2022-06-03_transfert-des-voix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tl;dr : *On essaie ici de deviner le transfert des voix entre les choix effectués entre deux scrutins d'un vote (ici les élections présidentielles 2022 en France).*\n",
    "\n",
    "Afin d'analyser les résultats des élections, par exemple les dernières élections présidentielles de 2022 en France, et de mieux comprendre la dynamique des choix de vote entre les différents groupes de population, il peut être utile d'utiliser des outils d'[apprentissage automatique](https://fr.wikipedia.org/wiki/Apprentissage_automatique) pour inférer des structures à première vue cachées dans la masse des données. En particulier, inspiré par cet [article du Monde](https://www.lemonde.fr/les-decodeurs/article/2022/05/04/election-presidentielle-2022-quels-reports-de-voix-entre-les-deux-tours_6124672_4355770.html), on peut se poser la question de savoir *si on peut extraire depuis les données brutes des élections une estimation des report de voix entre les choix de vote au premier tour et ceux qui sont effectués au deuxième tour*.\n",
    "\n",
    "Pour cela nous allons utiliser les outils mathématiques de l'apprentissage automatique et en particulier l'utilisation des probabilités. Cette théorie va nous permettre d'exprimer le fait que les résultats tels qu'ils sont obtenus peuvent présenter une variabilité mais que celle-ci réelle résulte de préférences de chaque individu dans la population votante. En particulier, on peut considérer que chaque individu va avoir une préférence pour chacun des candidats au premier et second tour et que les votes effectués vont correspondre à la réalisation de ces préférences (reppelons aussi qu'on ne peut voter que pour un candidat par scrutin). \n",
    "\n",
    "Bien sûr on a accès ni au vote de chaque individu et encore moins à ses préférences. Mais comme mais comme chaque bureau de vote présente des variabilités liées au contexte local et qui fait que cette population locale a une préférence pour certains choix plutôt que d'autres, on peut considérer chaque bureau de vote comme une population individuelle pour lequel nous allons essayer de prédire les résultats du vote au deuxième tour. Cette prediction, si elle est efficace, peut donner une idée du transfert de vote entre les deux tours qui a lieu en fonction des préférences des votes de chaque individu.\n",
    "\n",
    "<!-- TEASER_END -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Celà se modélise mathématiquement par un [processus de Bernoulli](https://fr.wikipedia.org/wiki/Processus_de_Bernoulli) relatif à ces préférences.\n",
    "\n",
    "Grâce à l'ouverture des données (notamment pour la recherche), on peut obtenir librement les résultats des [premier](https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-10-et-24-avril-2022-resultats-definitifs-du-1er-tour/) et [second](https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-10-et-24-avril-2022-resultats-definitifs-du-2nd-tour/) tours. Il est intéressant de noter que ses résultats sont indiqués pour chacun des bureaux de vote. \n",
    "\n",
    "Pour cela nous allons faire deux hypothèses: \n",
    "\n",
    "Tout d'abord nous allons estimer que pour chaque individu, il existe une préférence pour les candidats du premier tour ainsi que pour les candidats du deuxieme tour. Si on note les différentes alternatives au premier tour comme $i \\in \\{ \\text{'nul'}, \\text{'abstention'}, \\ldots, \\text{'Macron'}, \\text{'Poutou'} \\}$ et au deuxième tour  $j \\in \\{ \\text{'nul'}, \\text{'abstention'}, \\ldots, \\text{'Macron'}, \\text{'Le Pen'} \\}$. Alors, on peut écrire pour chaque individu $k$ les probabilités de vote $p^k_i$ et $q^k_j$ (chacune de ces valeurs étant comprises entre $0$ et $1$ représentant un biais de probabilité pour chacune des alternatives). On pourra vérifier que $\\forall k$ (pour tout individu),  $\\sum_i p^k_i = 1$ et $\\sum_j q^k_j = 1$.\n",
    "\n",
    "Avec une telle modélisation on peut prévoir les résultats du vote car les préférences de chaque individu pour tel ou tel choix doit se révéler au niveau de la population totale suivant le théorème central limite qui indique que la moyenne observée tend vers ces probabilités avec une précision (inverse de la variance) qui augmente linéairement avec le nombre d'observations. En particulier les résultats des votes au premier et second tour seront donnés par respectivement $\\frac 1 K \\cdot \\sum_k p^k_i$ et $\\frac 1 K \\cdot \\sum_k q^k_j$ avec $K$ la taille de la population (nous vérifierons ce point plus bas)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une deuxième hypothèse que nous allons faire et que si on considère la transition entre les préférences qui sont faites au premier tour et celles qui sont faites au second tour, les préférences peuvent évoluer avec chaque invidu mais que la transition est homogène au sein de la population (par exemple \"une personne qui choisit de s'abstenir s'abstiendra\"). C'est certe une hypothèse grossière mais assez générale pour refléter les tendances au niveau de la population globale. Cette hypothèse est basée sur la modélisation d'une séquence d'événements aléatoires basé sur un [processus de Markov](https://fr.wikipedia.org/wiki/Cha%C3%AEne_de_Markov). En particulier nous allons formaliser cette hypothèse en faisant l'hypothèse de l'existence d'une matrice de transition $M$ qui permet de prédire la préférence $\\hat{q}^k_j$ d'un individu au second tour à partir de ses préférences au premier tour. En particulier, cette hypothèse peut être formulée comme un simple produit matriciel :\n",
    "\n",
    "$$\n",
    "\\hat{q}^k_j = \\sum_i M_{i, j} \\cdot p^k_i \n",
    "$$\n",
    "\n",
    "En termes plus simples, cette formule exprime que la préférence d'un individu au second tour et le mélange de ses préférences individuelles au premier tour avec des poids indiquant les affinités entre les différentes alternatives aux deux tours. Il est important de noter qu'il existe une contrainte  pour chaque colonne de cette matrice de transition de sorte que la somme des différents éléments sur les différentes lignes de la matrice pour chaque colonne est égal à $1$ : $\\forall j$, $ \\sum_i M_{i, j} = 1$. Cette propriété découle des contraintes de représentation des préférences au premier et au deuxième tour que nous avons évoquées plus haut ($\\sum_i p^k_i = 1$ et $\\sum_j q^k_j = 1$).\n",
    "\n",
    "D'une certaine façon, cette matrice de transition décrit exactement les affinités de chacun des individus pour les différents choix de vote au niveau de la population globale. C'est donc un indicateur des report de vote qui vont être effectivement effectués entre les deux tours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Collecte des données\n",
    "\n",
    "La première partie de ce travail consiste à collecter les données et elle est représenté d'une façon utile. On va utiliser les données disponibles sur https://www.data.gouv.fr en se concentrant sur les résultats définitifs par bureau de vote. \n",
    "Commençons notre procédure avec le traitement des données du premier tour. Une fois que nous aurons décortiqué cette méthode, nous passerons au deuxième tour.\n",
    "\n",
    "### Données du premier tour\n",
    "\n",
    "Pour cela nous allons utiliser deux premières librairie python : `numpy` pour le traitement de données numériques puis `pandas` pour la représentation deces données sous forme de tableau tableaux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:41:18.737070Z",
     "iopub.status.busy": "2022-06-03T12:41:18.736428Z",
     "iopub.status.idle": "2022-06-03T12:41:19.007512Z",
     "shell.execute_reply": "2022-06-03T12:41:19.007838Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut directement enregistrer à partir de l'adresse des données puis extraire ses données numériques depuis le tableau Excel grâce a la [fonction suivante](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:41:19.011552Z",
     "iopub.status.busy": "2022-06-03T12:41:19.011070Z",
     "iopub.status.idle": "2022-06-03T12:41:19.012745Z",
     "shell.execute_reply": "2022-06-03T12:41:19.013223Z"
    }
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:41:19.017144Z",
     "iopub.status.busy": "2022-06-03T12:41:19.016592Z",
     "iopub.status.idle": "2022-06-03T12:42:22.689863Z",
     "shell.execute_reply": "2022-06-03T12:42:22.690256Z"
    }
   },
   "outputs": [],
   "source": [
    "fname = '/tmp/T1.xlsx'\n",
    "\n",
    "if not os.path.isfile(fname):\n",
    "    url = 'https://static.data.gouv.fr/resources/election-presidentielle-des-10-et-24-avril-2022-resultats-definitifs-du-1er-tour/20220414-152612/resultats-par-niveau-burvot-t1-france-entiere.xlsx' # XLSX\n",
    "    import urllib.request\n",
    "    urllib.request.urlretrieve(url, fname)\n",
    "\n",
    "T1 = pd.read_excel(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut avoir une première idée de ces données et du nombre total de bureaux de vote :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.698410Z",
     "iopub.status.busy": "2022-06-03T12:42:22.697910Z",
     "iopub.status.idle": "2022-06-03T12:42:22.730508Z",
     "shell.execute_reply": "2022-06-03T12:42:22.730865Z"
    }
   },
   "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>Code du département</th>\n",
       "      <th>Libellé du département</th>\n",
       "      <th>Code de la circonscription</th>\n",
       "      <th>Libellé de la circonscription</th>\n",
       "      <th>Code de la commune</th>\n",
       "      <th>Libellé de la commune</th>\n",
       "      <th>Code du b.vote</th>\n",
       "      <th>Inscrits</th>\n",
       "      <th>Abstentions</th>\n",
       "      <th>% Abs/Ins</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 95</th>\n",
       "      <th>Unnamed: 96</th>\n",
       "      <th>Unnamed: 97</th>\n",
       "      <th>Unnamed: 98</th>\n",
       "      <th>Unnamed: 99</th>\n",
       "      <th>Unnamed: 100</th>\n",
       "      <th>Unnamed: 101</th>\n",
       "      <th>Unnamed: 102</th>\n",
       "      <th>Unnamed: 103</th>\n",
       "      <th>Unnamed: 104</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69677</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>6</td>\n",
       "      <td>6ème circonscription</td>\n",
       "      <td>229</td>\n",
       "      <td>Zurich</td>\n",
       "      <td>0001</td>\n",
       "      <td>24868</td>\n",
       "      <td>14101</td>\n",
       "      <td>56.70</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.44</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>DUPONT-AIGNAN</td>\n",
       "      <td>Nicolas</td>\n",
       "      <td>189</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69678</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>11</td>\n",
       "      <td>11ème circonscription</td>\n",
       "      <td>231</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>0001</td>\n",
       "      <td>1709</td>\n",
       "      <td>942</td>\n",
       "      <td>55.12</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.32</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>DUPONT-AIGNAN</td>\n",
       "      <td>Nicolas</td>\n",
       "      <td>10</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69679</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>11</td>\n",
       "      <td>11ème circonscription</td>\n",
       "      <td>233</td>\n",
       "      <td>Nour-Soultan</td>\n",
       "      <td>0001</td>\n",
       "      <td>117</td>\n",
       "      <td>64</td>\n",
       "      <td>54.70</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>DUPONT-AIGNAN</td>\n",
       "      <td>Nicolas</td>\n",
       "      <td>1</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69680</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>2</td>\n",
       "      <td>2ème circonscription</td>\n",
       "      <td>234</td>\n",
       "      <td>Monterrey</td>\n",
       "      <td>0001</td>\n",
       "      <td>713</td>\n",
       "      <td>553</td>\n",
       "      <td>77.56</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.63</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>DUPONT-AIGNAN</td>\n",
       "      <td>Nicolas</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>2</td>\n",
       "      <td>2ème circonscription</td>\n",
       "      <td>235</td>\n",
       "      <td>Bahamas (Nassau)</td>\n",
       "      <td>0001</td>\n",
       "      <td>136</td>\n",
       "      <td>78</td>\n",
       "      <td>57.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "      <td>M</td>\n",
       "      <td>DUPONT-AIGNAN</td>\n",
       "      <td>Nicolas</td>\n",
       "      <td>1</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Code du département           Libellé du département  \\\n",
       "69677                  ZZ  Français établis hors de France   \n",
       "69678                  ZZ  Français établis hors de France   \n",
       "69679                  ZZ  Français établis hors de France   \n",
       "69680                  ZZ  Français établis hors de France   \n",
       "69681                  ZZ  Français établis hors de France   \n",
       "\n",
       "       Code de la circonscription Libellé de la circonscription  \\\n",
       "69677                           6          6ème circonscription   \n",
       "69678                          11         11ème circonscription   \n",
       "69679                          11         11ème circonscription   \n",
       "69680                           2          2ème circonscription   \n",
       "69681                           2          2ème circonscription   \n",
       "\n",
       "       Code de la commune Libellé de la commune Code du b.vote  Inscrits  \\\n",
       "69677                 229                Zurich           0001     24868   \n",
       "69678                 231                Taipei           0001      1709   \n",
       "69679                 233          Nour-Soultan           0001       117   \n",
       "69680                 234             Monterrey           0001       713   \n",
       "69681                 235      Bahamas (Nassau)           0001       136   \n",
       "\n",
       "       Abstentions  % Abs/Ins  ...  Unnamed: 95  Unnamed: 96  Unnamed: 97  \\\n",
       "69677        14101      56.70  ...           47         0.19         0.44   \n",
       "69678          942      55.12  ...           10         0.59         1.32   \n",
       "69679           64      54.70  ...            0         0.00         0.00   \n",
       "69680          553      77.56  ...            1         0.14         0.63   \n",
       "69681           78      57.35  ...            0         0.00         0.00   \n",
       "\n",
       "       Unnamed: 98  Unnamed: 99   Unnamed: 100  Unnamed: 101  Unnamed: 102  \\\n",
       "69677           12            M  DUPONT-AIGNAN       Nicolas           189   \n",
       "69678           12            M  DUPONT-AIGNAN       Nicolas            10   \n",
       "69679           12            M  DUPONT-AIGNAN       Nicolas             1   \n",
       "69680           12            M  DUPONT-AIGNAN       Nicolas             1   \n",
       "69681           12            M  DUPONT-AIGNAN       Nicolas             1   \n",
       "\n",
       "       Unnamed: 103  Unnamed: 104  \n",
       "69677          0.76          1.77  \n",
       "69678          0.59          1.32  \n",
       "69679          0.85          1.96  \n",
       "69680          0.14          0.63  \n",
       "69681          0.74          1.82  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données sont organisées suivant des colonnes qui vont représenter les différents choix et aussi d'autres métadonnées. Il va falloir faire quelques hypothèses pour récupérer les données utiles…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.735333Z",
     "iopub.status.busy": "2022-06-03T12:42:22.734846Z",
     "iopub.status.idle": "2022-06-03T12:42:22.737161Z",
     "shell.execute_reply": "2022-06-03T12:42:22.737497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code du département', 'Libellé du département',\n",
       "       'Code de la circonscription', 'Libellé de la circonscription',\n",
       "       'Code de la commune', 'Libellé de la commune', 'Code du b.vote',\n",
       "       'Inscrits', 'Abstentions', '% Abs/Ins',\n",
       "       ...\n",
       "       'Unnamed: 95', 'Unnamed: 96', 'Unnamed: 97', 'Unnamed: 98',\n",
       "       'Unnamed: 99', 'Unnamed: 100', 'Unnamed: 101', 'Unnamed: 102',\n",
       "       'Unnamed: 103', 'Unnamed: 104'],\n",
       "      dtype='object', length=105)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Première colonne concerne les nuls, blancs et abstention, que l'on peut enregistrer dans un nouveau tableau :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.742138Z",
     "iopub.status.busy": "2022-06-03T12:42:22.741661Z",
     "iopub.status.idle": "2022-06-03T12:42:22.746839Z",
     "shell.execute_reply": "2022-06-03T12:42:22.746477Z"
    }
   },
   "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>Nuls</th>\n",
       "      <th>Blancs</th>\n",
       "      <th>Abstentions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nuls  Blancs  Abstentions\n",
       "0     1      16          108\n",
       "1     1       3           38\n",
       "2     5      18          266\n",
       "3     6      15          265\n",
       "4     8      10          246"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = T1[['Nuls', 'Blancs', 'Abstentions']].copy()\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.753115Z",
     "iopub.status.busy": "2022-06-03T12:42:22.752509Z",
     "iopub.status.idle": "2022-06-03T12:42:22.755032Z",
     "shell.execute_reply": "2022-06-03T12:42:22.754686Z"
    }
   },
   "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>Nuls</th>\n",
       "      <th>Blancs</th>\n",
       "      <th>Abstentions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69677</th>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>14101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69678</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69679</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69680</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69682 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Nuls  Blancs  Abstentions\n",
       "0         1      16          108\n",
       "1         1       3           38\n",
       "2         5      18          266\n",
       "3         6      15          265\n",
       "4         8      10          246\n",
       "...     ...     ...          ...\n",
       "69677    31      40        14101\n",
       "69678     2       8          942\n",
       "69679     0       2           64\n",
       "69680     2       0          553\n",
       "69681     0       3           78\n",
       "\n",
       "[69682 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extraction des résultats de vote"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les 23 premières colonnes correspondent aux métadonnées :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.758928Z",
     "iopub.status.busy": "2022-06-03T12:42:22.758341Z",
     "iopub.status.idle": "2022-06-03T12:42:22.760542Z",
     "shell.execute_reply": "2022-06-03T12:42:22.760871Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code du département', 'Libellé du département',\n",
       "       'Code de la circonscription', 'Libellé de la circonscription',\n",
       "       'Code de la commune', 'Libellé de la commune', 'Code du b.vote',\n",
       "       'Inscrits', 'Abstentions', '% Abs/Ins', 'Votants', '% Vot/Ins',\n",
       "       'Blancs', '% Blancs/Ins', '% Blancs/Vot', 'Nuls', '% Nuls/Ins',\n",
       "       '% Nuls/Vot', 'Exprimés', '% Exp/Ins', '% Exp/Vot', 'N°Panneau',\n",
       "       'Sexe'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1.columns[:23]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les colonnes suivant la colonne numéro 23 vont concerner les résultats candidats par candidats :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.763975Z",
     "iopub.status.busy": "2022-06-03T12:42:22.763500Z",
     "iopub.status.idle": "2022-06-03T12:42:22.765380Z",
     "shell.execute_reply": "2022-06-03T12:42:22.765748Z"
    }
   },
   "outputs": [],
   "source": [
    "col_start = 23\n",
    "col_par_cdt = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut extraire les noms des candidats présents au premier tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.770111Z",
     "iopub.status.busy": "2022-06-03T12:42:22.769569Z",
     "iopub.status.idle": "2022-06-03T12:42:22.771823Z",
     "shell.execute_reply": "2022-06-03T12:42:22.772157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nom                   ARTHAUD\n",
       "Unnamed: 30           ROUSSEL\n",
       "Unnamed: 37            MACRON\n",
       "Unnamed: 44          LASSALLE\n",
       "Unnamed: 51            LE PEN\n",
       "Unnamed: 58           ZEMMOUR\n",
       "Unnamed: 65         MÉLENCHON\n",
       "Unnamed: 72           HIDALGO\n",
       "Unnamed: 79             JADOT\n",
       "Unnamed: 86          PÉCRESSE\n",
       "Unnamed: 93            POUTOU\n",
       "Unnamed: 100    DUPONT-AIGNAN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidats = T1.iloc[0][col_start::col_par_cdt]\n",
    "candidats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut par exemple extraire les résultats pour le premier bureau de vote et donner le nombre de suffrages exprimés pour chaque candidat :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.776573Z",
     "iopub.status.busy": "2022-06-03T12:42:22.776077Z",
     "iopub.status.idle": "2022-06-03T12:42:22.778265Z",
     "shell.execute_reply": "2022-06-03T12:42:22.778596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Voix              3\n",
       "Unnamed: 32       6\n",
       "Unnamed: 39     150\n",
       "Unnamed: 46      18\n",
       "Unnamed: 53     149\n",
       "Unnamed: 60      43\n",
       "Unnamed: 67      66\n",
       "Unnamed: 74       5\n",
       "Unnamed: 81      30\n",
       "Unnamed: 88      26\n",
       "Unnamed: 95       3\n",
       "Unnamed: 102     21\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "résultats = T1.iloc[0][(col_start+2)::col_par_cdt]\n",
    "résultats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grâce à ces connaissances, nous allons pouvoir maintenant récolter les données pour chaque candidat et pour tous les bureaux de vote en utilisant la fonction suivante :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.782654Z",
     "iopub.status.busy": "2022-06-03T12:42:22.782176Z",
     "iopub.status.idle": "2022-06-03T12:42:22.792340Z",
     "shell.execute_reply": "2022-06-03T12:42:22.792699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# colonne 25  résultats= [3 2 4 ... 0 0 0]\n",
      "# colonne 32  résultats= [ 6  7 30 ...  1  1  1]\n",
      "# colonne 39  résultats= [150  50 159 ...  25  96  25]\n",
      "# colonne 46  résultats= [18 10 24 ...  1  1  0]\n",
      "# colonne 53  résultats= [149  23 237 ...   3   4   3]\n",
      "# colonne 60  résultats= [43 14 69 ...  7 18 17]\n",
      "# colonne 67  résultats= [ 66  41 216 ...   9  19   2]\n",
      "# colonne 74  résultats= [ 5  3 15 ...  0  2  0]\n",
      "# colonne 81  résultats= [30 10 25 ...  4 10  0]\n",
      "# colonne 88  résultats= [26  8 40 ...  0  5  6]\n",
      "# colonne 95  résultats= [3 3 8 ... 0 1 0]\n",
      "# colonne 102  résultats= [21  0 13 ...  1  1  1]\n"
     ]
    }
   ],
   "source": [
    "for i_candidat, candidat in enumerate(candidats):\n",
    "    i_col = col_start + i_candidat*col_par_cdt + 2\n",
    "    print('# colonne', i_col, ' résultats=', T1.iloc[:, i_col].values)\n",
    "    df_1[candidat] = T1.iloc[:, i_col].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons récolté les données utiles dans un nouveau tableau :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.796244Z",
     "iopub.status.busy": "2022-06-03T12:42:22.795763Z",
     "iopub.status.idle": "2022-06-03T12:42:22.807618Z",
     "shell.execute_reply": "2022-06-03T12:42:22.807961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 69682 entries, 0 to 69681\n",
      "Data columns (total 15 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   Nuls           69682 non-null  int64\n",
      " 1   Blancs         69682 non-null  int64\n",
      " 2   Abstentions    69682 non-null  int64\n",
      " 3   ARTHAUD        69682 non-null  int64\n",
      " 4   ROUSSEL        69682 non-null  int64\n",
      " 5   MACRON         69682 non-null  int64\n",
      " 6   LASSALLE       69682 non-null  int64\n",
      " 7   LE PEN         69682 non-null  int64\n",
      " 8   ZEMMOUR        69682 non-null  int64\n",
      " 9   MÉLENCHON      69682 non-null  int64\n",
      " 10  HIDALGO        69682 non-null  int64\n",
      " 11  JADOT          69682 non-null  int64\n",
      " 12  PÉCRESSE       69682 non-null  int64\n",
      " 13  POUTOU         69682 non-null  int64\n",
      " 14  DUPONT-AIGNAN  69682 non-null  int64\n",
      "dtypes: int64(15)\n",
      "memory usage: 8.0 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df_1.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ceci nous permet par exemple d'extraire les résultats pour un candidat donné et pour tous les bureaux de vote :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.812560Z",
     "iopub.status.busy": "2022-06-03T12:42:22.812069Z",
     "iopub.status.idle": "2022-06-03T12:42:22.814355Z",
     "shell.execute_reply": "2022-06-03T12:42:22.814695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3\n",
       "1         3\n",
       "2         8\n",
       "3         4\n",
       "4         3\n",
       "         ..\n",
       "69677    47\n",
       "69678    10\n",
       "69679     0\n",
       "69680     1\n",
       "69681     0\n",
       "Name: POUTOU, Length: 69682, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1['POUTOU']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En particulier, on a le nombre suivant de bureaux de vote :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.818326Z",
     "iopub.status.busy": "2022-06-03T12:42:22.817791Z",
     "iopub.status.idle": "2022-06-03T12:42:22.820067Z",
     "shell.execute_reply": "2022-06-03T12:42:22.820414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69682"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et on peut calculer pour chaque alternative le nombre total de choix ainsi que le nombre total de choix dans les data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.823837Z",
     "iopub.status.busy": "2022-06-03T12:42:22.823355Z",
     "iopub.status.idle": "2022-06-03T12:42:22.830105Z",
     "shell.execute_reply": "2022-06-03T12:42:22.830458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Nuls               247151\n",
       " Blancs             543609\n",
       " Abstentions      12824169\n",
       " ARTHAUD            197094\n",
       " ROUSSEL            802422\n",
       " MACRON            9783058\n",
       " LASSALLE          1101387\n",
       " LE PEN            8133828\n",
       " ZEMMOUR           2485226\n",
       " MÉLENCHON         7712520\n",
       " HIDALGO            616478\n",
       " JADOT             1627853\n",
       " PÉCRESSE          1679001\n",
       " POUTOU             268904\n",
       " DUPONT-AIGNAN      725176\n",
       " dtype: int64,\n",
       " 48747876)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.sum(), df_1.sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Par exemple, on note qu'environ 13 millions de personnes se sont abstenues, alors que environ 10 millions de personnes ont voté pour Macron."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans le reste, nous allons complétement ignorer les votes 'Nuls' et 'Abstebnsions'. En effet, des expériencesz précedentes (cf github) ont montré que ces valuers sont moins prédictibles. Pour celà, nous utilisons la fonction [`DataFrame.drop`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.834084Z",
     "iopub.status.busy": "2022-06-03T12:42:22.833615Z",
     "iopub.status.idle": "2022-06-03T12:42:22.835776Z",
     "shell.execute_reply": "2022-06-03T12:42:22.836113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nuls', 'Blancs', 'Abstentions', 'ARTHAUD', 'ROUSSEL', 'MACRON',\n",
       "       'LASSALLE', 'LE PEN', 'ZEMMOUR', 'MÉLENCHON', 'HIDALGO', 'JADOT',\n",
       "       'PÉCRESSE', 'POUTOU', 'DUPONT-AIGNAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_1 = df_1.drop(columns=['Nuls', 'Blancs'])#, 'Abstentions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.839508Z",
     "iopub.status.busy": "2022-06-03T12:42:22.839030Z",
     "iopub.status.idle": "2022-06-03T12:42:22.841043Z",
     "shell.execute_reply": "2022-06-03T12:42:22.841377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nuls', 'Blancs', 'Abstentions', 'ARTHAUD', 'ROUSSEL', 'MACRON',\n",
       "       'LASSALLE', 'LE PEN', 'ZEMMOUR', 'MÉLENCHON', 'HIDALGO', 'JADOT',\n",
       "       'PÉCRESSE', 'POUTOU', 'DUPONT-AIGNAN'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sous un format graphique on peut représenter ainsi les résultats du vote au premier tour et pour cela nous allons utiliser la librairie `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:22.844378Z",
     "iopub.status.busy": "2022-06-03T12:42:22.843877Z",
     "iopub.status.idle": "2022-06-03T12:42:23.413609Z",
     "shell.execute_reply": "2022-06-03T12:42:23.413975Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:23.417960Z",
     "iopub.status.busy": "2022-06-03T12:42:23.417485Z",
     "iopub.status.idle": "2022-06-03T12:42:23.570757Z",
     "shell.execute_reply": "2022-06-03T12:42:23.571111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 5))\n",
    "k = df_1.sum()/df_1.sum().sum()\n",
    "ax = k.plot.bar(ax=ax)\n",
    "ax.set_xlabel('Choix 1er tour')\n",
    "ax.set_ylabel('Pourcentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque le fort taux d'abstention qui a été observé au premier tour, ainsi que les deux candidats qui se distinguent par le plus grand nombre de voix et qui sont sélectionnés pour le second tour."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Données du 2ème tour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons maintenant répéter la même opération pour les données obtenues au deuxième tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:23.575117Z",
     "iopub.status.busy": "2022-06-03T12:42:23.574649Z",
     "iopub.status.idle": "2022-06-03T12:42:45.511406Z",
     "shell.execute_reply": "2022-06-03T12:42:45.511875Z"
    }
   },
   "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>Code du département</th>\n",
       "      <th>Libellé du département</th>\n",
       "      <th>Code de la circonscription</th>\n",
       "      <th>Libellé de la circonscription</th>\n",
       "      <th>Code de la commune</th>\n",
       "      <th>Libellé de la commune</th>\n",
       "      <th>Code du b.vote</th>\n",
       "      <th>Inscrits</th>\n",
       "      <th>Abstentions</th>\n",
       "      <th>% Abs/Ins</th>\n",
       "      <th>...</th>\n",
       "      <th>Voix</th>\n",
       "      <th>% Voix/Ins</th>\n",
       "      <th>% Voix/Exp</th>\n",
       "      <th>Unnamed: 28</th>\n",
       "      <th>Unnamed: 29</th>\n",
       "      <th>Unnamed: 30</th>\n",
       "      <th>Unnamed: 31</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "      <th>Unnamed: 33</th>\n",
       "      <th>Unnamed: 34</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69677</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>6</td>\n",
       "      <td>6ème circonscription</td>\n",
       "      <td>229</td>\n",
       "      <td>Zurich</td>\n",
       "      <td>0001</td>\n",
       "      <td>24867</td>\n",
       "      <td>13153</td>\n",
       "      <td>52.89</td>\n",
       "      <td>...</td>\n",
       "      <td>10166</td>\n",
       "      <td>40.88</td>\n",
       "      <td>88.88</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>LE PEN</td>\n",
       "      <td>Marine</td>\n",
       "      <td>1272</td>\n",
       "      <td>5.12</td>\n",
       "      <td>11.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69678</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>11</td>\n",
       "      <td>11ème circonscription</td>\n",
       "      <td>231</td>\n",
       "      <td>Taipei</td>\n",
       "      <td>0001</td>\n",
       "      <td>1703</td>\n",
       "      <td>928</td>\n",
       "      <td>54.49</td>\n",
       "      <td>...</td>\n",
       "      <td>611</td>\n",
       "      <td>35.88</td>\n",
       "      <td>82.23</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>LE PEN</td>\n",
       "      <td>Marine</td>\n",
       "      <td>132</td>\n",
       "      <td>7.75</td>\n",
       "      <td>17.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69679</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>11</td>\n",
       "      <td>11ème circonscription</td>\n",
       "      <td>233</td>\n",
       "      <td>Nour-Soultan</td>\n",
       "      <td>0001</td>\n",
       "      <td>117</td>\n",
       "      <td>64</td>\n",
       "      <td>54.70</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>33.33</td>\n",
       "      <td>78.00</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>LE PEN</td>\n",
       "      <td>Marine</td>\n",
       "      <td>11</td>\n",
       "      <td>9.40</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69680</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>2</td>\n",
       "      <td>2ème circonscription</td>\n",
       "      <td>234</td>\n",
       "      <td>Monterrey</td>\n",
       "      <td>0001</td>\n",
       "      <td>715</td>\n",
       "      <td>510</td>\n",
       "      <td>71.33</td>\n",
       "      <td>...</td>\n",
       "      <td>181</td>\n",
       "      <td>25.31</td>\n",
       "      <td>89.60</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>LE PEN</td>\n",
       "      <td>Marine</td>\n",
       "      <td>21</td>\n",
       "      <td>2.94</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>ZZ</td>\n",
       "      <td>Français établis hors de France</td>\n",
       "      <td>2</td>\n",
       "      <td>2ème circonscription</td>\n",
       "      <td>235</td>\n",
       "      <td>Bahamas (Nassau)</td>\n",
       "      <td>0001</td>\n",
       "      <td>137</td>\n",
       "      <td>78</td>\n",
       "      <td>56.93</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>25.55</td>\n",
       "      <td>68.63</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>LE PEN</td>\n",
       "      <td>Marine</td>\n",
       "      <td>16</td>\n",
       "      <td>11.68</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Code du département           Libellé du département  \\\n",
       "69677                  ZZ  Français établis hors de France   \n",
       "69678                  ZZ  Français établis hors de France   \n",
       "69679                  ZZ  Français établis hors de France   \n",
       "69680                  ZZ  Français établis hors de France   \n",
       "69681                  ZZ  Français établis hors de France   \n",
       "\n",
       "       Code de la circonscription Libellé de la circonscription  \\\n",
       "69677                           6          6ème circonscription   \n",
       "69678                          11         11ème circonscription   \n",
       "69679                          11         11ème circonscription   \n",
       "69680                           2          2ème circonscription   \n",
       "69681                           2          2ème circonscription   \n",
       "\n",
       "       Code de la commune Libellé de la commune Code du b.vote  Inscrits  \\\n",
       "69677                 229                Zurich           0001     24867   \n",
       "69678                 231                Taipei           0001      1703   \n",
       "69679                 233          Nour-Soultan           0001       117   \n",
       "69680                 234             Monterrey           0001       715   \n",
       "69681                 235      Bahamas (Nassau)           0001       137   \n",
       "\n",
       "       Abstentions  % Abs/Ins  ...   Voix  % Voix/Ins  % Voix/Exp  \\\n",
       "69677        13153      52.89  ...  10166       40.88       88.88   \n",
       "69678          928      54.49  ...    611       35.88       82.23   \n",
       "69679           64      54.70  ...     39       33.33       78.00   \n",
       "69680          510      71.33  ...    181       25.31       89.60   \n",
       "69681           78      56.93  ...     35       25.55       68.63   \n",
       "\n",
       "       Unnamed: 28  Unnamed: 29  Unnamed: 30  Unnamed: 31  Unnamed: 32  \\\n",
       "69677            2            F       LE PEN       Marine         1272   \n",
       "69678            2            F       LE PEN       Marine          132   \n",
       "69679            2            F       LE PEN       Marine           11   \n",
       "69680            2            F       LE PEN       Marine           21   \n",
       "69681            2            F       LE PEN       Marine           16   \n",
       "\n",
       "       Unnamed: 33  Unnamed: 34  \n",
       "69677         5.12        11.12  \n",
       "69678         7.75        17.77  \n",
       "69679         9.40        22.00  \n",
       "69680         2.94        10.40  \n",
       "69681        11.68        31.37  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = '/tmp/T2.xlsx'\n",
    "\n",
    "if not os.path.isfile(fname):\n",
    "    url = 'https://static.data.gouv.fr/resources/election-presidentielle-des-10-et-24-avril-2022-resultats-definitifs-du-2nd-tour/20220428-142301/resultats-par-niveau-burvot-t2-france-entiere.xlsx' # XLSX\n",
    "    import urllib.request\n",
    "    urllib.request.urlretrieve(url, fname)\n",
    "\n",
    "T2 = pd.read_excel(fname)\n",
    "\n",
    "T2.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie que les données sont une nouvelle fois organisé suivant la même structure :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.516378Z",
     "iopub.status.busy": "2022-06-03T12:42:45.515783Z",
     "iopub.status.idle": "2022-06-03T12:42:45.518241Z",
     "shell.execute_reply": "2022-06-03T12:42:45.518634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code du département', 'Libellé du département',\n",
       "       'Code de la circonscription', 'Libellé de la circonscription',\n",
       "       'Code de la commune', 'Libellé de la commune', 'Code du b.vote',\n",
       "       'Inscrits', 'Abstentions', '% Abs/Ins', 'Votants', '% Vot/Ins',\n",
       "       'Blancs', '% Blancs/Ins', '% Blancs/Vot', 'Nuls', '% Nuls/Ins',\n",
       "       '% Nuls/Vot', 'Exprimés', '% Exp/Ins', '% Exp/Vot', 'N°Panneau', 'Sexe',\n",
       "       'Nom', 'Prénom', 'Voix', '% Voix/Ins', '% Voix/Exp', 'Unnamed: 28',\n",
       "       'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Unnamed: 32',\n",
       "       'Unnamed: 33', 'Unnamed: 34'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.522638Z",
     "iopub.status.busy": "2022-06-03T12:42:45.522011Z",
     "iopub.status.idle": "2022-06-03T12:42:45.524375Z",
     "shell.execute_reply": "2022-06-03T12:42:45.524895Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code du département', 'Libellé du département',\n",
       "       'Code de la circonscription', 'Libellé de la circonscription',\n",
       "       'Code de la commune', 'Libellé de la commune', 'Code du b.vote',\n",
       "       'Inscrits', 'Abstentions', '% Abs/Ins', 'Votants', '% Vot/Ins',\n",
       "       'Blancs', '% Blancs/Ins', '% Blancs/Vot', 'Nuls', '% Nuls/Ins',\n",
       "       '% Nuls/Vot', 'Exprimés', '% Exp/Ins', '% Exp/Vot', 'N°Panneau',\n",
       "       'Sexe'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2.columns[:23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.528644Z",
     "iopub.status.busy": "2022-06-03T12:42:45.528039Z",
     "iopub.status.idle": "2022-06-03T12:42:45.530300Z",
     "shell.execute_reply": "2022-06-03T12:42:45.530654Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Nom', 'Prénom', 'Voix', '% Voix/Ins', '% Voix/Exp', 'Unnamed: 28',\n",
       "       'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Unnamed: 32',\n",
       "       'Unnamed: 33', 'Unnamed: 34'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2.columns[23:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.534985Z",
     "iopub.status.busy": "2022-06-03T12:42:45.534404Z",
     "iopub.status.idle": "2022-06-03T12:42:45.536679Z",
     "shell.execute_reply": "2022-06-03T12:42:45.537103Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nom              MACRON\n",
       "Prénom         Emmanuel\n",
       "Voix                237\n",
       "% Voix/Ins        36.86\n",
       "% Voix/Exp        52.67\n",
       "Unnamed: 28           2\n",
       "Unnamed: 29           F\n",
       "Unnamed: 30      LE PEN\n",
       "Unnamed: 31      Marine\n",
       "Unnamed: 32         213\n",
       "Unnamed: 33       33.13\n",
       "Unnamed: 34       47.33\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T2.iloc[0, 23:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.541592Z",
     "iopub.status.busy": "2022-06-03T12:42:45.540958Z",
     "iopub.status.idle": "2022-06-03T12:42:45.543664Z",
     "shell.execute_reply": "2022-06-03T12:42:45.543294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nom            MACRON\n",
       "Unnamed: 30    LE PEN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_start = 23\n",
    "col_par_cdt = 7\n",
    "candidats = T2.iloc[0][col_start::col_par_cdt]\n",
    "candidats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une fois cette vérification faite nous pouvons extraire les données dans un nouveau tableau :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.548340Z",
     "iopub.status.busy": "2022-06-03T12:42:45.547833Z",
     "iopub.status.idle": "2022-06-03T12:42:45.552983Z",
     "shell.execute_reply": "2022-06-03T12:42:45.553404Z"
    },
    "tags": []
   },
   "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>Nuls</th>\n",
       "      <th>Blancs</th>\n",
       "      <th>Abstentions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>87</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nuls  Blancs  Abstentions\n",
       "0     5      42          146\n",
       "1     3      23           45\n",
       "2    22      71          322\n",
       "3    18      45          313\n",
       "4    21      87          303"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = T2[['Nuls', 'Blancs', 'Abstentions']].copy()\n",
    "#df_1 = df_1.rename(columns={\"Nuls\": \"1_Nuls\", \"Abstentions\": \"1_Abstentions\"})\n",
    "df_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions aussi que nous avons le même nombre de bureaux de vote :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.556806Z",
     "iopub.status.busy": "2022-06-03T12:42:45.556289Z",
     "iopub.status.idle": "2022-06-03T12:42:45.558601Z",
     "shell.execute_reply": "2022-06-03T12:42:45.558956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69682"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.562994Z",
     "iopub.status.busy": "2022-06-03T12:42:45.562299Z",
     "iopub.status.idle": "2022-06-03T12:42:45.566258Z",
     "shell.execute_reply": "2022-06-03T12:42:45.565585Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 [237  94 360 ...  39 181  35]\n",
      "32 [213  48 355 ...  11  21  16]\n"
     ]
    }
   ],
   "source": [
    "for i_candidat, candidat in enumerate(candidats):\n",
    "    i_col = col_start + i_candidat*col_par_cdt + 2\n",
    "    print(i_col, T2.iloc[:, i_col].values)\n",
    "    df_2[candidat] = T2.iloc[:, i_col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.573161Z",
     "iopub.status.busy": "2022-06-03T12:42:45.572575Z",
     "iopub.status.idle": "2022-06-03T12:42:45.575130Z",
     "shell.execute_reply": "2022-06-03T12:42:45.575569Z"
    }
   },
   "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>Nuls</th>\n",
       "      <th>Blancs</th>\n",
       "      <th>Abstentions</th>\n",
       "      <th>MACRON</th>\n",
       "      <th>LE PEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>146</td>\n",
       "      <td>237</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>45</td>\n",
       "      <td>94</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>71</td>\n",
       "      <td>322</td>\n",
       "      <td>360</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>45</td>\n",
       "      <td>313</td>\n",
       "      <td>426</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>87</td>\n",
       "      <td>303</td>\n",
       "      <td>449</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69677</th>\n",
       "      <td>49</td>\n",
       "      <td>227</td>\n",
       "      <td>13153</td>\n",
       "      <td>10166</td>\n",
       "      <td>1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69678</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>928</td>\n",
       "      <td>611</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69679</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69680</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>510</td>\n",
       "      <td>181</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69681</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>78</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69682 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Nuls  Blancs  Abstentions  MACRON  LE PEN\n",
       "0         5      42          146     237     213\n",
       "1         3      23           45      94      48\n",
       "2        22      71          322     360     355\n",
       "3        18      45          313     426     327\n",
       "4        21      87          303     449     353\n",
       "...     ...     ...          ...     ...     ...\n",
       "69677    49     227        13153   10166    1272\n",
       "69678     6      26          928     611     132\n",
       "69679     0       3           64      39      11\n",
       "69680     1       2          510     181      21\n",
       "69681     1       7           78      35      16\n",
       "\n",
       "[69682 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De la même façon que pour le premier tour, nous pouvons représenter les résultats totaux obtenus au second tour de façon graphique :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.614514Z",
     "iopub.status.busy": "2022-06-03T12:42:45.613990Z",
     "iopub.status.idle": "2022-06-03T12:42:45.713742Z",
     "shell.execute_reply": "2022-06-03T12:42:45.714268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 5))\n",
    "k = df_2.sum()/df_2.sum().sum()\n",
    "ax = k.plot.bar(ax=ax)\n",
    "ax.set_xlabel('Candidat')\n",
    "#ax.set_xlim(1, 10)\n",
    "#ax.set_xticks(np.arange(1, 10)+.5)\n",
    "#ax.set_xticklabels(np.arange(1, 10)) , rotation=45\n",
    "ax.set_ylabel('pourcentage');"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_2 = df_2.drop(columns=['Nuls', 'Blancs'])#, 'Abstentions'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nettoyage des données (non aux nans)\n",
    "\n",
    "Certains bureaux de vote n'ont pas de votants au premier ou au deuxieme. Ceci peut engendrer des problèmes numériques en générant des divisions par zéro, des `Not a Number (NaN)` dans le jargon informatique. Comme ceux-ci représentent un nombre très faible d'électeurs nous allons les ignorer par rapport au reste de la population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons d'abord compter le nombre de bureaux de vote qui n'ont aucun suffrage enregistré:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.718763Z",
     "iopub.status.busy": "2022-06-03T12:42:45.718173Z",
     "iopub.status.idle": "2022-06-03T12:42:45.735259Z",
     "shell.execute_reply": "2022-06-03T12:42:45.735702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 11)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_1.sum(axis=1)==0).sum(), (df_2.sum(axis=1)==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons \"effacer\" ces bureaux du vote en commençant par filtrer ceux qui n'ont aucun suffrage enregistré au premier tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.739882Z",
     "iopub.status.busy": "2022-06-03T12:42:45.739372Z",
     "iopub.status.idle": "2022-06-03T12:42:45.753626Z",
     "shell.execute_reply": "2022-06-03T12:42:45.754141Z"
    }
   },
   "outputs": [],
   "source": [
    "df_2.drop(df_2.loc[df_1.sum(axis=1)==0].index, inplace=True)\n",
    "df_1.drop(df_1.loc[df_1.sum(axis=1)==0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.758160Z",
     "iopub.status.busy": "2022-06-03T12:42:45.757631Z",
     "iopub.status.idle": "2022-06-03T12:42:45.762542Z",
     "shell.execute_reply": "2022-06-03T12:42:45.762895Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_1.sum(axis=1)==0).sum(), (df_2.sum(axis=1)==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et maintenant répéter la même procédure sur les bureaux de vote qui n'ont aucun suffrage enregistré au second tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.766819Z",
     "iopub.status.busy": "2022-06-03T12:42:45.766313Z",
     "iopub.status.idle": "2022-06-03T12:42:45.779581Z",
     "shell.execute_reply": "2022-06-03T12:42:45.779966Z"
    }
   },
   "outputs": [],
   "source": [
    "df_1.drop(df_1.loc[df_2.sum(axis=1)==0].index, inplace=True)\n",
    "df_2.drop(df_2.loc[df_2.sum(axis=1)==0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.783977Z",
     "iopub.status.busy": "2022-06-03T12:42:45.783371Z",
     "iopub.status.idle": "2022-06-03T12:42:45.788522Z",
     "shell.execute_reply": "2022-06-03T12:42:45.788884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_1.sum(axis=1)==0).sum(), (df_2.sum(axis=1)==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistiques de second ordre\n",
    "\n",
    "Comme cela est montré dans l'[article du Monde](https://www.lemonde.fr/les-decodeurs/article/2022/05/04/election-presidentielle-2022-quels-reports-de-voix-entre-les-deux-tours_6124672_4355770.html) on peut montrer la dépendance entre les choix qui sont effectués au premier tour et ceux qui sont effectués au deuxième tour. On va utiliser des représentations graphiques similaires à ceux de l'article pour d'une première part les répliquer et vérifier que la méthode est correct et d'un autre côté pour mieux comprendre comment nous pouvons tirer dans ses enseignements depuis ces observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.793011Z",
     "iopub.status.busy": "2022-06-03T12:42:45.792407Z",
     "iopub.status.idle": "2022-06-03T12:42:45.794154Z",
     "shell.execute_reply": "2022-06-03T12:42:45.794577Z"
    }
   },
   "outputs": [],
   "source": [
    "df_12 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.800965Z",
     "iopub.status.busy": "2022-06-03T12:42:45.800367Z",
     "iopub.status.idle": "2022-06-03T12:42:45.803414Z",
     "shell.execute_reply": "2022-06-03T12:42:45.803847Z"
    }
   },
   "outputs": [],
   "source": [
    "df_12['1_MÉLENCHON'] = df_1['MÉLENCHON'].copy()\n",
    "df_12['MACRON'] = df_2['MACRON'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.807629Z",
     "iopub.status.busy": "2022-06-03T12:42:45.807006Z",
     "iopub.status.idle": "2022-06-03T12:42:45.813807Z",
     "shell.execute_reply": "2022-06-03T12:42:45.814258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 69670 entries, 0 to 69681\n",
      "Data columns (total 2 columns):\n",
      " #   Column       Non-Null Count  Dtype\n",
      "---  ------       --------------  -----\n",
      " 0   1_MÉLENCHON  69670 non-null  int64\n",
      " 1   MACRON       69670 non-null  int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_12.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.818035Z",
     "iopub.status.busy": "2022-06-03T12:42:45.817475Z",
     "iopub.status.idle": "2022-06-03T12:42:45.822273Z",
     "shell.execute_reply": "2022-06-03T12:42:45.822699Z"
    }
   },
   "outputs": [],
   "source": [
    "df_12['1_MÉLENCHON'] = df_12['1_MÉLENCHON']/df_1.sum(axis=1)\n",
    "df_12['MACRON'] = df_12['MACRON']/df_2.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:45.826339Z",
     "iopub.status.busy": "2022-06-03T12:42:45.825744Z",
     "iopub.status.idle": "2022-06-03T12:42:46.183763Z",
     "shell.execute_reply": "2022-06-03T12:42:46.184172Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:46.217518Z",
     "iopub.status.busy": "2022-06-03T12:42:46.191728Z",
     "iopub.status.idle": "2022-06-03T12:42:47.003935Z",
     "shell.execute_reply": "2022-06-03T12:42:47.004309Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=df_12['1_MÉLENCHON'], y=df_12['MACRON'], kind='hist', height=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque effectivement une dépendance entre le choix. Un premier candidat effectué au premier tour et celui qui est effectué au second tour. Nous allons essayer d'inférer de façon plus précise cette dépendance grâce au modèle de transition que nous avons exposer au début de cet article."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modèle de prédiction du transfert des voix\n",
    "\n",
    "Maintenant que nous avons récolté les données pour chacun des deux tours, et que nous avons une idée qu'il existe une dépendance entre les choix qui sont faits entre un tour et le suivant, nous allons pouvoir utiliser des librairies de l'apprentissage automatique (machine Learning en anglais) pour pouvoir inférer le report de voix entre les deux tours : Pour cela nous allons utiliser un travail précédemment effectué appliqué à l'[exploration du comportement humain](https://laurentperrinet.github.io/sciblog/posts/2020-04-08-fitting-a-psychometric-curve-using-pytorch.html) ou alors pour [l'épidémiologie du Covid](https://laurentperrinet.github.io/sciblog/posts/2020-10-10-fitting-covid-data.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatage des données au format de la librairie `torch`\n",
    "\n",
    "Pendant un aperçu des résultats au premier tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.013169Z",
     "iopub.status.busy": "2022-06-03T12:42:47.012546Z",
     "iopub.status.idle": "2022-06-03T12:42:47.014899Z",
     "shell.execute_reply": "2022-06-03T12:42:47.015251Z"
    }
   },
   "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>Nuls</th>\n",
       "      <th>Blancs</th>\n",
       "      <th>Abstentions</th>\n",
       "      <th>ARTHAUD</th>\n",
       "      <th>ROUSSEL</th>\n",
       "      <th>MACRON</th>\n",
       "      <th>LASSALLE</th>\n",
       "      <th>LE PEN</th>\n",
       "      <th>ZEMMOUR</th>\n",
       "      <th>MÉLENCHON</th>\n",
       "      <th>HIDALGO</th>\n",
       "      <th>JADOT</th>\n",
       "      <th>PÉCRESSE</th>\n",
       "      <th>POUTOU</th>\n",
       "      <th>DUPONT-AIGNAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>150</td>\n",
       "      <td>18</td>\n",
       "      <td>149</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>266</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>159</td>\n",
       "      <td>24</td>\n",
       "      <td>237</td>\n",
       "      <td>69</td>\n",
       "      <td>216</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>265</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>201</td>\n",
       "      <td>19</td>\n",
       "      <td>210</td>\n",
       "      <td>68</td>\n",
       "      <td>194</td>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>246</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>229</td>\n",
       "      <td>28</td>\n",
       "      <td>227</td>\n",
       "      <td>74</td>\n",
       "      <td>227</td>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nuls  Blancs  Abstentions  ARTHAUD  ROUSSEL  MACRON  LASSALLE  LE PEN  \\\n",
       "0     1      16          108        3        6     150        18     149   \n",
       "1     1       3           38        2        7      50        10      23   \n",
       "2     5      18          266        4       30     159        24     237   \n",
       "3     6      15          265        6       13     201        19     210   \n",
       "4     8      10          246        8       18     229        28     227   \n",
       "\n",
       "   ZEMMOUR  MÉLENCHON  HIDALGO  JADOT  PÉCRESSE  POUTOU  DUPONT-AIGNAN  \n",
       "0       43         66        5     30        26       3             21  \n",
       "1       14         41        3     10         8       3              0  \n",
       "2       69        216       15     25        40       8             13  \n",
       "3       68        194       19     46        42       4             20  \n",
       "4       74        227       16     65        35       3             19  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons donc comptabilisé ces différentes alternatives au premier tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.019215Z",
     "iopub.status.busy": "2022-06-03T12:42:47.018588Z",
     "iopub.status.idle": "2022-06-03T12:42:47.021190Z",
     "shell.execute_reply": "2022-06-03T12:42:47.021580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_1.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De sorte que sur les bureaux de vote que nous avons validé nous avons les deux tableaux suivants :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.025739Z",
     "iopub.status.busy": "2022-06-03T12:42:47.025192Z",
     "iopub.status.idle": "2022-06-03T12:42:47.027585Z",
     "shell.execute_reply": "2022-06-03T12:42:47.028051Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((69670, 15), (69670, 5))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.values.shape, df_2.values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons maintenant utiliser la librairie `torch` pour enregistrer ses données sous la forme d'une matrice (ou *tenseur* dans le jargon de cette librairie) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.031497Z",
     "iopub.status.busy": "2022-06-03T12:42:47.030947Z",
     "iopub.status.idle": "2022-06-03T12:42:47.523961Z",
     "shell.execute_reply": "2022-06-03T12:42:47.524506Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "X_1, X_2 = df_1.values, df_2.values\n",
    "x_1, x_2 = torch.Tensor(X_1), torch.Tensor(X_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.528439Z",
     "iopub.status.busy": "2022-06-03T12:42:47.527824Z",
     "iopub.status.idle": "2022-06-03T12:42:47.530624Z",
     "shell.execute_reply": "2022-06-03T12:42:47.530986Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([69670, 15])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ainsi nous allons très facilement pouvoir représenter les données pour pouvoir les apprendre. Une pratique extrêmement importante dans l'apprentissage automatique et de séparer les données qui sont utilisés pour apprendre le modèle, avec celles qui sont utilisés pour tester ce modèle :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.535079Z",
     "iopub.status.busy": "2022-06-03T12:42:47.534527Z",
     "iopub.status.idle": "2022-06-03T12:42:47.539561Z",
     "shell.execute_reply": "2022-06-03T12:42:47.540255Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "dataset = TensorDataset(x_1, x_2)\n",
    "\n",
    "# Random split\n",
    "train_set_size = int(len(dataset) * 0.8)\n",
    "test_set_size = len(dataset) - train_set_size\n",
    "train_set, test_set = torch.utils.data.random_split(dataset, [train_set_size, test_set_size])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ainsi nous pourrons utiliser l'ensemble d'apprentissage au cours des différentes époques d'apprentissage utilisé plus bas :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.543963Z",
     "iopub.status.busy": "2022-06-03T12:42:47.543392Z",
     "iopub.status.idle": "2022-06-03T12:42:47.545610Z",
     "shell.execute_reply": "2022-06-03T12:42:47.545264Z"
    }
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_set, batch_size=32, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.548756Z",
     "iopub.status.busy": "2022-06-03T12:42:47.548210Z",
     "iopub.status.idle": "2022-06-03T12:42:47.554462Z",
     "shell.execute_reply": "2022-06-03T12:42:47.554860Z"
    }
   },
   "outputs": [],
   "source": [
    "for n_1, n_2 in train_loader:\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En particulier, nous allons couper les données de façon aléatoire puis il est représenté sous la forme de différents « paquet » dont la taille est fixé ici à 32, et nous verrons plus tard que si cette procédure permet d'accélérer l'apprentissage - la taille du paquet n'ayant qu'une influence sur la vitesse pour obtenir le résultat final (et non sur le résultat \"espéré\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.558892Z",
     "iopub.status.busy": "2022-06-03T12:42:47.558211Z",
     "iopub.status.idle": "2022-06-03T12:42:47.560856Z",
     "shell.execute_reply": "2022-06-03T12:42:47.561216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([32, 15]), torch.Size([32, 5]))"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_1.shape, n_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans chaque paquet (de bureaux de votes tirés au hasard sur la population française), nous pouvons compter le nombre de votes pour les différentes alternatives :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.565430Z",
     "iopub.status.busy": "2022-06-03T12:42:47.564813Z",
     "iopub.status.idle": "2022-06-03T12:42:47.568588Z",
     "shell.execute_reply": "2022-06-03T12:42:47.569146Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1050.,   60.,  286.,  365.,  906.,  284., 1444.,  252.,  543.,  101.,\n",
       "          590.,  826.,  960.,  208.,  440.,  774.,  888.,  551., 1347.,  698.,\n",
       "          105.,  968.,  126.,  965., 1256.,   33., 1259., 1119., 1328.,  843.,\n",
       "          853.,  200.]),\n",
       " tensor([1051.,   60.,  286.,  364.,  906.,  284., 1444.,  252.,  544.,  101.,\n",
       "          590.,  826.,  960.,  207.,  440.,  775.,  888.,  551., 1346.,  698.,\n",
       "          105.,  968.,  126.,  966., 1256.,   33., 1259., 1119., 1327.,  842.,\n",
       "          853.,  200.]))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_1, sum_2 = n_1.sum(axis=1), n_2.sum(axis=1)\n",
    "sum_1, sum_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons aussi vérifier que parmi toutes les alternatives, on peut calculer des fréquences d'occurrence, et que comme chaque individu peut faire un seul choix et un seul, la somme de ses fréquences d'occurrence pour chacun des paquets est égale à $1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.573188Z",
     "iopub.status.busy": "2022-06-03T12:42:47.572586Z",
     "iopub.status.idle": "2022-06-03T12:42:47.575562Z",
     "shell.execute_reply": "2022-06-03T12:42:47.575902Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,\n",
       "        1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,\n",
       "        1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,\n",
       "        1.0000, 1.0000, 1.0000, 1.0000, 1.0000])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(n_1/sum_1[:, None]).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pourrons aussi aisément utiliser les données qui sont représentés dans l'ensemble de test :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.579947Z",
     "iopub.status.busy": "2022-06-03T12:42:47.579374Z",
     "iopub.status.idle": "2022-06-03T12:42:47.583872Z",
     "shell.execute_reply": "2022-06-03T12:42:47.584258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([13934, 15]), torch.Size([13934, 5]))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_1, n_2 = dataset[test_set.indices]\n",
    "n_1.shape, n_2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modèle `torch` de transition de probabilités\n",
    "\n",
    "\n",
    "Maintenant que nous avons correctement formaté les données, nous allons exprimer dans le langage de la librairie `torch` le modèle qui nous permet d'exprimer la transition entre les préférences au premier tour et les préférences au premier second tour. En particulier la matrice de transition sera définie par une matrice de poids `self.lin.weight` de telle sorte que quand on l'utilise, la contrainte qui fait que la somme des éléments colonne par colonne est bien égal à $1$ (utilisation de `torch.softmax(self.lin.weight, axis=1)`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.589673Z",
     "iopub.status.busy": "2022-06-03T12:42:47.588955Z",
     "iopub.status.idle": "2022-06-03T12:42:47.590733Z",
     "shell.execute_reply": "2022-06-03T12:42:47.591089Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader, random_split\n",
    "import torch.nn.functional as F\n",
    "\n",
    "#torch.set_default_tensor_type(\"torch.DoubleTensor\")\n",
    "torch.set_default_tensor_type(\"torch.FloatTensor\")\n",
    "\n",
    "class TransfertVoix(torch.nn.Module):\n",
    "    def __init__(self, N_1er, N_2eme):#, device=None):\n",
    "        super(TransfertVoix, self).__init__()\n",
    "        #if device is None: device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        #M = torch.randn((N_1er, N_2eme))\n",
    "        #self.M = torch.nn.Parameter(M, requires_grad=True)#.to(device)\n",
    "        self.lin = torch.nn.Linear(N_2eme, N_1er, bias=False)\n",
    "\n",
    "    def forward(self, p_1):\n",
    "        M = torch.softmax(self.lin.weight, axis=1)\n",
    "        #p_2_pred = torch.matmul(p_1, M)\n",
    "        p_2_pred = torch.matmul(p_1, M)\n",
    "        return p_2_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce modèle va utiliser comme dimension le nombre de différentes alternatives au premier et deuxième tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.595065Z",
     "iopub.status.busy": "2022-06-03T12:42:47.594489Z",
     "iopub.status.idle": "2022-06-03T12:42:47.596939Z",
     "shell.execute_reply": "2022-06-03T12:42:47.597293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 5)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_1er, N_2eme = len(df_1.columns), len(df_2.columns)\n",
    "N_1er, N_2eme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De telle sorte que nous allons pouvoir instancier un tel modèle, sachant que la matrice de transition sera initialement choisie de façon totalement aléatoire et donc déconnecté des données à la première époque de cet apprentissage :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.600782Z",
     "iopub.status.busy": "2022-06-03T12:42:47.600214Z",
     "iopub.status.idle": "2022-06-03T12:42:47.602236Z",
     "shell.execute_reply": "2022-06-03T12:42:47.602654Z"
    }
   },
   "outputs": [],
   "source": [
    "trans = TransfertVoix(N_1er, N_2eme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.606003Z",
     "iopub.status.busy": "2022-06-03T12:42:47.605499Z",
     "iopub.status.idle": "2022-06-03T12:42:47.608522Z",
     "shell.execute_reply": "2022-06-03T12:42:47.609096Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[-0.0880,  0.1206, -0.1215, -0.4015, -0.4468],\n",
      "        [ 0.3960,  0.3146, -0.2126, -0.3328, -0.1046],\n",
      "        [-0.3464,  0.4095, -0.1480, -0.4153, -0.1329],\n",
      "        [-0.4339, -0.4065, -0.1792,  0.1325, -0.1053],\n",
      "        [-0.0423,  0.2690, -0.3161,  0.0996,  0.4200],\n",
      "        [-0.2028, -0.0877,  0.3372, -0.1575, -0.2847],\n",
      "        [ 0.1853,  0.4341, -0.1799, -0.1631,  0.0924],\n",
      "        [-0.3792,  0.1841, -0.4133, -0.0281,  0.4405],\n",
      "        [-0.2768, -0.3501, -0.2039, -0.2115, -0.1076],\n",
      "        [-0.3536, -0.3075,  0.2438,  0.2888, -0.2852],\n",
      "        [ 0.3602, -0.4141,  0.3879, -0.4003,  0.1286],\n",
      "        [-0.2111, -0.2393, -0.2401,  0.3166, -0.2232],\n",
      "        [ 0.0414, -0.4352, -0.3531,  0.0005, -0.2302],\n",
      "        [-0.4354, -0.3757,  0.3449, -0.0454,  0.3981],\n",
      "        [-0.3212, -0.1278, -0.4469, -0.1314, -0.1780]], requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for p in trans.parameters():print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.613050Z",
     "iopub.status.busy": "2022-06-03T12:42:47.612470Z",
     "iopub.status.idle": "2022-06-03T12:42:47.614877Z",
     "shell.execute_reply": "2022-06-03T12:42:47.615274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([15, 5])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.lin.weight.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.618542Z",
     "iopub.status.busy": "2022-06-03T12:42:47.618076Z",
     "iopub.status.idle": "2022-06-03T12:42:47.622064Z",
     "shell.execute_reply": "2022-06-03T12:42:47.621579Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.2161, 0.2662, 0.2089, 0.1579, 0.1509],\n",
       "        [0.2813, 0.2593, 0.1531, 0.1357, 0.1705],\n",
       "        [0.1534, 0.3266, 0.1870, 0.1432, 0.1899],\n",
       "        [0.1546, 0.1589, 0.1994, 0.2724, 0.2147],\n",
       "        [0.1705, 0.2327, 0.1296, 0.1965, 0.2707],\n",
       "        [0.1722, 0.1933, 0.2956, 0.1802, 0.1587],\n",
       "        [0.2177, 0.2792, 0.1511, 0.1537, 0.1984],\n",
       "        [0.1349, 0.2369, 0.1304, 0.1916, 0.3062],\n",
       "        [0.1902, 0.1768, 0.2046, 0.2031, 0.2253],\n",
       "        [0.1463, 0.1532, 0.2658, 0.2781, 0.1566],\n",
       "        [0.2667, 0.1229, 0.2742, 0.1247, 0.2115],\n",
       "        [0.1777, 0.1728, 0.1726, 0.3012, 0.1756],\n",
       "        [0.2489, 0.1546, 0.1678, 0.2390, 0.1897],\n",
       "        [0.1247, 0.1323, 0.2720, 0.1841, 0.2869],\n",
       "        [0.1832, 0.2223, 0.1616, 0.2215, 0.2114]], grad_fn=<SoftmaxBackward0>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.softmax(trans.lin.weight, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions que la contrainte sur chaque colonne de la matrice de transition et bien vérifiée :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.625701Z",
     "iopub.status.busy": "2022-06-03T12:42:47.625054Z",
     "iopub.status.idle": "2022-06-03T12:42:47.628617Z",
     "shell.execute_reply": "2022-06-03T12:42:47.629085Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,\n",
       "        1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],\n",
       "       grad_fn=<SumBackward1>)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.softmax(trans.lin.weight, axis=1).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exprimons maintenant pour chacun des bureaux de vote les probabilités de préférence qui sont exprimées localement :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.633141Z",
     "iopub.status.busy": "2022-06-03T12:42:47.632425Z",
     "iopub.status.idle": "2022-06-03T12:42:47.635000Z",
     "shell.execute_reply": "2022-06-03T12:42:47.635396Z"
    }
   },
   "outputs": [],
   "source": [
    "sum_1, sum_2 = n_1.sum(axis=1), n_2.sum(axis=1)\n",
    "p_1 = n_1/sum_1[:, None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette probabilité va pouvoir être multiplié par la matrice de transition de probabilité et nous vérifions dans les lignes suivantes la compatibilité entre les différentes dimensions des données représentées :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.639457Z",
     "iopub.status.busy": "2022-06-03T12:42:47.638781Z",
     "iopub.status.idle": "2022-06-03T12:42:47.642186Z",
     "shell.execute_reply": "2022-06-03T12:42:47.642580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([13934, 15]),\n",
       " tensor([1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000]))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_1.shape, p_1.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.646644Z",
     "iopub.status.busy": "2022-06-03T12:42:47.645994Z",
     "iopub.status.idle": "2022-06-03T12:42:47.648199Z",
     "shell.execute_reply": "2022-06-03T12:42:47.648625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([13934, 15]), torch.Size([15, 5]))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_1.shape, trans.lin.weight.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.652332Z",
     "iopub.status.busy": "2022-06-03T12:42:47.651627Z",
     "iopub.status.idle": "2022-06-03T12:42:47.656204Z",
     "shell.execute_reply": "2022-06-03T12:42:47.656558Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([13934, 5])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.matmul(p_1, torch.softmax(trans.lin.weight, axis=1)).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ces différentes vérifications nous permettent de valider l'utilisation directe du modèle pour prédire la probabilité de préférence dans ce bureau de vote au second tour à partir de celle observée au premier tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.660384Z",
     "iopub.status.busy": "2022-06-03T12:42:47.659716Z",
     "iopub.status.idle": "2022-06-03T12:42:47.663356Z",
     "shell.execute_reply": "2022-06-03T12:42:47.663858Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.1646, 0.2284, 0.2080, 0.1943, 0.2047], grad_fn=<MeanBackward1>),\n",
       " tensor(1., grad_fn=<SumBackward0>))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_2_pred = trans(n_1/sum_1[:, None])\n",
    "p_2_pred.mean(axis=0), p_2_pred.mean(axis=0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au cours de l'apprentissage, nous allons pouvoir comparer cette probabilité prédit avec celle qui a été effectivement observée :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.667867Z",
     "iopub.status.busy": "2022-06-03T12:42:47.667203Z",
     "iopub.status.idle": "2022-06-03T12:42:47.671360Z",
     "shell.execute_reply": "2022-06-03T12:42:47.670950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.0187, 0.0502, 0.2552, 0.3702, 0.3057]), tensor(1.))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_2 = n_2/sum_2[:, None]\n",
    "p_2.mean(axis=0), p_2.mean(axis=0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons aussi vérifier graphiquement que les résultats moyen des votes au second tour peuvent être inférer à partir des données de probabilité multiplié par la taille de chacun des bureaux de vote :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.688650Z",
     "iopub.status.busy": "2022-06-03T12:42:47.686336Z",
     "iopub.status.idle": "2022-06-03T12:42:47.801090Z",
     "shell.execute_reply": "2022-06-03T12:42:47.800688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 5))\n",
    "k = df_2.sum()/df_2.sum().sum()\n",
    "ax = k.plot.bar(ax=ax)\n",
    "p_2_average = (p_2*sum_2[:, None]).sum(axis=0)/sum_2.sum()\n",
    "ax.plot(p_2_average, 'r')\n",
    "ax.set_xlabel('Candidat')\n",
    "ax.set_ylabel('pourcentage');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ainsi que la concordance entre les résultats finaux obtenu est ce que nous représentons dans notre modèle :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.806025Z",
     "iopub.status.busy": "2022-06-03T12:42:47.805499Z",
     "iopub.status.idle": "2022-06-03T12:42:47.808688Z",
     "shell.execute_reply": "2022-06-03T12:42:47.809083Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Nuls            1.651714\n",
       " Blancs          4.582147\n",
       " Abstentions    28.010677\n",
       " MACRON         38.497926\n",
       " LE PEN         27.257536\n",
       " dtype: float64,\n",
       " tensor([ 1.6700,  4.5794, 28.0624, 38.5165, 27.1717]))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k*100, p_2_average*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.813093Z",
     "iopub.status.busy": "2022-06-03T12:42:47.812470Z",
     "iopub.status.idle": "2022-06-03T12:42:47.815505Z",
     "shell.execute_reply": "2022-06-03T12:42:47.815835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.1159)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.binary_cross_entropy(p_2_average, p_2_average, reduction=\"sum\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modèle `torch` d'apprentissage\n",
    "\n",
    "Maintenant que nous avons défini le modèle de transition des probabilités entre les préférences au premier tour et au second tour, nous pouvons maintenant écrire un algorithme d'apprentissage qui permet d'optimiser la concordance entre la prédiction et les observations. \n",
    "\n",
    "Nous allons utiliser les distributions observées $q$ et prédites $\\hat{q}$ (celle-ci dépendant de $M$) pour calculer un coût à minimiser\n",
    "$$\n",
    "\\mathcal{L} =  KL(q, \\hat{q})\n",
    "$$\n",
    "\n",
    "où la [divergence de Kullback-Leibler](https://fr.wikipedia.org/wiki/Divergence_de_Kullback-Leibler) est calculée comme\n",
    "\n",
    "$$\n",
    "KL(P, Q) = \\sum_j P_j \\cdot \\log \\frac {P_j}{Q_j}\n",
    "$$\n",
    "\n",
    "\n",
    "Cette divergence est l'équivalent d'une distance dans les espaces de probabilité. Mathématiquement, c'est une semi-normes car elle obéit à deux propriétés fondamentales: elle est toujours positive et est égale à zéro quand elle est appliquée à distributions identiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.829669Z",
     "iopub.status.busy": "2022-06-03T12:42:47.829090Z",
     "iopub.status.idle": "2022-06-03T12:42:47.830956Z",
     "shell.execute_reply": "2022-06-03T12:42:47.831442Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "\n",
    "learning_rate = 0.03\n",
    "beta1, beta2 = 0.95, 0\n",
    "# beta1, beta2 = 0.9, 0.999\n",
    "num_epochs = 2 ** 10 + 1\n",
    "batch_size = 2 ** 6\n",
    "do_optim = False\n",
    "do_optim = True\n",
    "\n",
    "def pdf_loss(p_pred, p, weight, loss_type):\n",
    "    if loss_type=='kl':\n",
    "        ind_nonzero = (p==0) + (p_pred==0)\n",
    "        p[ind_nonzero] = 1.\n",
    "        p_pred[ind_nonzero] = 1.\n",
    "        kl_div = p * (p.log() - p_pred.log())\n",
    "        loss_train = (kl_div * weight[:, None]).sum()\n",
    "    elif loss_type=='l1':\n",
    "        div = torch.absolute(p - p_pred)\n",
    "        loss_train = (div * weight[:, None]).sum() \n",
    "    else:\n",
    "        loss_train = F.binary_cross_entropy(p_pred, p, reduction=\"sum\", weight=weight[:, None])\n",
    "        loss_train -= F.binary_cross_entropy(p, p, reduction=\"sum\", weight=weight[:, None])\n",
    "    return loss_train\n",
    "\n",
    "def fit_data(\n",
    "    df_1,\n",
    "    df_2,\n",
    "    learning_rate=learning_rate,\n",
    "    batch_size=batch_size,\n",
    "    num_epochs=num_epochs,\n",
    "    loss_type='bce',\n",
    "    beta1=beta1,\n",
    "    beta2=beta2, \n",
    "    do_optim=do_optim, \n",
    "    split_ratio=.9, \n",
    "    seed=2022, # graine du générateur de nombre aléatoires utilisé dans le split test vs train\n",
    "    verbose=False\n",
    "):\n",
    "\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    N_1er, N_2eme = len(df_1.columns), len(df_2.columns)\n",
    "    trans = TransfertVoix(N_1er, N_2eme)\n",
    "    trans = trans.to(device)\n",
    "    trans.train()\n",
    "    \n",
    "    # apprentissage\n",
    "    if beta2==0: \n",
    "        optimizer = torch.optim.SGD(trans.parameters(), lr=learning_rate, momentum=beta1, nesterov=do_optim)\n",
    "    else:\n",
    "        optimizer = torch.optim.Adam(trans.parameters(), lr=learning_rate, betas=(beta1, beta2), amsgrad=do_optim)\n",
    "\n",
    "    if torch.cuda.is_available(): torch.cuda.empty_cache()    \n",
    "    # the data\n",
    "    X_1, X_2 = df_1.values, df_2.values\n",
    "    x_1, x_2 = torch.Tensor(X_1), torch.Tensor(X_2)\n",
    "\n",
    "    # split train and test\n",
    "    dataset = TensorDataset(x_1, x_2)\n",
    "    train_set_size = int(len(dataset) * split_ratio)\n",
    "    test_set_size = len(dataset) - train_set_size\n",
    "    train_set, test_set = random_split(dataset, [train_set_size, test_set_size], generator=torch.Generator().manual_seed(seed))\n",
    "    train_loader = DataLoader(train_set, batch_size=int(batch_size), shuffle=True)\n",
    "\n",
    "    for epoch in range(int(num_epochs)):\n",
    "        losses = []\n",
    "        for n_1, n_2 in train_loader:\n",
    "            n_1, n_2 = n_1.to(device), n_2.to(device)\n",
    "\n",
    "            sum_1, sum_2 = n_1.sum(axis=1), n_2.sum(axis=1)\n",
    "            p_1 = n_1/sum_1[:, None]\n",
    "            p_2 = n_2/sum_2[:, None]\n",
    "\n",
    "            p_2_pred = trans(p_1)\n",
    "            weight = sum_2 / train_set_size # /sum_2.sum() # donne un poids à chaque bureau de vote proportionnel à sa taille\n",
    "\n",
    "            loss_train = pdf_loss(p_2_pred, p_2, weight, loss_type=loss_type)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss_train.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            losses.append(loss_train.item())\n",
    "\n",
    "        if verbose and (epoch % (num_epochs // 32) == 0):\n",
    "            print(f\"Iteration: {epoch} / {num_epochs} - Loss: {np.sum(losses):.5e}\")\n",
    "\n",
    "    loss_train = np.sum(losses) # somme des loss à la dernière époque\n",
    "    loss_train = loss_train/split_ratio # normalise par rapport à la taille du train dataset\n",
    "\n",
    "    # Test\n",
    "    with torch.no_grad():\n",
    "        n_1, n_2 = dataset[test_set.indices]\n",
    "        sum_1, sum_2 = n_1.sum(axis=1), n_2.sum(axis=1)\n",
    "        p_2 = n_2/sum_2[:, None]\n",
    "        p_1 = n_1/sum_1[:, None]\n",
    "        p_2_pred = trans(p_1)\n",
    "        weight = sum_2 / test_set_size # normalise par rapport à la taille du test dataset\n",
    "        loss_test = pdf_loss(p_2_pred, p_2, weight, loss_type=loss_type)\n",
    "\n",
    "    return trans, loss_train, loss_test, p_1.detach().numpy(), p_2.detach().numpy(), p_2_pred.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T12:42:47.838108Z",
     "iopub.status.busy": "2022-06-03T12:42:47.836409Z",
     "iopub.status.idle": "2022-06-03T13:08:08.627234Z",
     "shell.execute_reply": "2022-06-03T13:08:08.627586Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_type='kl'\n",
      "Iteration: 0 / 1025 - Loss: 3.70322e+01\n",
      "Iteration: 32 / 1025 - Loss: 8.00898e+00\n",
      "Iteration: 64 / 1025 - Loss: 7.79831e+00\n",
      "Iteration: 96 / 1025 - Loss: 7.72095e+00\n",
      "Iteration: 128 / 1025 - Loss: 7.67729e+00\n",
      "Iteration: 160 / 1025 - Loss: 7.65271e+00\n",
      "Iteration: 192 / 1025 - Loss: 7.63514e+00\n",
      "Iteration: 224 / 1025 - Loss: 7.62084e+00\n",
      "Iteration: 256 / 1025 - Loss: 7.61023e+00\n",
      "Iteration: 288 / 1025 - Loss: 7.60159e+00\n",
      "Iteration: 320 / 1025 - Loss: 7.59273e+00\n",
      "Iteration: 352 / 1025 - Loss: 7.59356e+00\n",
      "Iteration: 384 / 1025 - Loss: 7.58869e+00\n",
      "Iteration: 416 / 1025 - Loss: 7.58291e+00\n",
      "Iteration: 448 / 1025 - Loss: 7.57853e+00\n",
      "Iteration: 480 / 1025 - Loss: 7.57781e+00\n",
      "Iteration: 512 / 1025 - Loss: 7.57510e+00\n",
      "Iteration: 544 / 1025 - Loss: 7.57345e+00\n",
      "Iteration: 576 / 1025 - Loss: 7.57048e+00\n",
      "Iteration: 608 / 1025 - Loss: 7.56798e+00\n",
      "Iteration: 640 / 1025 - Loss: 7.56749e+00\n",
      "Iteration: 672 / 1025 - Loss: 7.56612e+00\n",
      "Iteration: 704 / 1025 - Loss: 7.56486e+00\n",
      "Iteration: 736 / 1025 - Loss: 7.55993e+00\n",
      "Iteration: 768 / 1025 - Loss: 7.56216e+00\n",
      "Iteration: 800 / 1025 - Loss: 7.55778e+00\n",
      "Iteration: 832 / 1025 - Loss: 7.56123e+00\n",
      "Iteration: 864 / 1025 - Loss: 7.56200e+00\n",
      "Iteration: 896 / 1025 - Loss: 7.55750e+00\n",
      "Iteration: 928 / 1025 - Loss: 7.55842e+00\n",
      "Iteration: 960 / 1025 - Loss: 7.55721e+00\n",
      "Iteration: 992 / 1025 - Loss: 7.55595e+00\n",
      "Iteration: 1024 / 1025 - Loss: 7.55659e+00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_type='l1'\n",
      "Iteration: 0 / 1025 - Loss: 1.33196e+02\n",
      "Iteration: 32 / 1025 - Loss: 5.66033e+01\n",
      "Iteration: 64 / 1025 - Loss: 5.63176e+01\n",
      "Iteration: 96 / 1025 - Loss: 5.62326e+01\n",
      "Iteration: 128 / 1025 - Loss: 5.61676e+01\n",
      "Iteration: 160 / 1025 - Loss: 5.61466e+01\n",
      "Iteration: 192 / 1025 - Loss: 5.60992e+01\n",
      "Iteration: 224 / 1025 - Loss: 5.60895e+01\n",
      "Iteration: 256 / 1025 - Loss: 5.60742e+01\n",
      "Iteration: 288 / 1025 - Loss: 5.60796e+01\n",
      "Iteration: 320 / 1025 - Loss: 5.60805e+01\n",
      "Iteration: 352 / 1025 - Loss: 5.60440e+01\n",
      "Iteration: 384 / 1025 - Loss: 5.60504e+01\n",
      "Iteration: 416 / 1025 - Loss: 5.60609e+01\n",
      "Iteration: 448 / 1025 - Loss: 5.60564e+01\n",
      "Iteration: 480 / 1025 - Loss: 5.60472e+01\n",
      "Iteration: 512 / 1025 - Loss: 5.60362e+01\n",
      "Iteration: 544 / 1025 - Loss: 5.60396e+01\n",
      "Iteration: 576 / 1025 - Loss: 5.60343e+01\n",
      "Iteration: 608 / 1025 - Loss: 5.60274e+01\n",
      "Iteration: 640 / 1025 - Loss: 5.60431e+01\n",
      "Iteration: 672 / 1025 - Loss: 5.60369e+01\n",
      "Iteration: 704 / 1025 - Loss: 5.60163e+01\n",
      "Iteration: 736 / 1025 - Loss: 5.60454e+01\n",
      "Iteration: 768 / 1025 - Loss: 5.60221e+01\n",
      "Iteration: 800 / 1025 - Loss: 5.60282e+01\n",
      "Iteration: 832 / 1025 - Loss: 5.60290e+01\n",
      "Iteration: 864 / 1025 - Loss: 5.60272e+01\n",
      "Iteration: 896 / 1025 - Loss: 5.60183e+01\n",
      "Iteration: 928 / 1025 - Loss: 5.60114e+01\n",
      "Iteration: 960 / 1025 - Loss: 5.60299e+01\n",
      "Iteration: 992 / 1025 - Loss: 5.60116e+01\n",
      "Iteration: 1024 / 1025 - Loss: 5.60406e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_type='bce'\n",
      "Iteration: 0 / 1025 - Loss: 4.09472e+01\n",
      "Iteration: 32 / 1025 - Loss: 9.90133e+00\n",
      "Iteration: 64 / 1025 - Loss: 9.67422e+00\n",
      "Iteration: 96 / 1025 - Loss: 9.59947e+00\n",
      "Iteration: 128 / 1025 - Loss: 9.55688e+00\n",
      "Iteration: 160 / 1025 - Loss: 9.53168e+00\n",
      "Iteration: 192 / 1025 - Loss: 9.51553e+00\n",
      "Iteration: 224 / 1025 - Loss: 9.50445e+00\n",
      "Iteration: 256 / 1025 - Loss: 9.49103e+00\n",
      "Iteration: 288 / 1025 - Loss: 9.48711e+00\n",
      "Iteration: 320 / 1025 - Loss: 9.47912e+00\n",
      "Iteration: 352 / 1025 - Loss: 9.47516e+00\n",
      "Iteration: 384 / 1025 - Loss: 9.47045e+00\n",
      "Iteration: 416 / 1025 - Loss: 9.47025e+00\n",
      "Iteration: 448 / 1025 - Loss: 9.46174e+00\n",
      "Iteration: 480 / 1025 - Loss: 9.46103e+00\n",
      "Iteration: 512 / 1025 - Loss: 9.45700e+00\n",
      "Iteration: 544 / 1025 - Loss: 9.45706e+00\n",
      "Iteration: 576 / 1025 - Loss: 9.45492e+00\n",
      "Iteration: 608 / 1025 - Loss: 9.45437e+00\n",
      "Iteration: 640 / 1025 - Loss: 9.45021e+00\n",
      "Iteration: 672 / 1025 - Loss: 9.44847e+00\n",
      "Iteration: 704 / 1025 - Loss: 9.44583e+00\n",
      "Iteration: 736 / 1025 - Loss: 9.44639e+00\n",
      "Iteration: 768 / 1025 - Loss: 9.44761e+00\n",
      "Iteration: 800 / 1025 - Loss: 9.44721e+00\n",
      "Iteration: 832 / 1025 - Loss: 9.43728e+00\n",
      "Iteration: 864 / 1025 - Loss: 9.44098e+00\n",
      "Iteration: 896 / 1025 - Loss: 9.43843e+00\n",
      "Iteration: 928 / 1025 - Loss: 9.44168e+00\n",
      "Iteration: 960 / 1025 - Loss: 9.43872e+00\n",
      "Iteration: 992 / 1025 - Loss: 9.44106e+00\n",
      "Iteration: 1024 / 1025 - Loss: 9.44050e+00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for loss_type in [ 'kl', 'l1', 'bce',]:\n",
    "    print(f'{loss_type=}')\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, verbose=True, loss_type=loss_type)\n",
    "\n",
    "    fig, axs = plt.subplots(1, N_2eme, figsize=(13, 5))\n",
    "    for i_col, candidat in enumerate(df_2.columns):\n",
    "        #axs[i_col].scatter(p_2[:,i_col], p_2_pred[:,i_col], alpha=.005)\n",
    "        axs[i_col].plot([0, 1], [0, 1], 'r--')\n",
    "        sns.histplot(x=p_2[:, i_col], y=p_2_pred[:, i_col], ax=axs[i_col])\n",
    "        axs[i_col].set_xlabel(candidat)\n",
    "        axs[i_col].set_xlim(0, 1)\n",
    "        axs[i_col].set_ylim(0, 1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ces graphiques représentent en abscisse les probabilités observées et en ordonnée les probabilités prédites. L'intérieur du graphique représente en niveaux de bleu l'histogramme des différentes valeurs telles qu'elles sont observés sur la base de test, c'est-à-dire sur 10 % de l'ensemble des bureaux de vote."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut maintenant récupérer du modèle la matrice de transition qui a été inférée grâce a notre apprentissage automatique :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:08.632350Z",
     "iopub.status.busy": "2022-06-03T13:08:08.630795Z",
     "iopub.status.idle": "2022-06-03T13:08:08.634552Z",
     "shell.execute_reply": "2022-06-03T13:08:08.634960Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.1051117e-01, 2.3402784e-02, 5.1034647e-03, 1.3584508e-01,\n",
       "        4.2513749e-01],\n",
       "       [1.3189758e-02, 6.0958701e-01, 3.0688695e-03, 4.1748765e-03,\n",
       "        3.6997950e-01],\n",
       "       [2.1108342e-03, 1.9269541e-04, 8.9829373e-01, 2.0171998e-02,\n",
       "        7.9230756e-02],\n",
       "       [2.7441588e-01, 2.4624530e-02, 4.7215675e-03, 6.1894637e-03,\n",
       "        6.9004852e-01],\n",
       "       [1.6418426e-01, 3.9319155e-01, 5.4822816e-03, 3.2009218e-02,\n",
       "        4.0513271e-01],\n",
       "       [5.5745655e-05, 1.2465418e-04, 8.1309496e-05, 9.9965394e-01,\n",
       "        8.4356012e-05],\n",
       "       [1.8812412e-01, 2.8088453e-01, 2.2433496e-03, 3.6759085e-03,\n",
       "        5.2507210e-01],\n",
       "       [6.2122388e-05, 6.8668654e-05, 5.1389063e-05, 5.3146843e-05,\n",
       "        9.9976462e-01],\n",
       "       [7.1626771e-03, 7.6614358e-02, 5.4638863e-02, 2.0824806e-01,\n",
       "        6.5333611e-01],\n",
       "       [1.6121916e-02, 7.1782693e-02, 2.7742311e-01, 6.0657763e-01,\n",
       "        2.8094605e-02],\n",
       "       [5.4101113e-02, 2.1443865e-01, 3.7258046e-03, 7.2310627e-01,\n",
       "        4.6282150e-03],\n",
       "       [2.2628318e-04, 9.8243030e-04, 3.7243144e-04, 9.9814224e-01,\n",
       "        2.7658747e-04],\n",
       "       [2.3065766e-02, 1.2551677e-01, 3.1587842e-03, 8.4496987e-01,\n",
       "        3.2889245e-03],\n",
       "       [1.7124680e-01, 5.2805609e-01, 1.0715614e-02, 2.6337279e-02,\n",
       "        2.6364425e-01],\n",
       "       [8.3108833e-03, 3.3624385e-02, 1.7545348e-03, 2.2951560e-03,\n",
       "        9.5401508e-01]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = torch.softmax(trans.lin.weight, axis=1).detach().numpy()\n",
    "M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie dans un premier temps que la contrainte est toujours bien respectée :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:08.639350Z",
     "iopub.status.busy": "2022-06-03T13:08:08.638801Z",
     "iopub.status.idle": "2022-06-03T13:08:08.641093Z",
     "shell.execute_reply": "2022-06-03T13:08:08.641421Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.99999994, 1.        , 0.99999994,\n",
       "        1.        , 0.99999994, 1.0000001 , 1.        , 1.        ],\n",
       "       dtype=float32),\n",
       " array([1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.99999994, 1.        , 0.99999994,\n",
       "        1.        , 0.99999994, 1.0000001 , 1.        , 1.        ],\n",
       "       dtype=float32),\n",
       " (15, 5))"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.sum(axis=1), M.sum(axis=1), M.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons nous inspirer d'[un graphique de la galerie matplotlib](https://matplotlib.org/stable/gallery/misc/table_demo.html#sphx-glr-gallery-misc-table-demo-py) pour représenter la la proportion des report de voir en fonction de chacun des choix FE au deuxième tour :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:08.649144Z",
     "iopub.status.busy": "2022-06-03T13:08:08.648504Z",
     "iopub.status.idle": "2022-06-03T13:08:09.190033Z",
     "shell.execute_reply": "2022-06-03T13:08:09.190381Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13, 13))\n",
    "\n",
    "columns = df_2.columns\n",
    "rows = df_1.columns\n",
    "\n",
    "# Get some shades for the colors\n",
    "colors = plt.cm.tab20c(np.linspace(0, 1, len(rows)))\n",
    "n_rows = len(rows)\n",
    "\n",
    "index = np.arange(len(columns)) + 0.3\n",
    "bar_width = 0.4\n",
    "\n",
    "# Initialize the vertical-offset for the stacked bar chart.\n",
    "y_offset = np.zeros(len(columns))\n",
    "\n",
    "# Plot bars and create text labels for the table\n",
    "cell_text = []\n",
    "for row in range(n_rows):\n",
    "    ax.bar(index, M[row]*100, bar_width, bottom=y_offset, color=colors[row])\n",
    "    y_offset = y_offset + M[row]*100\n",
    "    cell_text.append([f'{x*100:1.1f}%' for x in M[row]])\n",
    "ax.set_ylim(0, 100)\n",
    "\n",
    "# Add a table at the bottom of the axes\n",
    "the_table = ax.table(cellText=cell_text,\n",
    "                      rowLabels=rows,\n",
    "                      rowColours=colors,\n",
    "                      colLabels=columns,\n",
    "                      loc='bottom')\n",
    "\n",
    "# Adjust layout to make room for the table:\n",
    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
    "\n",
    "plt.ylabel(\"Pourcentage de report des voix\")\n",
    "plt.yticks(np.linspace(0, 100, 6, endpoint=True))\n",
    "plt.xticks([])\n",
    "plt.title(\"Report des voix par résultat du 2nd tour\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ces données correspondent aux intuitions qu'on peut se faire quand au report des votes. Notamment on remarque que la plupart des individus qui s'abstiennent au second tour se sont abstenus au premier (avec une petite proportion de Mélenchon qui se sont abstenus). Remarque aussi que la majorité des électeurs de Le Pen au premier tour en voter pour la même personne au deuxième tour. Environ un quart des électeurs de Le Pen au second tour s'était abstenu au premier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:09.195276Z",
     "iopub.status.busy": "2022-06-03T13:08:09.194691Z",
     "iopub.status.idle": "2022-06-03T13:08:09.197107Z",
     "shell.execute_reply": "2022-06-03T13:08:09.197440Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 41.1,   2.3,   0.5,  13.6,  42.5],\n",
       "       [  1.3,  61. ,   0.3,   0.4,  37. ],\n",
       "       [  0.2,   0. ,  89.8,   2. ,   7.9],\n",
       "       [ 27.4,   2.5,   0.5,   0.6,  69. ],\n",
       "       [ 16.4,  39.3,   0.5,   3.2,  40.5],\n",
       "       [  0. ,   0. ,   0. , 100. ,   0. ],\n",
       "       [ 18.8,  28.1,   0.2,   0.4,  52.5],\n",
       "       [  0. ,   0. ,   0. ,   0. , 100. ],\n",
       "       [  0.7,   7.7,   5.5,  20.8,  65.3],\n",
       "       [  1.6,   7.2,  27.7,  60.7,   2.8],\n",
       "       [  5.4,  21.4,   0.4,  72.3,   0.5],\n",
       "       [  0. ,   0.1,   0. ,  99.8,   0. ],\n",
       "       [  2.3,  12.6,   0.3,  84.5,   0.3],\n",
       "       [ 17.1,  52.8,   1.1,   2.6,  26.4],\n",
       "       [  0.8,   3.4,   0.2,   0.2,  95.4]], dtype=float32)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(precision=1)\n",
    "np.set_printoptions(suppress=True)\n",
    "M*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe aussi quelques points intéressants dans les votes nuls qui proviennent majoritairement de voteur \"nul\" au premier tour (40 %) et ensuite par des électeurs ayant voté pour des candidats marginaux au premier tour (Arnaud, Lassalle, Hidalgo). On observe à peu près la même structure pour les voteurs \"blanc\" avec cette particularité que les individus ayant voté pour Jadot au premier tour ont relativement plus exprimé de votes \"blanc\" (~12 %) par rapport à ce qu'on observe pour les votes \"nul\". On rappelle qu'en France, un vote nul est un bulletin qui n'a pas été validé car il est par exemple raturé alors qu'un vote blanc est exprimé par un bulletin de vote totalement blanc (et qu'il faut préparer en avance). On peut donc expliquer ce dernier par rapport aux particularités du vote écologiques liés aux candidat Jadot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muni de cette matrice de transition on peut maintenant opérer une transformation de telle sorte à ce que nous allons regarder la distribution des report de voix pour chacun des choix qui sont faits au premier tour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:09.201841Z",
     "iopub.status.busy": "2022-06-03T13:08:09.201335Z",
     "iopub.status.idle": "2022-06-03T13:08:09.203501Z",
     "shell.execute_reply": "2022-06-03T13:08:09.203828Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 41.1,   1.3,   0.2,  27.4,  16.4,   0. ,  18.8,   0. ,   0.7,\n",
       "          1.6,   5.4,   0. ,   2.3,  17.1,   0.8],\n",
       "       [  2.3,  61. ,   0. ,   2.5,  39.3,   0. ,  28.1,   0. ,   7.7,\n",
       "          7.2,  21.4,   0.1,  12.6,  52.8,   3.4],\n",
       "       [  0.5,   0.3,  89.8,   0.5,   0.5,   0. ,   0.2,   0. ,   5.5,\n",
       "         27.7,   0.4,   0. ,   0.3,   1.1,   0.2],\n",
       "       [ 13.6,   0.4,   2. ,   0.6,   3.2, 100. ,   0.4,   0. ,  20.8,\n",
       "         60.7,  72.3,  99.8,  84.5,   2.6,   0.2],\n",
       "       [ 42.5,  37. ,   7.9,  69. ,  40.5,   0. ,  52.5, 100. ,  65.3,\n",
       "          2.8,   0.5,   0. ,   0.3,  26.4,  95.4]], dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MT = M.T\n",
    "MT /= MT.sum(axis=0)\n",
    "MT*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:09.238560Z",
     "iopub.status.busy": "2022-06-03T13:08:09.211143Z",
     "iopub.status.idle": "2022-06-03T13:08:09.789831Z",
     "shell.execute_reply": "2022-06-03T13:08:09.790209Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [78]\u001b[0m, in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m cell_text \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_rows):\n\u001b[0;32m---> 19\u001b[0m     ax\u001b[38;5;241m.\u001b[39mbar(index, MT[row]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, bar_width, bottom\u001b[38;5;241m=\u001b[39my_offset, color\u001b[38;5;241m=\u001b[39m\u001b[43mcolors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m]\u001b[49m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     20\u001b[0m     y_offset \u001b[38;5;241m=\u001b[39m y_offset \u001b[38;5;241m+\u001b[39m MT[row]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\n\u001b[1;32m     21\u001b[0m     cell_text\u001b[38;5;241m.\u001b[39mappend([\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m1.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m MT[row]])\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(25, 8))\n",
    "\n",
    "columns = df_1.columns\n",
    "rows = df_2.columns\n",
    "\n",
    "# named colors: https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "colors = ['darkviolet', 'steelblue', 'brown' ]\n",
    "n_rows = len(rows)\n",
    "\n",
    "index = np.arange(len(columns)) + 0.3\n",
    "bar_width = 0.4\n",
    "\n",
    "# Initialize the vertical-offset for the stacked bar chart.\n",
    "y_offset = np.zeros(len(columns))\n",
    "\n",
    "# Plot bars and create text labels for the table\n",
    "cell_text = []\n",
    "for row in range(n_rows):\n",
    "    ax.bar(index, MT[row]*100, bar_width, bottom=y_offset, color=colors[row], linewidth=1)\n",
    "    y_offset = y_offset + MT[row]*100\n",
    "    cell_text.append([f'{x*100:1.1f}%' for x in MT[row]])\n",
    "ax.set_ylim(0, 100)\n",
    "\n",
    "# Add a table at the bottom of the axes\n",
    "the_table = ax.table(cellText=cell_text,\n",
    "                      rowLabels=rows,\n",
    "                      rowColours=colors,\n",
    "                      colLabels=columns,\n",
    "                      loc='bottom')\n",
    "\n",
    "# Adjust layout to make room for the table:\n",
    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
    "\n",
    "plt.ylabel(\"Pourcentage de report des voix\")\n",
    "plt.yticks(np.linspace(0, 100, 6, endpoint=True))\n",
    "plt.xticks([])\n",
    "plt.title(\"Report des voix par résultat du 1er tour\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut aussi se restreindre aux votes exprimés :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:09.794840Z",
     "iopub.status.busy": "2022-06-03T13:08:09.794256Z",
     "iopub.status.idle": "2022-06-03T13:08:09.796691Z",
     "shell.execute_reply": "2022-06-03T13:08:09.797019Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.9,   7.3, 100. ,   0.7,   0. ,  24.2,  95.6,  99.4, 100. ,\n",
       "         99.6,   9.1,   0.2],\n",
       "       [ 99.1,  92.7,   0. ,  99.3, 100. ,  75.8,   4.4,   0.6,   0. ,\n",
       "          0.4,  90.9,  99.8]], dtype=float32)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shift = 3\n",
    "MT = M[shift:, shift:].T\n",
    "MT /= MT.sum(axis=0)\n",
    "MT*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:09.832249Z",
     "iopub.status.busy": "2022-06-03T13:08:09.805090Z",
     "iopub.status.idle": "2022-06-03T13:08:10.128141Z",
     "shell.execute_reply": "2022-06-03T13:08:10.128501Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "\n",
    "columns = df_1.columns[shift:]\n",
    "rows = df_2.columns[shift:]\n",
    "\n",
    "# named colors: https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "colors = ['steelblue', 'brown' ]\n",
    "n_rows = len(rows)\n",
    "\n",
    "index = np.arange(len(columns)) + 0.3\n",
    "bar_width = 0.4\n",
    "\n",
    "# Initialize the vertical-offset for the stacked bar chart.\n",
    "y_offset = np.zeros(len(columns))\n",
    "\n",
    "# Plot bars and create text labels for the table\n",
    "cell_text = []\n",
    "for row in range(n_rows):\n",
    "    ax.bar(index, MT[row]*100, bar_width, bottom=y_offset, color=colors[row], linewidth=1)\n",
    "    y_offset = y_offset + MT[row]*100\n",
    "    cell_text.append([f'{x*100:1.1f}%' for x in MT[row]])\n",
    "ax.set_ylim(0, 100)\n",
    "# Add a table at the bottom of the axes\n",
    "the_table = ax.table(cellText=cell_text, colLoc='center', \n",
    "                      rowLabels=rows, rowLoc='center',\n",
    "                      rowColours=colors,\n",
    "                      colLabels=columns,\n",
    "                      loc='bottom')\n",
    "the_table.auto_set_font_size(False)\n",
    "the_table.set_fontsize(10)\n",
    "# Adjust layout to make room for the table:\n",
    "plt.subplots_adjust(left=0.2, bottom=0.2)\n",
    "\n",
    "plt.ylabel(\"Pourcentage de report des voix\")\n",
    "plt.yticks(np.linspace(0, 100, 6, endpoint=True))\n",
    "plt.xticks([])\n",
    "plt.title(\"Report des voix exprimées connaissant le choix exprimé au 1er tour\")\n",
    "plt.savefig('2022-05-23_transfert-des-voix.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ce tableau donne le pourcentage de chances d'exprimer une voix pour un candidat ou pour l'autre en fonction du choix qu'on a exprimé au premier tour. \n",
    "\n",
    "Ce tableau montre des tendances claires, par exemple que si on a voté Macron, Jadot ou Pécresse au premier tour, alors on va certainement voter Macron au deuxième tour. Ses électeurs se montre particulièrement consensuel et suivent le « pacte républicain » mise en place pour faire un barrage au Front National. Il montre aussi que si on a voté Le Pen ou Dupont-Aignan au premier tour alors on va voter Le Pen au deuxième, un clair vote de suivi. (On pourra aussi remarquer les ~ 3 % des voix pour Macron au premier tour qui ont voté Le Pen au deuxième tour, soit tout de même environ 320k individus…)\n",
    "\n",
    "Connaissant les couleurs politiques d'autres candidats du premier tour, on peut être surpris que les électeurs de Arthaud, Roussel ou Hidalgo ont majoritairement choisi Le Pen au deuxième tour, signifiant alors un rejet du candidat Macron. Les électeurs de Zemmour sont aussi partagés, signifiant un rejet des deux alternatives. **Ce résultat est à prendre avec des pincettes car ces derniers candidats ont obtenu moins de votes et donc que le processus d'inférence est forcément moins précis car il y a moins de données disponibles.** Pour se rendre compte de la variabilité des résultats qu'on obtient là, je conseille au lecteur (*à l'électeur*) de relancer ces notebook en utilisant différents « graines » pour le générateur de nombre aléatoire qui permet de séparer les données (le paramètre `seed`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:10.132667Z",
     "iopub.status.busy": "2022-06-03T13:08:10.132146Z",
     "iopub.status.idle": "2022-06-03T13:08:10.134867Z",
     "shell.execute_reply": "2022-06-03T13:08:10.135193Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total des voix au 1er tour= 48747860, dont Macron = 9783058 (soit une pourcentage de 20.07%), \n",
      "-> nombre de reports de Macron du 1er vers Le Pen au 2eme = 825 personnes.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "Total des voix au 1er tour= {x_1.sum():.0f}, dont Macron = {x_1[:, 5].sum():.0f} (soit une pourcentage de {x_1[:, 5].sum()/x_1.sum()*100:.2f}%), \n",
    "-> nombre de reports de Macron du 1er vers Le Pen au 2eme = {x_1[:, 5].sum()*MT[1, 2]:.0f} personnes.\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Influence des parametres\n",
    "\n",
    "Finalement, on valide nos résultats en testant différentes paramétrisation de l'apprentissage et en donnant la valeur du loss calculé sur la base de test :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:08:10.139525Z",
     "iopub.status.busy": "2022-06-03T13:08:10.138940Z",
     "iopub.status.idle": "2022-06-03T13:16:32.217814Z",
     "shell.execute_reply": "2022-06-03T13:16:32.218187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: Loss avec do_optim=True = 1.05e+01 / Loss avec alternative choice do_optim=False = 1.05e+01 \n",
      "TEST: Loss avec do_optim=True = 1.05e+01 / Loss avec alternative choice do_optim=False = 1.05e+01 \n"
     ]
    }
   ],
   "source": [
    "trans, loss_train_alt, loss_test_alt, p_1, p_2, p_2_pred = fit_data(df_1, df_2, verbose=False, do_optim=not do_optim)\n",
    "print(f'TRAIN: Loss avec {do_optim=} = {loss_train:.2e} / Loss avec alternative choice do_optim={not do_optim} = {loss_train_alt:.2e} ')\n",
    "print(f'TEST: Loss avec {do_optim=} = {loss_test:.2e} / Loss avec alternative choice do_optim={not do_optim} = {loss_test_alt:.2e} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:16:32.221847Z",
     "iopub.status.busy": "2022-06-03T13:16:32.221312Z",
     "iopub.status.idle": "2022-06-03T13:16:32.223694Z",
     "shell.execute_reply": "2022-06-03T13:16:32.224045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "do_optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T13:16:32.230163Z",
     "iopub.status.busy": "2022-06-03T13:16:32.229457Z",
     "iopub.status.idle": "2022-06-03T14:15:45.972743Z",
     "shell.execute_reply": "2022-06-03T14:15:45.973257Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pour learning_rate= 3.00e-03, loss_train=1.06e+01 / loss_test=1.07e+01; le temps de calcul est 505.245 s\n",
      "Pour learning_rate= 6.46e-03, loss_train=1.06e+01 / loss_test=1.06e+01; le temps de calcul est 510.477 s\n",
      "Pour learning_rate= 1.39e-02, loss_train=1.05e+01 / loss_test=1.06e+01; le temps de calcul est 515.494 s\n",
      "Pour learning_rate= 3.00e-02, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 505.410 s\n",
      "Pour learning_rate= 6.46e-02, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 506.111 s\n",
      "Pour learning_rate= 1.39e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 504.868 s\n",
      "Pour learning_rate= 3.00e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 505.729 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "N_scan = 7\n",
    "results_train, results_test = [], []\n",
    "\n",
    "learning_rates = learning_rate * np.logspace(-1, 1, 7, base=10)\n",
    "for learning_rate_ in learning_rates:\n",
    "    tic = time.time()\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, learning_rate=learning_rate_, verbose=False)\n",
    "    print(f'Pour learning_rate= {learning_rate_:.2e}, {loss_train=:.2e} / {loss_test=:.2e}; le temps de calcul est {time.time()-tic:.3f} s')\n",
    "    results_train.append(loss_train)\n",
    "    results_test.append(loss_test)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (13, 8))\n",
    "ax.plot(learning_rates, results_train, '--')\n",
    "ax.plot(learning_rates, results_test)\n",
    "ax.set_xlabel = 'learning rate'\n",
    "ax.set_ylabel = 'Loss'\n",
    "ax.set_yscale('log');\n",
    "ax.set_xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T14:15:45.978990Z",
     "iopub.status.busy": "2022-06-03T14:15:45.978229Z",
     "iopub.status.idle": "2022-06-03T15:30:37.030918Z",
     "shell.execute_reply": "2022-06-03T15:30:37.031331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  8  32  72 128 200 288]\n",
      "Pour batch_size= 8, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 2179.454 s\n",
      "Pour batch_size= 32, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 752.049 s\n",
      "Pour batch_size= 72, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 481.874 s\n",
      "Pour batch_size= 128, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 378.228 s\n",
      "Pour batch_size= 200, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 371.919 s\n",
      "Pour batch_size= 288, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 327.269 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_train, results_test = [], []\n",
    "batch_sizes = (batch_size * np.arange(1, N_scan) ** 2) // 8\n",
    "print(batch_sizes)\n",
    "for batch_size_ in batch_sizes:\n",
    "    tic = time.time()\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, batch_size=batch_size_, verbose=False)\n",
    "    print(f'Pour batch_size= {batch_size_}, {loss_train=:.2e} / {loss_test=:.2e}; le temps de calcul est {time.time()-tic:.3f} s')\n",
    "    results_train.append(loss_train)\n",
    "    results_test.append(loss_test)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (13, 8))\n",
    "ax.plot(batch_sizes, results_train, '--')\n",
    "ax.plot(batch_sizes, results_test)\n",
    "ax.set_xlabel = 'batch size'\n",
    "ax.set_ylabel = 'Loss'\n",
    "ax.set_yscale('log');\n",
    "ax.set_xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T15:30:37.035396Z",
     "iopub.status.busy": "2022-06-03T15:30:37.034916Z",
     "iopub.status.idle": "2022-06-03T15:30:37.036889Z",
     "shell.execute_reply": "2022-06-03T15:30:37.037281Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.  1.  1.  0.9 0.9 0.8 0.5]\n"
     ]
    }
   ],
   "source": [
    "beta1s = 1 - (1 - beta1) * np.logspace(-1, 1, 7, base=10)\n",
    "print(beta1s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T15:30:37.043012Z",
     "iopub.status.busy": "2022-06-03T15:30:37.042179Z",
     "iopub.status.idle": "2022-06-03T16:30:34.811771Z",
     "shell.execute_reply": "2022-06-03T16:30:34.812119Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.  1.  1.  0.9 0.9 0.8 0.5]\n",
      "Pour beta1= 9.95e-01, loss_train=1.06e+01 / loss_test=1.05e+01; le temps de calcul est 527.605 s\n",
      "Pour beta1= 9.89e-01, loss_train=1.05e+01 / loss_test=1.06e+01; le temps de calcul est 509.534 s\n",
      "Pour beta1= 9.77e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 509.994 s\n",
      "Pour beta1= 9.50e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 513.016 s\n",
      "Pour beta1= 8.92e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 510.767 s\n",
      "Pour beta1= 7.68e-01, loss_train=1.06e+01 / loss_test=1.06e+01; le temps de calcul est 512.885 s\n",
      "Pour beta1= 5.00e-01, loss_train=1.07e+01 / loss_test=1.07e+01; le temps de calcul est 513.630 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_train, results_test = [], []\n",
    "beta1s = 1 - (1 - beta1) * np.logspace(-1, 1, 7, base=10)\n",
    "print(beta1s)\n",
    "for beta1_ in beta1s:\n",
    "    tic = time.time()\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, beta1=beta1_, verbose=False)\n",
    "    print(f'Pour beta1= {beta1_:.2e}, {loss_train=:.2e} / {loss_test=:.2e}; le temps de calcul est {time.time()-tic:.3f} s')\n",
    "    results_train.append(loss_train)\n",
    "    results_test.append(loss_test)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (13, 8))\n",
    "ax.plot(beta1s, results_train, '--')\n",
    "ax.plot(beta1s, results_test)\n",
    "ax.set_xlabel = 'beta1'\n",
    "ax.set_ylabel = 'Loss';\n",
    "ax.set_yscale('log');\n",
    "#ax.set_xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T16:30:34.818372Z",
     "iopub.status.busy": "2022-06-03T16:30:34.817792Z",
     "iopub.status.idle": "2022-06-03T17:34:52.519847Z",
     "shell.execute_reply": "2022-06-03T17:34:52.520190Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pour beta2= 9.95e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 568.161 s\n",
      "Pour beta2= 9.89e-01, loss_train=1.05e+01 / loss_test=1.06e+01; le temps de calcul est 549.940 s\n",
      "Pour beta2= 9.77e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 551.237 s\n",
      "Pour beta2= 9.50e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 549.098 s\n",
      "Pour beta2= 8.92e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 545.754 s\n",
      "Pour beta2= 7.68e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 541.892 s\n",
      "Pour beta2= 5.00e-01, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 551.355 s\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyQAAAHWCAYAAACGz00/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABtPElEQVR4nO3ddXzW5f7H8de1Zklso3t0h6iEgBhgYdDYiHHsOuo5x+MJT/xM7MRCpFExUY8IJt0xGA2CG7WA7V5dvz++2wiJbWz73vF+Ph57TO58z41xf+7r+nwuY61FRERERETEDUFuBxARERERkcClgkRERERERFyjgkRERERERFyjgkRERERERFyjgkRERERERFyjgkRERERERFwT4nYAbxcfH2+bNGnidgwREREREZ+2ePHiPdbahGMvV0FyCk2aNGHRokVuxxARERER8WnGmK3Hu1xbtkRERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERExDUqSERERERE/MG8p+DH59xOUWYqSERERERE/MGaj2HJBLdTlJkKEhERERERf5CbBfs2Ql6O20nKRAWJiIiIiIg/8GSBLYQ9691OUiYqSERERERE/EFulvM5dY27OcpIBYmIiIiIiK8ryIe8Q85/qyAREREREZEqVbw6ApC61r0c5aCCRERERETE1xUXJCZIBYmIiIiIiFQxT1FBUrs9pG+HnAx385SBChIREREREV/nyXQ+N+zhfE5b516WMlJBIiIiIiLi63KLCpIGRQWJDzW2qyAREREREfF1xVu2EttAWLRP9ZGoIBERERER8XXFW7YiYiGhNfy22t08ZaCCRERERETE1xVP2QqLcVZJtEIiIiIiIiJVpniFJDwaEtvCoT2QleZuplJSQSIiIiIi4utysyA4DELCoXZb5zIfaWxXQSIiIiIi4us8mU4zOzgrJOAz27ZUkIiIiIiI+DpPlrNdCyAqASJraYVERERERESqSG6W09AOYIyzSqIVEhERERERqRKeDAiPOfzn4klb1rqXqZRUkIiIiIiI+Lojt2yBU5DkZkL6dvcylZIKEhERERERX5ebdbipHXyqsV0FiYiIiIiIrzt2hSShtfPZBxrbVZCIiIiIiPg6TyaExx7+c7XqENtAKyQiIiIiIlLJrP39li0oamzXComIiIiIiFSm3IOAPXrLFjgFSdp6KMh3JVZpqSAREREREfFluVnO5yPH/oLT2F7ggf2bqz5TGaggERERERHxZZ5M53PYsQVJG+ezl2/bUkEiIiIiIuLLiguSY7dsJbQCDPymgkRERERERCpL8ZatY5vaQ6tBzWZaIRERERERkUpUskIS8/vrarf1+tG/KkhERERERHyZ5wRN7eA0tu/bCHk5VZupDFSQiIiIiIj4stzipvbo31+X2AZsIexZX7WZykAFiYiIiIiILzvVCokJhgPbqjZTGYS4HUBERERERE6DJxNMkNPEfqxaLeDPuyAkvOpzlZIKEhERERERX5ab5ZxBYszvrwsKgiDvLUZAW7ZERERERHybJ+v3Z5D4EBUkIiIiIiK+LDfz+P0jPkIFiYiIiIiIL/NkHn/Clo9QQSIiIiIi4su0ZUtERERERFyTm6UVEhERERERcYknE8Jj3U5RbipIRERERER8mSdTW7ZERERERMQF1mrLloiIiIiIuCTfA4X5GvsrIiIiIiIu8GQ6n1WQiIiIiIhIlcstKki0ZUtERERERKqcJ8v5rKZ2ERERERGpctqyJSIiIiIirsktWiEJU0EiIiIiIiJVrWSFRFu2RERERESkqpWskKggERERERGRqqYeEhERERERcY1HKyQiIiIiIuKW3CwIjYIg331Z77vJRUREREQCnSfTp7drgQoSERERERHf5cn06QlboIJERERERMR35Wb5dP8IqCAREREREfFdnixt2RIREREREZeoh0RERERERFyTm6ktWyIiIiIi4hJPlpraRURERETEJWpqFxERERERVxTkQX4OhMe6neS0qCAREREREfFFnkzns7ZsiYiIiIhIlcvNcj5ry5aIiIiIiFS5khUSjf0VEREREZGq5ilaIdGWLRERERERqXK5RSskYVohERERERGRqqYVEt9ljGlmjBlvjJnudhYRERERkXIJpB4SY8xbxphUY8yqk9xmoDEm2RiTYox5+IjLtxhjVhpjlhljFh3nfsHGmKXGmE/L9yWcPN/xcllrN1lrx5T3+UREREREXBdgU7beAQae6EpjTDDwEjAIaAuMNMa0PeIm/a21na213Y9z97uBtSd43ERjTMwxlyWVNl8pcomIiIiI+KaSLVsBsEJirZ0H7DvJTXoAKUUrD7nAZGDwqR7XGNMAuBh48wQ36Qt8ZIwJL7r9WOCFMuQrVy4REREREa+XmwnB4RAc6naS01JRPST1ge1H/HlH0WUAFvjKGLPYGHPzMfcbB/wRKDzeg1prpwGzgSnGmNHAjcDQ081ljKlljHkV6GKMeeR4dzTGXGqMeT09Pb0MTyciIiIiUkU8mT6/OgJV09Te21rbFWfb1O3GmHMAjDGXAKnW2sUnu7O19gkgB3gFuMxam3W6gay1e621t1prm1tr/3OC23xirb05Li7udJ9ORERERKTiebJ8fsIWVFxBshNoeMSfGxRdhrW2+HMq8CHONiqAXsBlxpgtOFupzjXGvH/sAxtj+gDti+77WEXlEhERERHxablZPn8GCVRcQbIQaGGMaWqMCQNGALOMMVHFTenGmCjgAmAVgLX2EWttA2ttk6Lbf2utvfrIBzXGdAFex+n7uAGoZYx5/HRznc4XKiIiIiLiFQJpy5YxZhLwM9DKGLPDGDOm6PLPjTH1rLX5wB04/R5rganW2tVAbeAHY8xyYAHwmbX2yzLkiwSGWWs3WmsLgWuBraXNd5JcIiIiIiK+zZPpF1u2jLXW7QxerXv37nbRot8dnyIiIiIi4q4XukGdjjD0bbeTlIoxZvHxjgEJyJPaRURERER8npraRURERCTgZeyCXcvdThGYPJkQHut2itOmgkREREREyidzN4w/H948T0VJVSsshLyDEKYVEhEREREJRJ4s+GAYHNoH1WrAtBucd+ylauQWHc2nLVsiIiIiEnAK8mH6jbB7pdNQPeQt2L8ZPnvA7WSBo7j4C5SxvyIiIiIiAFgLXz4EG2bDoCeg5YXQpDf0fQhWTIZlk9xOGBiKV0i0ZUsq3d6NkPmb2ylEREREHD+/CAvfhJ53Qo+xhy8/50Fo3Bs+ux/2bHAvX6DwFG/Z0gqJVLZvHoNn2sDk0bB+trNEKiIiIuKG1R/BV3+BtoPhvH8cfV1QMFz1BoSEO/0keTmuRAwYuUVbtrRCIpVuwGPQ8w7YPt9pHBvXAb59HPZvcTuZiIiIBJLtC+DDW6BBD7jiNQg6zsvI2Hpwxavw20r4+tGqzxhI1EMiVSa+BZz/D7hvLQx/H+q0h++fhuc6wXuDYeV0vQMhIiIilWvfJpg0AmLqwshJEFrtxLdteSGcfQcseB3WflJ1GQONx3+mbIW4HUBKKTgU2lzqfKTvgGUfwJIJMGOMM2qv4wjoei3Ubut2UhEREfEnh/bBxKFgC2H0dIiKP/V9BjwGW36Aj2+Hup2geqPKzxloSpratUIibohrAH3/CHcvh2s+hGb9YNF4eOVseGMALH5Xc8BFRETk9OXlwORRcGA7jJwM8Umlu19ImDMOuLAQZtwEBXmVmzMQlWzZ8v0VEhUkviwoCJqfC0PfgfvWwYX/carlT+6Cp1rBx3fA9oXOeD4RERGRsigshI//ANt+hitegUZnle3+NZvBpeOcPtjv/lMpEQOaJxOCQiAkwu0kp01btvxFVC04+w9w1m2wYxEseRdWzYSlEyChDXS9xtnWFVXL7aQiIiLiC779J6yaAef9DdpfVb7H6DAENn0H3z8DTfpA8/4VmTCw5WY5E7aMcTvJadMKib8xBhqeAYNfhAeS4dLnISwKZv8JnmkN066Hjd8673qIiIiIHM/id+CHZ6Db9dDrntN7rEFPQHxLmHkzZKVWQDgBnKZ2P5iwBSpI/Ft4DHS7Dsb+D277CbqPcd6lmHAFPN8J5j4B6TvdTikiIiLeZMM38Ol9kHQeXPT06b8DHxbp9JN4MpyxwXpTtGJ4MlSQiI+p3Q4G/dfpNblqPNRoCnP+BePaO5Mz1n6ihjMREZFAt3slTLsOEts6ParBFbS7v3Y7GPgfZ5fGT89VzGMGuuItW35APSSBJjTC2c/ZYQjs2wzLJsLS92HK1RCVAJ1HQZdrSz9FQ0RERPxD+k6YOAzCY2H01Ip/973bDbBpLvzvn9C4FzTsUbGPH2g8WRAR63aKCqEVkkBWsymc+xe4ZxWMmgoNz4SfXoQXu8HbF8HyyZB7yO2UIiIiUtlyMuCDYc7kptHTnBPXK5oxcOlzEFcfpo+B7AMV/xyBxI9WSFSQiLMc2/JCGDHRORH+vL9B5i5nn+fTrZx9pL8uczuliIiIVIaCPGfoTepaGPYO1Glfec9VrToMeRsyf4VZd+pogtPhyXRWs/yAChI5Wkxt6H0v3LkErv8MWg1ytnW93hde7QML3tA7GiIiIv7CWvjsPtj4P7jkWaeRvbI16A4D/gprZ8Gityr/+fyVJ8svDkUEFSRyIsZAk95w5etwfzJc9BRg4fMHnFWTmbfAlh/1zoaIiIgv++FZWPIe9LnfmcxZVc6+E5oPgC8fgd2rqu55/YW1kJupLVsSQKpVhx5j4dYf4Oa50Hk0JH8O71wEL3SDH8ZB5m9upxQREZGyWDkd/vd3aD8E+v+lap87KAiueM15jTH9Bsg9WLXP7+vyssEWaoVEAlS9znDJM86qyeWvQnRt+OYxeLYtTB4N62dDQb7bKUVERORktv4MH90GjXrC5S87BUJVi05wdmLs2QBf/LHqn9+XeTKdz35yDonG/kr5hEVC55HOx54NznLv8kmw7lOIqQddRkOXq6FGE7eTioiIyJH2bIDJI6F6Y2egTUi4e1ma9YNzHoB5T0LTftBxqHtZfElulvM5zD8KEq2QyOmLbwEX/BPuXQPDJjiHH817Cp7rBO8NhlUzIN/jdkoRERE5uAcmDgET7Iz3jazpdiLo+zA0PAs+vQf2bnQ7jW8oWSHRli2Ro4WEQdvL4OrpcO8q6Pcn2LsJpt/oNMJ/+Qj8tsbtlCIiIoEpLxsmjYDM3TBysnMemTcIDoGr3oSgEKefRG9inpqfbdlSQSKVI64B9HsI7l4O13zoLMkueANeORveGACL3z38l0lEREQqV2EhzLwZdiyCK9+Ahme4neho1Rs6vSy7lsM3f3M7jfcr2bKlFRKRUwsKgubnwtB34P51cOG/nb9En9wFT7WCj++A7Qs1PlhERKQyff2oc+7Hhf9ydjN4o9YXQ49b4JeXIfkLt9N4N09RQaIVEpEyioqHs2+HP/wCY76G9lc4/SXjz4OXz4afX4aDe91OKSIi4l8WvAE/vwg9boaz/uB2mpO74J9Qp6MzASx9p9tpvFdu0S4TrZCIlJMx0LAHDH7JGR986XPO1K7Zj8AzrWHaDbBxjrO8LCIiIuWX/KUzUrflIBj4X+ffYG8WEg5D3ob8XJhxk44SOBH1kIhUoIhY6HY9jP0WbvsJuo+BTXNgwuXwfCeY+6TeIRERESmPX5c6TeJ1OsKQ8RAU7Hai0olPgkuehW0/wbwn3E7jnTxZgIGwKLeTVAgVJOI9areDQf+F+9bBVeOhRlOY8ziMaw8Th8HaT6Agz+2UIiIi3u/ANvhgOETGw6ipvvfCtdNw6DQK5j4Bm+e5ncb75GY527W8fcWrlFSQiPcJjYAOQ+C6WXDXMuh9H+xeAVOuhmfawtd/hT0pbqcUERHxTtkHnDfy8nJg9FSIqe12ovK56EmolQQzxjrnp8hhngy/OYMEVJCIt6vZFAY8CvesgpFToMEZ8NOL8GI3ePsiWD4Zcg+5nVJERMQ75OfC1Gtg7wYYPgES27idqPzCo2HIW5C9Hz68Vb2lR/Jk+U3/CKggEV8RHAKtBsLID+C+NXDe3yBzF3x4CzzdGj6735ldLiIiEqisdU473zwPLnsBmvV1O9Hpq9vRGVWc8rUzDlgcxVu2/IQKEvE9MXWg971w5xK4/jOnUFn6Prx2jvOx4A1nuVpERCSQzHsSlk2Evg9D51Fup6k4Z9wErS9xDkzcudjtNN7Bk6UtWyJewRho0huufN05dPGip5zl3M8fgKdbwcxbYMuPOnRRRET83/LJMOdf0Gkk9HvY7TQVyxgY/KLzhuT0GyEn3e1E7vNkQnis2ykqjAoS8Q/VakCPsXDr93Dzd847Q8mfwzsXwYvd4YdxkJXqdkoREZGKt3kefHwHNOkDlz7vN5OXjlKthjOB88B2+OQevdmYm6ktWyJeyxio18WZX37/Orj8FYhKgG8eg2fawOTRsP4rKCxwO6mIiMjpS10Hk6+GWs1h+PsQEuZ2osrT6Ew498+weiYsec/tNO7ysy1bIW4HEKk0YVHOSknnUZC2HpZOgGUfwLpPIaYedLkauoyGGk3cTioiIlJ2mb/BxKHO6eajpkK16m4nqny97nVWhL54CBr28O0pYqdDTe0iPiihJVzwT7hvLQyb4BzCOO9JeK4TvDcYVs2AfI/bKUVEREon9yBMGg6H9sCoKVCjsduJqkZQEFzxurM6MO2GwBz9n++BglyN/RXxWSFh0PYyuHo63LMS+v0J9m50muSebg1fPgKpa91OKSIicmKFBTDjJmfc/ZC3oH5XtxNVrZjacMVrkLYWZj/idpqq58lyPqsgEfED1RtCv4fg7uVw9UxoWjQy+OWz4M3znP2pxX/pRUREvMXsPzmDWwb+H7Qa5HYadyQNgF73wOJ3YNVMt9NUrdxM57O2bIn4kaBg5xfbsHedRvgL/gU5GTDrTmd88Kw7YcciTfQQERH3/fIKzH8VzrodzrzZ7TTuOvcv0OAM+ORu2LfZ7TRVx1NUkGiFRMRPRcVDzzvg9vkw5mtodzmsnA5vDoBXejr/EBza53ZKEREJRGs/cbYWt7kULnjc7TTuCw51RgEbAzPGQH6u24mqRsmWLa2QiPg3Y5zpHYNfgvuT4dLnILQafPmws2oy7QbYOMc5iFFERKSy7VgEM8ZC/W5OU3eQXsIBTjP/ZS84J7h/+w+301SN3KKCJMx/Vkg09lfkVCJiodv1zsfuVc744OWTnTno1RtBl2ud0cJx9d1OKiIi/mjfZvhgOEQnwsjJEBbpdiLv0nYwdB8DP70ATftCi/PdTlS5SrZsaYVEJDDVaQ+D/s9ZNblqPNRoCnMeh3HtYeIwWPspFOS5nVJERPzFoX3OWSOF+TB6OkQnuJ3IO134b6jdHj68BTJ2uZ2mcqmHREQACI2ADkPgullw1zLofR/sXgFTRsMzbeHrx5xxwiIiIuWV74Ep18CBrTDiA+dMLTm+0AhnBHJeNswc64xG9lclW7a0QiIixWo2hQGPwj2rYOQUZ+LHTy/AC13h7Yuc7V2BeHCTiIiUn7Xw8R2w9QcY/DI06eV2Iu+X0AouehK2fA/fP+N2msrjUUEiIicSHAKtBsLID+C+NTDgMcj41Vk+fro1fHa/c4iViIjIqcz5F6ycCuc+Ch2Hup3Gd3QeDR2GwXf/hq0/uZ2mcngyIKSa87rDT6ggEakMMXWgz31w5xK47lNoeSEsmQCvneN8LHwTsg+4nVJERLzRkgkw70nocg30ud/tNL7FGLjkGajRxDnN3h9H9edm+VX/CKggEalcQUHQtA9c9QY8kAyDnnRGBX92v7Nq8uGtzjs4OnRRREQANn4Ln94Dzc+FS551XmBL2YTHwJC3ISsVPr7d//6N9WT51YQtUEEiUnWq1XBO1b31e7j5O+g80pnK9fYgeLE7/DDO+eUpIiKB6bfVMPU6iG8FQ991Dv6T8qnXGS74JyR/DvNfcztNxcrN8qv+EVBBIlL1jIF6XZx3vh5IhstfgagE+OYxeKYNTB4N67/y7wkhIiJytIxdznjfsCgYPdU5A0tOz5m3QstB8PWj8Osyt9NUHE8mhPvXz4cKEhE3hUU5hyre+CXcvhDOug22/QIfDIVxHeDbf8H+rW6nFBGRyuTJdH7v56TDqKkQ18DtRP7BGLj8ZYiMh+k3HD6/w9d5MrVlS0QqSUJLuOBxuG8tDHsPEts4TY3PdYL3LodVM52Z9CIi4j8K8mH6jfDbGhj6DtTt6HYi/xJZE4aMh/1b4NP7/KOfxA+3bPnPvDARfxESBm0HOx8HtsOyD2DpBOfdnWo1odNI6HqNU7CIiIjvsha+eBA2fAWXjIMW57udyD817gn9HnFGKTfrB11Gu53o9KipXUSqVPWG0O8huHs5XD0Tmp4DC16Hl8+CN893RkMWH5AkIiK+5afnYdFb0Ose6H6D22n8W5/7oUkf+PwBSFvvdprT48nU2F8RcUFQMCQNgGHvwv3r4IJ/OXuNZ90BT7eCWXfCjkX+sRQtIhIIVn8IX/8V2l3hHKQrlSsoGK58A0KrOTsO8rLdTlQ+BfmQnw1hKkhExE1R8dDzDrh9Ptz4FbS9HFZOhzcHwCs94ZdX/PMgKBERf7FtPsy8BRqeBZe/6pxZJZUvtq7z//u3VfDVX9xOUz65RbsitGVLRLyCMdDoTLj8Jbg/2dl/HBIBXz7srJpMvxE2feccxCgiIt5h70aYNMKZpDXiAwiNcDtRYGl5AZx9Byx8E9bMcjtN2RVPClNTu4h4nYhYZ/9x9xtg90qnt2TFFFg1A6o3dprgO4+G2HpuJxURCVwH98LEIc5/j54GUbXczROoBjwGW39ytj3X7QQ1GrudqPRKVki0ZUtEvFmdDnDRE86qyVXjnV+03z4Oz7aDicOc0+EL8txOKSISWPJyYPJISN8JIydDreZuJwpcIWEw5C2n73LGTb71b6JHBYmI+JLQCOgwBK77BO5aCr3vhV3LYcpoeKYtfP2Ys3VAREQqV2EhfHQrbJ8PV77mbLcVd9VsCpeOgx0LnHHAviLXP7dsqSARCQQ1m8GAv8K9q5135hp0h59egBe6wtsXw/IpvjtxRETE2/3v785UrfP/4UzVEu/Q/iroeh388Cyk/M/tNKVT3EOiFRIR8VnBIdBqEIyc5BQnA/4KGTvhw5vhqVbw2QPOKoqIiFSMRW/Bj+Og+xjoeZfbaeRYA/8LCW3gw1sg8ze305yaR1O2RMSfxNZ1Doq6cwlc9ym0vBCWvAevneN8LBzvnHUiIiLls/4r+Ox+aHEBDHrCmY4o3iUsEoa+7bzQ//AW759MWdzUrnNIRMSvBAVB0z5w1RvwQDIMehIKC+Cz+5xVkw9vdaaR6NBFEZHS27Ucpl0PtdvDkLedFWrxToltYNB/YdMcZzXLm5Vs2fKvFRL97RCRw6rVgDNvhh5j4delzorJyumwfBLUSoKu10KnkRCd6HZSERHvlb4DPhju/E4dNdXvXjz6pa7Xwaa5zlTKxr28d/CAJxOCwyAk3O0kFUorJCLye8ZA/a7OBJIHkmHwyxCVAF//FZ5pA1Ouhg1fOyspIiJyWE6GM2I99yCMnupsjxXvZ4zzb15cA5gxBrL3u53o+HKz/G7CFqggEZFTCYuCLqPhxi/h9oVw1m2w9WfncK9xHWDOv2H/VrdTioi4ryAPpl4Le5Jh2HtQu53biaQsIuKc7XWZu2DWnd65VdmT5ZcrbipIRKT0ElrCBY/DfWudf2wT28DcJ+C5TvDe5bBqJuR73E4pIlL1rIVP73X6EC4ZB837u51IyqNBNzjvb7D2E1j4pttpfs+T6XcN7aAeEhEpj5AwaDvY+TiwHZZNhKXvw/QboFpNp8+k6zVOwSIiEgi+fxqWToBzHnR+/4nvOut2p59k9p+h0VlQp4PbiQ7LzSzzGSQFhZZCawkN9t51CO9NJiK+oXpD6Pcw3L0crp7hTOxa8Dq8fBa8eT4smXB4brqIiD9aMQ2+/Sd0HA79/+x2GjldQUFwxavOUIJpN3jXv2Hl2LK1bPt+Wvz5C77fkFZJoU6fChIRqRhBwZB0nrOV6761ztaunAMw6w54uhXMugt2LPbOPbkiIuW15Uf4+A/QuDdc9oLOGvEXUfHOOPy9KfDFH91Oc1g5mtozsvMBiA733o1RKkhEpOJFJ0DPO+H2BXDjV9D2clg5Dd48F17pBb+8Aof2uZ1SROT0pK2HyaOgRhMY8b7fjWINeE3PcbbgLZsIy6e4ncbhKfuWrYycPABiq4VWRqIKoYJERCqPMc4s98tfgvuTnUbPkHD48mFn1WT6jbDpO+8/GVdE5FhZac60weBQGD3N2d4j/qfvQ9Cop3NY8N6Nbqcp2rJVxoIku6ggiVBBIiKBLiIWut8AN8+BW3+AbjdAyjfw3mB4vjPMexIyfnU7pYjIqeUegkkjICsVRk5xVkjEPwWHwFVvOoXntOvdnSRZWFi+LVs5zpatmAht2RIROaxOB7joCWfV5Mo3oXoj53TcZ9s5pxuv+8yZ5y8i4m0KC2DmWNi52Hmh2qCb24mkssXVh8tfgd0rnAOC3ZJ3ELBlbmrv3LA6t5zTjIjQ4MrJVQG8t1QSEf8XWg06DnU+9m1yRgcvnQjrv4To2tB5FHS5Bmo1dzupiIjjq0dh3acw8L/Q5hK300hVaTUIzrwN5r8CTftC64uqPkPxtK8ybtnqlRRPr6T4SghUcbRCIiLeoWYzGPBXuHc1jJwM9bvBj8/DC13hnUtgxVTIy3Y7pYgEsvmvwS8vwZm3wlm3uZ1Gqtr5f4e6nZypauk7qv75c4sKkjIejHjgUC4HPfmVEKjiqCAREe8SHOK8EzVyklOcDPir84t/5linEf6zB2DXCrdTikigWfe5M5Cj1cVw4b/dTiNuCAmHIW87W4pn3lL1Y+w9mc7nMm7ZunPSUka/Ob8SAlUcFSQi4r1i60Kf++HOJXDdJ9DiAljyHrzWB17rCwvHQ0662ylFxN/tXAIzxkDdzs7ZFEHeuxdfKlmt5nDuo7D1B9i1vGqfu7ggKUdTuzeP/IUALUiMMc2MMeONMdPdziIipRAU5MyDv+pNuH8dDHoSCvOdMYxPtYIPb4OtP+vQRRGpePu3OsM2ouJh1BQIi3I7kbit03AIDnfOJ6lKueXrIcnMziPWiydsQSkLEmPMW8aYVGPMqpPcZqAxJtkYk2KMefiIy7cYY1YaY5YZYxYdcXlDY8wcY8waY8xqY8zd5f0iTpbveLmstZustWPK+3wi4qLImnDmzc7o4LFzoNMIWPsJvD0QXjzD6TvJSnM7pYj4g+wDMHEoFHhg9HSITnQ7kXiDajWcgQYrp1XtGOByNrVn5OQR48VnkEDpV0jeAQae6EpjTDDwEjAIaAuMNMa0PeIm/a21na213Y+4LB+431rbFjgLuP2Y+2CMSTTGxBxzWVJp85Uil4j4KmOgfle4dBw8kAyDX4bIWvD1o/BMa5hyNWz42hnRKSJSVvm5zu+RfZtg+PuQ0MrtROJNOo+C7P2Q/EXVPWfu6WzZ8oMVEmvtPGDfSW7SA0gpWnnIBSYDg0/xmLustUuK/jsTWAvUP+ZmfYGPjDHhAMaYscALZchX5lwi4oPCoqDLaBgzG25f4EzA2fqTc4ryuA4w59/OtgsRkdKwFj65C7Z8D4NfcraMihypWX+IqQfLPqi65yxpai/9CklhoeXBC1rRv5V3r+5VVA9JfWD7EX/eweHiwgJfGWMWG2NuPt6djTFNgC7AUSMArLXTgNnAFGPMaOBGYOjp5jLG1DLGvAp0McY8coJMlxpjXk9PV8OsiE9JaAUX/gvuWwdD34WE1jD3CXiuE0y4AlZ/6O5JuyLi/b77LyyfBP3/7PQLiBwrKNjZMpzyNWTurprn9GSBCXLO8CqloCDD2HOacVazWpUY7PRVRVN7b2ttV5xtU7cbY456m8EYEw3MAO6x1mYce2dr7RNADvAKcJm1Nut0A1lr91prb7XWNrfW/ucEt/nEWntzXFzc6T6diLghJAzaXQ7XzIR7VkDfhyBtPUy7Hp5pA7P/DKnr3E4pIt5m2Qcw97/QeTSc86DbacSbdR4NthBWTKma58vNcs4gMabUd8nJK2BTWhY5ed69fbmiCpKdQMMj/tyg6DKstcWfU4EPcbZRAWCMCcUpRiZaa2ce74GNMX2A9kX3fayicolIAKneCPo/4hQmV8+AJr2dA85ePhPePB+WTDjcLCgigWvTdzDrTuck7kvGlemFnwSg+CRoeCYsnVg1Ux49mWU+g2TtrgzOfXouP2/aW0mhKkZFFSQLgRbGmKbGmDBgBDDLGBNV3JRujIkCLgBWFf3ZAOOBtdbaZ473oMaYLsDrOH0fNwC1jDGPn26ucn2FIuL7goIh6TwY9h7ctxYueBxyDsCsO5xDF2fdBTsWa3ywSCBKXQtTroVaLWD4BGeVVeRUOo+CPcnOWTWVzZNZjglbzgntsf4wZcsYMwn4GWhljNlhjBlTdPnnxph61tp84A6cfo+1wFRr7WqgNvCDMWY5sAD4zFr7ZdHD9gKuAc4tGgm8zBhz0TFPHQkMs9ZutNYWAtcCv+tMPVG+k+QSkUAXnQA973Sa4G+cDW0HOyMc3zwXXukFv7wKh042y0NE/Ebmbme8b2gEjJ4GEdquLaXU7goIqQbL3q/858rNKvuErew8AOK8fMqWsXon8KS6d+9uFy1adOobiojvy0mHVTOcLVy/LnEOvmpzKXS9Fpr0cQ5oFBH/4smCdy6CPSlww+dQr7PbicTXzBgL62fDA+udorayvHk+hEXCtR+X+i4T52/lzx+uYsGfBpAYW4nZSskYs/iYY0CAAD2pXUTkuCLioPuNcPMc5+DFbtc7E1Teuwxe6ALznoKMXW6nFJGKUlgAM8bA7pUw5C0VI1I+XUaDJx2SP6vc5ynPlq1sZ8uWvxyMKCISWOp0gIuegPuT4co3Ia4hfPtPeLYtfDAC1n0GBXlupxSR8rIWvngI1n8Jg56AVic8/1nk5Jqc4/wbsXRi5T5P8ZStMjinZTz/HNyOiFDvfsnv3RvKRETcFloNOg51PvZuhKXvw7KJsP4LiK7tNDR2uQZqNXc7qYiUxS8vw8I3nF6yHmPdTiO+LCgIOo2E75+CjF8htl7lPE85pmy1qxdHu3re3xPl3eWSiIg3qdUcznsM7l0DIyZBva7w4/PwQld45xJYMRXyst1OKSKnsuZj5yyiNpfBef9wO434g84jnTNJlk+qnMe31ilIytjUnpKaRUqq94+1V0EiIlJWwSHQ+iIYNRnuXQ0D/grp22HmWGd88OcPOnvSRcT7bF8IM2+GBt3hytc1rEIqRs1m0LiXc7BmZQyMys8BW1DmHpLHP1vD/VOXVXyeCqa/hSIipyO2LvS5H+5cCtfOghYXwOJ34dXe8Ho/WDjemd4lIu7btwkmjYCYujBysrMlU6SidB4Fe1Ng+4KKf+ziw3vL3NSe5/UN7aCCRESkYgQFQbO+cNWbcP86p0k2Pxc+uw+eagUf3gZbf9ahiyJuObTPOWvEFsDo6RAV73Yi8TdtL4fQKKfPsKLlZjqfy3oOSU4+sV5+BgmoIBERqXiRNeHMW+C2H2Hst9BpOKydBW8PhBfPcPpOstLcTikSOPJyYPJoOLDN6f+KT3I7kfij8GjnkN1VMyH3UMU+tifz8HOUQUZ2ntef0g4qSEREKo8xUL8bXPqcMz548EtOsfL1o/BMa5hyDWz4xjkLQUQqR2EhfPwH2PYTXP4KND7b7UTiz7qMdlYz1n1asY9b3i1bOXnEVvP+gsT713BERPxBeDR0udr5SEuGJe8501jWzoLYBs4/Yl2uhuqN3E4q4l/mPA6rZsCAx6DDELfTiL9r1BOqN3ZGxHccVnGPm1tUkJThHBJrLeOGd6ZRzaiKy1FJtEIiIlLVElrBhf+C+9bB0HedP899AsZ1hAlXwOoPId/jdkoR37f4Xfj+aeh2PfS+1+00EgiCgqDzaNg8z9kiWFHKsWXLGMPA9nVpWy+24nJUEhUkIiJuCQmDdpfDNTPhnhXQ9yFIWw/Trodn2jjnJKSuczuliG9K+QY+vReSzoOLnna2UIpUhU4jAAvLJ1fcY5YUJKVfIcnMyWPu+jT2Znn/G1wqSEREvEH1RtD/EacwGT3DmWc//1V4+UwYf4Gz/J970O2UIr5h90qYej0ktoWh7zhnB4lUlRqNoek5zrStipqsWLJlq/QrJJvSDnLdWwtYtv1AxWSoRCpIRES8SVAwtDgPhk9wtnRd8Dhk74ePb3fGB39yN+xcrPHBIieS8StMHOa8kzxqSpmbgEUqROfRsH8LbP2pYh7PU/aCJCMnD8AnmtpVkIiIeKvoBOh5J9y+AG6cDW0vg+VT4I1znYMX57/mnK0gIg5PplOMeDJh9FSIq+92IglUbS51GtCXfVAxj+fJdM44CSr9S/eM7HwAjf0VEZEKYAw0OgsufxkeSIZLnoXgUPjij/B0a5g+BjbNdcabigSqgnyn/yp1DQx7B+p0cDuRBLKwKKdHcPWHh1c3TkduZrlG/gI6GFFERCpYRBx0vxFu/g5u+R66XQcpX8N7l8ELXWDeU5Cxy+2UIlXLWvj8fqeR/ZJnnEZ2Ebd1Hg15B53x7qfLk1XmQxEziwqSGK2QiIhIpanbES560jl08co3IK4hfPtPeLYtfDAC1n3uvGss4u9+HAeL34He9zkjfkW8QaOzoGazitm2lZtVpv4RgIs71uPtG84gKiz49J+/knn/Go6IiJxcaDXnAK6Ow2DvRlg6wfkHcP0XEF0HOo9yDl2s1dztpCIVb9UM+OZv0P4qOPdRt9OIHGaM8/v328dh32ao2bT8j+Up+5at+tWrUb96tfI/ZxXSComIiD+p1RzO+xvcuxpGTIJ6XZx3j1/oCu9cAiumQl622ylFKsbWn+HD25zTsS9/pUwNvyJVotNIwJz+mSSerDIXJPM37eX7DWmn97xVRCskIiL+KDgUWl/kfGT86qyYLJ0AM8c6fSgdh0PXa9X4K75rTwpMHgnVG8KIiRAS7nYikd+LawDN+jm/g/s+VP6iOTezzFu2Xpu3idTMHPq0SCjfc1YhvZUgIuLvYuvBOQ/AnUvh2lmQdL6z3/7V3vB6P1j0FuRkuJ1SpPQO7oGJQ8AEwehpEFnT7UQiJ9blakjfBlt/KP9jeDLL1dTuCyN/QQWJiEjgCAqCZn1hyHinEX7g/0F+Lnx6LzzdCj76A2z7RYcuinfLy4ZJIyFzF4yc4jQNi3iz1hdDeBwsnVj+xyjHlq2M7HwVJCIi4sUia8JZt8JtP8LYb52G+DUfw1sXwotnwI/PQ5Zv7D2WAFJYCB/eAjsWwpWvQ8Mz3E4kcmqh1aD9lc7v2PKsRhfkQYHHOWixDDJy8nziDBJQQSIiEtiMgfrd4NLnnFWTwS85xcrXj8IzrWHKNbDhGygscDupCHzzV+dF3QWPQ9vBbqcRKb3OoyE/G9Z8VPb7ejKdz2XcspWR7TtbtnyjbBIRkcoXHu3sde5yNaSuc5rgl09yDvWKbVB03Wio3sjtpBKIFrwBP70AZ4yFs293O41I2TToDvEtneb2rteW7b7FBUkZm9qn3dpTKyQiIuLDElvDhf+C+9bC0HcgoSXM/T8Y1xEmXAmrP3L6T0SqwvrZ8MUfoeVAGPhfZ2VPxJcUn0my7WfnvKiyyM1yPpexh6RtvVga1Igs23O5RAWJiIicWEg4tLsCrvkQ7lnhjK1MS4Zp1zlbumb/2fmzSGX5dRlMuwHqdIQhb0Gwb7zjK/I7HUc4k+HKenK7p7ggKf0KyYFDuUz4ZSvb9x0q23O5RAWJiIiUTvVG0P8RpzAZPQMa94L5r8JLPWD8hbD0fcg96HZK8ScHtsMHw52+plFTICzK7UQi5RdbF5oPcLbClqUvL7d4y1bpV0i278vm0Y9WsW53ZhlDukMFiYiIlE1QMLQ4D4ZPcLZ0nf9POLQXPr4dnmoFn9wNOxdrfLCcnpx0+GAY5B1yzhqJqeN2IpHT12U0ZOyEzXNLf59yNLVn5uQBEBPhGyuKKkhERKT8ohOh111wx0K44UtocyksnwJvnOscvDj/NTi0z+2U4mvyc50Jb3vWO4VvYhu3E4lUjJaDIKJ62bZtecreQ5JRVJD4ypQtFSQiInL6jIHGZ8MVr8ADyXDxMxAU4jQiP90aZtwEm+c550iInIy1zmGdm+fCZS9As35uJxKpOKER0GEorP0Esg+U7j7FTe1lmLKVkZ0PoClbIiISoCLi4IwxcMtcuOV76HYdbPgK3r0UXugK856CjF1upxRvNe8pWPY+9H3YmUok4m86j4L8HFj9YeluX7JlqxwrJNW0QiIiIoGubke46Enn0MUr34C4BvDtP+HZdvDBCFj3ORTku51SvMXyKTDncWcaUb+H3U4jUjnqdYHEtrBsYulu78mEkAgILn1xMfyMhvzv/r5Eh/nGColvpBQREd8WWg06DnM+9m50Dl1c9gGs/wKi6zjvGHa5Gmo1dzupuGXz985ghCZ9nK1aOmtE/FXxmSRf/QXS1jvnPJ1MblaZD0WMiQglxkf6R0ArJCIiUtVqNYfz/gb3roYRH0C9zvDjOGc71zuXwIppkJfjckipUmnJMGU01GzmNLGHhLmdSKRydRgGJrh0qySerDJN2AL4ctUupi7cXs5wVU8FiYiIuCM4FFpf7Jwvce9qOPdROLANZt4ET7eCz/8Iu1e6nVIqW1YqTBwCweHOeN9qNdxOJFL5YmpDiwtgxZRTn0niySzTGSQA0xfv4O2ftpQ/XxVTQSIiIu6LrQfnPAB3LYNrZ0HSebD4bWd08Ov9YNFbkJPhdkqpaLmHnIMPD+5xCtMajd1OJFJ1Oo+CzF2w8duT3y43q0wN7QAZOfnE+sgZJKCCREREvElQEDTrC0PGO43wA/8P8j3OGNinW8FHf4Btv+jQRX9QWOCMg/51KVw1Hup3dTuRSNVqORCq1Tz1ti1PZpm3bGVk5/nMhC1QU7uIiHiryJpw1q1w5i2wcwkseRdWzXD+8Y5vCV2vhU4jISre7aRSHrP/DMmfwaAnoPVFbqcRqXohYc6gj0VvOQfIRtY8/u1ysyCsWZkeOjMn32cORQStkIiIiLczBhp0g8ued1ZNLnvROen4q784hy5OvRZSvjn1PmzxHr+8AvNfgbNudwpOkUDVeTQU5DpvtpxIeVZIcvJ85lBE0AqJiIj4kvBo6HqN85G6FpZMgOWTYM3HENfQGR3ceTRUb+h2UjmRdZ/Bl49A60vggn+6nUbEXXU7Qu0Ozspvj7HHv40nC8Jjy/SwPz58bgWEqzpaIREREd+U2AYG/hvuXwdD34H4FvDdf2FcB5hwJaz+CPJz3U4pR9qxGKaPcfpFrnwDgoLdTiTivi6jnV6q39b8/rrCAsg7WOZzSGIjQrVlS0REpMqEhEO7K+CaD+Hu5dD3j5C2DqZdB8+0cXoV0pLdTin7t8Ck4RCdCCMnQ1ik24lEvEOHoRAUAss/+P11uVnO5zJs2dqb5eHfn69lza++M5lQBYmIiPiPGo2h/5/gnpUwejo0Phvmvwov9YDxF8LSiZB70O2UgSd7P0wcCgV5zvclOtHtRCLeIyrembi1fIrzd+RInuKCpPRjf3dn5PD6vE1s23eoAkNWLhUkIiLif4KCocX5MPx9uG8tnP8POLQHPv4DPNUKPrnHmdyl8cGVL98DU65xVkhGfAAJLd1OJOJ9Oo+Gg6mQ8r+jLy9eISnDlq2M7HwAn2pqV0EiIiL+LToRet0NdyyCG76ENpfC8snwRn/n4MX5rzkjN6XiWQuz7oQt38Pgl6FJL7cTiXinFudDVAIse//oy8uxQpKZ46yyqIdERETE2xjjbOG64hV4IBkufsbZt/3FH53xwTNugs3zoLDQ7aT+Y86/YcUUOPcv0HGo22lEvFdwKHQcDslfwsG9hy/3FPWBlGWFJMdZIYnzoYMRVZCIiEjgiYiDM8bALXPhlnnOIYvrv4J3L4UXusL3T0PGLrdT+ral78O8J6DLNdDnAbfTiHi/zqOgMA9WTjt8WW7ZV0gOeoq2bPnQComx2j97Ut27d7eLFi1yO4aIiFS2vGxYMwuWvAdbfwATDC0vdIqVpPMh2Hf2Y7tu4xyYOASa9IHR05x3f0Xk1F7rC7YQbv3e+fOySfDRrXDXUqhZ+tPac/MLCQ02GGMqKWj5GGMWW2u7H3u5fruKiIgAhFaDTsOdj70bYekEZypX8ucQXcc5K6DL1WV6URCQflsDU6+F+FYw7F0VIyJl0eVq+PwB2L0S6nRwTmkHCCv9CglAWIhvbYLyrbQiIiJVoVZzOO9vcN8aZzJUvc7ww7PwfBdnW9eKaZCX43ZK75OxyxnvGxoJo6c6W+NEpPTaXwXBYbCs6EyS3KKCpAxbtiYt2MYzX6+vhHCVRwWJiIjIiQSHQuuLYdQUuHe105y9fyvMvAmebgWf/xF2r3I7pXfwZMEHwyDngFOMxDVwO5GI74msCa0ucoZB5Oc6f6+CQpwDYEtpzrpUvlq9uxJDVjwVJCIiIqURWw/OeRDuWgbXfgxJA2Dx2/BqL3i9Pyx6G3J852TkClWQD9NvhN9Ww9B3oG4ntxOJ+K7Oo+HQXtjwldPUHhbtTAkspYycPJ9qaAcVJCIiImUTFATN+sGQt+D+ZBj4X8jPgU/vcVZNProdts0PnEMXrXVGJ2+YDRc/5ZynICLl1/xcp29t2USnh6QM27UAMnPyiYnwrTZxFSQiIiLlFVkTzroNbvsJbvofdBgKaz6Cty6Al86En16Eg3vcTlm5fnoBFo13Dp/sfqPbaUR8X3CIM1xj/WzYt7nMBUlGTh6xPnQGCaggEREROX3GQIPucNnzzqrJZS86Dd1f/dk5dHHqtZDyDRQWuJ20Yq3+EL5+FNpeDgP+5nYaEf/ReTTYAtj+S5kORQQIMobqkb5VkPjWeo6IiIi3C4+Grtc4H6lrYckEWD4J1nwMcQ2dsZ6dR0P1hm4nPT3b5sPMW6DhmXDFa85WNhGpGAmtoH532LnI+Z1SBnMf7F9JoSqPfnuIiIhUlsQ2MPDfcP86GPI21EqC7/4D4zrA+1c5RUp+rtspy27vRpg8EuLqw4hJEBrhdiIR/9N5lPO5jCskvkgFiYiISGULCYf2V8K1H8Hdy51pXalrna1cz7SBr/4CaT5ybsDBvc5ZI9bC6OkQVcvtRCL+qf1VEBIB1aqX+i5pmR5ue38xCzbvq7xclUAFiYiISFWq0QTO/TPcs9J5Qd/4bPjlFXjpDBh/oXM6fO5Bt1MeX14OTB4F6Ttg5CTnAEkRqRzVqju/I3rfV+q77Mny8MWq3ezJ8lRerkqgHhIRERE3BAU7I3JbnA9ZqU6fyZL34OM/wBcPQYch0PVaqNelTGcQVJrCQvjoNqfJdug70OgstxOJ+L+mfcp088ycfACfO4dEBYmIiIjbohOdsbk974JtPzuFyfLJzsGLtTs4hUnHoVCthnsZv/0HrJ4J5/8D2l3hXg4ROaGM7DwAYqv51kt8bdkSERHxFsZA455wxatOI/zFTzsrKV88CE+1ghljYfP3VX/o4qK34YdnnXNGet5Vtc8tIqWWkVNUkGiFRERERE5btepwxk3Ox67lzvjgFVNh5VSo2Qy6XONM4YmpU7k5NnwNn90PSefDoCe9Y/uYiBxXkDHUiY3wuYMRja3qd1l8TPfu3e2iRYvcjiEiIgJ52bBmlrOla+sPYIKh5YXOlq6k850TnivSrhXw9iCnALrh8zKfGC0iciRjzGJrbfdjL9cKiYiIiK8IrQadhjsfe1Jg6QRY9gEkfw7RdaDLaOfgxZrNTv+50nfCB8OcE+dHTVUxIiKVRiskp6AVEhER8WoFebB+trNqkvI12EJoeg50vQ5aX1K+QwtzMpyVkf1bYcxsqN2u4nOLSIV7+bsUUlKzeGZYZ7ejHJdWSERERPxRcCi0ucT5SN/prJgsfQ9mjIGI6tBphNNvUqd96R6vIA+mXQdp62D0NBUjIj5k5Y50UlKz3I5RZpqyJSIi4i/i6kPfB+Gu5XDtx5A0ABa9Ba/2gtf7O9OycjJOfH9r4bP7YOO3cMk4aH5ulUUXkdOXnp3ncw3toIJERETE/wQFQbN+MOQtuD8ZBv7XaYj/9B54uhV8dDtsm//78cE/PONs/TrnQeh6jRvJRaScdqfnsHjrflrWjnY7Splpy5aIiIg/i6wJZ90GZ94KOxfDkndh5QxY9j7Et3ImdHUaAZu+g//9AzoMhf5/dju1iJTR899uoNBa/tAvye0oZaaCREREJBAYAw26Ox8X/htWf+ishnz1Z/jmb85tGveCwS/prBERH9SlYXXqxEbQsGak21HKTFO2TkFTtkRExK+lrnUOXdy3ES5/xVlRERGpBJqyJSIiIr+X2AYG/tvtFCJSTut2Z/Dzxr2MOrMR4SHBbscpFzW1i4iIiIj4qCe/TObZr9eTk1vodpRyU0EiIiIiIuKDFm3Zx//WpXJL3+bERfreuN9iKkhERERERHyMtZYnZicTHx3ODb2auB3ntKggERERERHxMXPXp7Fg8z7uGpBEZJhvt4WrIBERERER8TExEaEMbFeHEWc0cjvKafPtckpEREREJAB1a1yDbtd0cztGhdAKiYiIiIiIj8gvKOTFbzew72Cu21EqjAoSEREREREfMXPJTp76aj0LNu9zO0qFUUEiIiIiIuIDcvIKGPfNejo1rM6F7Wq7HafCqCAREREREfEBE+dv49f0HP54YSuMMW7HqTABW5AYY5oZY8YbY6a7nUVERERE5GSyPPm8NCeFXkm16JUU73acCnXaBYkx5i1jTKoxZtVJbjPQGJNsjEkxxjx8xOVbjDErjTHLjDGLKiPHiZ7bWrvJWjvmdJ5TRERERKQqHPLkc2bTmjx4YWu3o1S4ilgheQcYeKIrjTHBwEvAIKAtMNIY0/aIm/S31na21nY/wf0TjTExx1yWVJocpXhuERERERGvlxgbwStXd6Nzw+puR6lwp12QWGvnASdr8+8BpBStSOQCk4HBZXiKvsBHxphwAGPMWOCFUuY43ecWEREREXHVh0t3kJKa5XaMSlMVPST1ge1H/HlH0WUAFvjKGLPYGHPz8e5srZ0GzAamGGNGAzcCQ0/3uY0xtYwxrwJdjDGPHHtHY8ylxpjX09PTS/lUIiIiIiIVa1d6Ng/NWMkr3210O0qlcfuk9t7W2p3GmETga2PMuqKVjqNYa58wxkwGXgGaW2tPu0S01u4Fbj3J9Z8An3Tv3n3s6T6XiIiIiEh5PP+/FKy13HNeC7ejVJqqWCHZCTQ84s8Nii7DWlv8ORX4EGeL1e8YY/oA7Ytu81hFPLeIiIiIiDfbvOcgUxdtZ1SPRjSsGel2nEpTFQXJQqCFMaapMSYMGAHMMsZEFTerG2OigAuA303qMsZ0AV7H6f24AahljHn8dJ77tL8iEREREZFK9uzX6wkLDuKOc/13dQQqZuzvJOBnoJUxZocxZkzR5Z8bY+pZa/OBO3D6QNYCU621q4HawA/GmOXAAuAza+2Xx3mKSGCYtXajtbYQuBbYWpocJ3luERERERGvZa2lVnQYd5ybREJMuNtxKpWx1rqdwat1797dLlp0WkekiIiIiIgEPGPM4uMd9RGwJ7WLiIiIiHirXw9kU1gYGAsHKkhERERERLyItZYrX/6JB6evcDtKlVBBIiIiIiLiRdbtzmR3Rg5nNq3pdpQqoYJERERERMSLzElOBaBvqwSXk1QNFSQiIiIiIl7ku3VptKsXS+3YCLejVAkVJCIiIiIiXiI9O4/F2/bTv1Wi21GqjAoSERERESm31Mwcbp+4hNz8Qrej+IVqocG8eV13rurWwO0oVUYFiYiIiIiUWZYnH4CDngLW7srg6a+TXU7kH8JCgujfKpGm8VFuR6kyKkhEREREpEzW/JrBhc/OY9KCbTSNj+Ks5rV4be4mftiwx+1oPq2w0PLitxtISc1yO0qVUkEiIiIiIqU2e/Vuhrz6EwWFlvb14gB49OK2NE+I4r6py9h3MNflhL5r9a8ZPPXVelbsOOB2lCqlgkRERERETslay0tzUrhlwmJa1I5h1h296NDAKUiqhQXz/MguHDiUxyMzA+Mwv8owJzkVY+CcloEx7rdYiNsBRERERMT7Ld1+gCdnJzO4cz3+76qORIQGH3V9u3pxPH55exrVinQpoe+bk5xKxwbViY8OdztKlVJBIiIiIiInlFdQSGhwEF0b1WDqLWdzRpMaGGOOe9thZzQs+W9PfgHhIcHHvZ383r6DuSzbfoC7B7RwO0qV05YtERERETmuVTvTOffp71iweR8APZrWPGExcqSXv0vhipd+IievoLIj+o3k3ZlEhATTL4DOHymmgkREREREfufzlbuc5vUCS1R42VY62tSJZc2uDP7vy3WVlM7/nN28Fkv/ej4d68e5HaXKqSARERERkRLWWp77ZgN/mLiEtnVj+fiO3rSrV7YXyf1bJ3J9zya8/eMW5iSnVlJS/xMRGkxQ0KlXoPyNChIRERERKfHpil08+816ruxan0k3n0VCTPkarB8e1JrWdWJ4cNpy0jI9FZzSvyzdtp+Lnvuetbsy3I7iChUkIiIiIoK1FoCLO9Tl1au78fTQTqfVlB4RGsxzI7qQm1/Isu0HKiilf5qTnMa63RnUjYtwO4orVJCIiIiIBLhl2w9w0fM/sGP/IYKCDAPb1ylV8/qptKoTw48Pn8v5bWtXQEr/9V1yKl0b1aB6ZJjbUVyhgkREREQkgH28bCfDX/uZzJw8snMrfipWTEQoAJ+u+DVgtySdTFqmhxU70unXKrAOQzySziERERERCUCFhZZnv1nPC9+m0KNJTV65uiu1KulAvixPPn//ZA3Vq4XyyZ29f3eoYiCbuz4NICDH/RbTComIiIhIABr/w2Ze+DaF4d0b8v5NZ1ZaMQIQHR7C00M7sSE1i399trbSnscX1YuL4KquDWhXL9btKK7RComIiIhIABp1ZiNqRIVxVdf6FdIvcirntExgbJ+mvPH9Zs5pmaC+kiI9k+LpmRTvdgxXaYVEREREJEAs3rqfa99awKHcfKLCQxjSrUGVFCPFHriwFW3rxvLQjBWkZ+dV2fN6q98ycvj1QLbbMVyngkREREQkAMxcsoORr//C1r0H2ZuV60qG8JBgnh/ZhccubUtctVBXMniTd3/awjlPzCHLk+92FFdpy5aIiIiIHysotDw5O5lX527k7Ga1eHl0V2pEuTdeNikxmqTEaAAycvKIjQjcwmROchrdGtcgOjywX5JrhURERETEj/33i7W8Oncjo89sxHtjerhajBxpTnIqvf7zLat2prsdxRW703NYuyuD/q0Dd7pWscAux0RERET83LVnN6FpfDSjzmzkdpSjdG5QnajwEO6avJRP7+xNZFhgvSyduz4VIKDPHymmFRIRERERP7Ng8z7+9OFKCgstDWtGel0xAlAjKoxnhnVi856D/PPTNW7HqXJz1qVRNy6CVrVj3I7iusAqRUVERET83NSF2/nzRytpWCOSfYdyia/E80VOV8+keG7t25xXvtvIOS0SGNShrtuRqsw/Lm/H9n2HqnTKmbdSQSIiIiLiBwoKLf/5fC1v/rCZPi3ieXFUV5+YZHXveS35aeNeNqRmMcjtMFUoMSaCxJgIt2N4BRUkIiIiIn7gwWnLmbl0J9f3bMJfLm5DSLBv7MwPCwli2i1nExbiG3krwrRF2ykotIzo4X1b6dyggkRERETEDww7oyHdmtRg9JmN3Y5SZsXFyILN+9iQmumTX0NZvD5vE7VjI1SQFAmcUlRERETEz/y8cS9vfr8JgLOa1fL5F/Lv/7KVxz5ezfLtB9yOUml27D/EhtQsTdc6ggoSERERER/0wfxtXDN+PlMXbScnr8DtOBXin4PbUzs2grsnL/Xb08u/S04DoF8rnT9STAWJiIiIiA/JLyjkb7NW86cPV9KnRTwzbutJRGiw27EqRFxkKM8O78y2fYf426zVbsepFN8lp9KwZjWaJ0S5HcVrqCARERER8RHWWsa+t4h3ftrC2D5NefO6M4iJ8P5JWmXRo2lN7uifxPTFO/h+Q5rbcSqUtZaDngIGtK6tcb9HUFO7iIiIiI8wxnB+2zoMal+XYWc0dDtOpblrQAsa1YqiV/N4t6NUKGMMk24+i8JC63YUr6KCRERERMTLfb8hjZy8Qs5vW9srT12vaCHBQQzp1gCA1MwcakaG+cwY45MpLLQEBRmCgrQ6ciTf/86KiIiI+ClrLe/+tIXr317IK9+lYG1gvbO+Oz2HC56dx0tzNrodpUJc9Pz3PPNVstsxvI4KEhEREREvlFdQyF8+WsVjs1bTv1Ui7405M+D6DurERdCvZQLPf7uBxVv3uR3ntGzZc5B1uzOpGRXmdhSvo4JERERExMvk5BVw7fgFTJy/jdv6Nef1a7oRHR6YO+3/cXl76lWP4O7Jy8jIyXM7Trl9l5wKaNzv8aggEREREfEyEaHBtKkbyzPDOvHQwNYB3XMQGxHKuOFd2JWew18/WuV2nHKbk5xGs/gomsRr3O+xVJCIiIiIeInvklNZuysDgL9e2pYruzZwOZF36Na4BncPaEF+oSU3v9DtOGWWnVvAL5v2anXkBAJz7U9ERETEi1hrGf/DZv79+VoGtKnNG9d2dzuS17mjfxLG4JN9NHmFhdzRP4neLfxrjHFF0QqJiIiIiIty8wt5eMZKHv9sLee3rc1zIzq7HckrBQUZjDGkpGbx8IwV5BX4zkpJbEQodw5oQZdGNdyO4pVUkIiIiIi4JP1QHle/OZ8pi7Zz57lJvDK6G5Fh2sByMut2ZzB54XZe+N8Gt6OUirWWOcmpHPTkux3Fa6kgEREREXFJtbBgqoUF89yIztx/QauAbl4vrUs61mNItwa8OCeF+Zv2uh3nlDbtOcgNby/kw6U73Y7itVSQiIiIiFSx75JT2Xcwl7CQIN654QwGd67vdiSf8rfL2tGoZiT3TllG+iHvHgU8Z13xuN8El5N4LxUkIiIiIlXEWstrczdywzsLefbr9YBvNmm7LTo8hOdGdCE108PLc1PcjnNS3yWn0SIxmgY1It2O4rW0SVFERESkCnjyC3hk5kpmLtnJxR3r8qeL2rgdyad1alidd27oQfcm3tsoftCTz/zNe7mhV1O3o3g1FSQiIiIilWxPloeb31vEkm0HuPe8ltw1IEkrIxWgeIxulief9Ow86lev5nKio/2yaS95BVbbtU5BBYmIiIhIJTNAZk4+L4/uykUd6rodx69Yaxn95nwKCy0zbutJWIj3dCSc2zqRL+7uQ/OEaLejeDXv+Y6JiIiI+JmfN+4lr6CQWtHhfHF3HxUjlcAYw219m7NyZzpPf53sdpyjGGNoUzfWq4okb6T/OyIiIiIVzFrLS3NSGPnGL7z5/WYAQoL1squyDGxfh5E9GvH6vE38mLLH7TgApKRm8sfpy9m+75DbUbye/maIiIiIVKCcvALunryMJ2cnc3nnetzQq4nbkQLCo5e0oVl8FPdNXca+g7lux+GbtalMXbSDUBWip6T/QyIiIiIVJDUjh+Gv/8Ks5b/y4IWteHZ4ZyJCg92OFRAiw0J4fmQXWiTGkJtf6HYc5qxLpU3dWOrERbgdxeupqV1ERESkguw9mMvO/dm8dk03LmxXx+04AaddvTjev+lMt2OQkZPHoq37ueWcZm5H8QlaIRERERE5Tat2pgPQpm4s3/+xv4oRl+3J8nDrhMWkpGa68vw/bNhDQaGlf+tEV57f16gg8QGrdqaTmpGDtdbtKCIiInKEwkLLuG/Wc8kLP/DZil0AVAvTFi23FRZaFmzZx52TluHJL6jy58/JK6BN3Vi6NKxe5c/ti7Rlywdc//YC9mTlEhMRQlJiND2b1+LBC1sDkJqZQ62ocIKDdLiSiIhIVcrOLeCBacv5bOUuruxan/Pa6t1wb5EYG8GTQzoy5t1FPPFlMo9e0rZKn//Krg24smuDKn1OX6aCxMtZaxk3vAspqZmkpGWRkprF7nRPyfVXvvwTqZkemsVHkZQYTVJiNGc2rcXZzWu5mFpERMS/7UrPZux7i1j9awaPDGrNzec008nrXmZAm9pce3Zjxv+wmXNaJtC3ZdWclp6TV0BYcBBBerO41FSQeDljDL1bxNO7Rfxxr797QAs2pGax4bdMlu84wGcrdzGyh4ezm9eioNAy6Ll5NKoZSfPEaJonOAVLi8RoYiJCq/grERER8R8rd6Szdc8h3ry2OwPa1HY7jpzAny5qwy+b9vLU7GTOaRFfJUXjm99v4t2ftzL3wX5Ehumldmno/5KPG9q94VF/zskrIDvX2St5MDefFokxpKRmMW/9HnILnBF495/fkjsHtGDfwVzGfbPeWVkpKlYSYsL1Do+IiMgJbN93iIY1I7mgXR3m/bEmNaLC3I4kJxERGszr13Qnrlpolb2+mZOcRt24CBUjZaD/U34mIjS4ZN55bEQoL43uCkB+QSHb92eTkppFs4QoAHbuz2bmkp1kefJL7h8TEcK44Z0Z0KY2qRk5rNyZTlJiNA1qRKpPRUREAlZhoeXZb9bz6tyNTLu1J50bVlcx4iOaxDuve/IKClmxI51ujWtU2nPtP5jL0m37uePcFpX2HP5IBUmACAkOoml8FE2L/lICdGgQx8q/XcBvGR5SUrNK+lQa1YwE4KeNe7lnyjIAwkKCaBYfRfPEaB4e2JqGNSPJ8uQTEmR04JOIiPi1g5587pu6jNmrf2N494a0rRvrdiQph6dmJ/P2T1v45I7etKoTUynPMW9DGoUW+reqmn4Vf6GCJMAZY6gTF0GduIjf9amc17Y2M247m42pB0sa6lfuSCck2FkpmfDzVp6cvY6GNSNLtnwlJUZzaad6KlJERMQv7Nh/iLHvLSZ5dwaPXtKWG3s10dZmHzX2nGbMWLKTuyYt5eM7elXKa5W5yWnUjAqjY4PqFf7Y/kwFiZxQdHgI3RrXpFvjmse9/sxmNbmjfxIpaVlsTD3I9xv2UGgtl3epD8CzX69n4ZZ9JYVKcdGSGBtRlV+GiIhIuX26Yhc79h3irevPoF8rjfX1ZfHR4Tw1tCPXv72Q/3y+lr8Pbl/hzzGkewN6JsVrm3sZqSCRcuvaqAZdGx3eh5lfUMiu9BxCg53zNiPDgjmYW3BUn0qd2Ah++dMAAN79aQs5eQUlBYv6VERExFvsO5hLzagwbjmnGZd2qkf96tXcjiQVoF+rRG7s1ZS3ftxM31YJnNu6Yiek9Wx+/KmocnIqSKTChAQH0bCo/wTglr7NuaVvc6y1JX0qRzbQf7riVxZu2V/y57CQIC7uUJdnh3cG4PsNaSTEhNOkVpS2gImISJUoKLQ8OTuZqYu288mdvalfvZqKET/z0KBW/JaZQ+0K3rHx08Y9VAsNpkujymua91cqSKTSHdmncqRpt/Yk/VAeKWmZRU31WdSJc37pW2u5dcJiDuYWEGQo6VO5pFNdrujinHya5cknOlw/wiIiUjGyPPncM3kp36xNZfSZjUiMCXc7klSC8JBgXhrVteTP1toK6Qv6vy/WERxkmPmHXqf9WIFGr+bEVXGRoSfsU5l2a8+SZvqNRQXL9n3ZAKRn59Hp719ROzacpCMOfeydFE+zhOiq/jJERMTHbd93iJveXURKWhb/GNyOa89u4nYkqWS5+YX89eNVJCVGc1OfZqf1WHuyPCzfkc7957esoHSBRQWJeCVjDG3rxdK23olHKz40sLWzspKWVdKn8q8r2tMsIZqNaVk8MG35UdO/1KciIiIn8tKcFHalZ/PODWfQp4VGtgaC0GDD3oO5zPwymbOb16JdvbhyP9a89WkA9G+twQflYay1bmfwat27d7eLFi1yO4acQnGfSrXQYOIiQ1m1M53HP1tDSupB9mR5Sm43/rruDGhTmzW/ZvDVmt0lhUrT+CjCQ9SnIiISaA7l5hMZFsKh3HxSMzwlh+hJYNh3MJeB4+YRExHCp3f2oVpY+V4L3DlpKT9v3MuCPw0gSG98npAxZrG1tvuxl2uFRPxCcZ9Ksfb145h889kAR/WpdGpYHYAVOw7w3P82UFyPBxloVDOSCWPOpGHNSDbvOcj+Q7kkJUYTGxFa1V+OiIhUsoJCy38+X8sPKXuYcVtPosJDaBKvl0WBpmZUGM8O78zV4+fzz8/W8O8rOpT5May1LNqyj36tElSMlJP+5onfO16fyogejbi8S302pmWxMe1gSZ9KQlED4wfzt/LG95sBSIwJL1lJefSStoQGB5GTV0B4SJAOxxIR8UEZOXnc+cFS5q5P4/qeTQgPCXI7krioV1I8N/dpxsT527h7QIsyT98yxjDngX5HTRKVstGWrVPQlq3A9OuBbFb/mlEy/SslLYsDh3KZ+2B/AO74YAnz1qfR/IgDH9vUjeWcltp3LCLizbbsOciYdxeyde8h/jG4PaPObOR2JPECufmF/JaRc9TxBVLxtGVLpAzqVa9GverVOL/t8Q9MurBdHapHhpKSmsWc5DSmLd5Bu3qHC5IHpi0/6tBH9amIiHiHR2auZO/BXN4b00OH2EmJsBDnLDVrLbNX7+aCtnVKvf3q7slL6dm8FsPPUHFbXipIRMrh0k71uLRTvZI/px/KY/+h3JI/FxRaVuxI57OVu0r6VM5rk8ib150BwIvfbiAxNqKkWFGfiohI5covKCQkOIgnh3Ykv8CqeV2Oa96GPdz6/hIeHtSaW/s2P+Xtf8vI4eNlv9K6zomngsqpqSARqQBxkaHERR4uKopPm8/JK2BT2kFS0rKoXi205LIXvk3Bk19YcvvEmHBu6ducMb2bkl9QyILN+0hKjCYhJlx9KiIipyG/oJDHP1vLrvRsXhndjQY1tCVHTuycFvFc1KEOT81OpmfzWnRsUP2kt5+b7Iz77ddKW7ZPhwoSkUoUERr8u/NUIkKDWf33C9m+P/twj0pqFnWKmui2789m1JvzAYiJCHFWURKiGXlmI7o2qkFBobPkovNUREROLv1QHndMWsL3G/Ywtk9T1DUrp2KM4T9XdGTZtnncPXkZn97Zm6jwE79cnpOcSp3YCFrXianClP5HBYmIC0KCg2gaH0XT+Kjf9anUiY1g4k1nsuG3zJKT6uckpzGgjXPY0vzNe7nh7YU0jY8q2fLVPME5pb5GVJgbX46IiNfZlJbFTe8uYvv+QzxxVUeGndHQ7UjiI+IiQ3lmeGdGvvELf/9kNU8M6XTc2+UVFPL9hj1c2qmudjOcJhUkIl6mWlgwvZLi6ZV0dLNl8US8xJhwrj27MRvTDh7Vp/LR7b2oERXGN2t+Y9KCbU6hckRTvfpURCRQFBRabnp3EQey85h401n0aFrz1HcSOcJZzWrxyKDWtDpJb8iBQ3n0SqrFBW3rVGEy/6Sxv6egsb/i7Yr7VJolRBERGszHy3by0pwUNu85SF7B4b/fC/40gMTYCL7fkMbmPQdLxhWrT0VE/EXxaxpjDEu37Sc+OlxjXKVCFBRabZWuABr7K+KnivtUig3uXJ/BneuTX1BY0qeyec/hQx8/W7GLyQu3l9w+JiKENnVjmXLzWRhjSN6dSURoEA1qROqXr4j4jLyCQv42azXx0eHce35LujSq4XYk8ROvzd3Id8lpvH/TmUf9u5iamUNiTNkOUZTjC8iCxBjTDPgzEGetHeJ2HpHKcGSfChzuU/nPlR2457yWRc30Tp9Kdm5hySrJ3z9ZzU8b9xIWEkSzoj6V7o1rcH2vpoDeJRIR77P/YC5/mLiEnzft5Q/9mmOt1cqvVJj46HB+3rSXV75L4Y5zWwCw80A2vf77Lf93VQedP1IBSlWQGGPeAi4BUq217U9wm4HAc0Aw8Ka19r9HXBcMLAJ2WmsvOeLye4GbAAusBG6w1uaU9Ys4Wb7j5bLWbgLGGGOml/W5RHydMYY6cRHUiYugd4vfHwr2yKA2rNmVXjL9a/mOA2Tm5JcUJBeOm0deQWHJlq/midF0alCdVpowIiIuSEnNZMy7i9h1IIdnhnXiyq4N3I4kfubKrvWZuz6NZ7/ZQM+keLo2qsF3yakAdGuslbiKUNoVkneAF4H3jndlUcHxEnA+sANYaIyZZa1dU3STu4G1QOwR96kP3AW0tdZmG2OmAiOKnqv4NolAtrU284jLkqy1KaXJV4pcInKMDg3i6NAg7qjLCgsP96Jc1qkeybszSUnN4vsNe8gtKGRotwY8ObQT1lpufGchjWpGOg316lMRkUqU5cln2Gu/EGRg0s1n6cWhVApjDI9f0Z4l2/Zz9+SlfH5XH75LTqNBjWo0T4h2O55fKFVBYq2dZ4xpcpKb9ABSilYeMMZMBgYDa4wxDYCLgX8B9x3n+asZY/KASODXY67vC9xqjLnIWusxxowFrgQGlTLfCXOd4ksWkSMEHbFF664BLUr+u7hPpfjqjJx89h3KY+GWnWR58ktud9/5LblrQAsyc/L4YP62kslf6lMRkdMRHR7Cvy5vT8eG1alfvZrbccSPxUaEMm54Z0a/OZ8fU/bwY8oeruxaX2+2VZCK6iGpD2w/4s87gDOL/nsc8EfgqP0c1tqdxpingG1ANvCVtfarY24zzRjTFJhijJkG3Iiz2nFauYwxtXAKpC7GmEestf859o7GmEuBS5OSksrwdCKBpbhPpVhctVA+vr0X1lp+y/CU9KkUN5empGbxny/Wldw+PMS5/6OXtKVXUjzp2XnsSs+maXwU4SHBVf71iIj3y80v5LFZq+jTIoGLOtRlUIe6bkeSANG9SU1+eOhckndncii3gP6tEt2O5DcqtandGFPc17HYGNPvmOtq4KxWNAUOANOMMVdba98/8nbW2ieKVjZeAZpba7NON5e1di9w6ylu8wnwSffu3cee7vOJBJoT9al0aVSD5X+9gJS0zKNOqY+JcH4V/bBhD7d/sIQgA41qRpYc+nhDr6bUiYtQo6pIgNub5eG295ewYMs+rYiIKxJiwgkJMjwxpCNnN6/ldhy/UVEFyU7gyCNQGxRd1gu4zBhzERABxBpj3rfWXg2cB2y21qYBGGNmAj2BowoSY0wfoD3wIfAYcEcF5BIRl8RFhtKtcU26Nf79QWXdm9Tg+ZFdSEnNYmNRsTJv/R6uPqsxAON/2Mzr8zaVbPlKKupTOaNpTUKDg6r6SxGRKpS8O5Mx7y4kLdPD8yO7cFmnem5HkgBVIyqMYd0bnvqGUmoVVZAsBFoUba/aidOcPspauxp4BKBoheSBomIEnK1aZxljInG2bA3AmcRVwhjTBXgdZ4LWZmCiMeZxa+1fTidXeb9IEalctWMjfvciI7+gsKTPJCkxmj4tEkhJy2LmEqdPJcjAmn8MJDQY3v1pC6t2ph9VsKhPRcT37TyQzZUv/0hUeAhTbjmbzg2rux1JRCpQacf+TgL6AfHGmB3AY9ba8caYz4GbrLW/GmPuAGbjjNd9q6gYOSFr7fyisbtLgHxgKU7xcaRIYJi1dmNRjmuB60ubz1qbX9ZcIuJdQo5Y+ejXKpF+RXt2i/tUtu07RESo02+yKz2HOclpTFu8o+Q+9atX48eHzwWcQyEtlqTEaPWpiPiQ+tWrce/5LbmkYz3qxOkgOhF/Y6y1p75VAOvevbtdtGjRqW8oIl4j/VAeKWmZbEw9iCe/gGvObgLAwHHzWLfbmSJe3KdyXpva/OWStgCs/y2TOnERxEaEuhVdRIp48gv4+ydrGH1mI9rVizv1HUTE6xljFltrux97eUCe1C4i/u1EfSof3d6LTWkHSUnLKulTCQtxVmCstQx99WfSs/NIjAkv2fLVv1Ui/VsfXpVRU71I5UvL9HDLhEUs2XaAlonRKkhE/JwKEhEJGBGhwbStF0vberG/u85aeGpop8PTv4r6VCLDQujfOpGDnnx6/vdbmiVElRz4mJQYTccG1UmICXfhqxHxT6t/TWfsu4vYdyiXl0d35SKN9RXxeypIRERwDn88v21tzm9bu+Qyay25BYUAePILuaxTPVJSs47qU/nbpW25vldTtu87xP99ua6kUGme4PSpFPe3iMiprdqZztBXfyauWijTb+1J+/paGREJBCpIREROwBhT0vheMyqMf17evuS64j6V+tUjAUjL8rBiRzqfrdxFcWtekIG3rj+Dfq0S2bznIAu37CspWNSnIvJ7rerEMLJHI27t24zEWDWviwQKNbWfgpraRaQscvIKjupTGXFGQ+pVr8Z7P2/hrx8fHvJX3Kfy1NBO1KtejbRMD9ZaEmLC1aciASUnr4AnZyfzh37NqRWt7Y8i/kxN7SIiVeBEfSqjejSid1I8G9MOHtWnElvNWSl5+8fNvPzdRmIiQkoOfGyeGM2Y3k116KP4rdSMHMZOWMzy7QfoUD+Oy7vUdzuSiLhABYmISBUICQ6iWUI0zRKij+pTKXZJx3okxoSXrKzMSU7jy9W7ueWcZgA8MnMFS7cdoHlRsZKUGE2rOjG0rB1T1V+KSIVYuSOdse8tIiMnj9ev6cYF7eq4HUlEXKKCRETECxxvVeWgJ79k+1ar2jHsTs9hxY4DfF7Up9KydjRf3dsXgKdmJ5NXUOgULOpTES/3Y8oexry7kFpR4Uy/tedxJ9+JSOBQQSIi4qWiwg//ir6+V1Ou79UUONyncjA3v+T6pdv3s3Dz/pKpYACXdqrHCyO7ADBzyQ7qxEaQlBitPhVxXdu6sVzYrg5/ubitxmaLiJraT0VN7SLiK/ILCtm+P7ukR6Ve9QgGd66PJ7+ANo9+SWHRr/viPpWRPRoxrHtDCgst2/cfokGNSIKDVKhI5cjOLeD1eZu4rV/zkgNJRSSwqKldRMTPhQQH0TQ+iqbxUUf1qYQFB/HTwwOKCpXMkj6V/AKnQvk1PZu+T35HeIhz/+ItX4Pa16VVHfWoyOnblZ7N2PcWsfrXDLo0qs45LRPcjiQiXkQFiYiInzPGUCcugjpxEfRuEf+762MiQnniqo4lhcryHQf4bOUumic4jfOLt+7n/qnLSg58LO5TaVMnlmphOvhRTm7ptv3cPGEx2bkFjL+uu4oREfkdFSQiIgEurloow85oeNRlOXkFJf8dHhJEu3pxpKRmMW/9npI+lWm3ns0ZTWry88a9fLFqV8m4YvWpSLHZq3dz56Sl1I4NZ+JNZ2oqnIgclwoSERH5nYjQwysf7evH8dLorsDRfSpt6jqTkTbvOcjMJTvJ8hxuso+NCOGb+/uSGBPBih0HSMv0kJQYrT6VANOkVhR9kuJ5cmgnakaFuR1HRLyUmtpPQU3tIiKnZq3ltwxPSZ/K5j0HeezSdgQFGR6avoIpi7YDEBYSRLP4KFrViWHc8M4YY0jNzCE2IvSoIkh810FPPh8v+5WRPRpqlUxEjqKmdhERqTQn61P508VtGHZGA1JSs0pOqt+T5Sl5sfrgtBV8vyGNhjUjS7Z8dWxQnYs71nXjS5HTsGP/IW56dxHrf8ukc8PqOl9EREpFBYmIiFSquGqhdGtck26Nax73+ut6NqZTw+psLBpX/P2GPXRtfLggufrN+RRaWzL9KykhmpZ1YoiP1vkV3mTx1n3cMmExnvxC3r6hh4oRESk1FSQiIuKqc1vX5tzWh8cU5xcUkpFzuB+lYc1I1uzKOKpP5You9Xl2eGestTz68Soa1ogsKVjUp1L1Plq6kz9OX0G96hFMvvkMkhKj3Y4kIj5EBYmIiHiVkOCgoxqg/3NlB+Bwn8rGtCxiIpx/vjKy85m9+jfSMj0ltw8PCeLBC1txU59mZOcW8L91v9E8IZqm8VHqU6kkkWHB9GhakxdHdaF6pJrXRaRs1NR+CmpqFxHxfumH8khJy3K2faVl0bdlAr2S4lmx4wCXvfgjAEGGkj6VP/RvTrfGNcnJKyC3oJDYiFCXvwLfk5mTx8It+0pWt6y1amIXkZNSU7uIiPituMhQujWuQbfGNY66vHWdWD6/q0/JoY/FfSp5RafUz12fxi0TFpMYE364RyUxmos71KWWelROaNveQ9z03kK27D3ED3/sT2JshIoRESk3FSQiIuK3wkKCaFsv9oQN1i0So3loYGtnXHFaVkmfSq+keGpFhzN10XYmLdhWMv2redHnRjUjCQrQPpX5m/Zy6/uLKSi0vHXdGSTGRrgdSUR8nAoSEREJWM0Sormt3+EG7OI+lYQYZ3WkWmgw4SFBzElOY9riHSW3W/33C4kKD2HW8l/ZmJpVsrLi730qUxZu4y8fraJhzUjGX3cGTeOj3I4kIn5ABYmIiEiR4vNUil3aqR6XdqoHFPepZLJjfzZR4c4/nz9v3MPkhdspbscMMs7J9rPu6A04qwmhIUEkJUb7RZ9KaoaHs5rV4sVRXYmr5vtfj4h4BzW1n4Ka2kVE5GRy8grYlHawpE+lsNDywIWtALj0hR9YuTMdoKRPpXeLeP7QLwlwipzYaiFe3X+RkZPH1j2H6NAgDmstBYWWkOAgt2OJiA9SU7uIiEgliAgNPmGfyoujurD+N6dQKe5T2bLnYMn1A56ZS25+Ac2LDnxMSoymR9OadGlU43eP5YYtew4y5t2FpGfn8/0f+1MtLJiQYO8tnkTEN6kgERERqSSNa0XRuFYU57et/bvrCgstd/RvXrKyUtynMqZ3U7o0qoEnv4ArXvqJZglRR00AaxofRXhI5fep/JSyh9smLsEYeGV0N6qF+W9vjIi4SwWJiIiIC4KCDNf3anrUZemH8sgvLAScQx/rxEWwYkc6n63cVdKn8peL23BTn2akZuQw/sfNJCVEOyssFdin8v4vW/nbrNU0jY/izeu607iWmtdFpPKoIBEREfEScZGHC4qEmHDeuv4M4Og+lXZFW8M27znI2z9sIbegsOQ+iTHhjBvRmZ7N4/ktI6dkAlhCTHip+1Sstfy8aS99WsTz/MguxPhBM76IeDc1tZ+CmtpFRMRb5RcUsn1/9uEeldQs7jg3iabxUUxasI1HZq4EICYixNnylRDNAxe2onZsBNm5BYSFBBFcdJ5K+qE8snLzqV+9Gjl5BYQGH75ORKQiqKldRETEz4QEB9E0Poqm8b/vUxnUvg4Na0SSkpp5VJ/Kny9uA8Arczfy2tyNNI2PonliNGt+zSAqPJhZt/f267NURMT7qCARERHxQ9Ujw+jdIp7eLeKPe/1ZzWqSk1dASmoWK3ekY7E8dmnHgD2BXkTco4JEREQkAPVsHk/P5scvVkREqpJONhIREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdeoIBEREREREdcYa63bGbyaMSYN2OpyjDgg3eUMIuWhn12pSPp5Cmz6/vs2ff+8Tzywp4qfs7G1NuHYC1WQ+ABjzOvW2pvdziFSVvrZlYqkn6fApu+/b9P3z/sYYxZZa7u7nQO0ZctXfOJ2AJFy0s+uVCT9PAU2ff99m75/ckJaIRERERERCTBaIRERERERETe97naAYlohERERERER12iFREREREREXKOCJEAYY5oZY8YbY6a7nUWktPRzKxVJP0+BTd9/36fvof9SQVJJjDFbjDErjTHLjDGLTuNx3jLGpBpjVh3nuoHGmGRjTIox5uGTPY61dpO1dkx5c0hgMMZUN8ZMN8asM8asNcacXc7H0c9tgDPGtCr6/Vf8kWGMuaecj6WfJx9kjLnXGLPaGLPKGDPJGBNRzsfR998lxpi7i75/q8v797focfQ9lJNSQVK5+ltrOx9vgoExJtEYE3PMZUnHeYx3gIHHuX8w8BIwCGgLjDTGtDXGdDDGfHrMR2KFfDUSCJ4DvrTWtgY6AWuPvFI/t1Ja1trkot9/nYFuwCHgwyNvo58n/2WMqQ/cBXS31rYHgoERx9xG338vZoxpD4wFeuD8e3DJsd8ffQ/9izEmyhjzrjHmDWPM6Kp8bhUk7ukLfGSMCQcwxowFXjj2RtbaecC+49y/B5BS9G5BLjAZGGytXWmtveSYj9RK/DrETxhj4oBzgPEA1tpca+2BY26mn1spjwHARmvt1mMu18+TfwsBqhljQoBI4Ndjrtf337u1AeZbaw9Za/OBucCVx9xG30Mvd6LVqROsTF0JTLfWjgUuq8qcKkgqjwW+MsYsNsb87mRSa+00YDYwpagKvREYWobHrw9sP+LPO4ouOy5jTC1jzKtAF2PMI2V4HgkcTYE04G1jzFJjzJvGmKgjb6CfWymnEcCkYy/Uz5P/stbuBJ4CtgG7gHRr7VfH3Ebff++2CuhT9P8tErgIaHjkDfQ99AnvcMzq1IlWpoAGHP5+FFRhRkKq8skCTG9r7c6iJcavjTHrit4hKGGtfcIYMxl4BWhurc2qrDDW2r3ArZX1+OIXQoCuwJ3W2vnGmOeAh4FHj7yRfm6lLIwxYTjvtB33xYN+nvyTMaYGMBjnjY4DwDRjzNXW2vePvJ2+/97LWrvWGPN/wFfAQWAZx3mRqu+hd7PWzjPGNDnm4pKVKYCi799gnIKwAc73ukoXLbRCUkmK3h2iaInxQ5xv/lGMMX2A9kXXP1bGp9jJ0e9UNCi6TKS8dgA7rLXzi/48HadAOYp+bqWMBgFLrLW/He9K/Tz5rfOAzdbaNGttHjAT6HnsjfT9927W2vHW2m7W2nOA/cD6Y2+j76FPOtHK1EzgKmPMK8AnVRlIBUklKGoKiin+b+ACnKXPI2/TBeeEzMHADUAtY8zjZXiahUALY0zToncgRwCzKiK/BCZr7W5guzGmVdFFA4A1R95GP7dSDiM5znYt0M+Tn9sGnGWMiTTGGJzfJ8cOydD338sVN5IbYxrh9Bd8cMz1+h76EWvtQWvtDdba26y1E6vyuVWQVI7awA/GmOXAAuAza+2Xx9wmEhhmrd1orS0ErgWObfjEGDMJ+BloZYzZYYwZA1DUYHYHzt7NtcBUa+3qSvuKJFDcCUw0xqwAOgP/PuZ6/dxKqRW9IXM+zrtux6OfJz9VtNI6HVgCrMR5vfH6MTfT99/7zTDGrMF5t/z24ww60ffQN3ndypSx1rr5/CIiIiIiUkmKekg+LRrBTdHku/U4K5c7cVaqRrlZDGqFRERERETEDx1vdcobV6a0QiIiIiIiIq7RComIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLhGBYmIiIiIiLjm/wG/WH+8wZyRzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_train, results_test = [], []\n",
    "beta2s = 1 - (1 - .95) * np.logspace(-1, 1, 7, base=10)\n",
    "for beta2_ in beta2s:\n",
    "    tic = time.time()\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, beta2=beta2_, verbose=False)\n",
    "    print(f'Pour beta2= {beta2_:.2e}, {loss_train=:.2e} / {loss_test=:.2e}; le temps de calcul est {time.time()-tic:.3f} s')\n",
    "    results_train.append(loss_train)\n",
    "    results_test.append(loss_test)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (13, 8))\n",
    "ax.plot(beta2s, results_train, '--')\n",
    "ax.plot(beta2s, results_test)\n",
    "ax.set_xlabel = 'beta2 with Adam instead of SGD'\n",
    "ax.set_ylabel = 'Loss'\n",
    "ax.set_yscale('log');\n",
    "ax.set_xscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-03T17:34:52.526348Z",
     "iopub.status.busy": "2022-06-03T17:34:52.525745Z",
     "iopub.status.idle": "2022-06-03T19:11:24.051680Z",
     "shell.execute_reply": "2022-06-03T19:11:24.052053Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 129  513 1153 2049 3201 4609]\n",
      "Pour num_epochs=129, loss_train=1.06e+01 / loss_test=1.07e+01; le temps de calcul est 65.368 s\n",
      "Pour num_epochs=513, loss_train=1.05e+01 / loss_test=1.06e+01; le temps de calcul est 259.514 s\n",
      "Pour num_epochs=1153, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 572.166 s\n",
      "Pour num_epochs=2049, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 1017.403 s\n",
      "Pour num_epochs=3201, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 1586.171 s\n",
      "Pour num_epochs=4609, loss_train=1.05e+01 / loss_test=1.05e+01; le temps de calcul est 2290.620 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_train, results_test = [], []\n",
    "num_epochss = ((num_epochs-1) * np.arange(1, N_scan) ** 2) // 8 + 1\n",
    "print(num_epochss)\n",
    "for num_epochs_ in num_epochss:\n",
    "    tic = time.time()\n",
    "    trans, loss_train, loss_test, p_1, p_2, p_2_pred = fit_data(df_1, df_2, num_epochs=num_epochs_, verbose=False)\n",
    "    print(f'Pour num_epochs={num_epochs_}, {loss_train=:.2e} / {loss_test=:.2e}; le temps de calcul est {time.time()-tic:.3f} s')\n",
    "    results_train.append(loss_train)\n",
    "    results_test.append(loss_test)\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (13, 8))\n",
    "ax.plot(num_epochss, results_train, '--')\n",
    "ax.plot(num_epochss, results_test)\n",
    "ax.set_xlabel = 'batch size'\n",
    "ax.set_ylabel = 'Loss'\n",
    "ax.set_yscale('log');\n",
    "ax.set_xscale('log');"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Welcome To Colaboratory",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}