DecisionTrees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<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>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>mois</th>\n",
       "      <th>...</th>\n",
       "      <th>0-3-pa_rdb</th>\n",
       "      <th>0-3-pa_rdb_uc</th>\n",
       "      <th>0-3-taux_epargne</th>\n",
       "      <th>0-3-taux_epargne_financier</th>\n",
       "      <th>0-4-rdb</th>\n",
       "      <th>0-4-rdb_uc</th>\n",
       "      <th>0-4-pa_rdb</th>\n",
       "      <th>0-4-pa_rdb_uc</th>\n",
       "      <th>0-4-taux_epargne</th>\n",
       "      <th>0-4-taux_epargne_financier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>ST-HERBLAIN</td>\n",
       "      <td>145.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>145.0</td>\n",
       "      <td>7</td>\n",
       "      <td>352.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>104450.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>ST-HERBLAIN</td>\n",
       "      <td>54.50</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-21</td>\n",
       "      <td>230000.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>92.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>58.61</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>49.58</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 175 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_mutation  valeur_fonciere  code_postal  nom_commune  surface_carrez  \\\n",
       "0    2016-05-27         240000.0        44800  ST-HERBLAIN          145.00   \n",
       "1    2016-06-09         104450.0        44800  ST-HERBLAIN           54.50   \n",
       "2    2016-06-21         230000.0        44100       NANTES           92.00   \n",
       "3    2016-06-03         122000.0        44300       NANTES           58.61   \n",
       "4    2016-05-23         159000.0        44000       NANTES           49.58   \n",
       "\n",
       "    type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "0       Maison                145.0                          7   \n",
       "1  Appartement                 54.0                          2   \n",
       "2       Maison                 92.0                          3   \n",
       "3  Appartement                 59.0                          3   \n",
       "4  Appartement                 52.0                          3   \n",
       "\n",
       "   surface_terrain  mois  ...  0-3-pa_rdb  0-3-pa_rdb_uc  0-3-taux_epargne  \\\n",
       "0            352.0     5  ...  126.351721     111.880979     587654.396848   \n",
       "1             54.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "2            192.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "3             59.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "4             52.0     5  ...  126.351721     111.880979     587654.396848   \n",
       "\n",
       "   0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  0-4-pa_rdb  \\\n",
       "0                 2024.920706  154.887308  136.523181  127.109831   \n",
       "1                 2024.920706  154.887308  136.523181  127.109831   \n",
       "2                 2024.920706  154.887308  136.523181  127.109831   \n",
       "3                 2024.920706  154.887308  136.523181  127.109831   \n",
       "4                 2024.920706  154.887308  136.523181  127.109831   \n",
       "\n",
       "   0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \n",
       "0     112.328503     669926.012407                 2116.042138  \n",
       "1     112.328503     669926.012407                 2116.042138  \n",
       "2     112.328503     669926.012407                 2116.042138  \n",
       "3     112.328503     669926.012407                 2116.042138  \n",
       "4     112.328503     669926.012407                 2116.042138  \n",
       "\n",
       "[5 rows x 175 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "        '../expe_logement_dataset/datasets/origin_enriched_v3.csv',\n",
    "        header=0,\n",
    "        index_col=False\n",
    "    )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "qiles = pd.qcut(x=df.valeur_fonciere, q=500, duplicates='drop')\n",
    "counts = qiles.value_counts()\n",
    "rc = counts.reindex(qiles.cat.categories)\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: str(x), rc.index)), y=rc.values, fill='tozeroy'))\n",
    "fig.show()"
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  },
  {
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   "execution_count": 108,
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qiles = pd.qcut(x=df.surface_reelle_bati, q=2500, duplicates='drop')\n",
    "counts = qiles.value_counts()\n",
    "rc = counts.reindex(qiles.cat.categories)\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: str(x), rc.index)), y=rc.values, fill='tozeroy'))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "var gd = document.getElementById('fe434b81-47fa-40de-a17d-d012a7287c60');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qiles = pd.qcut(x=(df.valeur_fonciere)/(df.surface_reelle_bati), q=2500, duplicates='drop')\n",
    "counts = qiles.value_counts()\n",
    "rc = counts.reindex(qiles.cat.categories)\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: str(x), rc.index)), y=rc.values, fill='tozeroy'))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>code_postal</th>\n",
       "      <th>nom_commune</th>\n",
       "      <th>surface_carrez</th>\n",
       "      <th>type_local</th>\n",
       "      <th>surface_reelle_bati</th>\n",
       "      <th>nombre_pieces_principales</th>\n",
       "      <th>surface_terrain</th>\n",
       "      <th>mois</th>\n",
       "      <th>...</th>\n",
       "      <th>0-3-pa_rdb</th>\n",
       "      <th>0-3-pa_rdb_uc</th>\n",
       "      <th>0-3-taux_epargne</th>\n",
       "      <th>0-3-taux_epargne_financier</th>\n",
       "      <th>0-4-rdb</th>\n",
       "      <th>0-4-rdb_uc</th>\n",
       "      <th>0-4-pa_rdb</th>\n",
       "      <th>0-4-pa_rdb_uc</th>\n",
       "      <th>0-4-taux_epargne</th>\n",
       "      <th>0-4-taux_epargne_financier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>ST-HERBLAIN</td>\n",
       "      <td>145.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>145.0</td>\n",
       "      <td>7</td>\n",
       "      <td>352.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-09</td>\n",
       "      <td>104450.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>ST-HERBLAIN</td>\n",
       "      <td>54.50</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-21</td>\n",
       "      <td>230000.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>92.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>122000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>58.61</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>159000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>NANTES</td>\n",
       "      <td>49.58</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 175 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_mutation  valeur_fonciere  code_postal  nom_commune  surface_carrez  \\\n",
       "0    2016-05-27         240000.0        44800  ST-HERBLAIN          145.00   \n",
       "1    2016-06-09         104450.0        44800  ST-HERBLAIN           54.50   \n",
       "2    2016-06-21         230000.0        44100       NANTES           92.00   \n",
       "3    2016-06-03         122000.0        44300       NANTES           58.61   \n",
       "4    2016-05-23         159000.0        44000       NANTES           49.58   \n",
       "\n",
       "    type_local  surface_reelle_bati  nombre_pieces_principales  \\\n",
       "0       Maison                145.0                          7   \n",
       "1  Appartement                 54.0                          2   \n",
       "2       Maison                 92.0                          3   \n",
       "3  Appartement                 59.0                          3   \n",
       "4  Appartement                 52.0                          3   \n",
       "\n",
       "   surface_terrain  mois  ...  0-3-pa_rdb  0-3-pa_rdb_uc  0-3-taux_epargne  \\\n",
       "0            352.0     5  ...  126.351721     111.880979     587654.396848   \n",
       "1             54.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "2            192.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "3             59.0     6  ...  126.351721     111.880979     587654.396848   \n",
       "4             52.0     5  ...  126.351721     111.880979     587654.396848   \n",
       "\n",
       "   0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  0-4-pa_rdb  \\\n",
       "0                 2024.920706  154.887308  136.523181  127.109831   \n",
       "1                 2024.920706  154.887308  136.523181  127.109831   \n",
       "2                 2024.920706  154.887308  136.523181  127.109831   \n",
       "3                 2024.920706  154.887308  136.523181  127.109831   \n",
       "4                 2024.920706  154.887308  136.523181  127.109831   \n",
       "\n",
       "   0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \n",
       "0     112.328503     669926.012407                 2116.042138  \n",
       "1     112.328503     669926.012407                 2116.042138  \n",
       "2     112.328503     669926.012407                 2116.042138  \n",
       "3     112.328503     669926.012407                 2116.042138  \n",
       "4     112.328503     669926.012407                 2116.042138  \n",
       "\n",
       "[5 rows x 175 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df[(df.valeur_fonciere < 45000) | (df.valeur_fonciere > 1000000)].index, inplace=True)\n",
    "#df.drop(df[(df.surface_reelle_bati < 13)].index, inplace=True)\n",
    "df.drop(df[((df.valeur_fonciere)/(df.surface_reelle_bati) < 1200) | ((df.valeur_fonciere)/(df.surface_reelle_bati) > 10000)].index, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valeur_fonciere</th>\n",
       "      <th>code_postal</th>\n",
       "      <th>surface_carrez</th>\n",
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       "      <th>mois</th>\n",
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       "      <th>foot/vie_courante/townhall</th>\n",
       "      <th>...</th>\n",
       "      <th>0-3-pa_rdb</th>\n",
       "      <th>0-3-pa_rdb_uc</th>\n",
       "      <th>0-3-taux_epargne</th>\n",
       "      <th>0-3-taux_epargne_financier</th>\n",
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       "      <th>0-4-rdb_uc</th>\n",
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       "      <td>240000.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>145.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>145.0</td>\n",
       "      <td>7</td>\n",
       "      <td>352.0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>104450.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>54.50</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>230000.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>92.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
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       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>58.61</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
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       "      <td>111.880979</td>\n",
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       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>159000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>49.58</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   valeur_fonciere  code_postal  surface_carrez   type_local  \\\n",
       "0         240000.0        44800          145.00       Maison   \n",
       "1         104450.0        44800           54.50  Appartement   \n",
       "2         230000.0        44100           92.00       Maison   \n",
       "3         122000.0        44300           58.61  Appartement   \n",
       "4         159000.0        44000           49.58  Appartement   \n",
       "\n",
       "   surface_reelle_bati  nombre_pieces_principales  surface_terrain  mois  \\\n",
       "0                145.0                          7            352.0     5   \n",
       "1                 54.0                          2             54.0     6   \n",
       "2                 92.0                          3            192.0     6   \n",
       "3                 59.0                          3             59.0     6   \n",
       "4                 52.0                          3             52.0     5   \n",
       "\n",
       "   annee  foot/vie_courante/townhall  ...  0-3-pa_rdb  0-3-pa_rdb_uc  \\\n",
       "0   2016                         1.0  ...  126.351721     111.880979   \n",
       "1   2016                         0.0  ...  126.351721     111.880979   \n",
       "2   2016                         0.0  ...  126.351721     111.880979   \n",
       "3   2016                         0.0  ...  126.351721     111.880979   \n",
       "4   2016                         0.0  ...  126.351721     111.880979   \n",
       "\n",
       "   0-3-taux_epargne  0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  \\\n",
       "0     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "1     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "2     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "3     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "4     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "\n",
       "   0-4-pa_rdb  0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \n",
       "0  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "1  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "2  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "3  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "4  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "\n",
       "[5 rows x 171 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.drop(['date_mutation', 'mois_sin', 'mois_cos', 'longitude', 'latitude'], axis=1, inplace=True)\n",
    "df.drop(['date_mutation', 'mois_sin', 'mois_cos'], axis=1, inplace=True)\n",
    "\n",
    "df.drop(['nom_commune'], axis=1, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>104450.0</td>\n",
       "      <td>44800</td>\n",
       "      <td>54.50</td>\n",
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       "      <td>2</td>\n",
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       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>230000.0</td>\n",
       "      <td>44100</td>\n",
       "      <td>92.00</td>\n",
       "      <td>Maison</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122000.0</td>\n",
       "      <td>44300</td>\n",
       "      <td>58.61</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>159000.0</td>\n",
       "      <td>44000</td>\n",
       "      <td>49.58</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>126.351721</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 169 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   valeur_fonciere  code_postal  surface_carrez   type_local  \\\n",
       "0         240000.0        44800          145.00       Maison   \n",
       "1         104450.0        44800           54.50  Appartement   \n",
       "2         230000.0        44100           92.00       Maison   \n",
       "3         122000.0        44300           58.61  Appartement   \n",
       "4         159000.0        44000           49.58  Appartement   \n",
       "\n",
       "   surface_reelle_bati  nombre_pieces_principales  surface_terrain  mois  \\\n",
       "0                145.0                          7            352.0     5   \n",
       "1                 54.0                          2             54.0     6   \n",
       "2                 92.0                          3            192.0     6   \n",
       "3                 59.0                          3             59.0     6   \n",
       "4                 52.0                          3             52.0     5   \n",
       "\n",
       "   annee  foot/vie_courante/townhall  ...  0-3-pa_rdb  0-3-pa_rdb_uc  \\\n",
       "0   2016                         1.0  ...  126.351721     111.880979   \n",
       "1   2016                         0.0  ...  126.351721     111.880979   \n",
       "2   2016                         0.0  ...  126.351721     111.880979   \n",
       "3   2016                         0.0  ...  126.351721     111.880979   \n",
       "4   2016                         0.0  ...  126.351721     111.880979   \n",
       "\n",
       "   0-3-taux_epargne  0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  \\\n",
       "0     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "1     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "2     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "3     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "4     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "\n",
       "   0-4-pa_rdb  0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \n",
       "0  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "1  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "2  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "3  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "4  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "\n",
       "[5 rows x 169 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if col.endswith('/zoo') or col.startswith('/zoo'):\n",
    "        df.drop(col, axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>valeur_fonciere</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104450</td>\n",
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       "      <td>54</td>\n",
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       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>230000</td>\n",
       "      <td>44100</td>\n",
       "      <td>92</td>\n",
       "      <td>Maison</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "      <td>192</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>122000</td>\n",
       "      <td>44300</td>\n",
       "      <td>58</td>\n",
       "      <td>Appartement</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>159000</td>\n",
       "      <td>44000</td>\n",
       "      <td>49</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   valeur_fonciere code_postal  surface_carrez   type_local  \\\n",
       "0           240000       44800             145       Maison   \n",
       "1           104450       44800              54  Appartement   \n",
       "2           230000       44100              92       Maison   \n",
       "3           122000       44300              58  Appartement   \n",
       "4           159000       44000              49  Appartement   \n",
       "\n",
       "   surface_reelle_bati  nombre_pieces_principales  surface_terrain  mois  \\\n",
       "0                  145                          7              352     5   \n",
       "1                   54                          2               54     6   \n",
       "2                   92                          3              192     6   \n",
       "3                   59                          3               59     6   \n",
       "4                   52                          3               52     5   \n",
       "\n",
       "   annee  foot/vie_courante/townhall  ...  0-3-pa_rdb  0-3-pa_rdb_uc  \\\n",
       "0   2016                           1  ...  126.351721     111.880979   \n",
       "1   2016                           0  ...  126.351721     111.880979   \n",
       "2   2016                           0  ...  126.351721     111.880979   \n",
       "3   2016                           0  ...  126.351721     111.880979   \n",
       "4   2016                           0  ...  126.351721     111.880979   \n",
       "\n",
       "   0-3-taux_epargne  0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  \\\n",
       "0     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "1     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "2     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "3     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "4     587654.396848                 2024.920706  154.887308  136.523181   \n",
       "\n",
       "   0-4-pa_rdb  0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \n",
       "0  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "1  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "2  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "3  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "4  127.109831     112.328503     669926.012407                 2116.042138  \n",
       "\n",
       "[5 rows x 169 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['valeur_fonciere'] = df['valeur_fonciere'].astype(np.int32)\n",
    "df['code_postal'] = df['code_postal'].astype(str)\n",
    "df['surface_carrez'] = df['surface_carrez'].astype(np.int16)\n",
    "df['surface_reelle_bati'] = df['surface_reelle_bati'].astype(np.int16)\n",
    "df['nombre_pieces_principales'] = df['nombre_pieces_principales'].astype(np.int8)\n",
    "df['surface_terrain'] = df['surface_terrain'].astype(np.int16)\n",
    "df['annee'] = df['annee'].astype(np.int16)\n",
    "df['mois'] = df['mois'].astype(np.int8)\n",
    "for col in df.columns:\n",
    "    if col.startswith('foot/') or col.startswith('car/'):\n",
    "        df[col] = df[col].astype(np.int16)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>104450</td>\n",
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       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>122000</td>\n",
       "      <td>44300</td>\n",
       "      <td>58</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
       "      <td>136.523181</td>\n",
       "      <td>127.109831</td>\n",
       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>159000</td>\n",
       "      <td>44000</td>\n",
       "      <td>49</td>\n",
       "      <td>Appartement</td>\n",
       "      <td>52</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>111.880979</td>\n",
       "      <td>587654.396848</td>\n",
       "      <td>2024.920706</td>\n",
       "      <td>154.887308</td>\n",
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       "      <td>112.328503</td>\n",
       "      <td>669926.012407</td>\n",
       "      <td>2116.042138</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   valeur_fonciere code_postal  surface_carrez   type_local  \\\n",
       "0           240000       44800             145       Maison   \n",
       "1           104450       44800              54  Appartement   \n",
       "2           230000       44100              92       Maison   \n",
       "3           122000       44300              58  Appartement   \n",
       "4           159000       44000              49  Appartement   \n",
       "\n",
       "   surface_reelle_bati  nombre_pieces_principales  surface_terrain  mois  \\\n",
       "0                  145                          7              352     5   \n",
       "1                   54                          2               54     6   \n",
       "2                   92                          3              192     6   \n",
       "3                   59                          3               59     6   \n",
       "4                   52                          3               52     5   \n",
       "\n",
       "   annee  foot/vie_courante/townhall  ...  0-3-pa_rdb_uc  0-3-taux_epargne  \\\n",
       "0   2016                           1  ...     111.880979     587654.396848   \n",
       "1   2016                           0  ...     111.880979     587654.396848   \n",
       "2   2016                           0  ...     111.880979     587654.396848   \n",
       "3   2016                           0  ...     111.880979     587654.396848   \n",
       "4   2016                           0  ...     111.880979     587654.396848   \n",
       "\n",
       "   0-3-taux_epargne_financier     0-4-rdb  0-4-rdb_uc  0-4-pa_rdb  \\\n",
       "0                 2024.920706  154.887308  136.523181  127.109831   \n",
       "1                 2024.920706  154.887308  136.523181  127.109831   \n",
       "2                 2024.920706  154.887308  136.523181  127.109831   \n",
       "3                 2024.920706  154.887308  136.523181  127.109831   \n",
       "4                 2024.920706  154.887308  136.523181  127.109831   \n",
       "\n",
       "   0-4-pa_rdb_uc  0-4-taux_epargne  0-4-taux_epargne_financier  \\\n",
       "0     112.328503     669926.012407                 2116.042138   \n",
       "1     112.328503     669926.012407                 2116.042138   \n",
       "2     112.328503     669926.012407                 2116.042138   \n",
       "3     112.328503     669926.012407                 2116.042138   \n",
       "4     112.328503     669926.012407                 2116.042138   \n",
       "\n",
       "   surface_non_bati  \n",
       "0               207  \n",
       "1                 0  \n",
       "2               100  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 170 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['surface_non_bati'] = df['surface_terrain'] - df['surface_reelle_bati']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "   valeur_fonciere code_postal  surface_carrez   type_local  \\\n",
       "0           240000       44800             145       Maison   \n",
       "1           104450       44800              54  Appartement   \n",
       "2           230000       44100              92       Maison   \n",
       "3           122000       44300              58  Appartement   \n",
       "4           159000       44000              49  Appartement   \n",
       "\n",
       "   surface_reelle_bati  nombre_pieces_principales  surface_terrain  mois  \\\n",
       "0                  145                          7              352     5   \n",
       "1                   54                          2               54     6   \n",
       "2                   92                          3              192     6   \n",
       "3                   59                          3               59     6   \n",
       "4                   52                          3               52     5   \n",
       "\n",
       "   annee  foot/vie_courante/townhall  ...  1-1-pa_rdb_uc  1-1-taux_epargne  \\\n",
       "0   2016                           1  ...     110.883824     263774.646985   \n",
       "1   2016                           0  ...     110.883824     263774.646985   \n",
       "2   2016                           0  ...     110.883824     263774.646985   \n",
       "3   2016                           0  ...     110.883824     263774.646985   \n",
       "4   2016                           0  ...     110.883824     263774.646985   \n",
       "\n",
       "   1-1-taux_epargne_financier     0-1-rdb  0-1-rdb_uc  0-1-pa_rdb  \\\n",
       "0                 1515.378624  152.739321  135.302211  124.601199   \n",
       "1                 1515.378624  152.739321  135.302211  124.601199   \n",
       "2                 1515.378624  152.739321  135.302211  124.601199   \n",
       "3                 1515.378624  152.739321  135.302211  124.601199   \n",
       "4                 1515.378624  152.739321  135.302211  124.601199   \n",
       "\n",
       "   0-1-pa_rdb_uc  0-1-taux_epargne  0-1-taux_epargne_financier  \\\n",
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       "1     110.770505     449811.815154                  1838.41698   \n",
       "2     110.770505     449811.815154                  1838.41698   \n",
       "3     110.770505     449811.815154                  1838.41698   \n",
       "4     110.770505     449811.815154                  1838.41698   \n",
       "\n",
       "   surface_non_bati  \n",
       "0               207  \n",
       "1                 0  \n",
       "2               100  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "for y in range(0,5):\n",
    "    for t in range (1,5):\n",
    "        for v in ['rdb', 'rdb_uc', 'pa_rdb', 'pa_rdb_uc', 'taux_epargne', 'taux_epargne_financier']:\n",
    "            if (y not in [0, 1]) or (t != 1):\n",
    "                col = \"%i-%i-%s\" % (y, t, v)\n",
    "                df.drop(col, axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
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       "   valeur_fonciere  surface_carrez  surface_reelle_bati  \\\n",
       "0           240000             145                  145   \n",
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       "2           230000              92                   92   \n",
       "3           122000              58                   59   \n",
       "4           159000              49                   52   \n",
       "\n",
       "   nombre_pieces_principales  surface_terrain  mois  annee  \\\n",
       "0                          7              352     5   2016   \n",
       "1                          2               54     6   2016   \n",
       "2                          3              192     6   2016   \n",
       "3                          3               59     6   2016   \n",
       "4                          3               52     5   2016   \n",
       "\n",
       "   foot/vie_courante/townhall  foot/jardin/playground  \\\n",
       "0                           1                       2   \n",
       "1                           0                       5   \n",
       "2                           0                       5   \n",
       "3                           0                       0   \n",
       "4                           0                       2   \n",
       "\n",
       "   foot/vie_courante/pharmacy  ...  code_postal_44700  code_postal_44710  \\\n",
       "0                           2  ...                  0                  0   \n",
       "1                           1  ...                  0                  0   \n",
       "2                           0  ...                  0                  0   \n",
       "3                           1  ...                  0                  0   \n",
       "4                           2  ...                  0                  0   \n",
       "\n",
       "   code_postal_44800  code_postal_44830  code_postal_44840  code_postal_44860  \\\n",
       "0                  1                  0                  0                  0   \n",
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       "\n",
       "   code_postal_44880  code_postal_44980  type_local_Appartement  \\\n",
       "0                  0                  0                       0   \n",
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       "2                  0                  0                       0   \n",
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       "\n",
       "   type_local_Maison  \n",
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       "2                  1  \n",
       "3                  0  \n",
       "4                  0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "   valeur_fonciere  surface_carrez  surface_reelle_bati  \\\n",
       "0           240000             145                  145   \n",
       "1           104450              54                   54   \n",
       "2           230000              92                   92   \n",
       "3           122000              58                   59   \n",
       "4           159000              49                   52   \n",
       "\n",
       "   nombre_pieces_principales  surface_terrain  mois  annee  \\\n",
       "0                          7              352     5   2016   \n",
       "1                          2               54     6   2016   \n",
       "2                          3              192     6   2016   \n",
       "3                          3               59     6   2016   \n",
       "4                          3               52     5   2016   \n",
       "\n",
       "   foot/vie_courante/townhall  foot/jardin/playground  \\\n",
       "0                           1                       2   \n",
       "1                           0                       5   \n",
       "2                           0                       5   \n",
       "3                           0                       0   \n",
       "4                           0                       2   \n",
       "\n",
       "   foot/vie_courante/pharmacy  ...  code_postal_44640  code_postal_44700  \\\n",
       "0                           2  ...                  0                  0   \n",
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       "2                           0  ...                  0                  0   \n",
       "3                           1  ...                  0                  0   \n",
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       "\n",
       "   type_local_Appartement  \n",
       "0                       0  \n",
       "1                       1  \n",
       "2                       0  \n",
       "3                       1  \n",
       "4                       1  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('type_local_Maison', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "   valeur_fonciere  surface_carrez  surface_reelle_bati  \\\n",
       "0         240000.0           145.0                145.0   \n",
       "1         104450.0            54.0                 54.0   \n",
       "2         230000.0            92.0                 92.0   \n",
       "3         122000.0            58.0                 59.0   \n",
       "4         159000.0            49.0                 52.0   \n",
       "\n",
       "   nombre_pieces_principales  surface_terrain  mois   annee  \\\n",
       "0                        7.0            352.0   5.0  2016.0   \n",
       "1                        2.0             54.0   6.0  2016.0   \n",
       "2                        3.0            192.0   6.0  2016.0   \n",
       "3                        3.0             59.0   6.0  2016.0   \n",
       "4                        3.0             52.0   5.0  2016.0   \n",
       "\n",
       "   foot/vie_courante/townhall  foot/jardin/playground  \\\n",
       "0                         1.0                     2.0   \n",
       "1                         0.0                     5.0   \n",
       "2                         0.0                     5.0   \n",
       "3                         0.0                     0.0   \n",
       "4                         0.0                     2.0   \n",
       "\n",
       "   foot/vie_courante/pharmacy  ...  code_postal_44640  code_postal_44700  \\\n",
       "0                         2.0  ...                0.0                0.0   \n",
       "1                         1.0  ...                0.0                0.0   \n",
       "2                         0.0  ...                0.0                0.0   \n",
       "3                         1.0  ...                0.0                0.0   \n",
       "4                         2.0  ...                0.0                0.0   \n",
       "\n",
       "   code_postal_44710  code_postal_44800  code_postal_44830  code_postal_44840  \\\n",
       "0                0.0                1.0                0.0                0.0   \n",
       "1                0.0                1.0                0.0                0.0   \n",
       "2                0.0                0.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                0.0                0.0                0.0   \n",
       "\n",
       "   code_postal_44860  code_postal_44880  code_postal_44980  \\\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                0.0                0.0                0.0   \n",
       "\n",
       "   type_local_Appartement  \n",
       "0                     0.0  \n",
       "1                     1.0  \n",
       "2                     0.0  \n",
       "3                     1.0  \n",
       "4                     1.0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.T.drop_duplicates(keep='last').T\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Taille du dataset de test (fraction du dataset total)\n",
    "TEST_SIZE = 0.25\n",
    "\n",
    "# Random seed pour la sélection des observations\n",
    "SPLIT_RANDOM_SEED = 42\n",
    "\n",
    "# X et y\n",
    "X = df.drop(\"valeur_fonciere\", axis=1)\n",
    "y = df['valeur_fonciere']\n",
    "\n",
    "# Split du dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=SPLIT_RANDOM_SEED)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset : X_train shape (26784, 83) et y_train shape (26784,)\n",
      "Test dataset : X_test shape (8928, 83) et y_test shape (8928,)\n"
     ]
    }
   ],
   "source": [
    "print('Train dataset : X_train shape ' + str(X_train.shape) + ' et y_train shape ' + str(y_train.shape))\n",
    "print('Test dataset : X_test shape ' + str(X_test.shape) + ' et y_test shape ' + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error\n",
    "\n",
    "def meaae(y_true, y_pred):\n",
    "    return mean_absolute_error(y_true, y_pred)\n",
    "\n",
    "def medae(y_true, y_pred):\n",
    "    return median_absolute_error(y_true, y_pred)\n",
    "\n",
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
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    "scrolled": false
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    {
     "name": "stdout",
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     "text": [
      "MeaAE en prédiction : 2017\n",
      "MedAE en prédiction : 0\n",
      "RMSE en prédiction : 5581\n",
      "MeaAE en généralisation : 37599\n",
      "MedAE en généralisation : 21000\n",
      "RMSE en généralisation : 62624\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import ExtraTreeRegressor\n",
    "\n",
    "TRAIN_RANDOM_SEED=42\n",
    "\n",
    "# Instantiation du modèle.\n",
    "abr = AdaBoostRegressor(\n",
    "    base_estimator=ExtraTreeRegressor(\n",
    "        max_depth=32,\n",
    "        criterion='mse',\n",
    "        max_features=22\n",
    "    ),\n",
    "    n_estimators=50,\n",
    "    random_state=TRAIN_RANDOM_SEED\n",
    ")\n",
    "\n",
    "# Entraînement du modèle\n",
    "abr.fit(X_train, y_train)\n",
    "\n",
    "# Prédiction sur le corpus d'apprentissage afin de calculer l'erreur en prédiction\n",
    "y_train_predicted = abr.predict(X_train)\n",
    "print(\"MeaAE en prédiction : %i\" % meaae(y_train, y_train_predicted))\n",
    "print(\"MedAE en prédiction : %i\" % medae(y_train, y_train_predicted))\n",
    "print(\"RMSE en prédiction : %i\" % rmse(y_train, y_train_predicted))\n",
    "\n",
    "# Prédiction sur le corpus de test afin de calculer l'erreur en généralisation\n",
    "y_test_predicted = abr.predict(X_test)\n",
    "print(\"MeaAE en généralisation : %i\" % meaae(y_test, y_test_predicted))\n",
    "print(\"MedAE en généralisation : %i\" % medae(y_test, y_test_predicted))\n",
    "print(\"RMSE en généralisation : %i\" % rmse(y_test, y_test_predicted))"
   ]
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   "source": [
    "s = 5\n",
    "\n",
    "pe = ((y_test - y_test_predicted).abs() / y_test) * 100\n",
    "rpe = np.floor(pe / s) * s\n",
    "\n",
    "counts = rpe.value_counts()\n",
    "counts = counts.reindex(index=np.sort(counts.index)).cumsum()\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: \"[0;%i[\" % (x+s), counts.index)), y=(counts.values / len(rpe)) * 100, fill='tozeroy'))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               feature  importance\n",
      "0                       surface_carrez   19.477071\n",
      "1                  surface_reelle_bati   15.087650\n",
      "2            nombre_pieces_principales    6.386917\n",
      "3                      surface_terrain    4.362859\n",
      "82              type_local_Appartement    3.946921\n",
      "58                    surface_non_bati    3.642083\n",
      "24                 car/medical/doctors    1.761157\n",
      "23        car/vie_courante/convenience    1.448732\n",
      "19         car/distractions/restaurant    1.403308\n",
      "70                   code_postal_44470    1.261166\n",
      "27                car/distractions/bar    1.193750\n",
      "34                  car/medical/clinic    1.115151\n",
      "21           car/vie_courante/pharmacy    1.031459\n",
      "12        foot/distractions/restaurant    1.004643\n",
      "16   car/distractions/place_of_worship    0.980420\n",
      "37       foot/vie_courante/supermarket    0.978557\n",
      "14        car/vie_courante/supermarket    0.943958\n",
      "17                    car/tan/bus_stop    0.943885\n",
      "4                                 mois    0.929610\n",
      "9                    foot/tan/bus_stop    0.909448\n",
      "25            car/distractions/library    0.884191\n",
      "13               car/jardin/playground    0.877632\n",
      "46                             1-1-rdb    0.874331\n",
      "22                     car/jardin/park    0.872444\n",
      "31  foot/distractions/place_of_worship    0.847461\n",
      "38                foot/medical/doctors    0.834105\n",
      "59                   code_postal_44000    0.825009\n",
      "36       foot/vie_courante/convenience    0.774732\n",
      "18           car/vie_courante/townhall    0.769683\n",
      "26    car/vie_courante/social_facility    0.762713\n",
      "35                 foot/medical/clinic    0.746717\n",
      "32                    foot/jardin/park    0.732477\n",
      "7               foot/jardin/playground    0.726066\n",
      "8           foot/vie_courante/pharmacy    0.724060\n",
      "48                          1-1-pa_rdb    0.719081\n",
      "50                    1-1-taux_epargne    0.715232\n",
      "57          0-1-taux_epargne_financier    0.708069\n",
      "20                   car/tan/tram_stop    0.697503\n",
      "33                car/medical/hospital    0.686098\n",
      "55                       0-1-pa_rdb_uc    0.684449\n",
      "44                     car/tan/station    0.673740\n",
      "53                          0-1-rdb_uc    0.668014\n",
      "10           foot/distractions/library    0.664814\n",
      "49                       1-1-pa_rdb_uc    0.640989\n",
      "28             car/distractions/museum    0.633325\n",
      "47                          1-1-rdb_uc    0.632508\n",
      "42            car/distractions/gallery    0.625431\n",
      "30   foot/vie_courante/social_facility    0.613418\n",
      "54                          0-1-pa_rdb    0.612088\n",
      "56                    0-1-taux_epargne    0.610766\n",
      "15        car/distractions/arts_centre    0.593687\n",
      "11               foot/distractions/bar    0.587185\n",
      "51          1-1-taux_epargne_financier    0.581705\n",
      "52                             0-1-rdb    0.547879\n",
      "5                                annee    0.540199\n",
      "40       foot/distractions/arts_centre    0.519579\n",
      "67                   code_postal_44300    0.509038\n",
      "29               foot/medical/hospital    0.503825\n",
      "6           foot/vie_courante/townhall    0.474176\n",
      "39                  foot/tan/tram_stop    0.462941\n",
      "69                   code_postal_44400    0.442854\n",
      "62                   code_postal_44120    0.357466\n",
      "60                   code_postal_44100    0.322462\n",
      "76                   code_postal_44800    0.302207\n",
      "41            foot/distractions/museum    0.270673\n",
      "80                   code_postal_44880    0.261378\n",
      "43           foot/distractions/gallery    0.254153\n",
      "74                   code_postal_44700    0.214309\n",
      "65                   code_postal_44230    0.204791\n",
      "64                   code_postal_44220    0.188486\n",
      "45                    foot/tan/station    0.178832\n",
      "68                   code_postal_44340    0.167371\n",
      "66                   code_postal_44240    0.158178\n",
      "73                   code_postal_44640    0.116603\n",
      "63                   code_postal_44200    0.092164\n",
      "61                   code_postal_44115    0.090198\n",
      "72                   code_postal_44620    0.086271\n",
      "79                   code_postal_44860    0.075388\n",
      "81                   code_postal_44980    0.069796\n",
      "77                   code_postal_44830    0.067790\n",
      "78                   code_postal_44840    0.067494\n",
      "71                   code_postal_44610    0.026048\n",
      "75                   code_postal_44710    0.018981\n"
     ]
    }
   ],
   "source": [
    "# Obtention de l'importance des features\n",
    "importances = abr.feature_importances_\n",
    "\n",
    "# Création d'un DataFrame pour associer chaque feature à son importance\n",
    "importances = pd.concat([pd.Series(X_train.columns), pd.Series(importances * 100)], axis=1).rename({0: 'feature', 1: 'importance'}, axis=1)\n",
    "\n",
    "# Tri\n",
    "importances.sort_values(by='importance', ascending=False, inplace=True)\n",
    "\n",
    "#Affichage\n",
    "print(importances.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   6 | elapsed:    6.8s remaining:   13.6s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:    7.1s remaining:    7.1s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   6 | elapsed:  2.8min remaining:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:  2.9min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'base_estimator': AdaBoostRegressor(base_estimator=ExtraTreeRegressor(criterion='mse', max_depth=32, max_features=22,\n",
      "          max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
      "          min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,\n",
      "          min_weight_fraction_leaf=0.0, random_state=None,\n",
      "          splitter='random'),\n",
      "         learning_rate=1.0, loss='linear', n_estimators=50,\n",
      "         random_state=42)}\n",
      "MeaAE en prédiction : 10016\n",
      "MedAE en prédiction : 6600\n",
      "RMSE en prédiction : 14799\n",
      "MeaAE en généralisation : 38118\n",
      "MedAE en généralisation : 23500\n",
      "RMSE en généralisation : 61134\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import ExtraTreeRegressor\n",
    "\n",
    "TRAIN_RANDOM_SEED=42\n",
    "\n",
    "# Instantiation du modèle dont on veut optimiser les hyper-paramètres.\n",
    "model = AdaBoostRegressor(\n",
    "    random_state=TRAIN_RANDOM_SEED\n",
    ")\n",
    "\n",
    "# Grille d'hyper-paramètres à tester\n",
    "param_grid = {\n",
    "    'base_estimator': [\n",
    "        ExtraTreeRegressor(max_depth=29,criterion='mse',max_features=22,random_state=TRAIN_RANDOM_SEED),\n",
    "        AdaBoostRegressor(\n",
    "            base_estimator=ExtraTreeRegressor(\n",
    "                max_depth=32,\n",
    "                criterion='mse',\n",
    "                max_features=22\n",
    "            ),\n",
    "            n_estimators=50,\n",
    "            random_state=TRAIN_RANDOM_SEED\n",
    "        )\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Instantiation du GridSearchCV.\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, n_jobs=-1, verbose=10)\n",
    "\n",
    "# Entraînement du modèle\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "# Affichage des meilleurs paramètres\n",
    "print(grid.best_params_)\n",
    "\n",
    "# Prédiction sur le corpus d'apprentissage afin de calculer l'erreur en prédiction\n",
    "y_train_predicted = grid.predict(X_train)\n",
    "print(\"MeaAE en prédiction : %i\" % meaae(y_train, y_train_predicted))\n",
    "print(\"MedAE en prédiction : %i\" % medae(y_train, y_train_predicted))\n",
    "print(\"RMSE en prédiction : %i\" % rmse(y_train, y_train_predicted))\n",
    "\n",
    "# Prédiction sur le corpus de test afin de calculer l'erreur en généralisation\n",
    "y_test_predicted = grid.predict(X_test)\n",
    "print(\"MeaAE en généralisation : %i\" % meaae(y_test, y_test_predicted))\n",
    "print(\"MedAE en généralisation : %i\" % medae(y_test, y_test_predicted))\n",
    "print(\"RMSE en généralisation : %i\" % rmse(y_test, y_test_predicted))"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = 5\n",
    "\n",
    "pe = ((y_test - y_test_predicted).abs() / y_test) * 100\n",
    "rpe = np.floor(pe / s) * s\n",
    "\n",
    "counts = rpe.value_counts()\n",
    "counts = counts.reindex(index=np.sort(counts.index)).cumsum()\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: \"[0;%i[\" % (x+s), counts.index)), y=(counts.values / len(rpe)) * 100, fill='tozeroy'))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:38:00] ======== Monitor: Learner ========\n",
      "[11:38:00] GetGradient: 0.00668764s, 50 calls @ 133us\n",
      "[11:38:00] PredictRaw: 0.0117228s, 50 calls @ 234us\n",
      "[11:38:00] UpdateOneIter: 38.3336s, 50 calls @ 766672us\n",
      "[11:38:00] ======== Monitor: GBTree ========\n",
      "[11:38:00] BoostNewTrees: 37.5516s, 50 calls @ 751031us\n",
      "[11:38:00] CommitModel: 0.763298s, 50 calls @ 15265us\n",
      "[11:38:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hbasset/.local/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning:\n",
      "\n",
      "Series.base is deprecated and will be removed in a future version\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:38:01] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 910 extra nodes, 0 pruned nodes, max_depth=19\n",
      "[11:38:01] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 1240 extra nodes, 0 pruned nodes, max_depth=18\n",
      "[11:38:02] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 1498 extra nodes, 0 pruned nodes, max_depth=20\n",
      "[11:38:02] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 1948 extra nodes, 0 pruned nodes, max_depth=21\n",
      "[11:38:03] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 2378 extra nodes, 0 pruned nodes, max_depth=28\n",
      "[11:38:03] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 3070 extra nodes, 0 pruned nodes, max_depth=26\n",
      "[11:38:04] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 3640 extra nodes, 0 pruned nodes, max_depth=29\n",
      "[11:38:04] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4176 extra nodes, 0 pruned nodes, max_depth=26\n",
      "[11:38:05] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 5058 extra nodes, 0 pruned nodes, max_depth=30\n",
      "[11:38:05] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 5820 extra nodes, 0 pruned nodes, max_depth=32\n",
      "[11:38:06] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6608 extra nodes, 0 pruned nodes, max_depth=28\n",
      "[11:38:07] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 7374 extra nodes, 0 pruned nodes, max_depth=31\n",
      "[11:38:08] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 7880 extra nodes, 0 pruned nodes, max_depth=36\n",
      "[11:38:08] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8690 extra nodes, 0 pruned nodes, max_depth=32\n",
      "[11:38:09] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 9308 extra nodes, 0 pruned nodes, max_depth=35\n",
      "[11:38:10] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 9634 extra nodes, 0 pruned nodes, max_depth=37\n",
      "[11:38:11] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10504 extra nodes, 0 pruned nodes, max_depth=35\n",
      "[11:38:12] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 11254 extra nodes, 0 pruned nodes, max_depth=38\n",
      "[11:38:13] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 11572 extra nodes, 0 pruned nodes, max_depth=33\n",
      "[11:38:13] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 11944 extra nodes, 0 pruned nodes, max_depth=34\n",
      "[11:38:14] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12232 extra nodes, 0 pruned nodes, max_depth=38\n",
      "[11:38:15] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12842 extra nodes, 0 pruned nodes, max_depth=35\n",
      "[11:38:16] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 13156 extra nodes, 0 pruned nodes, max_depth=36\n",
      "[11:38:17] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 13568 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:18] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14114 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:19] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14698 extra nodes, 0 pruned nodes, max_depth=40\n",
      "[11:38:20] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 15042 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:20] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14684 extra nodes, 0 pruned nodes, max_depth=44\n",
      "[11:38:21] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 15096 extra nodes, 0 pruned nodes, max_depth=40\n",
      "[11:38:22] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 15366 extra nodes, 0 pruned nodes, max_depth=43\n",
      "[11:38:23] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 15968 extra nodes, 0 pruned nodes, max_depth=35\n",
      "[11:38:24] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16090 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:25] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16622 extra nodes, 0 pruned nodes, max_depth=43\n",
      "[11:38:26] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16948 extra nodes, 0 pruned nodes, max_depth=42\n",
      "[11:38:27] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 17160 extra nodes, 0 pruned nodes, max_depth=41\n",
      "[11:38:28] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 17502 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:29] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 17674 extra nodes, 0 pruned nodes, max_depth=38\n",
      "[11:38:30] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 17720 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:31] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18096 extra nodes, 0 pruned nodes, max_depth=38\n",
      "[11:38:32] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18292 extra nodes, 0 pruned nodes, max_depth=41\n",
      "[11:38:33] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 19032 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:34] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18770 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:34] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18918 extra nodes, 0 pruned nodes, max_depth=40\n",
      "[11:38:35] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 19108 extra nodes, 0 pruned nodes, max_depth=45\n",
      "[11:38:36] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 19458 extra nodes, 0 pruned nodes, max_depth=47\n",
      "[11:38:37] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 19756 extra nodes, 0 pruned nodes, max_depth=42\n",
      "[11:38:38] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20066 extra nodes, 0 pruned nodes, max_depth=44\n",
      "[11:38:39] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20014 extra nodes, 0 pruned nodes, max_depth=39\n",
      "[11:38:40] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20554 extra nodes, 0 pruned nodes, max_depth=37\n",
      "[11:38:41] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20650 extra nodes, 0 pruned nodes, max_depth=38\n",
      "MeaAE en prédiction : 2864\n",
      "MedAE en prédiction : 1485\n",
      "RMSE en prédiction : 5470\n",
      "MeaAE en généralisation : 41528\n",
      "MedAE en généralisation : 24804\n",
      "RMSE en généralisation : 66172\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "\n",
    "TRAIN_RANDOM_SEED=42\n",
    "\n",
    "# Instantiation du modèle.\n",
    "xgbr = XGBRegressor(\n",
    "    n_estimators=50,\n",
    "    max_depth=35,\n",
    "    random_state=TRAIN_RANDOM_SEED,\n",
    "    n_jobs=-1,\n",
    "    verbosity=3\n",
    ")\n",
    "\n",
    "# Entraînement du modèle\n",
    "xgbr.fit(X_train, y_train)\n",
    "\n",
    "# Prédiction sur le corpus d'apprentissage afin de calculer l'erreur en prédiction\n",
    "y_train_predicted = xgbr.predict(X_train)\n",
    "print(\"MeaAE en prédiction : %i\" % meaae(y_train, y_train_predicted))\n",
    "print(\"MedAE en prédiction : %i\" % medae(y_train, y_train_predicted))\n",
    "print(\"RMSE en prédiction : %i\" % rmse(y_train, y_train_predicted))\n",
    "\n",
    "# Prédiction sur le corpus de test afin de calculer l'erreur en généralisation\n",
    "y_test_predicted = xgbr.predict(X_test)\n",
    "print(\"MeaAE en généralisation : %i\" % meaae(y_test, y_test_predicted))\n",
    "print(\"MedAE en généralisation : %i\" % medae(y_test, y_test_predicted))\n",
    "print(\"RMSE en généralisation : %i\" % rmse(y_test, y_test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 13 candidates, totalling 39 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:   25.0s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:   56.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  28 out of  39 | elapsed:  2.8min remaining:  1.1min\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/externals/loky/process_executor.py:700: UserWarning:\n",
      "\n",
      "A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "\n",
      "[Parallel(n_jobs=-1)]: Done  32 out of  39 | elapsed:  3.1min remaining:   40.8s\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  39 | elapsed:  3.8min remaining:   19.0s\n",
      "[Parallel(n_jobs=-1)]: Done  39 out of  39 | elapsed:  4.0min finished\n",
      "/home/hbasset/.local/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning:\n",
      "\n",
      "Series.base is deprecated and will be removed in a future version\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 400}\n",
      "MeaAE en prédiction : 15628\n",
      "MedAE en prédiction : 11157\n",
      "RMSE en prédiction : 22008\n",
      "MeaAE en généralisation : 39426\n",
      "MedAE en généralisation : 23826\n",
      "RMSE en généralisation : 61636\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "TRAIN_RANDOM_SEED=42\n",
    "\n",
    "# Instantiation du modèle dont on veut optimiser les hyper-paramètres.\n",
    "model = XGBRegressor(\n",
    "    max_depth=9,\n",
    "    colsample_bytree=0.6,\n",
    "    colsample_bylevel=0.35,\n",
    "    colsample_bynode=1.0,\n",
    "    learning_rate=0.1,\n",
    "    booster='gbtree',\n",
    "    subsample=1,\n",
    "    gamma=0,\n",
    "    min_child_weight=1,\n",
    "    objective='reg:squarederror',\n",
    "    random_state=TRAIN_RANDOM_SEED,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "# Grille d'hyper-paramètres à tester\n",
    "param_grid = {\n",
    "    'n_estimators': range(400, 1050, 50)\n",
    "}\n",
    "\n",
    "# Instantiation du GridSearchCV.\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, n_jobs=-1, verbose=10)\n",
    "\n",
    "# Entraînement du modèle\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "# Affichage des meilleurs paramètres\n",
    "print(grid.best_params_)\n",
    "\n",
    "# Prédiction sur le corpus d'apprentissage afin de calculer l'erreur en prédiction\n",
    "y_train_predicted = grid.predict(X_train)\n",
    "print(\"MeaAE en prédiction : %i\" % meaae(y_train, y_train_predicted))\n",
    "print(\"MedAE en prédiction : %i\" % medae(y_train, y_train_predicted))\n",
    "print(\"RMSE en prédiction : %i\" % rmse(y_train, y_train_predicted))\n",
    "\n",
    "# Prédiction sur le corpus de test afin de calculer l'erreur en généralisation\n",
    "y_test_predicted = grid.predict(X_test)\n",
    "print(\"MeaAE en généralisation : %i\" % meaae(y_test, y_test_predicted))\n",
    "print(\"MedAE en généralisation : %i\" % medae(y_test, y_test_predicted))\n",
    "print(\"RMSE en généralisation : %i\" % rmse(y_test, y_test_predicted))"
   ]
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    "s = 5\n",
    "\n",
    "pe = ((y_test - y_test_predicted).abs() / y_test) * 100\n",
    "rpe = np.floor(pe / s) * s\n",
    "\n",
    "counts = rpe.value_counts()\n",
    "counts = counts.reindex(index=np.sort(counts.index)).cumsum()\n",
    "\n",
    "fig = go.Figure(data=go.Scatter(x=list(map(lambda x: \"[0;%i[\" % (x+s), counts.index)), y=(counts.values / len(rpe)) * 100, fill='tozeroy'))\n",
    "fig.show()"
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      "                               feature  importance\n",
      "34                  car/medical/clinic    0.109520\n",
      "82              type_local_Appartement    0.081716\n",
      "1                  surface_reelle_bati    0.073963\n",
      "0                       surface_carrez    0.059844\n",
      "2            nombre_pieces_principales    0.049691\n",
      "3                      surface_terrain    0.041330\n",
      "27                car/distractions/bar    0.028942\n",
      "23        car/vie_courante/convenience    0.024262\n",
      "19         car/distractions/restaurant    0.022348\n",
      "79                   code_postal_44860    0.018517\n",
      "64                   code_postal_44220    0.018386\n",
      "58                    surface_non_bati    0.018066\n",
      "73                   code_postal_44640    0.016986\n",
      "69                   code_postal_44400    0.015739\n",
      "42            car/distractions/gallery    0.015367\n",
      "67                   code_postal_44300    0.012908\n",
      "76                   code_postal_44800    0.012624\n",
      "59                   code_postal_44000    0.012240\n",
      "24                 car/medical/doctors    0.011578\n",
      "68                   code_postal_44340    0.011529\n",
      "15        car/distractions/arts_centre    0.010405\n",
      "11               foot/distractions/bar    0.010079\n",
      "26    car/vie_courante/social_facility    0.009345\n",
      "80                   code_postal_44880    0.009131\n",
      "36       foot/vie_courante/convenience    0.009114\n",
      "66                   code_postal_44240    0.008766\n",
      "35                 foot/medical/clinic    0.008653\n",
      "72                   code_postal_44620    0.008050\n",
      "65                   code_postal_44230    0.007978\n",
      "74                   code_postal_44700    0.007910\n",
      "44                     car/tan/station    0.007795\n",
      "62                   code_postal_44120    0.007185\n",
      "40       foot/distractions/arts_centre    0.007134\n",
      "12        foot/distractions/restaurant    0.007109\n",
      "38                foot/medical/doctors    0.006786\n",
      "20                   car/tan/tram_stop    0.006287\n",
      "33                car/medical/hospital    0.006179\n",
      "39                  foot/tan/tram_stop    0.005905\n",
      "28             car/distractions/museum    0.005846\n",
      "77                   code_postal_44830    0.005743\n",
      "16   car/distractions/place_of_worship    0.005699\n",
      "55                       0-1-pa_rdb_uc    0.005626\n",
      "18           car/vie_courante/townhall    0.005587\n",
      "14        car/vie_courante/supermarket    0.005584\n",
      "41            foot/distractions/museum    0.005517\n",
      "29               foot/medical/hospital    0.005462\n",
      "32                    foot/jardin/park    0.005363\n",
      "37       foot/vie_courante/supermarket    0.005345\n",
      "30   foot/vie_courante/social_facility    0.005329\n",
      "70                   code_postal_44470    0.005262\n",
      "81                   code_postal_44980    0.005182\n",
      "31  foot/distractions/place_of_worship    0.005168\n",
      "61                   code_postal_44115    0.005087\n",
      "17                    car/tan/bus_stop    0.005030\n",
      "75                   code_postal_44710    0.005022\n",
      "25            car/distractions/library    0.004913\n",
      "22                     car/jardin/park    0.004850\n",
      "52                             0-1-rdb    0.004746\n",
      "78                   code_postal_44840    0.004640\n",
      "9                    foot/tan/bus_stop    0.004602\n",
      "13               car/jardin/playground    0.004562\n",
      "21           car/vie_courante/pharmacy    0.004528\n",
      "48                          1-1-pa_rdb    0.004503\n",
      "50                    1-1-taux_epargne    0.004450\n",
      "54                          0-1-pa_rdb    0.004198\n",
      "51          1-1-taux_epargne_financier    0.004194\n",
      "57          0-1-taux_epargne_financier    0.004162\n",
      "53                          0-1-rdb_uc    0.004156\n",
      "63                   code_postal_44200    0.004112\n",
      "45                    foot/tan/station    0.004089\n",
      "46                             1-1-rdb    0.003998\n",
      "60                   code_postal_44100    0.003862\n",
      "8           foot/vie_courante/pharmacy    0.003662\n",
      "43           foot/distractions/gallery    0.003430\n",
      "10           foot/distractions/library    0.003410\n",
      "7               foot/jardin/playground    0.003284\n",
      "56                    0-1-taux_epargne    0.003137\n",
      "49                       1-1-pa_rdb_uc    0.003121\n",
      "71                   code_postal_44610    0.003109\n",
      "47                          1-1-rdb_uc    0.003041\n",
      "6           foot/vie_courante/townhall    0.003002\n",
      "4                                 mois    0.002573\n",
      "5                                annee    0.002444\n"
     ]
    }
   ],
   "source": [
    "# Obtention de l'importance des features\n",
    "importances = grid.best_estimator_.feature_importances_\n",
    "\n",
    "# Création d'un DataFrame pour associer chaque feature à son importance\n",
    "importances = pd.concat([pd.Series(X_train.columns), pd.Series(importances)], axis=1).rename({0: 'feature', 1: 'importance'}, axis=1)\n",
    "\n",
    "# Tri\n",
    "importances.sort_values(by='importance', ascending=False, inplace=True)\n",
    "\n",
    "#Affichage\n",
    "print(importances.to_string())"
   ]
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