P2_03_donnees_nutritionnelles_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "unusual-perspective",
   "metadata": {},
   "source": [
    "# <font color=#3A86FF>Données nutritionnelles - Exploration </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "radio-nothing",
   "metadata": {},
   "source": [
    "## Sommaire\n",
    "\n",
    "* [Chargement des donnees](#Chargement-des-donnees)  \n",
    "* [Analyses univariees](#Analyses-univariees)\n",
    "    * [Tendances centrales](#Tendances-centrales)\n",
    "    * [Boxplots](#Boxplots)\n",
    "    * [Histogrammes](#Histogrammes)\n",
    "    * [Nutrition Score et Grade](#Nutrition-Score-et-Grade)\n",
    "* [Analyse Multivariee](#Analyse-Multivariee)\n",
    "* [ACP](#ACP)\n",
    "    * [Préparation des données](#Preparation-des-donnees)\n",
    "    * [Centrage et réduction](#Centrage-et-reduction)\n",
    "    * [Calcul des composantes principales](#Calcul-des-composantes-principales)\n",
    "    * [Eboulis des valeurs propres](#Eboulis-des-valeurs-propres)\n",
    "    * [Cercle des corrélations](#Cercle-des-correlations)\n",
    "    * [Choix du plan factoriel](#Choix-du-plan-factoriel)\n",
    "    * [Projection des individus](#Projection-des-individus)\n",
    "* [Conclusion](#Conclusion) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "offensive-bicycle",
   "metadata": {},
   "source": [
    "## <font color=#FB5607>Chargement des donnees</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "mexican-coffee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "now = time.time()\n",
    "df = pd.read_csv(\"products_clean.csv\", encoding=\"utf-8\",sep=\",\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "concrete-mortality",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>quantity</th>\n",
       "      <th>countries_fr</th>\n",
       "      <th>categories_tags</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>states</th>\n",
       "      <th>countries_tags</th>\n",
       "      <th>brands_tags</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>countries</th>\n",
       "      <th>...</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>additives_n</th>\n",
       "      <th>cholesterol_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1kg</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-to-be-c...</td>\n",
       "      <td>en:france</td>\n",
       "      <td>ferme-t-y-r-nao</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:FR</td>\n",
       "      <td>...</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>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-complet...</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28 g (1 ONZ)</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>64.29</td>\n",
       "      <td>28.57</td>\n",
       "      <td>14.29</td>\n",
       "      <td>28.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-complet...</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>torn-glasser</td>\n",
       "      <td>28 g (0.25 cup)</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>60.71</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-complet...</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>grizzlies</td>\n",
       "      <td>28 g (0.25 cup)</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>17.86</td>\n",
       "      <td>17.86</td>\n",
       "      <td>57.14</td>\n",
       "      <td>3.57</td>\n",
       "      <td>5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-complet...</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>bob-s-red-mill</td>\n",
       "      <td>35 g (0.25 cup)</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>8.57</td>\n",
       "      <td>77.14</td>\n",
       "      <td>1.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  categories quantity countries_fr categories_tags categories_fr  \\\n",
       "0        NaN      1kg       France             NaN           NaN   \n",
       "1        NaN      NaN   États-Unis             NaN           NaN   \n",
       "2        NaN      NaN   États-Unis             NaN           NaN   \n",
       "3        NaN      NaN   États-Unis             NaN           NaN   \n",
       "4        NaN      NaN   États-Unis             NaN           NaN   \n",
       "\n",
       "                                              states    countries_tags  \\\n",
       "0  en:to-be-completed, en:nutrition-facts-to-be-c...         en:france   \n",
       "1  en:to-be-completed, en:nutrition-facts-complet...  en:united-states   \n",
       "2  en:to-be-completed, en:nutrition-facts-complet...  en:united-states   \n",
       "3  en:to-be-completed, en:nutrition-facts-complet...  en:united-states   \n",
       "4  en:to-be-completed, en:nutrition-facts-complet...  en:united-states   \n",
       "\n",
       "       brands_tags     serving_size countries  ... additives_fr additives_n  \\\n",
       "0  ferme-t-y-r-nao              NaN     en:FR  ...          NaN         NaN   \n",
       "1              NaN     28 g (1 ONZ)        US  ...          NaN         0.0   \n",
       "2     torn-glasser  28 g (0.25 cup)        US  ...          NaN         0.0   \n",
       "3        grizzlies  28 g (0.25 cup)        US  ...          NaN         0.0   \n",
       "4   bob-s-red-mill  35 g (0.25 cup)        US  ...          NaN         0.0   \n",
       "\n",
       "  cholesterol_100g nutrition-score-fr_100g energy_100g proteins_100g  \\\n",
       "0              NaN                     NaN         NaN           NaN   \n",
       "1            0.018                    14.0      2243.0          3.57   \n",
       "2            0.000                     0.0      1941.0         17.86   \n",
       "3              NaN                    12.0      2540.0         17.86   \n",
       "4              NaN                     NaN      1552.0          8.57   \n",
       "\n",
       "  carbohydrates_100g fat_100g sugars_100g saturated-fat_100g  \n",
       "0                NaN      NaN         NaN                NaN  \n",
       "1              64.29    28.57       14.29              28.57  \n",
       "2              60.71    17.86       17.86               0.00  \n",
       "3              17.86    57.14        3.57               5.36  \n",
       "4              77.14     1.43         NaN                NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "engaged-cemetery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 320772 entries, 0 to 320771\n",
      "Data columns (total 30 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   categories               84410 non-null   object \n",
      " 1   quantity                 104819 non-null  object \n",
      " 2   countries_fr             320492 non-null  object \n",
      " 3   categories_tags          84389 non-null   object \n",
      " 4   categories_fr            84411 non-null   object \n",
      " 5   states                   320726 non-null  object \n",
      " 6   countries_tags           320492 non-null  object \n",
      " 7   brands_tags              292352 non-null  object \n",
      " 8   serving_size             211331 non-null  object \n",
      " 9   countries                320492 non-null  object \n",
      " 10  additives_tags           154680 non-null  object \n",
      " 11  main_category_fr         84366 non-null   object \n",
      " 12  brands                   292360 non-null  object \n",
      " 13  main_category            84366 non-null   object \n",
      " 14  nutrition_grade_fr       221210 non-null  object \n",
      " 15  states_fr                320726 non-null  object \n",
      " 16  additives                248905 non-null  object \n",
      " 17  creator                  320770 non-null  object \n",
      " 18  product_name             303010 non-null  object \n",
      " 19  states_tags              320726 non-null  object \n",
      " 20  additives_fr             154680 non-null  object \n",
      " 21  additives_n              248939 non-null  float64\n",
      " 22  cholesterol_100g         144090 non-null  float64\n",
      " 23  nutrition-score-fr_100g  221210 non-null  float64\n",
      " 24  energy_100g              261113 non-null  float64\n",
      " 25  proteins_100g            259922 non-null  float64\n",
      " 26  carbohydrates_100g       243588 non-null  float64\n",
      " 27  fat_100g                 243891 non-null  float64\n",
      " 28  sugars_100g              244971 non-null  float64\n",
      " 29  saturated-fat_100g       229554 non-null  float64\n",
      "dtypes: float64(9), object(21)\n",
      "memory usage: 73.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "biological-copyright",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# identification des colonnes numériques et qualitatives\n",
    "colonnesNum=df.select_dtypes(np.number).columns.tolist()\n",
    "colonnesQual=df.select_dtypes('O').columns.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crude-exhibit",
   "metadata": {},
   "source": [
    "## <font color=#FB5607>Analyses univariees</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "varying-registration",
   "metadata": {},
   "source": [
    "### Tendances centrales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "breeding-sheet",
   "metadata": {},
   "outputs": [
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       "      <th>energy_100g</th>\n",
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       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>62.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.33</td>\n",
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       "      <th>176124</th>\n",
       "      <td>1.0</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",
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       "      <th>210219</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>13.80</td>\n",
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       "      <td>0.30</td>\n",
       "      <td>0.10</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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      "text/plain": [
       "        additives_n  cholesterol_100g  nutrition-score-fr_100g  energy_100g  \\\n",
       "219957          2.0               NaN                     11.0       1038.0   \n",
       "273260          3.0             0.016                     17.0       1602.0   \n",
       "150348          0.0             0.000                     10.0          0.0   \n",
       "143998          2.0               NaN                      NaN       1494.0   \n",
       "92360           2.0             0.000                     -6.0       1494.0   \n",
       "81642           0.0             0.000                     -5.0       1393.0   \n",
       "176124          1.0               NaN                      NaN          NaN   \n",
       "210219          0.0               NaN                     -4.0        387.0   \n",
       "204868          NaN               NaN                      NaN          NaN   \n",
       "217981          NaN               NaN                      NaN          NaN   \n",
       "\n",
       "        proteins_100g  carbohydrates_100g  fat_100g  sugars_100g  \\\n",
       "219957           0.00               60.00      0.00        60.00   \n",
       "273260           3.91               50.78     18.75        32.81   \n",
       "150348           0.00                0.00      0.00         0.00   \n",
       "143998           0.00               92.86      0.00        71.43   \n",
       "92360           12.50               75.00      1.79         3.57   \n",
       "81642           20.83               62.50      0.00         8.33   \n",
       "176124            NaN                 NaN       NaN          NaN   \n",
       "210219           6.50               13.80      0.30         0.30   \n",
       "204868            NaN                 NaN       NaN          NaN   \n",
       "217981            NaN                 NaN       NaN          NaN   \n",
       "\n",
       "        saturated-fat_100g  \n",
       "219957                0.00  \n",
       "273260                5.47  \n",
       "150348                0.00  \n",
       "143998                 NaN  \n",
       "92360                 0.00  \n",
       "81642                 0.00  \n",
       "176124                 NaN  \n",
       "210219                0.10  \n",
       "204868                 NaN  \n",
       "217981                 NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# echantillon de données numériques\n",
    "df[colonnesNum].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "unusual-thompson",
   "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>additives_n</th>\n",
       "      <th>cholesterol_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "      <td>320772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6447.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nunique</th>\n",
       "      <td>31.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>2503.0</td>\n",
       "      <td>5416.0</td>\n",
       "      <td>3378.0</td>\n",
       "      <td>4068.0</td>\n",
       "      <td>2197.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         additives_n  cholesterol_100g  nutrition-score-fr_100g  energy_100g  \\\n",
       "size        320772.0          320772.0                 320772.0     320772.0   \n",
       "median           1.0               0.0                     10.0       1100.0   \n",
       "mean             2.0               0.0                      9.0       1142.0   \n",
       "std              3.0               0.0                      9.0       6447.0   \n",
       "nunique         31.0             537.0                     55.0       3997.0   \n",
       "\n",
       "         proteins_100g  carbohydrates_100g  fat_100g  sugars_100g  \\\n",
       "size          320772.0            320772.0  320772.0     320772.0   \n",
       "median             5.0                21.0       5.0          6.0   \n",
       "mean               7.0                32.0      13.0         16.0   \n",
       "std                8.0                30.0      18.0         22.0   \n",
       "nunique         2503.0              5416.0    3378.0       4068.0   \n",
       "\n",
       "         saturated-fat_100g  \n",
       "size               320772.0  \n",
       "median                  2.0  \n",
       "mean                    5.0  \n",
       "std                     8.0  \n",
       "nunique              2197.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tableau de tendances (mediane, moyenne, ecart-type, valeurs uniques)\n",
    "df[colonnesNum].agg(['size','median','mean', 'std','nunique']).round(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-leader",
   "metadata": {},
   "source": [
    "### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "hungarian-christopher",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maskOutliers(s):\n",
    "    \"\"\"\n",
    "    Fonction qui retourne le mask qui retourne les lignes d'une Serie, correspondant aux outliers\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    s : pandas Series\n",
    "        une Serie de valeurs numériques\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    mask : Series\n",
    "        une Serie de booleens correspondant au masque qui retourne les valeurs atypiques/Outliers\n",
    "    \"\"\"\n",
    "    # premier Quantile\n",
    "    Q1 = s.quantile(0.25)\n",
    "    # 3e quantile\n",
    "    Q3 = s.quantile(0.75)\n",
    "    # Interquantile\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # les outliers sont toutes les valeurs qui sont inférieures à Q1-1.5*IQR et supérieure à  Q3+1.5*IQR\n",
    "    mask=(s < (Q1 - 1.5 * IQR)) |(s > (Q3 + 1.5 * IQR))\n",
    "    return mask\n",
    "\n",
    "# exclusion des outliers sur les variables quantitatives\n",
    "for name in colonnesNum:\n",
    "    mask=maskOutliers(df[name])\n",
    "    df.loc[mask,name]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "precise-appointment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "additives_n\n",
      "cholesterol_100g\n",
      "nutrition-score-fr_100g\n",
      "energy_100g\n",
      "proteins_100g\n",
      "carbohydrates_100g\n",
      "fat_100g\n",
      "sugars_100g\n",
      "saturated-fat_100g\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.rcdefaults()\n",
    "plt.rcParams[\"figure.figsize\"] = (100,5)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=9,figsize=(30,5))\n",
    "axes=axes.flatten()\n",
    "\n",
    "i=0\n",
    "for x in colonnesNum:\n",
    "        print(str(x)) #debug\n",
    "        # on dessine le boxplot en excluant les valeurs manquantes pour chaque variable quantitative\n",
    "        axes[i].boxplot(df[x].dropna())\n",
    "        axes[i].set_title(x,color='red')\n",
    "        axes[i].set_xlabel(str(round(100*df[x].isna().sum(axis=0)/len(df[x]),0))+'% NA')\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enhanced-hostel",
   "metadata": {},
   "source": [
    "On remarquera le boxplot bien équilibré du nutrition-score-fr_100g, comparé aux autres.\n",
    "Ainsi que la distribution sans tendances du cholesterol_100g."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unable-center",
   "metadata": {},
   "source": [
    "### Histogrammes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "prerequisite-inspection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "plt.rcParams[\"figure.figsize\"] = (100,5)\n",
    "plt.rcParams[\"axes.grid\"] = False\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=9,figsize=(30,5))\n",
    "\n",
    "df.hist(figsize=(14,14),ax=axes, xrot=45,grid=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excessive-happiness",
   "metadata": {},
   "source": [
    "On remarque une distribution bimodale sur le nutrition-score-fr_100g\n",
    "-> on aurant potentiellement deux groupes de produits (ceux avec des scores autour de 0 et ceux autour de 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cellular-credit",
   "metadata": {},
   "source": [
    "### Nutrition Score et Grade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empty-diagnosis",
   "metadata": {},
   "source": [
    "On cherche ici à savoir si le nutrition-score-fr_100g peut expliquer le nutrition_grade_fr par un essai de modelisation par regression linéaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "scientific-translation",
   "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>nutrition-score-fr_100g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-3.407644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.906673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>6.367561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>14.066934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>21.958587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    nutrition-score-fr_100g\n",
       "nutrition_grade_fr                         \n",
       "a                                 -3.407644\n",
       "b                                  0.906673\n",
       "c                                  6.367561\n",
       "d                                 14.066934\n",
       "e                                 21.958587"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"nutrition_grade_fr\").agg({\n",
    "    \"nutrition-score-fr_100g\":\"mean\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "administrative-jordan",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversion numerique de la note alphabetique de nutrition\n",
    "gradeNotationMapper={\"a\":5,\"b\":4,\"c\":3,\"d\":2,\"e\":1}\n",
    "nutritionGrade=df['nutrition_grade_fr'] # note alphabetique de nutrition par individu\n",
    "nutritionNote=nutritionGrade.replace(gradeNotationMapper)\n",
    "df['nutritionNote']=nutritionNote\n",
    "dfSub=df[['nutritionNote','nutrition-score-fr_100g']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "american-anger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          nutritionNote   R-squared:                       0.916\n",
      "Model:                            OLS   Adj. R-squared:                  0.916\n",
      "Method:                 Least Squares   F-statistic:                 2.398e+06\n",
      "Date:                Tue, 09 Mar 2021   Prob (F-statistic):               0.00\n",
      "Time:                        15:02:39   Log-Likelihood:            -1.0691e+05\n",
      "No. Observations:              221210   AIC:                         2.138e+05\n",
      "Df Residuals:                  221208   BIC:                         2.139e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       4.1117      0.001   3464.234      0.000       4.109       4.114\n",
      "nutrition-score-fr_100g    -0.1427   9.21e-05  -1548.558      0.000      -0.143      -0.142\n",
      "==============================================================================\n",
      "Omnibus:                     2694.949   Durbin-Watson:                   1.457\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4737.480\n",
      "Skew:                          -0.053   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.709   Cond. No.                         18.4\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Centrage Réduction\n",
    "dfSubStandardized = (dfSub - dfSub.mean()) / dfSub.std()\n",
    "\n",
    "X = dfSub[['nutrition-score-fr_100g']]\n",
    "# ajout de la constante explicative\n",
    "X=sm.add_constant(X)\n",
    "y=dfSub['nutritionNote']\n",
    "# definition du modele\n",
    "model = sm.OLS(y,X)\n",
    "\n",
    "# fittage du modele\n",
    "results=model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "julian-pharmacy",
   "metadata": {},
   "source": [
    "Résultats du calcul de régression linéaire sur 136607 observations:\n",
    "* $R^2$ elevé => score de prediction elevé\n",
    "* Coefficient negatif => plus le score est elevé, plus la note est mauvaise (proche de e)\n",
    "* P-value associé au coefficient teste l'hypothese nulle que le coefficient n'a pas d'effet. Ici il vaut 0, donc le coefficient est pertinent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "professional-sector",
   "metadata": {},
   "source": [
    "Représentation graphique des valeurs prédites par le modele par rapport aux données réelles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "alien-adventure",
   "metadata": {},
   "outputs": [],
   "source": [
    "# valeurs prédites de nutritionNote\n",
    "results.fittedvalues = results.params['nutrition-score-fr_100g'] * dfSub['nutrition-score-fr_100g'] + results.params['const']\n",
    "# valeurs résiduelles\n",
    "results.resid = dfSub['nutritionNote'] - results.fittedvalues\n",
    "# on ajoute les valeurs prédites dans le dataFrame\n",
    "dfSub['fitted'] = results.fittedvalues\n",
    "\n",
    "#dfSub.sort_values(by = 'nutrition-score-fr_100g', ascending = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "sharp-arena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Regression linéaire \\n 'nutritionNote~nutrition-score-fr_100g'\")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcdefaults()\n",
    "plt.scatter(dfSub['nutrition-score-fr_100g'], dfSub['nutritionNote'], label = 'nutritionNote' )\n",
    "plt.plot(dfSub['nutrition-score-fr_100g'], dfSub['fitted'], label = 'Fitted',color='orange' )\n",
    "plt.legend()\n",
    "plt.title(\"Regression linéaire \\n 'nutritionNote~nutrition-score-fr_100g'\",fontsize=14,fontweight='normal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "selected-theta",
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppression des valeurs manquantes\n",
    "dfNutritionScoreGradeComplete=df[['nutrition-score-fr_100g','nutrition_grade_fr']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "preliminary-seeking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'nutrition-score-fr_100g')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot\n",
    "plt.rcdefaults()\n",
    "plt.rcParams['agg.path.chunksize'] = 10000\n",
    "plt.plot('nutrition_grade_fr','nutrition-score-fr_100g','o', linestyle=\"None\",data=dfNutritionScoreGradeComplete)\n",
    "plt.xlabel(\"nutrition_grade_fr\")\n",
    "plt.ylabel(\"nutrition-score-fr_100g\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changing-conviction",
   "metadata": {},
   "source": [
    "Le nutrition grade est bon ('a') pour les nutritions score compris entre -15 et 20.\n",
    "Les mauvais grade ('e') correspondent au nutrition score compris entre 10 et 40.\n",
    "Cela confirme les resultats précédents, plus le nutrition score est elevé, plus le grade est mauvais."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reliable-compiler",
   "metadata": {},
   "source": [
    "## <font color=#FB5607>Analyse Multivariee</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "concrete-basket",
   "metadata": {},
   "source": [
    "On constate que le dataframe dfQual contient des centaines de milliers d'observations distinctes par variables.\\\n",
    "La variable \"nutrition_grade_fr\" presente 5 degré de notation.\n",
    "Nous voulons voir si la marque (brands) ou les additifs (additives_fr) peuvent expliquer la notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "compatible-thickness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320772, 21)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on selectionne les variables qualitatives\n",
    "dfQual=df[colonnesQual]\n",
    "dfQual.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "impressed-olympus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>categories</th>\n",
       "      <th>quantity</th>\n",
       "      <th>countries_fr</th>\n",
       "      <th>categories_tags</th>\n",
       "      <th>categories_fr</th>\n",
       "      <th>states</th>\n",
       "      <th>countries_tags</th>\n",
       "      <th>brands_tags</th>\n",
       "      <th>serving_size</th>\n",
       "      <th>countries</th>\n",
       "      <th>...</th>\n",
       "      <th>main_category_fr</th>\n",
       "      <th>brands</th>\n",
       "      <th>main_category</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th>states_fr</th>\n",
       "      <th>additives</th>\n",
       "      <th>creator</th>\n",
       "      <th>product_name</th>\n",
       "      <th>states_tags</th>\n",
       "      <th>additives_fr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84410</td>\n",
       "      <td>104819</td>\n",
       "      <td>320492</td>\n",
       "      <td>84389</td>\n",
       "      <td>84411</td>\n",
       "      <td>320726</td>\n",
       "      <td>320492</td>\n",
       "      <td>292352</td>\n",
       "      <td>211331</td>\n",
       "      <td>320492</td>\n",
       "      <td>...</td>\n",
       "      <td>84366</td>\n",
       "      <td>292360</td>\n",
       "      <td>84366</td>\n",
       "      <td>221210</td>\n",
       "      <td>320726</td>\n",
       "      <td>248905</td>\n",
       "      <td>320770</td>\n",
       "      <td>303010</td>\n",
       "      <td>320726</td>\n",
       "      <td>154680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>36982</td>\n",
       "      <td>13826</td>\n",
       "      <td>722</td>\n",
       "      <td>21142</td>\n",
       "      <td>21152</td>\n",
       "      <td>1021</td>\n",
       "      <td>725</td>\n",
       "      <td>50253</td>\n",
       "      <td>25423</td>\n",
       "      <td>1434</td>\n",
       "      <td>...</td>\n",
       "      <td>3543</td>\n",
       "      <td>58784</td>\n",
       "      <td>3543</td>\n",
       "      <td>5</td>\n",
       "      <td>1021</td>\n",
       "      <td>196069</td>\n",
       "      <td>3535</td>\n",
       "      <td>221347</td>\n",
       "      <td>1021</td>\n",
       "      <td>41537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Snacks sucrés,Biscuits et gâteaux,Biscuits</td>\n",
       "      <td>500 g</td>\n",
       "      <td>États-Unis</td>\n",
       "      <td>en:sugary-snacks,en:biscuits-and-cakes,en:bisc...</td>\n",
       "      <td>Snacks sucrés,Biscuits et gâteaux,Biscuits</td>\n",
       "      <td>en:to-be-completed, en:nutrition-facts-complet...</td>\n",
       "      <td>en:united-states</td>\n",
       "      <td>carrefour</td>\n",
       "      <td>240 ml (8 fl oz)</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>Boissons</td>\n",
       "      <td>Carrefour</td>\n",
       "      <td>en:beverages</td>\n",
       "      <td>d</td>\n",
       "      <td>A compléter,Informations nutritionnelles compl...</td>\n",
       "      <td>[ extra-virgin-olive-oil -&gt; en:extra-virgin-o...</td>\n",
       "      <td>usda-ndb-import</td>\n",
       "      <td>Ice Cream</td>\n",
       "      <td>en:to-be-completed,en:nutrition-facts-complete...</td>\n",
       "      <td>E322 - Lécithines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>301</td>\n",
       "      <td>4669</td>\n",
       "      <td>172998</td>\n",
       "      <td>802</td>\n",
       "      <td>802</td>\n",
       "      <td>168905</td>\n",
       "      <td>172998</td>\n",
       "      <td>3149</td>\n",
       "      <td>5496</td>\n",
       "      <td>169928</td>\n",
       "      <td>...</td>\n",
       "      <td>6054</td>\n",
       "      <td>2978</td>\n",
       "      <td>6054</td>\n",
       "      <td>62763</td>\n",
       "      <td>168905</td>\n",
       "      <td>397</td>\n",
       "      <td>169868</td>\n",
       "      <td>410</td>\n",
       "      <td>168905</td>\n",
       "      <td>8264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        categories quantity countries_fr  \\\n",
       "count                                        84410   104819       320492   \n",
       "unique                                       36982    13826          722   \n",
       "top     Snacks sucrés,Biscuits et gâteaux,Biscuits    500 g   États-Unis   \n",
       "freq                                           301     4669       172998   \n",
       "\n",
       "                                          categories_tags  \\\n",
       "count                                               84389   \n",
       "unique                                              21142   \n",
       "top     en:sugary-snacks,en:biscuits-and-cakes,en:bisc...   \n",
       "freq                                                  802   \n",
       "\n",
       "                                     categories_fr  \\\n",
       "count                                        84411   \n",
       "unique                                       21152   \n",
       "top     Snacks sucrés,Biscuits et gâteaux,Biscuits   \n",
       "freq                                           802   \n",
       "\n",
       "                                                   states    countries_tags  \\\n",
       "count                                              320726            320492   \n",
       "unique                                               1021               725   \n",
       "top     en:to-be-completed, en:nutrition-facts-complet...  en:united-states   \n",
       "freq                                               168905            172998   \n",
       "\n",
       "       brands_tags      serving_size countries  ... main_category_fr  \\\n",
       "count       292352            211331    320492  ...            84366   \n",
       "unique       50253             25423      1434  ...             3543   \n",
       "top      carrefour  240 ml (8 fl oz)        US  ...         Boissons   \n",
       "freq          3149              5496    169928  ...             6054   \n",
       "\n",
       "           brands main_category nutrition_grade_fr  \\\n",
       "count      292360         84366             221210   \n",
       "unique      58784          3543                  5   \n",
       "top     Carrefour  en:beverages                  d   \n",
       "freq         2978          6054              62763   \n",
       "\n",
       "                                                states_fr  \\\n",
       "count                                              320726   \n",
       "unique                                               1021   \n",
       "top     A compléter,Informations nutritionnelles compl...   \n",
       "freq                                               168905   \n",
       "\n",
       "                                                additives          creator  \\\n",
       "count                                              248905           320770   \n",
       "unique                                             196069             3535   \n",
       "top      [ extra-virgin-olive-oil -> en:extra-virgin-o...  usda-ndb-import   \n",
       "freq                                                  397           169868   \n",
       "\n",
       "       product_name                                        states_tags  \\\n",
       "count        303010                                             320726   \n",
       "unique       221347                                               1021   \n",
       "top       Ice Cream  en:to-be-completed,en:nutrition-facts-complete...   \n",
       "freq            410                                             168905   \n",
       "\n",
       "             additives_fr  \n",
       "count              154680  \n",
       "unique              41537  \n",
       "top     E322 - Lécithines  \n",
       "freq                 8264  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfQual.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "widespread-enough",
   "metadata": {},
   "source": [
    "Utilisons la regression lineaire par la méthode des moindres carrés pour déterminer si les variables explicatives \"brands\" et \"additives_fr\" peuvent expliquer \"nutrition_grade_fr\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "human-kuwait",
   "metadata": {},
   "source": [
    "### Tableau de contingence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "precious-binary",
   "metadata": {},
   "source": [
    "Pour pouvoir la corrélation entre ces variables catégorielles, nous calculons le tableau de contingences et traduisons les valeurs de \"nutrition_grade_fr\" en notes numériques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "single-cooler",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brands</th>\n",
       "      <th>additives_fr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>34975</td>\n",
       "      <td>13239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>33501</td>\n",
       "      <td>17754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>44526</td>\n",
       "      <td>25687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>61526</td>\n",
       "      <td>39721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>42044</td>\n",
       "      <td>29781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    brands  additives_fr\n",
       "nutrition_grade_fr                      \n",
       "a                    34975         13239\n",
       "b                    33501         17754\n",
       "c                    44526         25687\n",
       "d                    61526         39721\n",
       "e                    42044         29781"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#%%timeit\n",
    "tableauContingence=dfQual.groupby(\"nutrition_grade_fr\").agg({\n",
    "    \"brands\":\"count\",\n",
    "    \"additives_fr\":\"count\"\n",
    "})\n",
    "tableauContingence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "alive-miami",
   "metadata": {},
   "outputs": [],
   "source": [
    "tableauContingence['nutrition_grade_fr']=tableauContingence.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "sophisticated-assurance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>brands</th>\n",
       "      <th>additives_fr</th>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nutrition_grade_fr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>34975</td>\n",
       "      <td>13239</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>33501</td>\n",
       "      <td>17754</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>44526</td>\n",
       "      <td>25687</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>61526</td>\n",
       "      <td>39721</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>42044</td>\n",
       "      <td>29781</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    brands  additives_fr nutrition_grade_fr\n",
       "nutrition_grade_fr                                         \n",
       "a                    34975         13239                  0\n",
       "b                    33501         17754                  1\n",
       "c                    44526         25687                  2\n",
       "d                    61526         39721                  3\n",
       "e                    42044         29781                  4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableauContingence.loc[tableauContingence.nutrition_grade_fr=='a','nutrition_grade_fr']=0\n",
    "tableauContingence.loc[tableauContingence.nutrition_grade_fr=='b','nutrition_grade_fr']=1\n",
    "tableauContingence.loc[tableauContingence.nutrition_grade_fr=='c','nutrition_grade_fr']=2\n",
    "tableauContingence.loc[tableauContingence.nutrition_grade_fr=='d','nutrition_grade_fr']=3\n",
    "tableauContingence.loc[tableauContingence.nutrition_grade_fr=='e','nutrition_grade_fr']=4\n",
    "tableauContingence"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sticky-better",
   "metadata": {},
   "source": [
    "Utilisons la regression lineaire par la méthode des moindres carrés pour déterminer si les variables explicatives \"brands\" et \"additives_fr\" peuvent expliquer \"nutrition_grade_fr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "exterior-revision",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     nutrition_grade_fr   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.917\n",
      "Method:                 Least Squares   F-statistic:                     23.16\n",
      "Date:                Tue, 09 Mar 2021   Prob (F-statistic):             0.0414\n",
      "Time:                        15:02:42   Log-Likelihood:               -0.86622\n",
      "No. Observations:                   5   AIC:                             7.732\n",
      "Df Residuals:                       2   BIC:                             6.561\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const            2.2796      1.160      1.965      0.188      -2.713       7.272\n",
      "brands          -0.0002   5.55e-05     -3.508      0.073      -0.000    4.41e-05\n",
      "additives_fr     0.0003   5.99e-05      5.400      0.033    6.57e-05       0.001\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.715\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.589\n",
      "Skew:                          -0.690   Prob(JB):                        0.745\n",
      "Kurtosis:                       2.040   Cond. No.                     2.96e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.96e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hui-guan/opt/anaconda3/lib/python3.8/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X = tableauContingence[['brands','additives_fr']]\n",
    "X=sm.add_constant(X)\n",
    "y=tableauContingence['nutrition_grade_fr']\n",
    "model = sm.OLS(y.astype('int64'),X)\n",
    "\n",
    "results=model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "promising-investor",
   "metadata": {},
   "source": [
    "On remarque une valeur Adj.R-Squared très elevée indiquant un fort score de prediction.\\\n",
    "Les coefficients des valeurs explicatives sont proches de 0, avec une p-value proche de 0.05, on ne peut pas expliquer de manière fiable le nutrition_grade_fr par les variables brands et additives_fr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporated-calibration",
   "metadata": {},
   "source": [
    "## <font color=#FB5607>ACP</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standard-religion",
   "metadata": {},
   "source": [
    "### Preparation des donnees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "according-cattle",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtrage sur les variables quantitatives\n",
    "dataPCA=df[colonnesNum].dropna()\n",
    "nomsIndividus=df.iloc[dataPCA.index,df.columns.get_loc('product_name')] # Individus\n",
    "nutritionGrade=df.iloc[dataPCA.index,df.columns.get_loc('nutrition_grade_fr')] # note alphabetique de nutrition par individu\n",
    "variables=dataPCA.columns # Variables\n",
    "X=dataPCA.values # Valeurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "legendary-intake",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>additives_n</th>\n",
       "      <th>cholesterol_100g</th>\n",
       "      <th>nutrition-score-fr_100g</th>\n",
       "      <th>energy_100g</th>\n",
       "      <th>proteins_100g</th>\n",
       "      <th>carbohydrates_100g</th>\n",
       "      <th>fat_100g</th>\n",
       "      <th>sugars_100g</th>\n",
       "      <th>saturated-fat_100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150636</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>3.33</td>\n",
       "      <td>83.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80289</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151992</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>628.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104906</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>56.25</td>\n",
       "      <td>6.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105626</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>77.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        additives_n  cholesterol_100g  nutrition-score-fr_100g  energy_100g  \\\n",
       "150636          0.0               0.0                     11.0       1602.0   \n",
       "80289           1.0               0.0                     -2.0        753.0   \n",
       "151992          1.0               0.0                      2.0        628.0   \n",
       "104906          2.0               0.0                     18.0       1305.0   \n",
       "105626          6.0               0.0                     13.0       1360.0   \n",
       "\n",
       "        proteins_100g  carbohydrates_100g  fat_100g  sugars_100g  \\\n",
       "150636           3.33               83.33      0.00         20.0   \n",
       "80289            4.00               14.00     14.00         14.0   \n",
       "151992           3.00               16.00     10.00         11.0   \n",
       "104906           6.25               56.25      6.25          0.0   \n",
       "105626           5.00               77.50      0.00         45.0   \n",
       "\n",
       "        saturated-fat_100g  \n",
       "150636                0.00  \n",
       "80289                 1.00  \n",
       "151992                1.50  \n",
       "104906                6.25  \n",
       "105626                0.00  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataPCA.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-privacy",
   "metadata": {},
   "source": [
    "### Centrage et reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "democratic-implement",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn import decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "recognized-conclusion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136607, 9)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centreReduit = preprocessing.StandardScaler().fit(X)\n",
    "XCentreReduit = centreReduit.transform(X)\n",
    "XCentreReduit.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owned-surprise",
   "metadata": {},
   "source": [
    "### Calcul des composantes principales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "stable-module",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=9)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_comp=9 # n_comp <= nombre de variables\n",
    "pca = decomposition.PCA(n_components=n_comp)\n",
    "pca.fit(XCentreReduit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "figured-trace",
   "metadata": {},
   "source": [
    "### Eboulis des valeurs propres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "continuous-essay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3T2XLltWePXsSPefTTz/Vnj171KNHD82cOVNVq1aVv7+/jh49qi1btmjfvn06fvy4fH19tXz5cnXv3l3VqlVTiRIlFBAQoAMHDmjhwoXy8fFxa63YVq1aaeLEic4/PSe17m2rVq00YsQIdezYUStWrFBISIi2b9+u5cuXq0mTJpo/f36y3mvgwIH67bffNGbMGC1atEihoaG6//77FRkZqT/++EO///671q9f75zT6unfcfPmzeXr66vq1asrJCRE165d07Jly7Rr1y41b9482Verc+d7fTuZMmXStGnTVLduXT399NN6/vnnFRISol9//VU///yzihYtquHDhyd6XnBwsM6dO6fy5curfv36On/+vP73v//p6tWrmjx5svz8/JL9M7HqewekW6mzKAOAlJacpcBu/q+8/lsK6vDhw8bzzz9v5MqVy/Dx8TFq1qxprF27Nsn3OXXqlNGpUycjJCTEyJIli5EnTx6jadOmxh9//JHk/r/99pvx5JNPGr6+vkaOHDmMhg0bGvv27bunpcBiYmKMESNGGHXq1DHy589vZM2a1QgKCjJCQ0ONOXPmuPVzmzx5slGqVCkja9asRv78+Y133nnHuHz5cpJrwBqGYVy+fNkYOXKkUalSJSN79uyGj4+PUbhwYaNRo0bGjBkzjGvXrhmGYRi7du0yOnfubFSoUMEICAgwvL29jSJFihitW7c2du3a5VZGwzCMIkWKGJKMIkWK3HKfbdu2GXXq1DFy5cpl+Pn5GaGhocby5cudy29NnTrVZf9bfcbY2Fhj4sSJRrVq1YwcOXIY3t7eRsGCBY169eoZ48ePNy5evOiyvzu/41tlSfDpp58aDRo0MEJCQoxs2bIZAQEBRpUqVYyJEyc6f7Z34u73OqmcN9u+fbvRtGlTI0+ePEaWLFmMkJAQo1OnTsapU6cS7RsSEmKEhIQYp0+fNl577TUjMDDQ8Pb2NipXrmx8++23ifZPWApsxYoVt3x/q753QHrkMIwk/sYBAEA65XA4FBoamuQ82dSQcCnh5FxaGoDnMecWAAAAtkG5BQAAgG1QbgEAAGAbzLkFAACAbXDkFgAAALZBuQUAAIBtcBEHmZeJPHbsmPz8/G55vXMAAABYxzAMXbhwQfny5bvthVgot5KOHTumAgUKWB0DAAAAd3D06FHlz5//ltspt5LzMohHjx5Vjhw5LE4DAACAm0VFRalAgQJ3vHw15VZyTkXIkSMH5RYAACANu9MUUk4oAwAAgG1QbgEAAGAblFsAAADYBuUWAAAAtkG5BQAAgG1QbgEAAGAblFsAAADYBuUWAAAAtkG5BQAAgG1QbgEAAGAblFsAAADYBuUWAAAAtkG5BQAAgG1QbgEAAGAblFsAAADYBuUWAAAAtkG5BQAAgG1QbgEAAGAblFsAAADYBuUWAAAAtkG5BQAAgG1ktjoAAAAAPCQuTlqzRjp+XAoOlmrUkLy8rE6Vqiw9crt69Wo9++yzypcvnxwOh7755huX7YZhaMCAAcqXL598fHwUFhamnTt3uuwTHR2tjh07Kk+ePMqePbsaNGigv//+OxU/BQAAQBowf75UqJBUq5bUsqX5n4UKmeMZiKXl9tKlS3r44Yc1bty4JLePHDlSERERGjdunDZt2qS8efOqdu3aunDhgnOfLl26aMGCBZozZ47Wrl2rixcv6plnnlFcXFxqfQwAAABrzZ8vNW0q3XyALzLSHM9ABddhGIZhdQhJcjgcWrBggRo1aiTJPGqbL18+denSRT179pRkHqUNCgrSiBEj9Oabb+r8+fO6//77NXPmTDVv3lySdOzYMRUoUEA//PCD6tatm6z3joqKkr+/v86fP68cOXKkyOcDAABIEXFx5hHaW/3l2uGQ8ueXDh5M11MUktvX0uwJZQcPHtSJEydUp04d55i3t7dCQ0O1bt06SdKWLVt07do1l33y5cunMmXKOPdJSnR0tKKiolxuAAAA6YZhSMeOmfNr+/S5dbFN2PfoUXPfDCDNnlB24sQJSVJQUJDLeFBQkA4fPuzcJ2vWrMqVK1eifRKen5Rhw4Zp4MCBHk4MAADgQTEx0qFD0l9/mbcDB1zvX7ni3usdP54iMdOaNFtuEzgcDpfHhmEkGrvZnfbp1auXunXr5nwcFRWlAgUK3FtQAAAAd50/f72w3lxijx6V4uNv/VwvL6lgQSlXLum33+78XsHBnsudhqXZcps3b15J5tHZ4Bt+GSdPnnQezc2bN69iYmJ09uxZl6O3J0+e1OOPP37L1/b29pa3t3cKJQcAAPhPfLx5xPTmAptQYk+fvv3zfX2lokUT34oUkUJCpCxZrs+5jYw0pyDcLGHObY0aKfIR05o0W24LFy6svHnzatmyZapQoYIkKSYmRqtWrdKIESMkSZUqVVKWLFm0bNkyNWvWTJJ0/Phx7dixQyNHjrQsOwAAyECio12nD9x4O3hQunr19s8PDLx1gQ0KMsvp7Xh5SR9/bK6K4HC4FtyE544ena5PJnOHpeX24sWL2r9/v/PxwYMHtW3bNuXOnVsFCxZUly5dFB4eruLFi6t48eIKDw+Xr6+vWrZsKUny9/dX27Zt1b17dwUEBCh37tx65513VLZsWT311FNWfSwAAGCVlLqIwdmzrnNeb7z9/XfSR0wTeHmZR1mTKq9Fikh+fveer0kT6euvpc6dXU8uy5/fLLZNmtz7e6QTlpbbzZs3q1atWs7HCfNgX3nlFU2bNk09evTQlStX1K5dO509e1ZVqlTR0qVL5XfDl+Cjjz5S5syZ1axZM125ckVPPvmkpk2bJq8M8v9OAADAf+bPT7rcffzxnctdfLz5Z/1bFdizZ2///OzZb330tWBBc/pASmvSRGrYMMNfoSzNrHNrJda5BQAgnUu4iMHNtSbhz/Jffy09/bQ5TeDmlQcSpg9ER9/+PYKCbl1gAwPvPH0A9yS5fS3NzrkFAABIlrg484htUsfrEsaaNTP3u53MmZOePpBQYLNn93x2eBzlFgAApG+LF9/+IgbS9WJ7331Jl9eiRaUCBcyCi3SN3yAAAEhf4uOlbdvMUrt4sfTLL8l73oQJ0htvMH3A5ii3AAAg7Tt5Ulq6VFqyxLydOuX+azz0EMU2A6DcAgCAtOfaNWnDBvPI7JIl0pYtrtvvu0968kmpXj3pqaekWrW4iAEkUW4BAEBacfjw9TL7009SVJTr9goVzDJbt65UtaqUNev1bVzEAP+h3AIAAGtcuSKtWnW90P75p+v2PHmkOnXMQlunjrkU161wEQP8h3ILAABSh2FIu3ebRXbxYrPY3ri2rJeXeUS2bl2z0FasKGXKlPzX5yIGEOUWAACkpHPnzCkGCUdnjx513V6ggFlk69WTnnhCypnz3t7Py0sKC7u310C6RrkFAACeEx9vnvyVcHR2wwbXiydkyyaFhl4/OluiBCsYwKMotwAA4N6cOHF9ma6lS6V//3XdXqLE9aOzNWtKPj7W5ESGQLkFAADuiYmR1q27fnR22zbX7TlymMtz1a1r3kJCLImJjIlyCwAA7uzgQddlui5edN1eqdL1o7NVqkhZsliTExke5RYAACR26ZK0cuX1o7P79rluDwy8fmS2dm3zMZAGUG4BAIC5TNfOndePzq5ebU4/SJA5s/T449ePzj78sHvLdAGphHILAICdxMUlf53Xs2el5cuvF9rISNfthQpdvyLYE0+Yc2mBNI5yCwCAXcyfn/QVuj7+2LzAQVyctHnz9TL766/m0l0JfHzMNWITCu2DD7JMF9Idyi0AAHYwf77UtKk5veBGkZHSc89J1aqZVwc7c8Z1e+nS19ecrVHDXIcWSMcotwAApHdxceYR25uLrXR97JdfzP/09zdPAKtXT6pTx7xCGGAjlFsAANK7NWtcpyLcytix0ltvmSeHATbFaY4AAKRnsbHSvHnJ2zcggGIL2+MbDgBAehQdLU2fLo0cKf31V/KeExycspmANIByCwBAenLhgjRxohQRYS73JUm5c5tHcC9cSHrercNhrppQo0bqZgUswLQEAADSg3//lfr1k0JCpHffNYtt/vzS6NHSkSPS1Knmfjcv3ZXwePToW693C9gI5RYAgLTs6FFzJYSCBaXBg80LLzz4oDRlijkdoXNnKXt2cx3br7+WHnjA9fn585vjTZpYkx9IZUxLAAAgLfrzT2nECGnWLHPKgSRVrCj16iU1bpz0UdgmTaSGDZN/hTLAhii3AACkJVu2SMOGmRdlSJg/W6uWWWqfeurOVwzz8jKvMgZkUJRbAACsZhjSihVmqV2+/Pp4w4bSe+9Jjz1mXTYgnaHcAgBglfh4aeFCs9Ru3GiOeXlJLVtKPXpIZcpYmw9Ihyi3AACktmvXpP/9z5xTu2uXOZYtm9S2rfTOO1KhQpbGA9Izyi0AAKnlyhXp88+lDz+UDh82x3LkkNq3N1c9CAqyNh9gA5RbAABS2rlz0qefmmvNnjpljgUGSl27Sm+/Lfn7W5kOsBXKLQAAKeXECbPQjh8vRUWZY4UKmRdhePVVycfHynSALVFuAQDwtIMHpQ8+MC+0EB1tjpUuba580Ly5lCWLtfkAG6PcAgDgKTt2SMOHS3PmSHFx5thjj5lr1D7zjJSJC4MCKY1yCwDAvVq/3lzO67vvro/VqWOW2tDQO194AYDHUG4BALgbhiEtXWqW2lWrzDGHQ3ruOXP6QaVK1uYDMijKLQAA7oiLk+bNM6cfbN1qjmXJIrVqZV544aGHrM0HZHCUWwAAkiMmRpo507zwwr595pivr/TGG1L37lL+/NbmAyCJcgsAwO1dvChNniyNGiVFRppjuXJJHTuatzx5rM0HwAXlFgCApJw+LY0da97OnDHH8uWTunUzj9b6+VmbD0CSKLcAANwoMtI8SjtpknTpkjlWrJg5n/bllyVvb2vzAbgtyi0AAJI5j3bECGnGDOnaNXPs4YfN5byaNpW8vKzNByBZKLcAgIxt61ZzOa+vvzaX95KkGjXMUluvHmvUAukM5RYAkPEYhrR6tVlqlyy5Pv7MM+YatdWqWZcNwD2h3AIA7CUuTlqzRjp+XAoONo/CJkwpiI+XFi0yS+369eZYpkxS8+ZmqS1XzrrcADyCcgsAsI/586XOnaW//74+lj+/FBFhrlM7fLi0Y4c5njWr9Oqr0rvvSkWLWpMXgMdRbgEA9jB/vnniV8K82QR//y01a3b9sZ+f9PbbUpcu5pFdALZCuQUApH9xceYR25uL7Y0yZZIGDJA6dDAvwgDAljJZHQAAgHu2Zo3rVISkxMeb828ptoCtUW4BAOnfnYptguPHUzYHAMsxLQEAkH4ZhrRwodSnT/L2Z44tYHscuQUApE9r10rVq0uNGklHjtz+YgsOh1SggDktAYCtUW4BAOnLjh1SgwZmUV23TvLxMa8mNn26WWJvLrkJj0eP5hK6QAbAtAQAQPpw5IjUr580Y4Y5HcHLS2rbVurfX8qXz9wne/ak17kdPVpq0sSS2ABSF+UWAJC2nT4thYdLn3wiRUebY889Jw0dKj30kOu+TZpIDRve+gplAGyPcgsASJsuXZI+/lgaMUKKijLHwsLMq4xVqXLr53l5mfsByJAotwCAtOXaNWnKFPOCCydOmGMPP2yW2rp1b3/iGIAMj3ILAEgbDEOaN89c1mvvXnOsUCFpyBCpRQvzCmMAcAeUWwCA9VaskHr2lDZtMh/nySP17Su9+abk7W1tNgDpCuUWAGCdbduk996TliwxH2fPLnXvbt5y5LA0GoD0iXILAEh9Bw6YR2ZnzzYfZ84svfWW9P77UlCQtdkApGuUWwBA6jl50pxDO2GCeeKYZM6nHTxYKlrU2mwAbIFyCwBIeRcuSBER0ocfShcvmmN16kjDhkkVK1qbDYCtUG4BACknJkaaNEkaNEg6dcocq1TJXLv2ySetzQbAlii3AADPi4+X5s4159AeOGCOFStmXlWsaVOW9QKQYii3AADPMQxp6VKpVy9p61ZzLCjIvCBD27ZSliyWxgNgf5RbAIBnbNpkLuv188/mYz8/c+3aLl3MJb4AIBVQbgEA92bfPvOqYl99ZT7OmlVq317q3du8GAMApCLKLQDg7hw/bp4oNnmyFBcnORxSq1bSwIHmZXMBwAKUWwCAe86flz74QProI+nyZXOsfn0pPFwqV87abAAyPMotACB5rl6VPv3ULLGnT5tjjz1mLutVs6a12QDgP5RbAMDtxcVJX3xhXi73yBFzrEQJ8wIMDRua0xEAII2g3AIAkmYY0g8/mCsg7Nhhjj3wgDmn9pVXpMz8EwIg7eF/mQAAia1fby7jtWaN+ThnTnPt2o4dJR8fS6MBwO1QbgEA1+3ebS7h9c035uNs2aROncyjt7lyWRoNAJKDcgsAkP7+27yK2NSp5qVzM2WSXn3VHMuf3+p0AJBslFsAyMjOnpWGD5fGjDFXQ5CkRo3MFRFKlrQ0GgDcDcotAGREV65IY8eaKx6cO2eO1ahhLutVtaql0QDgXlBuAcBu4uLME8GOH5eCg83S6uVlbouNlaZNM6cbREaaY2XLmiX36adZ1gtAuke5BQA7mT9f6tzZnEObIH9+afRocx5t797Sn3+a4wULSoMHSy++eL38AkA6R7kFALuYP19q2tRcn/ZGf/9tjicICJD69JHefttcDQEAbIRyCwB2EBdnHrG9udjeyOEwl/Tq2VPy90+9bACQijJZHQAA4AFr1rhORUiKYUh16lBsAdga5RYA7OD4cc/uBwDpFOUWAOwgONiz+wFAOsWcWwCwg4RlvW7F4TBXTahRI3XyAIBFOHILAOlZfLzUr5/00kvXx25eqzbh8ejRLPkFwPYotwCQXl26JDVrZq5VK0k9ekhffSU98IDrfvnzS19/LTVpkvoZASCVMS0BANKjyEipQQPpt9+kLFmkSZOk1q3NbY0b3/oKZQBgc5RbAEhvNm2SGjY0y2uePNKCBVL16te3e3lJYWGWxQMAKzEtAQDSky+/lGrWNItt6dLSxo2uxRYAMjjKLQCkB4YhDRggNW8uXb0q1a8vrVsnFS5sdTIASFOYlgAAad2VK+Z82i+/NB937y6NGME8WgBIAuUWANKyY8fM+bWbN0uZM0sTJkht21qdCgDSLMotAKRVW7aYKyIcOyYFBEjz5kmhoVanAoA0jTm3AJAWff21uYTXsWNSyZLSr79SbAEgGSi3AJCWGIY0ZIj0/PPmXNt69aT166WiRa1OBgDpAuUWANKKK1ekF1+U+vY1H3fpIn33neTvb2ksAEhPmHMLAGnBiRNSo0bm9IPMmaVPPpHeeMPqVACQ7lBuAcBqW7eaJ479/beUK5d54litWlanAoB0iWkJAGClhEvn/v239NBD5pFbii0A3DXKLQBYwTCkYcOkJk2ky5elOnWkDRuk4sWtTgYA6RrlFgBS29Wr0ssvS717m487dJAWLZJy5rQ0FgDYAXNuASA1/fOP1LixubyXl5c0dqz09ttWpwIA26DcAkBq2b5devZZ6cgR8yjtV19JTz1ldSoAsJU0PS0hNjZW77//vgoXLiwfHx8VKVJEgwYNUnx8vHMfwzA0YMAA5cuXTz4+PgoLC9POnTstTA0ASVi4UHr8cbPYFi9unjhGsQUAj0vT5XbEiBGaMGGCxo0bp927d2vkyJH64IMPNHbsWOc+I0eOVEREhMaNG6dNmzYpb968ql27ti5cuGBhcgD4j2FII0eaa9heuiQ9+aRZbB980OpkAGBLabrcrl+/Xg0bNlT9+vVVqFAhNW3aVHXq1NHmzZslmUdtR48erT59+qhJkyYqU6aMpk+frsuXL2v27NkWpweQ4UVHS6++KvXsaZbct9+WfvzRXMsWAJAi0nS5rV69un766Sft3btXkvT7779r7dq1evrppyVJBw8e1IkTJ1SnTh3nc7y9vRUaGqp169bd8nWjo6MVFRXlcgMAjzp1yjxKO326lCmTeeLYp59KWbJYnQwAbC1Nn1DWs2dPnT9/XiVKlJCXl5fi4uI0dOhQtWjRQpJ04sQJSVJQUJDL84KCgnT48OFbvu6wYcM0cODAlAsOIGPbscM8cezQIcnfX/ryS3MdWwBAikvTR27nzp2rWbNmafbs2frtt980ffp0ffjhh5o+fbrLfg6Hw+WxYRiJxm7Uq1cvnT9/3nk7evRoiuQHkAEtWiRVrWoW26JFzQszUGwBINWk6SO37777rt577z298MILkqSyZcvq8OHDGjZsmF555RXlzZtXknkENzg42Pm8kydPJjqaeyNvb295e3unbHgAGYthSB99JL3zjnk/LEz6+mspIMDqZACQoaTpI7eXL19WpkyuEb28vJxLgRUuXFh58+bVsmXLnNtjYmK0atUqPf7446maFUAGFhMjvfaa1L27WWxff11asoRiCwAWSNNHbp999lkNHTpUBQsWVOnSpbV161ZFRESoTZs2kszpCF26dFF4eLiKFy+u4sWLKzw8XL6+vmrZsqXF6QFkCP/+Kz33nLR6tXniWESE1KmTdJupUQCAlJOmy+3YsWPVt29ftWvXTidPnlS+fPn05ptvql+/fs59evTooStXrqhdu3Y6e/asqlSpoqVLl8rPz8/C5AAyhF27zBPHDhyQcuSQ5syR/u//rE4FABmawzAMw+oQVouKipK/v7/Onz+vHDlyWB0HQHrw44/SCy9IUVFSkSLSd99JpUpZnQoAbCu5fS1Nz7kFgDTHMKTRo6VnnjGLbc2a5hXHKLYAkCZQbgEguWJipDfflLp2leLjpTZtpGXLpDx5rE4GAPhPmp5zCwBpxunTUtOm0sqV5sliH35ollxOHAOANIVyCwB38uef5jSEv/6S7rtP+t//zMcAgDSHcgsAt7N0qdSsmXT+vFSokHniWJkyVqcCANwCc24BICmGIY0bJz39tFlsq1UzTxyj2AJAmka5BYCbXbsmtW8vdewoxcVJr7wi/fSTFBhodTIAwB0wLQEAbnTmjDkN4aefzJPFRoyQ3nmHE8cAIJ2g3AJAgr17zRPF9u2TsmeXZs+WGjSwOhUAwA2UWwCQzCO1TZtK585JBQuaJ46VK2d1KgCAm5hzCwDjx0t165rFtmpVaeNGii0ApFOUWwAZV2ysedJYu3bmiWMvvST9/LMUFGR1MgDAXWJaAoCM6dw5qXlzcx1bSQoPl957jxPHACCdo9wCyHj27zdPHNuzR/L1lWbNkho3tjoVAMADKLcAMpYVK6TnnpPOnpXy55cWLpQqVLA6FQDAQ5hzCyDjmDRJqlPHLLZVqkibNlFsAcBmOHILwH7i4qQ1a6Tjx6XgYHMFhJ49pY8/Nre3aCF9/rnk42NtTgCAx1FuAdjL/PlS587S339fH/P2lqKjzftDhki9e3PiGADYFOUWgH3Mn29eiMEwXMcTiu0770h9+qR+LgBAqmHOLQB7iIszj9jeXGxvNHeuuR8AwLYotwDsYc0a16kISTl61NwPAGBblFsA9nD8uGf3AwCkS5RbAPYQHOzZ/QAA6RInlAGwh1Onbr/d4TAv2lCjRurkAQBYgiO3ANK/r782165NcPMyXwmPR4+WvLxSLRYAIPVRbgGkb199Jb3wgrkKQqtW5uMHHnDdJ39+swA3aWJNRgBAqmFaAoD068svpZYtzWL78svSlCnmkdnGjV2vUFajBkdsASCDoNwCSJ/mzJFeeskstq+8Yl5ON6HAenlJYWGWxgMAWINpCQDSn//9T3rxRbPYvvqqa7EFAGRolFsA6cvs2eYR2/h4qU0b6bPPKLYAACfKLYD0Y9Ys86Sx+HjptdekyZOlTPzPGADgOv5VAJA+zJxpnjQWHy+9/ro0cSLFFgCQCP8yAEj7pk83TxozDOnNN6UJEyi2AIAk8a8DgLRt2jTzpDHDkN56S/r0U4otAOCW+BcCQNo1ZYp50phhSO3aUWwBAHfEvxIA0qbPPzdPGjMMqX17ady4xJfVBQDgJpRbAGnP5MnXi23HjtLYsRRbAECyUG4BpC2TJklvvGHe79xZ+vhjii0AINkotwDSjokTzdUQJKlLF+mjjyi2AAC3UG4BpA3jx5urIUhS165SRATFFgDgNsotAOt98om5GoIkde8ujRpFsQUA3BXKLQBrjRsndehg3n/3XemDDyi2AIC7RrkFYJ0xY8zVECSpRw9pxAiKLQDgnlBuAVjj44/N1RAk6b33pOHDKbYAgHtGuQWQ+j76yFwNQZJ695bCwym2AACPoNwCSF0REVK3bub9Pn2kIUMotgAAj6HcAkg9H35oroYgSX37SoMHU2wBAB5FuQWQOj74wFwNQZL695cGDaLYAgA8jnILIOWNGGGuhiBJAwaYNwAAUgDlFkDKGjbMXA1BkgYONI/aAgCQQjJbHQCAjYWHmyeNSeb82vfftzYPAMD2OHILIGUMGXK92A4dSrEFAKQKyi0Azxs0yFwNQTKP3vbubW0eAECGwbQEAJ41cOD1E8aGD5d69rQ0DgAgY6HcAvAMwzBL7aBB5uORI68v/QUAQCqh3AK4d4ZhroIweLD5+IMPpHfesTYTACBDotwCuDeGIfXrZ55AJkmjRl2/vC4AAKmMcgvg7hmGuQpCeLj5OCJC6trV2kwAgAyNcgvg7hiGuQrC8OHm49Gjpc6dLY0EAADlFoD7DEPq1cu8rK4kjRkjdexobSYAAES5BeAuwzCX9/rgA/Px2LFShw7WZgIA4D+UWwDJZxjm8l6jRpmPx42T2re3NhMAADeg3AJIHsMwl/eKiDAff/qp9Pbb1mYCAOAmlFsAd2YY5vJeo0ebjydMkN5809JIAAAkhXIL4PYMw1ze6+OPzccTJ0pvvGFtJgAAboFyC+DWDMNc3mvsWPPx5MnSa69ZmwkAgNug3AJImmGYy3t98onkcJjFtm1bq1MBAHBblFsAiRmGubzXp5+axfazz6Q2baxOBQDAHVFuAbiKjzeL7fjxZrGdMkVq3drqVAAAJAvlFsB18fFSu3bmSWMOhzR1qvTKK1anAgAg2Si3AEzx8dJbb5lzax0Oafp0qVUrq1MBAOAWyi0As9i++aY5tzZTJrPYvvSS1akAAHAb5RbI6OLjpddfN+fWZsokzZwptWxpdSoAAO4K5RbIyOLjzXVrp041i+2sWVKLFlanAgDgrlFugYwqLs4sttOmSV5e0hdfSM2bW50KAIB7QrkFMqK4OHPd2hkzzGI7e7bUrJnVqQAAuGeUWyCjiYuTXn3VnFvr5SX973/S889bnQoAAI+g3AIZSVycuW7tF19ImTNLc+ZIzz1ndSoAADyGcgtkFLGxZrGdPdsstnPnSk2aWJ0KAACPotwCdhQXJ61ZIx0/LgUHS1WrmpfQnTPHLLZffik1bmx1SgAAPI5yC9jN/PlS587S339fH/Pxka5ckbJkkb76SmrY0Lp8AACkoEx3+8SYmBjt2bNHsbGxnswD4F7Mny81bepabCWz2EpS9+4UWwCArbldbi9fvqy2bdvK19dXpUuX1pEjRyRJnTp10vDhwz0eEEAyxcWZR2wN49b7fPGFuR8AADbldrnt1auXfv/9d61cuVLZsmVzjj/11FOaO3euR8MBcMOaNYmP2N7s6FFzPwAAbMrtObfffPON5s6dq8cee0wOh8M5XqpUKf31118eDQfADcePe3Y/AADSIbeP3J46dUqBgYGJxi9duuRSdgGksuBgz+4HAEA65Ha5feSRR7Ro0SLn44RCO3nyZFWtWtVzyQC4p0YNKSjo1tsdDqlAAXM/AABsyu1pCcOGDVO9evW0a9cuxcbG6uOPP9bOnTu1fv16rVq1KiUyAkiOqKhbb0v4q8ro0eYldwEAsCm3j9w+/vjj+uWXX3T58mUVLVpUS5cuVVBQkNavX69KlSqlREYAdxIfL730kvTPP9L990v58rluz59f+vprrkgGALC9u7qIQ9myZTV9+nRPZwFwtwYMkH74QcqWTVqyRCpXzvUKZTVqcMQWAJAhJKvcRkVFKUeOHM77t5OwH4BU8u230uDB5v3Jk6UKFcz7YWGWRQIAwCrJKre5cuXS8ePHFRgYqJw5cya5KoJhGHI4HIpjgXgg9ezZI7VqZd7v1MmcmgAAQAaWrHL7888/K3fu3JKkFStWpGggAMl04YLUuLH5nzVrSh9+aHUiAAAsl6xyGxoa6rxfuHBhFShQINHRW8MwdPToUc+mA5A0w5Bat5Z275YeeED68kspSxarUwEAYDm3V0soXLiwTp06lWj8zJkzKly4sEdCAbiD4cOl+fOlrFmlefNuv74tAAAZiNvlNmFu7c0uXryobNmyeSQUgNtYskTq08e8P26cVKWKtXkAAEhDkr0UWLdu3SSZVyTr27evfH19ndvi4uL066+/qnz58h4PCOAGBw5ILVqY0xJef928AQAAp2SX261bt0oyj9z+8ccfypo1q3Nb1qxZ9fDDD+udd97xfEIApsuXzYswnD1rHq0dO9bqRAAApDnJLrcJqyS0bt1aY8eOlZ+fX4qFAnCThCO1v/8uBQaaVxvz9rY6FQAAaY5bc25jY2M1a9YsHT58OKXyAEjKxx9Ls2dLmTNLX31lXk4XAAAk4la5zZw5s0JCQrhQA5CaVq6UEqb8jBplrmkLAACS5PZqCe+//7569eqlM2fOpEQeADc6elRq1kyKizOvPtaxo9WJAABI05I95zbBmDFjtH//fuXLl08hISHKnj27y/bffvvNY+GADO3qVem556RTp6Ty5aWJE6UkluEDAADXuV1uGzVqlAIxALgwDKlDB2nTJil3bmnBAumG5fcAAEDS3C63/fv3T4kcAG40aZL0+edSpkzSnDlSoUJWJwIAIF1we86tJJ07d06fffaZy9zb3377TZGRkR4NB2RI69dfn1sbHi7Vrm1tHgAA0hG3j9xu375dTz31lPz9/XXo0CG9/vrryp07txYsWKDDhw9rxowZKZETyBhOnJCaNpWuXTP/s0cPqxMBAJCuuH3ktlu3bmrdurX27dunbNmyOcf/7//+T6tXr/ZoOEmKjIzUSy+9pICAAPn6+qp8+fLasmWLc7thGBowYIDy5csnHx8fhYWFaefOnR7PAaS4mBjp+eelY8ekUqWkKVM4gQwAADe5XW43bdqkN998M9H4Aw88oBMnTngkVIKzZ8+qWrVqypIli3788Uft2rVLo0aNUs6cOZ37jBw5UhERERo3bpw2bdqkvHnzqnbt2rpw4YJHswAprnt3ae1aKUcO8wQyrgIIAIDb3J6WkC1bNkVFRSUa37Nnj+6//36PhEowYsQIFShQQFOnTnWOFbrhxBrDMDR69Gj16dNHTZo0kSRNnz5dQUFBmj17dpIlHEiTZsyQxo0z73/xhfTgg9bmAQAgnXL7yG3Dhg01aNAgXbt2TZLkcDh05MgRvffee3ruuec8Gm7hwoWqXLmynn/+eQUGBqpChQqaPHmyc/vBgwd14sQJ1alTxznm7e2t0NBQrVu37pavGx0draioKJcbYJnffpMS/o9Y//7SM89YmwcAgHTM7XL74Ycf6tSpUwoMDNSVK1cUGhqqYsWKyc/PT0OHDvVouAMHDmj8+PEqXry4lixZorfeekudOnVynrSWMA0iKCjI5XlBQUG3nSIxbNgw+fv7O28FChTwaG4g2f79V2rc2LxgwzPPSP36WZ0IAIB0ze1pCTly5NDatWv1888/67ffflN8fLwqVqyop556yuPh4uPjVblyZYWHh0uSKlSooJ07d2r8+PF6+eWXnfs5bjrpxjCMRGM36tWrl7p16+Z8HBUVRcFF6ouNlV54QTpyRCpeXJo501zXFgAA3DW3y22CJ554Qk888YQnsyQSHBysUqVKuYyVLFlS8+bNkyTlzZtXknkENzg42LnPyZMnEx3NvZG3t7e8vb1TIDHght69pZ9+krJnN08gu+FESQAAcHfuqtz+9NNP+umnn3Ty5EnFx8e7bJsyZYpHgklStWrVtGfPHpexvXv3KiQkRJJUuHBh5c2bV8uWLVOFChUkSTExMVq1apVGjBjhsRyAx335pfTBB+b9qVOl0qWtzQMAgE24XW4HDhyoQYMGqXLlygoODr7tn//vVdeuXfX4448rPDxczZo108aNGzVp0iRNmjRJkjkdoUuXLgoPD1fx4sVVvHhxhYeHy9fXVy1btkyxXMA92bFDatPGvN+jh7m2LQAA8AiHYRiGO08IDg7WyJEj1apVq5TK5OL7779Xr169tG/fPhUuXFjdunXT66+/7txuGIYGDhyoiRMn6uzZs6pSpYo++eQTlSlTJtnvERUVJX9/f50/f145cuRIiY8BmM6dkx55RNq/X3rqKenHH6XMdz07CACADCO5fc3tchsQEKCNGzeqaNGi9xwyraDcIlXEx0vPPiv98IMUEiJt2SIFBFidCgCAdCG5fc3tU7Nfe+01zZ49+57CARnSwIFmsc2WzTyBjGILAIDHuf330KtXr2rSpElavny5ypUrpyxZsrhsj4iI8Fg4wDYWLpQGDTLvT5ok/XcCJAAA8Cy3y+327dtVvnx5SdKOHTtctqXkyWVAurVnj5QwR71jx+v3AQCAx7ldblesWJESOQB7unDBvAJZVJRUo4Y0apTViQAAsDUuhwSkFMOQXn1V2r1bypfPXNv2pmk8AADAs5J15LZJkyaaNm2acuTIoSZNmtx23/nz53skGJDujRghzZtnFtp586T/rqgHAABSTrLKrb+/v3M+rb+/f4oGAmxh6VKpTx/z/rhx0mOPWZsHAIAMwu11bu2IdW7hUQcPSpUqSWfPSq+9Jk2ebHUiAADSvRRb5xbAbVy+bJ5Advas9Oij5lFbAACQapK9WkKtWrVclvr6+eefUyQQkG4ZhvTGG9Lvv0uBgeY8W29vq1MBAJChJLvctm7dOgVjADYwZoz0xReSl5e5MkL+/FYnAgAgw0l2uX3llVdSMgeQvq1aJXXvbt4fNUoKDbU2DwAAGRRzboF7dfSo9PzzUlyc9OKLUqdOVicCACDDStaR21y5ciX70rpnzpy5p0BAunL1qvTcc9KpU1L58tKkSRKXoQYAwDLJKrejR4923j99+rSGDBmiunXrqmrVqpKk9evXa8mSJerbt2+KhATSJMOQOnSQNm2ScueW5s+XfH2tTgUAQIbm9jq3zz33nGrVqqUOHTq4jI8bN07Lly/XN99848l8qYJ1bnFXJk6U3npLypRJ+vFHqU4dqxMBAGBbKbbO7ZIlS1SvXr1E43Xr1tXy5cvdfTkgfVq/XurY0bw/dCjFFgCANMLtchsQEKAFCxYkGv/mm28UEBDgkVBAmnbihNS0qXTtmjnftmdPqxMBAID/JHspsAQDBw5U27ZttXLlSuec2w0bNmjx4sX67LPPPB4QSFNiYsyVEY4dk0qVkqZO5QQyAADSELfLbevWrVWyZEmNGTNG8+fPl2EYKlWqlH755RdVqVIlJTICaUf37tLatVKOHNKCBZKfn9WJAADADdw+ocyOOKEMyTJjhpRwMZNvv5UaNLA2DwAAGUiKnVAGZEi//Sa9+aZ5v18/ii0AAGkU5Ra4k3//lRo3Ni/YUL++1L+/1YkAAMAtUG6B24mNlV54QTpyRCpWTJo1y1zXFgAApEn8Kw3cTu/e0k8/SdmzmyeQ5cxpdSIAAHAbd11u9+/fryVLlujKlSuSJM5Lg+18+aX0wQfm/SlTpDJlrM0DAADuyO1ye/r0aT311FN68MEH9fTTT+v48eOSpNdee03du3f3eEDAEjt2SG3amPfffVdq1szaPAAAIFncLrddu3ZV5syZdeTIEfn6+jrHmzdvrsWLF3s0HGCJc+fME8guXZKefFIKD7c6EQAASCa3L+KwdOlSLVmyRPnz53cZL168uA4fPuyxYIAl4uOlF1+U9u+XQkKkOXOkzG7/1wQAAFjE7SO3ly5dcjlim+Dff/+Vt7e3R0IBlhk4UPrhBylbNmn+fClPHqsTAQAAN7hdbmvWrKkZM2Y4HzscDsXHx+uDDz5QrVq1PBoOSFULF0qDBpn3J06UKla0Ng8AAHCb239v/eCDDxQWFqbNmzcrJiZGPXr00M6dO3XmzBn98ssvKZERSHl79kitWpn3O3SQXn7Z2jwAAOCuuH3ktlSpUtq+fbseffRR1a5dW5cuXVKTJk20detWFS1aNCUyAinrwgXzBLKoKKl6dSkiwupEAADgLjkMFqhVVFSU/P39df78eeXIkcPqOEhNhiE9/7w0b56UL5+0ZYuUN6/VqQAAwE2S29fcnpawffv2JMcdDoeyZcumggULcmIZ0o8RI8ximyWL9PXXFFsAANI5t8tt+fLl5XA4JF2/KlnCY0nKkiWLmjdvrokTJypbtmweigmkgKVLpT59zPtjx0pVq1qbBwAA3DO359wuWLBAxYsX16RJk/T7779r27ZtmjRpkh566CHNnj1bn3/+uX7++We9//77KZEX8IyDB6UXXjDXtW3bVnrjDasTAQAAD3D7yO3QoUP18ccfq27dus6xcuXKKX/+/Orbt682btyo7Nmzq3v37vrwww89GhbwiMuXzRPIzp6VHnlEGjdOuuGvDwAAIP1y+8jtH3/8oZCQkETjISEh+uOPPySZUxeOHz9+7+kATzMM8yjt779L999vzrdl+gwAALbhdrktUaKEhg8frpiYGOfYtWvXNHz4cJUoUUKSFBkZqaCgIM+lBDxlzBjpiy8kLy/pq6+kAgWsTgQAADzI7WkJn3zyiRo0aKD8+fOrXLlycjgc2r59u+Li4vT9999Lkg4cOKB27dp5PCxwT1atkrp3N+9/+KEUGmptHgAA4HF3tc7txYsXNWvWLO3du1eGYahEiRJq2bKl/Pz8UiJjimOd2wzg77+lSpWkkyelli2lWbOYZwsAQDqS3L7GRRxEubWluDhpzRrp+HEpIEB6/31p0ybp4YeldeskX1+rEwIAADek2EUcEuzatUtHjhxxmXsrSQ0aNLjblwQ8Y/58qXNn82jtjbJnN7dRbAEAsC23y+2BAwfUuHFj/fHHH3I4HIku5BAXF+fZhIA75s+XmjY1V0W42aVL0rZtUpEiqR4LAACkDrdXS+jcubMKFy6sf/75R76+vtq5c6dWr16typUra+XKlSkQEUimuDjziO2tZto4HFKXLuZ+AADAltwut+vXr9egQYN0//33K1OmTMqUKZOqV6+uYcOGqVOnTimREUieNWsST0W4kWFIR4+a+wEAAFtyu9zGxcXpvvvukyTlyZNHx44dk2RexGHPnj2eTQe4I7kXDuECIwAA2Jbbc27LlCmj7du3q0iRIqpSpYpGjhyprFmzatKkSSrCXEZYKTjYs/sBAIB0x+1y+/777+vSpUuSpCFDhuiZZ55RjRo1FBAQoDlz5ng8IJBsNWqYxfVWR2YdDil/fnM/AABgS26X27p16zrvFylSRLt27dKZM2eUK1cu54oJgCUyZTLLa1LlNuG7OXq0eeldAABgS27PuW3Tpo0uXLjgMpY7d25dvnxZbdq08VgwwG2zZ5sXavDykoKCXLflzy99/bXUpIk12QAAQKpw+wplXl5eOn78uAIDA13G//33X+XNm1exsbEeDZgauEKZDRw7JpUpI509Kw0eLPXqdf0KZcHB5lQEjtgCAJBuefwKZVFRUTIMQ4Zh6MKFC8qWLZtzW1xcnH744YdEhRdIFYYhvfmmWWwrVZJ69jSLbFiY1ckAAEAqS3a5zZkzpxwOhxwOhx588MFE2x0OhwYOHOjRcECyzJghff+9lDWrNG2alCWL1YkAAIBFkl1uV6xYIcMw9MQTT2jevHnKnTu3c1vWrFkVEhKifPnypUhI4JYiI82rkknSwIHm1AQAAJBhJbvchoaGSpIOHjyoAgUKKFMmt89FAzzLMKTXX5fOn5cefVR65x2rEwEAAIu5vRRYSEiIzp07p40bN+rkyZOKj4932f7yyy97LBxwW1OnSj/+KHl7m9MRMrv9dQYAADbjdhv47rvv9OKLL+rSpUvy8/NzWdvW4XBQbpE6jhyRunY17w8eLJUsaW0eAACQJrg9t6B79+7OtW7PnTuns2fPOm9nzpxJiYyAK8OQXntNioqSHntM6tbN6kQAACCNcLvcRkZGqlOnTvL19U2JPMCdTZ4sLVsmZctmTkdg/VoAAPAft8tt3bp1tXnz5pTIAtzZoUNS9+7m/fBw6aGHLI0DAADSFrfn3NavX1/vvvuudu3apbJlyyrLTWuKNmjQwGPhABfx8VLbttLFi1L16lKnTlYnAgAAaYzbl9+93RJgDodDcXFx9xwqtXH53XRi/HipXTvJx0favl0qVszqRAAAIJV4/PK7CW5e+gtIFQcOSO++a94fPpxiCwAAknRPV2K4evWqp3IAtxYfL7VpI126JNWsKXXoYHUiAACQRrldbuPi4jR48GA98MADuu+++3TgwAFJUt++ffX55597PCCgTz6RVq2Ssmc3L9zA1fEAAMAtuN0Shg4dqmnTpmnkyJHKmjWrc7xs2bL67LPPPBoO0P79Us+e5v2RI6UiRazNAwAA0jS3y+2MGTM0adIkvfjii/K6YX3RcuXK6c8///RoOGRw8fHSq69KV65ITzwhvfWW1YkAAEAad1cXcSiWxMk88fHxunbtmkdCAZKkMWOktWul++6TPv+c6QgAAOCO3G4LpUuX1po1axKNf/XVV6pQoYJHQgHau1fq1cu8P2qUVKiQpXEAAED64PZSYP3791erVq0UGRmp+Ph4zZ8/X3v27NGMGTP0/fffp0RGZDRxcVLr1tLVq1Lt2tLrr1udCAAApBNuH7l99tlnNXfuXP3www9yOBzq16+fdu/ere+++061a9dOiYzIaD76SFq/XvLzkz77THI4rE4EAADSCbevUGZHXKEsDdm9W6pQQYqONott27ZWJwIAAGlAcvua20duN23apF9//TXR+K+//qrNmze7+3LAdbGx5nSE6GipXj3zwg0AAABucLvctm/fXkePHk00HhkZqfbt23skFDKoUaOkjRslf39p8mSmIwAAALe5XW537dqlihUrJhqvUKGCdu3a5ZFQyIB27pT69TPvf/yxlD+/tXkAAEC65Ha59fb21j///JNo/Pjx48qc2e3FFwDp2jXplVekmBjpmWekl1+2OhEAAEin3C63tWvXVq9evXT+/Hnn2Llz59S7d29WS8DdGTlS2rJFyplTmjiR6QgAAOCuuX2o9cMPP1RoaKhCQkKcF23Ytm2bgoKCNHPmTI8HhM1t3y4NHGjeHztWypfP2jwAACBdu6ulwC5duqQvvvhCv//+u3x8fFSuXDm1aNFCWbJkSYmMKY6lwCxy7Zr06KPStm1Sw4bSggUctQUAAElKbl9z68jttWvX9NBDD+n777/XG2+8cc8hkcGFh5vFNnduacIEii0AALhnbs25zZIli6Kjo+WghOBebdsmDRli3v/kEylvXkvjAAAAe3D7hLKOHTtqxIgRio2NTYk8yAhiYszVEWJjpeeek5o3tzoRAACwCbdPKPv111/1008/aenSpSpbtqyyZ8/usn3+/PkeCwebGjLEPJEsTx7p00+ZjgAAADzG7XKbM2dOPffccymRBRnBli3mXFvJLLaBgdbmAQAAtuJ2uZ06dWpK5EBGEB1tTkeIi5OaNZOef97qRAAAwGbcnnML3LWBA83L7AYGmieRAQAAeJjbR24LFy5829USDhw4cE+BYFObNkkjRpj3J0ww59sCAAB4mNvltkuXLi6Pr127pq1bt2rx4sV69913PZULdnL1qjkdIT5eatlSatzY6kQAAMCm3C63nTt3TnL8k08+0ebNm+85EGyof39p925zLdsxY6xOAwAAbMxjc27/7//+T/PmzfPUy8Eu1q+XPvzQvD9xohQQYG0eAABgax4rt19//bVy587tqZeDHVy5IrVubU5HaNVKatDA6kQAAMDm3J6WUKFCBZcTygzD0IkTJ3Tq1Cl9+umnHg2HdO7996W9e6XgYOnjj61OAwAAMgC3y22jRo1cHmfKlEn333+/wsLCVKJECU/lQnq3dq300Ufm/cmTpVy5rM0DAAAyBIdhGIbVIawWFRUlf39/nT9/Xjly5LA6Tvp3+bL08MPS/v3Sq69KU6ZYnQgAAKRzye1rbh+5laS4uDh988032r17txwOh0qVKqUGDRrIy8vrrgPDRnr3Nott/vxSRITVaQAAQAbidrndv3+/nn76aUVGRuqhhx6SYRjau3evChQooEWLFqlo0aIpkRPpxapV1+fXfvaZlDOnpXEAAEDG4vZqCZ06dVLRokV19OhR/fbbb9q6dauOHDmiwoULq1OnTimREenFxYtSmzbm/ddek+rWtTYPAADIcNw+crtq1Spt2LDBZdmvgIAADR8+XNWqVfNoOKQz770nHTggFSggjRpldRoAAJABuX3k1tvbWxcuXEg0fvHiRWXNmtUjoZAO/fyz9Mkn5v0pUyROzAMAABZwu9w+88wzeuONN/Trr7/KMAwZhqENGzborbfeUgMW6c+YLly4Ph3hrbekp56yNg8AAMiw3C63Y8aMUdGiRVW1alVly5ZN2bJlU7Vq1VSsWDF9zEL9GVOPHtLhw1KhQtLIkVanAQAAGZjbc25z5sypb7/9Vvv27dPu3bslSaVKlVKxYsU8Hg7pwLJl0oQJ5v0pUyQ/P2vzAACADO2u1rmVpOLFizsL7Y2X40UGEhUltW1r3u/QQapVy9o8AAAgw3N7WoIkff755ypTpoxzWkKZMmX02WefeTob0rru3aWjR6UiRaThw61OAwAA4H657du3rzp37qxnn31WX331lb766is9++yz6tq1q95///2UyOg0bNgwORwOdenSxTlmGIYGDBigfPnyycfHR2FhYdq5c2eK5oCkxYvNizRI0tSpUvbs1uYBAADQXUxLGD9+vCZPnqwWLVo4xxo0aKBy5cqpY8eOGjJkiEcDJti0aZMmTZqkcuXKuYyPHDlSERERmjZtmh588EENGTJEtWvX1p49e+TH/M+Uce6ceZEGSercWapZ09I4AAAACdw+chsXF6fKlSsnGq9UqZJiY2M9EupmFy9e1IsvvqjJkycrV65cznHDMDR69Gj16dNHTZo0UZkyZTR9+nRdvnxZs2fPTpEskNStmxQZKRUrJoWHW50GAADAye1y+9JLL2n8+PGJxidNmqQXX3zRI6Fu1r59e9WvX19P3bR+6sGDB3XixAnVqVPHOebt7a3Q0FCtW7fulq8XHR2tqKgolxuSadEicxqCwyFNmyb5+lqdCAAAwOmuVkv4/PPPtXTpUj322GOSpA0bNujo0aN6+eWX1a1bN+d+ERER9xxwzpw5+u2337Rp06ZE206cOCFJCgoKchkPCgrS4cOHb/maw4YN08CBA+85W4Zz9qz0+uvm/W7dJC63DAAA0hi3y+2OHTtUsWJFSdJff/0lSbr//vt1//33a8eOHc79PLE82NGjR9W5c2ctXbpU2bJlu+V+N7+XYRi3ff9evXq5lPCoqCgVKFDgnvPaXufO0vHj0kMPSYMHW50GAAAgEbfL7YoVK1IiR5K2bNmikydPqlKlSs6xuLg4rV69WuPGjdOePXskmUdwg4ODnfucPHky0dHcG3l7e8vb2zvlgtvRt99KM2dKmTKZ0xF8fKxOBAAAkMhdrXObWp588kn98ccf2rZtm/NWuXJlvfjii9q2bZuKFCmivHnzatmyZc7nxMTEaNWqVXr88cctTG4zp09Lb75p3n/nHem/6SgAAABpzV1foSw1+Pn5qUyZMi5j2bNnV0BAgHO8S5cuCg8PV/HixVW8eHGFh4fL19dXLVu2tCKyPXXsKP3zj1SypMRcZQAAkIal6XKbHD169NCVK1fUrl07nT17VlWqVNHSpUtZ49ZT5s+X/vc/yctLmj5dus3cZwAAAKs5DMMwrA5htaioKPn7++v8+fPKkSOH1XHSjlOnpNKlzf/s3VsaOtTqRAAAIINKbl9L03NuYbEOHcxiW6aM1K+f1WkAAADuiHKLpH35pXnz8jJXR2B1CQAAkA5QbpHYP/9I7dqZ93v3lm5Yig0AACAto9zClWFIb79tLv9Vrpz0/vtWJwIAAEg2yi1czZkjLVggZc5sro6QNavViQAAAJKNcovrTpwwTyKTpL59pfLlLY0DAADgLsotTIYhvfWWdOaMVKGC1KuX1YkAAADcRrmF6YsvpG+/lbJkMacjZMlidSIAAAC3UW4hHTtmXmJXkvr3l8qWtTYPAADAXaLcZnSGIb3xhnTunLnkV8+eVicCAAC4a5TbjG76dGnRInNVhOnTzVUSAAAA0inKbUb2999Sly7m/UGDpNKlLY0DAABwryi3GZVhSK+/Lp0/L1WpInXvbnUiAACAe0a5zaimTJEWL5a8vaVp05iOAAAAbIFymxEdOSJ17WreHzJEKlHC2jwAAAAeQrnNaAxDattWunBBqlr1eskFAACwAcptRjNpkrR8uZQtmzkdwcvL6kQAAAAeQ7nNSA4dkt55x7w/bJj04IOWxgEAAPA0ym1GER9vTke4eFGqUUPq1MnqRAAAAB5Huc0oJkyQfv5Z8vU1V0rIxK8eAADYDw0nIzhwQHr3XfP+iBFSsWLW5gEAAEghLG5qV3Fx0po1UmSkNHKkdPmyFBYmtWtndTIAAIAUQ7m1o/nzpc6dzcvrJnA4pGbNmI4AAABsjXJrN/PnS02bmuvZ3sgwpPbtpaAgqUkTa7IBAACkMA7j2UlcnHnE9uZie6MuXcz9AAAAbIhyaydr1rhORbiZYUhHj5r7AQAA2BDTEixS6L1FHn/NBrtWaUwy9usU8YMWLr7ksfc9NLy+x14LAADgXnDk1kZO3pfLo/sBAACkN5RbG9mYv7SO+eVR/C22x0s65pdHG/OXTs1YAAAAqYZyayPxmbw08Mk3zPs3b/vvPwc++YbiM3mlai4AAIDUQrm1mSUPPa63G/XWCb88LuMn/PLo7Ua9teShxy1KBgAAkPI4ocyGljz0uJYVr6JH/96pwItndfK+XNqYvzRHbAEAgO1Rbm0qPpOXNhQsZ3UMAACAVMW0BAAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANhGmi63w4YN0yOPPCI/Pz8FBgaqUaNG2rNnj8s+hmFowIABypcvn3x8fBQWFqadO3dalBgAAABWStPldtWqVWrfvr02bNigZcuWKTY2VnXq1NGlS5ec+4wcOVIREREaN26cNm3apLx586p27dq6cOGChckBAABghcxWB7idxYsXuzyeOnWqAgMDtWXLFtWsWVOGYWj06NHq06ePmjRpIkmaPn26goKCNHv2bL355ptWxAYAAIBF0vSR25udP39ekpQ7d25J0sGDB3XixAnVqVPHuY+3t7dCQ0O1bt26W75OdHS0oqKiXG4AAABI/9JNuTUMQ926dVP16tVVpkwZSdKJEyckSUFBQS77BgUFObclZdiwYfL393feChQokHLBAQAAkGrSTbnt0KGDtm/frv/973+JtjkcDpfHhmEkGrtRr169dP78eeft6NGjHs8LAACA1Jem59wm6NixoxYuXKjVq1crf/78zvG8efNKMo/gBgcHO8dPnjyZ6Gjujby9veXt7Z1ygQEAAGCJNH3k1jAMdejQQfPnz9fPP/+swoULu2wvXLiw8ubNq2XLljnHYmJitGrVKj3++OOpHRcAAAAWS9NHbtu3b6/Zs2fr22+/lZ+fn3Merb+/v3x8fORwONSlSxeFh4erePHiKl68uMLDw+Xr66uWLVtanB4AAACpLU2X2/Hjx0uSwsLCXManTp2q1q1bS5J69OihK1euqF27djp79qyqVKmipUuXys/PL5XTAgAAwGpputwahnHHfRwOhwYMGKABAwakfCAAAACkaWl6zi0AAADgDsotAAAAbINyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANvIbHUAZEyF3ltkdQSPOTS8vtURAADAfzhyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANug3AIAAMA2KLcAAACwDcotAAAAbINyCwAAANug3AIAAMA2MlsdAMiICr23yOoIHnNoeH2rIwAA4MSRWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuZrQ4AIOMp9N4iqyN4zKHh9a2OAAC4AUduAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBtcoQwAUpmdrtAmcZU2AGkLR24BAABgG5RbAAAA2AblFgAAALZBuQUAAIBtUG4BAABgG5RbAAAA2AblFgAAALZBuQUAAIBtUG4BAABgG5RbAAAA2AblFgAAALZBuQUAAIBtUG4BAABgG5RbAAAA2AblFgAAALZBuQUAAIBtUG4BAABgG5RbAAAA2EZmqwMAADKWQu8tsjqCRx0aXt/qCABuwJFbAAAA2AblFgAAALZBuQUAAIBtMOcWAIBUxJxjIGVx5BYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANhGZqsDAACAjKPQe4usjuBRh4bXtzoCbsKRWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBuUWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBtcxAEAACCVcBGLlMeRWwAAANgG5RYAAAC2QbkFAACAbVBuAQAAYBu2KbeffvqpChcurGzZsqlSpUpas2aN1ZEAAACQymxRbufOnasuXbqoT58+2rp1q2rUqKH/+7//05EjR6yOBgAAgFRki3IbERGhtm3b6rXXXlPJkiU1evRoFShQQOPHj7c6GgAAAFJRul/nNiYmRlu2bNF7773nMl6nTh2tW7cuyedER0crOjra+fj8+fOSpKioqJQLepP46Mup9l4p7W5+bnx+Pr9dZPTPL7n/M+Dz8/nthM+fet0p4b0Mw7j9jkY6FxkZaUgyfvnlF5fxoUOHGg8++GCSz+nfv78hiRs3bty4cePGjVs6ux09evS23TDdH7lN4HA4XB4bhpFoLEGvXr3UrVs35+P4+HidOXNGAQEBt3wO0p+oqCgVKFBAR48eVY4cOayOg1TG7z9j4/efsfH7tyfDMHThwgXly5fvtvul+3KbJ08eeXl56cSJEy7jJ0+eVFBQUJLP8fb2lre3t8tYzpw5UyoiLJYjRw7+xy0D4/efsfH7z9j4/duPv7//HfdJ9yeUZc2aVZUqVdKyZctcxpctW6bHH3/colQAAACwQro/citJ3bp1U6tWrVS5cmVVrVpVkyZN0pEjR/TWW29ZHQ0AAACpyBbltnnz5jp9+rQGDRqk48ePq0yZMvrhhx8UEhJidTRYyNvbW/379080BQUZA7//jI3ff8bG7z9jcxjGndZTAAAAANKHdD/nFgAAAEhAuQUAAIBtUG4BAABgG5RbAAAA2AblFrYybNgwPfLII/Lz81NgYKAaNWqkPXv2WB0LFhk2bJgcDoe6dOlidRSkosjISL300ksKCAiQr6+vypcvry1btlgdC6kgNjZW77//vgoXLiwfHx8VKVJEgwYNUnx8vNXRkIpssRQYkGDVqlVq3769HnnkEcXGxqpPnz6qU6eOdu3apezZs1sdD6lo06ZNmjRpksqVK2d1FKSis2fPqlq1aqpVq5Z+/PFHBQYG6q+//uIqlBnEiBEjNGHCBE2fPl2lS5fW5s2b9eqrr8rf31+dO3e2Oh5SCUuBwdZOnTqlwMBArVq1SjVr1rQ6DlLJxYsXVbFiRX366acaMmSIypcvr9GjR1sdC6ngvffe0y+//KI1a9ZYHQUWeOaZZxQUFKTPP//cOfbcc8/J19dXM2fOtDAZUhPTEmBr58+flyTlzp3b4iRITe3bt1f9+vX11FNPWR0FqWzhwoWqXLmynn/+eQUGBqpChQqaPHmy1bGQSqpXr66ffvpJe/fulST9/vvvWrt2rZ5++mmLkyE1MS0BtmUYhrp166bq1aurTJkyVsdBKpkzZ45+++03bdq0yeoosMCBAwc0fvx4devWTb1799bGjRvVqVMneXt76+WXX7Y6HlJYz549df78eZUoUUJeXl6Ki4vT0KFD1aJFC6ujIRVRbmFbHTp00Pbt27V27VqroyCVHD16VJ07d9bSpUuVLVs2q+PAAvHx8apcubLCw8MlSRUqVNDOnTs1fvx4ym0GMHfuXM2aNUuzZ89W6dKltW3bNnXp0kX58uXTK6+8YnU8pBLKLWypY8eOWrhwoVavXq38+fNbHQepZMuWLTp58qQqVarkHIuLi9Pq1as1btw4RUdHy8vLy8KESGnBwcEqVaqUy1jJkiU1b948ixIhNb377rt677339MILL0iSypYtq8OHD2vYsGGU2wyEcgtbMQxDHTt21IIFC7Ry5UoVLlzY6khIRU8++aT++OMPl7FXX31VJUqUUM+ePSm2GUC1atUSLf+3d+9ehYSEWJQIqeny5cvKlMn1dCIvLy+WAstgKLewlfbt22v27Nn69ttv5efnpxMnTkiS/P395ePjY3E6pDQ/P79E86uzZ8+ugIAA5l1nEF27dtXjjz+u8PBwNWvWTBs3btSkSZM0adIkq6MhFTz77LMaOnSoChYsqNKlS2vr1q2KiIhQmzZtrI6GVMRSYLAVh8OR5PjUqVPVunXr1A2DNCEsLIylwDKY77//Xr169dK+fftUuHBhdevWTa+//rrVsZAKLly4oL59+2rBggU6efKk8uXLpxYtWqhfv37KmjWr1fGQSii3AAAAsA3WuQUAAIBtUG4BAABgG5RbAAAA2AblFgAAALZBuQUAAIBtUG4BAABgG5RbAAAA2AblFgAAALZBuQUAD5s2bZpy5sx5T6/RunVrNWrUyCN5UtKAAQNUvnz5W24PCwtTly5dUi3PzTzxuwCQvlBuASAdaN26tQYMGGB1jDsaMGCAy6Wu58+fr8GDB6fKexcqVCjRZZabN2+uvXv3psr7A0gbMlsdAADuRUxMDNeMT8Ny586d4u9xu++Aj4+PfHx8UjwDgLSDI7cA0pWwsDB16NBB3bp1U548eVS7dm1JUkREhMqWLavs2bOrQIECateunS5evOh8XsKfp5csWaKSJUvqvvvuU7169XT8+HHnPrGxserUqZNy5sypgIAA9ezZU6+88sodpwdMmzZNBQsWlK+vrxo3bqzTp08n2ue7775TpUqVlC1bNhUpUkQDBw5UbGzsXf8cZs2apcqVK8vPz0958+ZVy5YtdfLkSef2QYMGKV++fC5ZGjRooJo1ayo+Pl6StG7dOtWsWVM+Pj4qUKCAOnXqpEuXLt32fYcPH66goCD5+fmpbdu2unr16m33v3laQqFChRQeHq42bdrIz89PBQsW1KRJk1yeExkZqebNmytXrlwKCAhQw4YNdejQIef2hCkbw4YNU758+fTggw8qLCxMhw8fVteuXeVwOORwOCQlPS3B078LAGkL5RZAujN9+nRlzpxZv/zyiyZOnChJypQpk8aMGaMdO3Zo+vTp+vnnn9WjRw+X512+fFkffvihZs6cqdWrV+vIkSN65513nNtHjBihL774QlOnTtUvv/yiqKgoffPNN7fN8uuvv6pNmzZq166dtm3bplq1amnIkCEu+yxZskQvvfSSOnXqpF27dmnixImaNm2ahg4detc/g5iYGA0ePFi///67vvnmGx08eNBlOkCfPn1UqFAhvfbaa5KkCRMmaPXq1Zo5c6YyZcqkP/74Q3Xr1lWTJk20fft2zZ07V2vXrlWHDh1u+Z5ffvml+vfvr6FDh2rz5s0KDg7Wp59+6nb2UaNGqXLlytq6davatWunt99+W3/++ack83dUq1Yt3XfffVq9erXWrl3r/D8iMTExztf46aeftHv3bi1btkzff/+95s+fr/z582vQoEE6fvy4y/9puVFK/C4ApDEGAKQjoaGhRvny5e+435dffmkEBAQ4H0+dOtWQZOzfv9859sknnxhBQUHOx0FBQcYHH3zgfBwbG2sULFjQaNiw4S3fp0WLFka9evVcxpo3b274+/s7H9eoUcMIDw932WfmzJlGcHDwLV/3lVdeue373mzjxo2GJOPChQvOsb/++svw8/Mzevbsafj6+hqzZs1ybmvVqpXxxhtvuLzGmjVrjEyZMhlXrlxJ8j2qVq1qvPXWWy5jVapUMR5++OFb5goNDTU6d+7sfBwSEmK89NJLzsfx8fFGYGCgMX78eMMwDOPzzz83HnroISM+Pt65T3R0tOHj42MsWbLEMAzzZxMUFGRER0e7vFdISIjx0UcfuYxNnTr1nn8XANIX5twCSHcqV66caGzFihUKDw/Xrl27FBUVpdjYWF29elWXLl1S9uzZJUm+vr4qWrSo8znBwcHOP+WfP39e//zzjx599FHndi8vL1WqVMn5Z/yk7N69W40bN3YZq1q1qhYvXux8vGXLFm3atMnl6GBcXJyuXr2qy5cvy9fX182fgLR161YNGDBA27Zt05kzZ5wZjxw5olKlSkmSihQpog8//FBvvvmmmjdvrhdffNEl0/79+/XFF184xwzDUHx8vA4ePKiSJUsm+VnfeuutRJ91xYoVbmUvV66c877D4VDevHmdv4eEXH5+fi7PuXr1qv766y/n47Jly97VXOuU+F0ASFsotwDSnYSymuDw4cN6+umn9dZbb2nw4MHKnTu31q5dq7Zt2+ratWvO/bJkyeLyPIfDIcMwEo3d6ObtN7vTdkmKj4/XwIED1aRJk0TbsmXLdsfn3+zSpUuqU6eO6tSpo1mzZun+++/XkSNHVLduXZc/3UvS6tWr5eXlpUOHDik2NlaZM2d2ZnrzzTfVqVOnRK9fsGBBtzO5I6nfQ0I5j4+PV6VKlVxKd4L777/fef/m70Byefp3ASDtodwCSPc2b96s2NhYjRo1SpkymacSfPnll269hr+/v4KCgrRx40bVqFFDknlEb+vWrbddx7VUqVLasGGDy9jNjytWrKg9e/aoWLFibmW6lT///FP//vuvhg8frgIFCkgyfwY3mzt3rubPn6+VK1eqefPmGjx4sAYOHOjMtHPnTrcylSxZUhs2bNDLL7/sHLv5s96rihUrau7cuQoMDFSOHDncem7WrFkVFxd3x9f35O8CQNrDCWUA0r2iRYsqNjZWY8eO1YEDBzRz5kxNmDDB7dfp2LGjhg0bpm+//VZ79uxR586ddfbs2URHc2/UqVMnLV68WCNHjtTevXs1btw4lykJktSvXz/NmDFDAwYM0M6dO7V7927NnTtX77//vtsZJfPIatasWZ2fd+HChYnWkv3777/19ttva8SIEapevbqmTZumYcOGOctoz549tX79erVv317btm3Tvn37tHDhQnXs2PGW79u5c2dNmTJFU6ZM0d69e9W/f3/t3Lnzrj7Drbz44ovKkyePGjZsqDVr1ujgwYNatWqVOnfurL///vu2zy1UqJBWr16tyMhI/fvvv0nu4+nfBYC0h3ILIN0rX768IiIiNGLECJUpU0ZffPGFhg0b5vbr9OzZUy1atNDLL7+sqlWr6r777lPdunVv++fqxx57TJ999pnGjh2r8uXLa+nSpYmKUt26dfX9999r2bJleuSRR/TYY48pIiJCISEhbmeUzD/PT5s2TV999ZVKlSql4cOH68MPP3RuNwxDrVu31qOPPupc/aB27drq0KGDXnrpJV28eFHlypXTqlWrtG/fPtWoUUMVKlRQ3759FRwcfMv3bd68ufr166eePXuqUqVKOnz4sN5+++27+gy34uvrq9WrV6tgwYJq0qSJSpYsqTZt2ujKlSt3PJI7aNAgHTp0SEWLFnWZwnAjT/8uAKQ9DiM5E8YAIAOKj49XyZIl1axZs1S7yhYA4N4w5xYA/nP48GEtXbpUoaGhio6O1rhx43Tw4EG1bNnS6mgAgGRiWgIA/CdTpkyaNm2aHnnkEVWrVk1//PGHli9fnuSyWACAtIlpCQAAALANjtwCAADANii3AAAAsA3KLQAAAGyDcgsAAADboNwCAADANii3AAAAsA3KLQAAAGyDcgsAAADb+H8Dqz+sNaNcKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.style.use('default')\n",
    "# taille du schema\n",
    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
    "\n",
    "vectVarianceExpliquee = pca.explained_variance_ratio_*100 # renvoie les variences expliquées pour chacune des dimensions\n",
    "plt.bar(np.arange(len(vectVarianceExpliquee))+1, vectVarianceExpliquee)\n",
    "plt.plot(np.arange(len(vectVarianceExpliquee))+1, vectVarianceExpliquee.cumsum(),c=\"red\",marker='o')\n",
    "plt.xlabel(\"rang de l'axe d'inertie\")\n",
    "plt.ylabel(\"pourcentage d'inertie\")\n",
    "plt.title(\"Eboulis des valeurs propres\",fontsize=14,fontweight='normal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "biological-morrison",
   "metadata": {},
   "outputs": [],
   "source": [
    "composantesPrincipales = pca.components_ # triés par variance expliquée"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compressed-player",
   "metadata": {},
   "source": [
    "### Choix du plan factoriel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "peaceful-china",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selection du plan Factoriel\n",
    "#dimension 1\n",
    "d1=0\n",
    "#dimension 2\n",
    "d2=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "positive-commission",
   "metadata": {},
   "source": [
    "### Cercle des correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "geological-shock",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "plt.style.use('default')\n",
    "# taille du schema\n",
    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# configuration de la bordure\n",
    "plt.xlim(-1,1)\n",
    "plt.ylim(-1,1)\n",
    "\n",
    "# affichage des lignes horizontales et verticales\n",
    "plt.plot([-1, 1], [0, 0], color='grey', ls='--')\n",
    "plt.plot([0, 0], [-1, 1], color='grey', ls='--')\n",
    "\n",
    "# libelles des axes\n",
    "plt.xlabel('Composante Principale {} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))\n",
    "plt.ylabel('Composante Principale {} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))\n",
    "plt.title('Cercle des corrélations',color=\"black\",fontsize=14,fontweight='normal')\n",
    "# afficher le cercle\n",
    "cercle = plt.Circle((0,0),radius=1, color='b', fill=False)\n",
    "ax.set_aspect(1)\n",
    "ax.add_patch(cercle)\n",
    "\n",
    "\n",
    "#placement des étiquettes des p variables + fleches (partant du centre vers les points projetés)\n",
    "for i in range(len(variables)):\n",
    "    plt.annotate(variables[i],xy =(composantesPrincipales[d1][i],composantesPrincipales[d2][i]),xytext =(composantesPrincipales[d1][i]+0.01,composantesPrincipales[d2][i]),arrowprops=dict(arrowstyle=\"-\",\n",
    "                            connectionstyle=\"arc3\")) \n",
    "    plt.arrow(0,0,composantesPrincipales[d1][i],composantesPrincipales[d2][i].astype(np.float),length_includes_head=True,head_width=0.02, head_length=0.03,color=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-summary",
   "metadata": {},
   "source": [
    "### Projection des individus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "imported-prize",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dim Projeté:(136607, 9)- dim Origine:(136607, 9)\n"
     ]
    }
   ],
   "source": [
    "# Projection des individus sur les composantes principales\n",
    "XProjete = pca.transform(XCentreReduit)\n",
    "print(f\"dim Projeté:{XProjete.shape}- dim Origine:{XCentreReduit.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "israeli-brief",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#387E47'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preparation de la palette de couleur qui reprend les codes couleurs hexa du logo nutriscore\n",
    "custom_palette = {\n",
    "'d': '#E08530',#Orange\n",
    " 'c': '#F6CB45',#Jaune\n",
    " 'e': '#D44B29',#Rouge\n",
    " 'a': '#387E47',#Vert foncé\n",
    " 'b': '#91B849' #Vert   \n",
    "}\n",
    "custom_palette['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "amateur-literature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['d', 'c', 'e', 'a', 'b']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(custom_palette.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "designing-diversity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fafa689fa90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisation\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# taille du graphique\n",
    "plt.rcParams[\"figure.figsize\"] = (8,8)\n",
    "plt.rcParams['font.size'] = '6.5'\n",
    "\n",
    "# configuration de la bordure\n",
    "plt.xlim(-6,6)\n",
    "plt.ylim(-6,6)\n",
    "\n",
    "# affichage des lignes horizontales et verticales\n",
    "plt.plot([-6, 6], [0, 0], color='grey', ls='--')\n",
    "plt.plot([0, 0], [-6, 6], color='grey', ls='--')\n",
    "\n",
    "plt.xlabel('Composante Principale {} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))\n",
    "plt.ylabel('Composante Principale {} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))\n",
    "plt.title('Projection des individus',fontsize=14,fontweight='normal')\n",
    "\n",
    "\n",
    "\n",
    "# representer le nuage projeté sur le 1er plan factoriel (composé des 2 premieres composantes principales 0 et 1)\n",
    "plt.scatter(XProjete[:,d1],XProjete[:,d2],marker=\".\",color=\"black\")\n",
    "\n",
    "for i in range(len(nomsIndividus)):   # nombre d'individus\n",
    "    \n",
    "    p=np.array([XProjete[i,d1],XProjete[i,d2]])\n",
    "\n",
    "    color=nutritionGrade.values[i]\n",
    "\n",
    "    # on utilise la distance euclidienne pour n'afficher les labels que pour les individus loin du centre\n",
    "    distance= np.linalg.norm(p-np.array([0,0]))\n",
    "\n",
    "    if distance>4.7:\n",
    "        label=nomsIndividus.values[i]\n",
    "        #print(f\"label[{i}]:{label} | test:{not(pd.isnull(label))} | color:{color}\") # debug\n",
    "        if not pd.isnull(label):\n",
    "            plt.text(XProjete[i,d1]+0.1, XProjete[i,d2]+0.1, label ,rotation=45,color=custom_palette[color])\n",
    "    #plt.annotate(nomsIndividus.values[i],xy =(XProjete[i,0],XProjete[i,1]),xytext =(XProjete[i,0],XProjete[i,1]))\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "a_patch = mpatches.Patch(color=custom_palette['a'], label='a')\n",
    "b_patch = mpatches.Patch(color=custom_palette['b'], label='b')\n",
    "c_patch = mpatches.Patch(color=custom_palette['c'], label='c')\n",
    "d_patch = mpatches.Patch(color=custom_palette['d'], label='d')\n",
    "e_patch = mpatches.Patch(color=custom_palette['e'], label='e')\n",
    "\n",
    "\n",
    "plt.legend(title=\"nutrition grade\",handles=[a_patch, b_patch, c_patch, d_patch, e_patch])\n",
    "\n",
    "#import os\n",
    "#os.system('say \"jai fini de dessiner\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "angry-trade",
   "metadata": {},
   "source": [
    "Observations:\n",
    "* sur l'axe du nutrition score, la note de nutrition ne semble pas correlee\n",
    "* la composante energy / nutrition_score explique toute seule une composante\n",
    "* sugars et carbohydrates (glucides) expliquent fortement l'axe vertical positif\n",
    "* le gras explique modéremment l'axe vertical negatif en opposition avec les glucides/sucres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "authentic-helping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.201021194458008"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.time() - now"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "divine-olympus",
   "metadata": {},
   "source": [
    "## <font color=#FB5607>Conclusion</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-belgium",
   "metadata": {},
   "source": [
    "On peut distinguer 3 axes de pour definir la qualité nutritionnelle d'un produit alimentaire:\n",
    "* les aliments riches en protéines\n",
    "* les aliments riches en sucre lent/rapide\n",
    "* les aliments energetiques"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}