{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# TP sur le topic modeling\n",
    "\n",
    "François HU - Data scientist au DataLab de la Société Générale Assurances - *05/07/21*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n",
       "<script>\n",
       "function repeat_indent_string(n){\n",
       "    var a = \"\" ;\n",
       "    for ( ; n > 0 ; --n)\n",
       "        a += \"    \";\n",
       "    return a;\n",
       "}\n",
       "// look up into all sections and builds an automated menu //\n",
       "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n",
       "    var anchors = document.getElementsByClassName(\"section\");\n",
       "    if (anchors.length == 0) {\n",
       "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n",
       "    }\n",
       "    var i,t;\n",
       "    var text_menu = begin;\n",
       "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n",
       "    var ind = \"\";\n",
       "    var memo_level = 1;\n",
       "    var href;\n",
       "    var tags = [];\n",
       "    var main_item = 0;\n",
       "    var format_open = 0;\n",
       "    for (i = 0; i <= llast; i++)\n",
       "        tags.push(\"h\" + i);\n",
       "\n",
       "    for (i = 0; i < anchors.length; i++) {\n",
       "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n",
       "\n",
       "        var child = null;\n",
       "        for(t = 0; t < tags.length; t++) {\n",
       "            var r = anchors[i].getElementsByTagName(tags[t]);\n",
       "            if (r.length > 0) {\n",
       "child = r[0];\n",
       "break;\n",
       "            }\n",
       "        }\n",
       "        if (child == null) {\n",
       "            text_memo += \"null\\n\";\n",
       "            continue;\n",
       "        }\n",
       "        if (anchors[i].hasAttribute(\"id\")) {\n",
       "            // when converted in RST\n",
       "            href = anchors[i].id;\n",
       "            text_memo += \"#1-\" + href;\n",
       "            // passer à child suivant (le chercher)\n",
       "        }\n",
       "        else if (child.hasAttribute(\"id\")) {\n",
       "            // in a notebook\n",
       "            href = child.id;\n",
       "            text_memo += \"#2-\" + href;\n",
       "        }\n",
       "        else {\n",
       "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n",
       "            continue;\n",
       "        }\n",
       "        var title = child.textContent;\n",
       "        var level = parseInt(child.tagName.substring(1,2));\n",
       "\n",
       "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n",
       "\n",
       "        if ((level < lfirst) || (level > llast)) {\n",
       "            continue ;\n",
       "        }\n",
       "        if (title.endsWith('¶')) {\n",
       "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n",
       "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n",
       "        }\n",
       "        if (title.length == 0) {\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        while (level < memo_level) {\n",
       "            text_menu += end_format + \"</ul>\\n\";\n",
       "            format_open -= 1;\n",
       "            memo_level -= 1;\n",
       "        }\n",
       "        if (level == lfirst) {\n",
       "            main_item += 1;\n",
       "        }\n",
       "        if (keep_item != -1 && main_item != keep_item + 1) {\n",
       "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n",
       "            continue;\n",
       "        }\n",
       "        while (level > memo_level) {\n",
       "            text_menu += \"<ul>\\n\";\n",
       "            memo_level += 1;\n",
       "        }\n",
       "        text_menu += repeat_indent_string(level-2);\n",
       "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n",
       "        format_open += 1;\n",
       "    }\n",
       "    while (1 < memo_level) {\n",
       "        text_menu += end_format + \"</ul>\\n\";\n",
       "        memo_level -= 1;\n",
       "        format_open -= 1;\n",
       "    }\n",
       "    text_menu += send;\n",
       "    //text_menu += \"\\n\" + text_memo;\n",
       "\n",
       "    while (format_open > 0) {\n",
       "        text_menu += end_format;\n",
       "        format_open -= 1;\n",
       "    }\n",
       "    return text_menu;\n",
       "};\n",
       "var update_menu = function() {\n",
       "    var sbegin = \"\";\n",
       "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n",
       "    var send = \"\";\n",
       "    var begin_format = '<li>';\n",
       "    var end_format = '</li>';\n",
       "    var keep_item = -1;\n",
       "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n",
       "       begin_format, end_format);\n",
       "    var menu = document.getElementById(\"my_id_menu_nb\");\n",
       "    menu.innerHTML=text_menu;\n",
       "};\n",
       "window.setTimeout(update_menu,2000);\n",
       "            </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jyquickhelper import add_notebook_menu\n",
    "add_notebook_menu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Les topic models (*modèles thématiques* en français) sont une famille de modèles qui sont capables de découvrir les topics (*thèmes* en français) d'une collection de documents textuels. Dans ce contexte, le terme \"**topic**\" désigne des groupes de mots qui se retrouvent souvent ensemble dans un même document. Par exemple, dans un recueil d'articles de journaux, un topic model peut identifier un topic composé des mots :\n",
    "- \"homme politique\", \"droit\" et \"parlement\", et un autre caractérisé par \n",
    "- \"joueur\", \"match\" et \"carton rouge\"\n",
    "\n",
    "Les topics modeling ne peuvent pas affecter un titre à ces topics : c'est notre tâche d'interpréter ces topics et de leur donner des étiquettes telles que **politique** et **football**.\n",
    "\n",
    "La particularité de ces méthodes c'est que nous n'avons pas de labels : ce sont des tâches **non-supervisées**.\n",
    "\n",
    "L'un des modèles les plus populaires est le LDA. Le LDA est un modèle génératif qui considère chaque document comme un mélange de topics. Ce sont ces topics qui seront en charge de générer les mots. Par exemple, \n",
    "\n",
    "- le topic **football** générera le mot \"ballon\" avec une probabilité élevée, \n",
    "- tandis que le topic **politique** aura une probabilité beaucoup plus élevée pour générer le mot \"politicien\" que pour générer le mot \"ballon\""
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Données : Grand Débat National\n",
    "\n",
    "- L'un des contextes où la modélisation des topics est très utile est celui des questions ouvertes. Il nous permet d'explorer les différents topics abordés dans les réponses des gens. \n",
    "\n",
    "- Dans ce notebook nous allons explorer un ensemble complet de réponses du [Grand Débat National](https://granddebat.fr/), un débat public organisé par le président Macron. Le but du débat était de mieux comprendre les besoins et les opinions des Français suite aux manifestations des gilets jaunes. Les résultats de ce débat sont maintenant disponibles sous forme de [données ouvertes](https://granddebat.fr/pages/donnees-ouvertes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 2. Charger les données\n",
    "\n",
    "Nous allons tout d'abord importer un des fichiers csv sur la transition écologique dans un dataframe [pandas](https://pandas.pydata.org/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%%capture captured\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "chemin = ... # TODO\n",
    "\n",
    "raw_data = pd.read_csv(chemin, error_bad_lines=False, warn_bad_lines=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Chacune des lignes de ce dataframe ``raw_data`` contient des réponses répondant à une liste de questions sur la transition écologique. Certaines de ces questions sont à choix multiples, tandis que d'autres sont des questions ouvertes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reference', 'title', 'createdAt', 'publishedAt', 'updatedAt',\n",
       "       'trashed', 'trashedStatus', 'authorId', 'authorType', 'authorZipCode',\n",
       "       'Quel est aujourd'hui pour vous le problème concret le plus important dans le domaine de l'environnement ?',\n",
       "       'Que faudrait-il faire selon vous pour apporter des réponses à ce problème ?',\n",
       "       'Diriez-vous que votre vie quotidienne est aujourd'hui touchée par le changement climatique ?',\n",
       "       'Si oui, de quelle manière votre vie quotidienne est-elle touchée par le changement climatique ?',\n",
       "       'À titre personnel, pensez-vous pouvoir contribuer à protéger l'environnement ?',\n",
       "       'Si oui, que faites-vous aujourd'hui pour protéger l'environnement et/ou que pourriez-vous faire ?',\n",
       "       'Qu'est-ce qui pourrait vous inciter à changer vos comportements comme par exemple mieux entretenir et régler votre chauffage, modifier votre manière de conduire ou renoncer à prendre votre véhicule pour de très petites distances ?',\n",
       "       'Quelles seraient pour vous les solutions les plus simples et les plus supportables sur un plan financier pour vous inciter à changer vos comportements ?',\n",
       "       'Par rapport à votre mode de chauffage actuel, pensez-vous qu'il existe des solutions alternatives plus écologiques ?',\n",
       "       'Si oui, que faudrait-il faire pour vous convaincre ou vous aider à changer de mode de chauffage ?',\n",
       "       'Avez-vous pour vos déplacements quotidiens la possibilité de recourir à des solutions de mobilité alternatives à la voiture individuelle comme les transports en commun, le covoiturage, l'auto-partage, le transport à la demande, le vélo, etc. ?',\n",
       "       'Si oui, que faudrait-il faire pour vous convaincre ou vous aider à utiliser ces solutions alternatives ?',\n",
       "       'Si non, quelles sont les solutions de mobilité alternatives que vous souhaiteriez pouvoir utiliser ?',\n",
       "       'Et qui doit selon vous se charger de vous proposer ce type de solutions alternatives ?',\n",
       "       'Que pourrait faire la France pour faire partager ses choix en matière d'environnement au niveau européen et international ?',\n",
       "       'Y a-t-il d'autres points sur la transition écologique sur lesquels vous souhaiteriez vous exprimer ?'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Nous allons étudier la dernière question : \n",
    "\n",
    "``Y a-t-il d'autres points sur la transition écologique sur lesquels vous souhaiteriez vous exprimer ?`` \n",
    "\n",
    "car cette dernière laisse plus de liberté aux personnes. Notre objectif est d'analyser les topics sur lesquels portent leurs réponses grâce au modèle LDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "question = \"Y a-t-il d'autres points sur la transition écologique sur lesquels vous souhaiteriez vous exprimer ?\"\n",
    "data = raw_data[question]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Multiplier les centrales géothermiques\n",
       "1    Les problèmes auxquels se trouve confronté l’e...\n",
       "2                                                  NaN\n",
       "3                                                  NaN\n",
       "4      Une vrai politique écologique et non économique\n",
       "Name: Y a-t-il d'autres points sur la transition écologique sur lesquels vous souhaiteriez vous exprimer ?, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons remarquer qu'il y a beaucoup de données manquantes (comme toute question ouverte, les personnes décident oui ou non d'écrire un commentaire). Une étape de néttoyage est donc nécessaire."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 3. Nettoyer et vectoriser les documents\n",
    "\n",
    "Avant d'entraîner notre modèle LDA, nous avons besoin de tokenizer notre texte. Nous allons tokenizer grâce à la librarie [spaCy](https://spacy.io/) car nous allons effectuer seulement quelques prétraitements de base. Nous allons juste initialiser un modèle vierge pour la langue française."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remarques :** Après avoir installé la librairie SpaCy (pip install spacy), il faut télécharger un pipeline entraîné pour la langue française.\n",
    "\n",
    "``python -m spacy download fr_core_news_sm``\n",
    "\n",
    "spaCy fournit une variété d'annotations linguistiques pour vous donner un aperçu de la structure grammaticale d'un texte. Cela inclut entre autres des preprocessing classique en NLP à savoir la **lemmatisation**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load(\"fr_core_news_sm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Supprimons toutes les lignes du dataframe qui n'ont pas de réponse pour notre question (les `NaN`s ci-dessus). Ce nouveau dataframe s'appellera ``textes``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "... # TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Multiplier les centrales géothermiques\n",
       "1    Les problèmes auxquels se trouve confronté l’e...\n",
       "4      Une vrai politique écologique et non économique\n",
       "5    Les bonnes idées ne grandissent que par le par...\n",
       "6    Pédagogie dans ce sens là dés la petite école ...\n",
       "Name: Y a-t-il d'autres points sur la transition écologique sur lesquels vous souhaiteriez vous exprimer ?, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Ensuite, nous utilisons spaCy pour effectuer notre premier prétraitement (cela peut prendre quelques minutes) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 33s, sys: 42.2 s, total: 4min 16s\n",
      "Wall time: 4min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "spacy_docs = list(nlp.pipe(textes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Nous avons maintenant une liste de documents spaCy. Nous allons transformer chaque document spaCy en une liste de tokens. Au lieu des tokens originaux, nous allons travailler avec les lemmes à la place. Cela permettra à notre modèle de mieux généraliser. En effet, nous voulons par exemple que \"continuation\" et \"continuations\" représentent la même signification. Voici la liste complète des prétraitements : \n",
    " \n",
    "1. supprimer tous les **mots de moins de 3 caractères**,\n",
    "2. supprimer tous les **stop-words**, et\n",
    "3. **lemmatiser** les mots restants et,\n",
    "4. mettre ces mots en **minuscules**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.55 s, sys: 155 ms, total: 1.71 s\n",
      "Wall time: 1.72 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "docs = []\n",
    "for doc in spacy_docs:\n",
    "    tokens = []\n",
    "    for token in doc:\n",
    "        if len(token.orth_) > 3 and not token.is_stop: # prétraitements 1 et 2\n",
    "            ... # TODO: prétraitements 3 et 4\n",
    "    docs.append( tokens )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici un aperçu du **premier** document tokenisé :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['multiplier', 'centrale', 'géothermique']\n"
     ]
    }
   ],
   "source": [
    "print(docs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**deuxième** document tokenisé :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['problème', 'trouve', 'confronter', 'ensemble', 'planète', 'dénoncent', 'parfaire', 'désordre', 'gilet', 'jaune', 'france', 'il', 'surpopulation', 'mondial', 'population', 'passer', 'd’1,5', 'milliard', 'habitant', '1900', 'milliard', '2020', 'monter', 'bientôt', 'milliard', '2040', 'progrès', 'communication', 'village', 'mondial', 'individu', 'fondre', 'asie', 'fondre', 'afrique', 'passer', 'quartiers', 'campagne', 'pays', 'aspir', 'vivr', 'blâmer', 'lotir', 'concitoyen', 'logement', 'nourriture', 'bien', 'consommation', 'déplacement', 'etc.', 'mère', 'problème', 'bien', 'solution', 'problème', 'stabilisation', 'croissance', 'démographique', 'partage', 'richesse', 'partage', 'terre', 'partage', 'protection', 'biodiversité', 'règlement', 'conflit', 'lutte', 'contre', 'déforestation', 'lutte', 'contre', 'dérèglement', 'climatique', 'règlement', 'conflit', 'stabilisation', 'migration', 'concurrence', 'commercial', 'mondial', 'etc.', 'français', 'européen', 'mondial', 'france', 'jouer', 'rôle', 'moteur', 'autour', 'dérouler', 'grand', 'débat', 'paraître', 'anecdotique']\n"
     ]
    }
   ],
   "source": [
    "print(docs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Afin de conserver un peu l'ordre des mots lors de notre modélisation, nous allons tenir en compte les **bigrammes fréquents**. Pour cela, nous allons utiliser la bibliothèque [Gensim](https://radimrehurek.com/gensim/) (excellente bibliothèque NLP pour les topics modeling et word embeddings)\n",
    "\n",
    "Voici le processus retenu : \n",
    "\n",
    "- **Identifier les bigrammes fréquents** dans le corpus, \n",
    "- **Ajouter à la liste des tokens** pour les documents dans lesquels ils apparaissent. \n",
    "\n",
    "Cela signifie que les bigrammes ne seront pas dans leur position correcte dans le texte, mais comme les topic models sont des modèles de **bag-of-words** (*sac-de-mots* en français) qui ignorent la position des mots, cela ne pose pas de problèmes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from gensim.models import Phrases\n",
    "\n",
    "bigram = Phrases(docs, min_count=10)\n",
    "\n",
    "for index in range(len(docs)):\n",
    "    for token in bigram[docs[index]]:\n",
    "        if '_' in token:  # les bigrammes peuvent être reconnus par \"_\" qui concatène les mots\n",
    "            ... # TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pédagogie', 'sens', 'petit', 'école', 'sensibilisation', 'parc', 'naturel', 'enfant', 'devenir', 'prescripteur', 'génération', 'futur', 'urgence', 'génération_futur']\n"
     ]
    }
   ],
   "source": [
    "print(docs[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Passons à la dernière étape du prétraitement spécifique à Gensim. Nous allons tout d'abord créer une représentation dictionnaire des documents. Ce dictionnaire mappera chaque mot à un identifiant unique et nous aidera à créer des représentations en sac-de-mot de chaque document. Ces représentations en sac-de-mots contiennent les identificateurs des mots du document ainsi que leur fréquence (ici en nombre d'occurences). De plus, nous pouvons supprimer les mots les moins fréquents et les plus fréquents du vocabulaire. Cela améliorera la qualité de notre modèle et accélèrera son entraînement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de mots unique dans les documents initiaux : 33319\n",
      "Nombre de mots unique dans les documents après avoir enlevé les mots fréquents/peu fréquents : 12869\n",
      "Exemple : [(170, 1), (171, 1), (172, 1), (173, 1), (174, 1), (175, 1), (176, 1), (177, 1), (178, 1), (179, 1), (180, 1), (181, 1), (182, 1)]\n"
     ]
    }
   ],
   "source": [
    "from gensim.corpora import Dictionary\n",
    "\n",
    "dictionary = Dictionary(docs)\n",
    "print('Nombre de mots unique dans les documents initiaux :', len(dictionary))\n",
    "\n",
    "dictionary.filter_extremes(no_below=3, no_above=0.25)\n",
    "print('Nombre de mots unique dans les documents après avoir enlevé les mots fréquents/peu fréquents :', len(dictionary))\n",
    "\n",
    "print(\"Exemple :\", dictionary.doc2bow(docs[4]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Ensuite, nous créons des représentations en Bag-of-Words pour chaque document du corpus voir la méthode [doc2bow](https://radimrehurek.com/gensim/corpora/dictionary.html) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "corpus = ... # TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 4. Topic Modeling avec LDA\n",
    "\n",
    "Maintenant, il est temps d'entraîner notre LDA ! Pour ce faire, nous utilisons les paramètres suivants : \n",
    "\n",
    "- **corpus** : les représentations en sac-de-mots de nos documents\n",
    "- **id2token** : le mappage des index aux mots\n",
    "- **num_topics** : le nombre de topics que le modèle doit identifier (fixons à <font color = \"red\"><b>10</b></font>)\n",
    "- **chunksize** : le nombre de documents que le modèle voit à chaque mise à jour (fixons à <font color = \"red\"><b>1 000</b></font>)\n",
    "- **passes** : le nombre de fois où nous montrons le corpus total au modèle pendant l'entraînement (fixons à <font color = \"red\"><b>5</b></font>)\n",
    "- **random_state** : nous utilisons une graine pour assurer la reproductibilité (fixons à <font color = \"red\"><b>1</b></font>)\n",
    "\n",
    "Remarquons que l'entraînement peut durer quelques minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 38.6 s, sys: 216 ms, total: 38.9 s\n",
      "Wall time: 38 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from gensim.models import LdaModel\n",
    "\n",
    "model = ... # TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 5. Résultats et visualisation\n",
    "\n",
    "Voyons ce que le modèle a appris. Pour ce faire, affichons les dix mots les plus caractéristiques pour chacun des topics. Nous obeservons déjà des tendances intéressantes : si certains topics sont plus généraux (comme le topic 3), d'autres font référence à des topics très pertinents : \n",
    "- véhicules électriques (**topic 1**), \n",
    "- énergie (alternative) (**topic 2**), \n",
    "- agriculture (**topic 6**), \n",
    "- déchets et recyclage (**topic 7**) et \n",
    "- fiscalité (**topic 9**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********\n",
      "* topic 1 *\n",
      "***********\n",
      "1 : 0.041*\"produit\" + 0.035*\"agriculture\" + 0.023*\"pesticide\" + 0.022*\"environnement\" + 0.018*\"interdir\" + 0.017*\"agriculteur\" + 0.016*\"animal\" + 0.015*\"santé\" + 0.015*\"industriel\" + 0.014*\"chasse\"\n",
      "\n",
      "***********\n",
      "* topic 2 *\n",
      "***********\n",
      "2 : 0.043*\"terme\" + 0.027*\"long\" + 0.022*\"logement\" + 0.017*\"bâtiment\" + 0.017*\"mode\" + 0.015*\"construction\" + 0.015*\"court\" + 0.014*\"long_terme\" + 0.011*\"chauffage\" + 0.011*\"public\"\n",
      "\n",
      "***********\n",
      "* topic 3 *\n",
      "***********\n",
      "3 : 0.021*\"falloir\" + 0.017*\"faire\" + 0.012*\"bien\" + 0.012*\"france\" + 0.010*\"pays\" + 0.010*\"planète\" + 0.009*\"problème\" + 0.008*\"être\" + 0.008*\"politique\" + 0.008*\"monde\"\n",
      "\n",
      "***********\n",
      "* topic 4 *\n",
      "***********\n",
      "4 : 0.070*\"énergie\" + 0.042*\"nucléaire\" + 0.019*\"renouvelable\" + 0.017*\"éolien\" + 0.017*\"développer\" + 0.014*\"centrale\" + 0.013*\"production\" + 0.012*\"fossile\" + 0.012*\"recherche\" + 0.012*\"falloir\"\n",
      "\n",
      "***********\n",
      "* topic 5 *\n",
      "***********\n",
      "5 : 0.010*\"public\" + 0.010*\"investissement\" + 0.009*\"pouvoir\" + 0.009*\"aide\" + 0.008*\"coût\" + 0.008*\"économie\" + 0.008*\"travail\" + 0.007*\"entreprise\" + 0.007*\"dépense\" + 0.007*\"mettre\"\n",
      "\n",
      "***********\n",
      "* topic 6 *\n",
      "***********\n",
      "6 : 0.064*\"écologique\" + 0.058*\"transition\" + 0.036*\"transition_écologique\" + 0.028*\"faire\" + 0.018*\"falloir\" + 0.013*\"citoyen\" + 0.012*\"taxe\" + 0.012*\"entreprise\" + 0.011*\"pollueur\" + 0.011*\"écologie\"\n",
      "\n",
      "***********\n",
      "* topic 7 *\n",
      "***********\n",
      "7 : 0.020*\"produit\" + 0.014*\"local\" + 0.012*\"ville\" + 0.012*\"animal\" + 0.011*\"grand\" + 0.009*\"zone\" + 0.008*\"centre\" + 0.008*\"favoriser\" + 0.006*\"commerce\" + 0.006*\"consommation\"\n",
      "\n",
      "***********\n",
      "* topic 8 *\n",
      "***********\n",
      "8 : 0.047*\"transport\" + 0.024*\"taxer\" + 0.014*\"route\" + 0.014*\"camion\" + 0.014*\"avion\" + 0.011*\"taxe\" + 0.010*\"vitesse\" + 0.010*\"carburant\" + 0.009*\"poids\" + 0.009*\"routier\"\n",
      "\n",
      "***********\n",
      "* topic 9 *\n",
      "***********\n",
      "9 : 0.031*\"véhicule\" + 0.022*\"voiture\" + 0.016*\"diesel\" + 0.014*\"falloir\" + 0.014*\"plastique\" + 0.012*\"faire\" + 0.012*\"produit\" + 0.012*\"emballage\" + 0.011*\"acheter\" + 0.011*\"déchet\"\n",
      "\n",
      "***********\n",
      "* topic 10 *\n",
      "***********\n",
      "10 : 0.048*\"électrique\" + 0.035*\"voiture\" + 0.020*\"voiture_électrique\" + 0.017*\"panneau\" + 0.017*\"batterie\" + 0.016*\"véhicule\" + 0.015*\"solaire\" + 0.014*\"hydrogène\" + 0.012*\"électricité\" + 0.011*\"écologique\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for (topic, words) in model.print_topics():\n",
    "    print(\"***********\")\n",
    "    print(\"* topic\", topic+1, \"*\")\n",
    "    print(\"***********\")\n",
    "    print(topic+1, \":\", words)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Une autre façon d'observer les topics est de les **visualiser**. Ceci peut être fait avec la bibliothèque [pyLDAvis](https://github.com/bmabey/pyLDAvis). PyLDAvis nous illustre :\n",
    "\n",
    "- à quel point les sujets sont **populaires** dans notre corpus, \n",
    "- à quel point les sujets sont **similaires**\n",
    "- et quels sont les **mots les plus importants** pour ce sujet. \n",
    "\n",
    "Notez que cela peut prendre quelques minutes pour charger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/pyLDAvis/_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  default_term_info = default_term_info.sort_values(\n",
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n",
      "/Users/Faugon/opt/anaconda3/envs/dsa_nlp/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  from imp import reload\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el35101404114667438726693513972\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"pays\", \"pays\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"permettre\", \"pesticide\", \"pesticide\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"petit\", \"peur\", \"photovoltaique\", \"photovolta\\u00efque\", \"photovolta\\u00efque\", \"physiquement\", \"phytosanitaire\", \"pile\", \"pile_combustible\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place_pratique\", \"plan\\u00e8te\", \"plan\\u00e8te\", \"plan\\u00e8te\", \"plan\\u00e8te\", \"plan\\u00e8te\", \"plastique\", \"plastique\", \"pluie\", \"pluvial\", \"poids\", \"poids\", \"poids\", \"poids_lourd\", \"politique\", \"politique\", \"politique\", \"politique\", \"politique\", \"politique\", \"politique\", \"polluant\", \"polluant\", \"polluant\", \"polluant\", \"polluant\", \"polluant\", \"polluant\", \"polluer\", \"polluer\", \"polluer\", \"polluer\", \"polluer\", \"pollueur\", \"pollueur\", \"pollueur\", \"pollueur_payeur\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pollution\", \"pompe_chaleur\", \"pompe_chaleur\", \"pompe_chaleur\", \"populaire\", \"porter_atteinte\", \"porteur\", \"poubelle\", \"poubelle\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pouvoir\", \"pratique\", \"pratique\", \"pratique\", \"pratique\", \"pratique\", \"prendre\", \"prendre\", \"prendre\", \"prendre\", \"prendre\", \"prendre\", \"prendre\", \"prendre\", \"prime\", \"prime\", \"principe_pollueur\", \"priorit\\u00e9\", \"priorit\\u00e9\", \"priorit\\u00e9\", \"priorit\\u00e9\", \"prix\", \"prix\", \"prix\", \"prix\", \"prix\", \"prix\", \"prix\", \"prix\", \"probl\\u00e8me\", \"probl\\u00e8me\", \"probl\\u00e8me\", \"probl\\u00e8me\", \"probl\\u00e8me\", \"probl\\u00e8me\", \"producteur\", \"producteur\", \"producteur\", \"producteur\", \"productif\", \"production\", \"production\", \"production\", \"production\", \"production\", \"production\", \"production\", \"production\", \"production_respectueuser\", \"productivit\\u00e9\", \"produire\", \"produire\", \"produire\", \"produire\", \"produire\", \"produire\", \"produire\", \"produire\", \"produire_\\u00e9lectricit\\u00e9\", \"produit\", \"produit\", \"produit\", \"produit\", \"produit\", \"produit\", \"produit_chimique\", \"produit_issu\", \"produit_nocif\", \"produit_toxique\", \"projet\", \"projet\", \"projet\", \"projet\", \"projet\", \"projet\", \"projet\", \"propre\", \"propre\", \"propre\", \"propre\", \"propre\", \"propre\", \"propre\", \"propre\", \"propri\\u00e9taire\", \"propri\\u00e9taire\", \"propri\\u00e9taire\", \"propri\\u00e9taire\", \"protection\", \"protection\", \"protection\", \"protection_environnement\", \"prot\\u00e9ger\", \"prot\\u00e9ger\", \"prot\\u00e9ger\", \"proximit\\u00e9\", \"proximit\\u00e9\", \"proximit\\u00e9\", \"pr\\u00e9sident\", \"pr\\u00e9sident\", \"pr\\u00e9sident\", \"pr\\u00eat\", \"pr\\u00eat\", \"pr\\u00eat\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"public\", \"punitif\", \"p\\u00e9trole\", \"p\\u00e9trole\", \"p\\u00e9trole\", \"p\\u00e9trole\", \"p\\u00eache\", \"p\\u00eache\", \"question\", \"question\", \"question\", \"question\", \"questionnaire\", \"questionnaire\", \"radar\", \"rail\", \"ramassage\", \"rare\", \"recharge\", \"rechargement\", \"recherche\", \"recherche\", \"recherche\", \"recherche\", \"recherche\", \"recherche\", \"recherche\", \"reconversion\", \"recr\\u00e9er\", \"recyclage\", \"recyclage\", \"recyclage\", \"recyclage\", \"reduire\", \"remboursement\", \"renouvelable\", \"rentable\", \"rentable\", \"rentable\", \"respecte\", \"respectueuser\", \"respectueux\", \"ressource\", \"ressource\", \"ressource\", \"ressource\", \"ressource\", \"ressource\", \"ressource\", \"retarder\", \"retenue\", \"retraitement\", \"revenu\", \"rien\", \"rien\", \"rien\", \"rien\", \"rien\", \"rien\", \"rien\", \"route\", \"route\", \"routier\", \"r\\u00e9acteur\", \"r\\u00e9chauffement\", \"r\\u00e9chauffement_climatique\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duire\", \"r\\u00e9duit\", \"r\\u00e9novation\", \"r\\u00e9novation\", \"r\\u00e9novation\", \"r\\u00e9parateur\", \"sac\", \"salaire\", \"salari\\u00e9\", \"sant\\u00e9\", \"sant\\u00e9\", \"sant\\u00e9\", \"sant\\u00e9\", \"sant\\u00e9\", \"satisfaisant\", \"sauver\", \"sauver_plan\\u00e8te\", \"savoir\", \"savoir\", \"savoir\", \"savoir\", \"savoir\", \"savoir\", \"scientifique\", \"scolaire\", \"service\", \"service\", \"service\", \"service\", \"service_public\", \"service_public\", \"situer\", \"si\\u00e8cle\", \"social\", \"social\", \"social\", \"social\", \"solaire\", \"solaire\", \"soleil\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"souhaitable\", \"souhaite\", \"souhaite\", \"source\", \"source\", \"source\", \"source\", \"source\", \"source\", \"source_\\u00e9nergie\", \"soutient\", \"stockage\", \"stockage\", \"stockage_\\u00e9nergie\", \"style\", \"substitution\", \"sujet\", \"sujet\", \"sujet\", \"sujet\", \"superflus\", \"supporter\", \"supporter\", \"surface\", \"surface\", \"surpopulation\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me\", \"syst\\u00e8me_\\u00e9conomique\", \"s\\u00e9cheresse\", \"s\\u00e9rieux\", \"taper\", \"tard\", \"taux\", \"taux\", \"taxe\", \"taxe\", \"taxe\", \"taxe\", \"taxer\", \"taxer\", \"taxer\", \"taxer\", \"taxer\", \"taxer_carburant\", \"technologie\", \"technologie\", \"temps\", \"temps\", \"temps\", \"temps\", \"temps\", \"temps\", \"temps\", \"temps\", \"temps\", \"tenter\", \"terme\", \"terme\", \"terme\", \"terme\", \"terre\", \"terre\", \"terre\", \"terre\", \"terre_agricole\", \"terrestre\", \"thermique\", \"thermique\", \"thermique\", \"thermique\", \"thermique\", \"thermique\", \"toxique\", \"train\", \"train\", \"transition\", \"transition\", \"transition\", \"transition_\\u00e9cologique\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport\", \"transport_a\\u00e9rien\", \"transport_commun\", \"transport_ferroviaire\", \"transport_routier\", \"travail\", \"travail\", \"travail\", \"travail\", \"travail\", \"traverser\", \"trier\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trop\", \"trouver_moyen\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"tr\\u00e8s\", \"urbain\", \"urbain\", \"urbain\", \"urbanisation\", \"urgence\", \"urgence\", \"urgence\", \"urgence\", \"utilisation\", \"utilisation\", \"utilisation\", \"utilisation\", \"utilisation\", \"utilisation\", \"utilisation\", \"utiliser\", \"utiliser\", \"utiliser\", \"utiliser\", \"utiliser\", \"utiliser\", \"utiliser\", \"utiliser\", \"utopique\", \"valider\", \"vehicule\", \"vent\", \"vent\", \"verre\", \"ville\", \"ville\", \"ville\", \"ville\", \"ville\", \"vitesse\", \"vivre_d\\u00e9cemment\", \"voir\", \"voir\", \"voir\", \"voir\", \"voir\", \"voir\", \"voir\", \"voir\", \"voir\", \"voiture\", \"voiture\", \"voiture\", \"voiture_hybride\", \"voiture_hydrog\\u00e8ne\", \"voiture_\\u00e9lectrique\", \"voix\", \"vouloir\", \"vouloir\", \"vouloir\", \"vouloir\", \"vouloir\", \"vouloir\", \"v\\u00e9g\\u00e9tal\", \"v\\u00e9hicule\", \"v\\u00e9hicule\", \"v\\u00e9hicule\", \"v\\u00e9hicule_diesel\", \"v\\u00e9hicule_essence\", \"v\\u00e9hicule_polluant\", \"v\\u00e9hicule_\\u00e9lectrique\", \"v\\u00e9hicule_\\u00e9lectrique\", \"v\\u00e9lo\", \"v\\u00e9lo\", \"v\\u00e9lo\", \"zone\", \"zone\", \"zone\", \"zone\", \"zone\", \"zone\", \"zone_commercial\", \"\\u00e9clairage_public\", \"\\u00e9co-responsable\", \"\\u00e9cole\", \"\\u00e9cole\", \"\\u00e9cologie\", \"\\u00e9cologie\", \"\\u00e9cologie\", \"\\u00e9cologie\", \"\\u00e9cologie\", \"\\u00e9cologique\", \"\\u00e9cologique\", \"\\u00e9cologique\", \"\\u00e9cologique\", \"\\u00e9cologique\", \"\\u00e9cologique\", \"\\u00e9cologiste\", \"\\u00e9cologiste\", \"\\u00e9conomie\", \"\\u00e9conomie\", \"\\u00e9conomie\", \"\\u00e9conomie\", \"\\u00e9conomie\", \"\\u00e9conomique\", \"\\u00e9conomique\", \"\\u00e9conomique\", \"\\u00e9conomique\", \"\\u00e9couter\", \"\\u00e9crire\", \"\\u00e9lectricit\\u00e9\", \"\\u00e9lectricit\\u00e9\", \"\\u00e9lectrique\", \"\\u00e9lectrique\", \"\\u00e9lectrom\\u00e9nager\", \"\\u00e9levage\", \"\\u00e9levage\", \"\\u00e9nergie\", \"\\u00e9nergie\", \"\\u00e9nergie\", \"\\u00e9nergie\", \"\\u00e9nergie_fossile\", \"\\u00e9nergie_propre\", \"\\u00e9nergie_renouvelable\", \"\\u00e9nergique\", \"\\u00e9nerg\\u00e9tique\", \"\\u00e9nerg\\u00e9tique\", \"\\u00e9nerg\\u00e9tique\", \"\\u00e9nerg\\u00e9tique\", \"\\u00e9olien\", \"\\u00e9olien\", \"\\u00e9olien_solaire\", \"\\u00e9olienne\", \"\\u00e9olienne\", \"\\u00e9quiper\", \"\\u00e9quiper\", \"\\u00e9quiper\", \"\\u00e9quiper\", \"\\u00e9tablir\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9tat\", \"\\u00e9teindre\", \"\\u00e9thanol\", \"\\u00e9tranger\", \"\\u00e9tranger\", \"\\u00e9tranger\", \"\\u00e9tranger\", \"\\u00e9tranger\", \"\\u00e9tranger\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u00eatre\", \"\\u0153uvre\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el35101404114667438726693513972\", ldavis_el35101404114667438726693513972_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el35101404114667438726693513972\", ldavis_el35101404114667438726693513972_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el35101404114667438726693513972\", ldavis_el35101404114667438726693513972_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "0      0.336377 -0.035428       1        1   5.723847\n",
       "1     -0.108411  0.278279       2        1   3.894784\n",
       "2     -0.155007 -0.149856       3        1  16.259059\n",
       "3     -0.043820  0.143454       4        1   9.113975\n",
       "4     -0.122107 -0.080526       5        1  17.173217\n",
       "5     -0.147990 -0.196883       6        1  10.621751\n",
       "6      0.164253 -0.028587       7        1  11.626945\n",
       "7      0.063938 -0.067548       8        1   9.463941\n",
       "8      0.039968 -0.016125       9        1   8.444670\n",
       "9     -0.027200  0.153220      10        1   7.677812, topic_info=           Term         Freq        Total Category  logprob  loglift\n",
       "256     énergie  5072.000000  5072.000000  Default  30.0000  30.0000\n",
       "88   écologique  6136.000000  6136.000000  Default  29.0000  29.0000\n",
       "466  transition  4761.000000  4761.000000  Default  28.0000  28.0000\n",
       "252   transport  3425.000000  3425.000000  Default  27.0000  27.0000\n",
       "167  électrique  3118.000000  3118.000000  Default  26.0000  26.0000\n",
       "..          ...          ...          ...      ...      ...      ...\n",
       "355   thermique   214.471811   347.079696  Topic10  -5.6185   2.0855\n",
       "568     vouloir   279.554187  1290.288484  Topic10  -5.3534   1.0374\n",
       "184       faire   314.006109  6845.291812  Topic10  -5.2372  -0.5151\n",
       "39       france   273.045335  3668.796888  Topic10  -5.3770  -0.0311\n",
       "670     arrêter   261.890143  2303.166047  Topic10  -5.4187   0.3927\n",
       "\n",
       "[666 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "9816      4  0.989912   \\n\\n  \n",
       "3786      5  0.994599   \\n    \n",
       "8902      5  0.995374  \\n     \n",
       "6944      9  0.991927     2022\n",
       "6532      9  0.988880     4000\n",
       "...     ...       ...      ...\n",
       "1404      6  0.130633     être\n",
       "1404      7  0.029738     être\n",
       "1404      9  0.107268     être\n",
       "1404     10  0.024427     être\n",
       "642       5  0.994964    œuvre\n",
       "\n",
       "[1476 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim_models\n",
    "import warnings\n",
    "\n",
    "pyLDAvis.enable_notebook()\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "\n",
    "pyLDAvis.gensim_models.prepare(model, corpus, dictionary, sort_topics=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Enfin, examinons les topics que le modèle reconnaît dans certains des documents individuels. Nous voyons ici comment LDA tend à attribuer une probabilité élevée à un faible nombre de sujets pour chaque document, ce qui rend ses résultats très interprétables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********\n",
      "* doc 1   *\n",
      "***********\n",
      "Multiplier les centrales géothermiques\n",
      "[(4, 0.77492505)]\n",
      "\n",
      "***********\n",
      "* doc 2   *\n",
      "***********\n",
      "Les problèmes auxquels se trouve confronté l’ensemble de la planète et que dénoncent, dans le plus parfait désordre, les gilets jaunes de France ne sont-ils pas dus, avant tout, à la surpopulation mondiale ? Cette population est passée d’1,5 milliards d’habitants en 1900 à 7 milliards en 2020 et montera bientôt à 10 milliards vers 2040.  Avec les progrès de la communication dans ce village mondial, chaque individu, du fin fond de l’Asie au fin fond de l’Afrique, en passant par les « quartiers » et les « campagnes » de notre pays, aspire à vivre – et on ne peu l’en blâmer – comme les moins mal lotis de nos concitoyens (logement, nourriture, biens de consommation, déplacement,etc.).  Voilà la mère de tous les problèmes. Si tel est bien le cas, la solution à tous les problèmes (stabilisation de la croissance démographique, partage des richesses, partage des terres, partage de l’eau, protection de la biodiversité, règlement des conflits, lutte contre la déforestation, lutte contre dérèglement climatique, règlement des conflits, stabilisation des migrations, concurrence commerciale mondiale, etc.) ne sera ni française, ni européenne, mais mondiale. La France se doit d’y jouer un rôle moteur.  Le reste, autour duquel se déroulera « le Grand débat », paraît assez anecdotique.\n",
      "[(3, 0.8163948)]\n",
      "\n",
      "***********\n",
      "* doc 3   *\n",
      "***********\n",
      "Une vrai politique écologique et non économique\n",
      "[(6, 0.8199855)]\n",
      "\n",
      "***********\n",
      "* doc 4   *\n",
      "***********\n",
      "Les bonnes idées ne grandissent que par le partage.\n",
      "\n",
      "En ces jours pénibles où s'affrontent le peuple et ceux entre lesquels ils ont remis leur sort. (tous concernés puis qu’élus).\n",
      "\n",
      "Une idée qui flotte en ce moment sur la mobilité propre dans les zones non ou mal desservies et qui est encore expérimentale dans quelques communes.\n",
      "\n",
      "L'avenir  est entre vos mains et les nôtres. Pourquoi ne pas planifier l'installation de flotte de véhicules électriques partagés en location, dans ces zones isolées.\n",
      "\n",
      "En construisant un partenariat avec : état, région, département, commune ou communauté, professionnel de la location, entreprises locales, constructeurs de véhicules, les bailleurs sociaux, pour la mise en place. Avec l'apport financier de la TICPE qui baissera au fil de la décroissance de la consommation, il faudra penser à la déconnecter du budget de fonctionnement de l'état !\n",
      "\n",
      "Avec un forfait de location pour les utilisateurs, adapté à leur ressources et peut être tout simplement basé sur leurs dépenses de mobilité actuelles. Ainsi pas d'investissements inaccessibles pour eux.\n",
      "\n",
      "Tout le monde y gagnerait, cela  pourrait prendre l'image du relais de poste comme par le passé et réglerait le problème d'autonomie actuel et engendrerait quelques emplois de service, nouveaux.\n",
      "[(5, 0.5128911), (9, 0.15113485), (10, 0.13670318)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Nous en affichons que 4\n",
    "n_doc = 4\n",
    "i = 0\n",
    "for (text, doc) in zip(textes[:n_doc], docs[:n_doc]):\n",
    "    i += 1\n",
    "    print(\"***********\")\n",
    "    print(\"* doc\", i, \"  *\")\n",
    "    print(\"***********\")\n",
    "    print(text)\n",
    "    print([(topic+1, prob) for (topic, prob) in model[dictionary.doc2bow(doc)] if prob > 0.1])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Conclusion**\n",
    "\n",
    "De nombreuses collections de textes non structurés ne sont pas accompagnées de labels. Les topic models (*modèles thématiques* en français) tels que le LDA sont une technique utile pour découvrir les topics les plus importants dans ces documents. **Gensim** facilite l'apprentissage sur ces sujets et **pyLDAvis** présente les résultats d'une manière visuellement attrayante. Ensemble, ils forment une puissante boîte à outils pour mieux comprendre et explorer des sous-ensembles de textes connexes. Si ces résultats sont souvent déjà très révélateurs, il est également possible de les utiliser comme point de départ, par exemple pour un exercice de labellisation pour la classification supervisée de textes. En somme, les modèles thématiques devraient figurer dans la boîte à outils de chaque data scientist comme un moyen très rapide d'obtenir un aperçu des grandes collections de documents."
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