_isole'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_non isole'\u001b[0m,\n",
       "    \u001b[32m'alea_argiles_Fort'\u001b[0m\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rich.print(list(df_preproc.columns)) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La variable cible `l_valeur_fonciere` correspond au logarithme du prix de ventes des biens (`valeur_fonciere`). On décide alors d'exprimer les variables numériques continues sur une échelle logarithmique pour minimiser les effets d'échelle de ces variables. Notons que l'on n'applique pas la transformation logarithmique aux variables numériques discrètes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove continuous variables which have a counterpart expressed in log scale\n",
    "\n",
    "to_remove = [\n",
    "    'valeur_fonciere',\n",
    "    'valeur_fonciere_m2', \n",
    "    'surface_reelle_bati',\n",
    "    'surface_terrain', \n",
    "    'hauteur_mean',\n",
    "    'altitude_sol_mean',\n",
    "    'conso_ener_mean',\n",
    "    'estim_ges_mean',\n",
    "    'conso_ener_std',\n",
    "    'estim_ges_std',\n",
    "    'conso_ener_min',\n",
    "    'estim_ges_min',\n",
    "    'conso_ener_max',\n",
    "    'estim_ges_max',\n",
    "    'ratio_ges_conso',\n",
    "    'distance_transport', \n",
    "    'distance_park', \n",
    "    'distance_batiment_historique_plus_proche', \n",
    "    'nom_commune',\n",
    "]\n",
    "\n",
    "to_remove = [col for col in to_remove if col in df_preproc.columns]\n",
    "\n",
    "df_preproc.drop(labels=to_remove, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_mutation</th>\n",
       "      <th>dependance</th>\n",
       "      <th>enr_solaire_photovoltaique</th>\n",
       "      <th>baie_u</th>\n",
       "      <th>mur_u_ext</th>\n",
       "      <th>pb_u</th>\n",
       "      <th>ph_u</th>\n",
       "      <th>prc_s_vitree_ext</th>\n",
       "      <th>presence_balcon</th>\n",
       "      <th>presence_climatisation</th>\n",
       "      <th>baie_orientation_indetermine</th>\n",
       "      <th>baie_orientation_ouest</th>\n",
       "      <th>baie_orientation_est</th>\n",
       "      <th>baie_orientation_horizontale</th>\n",
       "      <th>baie_orientation_est_ou_ouest</th>\n",
       "      <th>baie_orientation_sud</th>\n",
       "      <th>qpv</th>\n",
       "      <th>nb_lot_garpark</th>\n",
       "      <th>nb_lot_tot</th>\n",
       "      <th>nb_log</th>\n",
       "      <th>nb_lot_tertiaire</th>\n",
       "      <th>l_valeur_fonciere</th>\n",
       "      <th>l_surface_reelle_bati</th>\n",
       "      <th>l_hauteur_mean</th>\n",
       "      <th>l_altitude_sol_mean</th>\n",
       "      <th>l_conso_ener_mean</th>\n",
       "      <th>l_estim_ges_mean</th>\n",
       "      <th>l_conso_ener_std</th>\n",
       "      <th>l_estim_ges_std</th>\n",
       "      <th>l_conso_ener_min</th>\n",
       "      <th>l_estim_ges_min</th>\n",
       "      <th>l_conso_ener_max</th>\n",
       "      <th>l_estim_ges_max</th>\n",
       "      <th>l_ratio_ges_conso</th>\n",
       "      <th>l_baie_u</th>\n",
       "      <th>l_mur_u_ext</th>\n",
       "      <th>l_pb_u</th>\n",
       "      <th>l_ph_u</th>\n",
       "      <th>l_prc_s_vitree_ext</th>\n",
       "      <th>l_valeur_fonciere_ma7</th>\n",
       "      <th>l_valeur_fonciere_ma14</th>\n",
       "      <th>l_valeur_fonciere_ma30</th>\n",
       "      <th>l_valeur_fonciere_ma90</th>\n",
       "      <th>nombre_pieces_principales_1</th>\n",
       "      <th>nombre_pieces_principales_2</th>\n",
       "      <th>nombre_pieces_principales_4</th>\n",
       "      <th>nombre_pieces_principales_5</th>\n",
       "      <th>nombre_pieces_principales_6</th>\n",
       "      <th>trimestre_1</th>\n",
       "      <th>trimestre_2</th>\n",
       "      <th>trimestre_4</th>\n",
       "      <th>mois_1</th>\n",
       "      <th>mois_10</th>\n",
       "      <th>mois_11</th>\n",
       "      <th>mois_12</th>\n",
       "      <th>mois_2</th>\n",
       "      <th>mois_3</th>\n",
       "      <th>mois_4</th>\n",
       "      <th>mois_5</th>\n",
       "      <th>mois_7</th>\n",
       "      <th>mois_8</th>\n",
       "      <th>mois_9</th>\n",
       "      <th>periode_construction_1949-1970</th>\n",
       "      <th>periode_construction_1970-1988</th>\n",
       "      <th>periode_construction_1989-1999</th>\n",
       "      <th>periode_construction_2000-2005</th>\n",
       "      <th>periode_construction_2006-2012</th>\n",
       "      <th>periode_construction_&gt;2012</th>\n",
       "      <th>periode_construction_bad inf</th>\n",
       "      <th>periode_construction_bad sup</th>\n",
       "      <th>periode_construction_max_APRES_2011</th>\n",
       "      <th>periode_construction_max_AVANT_1949</th>\n",
       "      <th>periode_construction_max_DE_1949_A_1960</th>\n",
       "      <th>periode_construction_max_DE_1961_A_1974</th>\n",
       "      <th>periode_construction_max_DE_1994_A_2000</th>\n",
       "      <th>periode_construction_max_DE_2001_A_2010</th>\n",
       "      <th>periode_construction_max_non renseigné</th>\n",
       "      <th>mur_pos_isol_ext_ITE</th>\n",
       "      <th>mur_pos_isol_ext_ITE+ITR</th>\n",
       "      <th>mur_pos_isol_ext_ITI+ITE</th>\n",
       "      <th>mur_pos_isol_ext_ITI+ITR</th>\n",
       "      <th>mur_pos_isol_ext_ITR</th>\n",
       "      <th>mur_pos_isol_ext_isole</th>\n",
       "      <th>mur_pos_isol_ext_non isole</th>\n",
       "      <th>alea_argiles_Fort</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_mutation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-554686</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.768485</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>4.204693</td>\n",
       "      <td>4.928702</td>\n",
       "      <td>3.152736</td>\n",
       "      <td>4.041295</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>4.252772</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>5.472691</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>-3.912023</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554687</th>\n",
       "      <td>2017-07-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.713572</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554690</th>\n",
       "      <td>2017-07-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.884489</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554698</th>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.751942</td>\n",
       "      <td>4.343805</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>5.462984</td>\n",
       "      <td>2.821379</td>\n",
       "      <td>3.964615</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>5.088213</td>\n",
       "      <td>2.240710</td>\n",
       "      <td>5.776103</td>\n",
       "      <td>3.332205</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>-0.843970</td>\n",
       "      <td>0.371564</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.370421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-554702</th>\n",
       "      <td>2017-07-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.976659</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>5.364573</td>\n",
       "      <td>2.332144</td>\n",
       "      <td>3.543854</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>5.141664</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>5.613128</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>-1.021651</td>\n",
       "      <td>-1.309333</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>-1.748700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_mutation  dependance  enr_solaire_photovoltaique  baie_u  \\\n",
       "id_mutation                                                                 \n",
       "2017-554686    2017-07-04           1                         0.0     2.3   \n",
       "2017-554687    2017-07-04           1                         0.0     2.3   \n",
       "2017-554690    2017-07-05           1                         0.0     2.3   \n",
       "2017-554698    2017-07-03           1                         0.0     2.3   \n",
       "2017-554702    2017-07-06           1                         0.0     2.2   \n",
       "\n",
       "             mur_u_ext  pb_u  ph_u  prc_s_vitree_ext  presence_balcon  \\\n",
       "id_mutation                                                             \n",
       "2017-554686       0.43  1.45   1.2             0.254              0.0   \n",
       "2017-554687       0.43  1.45   1.2             0.254              0.0   \n",
       "2017-554690       0.43  1.45   1.2             0.254              0.0   \n",
       "2017-554698       0.43  1.45   1.2             0.254              0.0   \n",
       "2017-554702       0.36  0.27   0.2             0.174              0.0   \n",
       "\n",
       "             presence_climatisation  baie_orientation_indetermine  \\\n",
       "id_mutation                                                         \n",
       "2017-554686                     0.0                           0.0   \n",
       "2017-554687                     0.0                           0.0   \n",
       "2017-554690                     0.0                           0.0   \n",
       "2017-554698                     0.0                           0.0   \n",
       "2017-554702                     0.0                           0.0   \n",
       "\n",
       "             baie_orientation_ouest  baie_orientation_est  \\\n",
       "id_mutation                                                 \n",
       "2017-554686                     0.0                   0.0   \n",
       "2017-554687                     0.0                   0.0   \n",
       "2017-554690                     0.0                   0.0   \n",
       "2017-554698                     0.0                   0.0   \n",
       "2017-554702                     1.0                   0.0   \n",
       "\n",
       "             baie_orientation_horizontale  baie_orientation_est_ou_ouest  \\\n",
       "id_mutation                                                                \n",
       "2017-554686                           0.0                            0.0   \n",
       "2017-554687                           0.0                            0.0   \n",
       "2017-554690                           0.0                            0.0   \n",
       "2017-554698                           0.0                            0.0   \n",
       "2017-554702                           0.0                            0.0   \n",
       "\n",
       "             baie_orientation_sud  qpv  nb_lot_garpark  nb_lot_tot  nb_log  \\\n",
       "id_mutation                                                                  \n",
       "2017-554686                   0.0  0.0            90.0       269.0   179.0   \n",
       "2017-554687                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554690                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554698                   0.0  0.0            35.0        89.0    36.5   \n",
       "2017-554702                   0.0  0.0           108.0       213.0    81.0   \n",
       "\n",
       "             nb_lot_tertiaire  l_valeur_fonciere  l_surface_reelle_bati  \\\n",
       "id_mutation                                                               \n",
       "2017-554686               0.0          10.768485               3.496508   \n",
       "2017-554687               0.0          11.884489               3.713572   \n",
       "2017-554690               0.0          11.884489               3.737670   \n",
       "2017-554698               0.0          11.751942               4.343805   \n",
       "2017-554702               0.0          11.976659               3.931826   \n",
       "\n",
       "             l_hauteur_mean  l_altitude_sol_mean  l_conso_ener_mean  \\\n",
       "id_mutation                                                           \n",
       "2017-554686        3.496508             4.204693           4.928702   \n",
       "2017-554687        2.302585             4.369448           5.462984   \n",
       "2017-554690        2.302585             4.369448           5.462984   \n",
       "2017-554698        2.302585             4.369448           5.462984   \n",
       "2017-554702        1.791759             4.442651           5.364573   \n",
       "\n",
       "             l_estim_ges_mean  l_conso_ener_std  l_estim_ges_std  \\\n",
       "id_mutation                                                        \n",
       "2017-554686          3.152736          4.041295         2.944439   \n",
       "2017-554687          2.821379          3.964615         1.757858   \n",
       "2017-554690          2.821379          3.964615         1.757858   \n",
       "2017-554698          2.821379          3.964615         1.757858   \n",
       "2017-554702          2.332144          3.543854         0.788457   \n",
       "\n",
       "             l_conso_ener_min  l_estim_ges_min  l_conso_ener_max  \\\n",
       "id_mutation                                                        \n",
       "2017-554686          4.252772         0.693147          5.472691   \n",
       "2017-554687          5.088213         2.240710          5.776103   \n",
       "2017-554690          5.088213         2.240710          5.776103   \n",
       "2017-554698          5.088213         2.240710          5.776103   \n",
       "2017-554702          5.141664         2.079442          5.613128   \n",
       "\n",
       "             l_estim_ges_max  l_ratio_ges_conso  l_baie_u  l_mur_u_ext  \\\n",
       "id_mutation                                                              \n",
       "2017-554686         3.931826          -3.912023  0.832909    -0.843970   \n",
       "2017-554687         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554690         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554698         3.332205          -2.995732  0.832909    -0.843970   \n",
       "2017-554702         2.708050          -2.995732  0.788457    -1.021651   \n",
       "\n",
       "               l_pb_u    l_ph_u  l_prc_s_vitree_ext  l_valeur_fonciere_ma7  \\\n",
       "id_mutation                                                                  \n",
       "2017-554686  0.371564  0.182322           -1.370421                    NaN   \n",
       "2017-554687  0.371564  0.182322           -1.370421                    NaN   \n",
       "2017-554690  0.371564  0.182322           -1.370421                    NaN   \n",
       "2017-554698  0.371564  0.182322           -1.370421                    NaN   \n",
       "2017-554702 -1.309333 -1.609438           -1.748700                    NaN   \n",
       "\n",
       "             l_valeur_fonciere_ma14  l_valeur_fonciere_ma30  \\\n",
       "id_mutation                                                   \n",
       "2017-554686                     NaN                     NaN   \n",
       "2017-554687                     NaN                     NaN   \n",
       "2017-554690                     NaN                     NaN   \n",
       "2017-554698                     NaN                     NaN   \n",
       "2017-554702                     NaN                     NaN   \n",
       "\n",
       "             l_valeur_fonciere_ma90  nombre_pieces_principales_1  \\\n",
       "id_mutation                                                        \n",
       "2017-554686                     NaN                            1   \n",
       "2017-554687                     NaN                            0   \n",
       "2017-554690                     NaN                            0   \n",
       "2017-554698                     NaN                            0   \n",
       "2017-554702                     NaN                            0   \n",
       "\n",
       "             nombre_pieces_principales_2  nombre_pieces_principales_4  \\\n",
       "id_mutation                                                             \n",
       "2017-554686                            0                            0   \n",
       "2017-554687                            1                            0   \n",
       "2017-554690                            1                            0   \n",
       "2017-554698                            0                            1   \n",
       "2017-554702                            1                            0   \n",
       "\n",
       "             nombre_pieces_principales_5  nombre_pieces_principales_6  \\\n",
       "id_mutation                                                             \n",
       "2017-554686                            0                            0   \n",
       "2017-554687                            0                            0   \n",
       "2017-554690                            0                            0   \n",
       "2017-554698                            0                            0   \n",
       "2017-554702                            0                            0   \n",
       "\n",
       "             trimestre_1  trimestre_2  trimestre_4  mois_1  mois_10  mois_11  \\\n",
       "id_mutation                                                                    \n",
       "2017-554686            0            0            0       0        0        0   \n",
       "2017-554687            0            0            0       0        0        0   \n",
       "2017-554690            0            0            0       0        0        0   \n",
       "2017-554698            0            0            0       0        0        0   \n",
       "2017-554702            0            0            0       0        0        0   \n",
       "\n",
       "             mois_12  mois_2  mois_3  mois_4  mois_5  mois_7  mois_8  mois_9  \\\n",
       "id_mutation                                                                    \n",
       "2017-554686        0       0       0       0       0       1       0       0   \n",
       "2017-554687        0       0       0       0       0       1       0       0   \n",
       "2017-554690        0       0       0       0       0       1       0       0   \n",
       "2017-554698        0       0       0       0       0       1       0       0   \n",
       "2017-554702        0       0       0       0       0       1       0       0   \n",
       "\n",
       "             periode_construction_1949-1970  periode_construction_1970-1988  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686                               0                               0   \n",
       "2017-554687                               0                               0   \n",
       "2017-554690                               0                               0   \n",
       "2017-554698                               0                               0   \n",
       "2017-554702                               0                               0   \n",
       "\n",
       "             periode_construction_1989-1999  periode_construction_2000-2005  \\\n",
       "id_mutation                                                                   \n",
       "2017-554686                               0                               0   \n",
       "2017-554687                               0                               0   \n",
       "2017-554690                               0                               0   \n",
       "2017-554698                               0                               0   \n",
       "2017-554702                               0                               0   \n",
       "\n",
       "             periode_construction_2006-2012  periode_construction_>2012  \\\n",
       "id_mutation                                                               \n",
       "2017-554686                               0                           0   \n",
       "2017-554687                               0                           0   \n",
       "2017-554690                               0                           0   \n",
       "2017-554698                               0                           0   \n",
       "2017-554702                               1                           0   \n",
       "\n",
       "             periode_construction_bad inf  periode_construction_bad sup  \\\n",
       "id_mutation                                                               \n",
       "2017-554686                             0                             0   \n",
       "2017-554687                             0                             0   \n",
       "2017-554690                             0                             0   \n",
       "2017-554698                             0                             0   \n",
       "2017-554702                             0                             0   \n",
       "\n",
       "             periode_construction_max_APRES_2011  \\\n",
       "id_mutation                                        \n",
       "2017-554686                                    0   \n",
       "2017-554687                                    0   \n",
       "2017-554690                                    0   \n",
       "2017-554698                                    0   \n",
       "2017-554702                                    0   \n",
       "\n",
       "             periode_construction_max_AVANT_1949  \\\n",
       "id_mutation                                        \n",
       "2017-554686                                    0   \n",
       "2017-554687                                    0   \n",
       "2017-554690                                    0   \n",
       "2017-554698                                    0   \n",
       "2017-554702                                    0   \n",
       "\n",
       "             periode_construction_max_DE_1949_A_1960  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_1961_A_1974  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_1994_A_2000  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        0   \n",
       "\n",
       "             periode_construction_max_DE_2001_A_2010  \\\n",
       "id_mutation                                            \n",
       "2017-554686                                        0   \n",
       "2017-554687                                        0   \n",
       "2017-554690                                        0   \n",
       "2017-554698                                        0   \n",
       "2017-554702                                        1   \n",
       "\n",
       "             periode_construction_max_non renseigné  mur_pos_isol_ext_ITE  \\\n",
       "id_mutation                                                                 \n",
       "2017-554686                                       0                     0   \n",
       "2017-554687                                       0                     0   \n",
       "2017-554690                                       0                     0   \n",
       "2017-554698                                       0                     0   \n",
       "2017-554702                                       0                     0   \n",
       "\n",
       "             mur_pos_isol_ext_ITE+ITR  mur_pos_isol_ext_ITI+ITE  \\\n",
       "id_mutation                                                       \n",
       "2017-554686                         0                         0   \n",
       "2017-554687                         0                         0   \n",
       "2017-554690                         0                         0   \n",
       "2017-554698                         0                         0   \n",
       "2017-554702                         0                         0   \n",
       "\n",
       "             mur_pos_isol_ext_ITI+ITR  mur_pos_isol_ext_ITR  \\\n",
       "id_mutation                                                   \n",
       "2017-554686                         0                     0   \n",
       "2017-554687                         0                     0   \n",
       "2017-554690                         0                     0   \n",
       "2017-554698                         0                     0   \n",
       "2017-554702                         0                     0   \n",
       "\n",
       "             mur_pos_isol_ext_isole  mur_pos_isol_ext_non isole  \\\n",
       "id_mutation                                                       \n",
       "2017-554686                       0                           0   \n",
       "2017-554687                       0                           0   \n",
       "2017-554690                       0                           0   \n",
       "2017-554698                       0                           0   \n",
       "2017-554702                       0                           0   \n",
       "\n",
       "             alea_argiles_Fort  \n",
       "id_mutation                     \n",
       "2017-554686                  1  \n",
       "2017-554687                  0  \n",
       "2017-554690                  0  \n",
       "2017-554698                  0  \n",
       "2017-554702                  0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preproc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32769, 85)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_preproc.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/test split \n",
    "\n",
    "Pour obtenir les jeux de données d'entrainement et de test, on applique une séparation temporelle selon la date de transaction (`date_mutation`). Cette méthode est nécessaire puisque la matrice des features comprend des variables représentant l'évolution du prix du marché à court/moyen/long terme (il s'agit des variables de la forme `valeur_fonciere_ma[ma_window]`). Les 80% premières transactions constituent l'échantillon d'entrainement et sont utilisées pour la recherche du meilleur modèle. Les 20% restantes sont utilisées pour tester la performance des modèles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test, train_dates, test_dates = temporal_train_test_split(df_preproc, date_var=\"date_mutation\", train_prop=.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Période d'entrainement : 2017-07-01 / 2021-07-30\n",
      "Période de test : 2021-07-30 / 2022-06-30\n"
     ]
    }
   ],
   "source": [
    "print(f\"Période d'entrainement : {train_dates.min()} / {train_dates.max()}\") \n",
    "print(f\"Période de test : {test_dates.min()} / {test_dates.max()}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = \"l_valeur_fonciere\"\n",
    "\n",
    "X_tr, features = get_feature_vector(df_train, return_features=True, target=target, return_df=False)\n",
    "y_tr = get_target_vector(df_train, target=target, return_series=False) \n",
    "\n",
    "X_te = get_feature_vector(df_test, target=target, return_df=False)\n",
    "y_te = get_target_vector(df_test, target=target, return_series=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18,571 exemples d'entrainement pour 83 features.\n",
      "6,024 exemples test.\n"
     ]
    }
   ],
   "source": [
    "print(f\"{X_tr.shape[0]:,} exemples d'entrainement pour {X_tr.shape[1]} features.\")\n",
    "print(f\"{X_te.shape[0]:,} exemples test.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'dependance'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'enr_solaire_photovoltaique'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'pb_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'ph_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'prc_s_vitree_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'presence_balcon'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'presence_climatisation'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_indetermine'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_ouest'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_est'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_horizontale'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_est_ou_ouest'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'baie_orientation_sud'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'qpv'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_garpark'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tot'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_log'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tertiaire'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_surface_reelle_bati'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_hauteur_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_altitude_sol_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_ratio_ges_conso'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_baie_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_pb_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_ph_u'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_prc_s_vitree_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma7'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma14'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma30'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma90'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_5'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_6'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'trimestre_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_10'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_11'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_12'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_5'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_7'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_8'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mois_9'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1949-1970'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1970-1988'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_1989-1999'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2000-2005'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_2006-2012'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_&gt;2012'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad inf'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_bad sup'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_APRES_2011'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_AVANT_1949'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1949_A_1960'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1961_A_1974'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_1994_A_2000'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_DE_2001_A_2010'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'periode_construction_max_non renseigné'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITE+ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITE'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITI+ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_ITR'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_isole'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_pos_isol_ext_non isole'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'alea_argiles_Fort'</span>\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[32m'dependance'\u001b[0m,\n",
       "    \u001b[32m'enr_solaire_photovoltaique'\u001b[0m,\n",
       "    \u001b[32m'baie_u'\u001b[0m,\n",
       "    \u001b[32m'mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'pb_u'\u001b[0m,\n",
       "    \u001b[32m'ph_u'\u001b[0m,\n",
       "    \u001b[32m'prc_s_vitree_ext'\u001b[0m,\n",
       "    \u001b[32m'presence_balcon'\u001b[0m,\n",
       "    \u001b[32m'presence_climatisation'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_indetermine'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_ouest'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_est'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_horizontale'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_est_ou_ouest'\u001b[0m,\n",
       "    \u001b[32m'baie_orientation_sud'\u001b[0m,\n",
       "    \u001b[32m'qpv'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_garpark'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_tot'\u001b[0m,\n",
       "    \u001b[32m'nb_log'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_tertiaire'\u001b[0m,\n",
       "    \u001b[32m'l_surface_reelle_bati'\u001b[0m,\n",
       "    \u001b[32m'l_hauteur_mean'\u001b[0m,\n",
       "    \u001b[32m'l_altitude_sol_mean'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_mean'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_mean'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_std'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_std'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_min'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_min'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_max'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_max'\u001b[0m,\n",
       "    \u001b[32m'l_ratio_ges_conso'\u001b[0m,\n",
       "    \u001b[32m'l_baie_u'\u001b[0m,\n",
       "    \u001b[32m'l_mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'l_pb_u'\u001b[0m,\n",
       "    \u001b[32m'l_ph_u'\u001b[0m,\n",
       "    \u001b[32m'l_prc_s_vitree_ext'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma7'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma14'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma30'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma90'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_1'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_2'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_4'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_5'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_6'\u001b[0m,\n",
       "    \u001b[32m'trimestre_1'\u001b[0m,\n",
       "    \u001b[32m'trimestre_2'\u001b[0m,\n",
       "    \u001b[32m'trimestre_4'\u001b[0m,\n",
       "    \u001b[32m'mois_1'\u001b[0m,\n",
       "    \u001b[32m'mois_10'\u001b[0m,\n",
       "    \u001b[32m'mois_11'\u001b[0m,\n",
       "    \u001b[32m'mois_12'\u001b[0m,\n",
       "    \u001b[32m'mois_2'\u001b[0m,\n",
       "    \u001b[32m'mois_3'\u001b[0m,\n",
       "    \u001b[32m'mois_4'\u001b[0m,\n",
       "    \u001b[32m'mois_5'\u001b[0m,\n",
       "    \u001b[32m'mois_7'\u001b[0m,\n",
       "    \u001b[32m'mois_8'\u001b[0m,\n",
       "    \u001b[32m'mois_9'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1949-1970'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1970-1988'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_1989-1999'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_2000-2005'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_2006-2012'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_>2012'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_bad inf'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_bad sup'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_APRES_2011'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_AVANT_1949'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1949_A_1960'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1961_A_1974'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_1994_A_2000'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_DE_2001_A_2010'\u001b[0m,\n",
       "    \u001b[32m'periode_construction_max_non renseigné'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITE'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITE+ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITI+ITE'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITI+ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_ITR'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_isole'\u001b[0m,\n",
       "    \u001b[32m'mur_pos_isol_ext_non isole'\u001b[0m,\n",
       "    \u001b[32m'alea_argiles_Fort'\u001b[0m\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rich.print(features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection \n",
    "\n",
    "On applique deux méthodes sur le jeu de données d'entrainement pour identifier les variables explicatives pouvant expliquer au mieux la valeur foncière des biens. \n",
    "\n",
    "La première méthode est l'**information mutuelle**. *Mutual information (MI) between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency. MI is based on the KL divergence.* On sélectionne ensuite les features ayant une MI supérieure à un certain seuil. \n",
    "\n",
    "La seconde méthode consiste à calculér la ***Mean Decrease Gini*** (MDG) à partir de l'estimateur `RandomForestRegressor` et de sélectionner les features ayant une MDG supérieure à certain seuil.  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "if len(X_tr) <= 150000:     \n",
    "    # compute mutual information between features and target\n",
    "    # if dataset is small enough\n",
    "    mi_values = compute_mutual_info(X_tr, y_tr, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor fitted.\n",
      "Train MAPE: 0.39%\n"
     ]
    }
   ],
   "source": [
    "mdg_values = compute_rf_importances(X_tr, y_tr, features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une fois les métriques d'importance calculées, on définit des seuils pour la sélection des variables. Par exemple, si le seuil est fixé à `75%`, on sélectionne les variables dont l'importance est supérieure au 3ème quantile des valeurs d'importance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi_threshold = \"75%\"\n",
    "mdg_threshold = \"75%\"\n",
    "\n",
    "important_features_mi = select_important_features(mi_values, threshold=mi_threshold)\n",
    "important_features_mdg = select_important_features(mdg_values, threshold=mdg_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yxCckNxjQ7nfLHzkAC3qOFwIbjrDdUHFP4YmxDVpvhPEs6vm+iMf/u/gi8M9VdW4b72iAqvpfurvvpMua7wTuoLum5/b0uyXwXyOMR5IkraI22GADpkyZwgUXXMAZZ5zBIYccAsCxxx7L5ptvzo033siiRYvYYIOBaRRUFcB/VtXE3nJzMMAcTJIkjYHVbZkwdHeJD6e723t+K9sEuKeqFtEttxhso+kL6PaVWRcgydZJNgZ+CWyXZP0kT6NberysrgAOTLJR6/ugVjaoqnoQuDvJgS2m9ZNsNMK4R3p9y2MTHk8k37a4MMnT2jJtgL+ie0Lzf4HrgRcneX47fwjdHXlJkrSamzp1KieffDJXXHEF+++/PwDz589niy22YK211uK0005bvD/gE7zyla8EeKY52FIxB5MkSSvM6jgZeCHwp8CP2wbJAF8C3pbkRmBbBr+TfBJwKzA73cbQXwXWqapfAd8Bbm5/b1jWwKpqNnAKcB1wLXBSVQ3X3zTgyCQ3AVcBfzKSuAfpZ6T1lsbRwJlJZgH39pS/BLg5ye10b62bDlBVfwQ+QJcU/wz4TlXdspwxSJKkVcB+++3HZZddxr777st663XzUe973/s49dRTmTBhArfddhsbb/zkObC/+qu/gm7VhjnYyB2NOZgkSVpB0pZuSGNm8uTJNXPmzLEOQ5IkrSBJZlXV5LGOQ48z/5Ikac03VA62Oj4ZKEmSJEmSJGkZrJIvEEmyI3DagOIFVbX7WMQDkOQEYI8BxTOq6uSxiGckVsXfUZIkrbrmzp3LtGnTnlC2/vrrc+21145RROZgkiRJo22VnAysqrnAxLGOo1dVvX+sY1haq+LvKEmSVl077rgjc+bMGeswnsAcTJIkaXS5TFiSJEmSJEnqE04GSpIkSZIkSX3CyUBJkiRJkiSpTzgZKEmSJEmSJPUJJwMlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+sQ6Yx2A9MDDj3L27feMdRiSVnGv32aLsQ5BktYY8xbOY8b9M8Y6DACmbzp9rEOQJKmv+GSgJEmSJEmS1CdWucnAJA+NYl/jk9w8Wv0NMca2SeYkuSHJC0epz2cnOWs0+loRkrwuyU3tumcm2bPn3NuS/KJ93jaWcUqSpJEbN27cqPV11113scMOO4xaf4Pp0xxs2yRXJ1mQ5MODnF+7/R4/HIv4JEnS6sFlwiOUZO2qWjjIqQOBs6rqs6M1VlX9Gjh4KWJbp6r+OFrjj8DFwLlVVUl2Ar4DbJvk6cCngclAAbOSnFtV96/E2CRJ0hrEHOwJfgscSXftg5kO/Ax46soKSJIkrX5WuScDlyTJt5Mc0PP9lCQHtycAr0gyu31ePkjbtZMck+T69lTbu1v5lN67p0mOT3JYO74ryReSzAbeOEifrwaOAt6b5JJW9qEkN7fPUa1sfJKfJflakluSXJhkw3buRUl+nOTGFvsLe59oHCbuK5KcC9w6VL0hfscpSS5L8v0kdyT5fJJDk1yXZO7iu+tJXpvk2naH+cdJNgeoqoeqqlp3G9NN/AG8Erioqn7bJgAvAvZf4j+qJEla5R1yyCGcd955j30/7LDDOOuss7jrrrvYa6+9mDRpEpMmTeKqq656UtuFCxcCbGkONio52Lyquh54dJC+twQOAE4aanxJkiRYzSYDgTOANwEkWQ94BXAeMA/486qaBEwFjhuk7TuA+VW1K7Ar8M4kzx/BmPdV1aSq+vbAE1X1I+ArwLFVtU+SXYDDgd2Bl7Yxdm7VXwycUFXbAw8Ab2jlp7fyCcDLgYFv0lhS3JOA6VW19TJc3wTgPcBLgGnA1lW1G10CeUSr81PgpVW1M/Bt4K8XN05yUJLb6H7/t7fi5wC/6hnj7lb2JEnelW6J8cz599+3hDAlSdJYmzp1Kt/5zncAeOSRR7j44os54IADeNaznsVFF13E7NmzOeOMMzjyyCOf1PZf//VfARaagz1muXKwJfiXVm/RUBV686+H7h21nXkkSdJqZnVbJvzvwIwk69M9cXZ5Vf0hySbA8UkmAguBrQdpux+wU5LFSz82oUsOHxlmzDOWIr49gXOq6ncASc4G9gLOBe6sqjmt3ixgfJKnAM+pqnMAqurh1m6kcV9XVXcOU+9OBnd9Vd3TxvsP4MJWPhfYpx1vCZyRZAtgvd6+WsznJNkb+Dtg32F/nR5VdSJwIsCLdphQw1SXJElj6FWvehXTp09nwYIFnH/++ey9995suOGGzJ8/nw984APMmTOHtddem5///OdPanvhhRcCPCPJnFZkDrYcOdhgkrwGmFdVs5JMGapeb/611c5bmX9JktSnVqvJwKp6OMmldMtRp9LdKQX4IPA/dHda1wIeHqR5gCOq6oInFHYvv+h9QnKDAe1+t/yRA7Cg53ghsOEI2w0V9xSeGNug9UYYz6Ke74t4/L+LLwL/XFXntvGOHthJVV2e5AVJNgP+C5jSc3pL4NIRxiNJklZRG2ywAVOmTOGCCy7gjDPO4JBDDgHg2GOPZfPNN+fGG29k0aJFbLDBwDQK2s4i/1lVE3vLzcGA5cjBBtgD+Iu2fHoD4KlJvlFVbxlhTJIkqY+sbsuEobtLfDjd3d7zW9kmwD1VtYhuucXag7S7gG5fmXUBkmydZGPgl8B2SdZP8jS6pcfL6grgwCQbtb4PamWDqqoHgbuTHNhiWj/JRiOMe6TXtzw2oZvgA3jszcBtj52040nA+sB9LYb9kmyaZFO6O+UjTYwlSdIqbOrUqZx88slcccUV7L9/tyXw/Pnz2WKLLVhrrbU47bTTFu8P+ASvfOUrAZ5pDrZUBs3BhlJVf1NVW1bVeOAQ4CdOBEqSpKGsjpOBFwJ/Cvy4qhYvL/kS8LYkNwLbMvid5JOAW4HZbWPorwLrVNWv6N6Ge3P7e8OyBlZVs4FTgOuAa4GTqmq4/qYBRya5CbgK+JORxD1IPyOttzSOBs5MMgu4t6f8DcDNbbnPCcDU6vyWbsnw9e3zt61MkiSt5vbbbz8uu+wy9t13X9Zbbz0A3ve+93HqqacyYcIEbrvtNjbe+MlzYH/1V38F3aoNc7CRO5pBcrAkf5LkbuBDwCeT3J3ENwdLkqSlksdfCiuNjcmTJ9fMmTPHOgxJkrSCJJlVVZPHOg49zvxLkqQ131A52Or4ZKAkSZIkSZKkZbBKvkAkyY7AaQOKF1TV7mMRD0CSE+g2Z+41o6pOHot4RmJV/B0lSdKqa+7cuUybNu0JZeuvvz7XXnvtGEVkDiZJkjTaVsnJwKqaC0wc6zh6VdX7xzqGpbUq/o6SJGnVteOOOzJnzpyxDuMJzMEkSZJGl8uEJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvqEk4GSJEmSJElSn3AyUJIkSZIkSeoTTgZKkiRJkiRJfWKdsQ5AeuDhRzn79nvGOgxJK8nrt9lirEOQpL43b+E8Ztw/Y6zDYPqm08c6BEmS+o5PBkqSJEmSJEl9wslASZIkSZIkqU+scpOBSR4axb7GJ7l5tPobYoxtk8xJckOSF45Sn89OctZo9LUitGu+OsmCJB8e5Pza7ff44VjEJ0mSlt64ceNGra+77rqLHXbYYdT6G0yf5mCvS3JTu+6ZSfbsOfe2JL9on7eNZZySJGnV5p6BI5Rk7apaOMipA4GzquqzozVWVf0aOHgpYlunqv44WuOPwG+BI+mufTDTgZ8BT11ZAUmSpDWTOdgTXAycW1WVZCfgO8C2SZ4OfBqYDBQwK8m5VXX/SoxNkiStJla5JwOXJMm3kxzQ8/2UJAe3JwCvSDK7fV4+SNu1kxyT5Pp2R/XdrXxK7xNsSY5Pclg7vivJF5LMBt44SJ+vBo4C3pvkklb2oSQ3t89RrWx8kp8l+VqSW5JcmGTDdu5FSX6c5MYW+wt7n2gcJu4rkpwL3DpUvSF+xylJLkvy/SR3JPl8kkOTXJdk7uK760lem+Tadsf9x0k2B6iqeVV1PfDoIH1vCRwAnDT0vyQkeVe7oz1z/v33LamqJEkaY4cccgjnnXfeY98PO+wwzjrrLO666y722msvJk2axKRJk7jqqque1HbhwoUAW5qDjUoO9lBVVetuY7qJP4BXAhdV1W/bBOBFwP6DjP9Y/vXQvaO2GEeSJK1mVqvJQOAM4E0ASdYDXgGcB8wD/ryqJgFTgeMGafsOYH5V7QrsCrwzyfNHMOZ9VTWpqr498ERV/Qj4CnBsVe2TZBfgcGB34KVtjJ1b9RcDJ1TV9sADwBta+emtfALwcmDga3WXFPckYHpVbb0M1zcBeA/wEmAasHVV7UY3iXdEq/NT4KVVtTPwbeCvl/RDNf/S6i1aUqWqOrGqJlfV5E02fcYIupUkSWNl6tSpfOc73wHgkUce4eKLL+aAAw7gWc96FhdddBGzZ8/mjDPO4Mgjj3xS23/9138FWGgO9pjlysGSHJTkNroc+O2t+DnAr3rGuLuVDfzdHsu/xm02esvCJUnS6mV1Wyb878CMJOvT3e28vKr+kGQT4PgkE4GFwNaDtN0P2CnJ4qUfm9Alh48MM+YZSxHfnsA5VfU7gCRnA3sB5wJ3VtWcVm8WMD7JU4DnVNU5AFX1cGs30rivq6o7h6l3J4O7vqruaeP9B3BhK58L7NOOtwTOSLIFsN4S+qL18xpgXlXNSjJlSXUlSdLq41WvehXTp09nwYIFnH/++ey9995suOGGzJ8/nw984APMmTOHtddem5///OdPanvhhRcCPCPJnFZkDrYcOViL+ZwkewN/B+w77K8jSZLUY7WaDKyqh5NcSrcUYirdnVKADwL/Q3endS3g4UGaBziiqi54QmG38XLvE5IbDGj3u+WPHIAFPccLgQ1H2G6ouKfwxNgGrTfCeBb1fF/E4/9dfBH456o6t4139DB97gH8RVu6swHw1CTfqKq3jDAmSZK0Ctpggw2YMmUKF1xwAWeccQaHHHIIAMceeyybb745N954I4sWLWKDDQamUdBWtf5nVU3sLTcHA5YjB6uqy5O8IMlmwH8BU3pObwlcOsJ4JElSn1ndlglDd5f4cLq7vee3sk2Ae6pqEd1yi7UHaXcB3b4y6wIk2TrJxsAvge2SrJ/kaXRLj5fVFcCBSTZqfR/UygZVVQ8Cdyc5sMW0fpKNRhj3SK9veWxCl1wCDPtWuqr6m6rasqrGA4cAP3EiUJKkNcPUqVM5+eSTueKKK9h//247uvnz57PFFluw1lprcdpppy3eH/AJXvnKVwI80xxsqQyag7V9DtOOJwHrA/e1GPZLsmmSTemeVhzp5KQkSeozq+Nk4IXAnwI/rqrFy0u+BLwtyY3Atgx+J/kk4FZgdtsY+qvAOlX1K7o3sd3c/t6wrIFV1WzgFOA64FrgpKoarr9pwJFJbgKuAv5kJHEP0s9I6y2No4Ezk8wC7l1cmORPktwNfAj4ZJK7k/jmYEmS1mD77bcfl112Gfvuuy/rrbceAO973/s49dRTmTBhArfddhsbb/zkObC/+qu/gm7VhjnYyB3NIDkY3X6HN7cl1ycAU6vzW7olw9e3z9+2MkmSpCfJ4y8kk8bG5MmTa+bMmWMdhiRJWkGSzKqqyWMdhx5n/iVJ0ppvqBxsdXwyUJIkSZIkSdIyWCVfIJJkR+C0AcULqmr3sYgHIMkJdC/I6DWjqk4ei3hGYlX8HSVJ0qpr7ty5TJs27Qll66+/Ptdee+0YRWQOJkmSNNpWycnAqpoLTBzrOHpV1fvHOoaltSr+jpIkadW14447MmfOnLEO4wnMwSRJkkaXy4QlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9Yp2xDkB64OFHOfv2e8Y6DKnvvH6bLcY6BEnSGJm3cB4z7p+xwseZvun0FT6GJElaOj4ZKEmSJEmSJPWJ1WoyMMlDo9jX+CQ3j1Z/Q4yxbZI5SW5I8sJR6vPZSc4ajb5WhCQfadc8J8nNSRYmefpYxyVJklaucePGLVO7xfley9X+sqd8cpLj2vGUJC9fhr5PSXLwMgU28jEOS3L8ihxDkiRpeaxWk4GrqiRrD3HqQOCsqtq5qv5jNMaqql9X1YiT2CQrdSl4VR1TVROraiLwN8BlVfXblRmDJElaI4wHHpsMrKqZVXVk+zoFWOrJQEmSJK1Bk4FJvp3kgJ7vpyQ5uN1VviLJ7PZ5UuKYZO0kxyS5PslNSd7dyqck+WFPveOTHNaO70ryhSSzgTcO0uergaOA9ya5pJV9qD0td3OSo1rZ+CQ/S/K1JLckuTDJhu3ci5L8OMmNLfYX9j7ROEzcVyQ5F7h1qHpD/I5TklyW5PtJ7kjy+SSHJrkuydzFTzgmeW2Sa9tTjz9Osvkg3b0Z+NYS/tkkSVIfSjIuycUtv5mb5HWDVPs8sFdbbfDBxXlZkvHAe4APtnN7DXzir+fpwrT87fYkPwae1VNnl5bzzEpyQZIhN1JNcmSSW1se9e1W9vQk32tl1yTZaYTXfkqSL7c2d7Tr+nrLB0/pqbdfkqvbb3RmknGt/FMtp7s5yYlJ0sovbbnpdUl+nmSvkcQjSZL6zxozGQicAbwJIMl6wCuA84B5wJ9X1SRgKnDcIG3fAcyvql2BXYF3Jnn+CMa8r6omVdW3B56oqh8BXwGOrap9kuwCHA7sDry0jbFzq/5i4ISq2h54AHhDKz+9lU+gu/s98C0bS4p7EjC9qrZehuubQJdkvwSYBmxdVbsBJwFHtDo/BV5aVTsD3wb+ureDJBsB+wPfHWyAJO9KMjPJzPn337eEUCRJ0hroYeCglp/tA/zT4kmtHh8DrmgrDo5dXFhVd/F4jjWxqq5YwjgHAdsA2wFvpT1NmGRd4IvAwVW1C/B14HNL6OdjwM5VtRNdjgTwGeCGVvZx4N+Gv+zHbAq8DPggcC5wLLA9sGOSiUk2Az4J7Nt+o5nAh1rb46tq16raAdgQeE1Pv+u0nO0o4NMDB+3Nvx66d9R235EkSauZNeltwv8OzEiyPt0k1OVV9YckmwDHJ5kILAS2HqTtfsBOPXeUN6GboHtkmDHPWIr49gTOqarfASQ5G9iLLgG8s6rmtHqzgPFJngI8p6rOAaiqh1u7kcZ9XVXdOUy9Oxnc9VV1TxvvP4ALW/lcuoQdYEvgjHYXfb1B+notcOVQS4Sr6kTgRIAX7TChhohDkiStmQL8fZK9gUXAc4DNgf8e5XH2Br5VVQuBXyf5SSvfBtgBuKjlVmvz5JuuvW4CTk/yPeB7rWxP2g3cqvpJkmckeeoI4/pBVVWSucD/VNVcgCS30C2P3pJuAvPKFt96wNWt7T5J/hrYCHg6cAvwg3bu7PZ3VuvnCXrzr6123sr8S5KkPrXGTAZW1cNJLgVeSfcE4OKn9T4I/A/d025r0d2JHijAEVV1wRMKkz154tOTGwxo97vljxyABT3HC+nu8o7EUHFP4YmxDVpvhPEs6vm+iMf/m/ki8M9VdW4b7+gBfRyCS4QlSdLgDgWeCexSVY8muYsn51lL44+0nC3JWnSTZ0sS4JaqetkI+z+AbmLxtcAnkuy4rIE2vbnVwLxrHbp88KKqenNvoyQbAF8CJlfVr5IczRN/t8V9LWQNyvMlSdLoWpOWCUP3pN7hdE/cnd/KNgHuqapFdEteB3vZxwV0e/utC5Bk6yQbA78EtkuyfpKn0S09XlZXAAcm2aj1fVArG1RVPQjcneTAFtP6bentSOIe6fUtj02A/2rHb+s90Z7G/FPg+8s5hiRJWjNtAsxrE4H7AM8bpM6DwFOGaD/w3F3ALu34L4B12/HlwNR0+ydvweMrHG4HnpnkZdAtG06y/WADtcnF51bVJcBHW+zj6PK4Q1udKcC9VfW/S7jmpXENsEeSF7X+N06yNY9P/N3b9hBcoW9GliRJa6Y17Y7hhcBpwPeravES3y8B303yVroJwsGe5juJbinF7LZfzW+AA9sd1+8AN9Mtg71hWQOrqtltU+jrFo9ZVTek2wR7KNOAryb5W+BRuheVLBou7kH6GWm9pXE0cGaS+4GfAL17EB4EXLh4SbQkSdIApwM/aMtkZwK3DVLnJmBhkhuBU3hiHvYD4Kx0Lx45Avga8P1WtzffOwf4M+BW4D9pS22r6pG2fcpx7SbmOsC/0C25HWht4ButXoDjquqB9lTe15PcBPyeATdHl0dV/SbdS+u+1bbAAfhkVf08ydfoctP/Bq4frTElSVL/SJXbhWhsTZ48uWbOnDnWYUiSpBUkyayqmjzWcehx5l+SJK35hsrB1rRlwpIkSZIkSZKGsNotE24bNp82oHhBVe0+FvEAJDkB2GNA8YyqOnks4hmJVfF3lCRJa5a5c+cybdo06PZgntOKV9l8YzRzuiSfoNvipdeZVfW5ZY1PkiRpNLhMWGPOZSqSJK3ZXCa86jH/kiRpzecyYUmSJEmSJKnPORkoSZIkSZIk9QknAyVJkiRJkqQ+4WSgJEmSJEmS1CecDJQkSZIkSZL6hJOBkiRJkiRJUp9wMlCSJEmSJEnqE+uMdQDSAw8/ytm33zPWYUhj6vXbbDHWIUiS+si8hfOYcf+MUe93+qbTR71PSZI0unwyUJIkSZIkSeoTa/xkYJKHlqddkvFJ/rKnfHKS49rxlCQvX4a+T0ly8LLEtRRjHJbk+BU5hiRJ0mha1rxtiL6OSrJRz/cfJXnaaPUvSZK0ulrjJwNHwXjgscnAqppZVUe2r1OApZ4MlCRJ0gp3FPDYZGBVvbqqHhizaCRJklYRfT0ZmGRckouTzE4yN8nrBqn2eWCvJHOSfLA9DfjDJOOB9wAfbOf2GvjEX8/ThUlyfJLbk/wYeFZPnV2SXJZkVpILkgy5cViSI5PcmuSmJN9uZU9P8r1Wdk2SnUZ47ack+XJrc0e7rq8n+VmSU3rq7Zfk6vYbnZlkXCv/VJLrk9yc5MQkaeWXJvlCkuuS/DzJXiOJR5IkaThJPtLyj5uSfKaVbZzkvCQ3trxkapIjgWcDlyS5pNW7K8lmbdXHbS0X+nmS05Psm+TKJL9IstsSxn9mkouS3JLkpCS/TLJZO/eWlv/MSfLVJGu3zyktrrlJPriEvi9NcmySmS0f2zXJ2S2mz/bU+17LG29J8q5W9rxWb7MkayW5Isl+o/OrS5KkNU1fTwYCDwMHVdUkYB/gnxZPavX4GHBFVU2sqmMXF1bVXcBXgGPbuSuWMM5BwDbAdsBbaU8TJlkX+CJwcFXtAnwd+NwS+vkYsHNV7UQ3EQnwGeCGVvZx4N+Gv+zHbAq8DPggcC5wLLA9sGOSiS25/SSwb/uNZgIfam2Pr6pdq2oHYEPgNT39rlNVu9Hdkf/0YAMneVdLdmfOv/++pQhZkiT1oza59WJgN2AisEuSvYH9gV9X1YSWl5xfVccBvwb2qap9BunuRcA/Adu2z18CewIfpsunhvJp4CdVtT1wFrBVi+0lwFRgj6qaCCwEDm1xPqeqdqiqHYGTh7nMR6pqMl2O+X3g/cAOwGFJntHqvL3ljZOBI5M8o6p+CXwB+DLwf4Bbq+rCgZ335l8P3TtqK7IlSdJqpt/fJhzg71siuQh4DrA58N+jPM7ewLeqaiHw6yQ/aeXb0CV4F7U5yLWBJb1W9ybg9CTfA77XyvYE3gBQVT9J8owkTx1hXD+oqkoyF/ifqpoLkOQWuuXRW9JNYF7Z4lsPuLq13SfJX9Mtv3k6cAvwg3bu7PZ3VuvnSarqROBEgBftMKFGGK8kSepf+7XPDe37OLrJwSvobuh+AfjhMDdoF7tzQN5zcU9ONH4J7faku8lLVZ2f5P5W/gpgF+D6ljNtCMyjy41ekOSLwHnAkyboBji3/Z0L3FJV97QY7wCeC9xHNwF4UKv3XLrf4L6qOinJG+luGE8crPPe/Gurnbcy/5IkqU/1+2TgocAzgV2q6tEkdwEbLEd/f6Q9bZlkLbrJsyUJXaL3shH2fwDdxOJrgU8k2XFZA20WtL+Leo4Xf1+H7q72RVX15t5GSTYAvgRMrqpfJTmaJ/5ui/taiP+NSZKk0RHgH6rqq086kUwCXg18NsnFVfW3w/Q1MO/pzYmWJXcJcGpV/c0gsU0AXkk3Sfcm4O0jiGvQ3CzJFGBf4GVV9fskl9JysHQvS9my1R8HPLgM1yFJkvpAvy8T3gSY1yYC9wGeN0idB4GnDNF+4Lm76O4KA/wFsG47vhyY2vaN2YJuSTLA7cAzk7wMumXDSbYfbKA2ufjcqroE+GiLfRzd3fBDW50pwL1V9b9LuOalcQ2wR5IXtf43TrI1j0/83ZtuD8EV+mZkSZIk4ALg7Xl8/+LnJHlWkmcDv6+qbwDHAJNa/SXlcMvqSroJvcXLljdt5RcDByd5Vjv39LaP32bAWlX1XbqtVyYN0ufS2AS4v00Ebgu8tOfcF4DTgU8BX1vOcSRJ0hqs35/aOh34QVsSMhO4bZA6NwELk9wInMLjS1OgW/pxVroXjxxBl3h9v9U9H/hdq3cO8GfArcB/0pbaVtUj6V44clySTej+Pf6FbsntQGsD32j1AhxXVQ+0p/K+nuQm4PfA25bhdxhUVf0myWHAt5Ks34o/WVU/T/I14Ga6JdXXj9aYkiRJg6mqC9vefFe3pbgPAW+h2//vmCSLgEeB97YmJwLnJ/n1EPsGLovP0OVF0+jyuf8GHqyqe5N8Eriw3cB9lG6/vz8AJ7cygCc9ObiUzgfek+RndDeVrwFI8qfArnR7Fi5M8oYkh1fVcHsUSpKkPpQqtwvR2Jo8eXLNnDlzrMOQJEkrSJJZ7cUYq7V2c3RhVf2xrez4cnthyGrH/EuSpDXfUDlYvz8ZKEmSJI3UVsB32pN+jwDvHON4JEmSllpfTAa2F22cNqB4QVXtPhbxDCfJCcAeA4pnLMtSjySfAN44oPjMqvrcssYnSZK0oqwKeVuSw4HpA4qvrKr3AzsvZ9+jludJkiQtC5cJa8y5TEWSpDXbmrJMeE1i/iVJ0ppvqBys398mLEmSJEmSJPUNJwMlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hPrjHUA0gMPP8rZt98z1mFIY+L122wx1iFIkvrQvIXzmHH/jFHrb/qm00etL0mStGL5ZKAkSZIkSZLUJ5wMlCRJkiRJkvrEmEwGJhmf5OaVON57krx1ZY23LJJMTnLcMrZd5utLMiXJD5el7RD9fT3JvJX57ytJklaccePGjVpfKyMHTLJtkjlJbkjywlHq89lJzhqNvlaEJB9p1zwnyc1JFiZ5+ljHJUmSVk2r9J6BSdauqoXL209VfWU04llRkqxTVTOBmcvSfhW7vlOA44F/G+M4JEnSGmwJeeKBwFlV9dnRGquqfg0cvBSxrVNVfxyt8YdTVccAx7SxXwt8sKp+u7LGlyRJq5dhnwxsd3B/luRrSW5JcmGSDZNMTHJNkpuSnJNk01b/0iTHJpnZ2u2a5Owkv0jSm5Stk+T0VuesJBu19ncl+UKS2cAbk+yX5Ooks5OcmWTI29Ot7T8mmZvkuiQvauVHJ/lwO35hkvOTzEpyRZJtW/nm7TpubJ+Xt/K3tL7mJPlqkrXb55R253Vukg8uIaZLk8zouVO7W09MpyW5Ejit9wm9du7rre0dSY7s6e+t7Te/Mclpg1zfUOPt1n7HG5JclWSbQWLduI17Xav3ula+fc9vcFOSFw91vVV1OWDyKUlSHzjkkEM477zzHvt+2GGHcdZZZ3HXXXex1157MWnSJCZNmgSw8cC2LZ86Jsn1Lb94dyt/wqqFJMcnOawdPyFPHKTPVwNHAe9Nckkr+1DLiW5OclQrGzS/bedelOTHLdea3XLHx55oHCbuK5KcC9w6VL3BtLaXJfl+y/0+n+TQln/NTXvCMclrk1zb8rQfJ9l8kO7eDHxrqLEkSZJGukz4xcAJVbU98ADwBronvz5aVTsBc4FP99R/pKomA18Bvg+8H9gBOCzJM1qdbYAvVdVLgP8F3tfT/r6qmgT8GPgksG/7PhP40DCxzq+qHemeTvuXQc6fCBxRVbsAHwa+1MqPAy6rqgnAJOCWJC8BpgJ7VNVEYCFwKDAReE5V7dDGOnmYmDZq7d8HfL2nfLt2bW8epM22wCuB3YBPJ1k3yfZ0v8eftTiHem3bYOPdBuxVVTsDnwL+fpB2nwB+UlW7AfsAxyTZGHgPMKP1ORm4e5jrHVaSd6WbMJ45//77lrc7SZI0BqZOncp3vvMdAB555BEuvvhiDjjgAJ71rGdx0UUXMXv2bM444wyArQZp/g66vG1XYFfgnUmeP4Jh76uqSVX17YEnqupHdPnnsVW1T5JdgMOB3YGXtjF2btUHy28BTm/lE4CXA/csRdyTgOlVtfUyXN8EupzrJcA0YOuWk50EHNHq/BR4acvnvg38dW8H6W6u7w98d7ABevOvh+59aAmhSJKkNdlIlwnfWVVz2vEs4IXA06rqslZ2KnBmT/1z29+5wC1VdQ9AkjuA59IlXL+qqitbvW8ARwL/t30/o/19Kd2E2ZVJANYDrh4m1m/1/D2290S6pwpfDpzZ+gNYv/39M+CtAG3Jyfwk04BdgOtb/Q2BecAPgBck+SJwHnDhSGKqqsuTPDXJ01r5uVX1hyHanFdVC4AFSeYBm7cYz6yqe1t/Qz2BN9h4TwFObU/1FbDuIO32A/5i8VOGwAZ0yfvVwCeSbAmcXVW/GOZ6h1VVJ9JNzPKiHSbU8vYnSZJWvle96lVMnz6dBQsWcP7557P33nuz4YYbMn/+fD7wgQ8wZ84c1l57behyioH2A3ZKsnj57SZ0E3SPDDPsGcOc77UncE5V/Q4gydnAXnS56sD8dnySp9Dd8D0HoKoebu1GGvd1VXXnMPXuZHDX9+TM/8Hj+eVcupu0AFsCZyTZgi4vHtjXa4Erh8oRe/OvrXbeyvxLkqQ+NdLJwAU9xwuBp42w/qIBbRf1jDkwAen9/rv2N8BFQzw5N5Qa4hi6JyEfaE+4jUSAU6vqb550IplA9+Tee4A3AW8fYUy93383sGKPgb/50uzvONh4fwdcUlUHJRkPXDpIuwBvqKrbB5T/LMm1wAHAj5K8u6p+shTxSJKkNdAGG2zAlClTuOCCCzjjjDM45JBDADj22GPZfPPNufHGG1m0aBHrrrvuYKtRQrda44InFCZ78sTVKwMnEpeUPy2NgbnWhiNsN1TcU3hibIPWG2E8vTl0b/78ReCfq+rcNt7RA/o4BJcIS5KkYSzr24TnA/cn2at9nwZctoT6g9kqycva8V/SLXsY6Bpgjzy+99/GSbYept+pPX+f8BRhVf0vcGeSN7b+0ib1AC4G3tvK106ySSs7OMmzWvnTkzwvyWbAWlX1Xbplu5NGElNLbudX1fxh6g/lJ3T7KD5jcTxLMd4mwH+184cN0e4C4Ii029+Ll9EkeQFwR1UdR7fse6dljF+SJK1hpk6dysknn8wVV1zB/vvvD8D8+fPZYostWGuttTjttNOGanoB3d5+6wIk2bptT/JLYLsk67fVDa9YjvCuAA5MslHr+6BWNqiqehC4O8mBLab129LbkcQ90utbHr353Nt6T7Tc9U/pcjVJkqQhLetkIHQJyDFJbqLbQ+9vl7L97cD7k/wM2BT48sAKVfUbuomrb7VxrqbbS29JNm11pwODvdjjUOAdSW4EbgFe18qnA/skmUu3VGS7qrqVbrLvwtbnRcAWwHOAS5PMoVvi/KQnBwd4OMkNdHvYvGOYukOqqluAzwGXtfj/eSnG+0fgH1r5UE8Z/h3d8uGbktzSvkP35OPN7Xp3YAlvCk7yLbp/p22S3J1kma9XkiSt+vbbbz8uu+wy9t13X9Zbbz0A3ve+93HqqacyYcIEbrvtNuiebhvoJOBWYHa6l3N8FVinqn4FfAe4uf29YVljq6rZwCnAdcC1wElVNVx/04AjW+53FfAnI4l7kH5GWm9pHE233c0s4N4B5w4CLly8JFqSJGkoqVpztgtJchcwefGeequCJJcCH66qmWvieKNh8uTJNXPmahOuJElaSklmtZfLaRVh/iVJ0ppvqBxseZ4MlCRJkiRJkrQaWd6lCmMiyTnA8wcUf7Sqxo9BOAAkOQHYY0DxjKqasjLjWBnjtT0LLx7k1Cuq6r4VPb4kSVq55s6dy7Rp055Qtv7663PttdeOUURLzL1OHot4RiLJjsDATRQXVNXuYxGPJEnqT6vlZGBVHTTWMQxUVe8f6xhWljbhN3Gs45AkSSvHjjvuyJw5c8Y6jCdYHXOvqpqLOZQkSRpjLhOWJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9wslASZIkSZIkqU84GShJkiRJkiT1iXXGOgDpgYcf5ezb7xnrMDRKXr/NFmMdgiRJGsa8hfOYcf+MpW43fdPpKyAaSZK0MvlkoCRJkiRJktQnnAxcjSW5NMnkEdY9LMnxw9Q5MMl2I+jn2SMY76gkG40kNkmSpKEkeWgJ56Yk+eEojTMxyatHoy9JkqRVmZOB6nUgsMTJQOAwYNjJQOAowMlASZK0upgIjNpkYJK1R6svSZKk0eRk4GogyfgkP0vytSS3JLkwyYbt9LQkc5LcnGS3pejvJ0luSnJxkq2SvBz4C+CY1t8LB2l3MDAZOL3V2TDJK5LckGRukq8nWT/JkXQThpckuWSUfgZJkqTBjEtyVpLbkpyeJABJPpXk+pYjndhT/tjKiiSbJbkryXrA3wJTW44zNcnGLbe5ruU6r2ttnrDaIskPk0xpxw8l+ackNwIvGyzYNt4/tHFmJpmU5IIk/5HkPT31PtLivynJZ3rKv5dkVssJ39VT/lCSzyW5Mck1STYftV9YkiStUZwMXH28GDihqrYHHgDe0Mo3qqqJwPuAr4+wry8Cp1bVTsDpwHFVdRVwLvCRqppYVf8xsFFVnQXMBA5tYxZwCjC1qnakeyHNe6vqOODXwD5Vtc9gASR5V0uAZ86//74Rhi1JkvQkO9OtSNgOeAGwRys/vqp2raodgA2B1wzVQVU9AnwKOKPlQWcAnwB+UlW7AfvQ3TDdeJhYNgauraoJVfXTJdT7z5ZLXUGXSx0MvBT4DECS/ehyv93onljcJcnere3bq2oXuhu0RyZ5Rs/Y11TVBOBy4J0DB+3Nvx66d8jV15IkaQ3nZODq486qmtOOZwHj2/G3AKrqcuCpSZ42gr5eBnyzHZ8G7LmMMW3T4vp5+34qsPcS6j+mqk6sqslVNXmTTZ8xfANJkqTBXVdVd1fVImAOj+dI+yS5Nslc4M+A7Zey3/2AjyWZA1wKbABsNUybhcB3R9D3ue3vXLrJwwer6jfAgpbL7dc+NwCzgW3pJgehmwC8EbgGeG5P+SPA4v0Te3PFx/TmX+M2GzeCMCVJ0pponbEOQCO2oOd4Id0dbuiezus18LskSdKabGCOtE6SDYAvAZOr6ldJjqabzAP4I4/fEN+AoQV4Q1Xd/oTCZBeeeEO9t4+Hq2rhUsS8aED8i+jy8wD/UFVfHTD2FGBf4GVV9fskl/aM/2hVLc4DF2KeL0mShuCTgau/qQBJ9gTmV9X8EbS5CjikHR9Kt0QF4EHgKcO07a1zOzA+yYva92nAZUvRlyRJ0oqweILs3iTj6JbhLnYXsEs77i0fmLtcABzRs9fgzj3tJyZZK8lz6ZbyjrYLgLe32EnynCTPAjYB7m8TgdvSLS2WJElaKk4Grv4eTnID8BXgHSNscwRweJKb6CbwprfybwMfaZtkP+kFIs0pwFfakpkAhwNntiU4i1ocACcC5/sCEUmStLJV1QPA14Cb6SbWru85/X+B97b8abOe8kuA7Ra/QAT4O2Bd4KYkt7TvAFcCdwK3AsfRLeMd7fgvpNvS5eqWY51FN1F5Pt2Tjz8DPk+3VFiSJGmp5PHVBNLYmDx5cs2cOXOsw5AkSStIkllVNXms49DjzL8kSVrzDZWD+WSgJEmSJEmS1CfcWHgNk+RwHl/2u9iVVfX+peznBGCPAcUzqurk5YlPkiRpaSXZEThtQPGCqtp9LOIZiSTnAM8fUPzRqrpgLOKRJElazMnANUybrFvuCbulnTyUJElaUapqLjBxrONYGlV10FjHIEmSNBiXCUuSJEmSJEl9wslASZIkSZIkqU84GShJkiRJkiT1CScDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvqEk4GSJEmSJElSn1hnrAOQHnj4Uc6+/Z6xDkPL4PXbbDHWIUiSpGUwb+E8Ztw/Y6naTN90+gqKRpIkrUw+GShJkiRJkiT1CScD+1ySS5NMHmHdw5Icv6JjkiRJWpOZf0mSpLHkZKAkSZIkSZLUJ5wM7BNJxif5WZKvJbklyYVJNmynpyWZk+TmJLstRX8/SXJTkouTbNXKX5jkmiRzk3w2yUMr7KIkSZJWYeZfkiRpVeRkYH95MXBCVW0PPAC8oZVvVFUTgfcBXx9hX18ETq2qnYDTgeNa+QxgRlXtCNw9VOMk70oyM8nM+ffft9QXIkmStJpYJfOvh+51vlCSpH7lZGB/ubOq5rTjWcD4dvwtgKq6HHhqkqeNoK+XAd9sx6cBe/aUn9mOvzmw0WJVdWJVTa6qyZts+oyRxi9JkrS6WSXzr3GbjRtp/JIkaQ3jZGB/WdBzvBBYpx3XgHoDv0uSJGnZmH9JkqRVipOBApgKkGRPYH5VzR9Bm6uAQ9rxocAV7fgaHl/+csjARpIkSQLMvyRJ0hhZZ/gq6gMPJ7kBWBd4+wjbHAGcnOQjwG+Aw1v5UcA3knwCOB8YSWIrSZLUb8y/JEnSmEiVKxI0epJsBPyhqirJIcCbq+p1S2ozefLkmjlz5soJUJIkrXRJZlXV5LGOY01l/iVJkgYzVA7mk4EabbsAxycJ3RvzRnqnW5IkScvG/EuSJI2Yk4F6kiSHA9MHFF9ZVe8frm1VXQFMWCGBSZIkraHMvyRJ0sriZKCepKpOBk4e6zgkSZL6hfmXJElaWXybsCRJkiRJktQnnAyUJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9wslASZIkSZIkqU+sM9YBSA88/Chn337PWIeh5vXbbDHWIUiSpBVs3sJ5zLh/xlK1mb7p9BUUjSRJWpl8MlCSJEmSJEnqE04GSpIkSZIkSX2irycDkzw01jFIkiRJkiRJK4t7BvapJOtU1R/HOg5JkiQ9WZJPAG8D5gG/AmYBrwFuBP6ULo9/OzATuAOYWFUPtLa/APasqv9Z+ZFLkqRVXV8/GTicJB9NMjfJjUk+38omJrkmyU1JzkmyaSu/NMkXklyX5OdJ9mrl27eyOa3Ni1v5h5Lc3D5HDRPHW3r6+GqStVv5Q0k+1+K7JsnmrfyZSb6b5Pr22aOVH53ktCRXAqcNMdZhSb6X5KIkdyX5QIv1hjbG01u9d7a+b2xjbdTKv5/kre343UlOX95/B0mSpH6SZBfgEGAi8Gpg157TG1XVROB9wNerahHwfeCg1nZ34JdOBEqSpKE4GTiEJK8CXgfsXlUTgH9sp/4N+GhV7QTMBT7d02ydqtoNOKqn/D3AjJa0TQbubgne4cDuwEuBdybZeYg4XgJMBfZofSwEDm2nNwauafFdDryzlc8Ajq2qXYE3ACf1dLkdsG9VvXkJl78D8Hq6xPNzwO+ramfgauCtrc7ZVbVrG/tnwDta+buAT7XJ0P8DHDHEdb0rycwkM+fff98SQpEkSeo7ewHnVNXvq+p/gXN7zn0LoKouB56a5GnAGXT5InSTiGcM1mlv/vXQve6WI0lSv3KZ8ND2BU6uqt8DVNVvk2wCPK2qLmt1TgXO7Glzdvs7Cxjfjq8GPpFkS7oJtF8k2ZMuwfsdQJKz6ZK+GwaJ4xXALsD1SQA2pFsuAvAI8MOeMf+8J/btWn3oEsVx7fjcqvrDMNd+SVU9CDyYZD7wg1Y+F9ipHe+Q5LPA04BxwAUAVfU/ST4FXAIcVFW/HWyAqjoROBHgRTtMqGHikSRJUmdg3lR0+eaLkjwTOBD47KANe/KvrXbeyvxLkqQ+5ZOBo2tB+7uQNtFaVd8E/gL4A/CjJH+2lH0GOLWqJrbPNlV1dDv3aFUtTuQeG5Pu3/WlPW2eU1WLb//+bimuA2BRz/dFPWOcAnygqnYEPgNs0NNmR+A+4NkjukJJkiT1uhw4MMmGSZ4CvLbn3FSAdnN5flXNb/ngOcA/Az+rKpddSJKkITkZOLSLgMN79sJ7elXNB+5fvB8gMA24bKgOWrsXAHdU1XF0+7nsBFxBl+BtlGRjuj1erhiii4uBg5M8a3EcSZ43TOwX0rM8N8nEYeovi6cA9yRZl8eXLZNkN+BVwM7Ah5M8fwWMLUmStMaqqtl0S31vBP4duL7n9MNJbgC+wuPbtNDqv4UhlghLkiQt5jLhIVTV+W0SbWaSR4AfAR+ne6vbV9ok4R10e/8tyZuAaUkeBf4b+Pu25PgU4LpW56SqGmyJMFV1a5JPAhcmWQt4FHg/8MsljHkkcEKSm+j+jS+n27twNP1/wLXAb9rfpyRZH/gacHhV/TrJ/wG+nuTPep5glCRJ0jCq6nN0ezeT5OieU9+oqqMGqT+TbkWJJEnSEsU5Go21yZMn18yZM8c6DEmStIIkmVVVk8c6jtVVmwx8CHgN8OE28bdczL8kSVrzDZWD+WSgJEmStArr2S/6/45lHJIkac3Q95OBSXYEThtQvKCqdl/JcTyDbn/AgV6xIjaBTvJK4AsDiu+sqoNGeyxJkiRJkiStGvp+MrCq5gITV4E47luZcVTVBcAFK2s8SZIkSZIkjT3fJixJkiRJkiT1CScDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvqEk4GSJEmSJElSn3AyUJIkSZIkSeoT64x1ANIDDz/K2bffM9ZhCHj9NluMdQiSJGklmLdwHjPunzHi+tM3nb4Co5EkSSuTTwZKkiRJkiRJfcLJQEmSJEmSJKlPOBm4giS5NMnkEdY9LMnxw9Q5MMl2oxPdipPklCQHj3UckiRp1ZXkoVHs66gkG/V8/1GSp41W/5IkSWsaJwNXHwcCK2wyMMly7x85Gn1IkiQtpaOAxyYDq+rVVfXAmEUjSZK0inMycDklGZ/kZ0m+luSWJBcm2bCdnpZkTpKbk+y2FP39JMlNSS5OslWSlwN/ARzT+nvhEG13be3mJDkmyc09fV6RZHb7vLyVT2nl5wK3tnq3JTm9XdNZi++0J/lUkuvbtZyYJK380iT/kmQmMH1APH/XnhRce5BY35VkZpKZ8++/b0S/tSRJ6g9JPtLyjpuSfKaVbZzkvCQ3tnxkapIjgWcDlyS5pNW7K8lmPXnNKUl+3vKbfZNcmeQXS8rNkjwzyUUttzspyS+TbNbOvSXJdS3f+mqStdvnlBbX3CQfXELflyY5tuVBP2v529ktps/21HvSOK38y63tLYt/m57r/kzL9eYm2XaQsR/Lvx66d9QezpQkSasZJwNHx4uBE6pqe+AB4A2tfKOqmgi8D/j6CPv6InBqVe0EnA4cV1VXAecCH6mqiVX1H0O0PRl4dxtzYU/5PODPq2oSMBU4rufcJGB6VW3dvm8DfKmqXgL8b4sd4Piq2rWqdgA2BF7T08d6VTW5qv5pcUGSY4BnAodXVW8sAFTVia3N5E02fcawP4okSeoPSfajy612AyYCuyTZG9gf+HVVTWj5yPlVdRzwa2CfqtpnkO5eBPwTsG37/CWwJ/Bh4ONLCOPTwE9abncWsFWL7SV0udQePfnWoS3O51TVDlW1I11OtiSPVNVk4CvA94H3AzsAhyV5xhLGAfhEa7sT8KdJdurp996W7325XeMT9OZf4zYbN0yIkiRpTeVk4Oi4s6rmtONZwPh2/C2AqroceOoI9695GfDNdnwaXcI6rNb3U6rq6lb0zZ7T6wJfSzIXOJMnLje+rqru7Pn+q6q6sh1/o2f8fZJc2/r4M2D7njZnDAjn/wM2qar3VFWNJH5JkqRmv/a5AZhNN4n3YmAu8OdJvpBkr6qaP4K+7qyquVW1CLgFuLjlJnN5PF8bzJ7AtwGq6nzg/lb+CmAX4Pokc9r3FwB3AC9I8sUk+9PdUF2Sc9vfucAtVXVPVS1o/Tx3CeMAvCnJbLrfZ3uemNed3f725qOSJElP4B5vo2NBz/FCuifnAAZOhI3VxNgHgf8BJtBNAD/cc+53A+o+KeYkGwBfAiZX1a+SHA1ssIQ+rqe7i//0qvrt8gYvSZL6SoB/qKqvPulEMgl4NfDZJBdX1d8O01dvjrao5/sili0PDt0Kjr8ZJLYJwCuB9wBvAt4+grh6Y+qNa9Bxkjyf7om/Xavq/iSn8MScbHFfCzHPlyRJQ/DJwBVrKkCSPYH5I7yDfRVwSDs+FLiiHT8IPGWoRm2j7AeT7N6KDuk5vQlwT7srPg140h5+PbZK8rJ2/JfAT3k8ybw3yThguLcFnw98HjgvyZAxS5IkDeIC4O0t5yDJc5I8K8mzgd9X1TeAY+i2OoFhcqRldCXdhN7iZcubtvKLgYOTPKude3qS57X9BNeqqu8Cn+yJbVkNOg7wVLqbsPOTbA68ajnHkSRJfcg7hivWw0luoFumu6S7w72OAE5O8hHgN8DhrfzbdEt9jwQOHmLfwHe0OouAy4DFk49fAr6b5K10E3UDn+TrdTvw/iRfB24FvlxVv0/yNeBm4L/pnvxboqo6s00Enpvk1VX1h+HaSJIkVdWFbc+8q9O9r+wh4C10+/8d0/KcR4H3tiYnAucn+fUQ+wYui88A30oyDbiaLv95sKruTfJJ4MIka7U43g/8gS5/W3yj/UlPDi6Nqrp1sHGq6pqWW94G/Ipu0lKSJGmpxC3d1hxJxlXVQ+34Y8AWVTV9mGa97ccDP2ybcq80kydPrpkzZ67MISVJ0kqUZFZ76cVqIcn6wMKq+mNbMfHl9iKPNYb5lyRJa76hcjCfDFyzHJDkb+j+XX8JHDa24UiSJK2WtgK+057KewR45xjHI0mSNGqcDFyJkhwODHxS78qqev9S9nMCsMeA4hlVdTJPfrPviFXVXcBKfSpQkiT1pyQ7AqcNKF5QVbsPVn8FxbCk3Gzn5ex7SfmaJEnSmHEycCVqyd9yJ4BLO3koSZK0qqmqucDEMY5hVHKzIfo2X5MkSask3yYsSZIkSZIk9QknAyVJkiRJkqQ+4WSgJEmSJEmS1CecDJQkSZIkSZL6hJOBkiRJkiRJUp9wMlCSJEmSJEnqE+uMdQDSAw8/ytm33zPWYQh4/TZbjHUIkiRpJZi3cB4z7p8xorrTN52+gqORJEkrk08GSpIkqe8kOTrJh8dg3ClJfriyx5UkSVqsLyYDkzw01jFIkiRJkiRJY60vJgP7WRKXgkuSJAFJPpHk50l+CmzTyl6Y5Pwks5JckWTbVn5Kkq8kmdnavKaVr53kmCTXJ7kpybtb+ZQklyY5K8ltSU5PknZu/1Y2G3h9Tzy7Jbk6yQ1JrkqyOKbDkpzd4vpFkn/sabN/ktlJbkxycSvbOMnXk1zX+nrdyvlFJUnS6sjJQCDJR5PMbUnV51vZxCTXtCTvnCSbtvJLk3yhJVs/T7JXK9++lc1pbV7cyj+U5Ob2OWqYON7S08dXk6zdyh9K8rkW3zVJNm/lz0zy3ZaMXp9kj1Z+dJLTklwJnDbEWIcl+V6Si5LcleQDLdYb2hhPb/WGSpBfm+TaVv/HPTEd3ZLRS5PckeTI5f33kSRJWl5JdgEOASYCrwZ2badOBI6oql2ADwNf6mk2HtgNOAD4SpINgHcA86tq19bHO5M8v9XfGTgK2A54AbBHa/M14LXALsCf9PR/G7BXVe0MfAr4+55zE4GpwI7A1CTPTfLM1tcbqmoC8MZW9xPAT6pqN2Af4JgkGy/9ryRJkvpB308GJnkV8Dpg95ZULb7z+m/AR6tqJ2Au8OmeZuu0ZOuonvL3ADOqaiIwGbi7JZ2HA7sDL6VLFnceIo6X0CV8e7Q+FgKHttMbA9e0+C4H3tnKZwDHtmT0DcBJPV1uB+xbVW9ewuXvQHd3elfgc8DvWzJ6NfDWVmeoBPmnwEtb/W8Df93T77bAK+mS508nWXeQ631Xu9M+c/799y0hREmSpFGxF3BOVf2+qv4XOBfYAHg5cGaSOcBXgd63aX2nqhZV1S+AO+hynP2At7b61wLPAF7c6l9XVXdX1SJgDt1k4rbAnVX1i6oq4Bs9/W/Sxr4ZOBbYvufcxVU1v6oeBm4FnkeXT15eVXcCVNVvW939gI+1mC5t17XVwB+gN/966F530ZEkqV+5hBT2BU6uqt9Dl1Ql2QR4WlVd1uqcCpzZ0+bs9ncWXZIH3QTaJ5JsCZxdVb9Isidd0vk7gCRn0yWiNwwSxyvo7hZf31aUbAjMa+ceARZvND0L+POe2Ldr9QGemmRcOz63qv4wzLVfUlUPAg8mmQ/8oJXPBXZqfS1OkBe3Wb/93RI4I8kWwHrAnT39nldVC4AFSeYBmwN39w5cVSfSTTTyoh0m1DBxSpIkrQhrAQ+0G7GDGZijFBC6G6UX9J5IMgVY0FO0kOFz7b+jy8cOSjKebiJvsaXpK3RPC96+pMF686+tdt7K/EuSpD7V908GLqPFydljiVlVfRP4C+APwI+S/NlS9hng1Kqa2D7bVNXR7dyj7U7yE8ak+/d7aU+b51TV4tu8v1uK6wBY1PN9URvjsQS55/OSVueLwPFVtSPwbro70IP1O5JEWJIkaUW7HDgwyYZJnkK3bPf3wJ1J3giQzoSeNm9MslaSF9It+70duAB47+KVD0m2HmZJ7m3A+NYHQO+qjU2A/2rHh43gGq4B9l68LHnxti4tpiOSx/YoHHQliiRJEjgZCHARcHiSjaBLqqpqPnD/4v0AgWnAZUN10Nq9ALijqo4Dvg/sBFxBl3Ru1JLEg1rZYC4GDk7yrMVxJHneMLFfCBzRE8PEYeovlbaEZqgEuTd5fdtojitJkjTaqmo2cAZwI/DvwPXt1KHAO5LcCNxCt33MYv8JXNfqv6ct2T2Jbtnu7La896ss4cZna/Mu4Lx0LxCZ13P6H4F/SHLDkvro6es3ra+zW7xntFN/B6wL3JTklvZdkiRpUH3/xFZVnd8m0WYmeQT4EfBxugmur7RJwjvo9v5bkjcB05I8Cvw38PdtyfEpdEkkwElVNdgSYarq1iSfBC5MshbwKPB+4JdLGPNI4IQkN9H9W15Ot3fhaDoU+HKLbV26/QFvBI6mWz58P/AT4PlD9iBJkrQKqKrP0e2TPND+QzT5cVU9Ibdq+wF+vH16XUrPMt+q+kDP8fl0ewcOjOdqYOueok+28lOAU3rqvabn+N/pJid7+/kD3UoNSZKkYeXx1afS2Jg8eXLNnDlzrMOQJEkrSJJZVTV5rONYGu2G7g+r6qyxjmVFMP+SJGnNN1QO1vdPBkqSJEkDVdVhYx2DJEnSitA3k4FJdgROG1C8oKp2X8lxPINuf8CBXlFV962A8V4JfGFA8Z1VddBojyVJkiRJkqRVW99MBlbVXGDiKhDHfSszjqq6gO4Nc5IkSZIkSepzvk1YkiRJkiRJ6hNOBkqSJEmSJEl9wslASZIkSZIkqU84GShJkiRJkiT1CScDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvrEOmMdgPTAw49y9u33jHUYfeP122wx1iFIkqQxNm/hPGbcP2PYetM3nb4SopEkSSuTTwZKkiRJkiRJfWJMJgOTjE9y80oc7z1J3rqyxlsWSSYnOW4Z2y7z9SWZkuSHy9J2kL6em+SSJLcmuSWJt5IlSdJyS/LQKPZ1VJKNer7/KMnTRqv/laHlby9fwvlR+70kSdKaZ5VeJpxk7apauLz9VNVXRiOeFSXJOlU1E5i5LO1Xoev7I/B/qmp2kqcAs5JcVFW3jnVgkiRJzVHAN4DfA1TVq8c0mmUzBXgIuGqM45AkSauhYZ8MbE/x/SzJ19rTXhcm2TDJxCTXJLkpyTlJNm31L01ybJKZrd2uSc5O8oskn+3pep0kp7c6Zy2+Q5vkriRfSDIbeGOS/ZJcnWR2kjOTjFtCrHcl+cckc5Ncl+RFrfzoJB9uxy9Mcn6SWUmuSLJtK9+8XceN7fPyVv6W1tecJF9Nsnb7nJLk5jbWB5cQ06VJZrT2NyfZrSem05JcCZzW+4ReO/f11vaOJEf29PfW9pvfmOS0Qa5vqPF2a7/jDUmuSrLNILFu3Ma9rtV7XSvfvuc3uCnJiwe71qq6p6pmt+MHgZ8Bzxnqt5EkSRoNST6S5PqWp3ymlW2c5LyWM92cZGrLqZ4NXJLkklbvriSbtZz3tpbj/bzlqfsmubLlsbstYfxnJrmo5conJfllks3aueXNJY9Mt+ripiTfTjIeeA/wwdbnXkme3/K8uXlivi1JkvQkI10m/GLghKraHngAeAPwb8BHq2onYC7w6Z76j1TVZOArwPeB9wM7AIcleUarsw3wpap6CfC/wPt62t9XVZOAHwOfBPZt32cCHxom1vlVtSNwPPAvg5w/ETiiqnYBPgx8qZUfB1xWVROAScAtSV4CTAX2qKqJwELgUGAi8Jyq2qGNdfIwMW3U2r8P+HpP+Xbt2t48SJttgVcCuwGfTrJuku3pfo8/a3EOtQx3sPFuA/aqqp2BTwF/P0i7TwA/qardgH2AY5JsTJdwzmh9TgbuHuZ6aYnqzsC1Q5x/V7oJ45nz779vuO4kSZIGlWQ/ulx1N7ocbZckewP7A7+uqglVtQNwflUdB/wa2Keq9hmkuxcB/0SXh20L/CWwJ13O+PElhPFpuhxqe+AsYKsW22jkkh8Ddm4593uq6i66HPvYqppYVVcAM4Avt76GfCtbb/710L2uJJYkqV+NdDLwzqqa045nAS8EnlZVl7WyU4G9e+qf2/7OBW5pT4wtAO4AntvO/aqqrmzH36BLtBY7o/19Kd2E2ZVJ5gBvA543TKzf6vn7st4T6Z4qfDlwZuvvq8DiV6v+GfBlgKpaWFXzgVcAuwDXt/qvAF7QruMFSb6YZH+6ycxhY6qqy4Gn5vF9ac6tqj8M0ea8qlpQVfcC84DNW4xntjKq6rdLMd4m7bpvBo4Fth+k3X7Ax9q1XgpsQJfMXg18PMlHgectIWbgsd/5u8BRVTXob1NVJ1bV5KqavMmmzxisiiRJ0kjs1z43ALPpJvFeTJeH/nm6FSd7tdxuOHdW1dyqWgTcAlxcVdX6Gr+EdnsC3waoqvOB+1v5aOSSNwGnJ3kL3ZYsg9mDx3Pg04bqqDf/GrfZkIttJEnSGm6kewYu6DleCDxthPUXDWi7qGfMGtCm9/vv2t8AFw3x5NxQaohj6CY/H2h3ZkciwKlV9TdPOpFMoHty7z3Am4C3jzCm3u+/G1ixx8DffGn2dxxsvL8DLqmqg9pTe5cO0i7AG6rq9gHlP0tyLXAA8KMk766qnww2cJJ16SYCT6+qs5ciZkmSpGUR4B+q6qtPOpFMAl4NfDbJxVX1t8P0NTBv7c1pl2Wv7dHIJQ+gu+n+WuATSXYcot7A/E+SJGlQy/o24fnA/Un2at+nAZctof5gtkqy+Mm9vwR+Okida4A98vjefxsn2XqYfqf2/L2690R7Su3OJG9s/aUlYgAXA+9t5Wsn2aSVHZzkWa386Ume1/aAWauqvku3bHfSSGJKsifdMuaR3JkezE/o9lF8xuJ4lmK8TYD/aucPG6LdBcARSdLa79z+vgC4oy2t+T6w02CNW7t/BX5WVf+8dJcmSZK0TC4A3t5WJpDkOUmeleTZwO+r6hvAMTyerz0IPGWUY7iSbkJv8bLlTVv5cuWSSdYCnltVlwAfpcvnxg1yDVcCh7TjQ0fzwiRJ0ppned4m/DbgK+le/HEHcPhStr8deH+SrwO30pbo9qqq3yQ5DPhWkvVb8SeBny+h302T3ER3J3ewJwoPBb6c5JPAunRLOm6k23/vxCTvoHsS771VdXWrd2FLxh6l2//wD8DJrQzgSXd7B3g4yQ1tvCU9QbhEVXVLks8BlyVZSLcc5rARjvePwKntes4bYoi/o9tn8aZ2bXcCr6FLbqcleRT4bwbfbxC6JSrTgLltKQzAx6vqRyO+SEmSpKVQVRe2vfmubvczHwLeQrf/3zFJFtHlcO9tTU4Ezk/y6yH2DVwWn6HLV6fR3Yz+b+DBqrp3OXPJtYFvtJvUAY6rqgeS/AA4K93L3o6gy2O/2bZ0+f4oXZMkSVpDpdsGZc2Q5C5g8uI99VYFSS4FPlxVM9fE8UbD5MmTa+bM1SZcSZK0lJLMai+XWyO1m9YLq+qPbeXLl5diW5oxYf4lSdKab6gcbHmeDJQkSZLUvXDtO+1Jv0eAd45xPJIkSUNaLScDk5wDPH9A8UeravwYhANAkhPolsn2mlFVU1ZmHCtjvLZn4cWDnHpFVd23oseXJEn9p704Y+CbchdU1e4rMYbD6Zbk9rqyqt4P7LycfQ+VS568PP1KkiQNtFpOBlbVQWMdw0AtCewLbcJv4ljHIUmS+kdVzWWM8482MbdCJuf6KZeUJElja1nfJixJkiRJkiRpNeNkoCRJkiRJktQnnAyUJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9Yp2xDkB64OFHOfv2e8Y6jL7x+m22GOsQJEnSGJu3cB4z7p8xbL3pm05fCdFIkqSVyScDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMXEpJHhrFvo5KslHP9x8ledpo9b8yJJmS5OVLOD9qv5ckSVp9jBs3bqxDkCRJ0iCcDBxbRwGPTQZW1aur6oExi2bZTAGGnAyUJEnqN0ncl1uSJK2ynAwcZUk+kuT6JDcl+Uwr2zjJeUluTHJzkqlJjgSeDVyS5JJW764kmyUZn+S2JKck+XmS05Psm+TKJL9IstsSxn9mkouS3JLkpCS/TLJZO/eWJNclmZPkq0nWbp9TWlxzk3xwCX0fmeTWdm3fTjIeeA/wwdbnXkmen+Tq1tdnl9DXu5LMTDJz/v33LdNvLUmSVl9JPtryhRuTfL6VTUxyTcs1zkmyaSu/NMkXWh7z8yR7tfLte3Kbm5K8uJV/qOU2Nyc5apg4npQftfKHknyuxXdNks1b+TOTfLfle9cn2aOVH53ktCRXAqcNMdZhSb7XcrW7knygxXpDG+Pprd4Lk5yfZFaSK5Js28pfm+TaVv/HPTEdneTr7Xe6o+WZg43/WP710L0u3pAkqV85GTiKkuwHvBjYDZgI7JJkb2B/4NdVNaGqdgDOr6rjgF8D+1TVPoN09yLgn4Bt2+cvgT2BDwMfX0IYnwZ+UlXbA2cBW7XYXgJMBfaoqonAQuDQFudzqmqHqtoROHkJfX8M2LmqdgLeU1V3AV8Bjq2qiVV1BTAD+HLra8hXBFfViVU1uaomb7LpM5YwpCRJWtMkeRXwOmD3qpoA/GM79W/AR1uuMZcur1lsnarajW5lxeLy9wAzWm4zGbg7yS7A4cDuwEuBdybZeYg4hsqPADYGrmnxXQ68s5XPoMt9dgXeAJzU0+V2wL5V9eYlXP4OwOuBXYHPAb+vqp2Bq4G3tjonAkdU1S50ud+XWvlPgZe2+t8G/rqn322BV9LloZ9Osu7AgXvzr3GbuYxbkqR+5RKG0bVf+9zQvo+jmxy8AvinJF8AftgmzYZzZ1XNBUhyC3BxVVWSucD4JbTbEzgIoKrOT3J/K38FsAtwfRKADYF5wA+AFyT5InAecOES+r4JOD3J94DvDVFnD7rEGLq74l9YQn+SJKk/7QucXFW/B6iq3ybZBHhaVV3W6pwKnNnT5uz2dxaP50JXA59IsiVwdlX9IsmewDlV9TuAJGcDe/F4ftZrqPwI4BHghz1j/nlP7Nu1+gBPTbJ4Zu3cqvrDMNd+SVU9CDyYZD5dLgbd5OdOra+XA2f2jLF++7slcEaSLYD1gDt7+j2vqhYAC5LMAzYH7h4mFkmS1IecDBxdAf6hqr76pBPJJODVwGeTXFxVfztMXwt6jhf1fF/Esv27BTi1qv5mkNgm0N1Jfg/wJuDtQ/RxALA38Fq6xHvHIerVMsQnSZK0JItzoYW0XKiqvpnkWroc5UdJ3r2UfQ6ZHwGPVtXinOaxMelW1ry0qh5+QkfdxN3vRjDmcDneWsAD7UnFgb4I/HNVnZtkCnD0EP32xitJkvQELhMeXRcAb198dzjJc5I8K8mz6ZaAfAM4BpjU6j8IPGWUY7iSbkJv8bLlTVv5xcDBSZ7Vzj09yfPS7Se4VlV9F/hkT2xPkGQt4LlVdQnwUWATuicfB17DlcAh7fhQJEmSnuwi4PAkG0GXl1TVfOD+xfsBAtOAy4bqoLV7AXBH237l+8BOdCsyDkyyUZKN6VZMDLUqY9D8aJjYLwSO6Ilh4jD1l0pV/S9wZ5I3tv7TbtxCl3/9Vzt+22iOK0mS+od3DEdRVV3Y9p65ut0dfgh4C93+f8ckWQQ8Cry3NTkROD/Jr4fYN3BZfAb4VpJpdEtn/ht4sKruTfJJ4MI2sfco8H7gD8DJrQxgsDvjAGsD32hLeAIcV1UPJPkBcFaS19ElxtOBbyb5KF1SLkmS9ARtK5OJwMwkjwA/otsT+W3AV9ok4R10e/8tyZuAaUkepct5/r4tOT4FuK7VOamqBlsiTFXdOkR+9MsljHkkcEKSm+hy6cvpVleMpkOBL7fY1qXbH/BGuicBz2zbwPwEeP4ojytJkvpAHl/9oDVBkvWBhVX1xyQvo3uZx8QxDmuJJk+eXDNnzhzrMCRJ0gqSZFZVTR7rOPQ48y9JktZ8Q+VgPhm45tkK+E67u/0Ij7/5TpIkSZIkSX3OycBl0F6ccdqA4gVVtftKjOFwuiW5va6sqvcDOy9n3yfQvRW414yqOnl5+pUkSf1l7ty5TJs2Dbq3785pxSs1ZwJI8gy6/QEHekVV3bcCxnsl8IUBxXdW1UGjPZYkSdLScpmwxpzLVCRJWrO5THjVY/4lSdKab6gczLcJS5IkSZIkSX3CyUBJkiRJkiSpTzgZKEmSJEmSJPUJJwMlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+sQ6Yx2A9MDDj3L27feMdRh94/XbbDHWIUiSpDE2b+E8Ztw/Y9h60zedvhKikSRJK5NPBo6xJEcn+fAYjDslyQ9X9riSJEmSJEkaO04GSpIkSUshyaVJJo+w7mFJjh+mzoFJthtBP88ewXhHJdloJLFJkqT+5GTgGEjyiSQ/T/JTYJtW9sIk5yeZleSKJNu28lOSfCXJzNbmNa187STHJLk+yU1J3t3Kp7QE9awktyU5PUnauf1b2Wzg9T3x7Jbk6iQ3JLkqyeKYDktydovrF0n+safN/klmJ7kxycWtbOMkX09yXevrdSvnF5UkSVqtHQgscTIQOAwYdjIQOApwMlCSJA3JycCVLMkuwCHARODVwK7t1InAEVW1C/Bh4Es9zcYDuwEHAF9JsgHwDmB+Ve3a+nhnkue3+jvTJYLbAS8A9mhtvga8FtgF+JOe/m8D9qqqnYFPAX/fc24iMBXYEZia5LlJntn6ekNVTQDe2Op+AvhJVe0G7AMck2Tjpf+VJEmSxl6S8Ul+luRrSW5JcmGSDdvpaUnmJLk5yW5L0d9P2o3ci5NsleTlwF/Q5U1zkrxwkHYHA5OB01udDZO8ot18ndtuxq6f5Ei6CcNLklwySj+DJElawzgZuPLtBZxTVb+vqv8FzgU2AF4OnJlkDvBVoPctD9+pqkVV9QvgDmBbYD/gra3+tcAzgBe3+tdV1d1VtQiYQzeZuC1wZ1X9oqoK+EZP/5u0sW8GjgW27zl3cVXNr6qHgVuB5wEvBS6vqjsBquq3re5+wMdaTJe269pqsB8hybva044z599/34h+OEmSpDHwYuCEqtoeeAB4QyvfqKomAu8Dvj7Cvr4InFpVOwGnA8dV1VV0+eBHqmpiVf3HwEZVdRYwEzi0jVnAKcDUqtqR7qWA762q44BfA/tU1T4D++nNvx6696ERhixJktY0TgauGtYCHmgJ4OLPS3rO14D6BYTuScLF9Z9fVRe28wt66i5k+LdG/x1wSVXtQPfk4AY955amr9A9Lbg4pq2q6meDVayqE6tqclVN3mTTZwwTniRJ0pi5s6rmtONZdDdZAb4FUFWXA09N8rQR9PUy4Jvt+DRgz2WMaZsW18/b91OBvYdr1Jt/jdts3DIOLUmSVndOBq58lwMHtuUdT6GbfPs9cGeSNwKkM6GnzRuTrNWWjbwAuB24AHhvknVbm62HWZJ7GzC+Z+nJm3vObQL8Vzs+bATXcA2w9+JlyUme3sovAI7o2aNw5xH0JUmStCob6sboYDdrJUmSVnlOBq5kVTUbOAO4Efh34Pp26lDgHUluBG4Bel++8Z/Ada3+e9qS3ZPolu3Obst7v8oSntprbd4FnNdeIDKv5/Q/Av+Q5IYl9dHT129aX2e3eM9op/4OWBe4Kckt7bskSdKaaCpAkj3p9nGeP4I2V9HtHQ1d7ndFO34QeMowbXvr3E53k/dF7fs04LKl6EuSJPWxYSd+NPqq6nPA5wY5tf8QTX5cVe8Z0Mci4OPt0+vS9llc7wM9x+fT7R04MJ6rga17ij7Zyk+h249mcb3X9Bz/O93kZG8/fwDePcQ1SJIkrUkebjdS1wXePsI2RwAnJ/kI8Bvg8Fb+beBr7QUgBw+2byBdTvaVJH+gW258ON2ez+vQ3Vz+Sqt3InB+kl8Ptm+gJElSundJaFWV5BTgh23j6DXS5MmTa+bMmWMdhiRJWkGSzKqqyWMdhx5n/iVJ0ppvqBzMJwNXcVV12FjHIEmSJEmSpDWDk4GSJEnSckpyODB9QPGVVfX+peznBGCPAcUzqurk5YlPkiRpMScDJUmSpOXUJuuWe8JuaScPJUmSlpZvE5YkSZIkSZL6hJOBkiRJkiRJUp9wMlCSJEmSJEnqE04GSpIkSZIkSX3CyUBJkiRJkiSpTzgZKEmSJEmSJPUJJwMlSZIkSZKkPrHOWAcgPfDwo5x9+z1jHcYa5/XbbDHWIUiSpFXUvIXzmHH/jGHrTd90+kqIRpIkrUw+GShJkiRJkiT1CScDmyQPjXUMq6skf5tk37GOQ5Ik9bcklyaZPMK6hyU5fpg6BybZbgT9PHtp4hys75HkU+ZckiRpNLhMWCRZp6r+uKztq+pToxmPJEnSKuJA4IfArUuocxhwM/DrkXaaZJ2BfY8knxqqTpK1q2rhSMeXJEn9zScDRyjJR5PMTXJjks+3solJrklyU5Jzkmzayi9N8oUk1yX5eZK9Wvn2rWxOa/PiVv6hJDe3z1HDxPGWnj6+mmTtVv5Qks+1+K5Jsnkrf2aS7ya5vn32aOVHJzktyZXAaUOMdViS7yW5KMldST7QYr2hjfH0Vu+UJAe347uSfCbJ7PZ7bbv8v74kSdLjkoxP8rMkX0tyS5ILk2zYTk9redLNSXZbiv5+0vKzi5NsleTlwF8Ax7T+XjhIu4OBycDprc6GSXZJclmSWUkuSLJFq3tpkn9JMhP46MC+B+RTn2p5281JTkySVj4w5/pCktnAG5Psl+TqloOdmWTccv3IkiRpjeVk4AgkeRXwOmD3qpoA/GM79W/AR6tqJ2Au8OmeZutU1W7AUT3l7wFmVNVEusTx7iS7AIcDuwMvBd6ZZOch4ngJMBXYo/WxEDi0nd4YuKbFdznwzlY+Azi2qnYF3gCc1NPldsC+VfXmJVz+DsDrgV2BzwG/r6qdgauBtw7R5t6qmgR8GfjwENfyriQzk8ycf/99SxhekiRpUC8GTqiq7YEH6PIcgI1anvQ+4Osj7OuLwKktpzsdOK6qrgLOBT5SVROr6j8GNqqqs4CZwKFtzD+2vg6uql3a+J/rabJeVU2uqs8N0/fxVbVrVe0AbAi8Zoi472s514+BT9LldZNaTB8aWLk3/3roXnfIkSSpX7lMeGT2BU6uqt8DVNVvk2wCPK2qLmt1TgXO7Glzdvs7Cxjfjq8GPpFkS+DsqvpFkj2Bc6rqdwBJzgb2Am4YJI5XALsA17cbxBsC89q5R+iWmiwe8897Yt+u1Qd4as+d4nOr6g/DXPslVfUg8GCS+cAPWvlcYKch2vRe++sHq1BVJwInArxohwk1TAySJEkD3VlVc9pxb771LYCqujzJU5M8raoeGKavl/F4znIaj9/4XVrb0N1IvajlXmsD9/ScP2OE/eyT5K+BjYCnA7fweA7Wa3F/L6W7yXtlG3c9urzzCXrzr6123sr8S5KkPuVk4IqzoP1dSPudq+qbSa4FDgB+lOTdS9ln6O5a/80g5x6tqsVJ3WNj0j39+dKqevgJHXWJ4u9GMOaCnuNFPd8XMfR/P0+6dkmSpFHWm6MspLtJCjBwkmtlTnoFuKWqXjbE+WFzryQbAF8CJlfVr5IcDWwwTH8B/v/27jzasqq89/73J6i00pOBGkAUURop4NAIYiRinyggERVR0KuSEJoYiXr1Kpp4hfAm3lJRBF5BCEGDYiTGSyN9kK6AgiowYGh8YyAiCAgojcXz/rHnoTaH03e7ztnfzxh7nLXnms2z1jlV+xlzrTX3+WM87SFJkgT4mPB4nQ8clGQ1gCTrVtWDwP2D6wECBwCXjNRBa7cZcHtVfQn4Pp076y4D9kqyWpLVgb1b2XAuAPZNsuFgHEk2GSP284BDu2JYMEZ9SZKkuWw/gPb0xYMtZxvLj4F3tu39WZ6LPQSsOUbb7jq3ABskeWWL4dlJthpHu26DE3/3tqc59h07fK4Edkvykjbu6kleOo52kiSpDzkZOA5VdQ6ddV0WJVnM8nXw3kdn4ecbgQXA58bo6h3A0tbH1sCpVXUdcApwNXAVcFJVDfeIMFV1M531YM5rY54PbDTGmIcBA21B7JvprFsoSZI0Xz2a5HrgeOAD42xzKJ0LvzfSucB7eCv/FnBk+/K0Z3yBSHMKcHzL71aiM3l3TJIbgMXAriO0G7bv9kjziXS+ofhc4Jqxgq+qX9L5VuMz2jFcAfglbpIkaVhZ/mSp1BsDAwO1aNGiXochSZJmSJJrq2qg13FoOfMvSZLmv5FyMO8MlCRJkiRJkvqEX+7QJck2dL5BrttjVbXzLMexHp31AYd6bVXdNwPjvQE4ZkjxHVW193SPJUmSNJuSHMTyx34HXV5Vh0ywn+OA3YYUL6yqk6cSnyRJ0mxzMrBLVS2hs/Zfr+O4bzbjqKpz6axJI0mSNK+0ybopT9hNdPJQkiRpReVjwpIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9wslASZIkSZIkqU84GShJkiRJkiT1CScDJUmSJEmSpD6xcq8DkB549AnOuuXuXocx5+2zxUa9DkGSJM0R9yy7h4X3Lxyz3uHrHD4L0UiSpNnknYGSJEmSJElSn3AyUJIkSZIkSeoTTgb2SJKHex3DTEpyRJLVeh2HJElasc33nGgmJflckj17HYckSZpbnAyc55L0al3IIwAnAyVJkkYw1Tytqj5dVT+arngkSVJ/cDJwBZTkNUkuSfL9JLcnOTrJ/kmuTrIkyYtbvVOS7NvV7uGu9pclORu4eYQxNk2ytOv9R5McNUpML05yTpJrW98vS7JykmuSvKbV+UKSzyc5DHg+cFGSi6Z+RiRJUr9K8rGW/9yQ5OhWtiDJlUluTPK9JOu08ouTHNNypluT7N7Kt2pli1ubzVv5R5Isba8jxojjPV19fD3JSq384Zb/3NBi+r1WvkGS77Zc6Zoku7Xyo5KcluRy4LQRxjowyT8nOT/JnUn+vMV6fRtj3VbvqVyw1ftskuva+XrZ1M++JEmaj5wMXHFtCxwMvBw4AHhpVe0EnAQcOo722wOHV9VLpymeE4BDq2oH4KPAV6vqd8CBwNfaIypvBD5bVV8C7gL2qKo9hussyYeSLEqy6MH775umECVJ0nyS5E3A24Cdq2pb4G/brlOBj1XVK4AlwGe6mq3ccqYjusoPBhZW1QJgAPh5kh2Ag4CdgV2ADybZboQ4Xg7sB+zW+lgG7N92rw5c2eK7FPhgK18IfLGqdgTeTieHG7QlsGdVvWuUw98a2AfYEfg88Juq2g64AnjvCG3urartga/RydeGHsdT+dfD9/p0tiRJ/apXj5BqbNdU1d0ASW4DzmvlS4BhJ9iGuLqq7piOQJKsAewKnJlksPi5AFV1U5LTgB8Ar6yqx8fTZ1WdQGeCkZdsvW1NR5ySJGne2RM4uap+A1BVv0qyFrB2VV3S6nwTOLOrzVnt57XApm37CuCTSV4InFVVP03yKuB7VfUIQJKzgN2B64eJ47XADsA1LRdaFbin7XucTh40OObrumLfsit3el7LqQDOrqrfjnHsF1XVQ8BDSR4E/qWVLwFeMUKb7mPfZ+jO7vxr4+02Nv+SJKlPORm44nqsa/vJrvdPsvz39jva3Z1JngU8p6vNI2P0/1TbZpVR6j4LeKBdCR/ONsADwIZjjClJkjTTBnOmZbScqar+MclVwFuAHyb58AT7DPDNqvrEMPueqKrBibWnxqSTP+1SVY8+raPO5OBYeRqMLxccqc2yUepIkqQ+52PCc9uddK5SA7wVePYE2v4C2DDJekmeC/zRSBWr6tfAHUn+BCAd27btfYB1gVcDX06ydmv2ELDmBOKRJEka6nzgoCSrASRZt6oeBO4fXA+QznIql4zUQWu3GXB7W8rk+3TurLsM2CvJaklWB/ZuZcO5ANg3yYaDcSTZZIzYz6NraZckC8aoL0mSNCucDJzbTgT+IMkNwCsZ31VmAKrqCeBzwNV0Eu1/H6PJ/sAH2lg3AW9Lsj5wNPA/qupW4Ct01seBziMo5/gFIpIkabKq6hzgbGBRksUsXwfvfcCxSW4EFtDJaUbzDmBp62Nr4NSqug44hU4udBVwUlUN94gwVXUz8CngvDbm+cBGY4x5GDDQvrDkZjrrFkqSJPVclj/VIPXGwMBALVq0qNdhSJKkGZLk2qoa6HUcWs78S5Kk+W+kHMw7AyVJkiRJkqQ+4cLCPZRkG+C0IcWPVdXO0zjGenTWuRnqtVV13zD1jwN2G1K8sKpOnq6YJEmSus1GTjTOOCaUN03DeG8AjhlSfEdV7T3dY0mSJA1yMrCHqmoJnXVuZnKM+yYyRlUdMnPRSJIkPdNs5ETjjGNCedM0jHcucO5sjSdJkgQ+JixJkiRJkiT1DScDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvqEk4GSJEmSJElSn3AyUJIkSZIkSeoTK/c6AOmBR5/grFvu7nUYc8o+W2zU6xAkSdIcds+ye1h4/8JR6xy+zuGzFI0kSZpN3hkoSZIkSZIk9QknAyVJkiRJkqQ+4WTgCJI8PI19HZFkta73P0yy9nT1L0mSpI4kFycZGGfdA5N8ZYw6eyXZcnqimzlJTkmyb6/jkCRJKz4nA2fHEcBTk4FV9eaqeqBn0UiSJGm89gJmbDIwyZTX8J6OPiRJUv9wMnCSkhyZ5JokNyb5bCtbPcm/JrkhydIk+yU5DHg+cFGSi1q9O5Osn2TTJP/eruTemuT0JHsmuTzJT5PsNMr4GyQ5P8lNSU5K8rMk67d970lydZLFSb6eZKX2OqXFtSTJX4zS98VJvphkUZKfJNkxyVktpr/pqvfPSa5tMXyolW3S6q2f5FlJLkvy+mHG+FDrf9GD99832V+DJEnqUy2P+kmSE1sucl6SVdvuA1oetHS0fGqY/i5sud0FSTZOsivwVuDY1t+LR2i7Y2u3OMmxSZZ29XlZkuvaa9dW/ppWfjZwc1dOeHo7pu8MPlWS5NMt51ya5IQkaeUXJ/k/SRYBhw+J569b3rfSkPKn8q+H7522h2AkSdIc42TgJLTJrc2BnYAFwA5JXg28Ebirqratqq2Bc6rqS8BdwB5Vtccw3b0E+DvgZe31buBVwEeB/zlKGJ8BLqyqrYDvABu32F4O7AfsVlULgGXA/i3OF1TV1lW1DXDyGIf5eFUNAMcD3wcOAbYGDkyyXqvz/qraARgADkuyXlX9DDgG+Brwl8DNVXXe0M6r6oSqGqiqgbXWWW/obkmSpPHYHDiu5UMPAG9v5au1POjPgG+Ms68vA9+sqlcApwNfqqofA2cDR1bVgqq6bYS2JwMf7sq9Bt0DvK6qtqeTn32pa9/2wOFV9dL2fgvgq1X1cuDXLXaAr1TVji23XBX4o64+ntPyqb8bLEhyLLABcFBVdcfytPxrjfXXGNdJkSRJ84+TgZPz+va6HriOziTe5sAS4HVJjkmye1U9OI6+7qiqJVX1JHATcEFVVetr01HavQr4FkBVnQPc38pfC+wAXJNkcXu/GXA7sFmSLyd5I50kczRnt59LgJuq6u6qeqz18/tt32FJbgCubGWbt3hOAp4HHExnUlOSJGkm3FFVi9v2tSzPnc4AqKpLgedlfGs1vxL4x7Z9Gp1ca0yt7zWr6opW9I9du58NnJhkCXAmT3/c+OqquqPr/X9W1eVt+x+6xt8jyVWtjz8Etupq8+0h4fwvYK2qOrjlk5IkSc/g+iKTE+ALVfX1Z+xItgfeDPxNkguq6nNj9PVY1/aTXe+fZHK/n9C5qv2JYWLbFngDnUm6dwDvH0dc3TE9FVeS1wB7Aq+sqt8kuRhYpY2zGvDCVn8N4KFJHIckSdJYunOUZXTunAMYOhHWq4mxvwB+AWxL5yL8o137HhlS9xkxJ1kF+CowUFX/meQoWr41Qh/X0HliZd2q+tVUg5ckSfOTdwZOzrnA+5OsAZDkBUk2TPJ84DdV9Q/AsXQe/4DOZNia0xzD5XQm9AYfW16nlV8A7Jtkw7Zv3baO3/rAs6rqu8CnumKbrLWA+9tE4MuAXbr2HUPn8ZpPAydOcRxJkqSJ2g8gyauAB8f5tMaPgXe27f2By9r2qHlc+1K4h5Ls3Ire2bV7LeDu9gTIAcBKjGzjJK9s2+8G/o3lE3/3trxzrG8LPgc4GvjXJNOde0qSpHnCOwMnoarOa2vzXdHWcH4YeA+d9f+OTfIk8ATwp63JCcA5Se4aYd3AyfgscEaSA4ArgP8GHqqqe5N8CjgvybNaHIcAvwVObmUAz7hzcILOAQ5O8hPgFjqPCpPkD4Ad6axZuCzJ25McVFUjrlG49irPZp8tNppiOJIkSU95NMn1dB7THe1JiG6H0smVjgR+CRzUyr9F51Hfw4B9R1g38AOtzpPAJcDg5ONXge8meS+d3GnonXzdbgEOSfIN4Gbga+2i64nAUjq53jVjHURVndkmAs9O8uaq+u1w9TZcaUMOX+fw4XZJkqR5Li4nMjcleS6wrKp+164if60tWj3nDAwM1KJFi3odhiRJmiFJrm1fTDYvJVmjqh5u2x8HNqqqcc+0JdkU+EH7kpBZYf4lSdL8N1IO5p2Bc9fGwD+1O/0eBz7Y43gkSZL61VuSfIJObv0z4MDehiNJkjQyJwNHkWQbOt8m1+2xqtp5uPozFMNBwNAry5dX1SHAdlPs+zhgtyHFC0d7pFeSJGmuGSOfmkg/o+VOQ7/Zd9yq6k5g1u4KlCRJ/c3JwFFU1RJgQY9jOBmYkcm5iSbAkiRJc9F05VPmTpIkaT7w24QlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+oSTgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBkqSJEmSJEl9YuVeByA98OgTnHXL3b0OY07YZ4uNeh2CJEmaB+5Zdg8L71844v7D1zl8FqORJEmzyTsDJUmSJEmSpD7hZOAwkjzc6xjmgiRrJ/mzUfafkmTf2YxJkiSt+My1JEmSesfJQD1Nkok8Or42MOJkoCRJkp5ugrmWJEnStHMycBKSfCzJkiQ3JDm6lS1IcmWSG5N8L8k6rfziJMckuTrJrUl2b+VbtbLFrc3mrfwjSZa21xFjxPGerj6+nmSlVv5wks+3+K5M8nutfIMk301yTXvt1sqPSnJaksuB00YYa7h4jwZe3MqOTcdXktyS5EfAhtNwuiVJUp/p01zrwCT/nOT8JHcm+fMW6/VtjHVbvQ+2vm9oY63Wyr+f5L1t+8NJTp/q70GSJM1PTgZOUJI3AW8Ddq6qbYG/bbtOBT5WVa8AlgCf6Wq2clXtBBzRVX4wsLCqFgADwM+T7AAcBOwM7AJ8MMl2I8TxcmA/YLfWxzJg/7Z7deDKFt+lwAdb+ULgi1W1I/B24KSuLrcE9qyqd41w6M+IF/g4cFtVLaiqI4G9gS1aX+8Fdh2hL5J8KMmiJIsevP++kapJkqQ+08e5FsDWwD7AjsDngd9U1XbAFXRyK4CzqmrHNvZPgA+08g8Bn26ToX8JHDrMMT2Vfz18r09qS5LUr3xMYeL2BE6uqt8AVNWvkqwFrF1Vl7Q63wTO7GpzVvt5LbBp274C+GSSF9JJ6n6a5FXA96rqEYAkZwG7A9cPE8drgR2Aa5IArArc0/Y9Dvyga8zXdcW+ZasP8Lwka7Tts6vqt6Mc93DxDq3zauCMqloG3JXkwpE6q6oTgBMAXrL1tjXKuJIkqb/0a64FcFFVPQQ8lORB4F9a+RLgFW176yR/Q2e5ljWAcwGq6hdJPg1cBOxdVb8a2nl3/rXxdhubf0mS1KecDJwdj7Wfy2jnvKr+MclVwFuAHyb58AT7DPDNqvrEMPueqKrBBO+pMencCbpLVT36tI46Cesjow02Qry3TzBmSZKkmTDnc63msa7tJ7veP9k1xinAXlV1Q5IDgdd0tdkGuA94/jjGkiRJfcrHhCfufOCgrvVZ1q2qB4H7B9eoAQ4ALhmpg9ZuM+D2qvoS8H06V3svA/ZKslqS1ek8dnvZCF1cAOybZMPBOJJsMkbs59H1yEiSBWPUHyveh4A1u6pdCuyXZKUkGwF7jLd/SZKkpi9zrQlYE7g7ybNZ/tgySXYC3gRsB3w0yYtmYGxJkjQPeGfgBFXVOS2xW5TkceCHwP8E3gcc3xLX2+msRzOadwAHJHkC+G/gf7fHYE4Brm51Tqqq4R5boapuTvIp4LwkzwKeAA4BfjbKmIcBxyW5kc7v/lI66+mMx0jxXp5kKfB/gb8C/hC4Gfj/6DyeI0mSNG59nGuN1/8CrgJ+2X6umeS5wInAQVV1V5K/BL6R5A+77mCUJEkCIOYH6rWBgYFatGhRr8OQJEkzJMm1VTXQ6zi0nPmXJEnz30g5mI8JS5IkSZIkSX3Cx4RHkGQb4LQhxY9V1c6zHMd6dNasGeq1VXXfDIz3BuCYIcV3VNXe0z2WJEnqX+ZaT2OuJUmSZo2TgSOoqiXAghUgjvtmM46qOhc4d7bGkyRJ/clcS5IkqTd8TFiSJEmSJEnqE04GSpIkSZIkSX3CyUBJkiRJkiSpTzgZKEmSJEmSJPUJJwMlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+sTKvQ5AeuDRJzjrlrt7HcYKbZ8tNup1CJIkaR65Z9k9LLx/4Yj7D1/n8FmMRpIkzSbvDNSMSHJgkuf3Og5JkiRJkiQt52TgPJeOXvyeDwScDJQkSZOS5OFp7OuIJKt1vf9hkrWnq/9eSfLjXscgSZLmHicDV1BJNk3y70lOSXJrktOT7Jnk8iQ/TbJTkqOSfLSrzdLWbtMktyQ5FVgK/P4IYzzctb1vklNGiWeDJN9Nck177dbKv5/kvW37wy3OfYEB4PQki5OsOi0nRZIkaXKOAJ6aDKyqN1fVAz2LZppU1a69jkGSJM09Tgau2F4C/B3wsvZ6N/Aq4KPA/xyj7ebAV6tqq6r62TTEshD4YlXtCLwdOKmVfwj4dJLdgb8EDq2q7wCLgP2rakFV/XYaxpckSXpKkiPbBcobk3y2la2e5F+T3NAuku6X5DA6TytclOSiVu/OJOuP5+LrKONvkOT8JDclOSnJz5Ks3/a9J8nV7aLo15Os1F6ntLiWJPmLUfq+OMkXkyxK8pMkOyY5q8X0N131Hm4/X9PafKcdz+lJMj1nWpIkzTd+gciK7Y6qWgKQ5CbggqqqJEuATYHFo7T9WVVdOY2x7Als2ZVXPi/JGlX1iySfBi4C9q6qX42nsyQfojORyPrPf8E0hilJkua7JK+nc+FzJyDA2UleDWwA3FVVb2n11qqqB5N8BNijqu4dpruXAH8CvB+4huUXX99K5+LrXiOE8Rngwqr6QpI3Ah9oY74c2A/YraqeSPJVYH/gJuAFVbV1q7f2GIf5eFUNJDkc+D6wA/Ar4LYkX6yq+4bU3w7YCrgLuBzYDfi37grd+dc6L1xnjOElSdJ85Z2BK7bHuraf7Hr/JJ2J3N/x9N/hKl3bj4yj/xqh7XCeBezS7vRbUFUvqKrBx4y3Ae5jAmsEVtUJVTVQVQNrrbPeeJtJkiQBvL69rgeuo/MExebAEuB1SY5JsntVPTiOvu6oqiVV9SSdCbsLqqpaX5uO0u5VwLcAquoc4P5W/lo6E3fXJFnc3m8G3A5sluTLbfLw12PEdXb7uQS4qarurqrHWj/DLQFzdVX9vB3H4uFi786/1lh/jTGGlyRJ85WTgXPbncD2AEm2B140wfa/SPLy9gUje49R9zzg0ME3SRa0nzsBb6JzNfqjSQZjeAhYc4LxSJIkjUeAL3RdpHxJVf2/VXUrndxoCfA37emFsYx18XUysX2zK7Ytquqoqrof2Ba4GDiY5UuujBVXd0yjxdVdZ9kkY5ckSX3AycC57bvAuu0R4j8Hbp1g+48DPwB+DNw9Rt3DgIG2Ls/NwMFJngucCLy/qu6is2bgN9oaNacAx/sFIpIkaQacC7w/yRoASV6QZMMkzwd+U1X/ABxLu2jKzFykvBx4Rxv/9cDgc7cXAPsm2bDtWzfJJm09wWdV1XeBT3XFJkmSNKu8YriCqqo7ga273h84wr7Xj9DF1iOUd4/xHeA744znXjrr3wy1bVeds1n+SMt320uSJGlaVdV5bW2+K9p6xg8D76Gz/t+xSZ4EngD+tDU5ATgnyV1Vtcc0hfFZ4IwkBwBXAP8NPFRV9yb5FHBee/riCeAQ4LfAya0M4BPTFIckSdKEpLMkitQ7AwMDtWjRol6HIUmSZkiSa6tqoNdxTKf2hMSyqvpdklcCX6uqBT0Oa9zMvyRJmv9GysG8M7APJLkKeO6Q4gMGv6l4SN1P0vlGvW5nVtXnZyo+SZKkOWhj4J/anX6PAx/scTySJEnj4mRgH6iqnSdQ9/OAE3+SJKnnkmwDnDak+LGJ5DbTEMNBwOFDii+vqkPofIHaVPo+DthtSPHCqjp5Kv1KkiSNxslASZIkrZDaUwwLehzDycCMTM61CUVJkqRZ5bcJS5IkSZIkSX3CyUBJkiRJkiSpTzgZKEmSJEmSJPUJJwMlSZIkSZKkPuFkoCRJkiRJktQnnAyUJEmSJEmS+sTKvQ5AeuDRJzjrlrt7Hcas2WeLjXodgiRJ6nP3LLuHhfcvHHbf4escPsvRSJKk2eSdgZIkSZIkSVKfcDJQkiRJkiRJ6hNOBs6AJBcnGRhn3QOTfGWMOnsl2XIc/Tx/InEO13eSzyXZc4w2Y9aRJEmaqCQP9zqGuSDJ2kn+bJT9pyTZdzZjkiRJc4eTgXPDXsCok4HAgcCEJgOTrDy076r6dFX9aLR2I9VJstJExpckSVJHy8vGa21gxMlASZKk0TgZOAVJNk3ykyQnJrkpyXlJVm27D0iyOMnSJDtNoL8Lk9yY5IIkGyfZFXgrcGzr78XDtNsXGABOb3VWTbJDkkuSXJvk3CQbtboXJ/k/SRYBHxvad/eV5CSfTnJNO4YTkqSVd9e5M8kxSa4D/iTJ65NckeS6JGcmWWOEY/1QkkVJFj14/30TOOuSJKlfJflYkiVJbkhydCtbkOTKlj99L8k6rfzilqNcneTWJLu38q1a2eLWZvNW/pGW8yxNcsQYcbynq4+vD14QTfJwks+3+K5M8nutfIMk32151TVJdmvlRyU5LcnlwGkjjDVcvEcDL25lx6bjK0luSfIjYMMR+noq/3r4Xm/ClCSpXzkZOHWbA8dV1VbAA8DbW/lqVbWAzlXbb4yzry8D36yqVwCnA1+qqh8DZwNHVtWCqrptaKOq+g6wCNi/jfm71te+VbVDG//zXU2eU1UDVfX5Mfr+SlXtWFVbA6sCfzRC3PdV1fbAj4BPAXu294uAjwzXoKpOaDEMrLXOemOeGEmS1N+SvAl4G7BzVW0L/G3bdSrwsZY/LQE+09Vs5araCTiiq/xgYGHLmQaAnyfZATgI2BnYBfhgku1GiOPlwH7Abq2PZcD+bffqwJUtvkuBD7byhcAXq2pHOrniSV1dbkknd3rXCIf+jHiBjwO3tfztSGBvYIvW13uBXYfrqDv/WmP9Ya/XSpKkPjCRxxE0vDuqanHbvhbYtG2fAVBVlyZ5XpK1q+qBMfp6JbBP2z6N5UnuRG0BbA2c327mWwm4u2v/t8fZzx5J/gpYDVgXuAn4l2HqDfa3C50k9PI27nOAKyYavCRJ0jD2BE6uqt8AVNWvkqwFrF1Vl7Q63wTO7GpzVvvZnaNdAXwyyQuBs6rqp0leBXyvqh4BSHIWsDtw/TBxvBbYAbim5TurAve0fY8DP+ga83VdsW/Z6gM8r+vpibOr6rejHPdw8Q6t82rgjKpaBtyV5MJR+pMkSX3OycCpe6xrexmdhBCghtQb+n4mBbipql45wv5HxuwgWQX4KjBQVf+Z5ChglTH6C3D+KFe2JUmSZtNgnraMlvdW1T8muQp4C/DDJB+eYJ+h8yTHJ4bZ90RVDeZ8T41J52mcXarq0ad11JnUGzUvGyHe2ycYsyRJ0lN8THjm7AfQrjQ/WFUPjqPNj4F3tu39gcva9kPAmmO07a5zC7BBkle2GJ6dZKtxtOs2OPF3b7tyPZ5vpLsS2C3JS9q4qyd56TjaSZIkjeV84KAkqwEkWbflV/cPrgcIHABcMlIHrd1mwO1V9SXg+8Ar6ORceyVZLcnqdB67vWyELi4A9k2y4WAcSTYZI/bzgEO7YlgwRv2x4h2av10K7JdkpXTWid5jvP1LkqT+42TgzHk0yfXA8cAHxtnmUDpJ7o10ktnDW/m3gCOTXJ9hvkCkOQU4PsliOo8F7wsck+QGYDEjrB0zUt/tkeYTgaXAucA1YwVfVb+k863GZ7RjuAJ42VjtJEmSxlJV59BZ63hRy3c+2na9j86Xod0ILAA+N0ZX7wCWtj62Bk6tquvo5FJXA1cBJ1XVcI8IU1U301kj+bw25vnARmOMeRgw0L4A5GY66wCO13Dx3kdnWZalSY4Fvgf8FLiZzhqKLtMiSZJGlOVPMki9MTAwUIsWLep1GJIkaYYkubaqBnodh5Yz/5Ikaf4bKQfzzkBJkiRJkiSpT/gFIrMkyUEsf+x30OVVdcgE+zkO2G1I8cKqOnkq8UmSJK0IkmwDnDak+LGq2nmW41iPzvqAQ722PaY73eO9AThmSPEdVbX3dI8lSZL6m5OBs6RN1k15wm6ik4eSJElzSVUtobP2X6/juG8246iqc+ms0yxJkjSjfExYkiRJkiRJ6hN+gYh6LslDwC29jmOOWB+4t9dBzCGer4nxfI2f52piPF8TMx/P1yZVtUGvg9ByfZR/zcd/T8PxOOcXj3N+8Tjnl7l2nMPmYD4mrBXBLX7D4PgkWeS5Gj/P18R4vsbPczUxnq+J8XxplvRF/tUv/548zvnF45xfPM75Zb4cp48JS5IkSZIkSX3CyUBJkiRJkiSpTzgZqBXBCb0OYA7xXE2M52tiPF/j57maGM/XxHi+NBv65e/M45xfPM75xeOcXzzOOcQvEJEkSZIkSZL6hHcGSpIkSZIkSX3CyUDNmCRvTHJLkv9I8vFh9j83ybfb/quSbNq17xOt/JYkb5jVwHtksucryaZJfptkcXsdP+vBz7JxnKtXJ7kuye+S7Dtk3/uS/LS93jd7UffOFM/Xsq6/rbNnL+reGcf5+kiSm5PcmOSCJJt07eurv68pniv/tp65/+AkS9o5+bckW3bt67vPRU1ev+Rg/ZI79Uve0y/5Sr/kGf2SI/TLZ/tkj3O+/X/bVe/tSSrJQFfZnPl9AlBVvnxN+wtYCbgN2Ax4DnADsOWQOn8GHN+23wl8u21v2eo/F3hR62elXh/TCny+NgWW9voYVrBztSnwCuBUYN+u8nWB29vPddr2Or0+phX1fLV9D/f6GFbA87UHsFrb/tOuf4t99fc1lXPl39aI5+t5XdtvBc5p2333uehr8q9+ycH6JXfql7ynX/KVfskz+iVH6JfP9ike57z6/7bVWxO4FLgSGJhrv8/Bl3cGaqbsBPxHVd1eVY8D3wLeNqTO24Bvtu3vAK9Nklb+rap6rKruAP6j9TefTeV89Zsxz1VV3VlVNwJPDmn7BuD8qvpVVd0PnA+8cTaC7qGpnK9+NJ7zdVFV/aa9vRJ4Ydvut7+vqZyrfjSe8/XrrrerA4MLO/fj56Imr19ysH7Jnfol7+mXfKVf8ox+yRH65bN9Ksc5l4zncwXgr4FjgEe7yubS7xPwMWHNnBcA/9n1/uetbNg6VfU74EFgvXG2nW+mcr4AXpTk+iSXJNl9poPtsan8ffi3NfFjXiXJoiRXJtlrWiNbMU30fH0A+L+TbDvXTeVcgX9bw56vJIckuQ34W+CwibSVmn7Jwfold+qXvKdf8pV+yTP6JUfol8/2qRwnzKP/b5NsD/x+Vf3rRNuuaFbudQCSpuxuYOOqui/JDsA/J9lqyNUZabI2qar/SrIZcGGSJVV1W6+DWhEkeQ8wAPxBr2NZ0Y1wrvzbGkZVHQccl+TdwKeAFXpNKGmOMneaf+bdZ0q/5Bn9kCP0y2f7CMc5b/6/TfIs4O+BA3scyrTwzkDNlP8Cfr/r/Qtb2bB1kqwMrAXcN862882kz1e7Ffk+gKq6ls76BC+d8Yh7Zyp/H/5tTfCYq+q/2s/bgYuB7aYzuBXQuM5Xkj2BTwJvrarHJtJ2HpnKufJva+y/j28Be02yrfpbv+Rg/ZI79Uve0y/5Sr/kGf2SI/TLZ/ukj3Oe/X+7JrA1cHGSO4FdgLPbl4jMpd9nx1iLCvryNZkXnbtOb6ezeObg4ptbDalzCE9f1Pmf2vZWPH3xzdtZwRff7PH52mDw/NBZ7PS/gHV7fUy9PFdddU/hmQtp30Fn0eV12va8PVfTcL7WAZ7bttcHfsowi+jOp9c4/y1uRyeR2XxIeV/9fU3xXPm3Nfz52rxr+4+BRW277z4XfU3+NcWcYs78rU3xOOdM7jTFz/E587k0xeOcM58pU/zsnFe/z1GOc779Puf8Z/sUj3Ne/n/b6l/M8i8QmTO/z6fi73UAvubvC3gzcGv7T/6TrexzdK78AKwCnElncc2rgc262n6ytbsFeFOvj2VFPl/A24GbgMXAdcAf9/pYVoBztSOddRoeoXOnw01dbd/fzuF/AAf1+lhW5PMF7AosaR9sS4AP9PpYVpDz9SPgF+3f3GLg7H79+5rsufJva8TztbDr//OL6EpA+/Fz0dfkX5PNKdq+OfO3NtnjZI7lTpP9HG/75szn0mSPc659pkz2s3Me/j7nRY4wjuOcF5/tkz3O+fb/7ZC6F9MmA+fa77OqSAtakiRJkiRJ0jznmoGSJEmSJElSn3AyUJIkSZIkSeoTTgZKkiRJkiRJfcLJQEmSJEmSJKlPOBkoSZIkSZIk9QknAyVpHkhSSf6h6/3KSX6Z5AdjtFs7yZ+Nc4yHJ1I+pM7uSW5KsjjJquMZb6qSvCbJrl3vD07y3tkYW5IkzS+TzbWmYdxTktyR5IYktyY5NckLZ3LMmZLkI0n+PcmSdjx/n+TZbd8Pk6w9RvvPJdlzVoKV5jknAyVpfngE2Lprou11wH+No93awLgmA6dof+ALVbWgqn47VuUkK0/DmK8BnpoMrKrjq+rUaehXkiT1n8nmWtPhyKraFtgCuB64MMlzptLhNOVaExnvYOD1wC5VtQ2wI3APsCpAVb25qh4YrY+q+nRV/WimY5X6gZOBkjR//BB4S9t+F3DG4I4kRyX5aNf7pUk2BY4GXtzu2Ds2yRpJLkhyXbtq+7bxDt7uxLs4yXfaVd/T0/E/gHcAf91VdmyLYUmS/braX5bkbODm9v6SJN9PcnuSo5Psn+Tq1u7Frd0fJ7kqyfVJfpTk99qxHQz8RTu23bvPQZIFSa5McmOS7yVZp5VfnOSYNsatSXaf7C9DkiTNO6PlWqsn+UbLIa4fzKGSbNrym+vaa9dWPmzeNNrg1fFF4L+BN7V+Xp/kitb3mUnWaOU7JvlxuwPv6iRrJjkwydlJLgQumETMGyW5tOVWSwfzpJFiGOKTwJ8OTvhV1eNVdXRV/br1cWeS9dvYP0lyYjpPlZw3OAGbzl2S+074tybpGZwMlKT541vAO5OsArwCuGocbT4O3Nbu2DsSeBTYu6q2B/YA/m6sxHSI7YAjgC2BzYDdquok4Gw6V7X3B/YBFgDbAnsCxybZqLXfHji8ql7a3m9LZ1Lv5cABwEuraifgJODQVuff6Fxl3q6dg7+qqjuB44EvtmO7bEicpwIfq6pXAEuAz3TtW7mNccSQckmS1N9Gy7U+CVzYcog96OQ3q9O5++11LbfaD/hSV5tn5E3jjOM64GVJ1gc+BezZ+l8EfCSduwa/TSenGsy3Bp/M2B7Yt6r+YBIxvxs4t6oW0MnRFo8UQ3ewSZ4HrFFVd4zz+DYHjquqrYAHgLePs52kcZrVW4MlSTOnqm5sd8S9i86V68kI8L+TvBp4EngB8Ht0rkCPx9VV9XOAJIuBTelM1nV7FXBGVS0DfpHkEjqPivy6te9OFK+pqrtbf7cB57XyJXSSVoAXAt9uE4rPAUZNNJOsBaxdVZe0om8CZ3ZVOav9vLbFL0mSNFau9XrgrVn+JMYqwMbAXcBXkiwAlgEv7WoznrxpOIMXanehM5F4ebt2+xzgCjqPE99dVde0uAfvvgM4v6p+NcmYrwG+kc46f/9cVYuT/MEIMYwcfPIG4Bg6y9W8u6p+PKTKHVW1uG2bj0kzwMlASZpfzgb+Hzrr5a3XVf47nn43+CojtN8f2ADYoaqeSHLnKHWH81jX9jIm/jnzyCj9Pdn1/smuvr8M/H1VnZ3kNcBRExxzqMExJhO/JEma30bKtQK8vapu6a6c5CjgF3TupHsWnacwBk02b9oOuKCNeX5VvWvImNuM0rY715pQzFV1abtg/BbglCR/D9w/XAzdqurXSR5O8qKquqOqzgXOTefLV4Zb+3DoeZmVL5+T+omPCUvS/PIN4LNVtWRI+Z10HgshyfbAi1r5Q8CaXfXWAu5pE4F7AJvMQIyXAfslWSnJBsCrgaun0N9aLF/A+31d5UOPDYCqehC4v2s9wAOAS4bWkyRJGsZIuda5wKGDy6sk2a6Vr0XnLr0n6eQcK0124HQcBmwEnANcCeyW5CVt/+pJXgrcAmyUZMdWvmaG/8KQCcWcZBPgF1V1Ip0lW7YfJYahvgB8Le0bg9uYE7ngLGkaORkoSfNIVf28qr40zK7vAusmuQn4c+DWVv8+Oo91LE1yLHA6MJBkCfBe4N9nIMzvATcCNwAX0lnjb7yPIQ/nKODMJNcC93aV/wuwd1vkeugXgbyPzro4N9JZv/BzUxhfkiT1iVFyrb8Gng3c2PKtv27lXwXel+QG4GU88ymI8Ti2tb+VztIqe7Qv4PglcCBwRstprgBeVlWP01nr78ut3fkMP/E20ZhfA9yQ5PrW/8KRYhhmrK/RuZvxqlbvcjrfjHz9JM6HpClKVfU6BkmSJEmSJEmzwDsDJUmSJEmSpD7hZKAkSZIkSZLUJ5wMlCRJkiRJkvqEk4GSJEmSJElSn3AyUJIkSZIkSeoTTgZKkiRJkiRJfcLJQEmSJEmSJKlPOBkoSZIkSZIk9Yn/H8J/qHWIcRKPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(20, 8))\n",
    "\n",
    "mi_values[important_features_mi]\\\n",
    "    .sort_values()\\\n",
    "    .plot.barh(ax=axes[0], color=\"lightblue\")\n",
    "\n",
    "axes[0].set_xlabel(\"Mutal Information\")\n",
    "axes[0].set_title(f\"Thresold: {mi_threshold}\")\n",
    "\n",
    "mdg_values[important_features_mdg]\\\n",
    "    .sort_values()\\\n",
    "    .plot.barh(ax=axes[1], color=\"lightgreen\")\n",
    "\n",
    "axes[1].set_xlabel(\"Mean Decrease Gini\")\n",
    "axes[1].set_title(f\"Thresold: {mdg_threshold}\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">25</span> features sélectionnées\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;36m25\u001b[0m features sélectionnées\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tot'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'qpv'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_max'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_altitude_sol_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'dependance'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_std'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tertiaire'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_surface_reelle_bati'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma14'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_garpark'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma30'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_min'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_hauteur_mean'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma90'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_mur_u_ext'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nb_log'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma7'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_2'</span>\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[32m'nb_lot_tot'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_max'\u001b[0m,\n",
       "    \u001b[32m'qpv'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_max'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_std'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_min'\u001b[0m,\n",
       "    \u001b[32m'mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'l_altitude_sol_mean'\u001b[0m,\n",
       "    \u001b[32m'dependance'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_std'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_tertiaire'\u001b[0m,\n",
       "    \u001b[32m'l_surface_reelle_bati'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma14'\u001b[0m,\n",
       "    \u001b[32m'l_conso_ener_mean'\u001b[0m,\n",
       "    \u001b[32m'nb_lot_garpark'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma30'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_mean'\u001b[0m,\n",
       "    \u001b[32m'l_estim_ges_min'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_1'\u001b[0m,\n",
       "    \u001b[32m'l_hauteur_mean'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma90'\u001b[0m,\n",
       "    \u001b[32m'l_mur_u_ext'\u001b[0m,\n",
       "    \u001b[32m'nb_log'\u001b[0m,\n",
       "    \u001b[32m'l_valeur_fonciere_ma7'\u001b[0m,\n",
       "    \u001b[32m'nombre_pieces_principales_2'\u001b[0m\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the union of the two sets of important features\n",
    "selected_features = list(set(important_features_mi + important_features_mdg))\n",
    "\n",
    "rich.print(f\"{len(selected_features)} features sélectionnées\")\n",
    "rich.print(selected_features) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr_reduced = get_feature_vector(df_train, features=selected_features, return_df=False)\n",
    "X_te_reduced = get_feature_vector(df_test, features=selected_features, return_df=False)\n",
    "\n",
    "y_tr = get_target_vector(df_train, \"l_valeur_fonciere\", return_series=False) \n",
    "y_te = get_target_vector(df_test, \"l_valeur_fonciere\", return_series=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18571, 25)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tr_reduced.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisation des features sélectionnées"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric features: 21\n",
      "Dummy features: 4\n"
     ]
    }
   ],
   "source": [
    "numeric_features = [var for var in selected_features if is_numeric(df_preproc[var])]\n",
    "category_features = [var for var in selected_features if var not in numeric_features] \n",
    "\n",
    "print(f\"Numeric features: {len(numeric_features)}\")\n",
    "print(f\"Dummy features: {len(category_features)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>spearman</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>l_surface_reelle_bati</td>\n",
       "      <td>0.600675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l_valeur_fonciere_ma90</td>\n",
       "      <td>0.532974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>l_valeur_fonciere_ma30</td>\n",
       "      <td>0.524832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>l_valeur_fonciere_ma14</td>\n",
       "      <td>0.506385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>l_valeur_fonciere_ma7</td>\n",
       "      <td>0.478896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>l_altitude_sol_mean</td>\n",
       "      <td>0.246237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nb_lot_garpark</td>\n",
       "      <td>0.196657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nb_lot_tot</td>\n",
       "      <td>0.098175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nb_log</td>\n",
       "      <td>0.067699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>l_hauteur_mean</td>\n",
       "      <td>0.020371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>l_estim_ges_min</td>\n",
       "      <td>0.008585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>l_estim_ges_max</td>\n",
       "      <td>-0.058661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>l_estim_ges_mean</td>\n",
       "      <td>-0.069191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nb_lot_tertiaire</td>\n",
       "      <td>-0.082848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>l_estim_ges_std</td>\n",
       "      <td>-0.108723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mur_u_ext</td>\n",
       "      <td>-0.133569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>l_mur_u_ext</td>\n",
       "      <td>-0.133569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>l_conso_ener_min</td>\n",
       "      <td>-0.152986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>l_conso_ener_std</td>\n",
       "      <td>-0.181692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>l_conso_ener_max</td>\n",
       "      <td>-0.258083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>l_conso_ener_mean</td>\n",
       "      <td>-0.260883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature  spearman\n",
       "0    l_surface_reelle_bati  0.600675\n",
       "1   l_valeur_fonciere_ma90  0.532974\n",
       "2   l_valeur_fonciere_ma30  0.524832\n",
       "3   l_valeur_fonciere_ma14  0.506385\n",
       "4    l_valeur_fonciere_ma7  0.478896\n",
       "5      l_altitude_sol_mean  0.246237\n",
       "6           nb_lot_garpark  0.196657\n",
       "7               nb_lot_tot  0.098175\n",
       "8                   nb_log  0.067699\n",
       "9           l_hauteur_mean  0.020371\n",
       "10         l_estim_ges_min  0.008585\n",
       "11         l_estim_ges_max -0.058661\n",
       "12        l_estim_ges_mean -0.069191\n",
       "13        nb_lot_tertiaire -0.082848\n",
       "14         l_estim_ges_std -0.108723\n",
       "15               mur_u_ext -0.133569\n",
       "16             l_mur_u_ext -0.133569\n",
       "17        l_conso_ener_min -0.152986\n",
       "18        l_conso_ener_std -0.181692\n",
       "19        l_conso_ener_max -0.258083\n",
       "20       l_conso_ener_mean -0.260883"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor_method = \"spearman\"\n",
    "\n",
    "cor = df_preproc[numeric_features + [\"l_valeur_fonciere\"]]\\\n",
    "    .corr(method=cor_method)[\"l_valeur_fonciere\"]\\\n",
    "    .drop(\"l_valeur_fonciere\")\\\n",
    "    .sort_values(ascending=False)\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns={\"index\": \"feature\", \"l_valeur_fonciere\": \"spearman\"})\n",
    "\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "\n",
    "if len(category_features) % ncols == 0:\n",
    "    nrows = len(category_features) // ncols\n",
    "else:\n",
    "    nrows = len(category_features) // ncols + 1\n",
    "\n",
    "fig, axes = plt.subplots(nrows, ncols, figsize=(ncols*5, nrows*4))\n",
    "\n",
    "for var, ax in zip(category_features, axes.flatten()):\n",
    "    sns.histplot(x=\"l_valeur_fonciere\", hue=var, data=df_preproc, ax=ax, stat=\"density\", alpha=.5); "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modèles de régression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# indicate if we want to use the reduced feature matrix based on feature selection\n",
    "\n",
    "use_selected_features = True \n",
    "\n",
    "if use_selected_features: \n",
    "    X_tr_, X_te_ = X_tr_reduced, X_te_reduced\n",
    "else:\n",
    "    X_tr_, X_te_ = X_tr, X_te"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sans optimisation\n",
    "\n",
    "L'objectif est d'entrainer plusieurs régresseurs pour déterminer le type de modèle qui semble le plus adapté pour estimer la relation entre `l_valeur_fonciere` et les features sélectionnées. \n",
    "\n",
    "Régresseurs :\n",
    "- `DummyRegressor`, \n",
    "- `LinearRegression`, \n",
    "- `Ridge`, \n",
    "- `DecisionTreeRegressor`, \n",
    "- `RandomForestRegressor`, \n",
    "- `XGBRegressor`, \n",
    "- `MLPRegressor`. \n",
    "\n",
    "On garde les paramètres par défaut de chaque estimateur. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [\n",
    "    DummyRegressor(), \n",
    "    LinearRegression(),\n",
    "    Ridge(),\n",
    "    KNeighborsRegressor(n_neighbors=10),\n",
    "    SVR(max_iter=2000),\n",
    "    DecisionTreeRegressor(), \n",
    "    RandomForestRegressor(),\n",
    "    XGBRegressor(), \n",
    "]\n",
    "\n",
    "tree_based = [\"DecisionTreeRegressor\", \"RandomForestRegressor\", \"XGBRegressor\"]\n",
    "\n",
    "results = {\n",
    "    \"train\": {}, \n",
    "    \"test\": {}\n",
    "}\n",
    "\n",
    "models = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale feature matrix for non-tree based models\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_tr_scaled, scaler = scale_features(scaler, X_tr_, fit=True)\n",
    "X_te_scaled = scale_features(scaler, X_te_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting SVR (scaled)...:  50%|█████     | 4/8 [01:11<01:45, 26.49s/it]                c:\\Users\\pemma\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\svm\\_base.py:299: ConvergenceWarning: Solver terminated early (max_iter=2000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  warnings.warn(\n",
      "Fitting XGBRegressor...: 100%|██████████| 8/8 [24:01<00:00, 180.18s/it]         \n"
     ]
    }
   ],
   "source": [
    "loop = tqdm(estimators) \n",
    "\n",
    "for estimator in loop:\n",
    "\n",
    "    model_name = estimator.__class__.__name__\n",
    "    model = CustomRegressor(estimator)\n",
    "\n",
    "    if model_name in tree_based:\n",
    "        features_tr = X_tr_\n",
    "        features_te = X_te_\n",
    "    else: \n",
    "        model_name += \" (scaled)\"\n",
    "        features_tr = X_tr_scaled\n",
    "        features_te = X_te_scaled\n",
    "\n",
    "    loop.set_description(f\"Fitting {model_name}...\")\n",
    "\n",
    "    model.fit(features_tr, y_tr)\n",
    "\n",
    "    models[model_name] = model\n",
    "\n",
    "    metrics_train = compute_metrics(model, features_tr, y_tr, to_prices=True)\n",
    "    metrics_test = compute_metrics(model, features_te, y_te, to_prices=True)\n",
    "\n",
    "    results[\"train\"][model_name] = metrics_train\n",
    "    results[\"test\"][model_name] = metrics_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                       Metrics computed on valeur_fonciere (train set)                       </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Model                       </span>┃<span style=\"font-weight: bold\"> MAE           </span>┃<span style=\"font-weight: bold\"> % MAPE       </span>┃<span style=\"font-weight: bold\"> MSE           </span>┃<span style=\"font-weight: bold\"> % R²         </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
       "│ DummyRegressor (scaled)     │ 270309.01     │ 64.08        │ 1.76e+11      │ -7.64        │\n",
       "│ LinearRegression (scaled)   │ 91407.76      │ 20.48        │ 2.35e+10      │ 85.64        │\n",
       "│ Ridge (scaled)              │ 91417.57      │ 20.48        │ 2.35e+10      │ 85.64        │\n",
       "│ KNeighborsRegressor         │ 96323.33      │ 20.33        │ 2.67e+10      │ 83.66        │\n",
       "│ (scaled)                    │               │              │               │              │\n",
       "│ SVR (scaled)                │ 284307.77     │ 51.03        │ 1.38e+11      │ 15.71        │\n",
       "│ DecisionTreeRegressor       │ 169.42        │ 0.04         │ 5.11e+07      │ 99.97        │\n",
       "│ RandomForestRegressor       │ 33373.4       │ 6.9          │ 3.27e+09      │ 98.0         │\n",
       "│ XGBRegressor                │ 76898.83      │ 17.27        │ 1.62e+10      │ 90.06        │\n",
       "└─────────────────────────────┴───────────────┴──────────────┴───────────────┴──────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                       Metrics computed on valeur_fonciere (train set)                       \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mModel                      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMAE          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% MAPE      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMSE          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% R²        \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
       "│ DummyRegressor (scaled)     │ 270309.01     │ 64.08        │ 1.76e+11      │ -7.64        │\n",
       "│ LinearRegression (scaled)   │ 91407.76      │ 20.48        │ 2.35e+10      │ 85.64        │\n",
       "│ Ridge (scaled)              │ 91417.57      │ 20.48        │ 2.35e+10      │ 85.64        │\n",
       "│ KNeighborsRegressor         │ 96323.33      │ 20.33        │ 2.67e+10      │ 83.66        │\n",
       "│ (scaled)                    │               │              │               │              │\n",
       "│ SVR (scaled)                │ 284307.77     │ 51.03        │ 1.38e+11      │ 15.71        │\n",
       "│ DecisionTreeRegressor       │ 169.42        │ 0.04         │ 5.11e+07      │ 99.97        │\n",
       "│ RandomForestRegressor       │ 33373.4       │ 6.9          │ 3.27e+09      │ 98.0         │\n",
       "│ XGBRegressor                │ 76898.83      │ 17.27        │ 1.62e+10      │ 90.06        │\n",
       "└─────────────────────────────┴───────────────┴──────────────┴───────────────┴──────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                       Metrics computed on valeur_fonciere (test set)                        </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Model                       </span>┃<span style=\"font-weight: bold\"> MAE           </span>┃<span style=\"font-weight: bold\"> % MAPE       </span>┃<span style=\"font-weight: bold\"> MSE           </span>┃<span style=\"font-weight: bold\"> % R²         </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
       "│ DummyRegressor (scaled)     │ 289119.72     │ 58.33        │ 2.09e+11      │ -12.13       │\n",
       "│ LinearRegression (scaled)   │ 92785.19      │ 18.79        │ 2.43e+10      │ 86.93        │\n",
       "│ Ridge (scaled)              │ 92794.92      │ 18.79        │ 2.43e+10      │ 86.93        │\n",
       "│ KNeighborsRegressor         │ 115383.57     │ 22.87        │ 3.74e+10      │ 79.93        │\n",
       "│ (scaled)                    │               │              │               │              │\n",
       "│ SVR (scaled)                │ 300662.54     │ 51.03        │ 1.53e+11      │ 17.82        │\n",
       "│ DecisionTreeRegressor       │ 134291.12     │ 27.06        │ 4.79e+10      │ 74.3         │\n",
       "│ RandomForestRegressor       │ 93317.06      │ 18.54        │ 2.49e+10      │ 86.63        │\n",
       "│ XGBRegressor                │ 87536.55      │ 18.01        │ 2.26e+10      │ 87.85        │\n",
       "└─────────────────────────────┴───────────────┴──────────────┴───────────────┴──────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                       Metrics computed on valeur_fonciere (test set)                        \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mModel                      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMAE          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% MAPE      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMSE          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% R²        \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩\n",
       "│ DummyRegressor (scaled)     │ 289119.72     │ 58.33        │ 2.09e+11      │ -12.13       │\n",
       "│ LinearRegression (scaled)   │ 92785.19      │ 18.79        │ 2.43e+10      │ 86.93        │\n",
       "│ Ridge (scaled)              │ 92794.92      │ 18.79        │ 2.43e+10      │ 86.93        │\n",
       "│ KNeighborsRegressor         │ 115383.57     │ 22.87        │ 3.74e+10      │ 79.93        │\n",
       "│ (scaled)                    │               │              │               │              │\n",
       "│ SVR (scaled)                │ 300662.54     │ 51.03        │ 1.53e+11      │ 17.82        │\n",
       "│ DecisionTreeRegressor       │ 134291.12     │ 27.06        │ 4.79e+10      │ 74.3         │\n",
       "│ RandomForestRegressor       │ 93317.06      │ 18.54        │ 2.49e+10      │ 86.63        │\n",
       "│ XGBRegressor                │ 87536.55      │ 18.01        │ 2.26e+10      │ 87.85        │\n",
       "└─────────────────────────────┴───────────────┴──────────────┴───────────────┴──────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_regression_report(results[\"train\"], title=\"Metrics computed on valeur_fonciere (train set)\")\n",
    "display_regression_report(results[\"test\"], title=\"Metrics computed on valeur_fonciere (test set)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.300000012, max_bin=256,\n",
       "                                       max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=100, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CustomRegressor</label><div class=\"sk-toggleable__content\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.300000012, max_bin=256,\n",
       "                                       max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=100, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012, max_bin=256,\n",
       "             max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "             max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy='depthwise',\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints='',\n",
       "                                       learning_rate=0.300000012, max_bin=256,\n",
       "                                       max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints='()',\n",
       "                                       n_estimators=100, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor='auto',\n",
       "                                       random_state=0, ...))"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = models[\"XGBRegressor\"]\n",
    "best_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = get_predictions(best_model, X_te_, y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = sns.color_palette(palette=\"Set2\", n_colors=2)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "fig.suptitle(f\"Predictions on test set\", fontsize=12)\n",
    "\n",
    "for target, ax, color in zip([\"l_valeur_fonciere\", \"valeur_fonciere\"], axes.flatten(), colors):\n",
    "\n",
    "    plot_predictions(\n",
    "        y_true=preds[target][\"y_true\"],\n",
    "        y_pred=preds[target][\"y_pred\"],\n",
    "        color=color,\n",
    "        title=target,\n",
    "        ax=ax); "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimisation via `optuna`\n",
    "\n",
    "`optuna` est un framework python permerttant de trouver les paramètres maximisant (ou minimisant) une fonction objectif. `optuna` utilise un historique des essais pour déterminer les valeurs d'hyperparamètres à essayer pour les prochains essais. À l'aide de ces données, il estime les zones les plus prometteuses pour atteindre l'objectif et essaie des valeurs dans cette zone. `optuna` estime ensuite une région encore plus prometteuse sur la base du nouveau résultat, et ainsi de suite jusqu'à convergence de la fonction cible. \n",
    "\n",
    "Pour afficher le dashboard résumant l'optimisation : \n",
    "\n",
    "```\n",
    "optuna-dashboard sqlite:///./backup/__optuna/db.sqlite3\n",
    "\n",
    "Listening on http://localhost:8080/\n",
    "Hit Ctrl-C to quit.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiation study `optuna`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'storage'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'sqlite:///../backup/__optuna/db.sqlite3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'study_name'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'optim-seine-et-marne-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'pruner'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">OptimPruner</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">n_warmup_trials</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>, <span style=\"color: #808000; text-decoration-color: #808000\">n_trials_to_prune</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span><span style=\"font-weight: bold\">)</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'storage'\u001b[0m: \u001b[32m'sqlite:///../backup/__optuna/db.sqlite3'\u001b[0m,\n",
       "    \u001b[32m'study_name'\u001b[0m: \u001b[32m'optim-seine-et-marne-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'pruner'\u001b[0m: \u001b[1;35mOptimPruner\u001b[0m\u001b[1m(\u001b[0m\u001b[33mn_warmup_trials\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[33mn_trials_to_prune\u001b[0m=\u001b[1;36m10\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pruner = OptimPruner(n_warmup_trials=5, n_trials_to_prune=10)\n",
    "\n",
    "version = 0\n",
    "\n",
    "if df_args[\"departement\"] is not None:\n",
    "    study_name = f\"optim-{df_args['departement']}-{df_args['property_type']}-xgb-v{version}\".lower()\n",
    "elif df_args[\"region\"] is not None:\n",
    "    df_args[\"region\"] = f\"optim-{df_args['region']}-{df_args['property_type']}-xgb-v{version}\".lower()\n",
    "else:\n",
    "    study_name = f\"optim-{df_args['geo_area']}-{df_args['property_type']}-xgb-v{version}\".lower()\n",
    "\n",
    "optuna_args = {\n",
    "    \"storage\": f\"sqlite:///{BACKUP_DIR}__optuna/db.sqlite3\", \n",
    "    \"study_name\": study_name, \n",
    "    \"pruner\": pruner\n",
    "}\n",
    "\n",
    "rich.print(optuna_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-04-14 20:08:51,090]\u001b[0m A new study created in RDB with name: optim-seine-et-marne-flats-xgb-v0\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "try: \n",
    "    study = optuna.create_study(direction=\"minimize\", **optuna_args)\n",
    "except:\n",
    "    optuna.delete_study(study_name=optuna_args[\"study_name\"], storage=optuna_args[\"storage\"])\n",
    "    study = optuna.create_study(direction=\"minimize\", **optuna_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-paris-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-paris-flats-xgb-v1'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-paris-flats-xgb-v2'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-marseille-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-hauts-de-seine-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-val-de-marne-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-val-de-marne-flats-xgb-v3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-paris-flats-xgb-v3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-seine-saint-denis-flats-xgb-v3'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">\"optim-val-d'oise-flats-xgb-v0\"</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-paris-flats-xgb-v4'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-essonne-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-essonne-houses-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-yvelines-houses-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-seine-saint-denis-houses-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-val-de-marne-houses-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-seine-et-marne-houses-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">\"optim-val-d'oise-houses-xgb-v0\"</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-yvelines-flats-xgb-v0'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'optim-seine-et-marne-flats-xgb-v0'</span>\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[32m'optim-paris-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-paris-flats-xgb-v1'\u001b[0m,\n",
       "    \u001b[32m'optim-paris-flats-xgb-v2'\u001b[0m,\n",
       "    \u001b[32m'optim-marseille-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-hauts-de-seine-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-val-de-marne-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-val-de-marne-flats-xgb-v3'\u001b[0m,\n",
       "    \u001b[32m'optim-paris-flats-xgb-v3'\u001b[0m,\n",
       "    \u001b[32m'optim-seine-saint-denis-flats-xgb-v3'\u001b[0m,\n",
       "    \u001b[32m\"optim-val-d'oise-flats-xgb-v0\"\u001b[0m,\n",
       "    \u001b[32m'optim-paris-flats-xgb-v4'\u001b[0m,\n",
       "    \u001b[32m'optim-essonne-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-essonne-houses-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-yvelines-houses-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-seine-saint-denis-houses-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-val-de-marne-houses-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-seine-et-marne-houses-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m\"optim-val-d'oise-houses-xgb-v0\"\u001b[0m,\n",
       "    \u001b[32m'optim-yvelines-flats-xgb-v0'\u001b[0m,\n",
       "    \u001b[32m'optim-seine-et-marne-flats-xgb-v0'\u001b[0m\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get all studies in the database\n",
    "all_studies = optuna.get_all_study_summaries(storage=optuna_args[\"storage\"])\n",
    "\n",
    "# display name of all studies   \n",
    "rich.print([study.study_name for study in all_studies])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recherche modèle optimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = CustomRegressor(estimator=XGBRegressor())\n",
    "\n",
    "optim_args = {\n",
    "    \"regressor\": regressor,\n",
    "    \"X_tr\": X_tr_,\n",
    "    \"y_tr\": y_tr,\n",
    "    \"X_te\": X_te_,\n",
    "    \"y_te\": y_te, \n",
    "    \"to_prices\": False, \n",
    "    \"metric\": \"mdape\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function optuna_objective in module lib.model.optimize:\n",
      "\n",
      "optuna_objective(trial: optuna.trial._trial.Trial, regressor: lib.model.estimator.CustomRegressor, X_tr: numpy.ndarray, y_tr: numpy.ndarray, X_te: numpy.ndarray, y_te: numpy.ndarray, metric: str = 'mdape', to_prices: bool = True) -> float\n",
      "    Description.\n",
      "    Objective function to optimize hyperparams of CustomRegressor model using optuna.\n",
      "    \n",
      "    Args:\n",
      "        trial (Trial): optuna trial\n",
      "        X_tr (np.ndarray): training features\n",
      "        y_tr (np.ndarray): training target\n",
      "        X_te (np.ndarray): test features\n",
      "        y_te (np.ndarray): test target\n",
      "        metric (str): metric to optimize (default: \"mdape\")\n",
      "        to_prices (bool): convert target to prices (default: True)\n",
      "    \n",
      "    Returns:\n",
      "        float: median absolute percentage error between true and predicted values \n",
      "            ('valeur_fonciere' or 'l_valeur_fonciere') depending on to_prices.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(optuna_objective)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f1e441700b04b1db591a6eddaf970f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-04-14 20:09:00,514]\u001b[0m Trial 3 finished with value: 0.5929257559154751 and parameters: {'n_estimators': 502, 'max_depth': 8, 'learning_rate': 0.0169811759113501, 'subsample': 0.5837519449742844, 'reg_alpha': 0.003143436366055107, 'reg_lambda': 11.63833493290687}. Best is trial 3 with value: 0.5929257559154751.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:00,542]\u001b[0m Trial 2 finished with value: 0.5929257559154751 and parameters: {'n_estimators': 179, 'max_depth': 9, 'learning_rate': 0.002773105490012268, 'subsample': 0.5007560334672052, 'reg_alpha': 0.33993723654665914, 'reg_lambda': 206.05896378721155}. Best is trial 3 with value: 0.5929257559154751.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:11,351]\u001b[0m Trial 1 finished with value: 0.008222349123149609 and parameters: {'n_estimators': 337, 'max_depth': 5, 'learning_rate': 0.04502696385915511, 'subsample': 0.7702600952291, 'reg_alpha': 3.0223436424830794, 'reg_lambda': 86.65492443001565}. Best is trial 1 with value: 0.008222349123149609.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:12,741]\u001b[0m Trial 6 finished with value: 0.7661072655342519 and parameters: {'n_estimators': 113, 'max_depth': 7, 'learning_rate': 0.0020885814917136437, 'subsample': 0.613224156266793, 'reg_alpha': 308.31339644237437, 'reg_lambda': 457.18660296472103}. Best is trial 1 with value: 0.008222349123149609.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:27,277]\u001b[0m Trial 0 finished with value: 0.008145075797708977 and parameters: {'n_estimators': 922, 'max_depth': 4, 'learning_rate': 0.05873966572567065, 'subsample': 0.586924784886372, 'reg_alpha': 0.028779314574102162, 'reg_lambda': 0.2529172732178983}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:33,502]\u001b[0m Trial 8 finished with value: 0.024679227733801143 and parameters: {'n_estimators': 273, 'max_depth': 4, 'learning_rate': 0.01468867979556766, 'subsample': 0.9831369442887002, 'reg_alpha': 4.521946293554371, 'reg_lambda': 316.2188475672939}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:42,496]\u001b[0m Trial 5 finished with value: 0.008468907969089938 and parameters: {'n_estimators': 968, 'max_depth': 5, 'learning_rate': 0.008168142350428146, 'subsample': 0.5480021290492256, 'reg_alpha': 0.005025822456792357, 'reg_lambda': 0.13600106643695234}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:44,255]\u001b[0m Trial 4 finished with value: 0.008468907969089938 and parameters: {'n_estimators': 525, 'max_depth': 9, 'learning_rate': 0.005711329625698168, 'subsample': 0.9052970798578411, 'reg_alpha': 0.015342485853568582, 'reg_lambda': 95.7512551647366}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:49,739]\u001b[0m Trial 11 finished with value: 0.360874487494813 and parameters: {'n_estimators': 368, 'max_depth': 4, 'learning_rate': 0.002692245813648822, 'subsample': 0.5509024591207629, 'reg_alpha': 0.0242189895570345, 'reg_lambda': 11.744762543201206}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:09:58,991]\u001b[0m Trial 12 finished with value: 0.1475363228271869 and parameters: {'n_estimators': 466, 'max_depth': 9, 'learning_rate': 0.004086918401907284, 'subsample': 0.8845895511055389, 'reg_alpha': 26.161995560052784, 'reg_lambda': 0.5698200483075999}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:10:08,539]\u001b[0m Trial 10 finished with value: 0.015152119244117773 and parameters: {'n_estimators': 813, 'max_depth': 4, 'learning_rate': 0.005590639784282848, 'subsample': 0.9531039615905121, 'reg_alpha': 5.313977498531261, 'reg_lambda': 1.5572911595812073}. Best is trial 0 with value: 0.008145075797708977.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:10:24,133]\u001b[0m Trial 13 finished with value: 0.008101789082431855 and parameters: {'n_estimators': 845, 'max_depth': 3, 'learning_rate': 0.09885866564801987, 'subsample': 0.6889892132775052, 'reg_alpha': 0.1275234654924985, 'reg_lambda': 0.0035927739009666286}. Best is trial 13 with value: 0.008101789082431855.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:10:34,851]\u001b[0m Trial 7 finished with value: 0.007883256041155595 and parameters: {'n_estimators': 804, 'max_depth': 10, 'learning_rate': 0.024540245304111776, 'subsample': 0.7364777658357227, 'reg_alpha': 7.037753256535541, 'reg_lambda': 0.003720989968750597}. Best is trial 7 with value: 0.007883256041155595.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:10:37,297]\u001b[0m Trial 9 finished with value: 0.25255888872756926 and parameters: {'n_estimators': 492, 'max_depth': 8, 'learning_rate': 0.002726906113506395, 'subsample': 0.597581885139417, 'reg_alpha': 0.003037874308433324, 'reg_lambda': 0.002325103743417478}. Best is trial 7 with value: 0.007883256041155595.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:10:49,541]\u001b[0m Trial 15 finished with value: 0.008162819710075977 and parameters: {'n_estimators': 738, 'max_depth': 3, 'learning_rate': 0.08655968007423662, 'subsample': 0.678216695409431, 'reg_alpha': 0.13024828274345784, 'reg_lambda': 0.0017534665008240094}. Best is trial 7 with value: 0.007883256041155595.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:11:08,754]\u001b[0m Trial 14 finished with value: 0.007853755574789123 and parameters: {'n_estimators': 728, 'max_depth': 6, 'learning_rate': 0.07819537853647986, 'subsample': 0.7423523129344359, 'reg_alpha': 0.3558652638892015, 'reg_lambda': 0.0012228821795293437}. Best is trial 14 with value: 0.007853755574789123.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:11:54,825]\u001b[0m Trial 18 finished with value: 0.00845196195624867 and parameters: {'n_estimators': 684, 'max_depth': 10, 'learning_rate': 0.029189404965827618, 'subsample': 0.759160531271218, 'reg_alpha': 40.48090471917677, 'reg_lambda': 0.019160976862284747}. Best is trial 14 with value: 0.007853755574789123.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:12:02,340]\u001b[0m Trial 19 finished with value: 0.009166252078676006 and parameters: {'n_estimators': 669, 'max_depth': 10, 'learning_rate': 0.028851125736205345, 'subsample': 0.7940609274662385, 'reg_alpha': 98.32279058729932, 'reg_lambda': 0.01863412607746791}. Best is trial 14 with value: 0.007853755574789123.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:12:52,531]\u001b[0m Trial 17 finished with value: 0.008196756928553102 and parameters: {'n_estimators': 710, 'max_depth': 10, 'learning_rate': 0.08649903128197477, 'subsample': 0.6984153542979579, 'reg_alpha': 0.2938268023949321, 'reg_lambda': 0.0014730924411298992}. Best is trial 14 with value: 0.007853755574789123.\u001b[0m\n",
      "\u001b[32m[I 2023-04-14 20:12:54,659]\u001b[0m Trial 16 finished with value: 0.008313810221088926 and parameters: {'n_estimators': 718, 'max_depth': 10, 'learning_rate': 0.08535414611818858, 'subsample': 0.7089851549177997, 'reg_alpha': 0.241766169545049, 'reg_lambda': 0.0013881356569006907}. Best is trial 14 with value: 0.007853755574789123.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "study.optimize(\n",
    "    func=lambda trial: optuna_objective(trial, **optim_args),\n",
    "    n_trials=20,\n",
    "    n_jobs=CPU_COUNT/2,\n",
    "    show_progress_bar=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FrozenTrial</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">number</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>, <span style=\"color: #808000; text-decoration-color: #808000\">values</span>=<span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007853755574789123</span><span style=\"font-weight: bold\">]</span>, <span style=\"color: #808000; text-decoration-color: #808000\">datetime_start</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2023</span>, \n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">560693</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">datetime_complete</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2023</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">14</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, \n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">694794</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #808000; text-decoration-color: #808000\">params</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'learning_rate'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07819537853647986</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_depth'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'n_estimators'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">728</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'reg_alpha'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3558652638892015</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'reg_lambda'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0012228821795293437</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'subsample'</span>: \n",
       "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7423523129344359</span><span style=\"font-weight: bold\">}</span>, <span style=\"color: #808000; text-decoration-color: #808000\">distributions</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'learning_rate'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, \n",
       "<span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'max_depth'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">IntDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'n_estimators'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">IntDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1000</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, <span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'reg_alpha'</span>: \n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1000.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'reg_lambda'</span>: \n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1000.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'subsample'</span>: \n",
       "<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">FloatDistribution</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">high</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #808000; text-decoration-color: #808000\">log</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">low</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5</span>, <span style=\"color: #808000; text-decoration-color: #808000\">step</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)}</span>, <span style=\"color: #808000; text-decoration-color: #808000\">user_attrs</span>=<span style=\"font-weight: bold\">{}</span>, <span style=\"color: #808000; text-decoration-color: #808000\">system_attrs</span>=<span style=\"font-weight: bold\">{}</span>, \n",
       "<span style=\"color: #808000; text-decoration-color: #808000\">intermediate_values</span>=<span style=\"font-weight: bold\">{}</span>, <span style=\"color: #808000; text-decoration-color: #808000\">trial_id</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">621</span>, <span style=\"color: #808000; text-decoration-color: #808000\">state</span>=<span style=\"color: #800080; text-decoration-color: #800080\">TrialState</span>.COMPLETE, <span style=\"color: #808000; text-decoration-color: #808000\">value</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mFrozenTrial\u001b[0m\u001b[1m(\u001b[0m\u001b[33mnumber\u001b[0m=\u001b[1;36m14\u001b[0m, \u001b[33mvalues\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;36m0.007853755574789123\u001b[0m\u001b[1m]\u001b[0m, \u001b[33mdatetime_start\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2023\u001b[0m, \n",
       "\u001b[1;36m4\u001b[0m, \u001b[1;36m14\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m10\u001b[0m, \u001b[1;36m8\u001b[0m, \u001b[1;36m560693\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mdatetime_complete\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2023\u001b[0m, \u001b[1;36m4\u001b[0m, \u001b[1;36m14\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m11\u001b[0m, \u001b[1;36m8\u001b[0m, \n",
       "\u001b[1;36m694794\u001b[0m\u001b[1m)\u001b[0m, \u001b[33mparams\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'learning_rate'\u001b[0m: \u001b[1;36m0.07819537853647986\u001b[0m, \u001b[32m'max_depth'\u001b[0m: \u001b[1;36m6\u001b[0m, \u001b[32m'n_estimators'\u001b[0m: \u001b[1;36m728\u001b[0m, \n",
       "\u001b[32m'reg_alpha'\u001b[0m: \u001b[1;36m0.3558652638892015\u001b[0m, \u001b[32m'reg_lambda'\u001b[0m: \u001b[1;36m0.0012228821795293437\u001b[0m, \u001b[32m'subsample'\u001b[0m: \n",
       "\u001b[1;36m0.7423523129344359\u001b[0m\u001b[1m}\u001b[0m, \u001b[33mdistributions\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'learning_rate'\u001b[0m: \u001b[1;35mFloatDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;92mTrue\u001b[0m, \n",
       "\u001b[33mlow\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.001\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'max_depth'\u001b[0m: \u001b[1;35mIntDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m10\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mlow\u001b[0m=\u001b[1;36m3\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \n",
       "\u001b[32m'n_estimators'\u001b[0m: \u001b[1;35mIntDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m1000\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mlow\u001b[0m=\u001b[1;36m100\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'reg_alpha'\u001b[0m: \n",
       "\u001b[1;35mFloatDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m1000\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mlow\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.001\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'reg_lambda'\u001b[0m: \n",
       "\u001b[1;35mFloatDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m1000\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mlow\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.001\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m, \u001b[32m'subsample'\u001b[0m: \n",
       "\u001b[1;35mFloatDistribution\u001b[0m\u001b[1m(\u001b[0m\u001b[33mhigh\u001b[0m=\u001b[1;36m1\u001b[0m\u001b[1;36m.0\u001b[0m, \u001b[33mlog\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mlow\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.5\u001b[0m, \u001b[33mstep\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\u001b[1m}\u001b[0m, \u001b[33muser_attrs\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m, \u001b[33msystem_attrs\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m, \n",
       "\u001b[33mintermediate_values\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m, \u001b[33mtrial_id\u001b[0m=\u001b[1;36m621\u001b[0m, \u001b[33mstate\u001b[0m=\u001b[35mTrialState\u001b[0m.COMPLETE, \u001b[33mvalue\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rich.print(study.best_trial) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Entrainement `XGBRegressor`\n",
    "\n",
    "Utilisation des hyperparamètres optimaux pour entrainer `XGBRegressor`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'learning_rate'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07819537853647986</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'max_depth'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'n_estimators'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">728</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'reg_alpha'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.3558652638892015</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'reg_lambda'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0012228821795293437</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'subsample'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7423523129344359</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'learning_rate'\u001b[0m: \u001b[1;36m0.07819537853647986\u001b[0m,\n",
       "    \u001b[32m'max_depth'\u001b[0m: \u001b[1;36m6\u001b[0m,\n",
       "    \u001b[32m'n_estimators'\u001b[0m: \u001b[1;36m728\u001b[0m,\n",
       "    \u001b[32m'reg_alpha'\u001b[0m: \u001b[1;36m0.3558652638892015\u001b[0m,\n",
       "    \u001b[32m'reg_lambda'\u001b[0m: \u001b[1;36m0.0012228821795293437\u001b[0m,\n",
       "    \u001b[32m'subsample'\u001b[0m: \u001b[1;36m0.7423523129344359\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params_opt = study.best_params\n",
    "rich.print(params_opt)\n",
    "\n",
    "xgb_opt = CustomRegressor(estimator=XGBRegressor(**params_opt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CustomRegressor</label><div class=\"sk-toggleable__content\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.07819537853647986,\n",
       "             max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=728, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.07819537853647986,\n",
       "             max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=728, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy='depthwise',\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints='',\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints='()',\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor='auto',\n",
       "                                       random_state=0, ...))"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_opt.fit(X_tr_, y_tr)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diganostic modèle optimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_opt_metrics = {\n",
    "    \"train\": {\"XGBRegressor\": compute_metrics(xgb_opt, X_tr_, y_tr)}, \n",
    "    \"test\": {\"XGBRegressor\": compute_metrics(xgb_opt, X_te_, y_te)}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                         Fine-tuned XGBRegressor metrics (train set)                         </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Model                     </span>┃<span style=\"font-weight: bold\"> MAE        </span>┃<span style=\"font-weight: bold\"> % MAPE     </span>┃<span style=\"font-weight: bold\"> %MDAPE     </span>┃<span style=\"font-weight: bold\"> MSE       </span>┃<span style=\"font-weight: bold\"> % R²       </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 11198.32   │ 6.9        │ 4.95       │ 2.77e+08  │ 93.33      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                         Fine-tuned XGBRegressor metrics (train set)                         \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mModel                    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMAE       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% MAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m%MDAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMSE      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% R²      \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 11198.32   │ 6.9        │ 4.95       │ 2.77e+08  │ 93.33      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                         Fine-tuned XGBRegressor metrics (test set)                          </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Model                     </span>┃<span style=\"font-weight: bold\"> MAE        </span>┃<span style=\"font-weight: bold\"> % MAPE     </span>┃<span style=\"font-weight: bold\"> %MDAPE     </span>┃<span style=\"font-weight: bold\"> MSE       </span>┃<span style=\"font-weight: bold\"> % R²       </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 23061.82   │ 13.15      │ 9.46       │ 1.18e+09  │ 76.18      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                         Fine-tuned XGBRegressor metrics (test set)                          \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mModel                    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMAE       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% MAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m%MDAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMSE      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% R²      \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 23061.82   │ 13.15      │ 9.46       │ 1.18e+09  │ 76.18      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_regression_report(xgb_opt_metrics[\"train\"], title=\"Fine-tuned XGBRegressor metrics (train set)\")\n",
    "display_regression_report(xgb_opt_metrics[\"test\"], title=\"Fine-tuned XGBRegressor metrics (test set)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_preds = {\n",
    "    \"train\": get_predictions(xgb_opt, X_tr_, y_tr), \n",
    "    \"test\": get_predictions(xgb_opt, X_te_, y_te)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dataset, preds in xgb_preds.items():\n",
    "\n",
    "    fig, axes = plt.subplots(ncols=2, figsize=(16, 6))\n",
    "\n",
    "    title = f\"Fine-tuned XGBRegressor predictions ({dataset} set)\"\n",
    "    fig.suptitle(title, fontsize=12)\n",
    "\n",
    "    for target, ax in zip([\"l_valeur_fonciere\", \"valeur_fonciere\"], axes.flatten()):\n",
    "\n",
    "        plot_predictions(\n",
    "            y_true=preds[target][\"y_true\"],\n",
    "            y_pred=preds[target][\"y_pred\"],\n",
    "            title=target,\n",
    "            ax=ax);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrainement sur dataset complet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate train and test sets to retrain model \n",
    "\n",
    "X = np.concatenate([X_tr_, X_te_])\n",
    "y = np.concatenate([y_tr, y_te])\n",
    "\n",
    "assert X.shape[0] == y.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CustomRegressor</label><div class=\"sk-toggleable__content\"><pre>CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy=&#x27;depthwise&#x27;,\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints=&#x27;&#x27;,\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints=&#x27;()&#x27;,\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                       random_state=0, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.07819537853647986,\n",
       "             max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=728, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "             grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "             interaction_constraints=&#x27;&#x27;, learning_rate=0.07819537853647986,\n",
       "             max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=728, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "CustomRegressor(estimator=XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                       callbacks=None, colsample_bylevel=1,\n",
       "                                       colsample_bynode=1, colsample_bytree=1,\n",
       "                                       early_stopping_rounds=None,\n",
       "                                       enable_categorical=False,\n",
       "                                       eval_metric=None, feature_types=None,\n",
       "                                       gamma=0, gpu_id=-1,\n",
       "                                       grow_policy='depthwise',\n",
       "                                       importance_type=None,\n",
       "                                       interaction_constraints='',\n",
       "                                       learning_rate=0.07819537853647986,\n",
       "                                       max_bin=256, max_cat_threshold=64,\n",
       "                                       max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                       max_depth=6, max_leaves=0,\n",
       "                                       min_child_weight=1, missing=nan,\n",
       "                                       monotone_constraints='()',\n",
       "                                       n_estimators=728, n_jobs=0,\n",
       "                                       num_parallel_tree=1, predictor='auto',\n",
       "                                       random_state=0, ...))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_opt.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_opt_metrics[\"all\"] = {\"XGBRegressor\": compute_metrics(xgb_opt, X_te_, y_te)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                            Fine-tuned XGBRegressor metrics (all)                            </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Model                     </span>┃<span style=\"font-weight: bold\"> MAE        </span>┃<span style=\"font-weight: bold\"> % MAPE     </span>┃<span style=\"font-weight: bold\"> %MDAPE     </span>┃<span style=\"font-weight: bold\"> MSE       </span>┃<span style=\"font-weight: bold\"> % R²       </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 13852.09   │ 7.83       │ 5.66       │ 4.38e+08  │ 91.19      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[3m                            Fine-tuned XGBRegressor metrics (all)                            \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mModel                    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMAE       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% MAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m%MDAPE    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mMSE      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m% R²      \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
       "│ XGBRegressor              │ 13852.09   │ 7.83       │ 5.66       │ 4.38e+08  │ 91.19      │\n",
       "└───────────────────────────┴────────────┴────────────┴────────────┴───────────┴────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_regression_report(xgb_opt_metrics[\"all\"], title=\"Fine-tuned XGBRegressor metrics (all)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sauvegarde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'version': 0, 'geo_area': 'Seine-et-Marne', 'property_type': 'flats'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if use_selected_features: \n",
    "    feature_names = selected_features\n",
    "else:\n",
    "    feature_names = features\n",
    "\n",
    "if df_args[\"departement\"] is not None:\n",
    "    geo_area = df_args[\"departement\"]\n",
    "elif df_args[\"region\"] is not None:\n",
    "    geo_area = df_args[\"region\"]\n",
    "else:\n",
    "    geo_area = df_args[\"geo_area\"]\n",
    "\n",
    "backup_args = {\n",
    "    \"version\": 0, \n",
    "    \"geo_area\": geo_area, \n",
    "    \"property_type\": df_args[\"property_type\"]\n",
    "}\n",
    "\n",
    "backup_args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor and feature names saved at ../backup/models/xgbregressor-seine-et-marne-flats-v0.pkl\n"
     ]
    }
   ],
   "source": [
    "save_model(\n",
    "    path=f\"{BACKUP_DIR}models\",\n",
    "    model=xgb_opt, \n",
    "    feature_names=feature_names, \n",
    "    metrics=xgb_opt_metrics, \n",
    "    **backup_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Succesfully loaded XGBRegressor, feature names and metrics from ../backup/models/xgbregressor-seine-et-marne-flats-v0.pkl.\n"
     ]
    }
   ],
   "source": [
    "# load best model and feature names as dict \n",
    "\n",
    "xgb_loader = load_model(\n",
    "    path=f\"{BACKUP_DIR}models\",\n",
    "    estimator_name=\"XGBRegressor\", \n",
    "    **backup_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Features used to fit model:\n",
       "<span style=\"font-weight: bold\">[</span><span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tot'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_max'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'qpv'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_max'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_std'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_min'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mur_u_ext'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_altitude_sol_mean'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'dependance'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_std'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_tertiaire'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_surface_reelle_bati'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma14'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_conso_ener_mean'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'nb_lot_garpark'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma30'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_mean'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_estim_ges_min'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_1'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_hauteur_mean'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma90'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_mur_u_ext'</span>, \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'nb_log'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'l_valeur_fonciere_ma7'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'nombre_pieces_principales_2'</span><span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Features used to fit model:\n",
       "\u001b[1m[\u001b[0m\u001b[32m'nb_lot_tot'\u001b[0m, \u001b[32m'l_conso_ener_max'\u001b[0m, \u001b[32m'qpv'\u001b[0m, \u001b[32m'l_estim_ges_max'\u001b[0m, \u001b[32m'l_estim_ges_std'\u001b[0m, \n",
       "\u001b[32m'l_conso_ener_min'\u001b[0m, \u001b[32m'mur_u_ext'\u001b[0m, \u001b[32m'l_altitude_sol_mean'\u001b[0m, \u001b[32m'dependance'\u001b[0m, \u001b[32m'l_conso_ener_std'\u001b[0m, \n",
       "\u001b[32m'nb_lot_tertiaire'\u001b[0m, \u001b[32m'l_surface_reelle_bati'\u001b[0m, \u001b[32m'l_valeur_fonciere_ma14'\u001b[0m, \u001b[32m'l_conso_ener_mean'\u001b[0m, \n",
       "\u001b[32m'nb_lot_garpark'\u001b[0m, \u001b[32m'l_valeur_fonciere_ma30'\u001b[0m, \u001b[32m'l_estim_ges_mean'\u001b[0m, \u001b[32m'l_estim_ges_min'\u001b[0m, \n",
       "\u001b[32m'nombre_pieces_principales_1'\u001b[0m, \u001b[32m'l_hauteur_mean'\u001b[0m, \u001b[32m'l_valeur_fonciere_ma90'\u001b[0m, \u001b[32m'l_mur_u_ext'\u001b[0m, \n",
       "\u001b[32m'nb_log'\u001b[0m, \u001b[32m'l_valeur_fonciere_ma7'\u001b[0m, \u001b[32m'nombre_pieces_principales_2'\u001b[0m\u001b[1m]\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'train'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'XGBRegressor'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">11198.317336404878</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.06903362213409571</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'median_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.04949359650805875</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_squared_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">276738591.2973528</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'r2_score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9332743872489556</span>\n",
       "        <span style=\"font-weight: bold\">}</span>\n",
       "    <span style=\"font-weight: bold\">}</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'test'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'XGBRegressor'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">23061.820263671874</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.13152041110504056</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'median_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.09463330455288213</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_squared_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1183109167.1376987</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'r2_score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7617725395514552</span>\n",
       "        <span style=\"font-weight: bold\">}</span>\n",
       "    <span style=\"font-weight: bold\">}</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'all'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'XGBRegressor'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13852.090785918825</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07828144532398693</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'median_absolute_percentage_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.05659991108067265</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'mean_squared_error'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">437699921.11872673</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'r2_score'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9118660022735613</span>\n",
       "        <span style=\"font-weight: bold\">}</span>\n",
       "    <span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'train'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "        \u001b[32m'XGBRegressor'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'mean_absolute_error'\u001b[0m: \u001b[1;36m11198.317336404878\u001b[0m,\n",
       "            \u001b[32m'mean_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.06903362213409571\u001b[0m,\n",
       "            \u001b[32m'median_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.04949359650805875\u001b[0m,\n",
       "            \u001b[32m'mean_squared_error'\u001b[0m: \u001b[1;36m276738591.2973528\u001b[0m,\n",
       "            \u001b[32m'r2_score'\u001b[0m: \u001b[1;36m0.9332743872489556\u001b[0m\n",
       "        \u001b[1m}\u001b[0m\n",
       "    \u001b[1m}\u001b[0m,\n",
       "    \u001b[32m'test'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "        \u001b[32m'XGBRegressor'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'mean_absolute_error'\u001b[0m: \u001b[1;36m23061.820263671874\u001b[0m,\n",
       "            \u001b[32m'mean_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.13152041110504056\u001b[0m,\n",
       "            \u001b[32m'median_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.09463330455288213\u001b[0m,\n",
       "            \u001b[32m'mean_squared_error'\u001b[0m: \u001b[1;36m1183109167.1376987\u001b[0m,\n",
       "            \u001b[32m'r2_score'\u001b[0m: \u001b[1;36m0.7617725395514552\u001b[0m\n",
       "        \u001b[1m}\u001b[0m\n",
       "    \u001b[1m}\u001b[0m,\n",
       "    \u001b[32m'all'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "        \u001b[32m'XGBRegressor'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'mean_absolute_error'\u001b[0m: \u001b[1;36m13852.090785918825\u001b[0m,\n",
       "            \u001b[32m'mean_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.07828144532398693\u001b[0m,\n",
       "            \u001b[32m'median_absolute_percentage_error'\u001b[0m: \u001b[1;36m0.05659991108067265\u001b[0m,\n",
       "            \u001b[32m'mean_squared_error'\u001b[0m: \u001b[1;36m437699921.11872673\u001b[0m,\n",
       "            \u001b[32m'r2_score'\u001b[0m: \u001b[1;36m0.9118660022735613\u001b[0m\n",
       "        \u001b[1m}\u001b[0m\n",
       "    \u001b[1m}\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb_opt = xgb_loader[\"model\"]\n",
    "\n",
    "feature_names = xgb_loader[\"feature_names\"]\n",
    "rich.print(f\"Features used to fit model:\\n{feature_names}\")\n",
    "\n",
    "metrics = xgb_loader[\"metrics\"]\n",
    "rich.print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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