Session 1 - correction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette première session de NLP va vous donner les bases de la manipulation de texte en vue du data challenge qui vous attend après l'été !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting fr_core_news_md==2.3.0\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/fr_core_news_md-2.3.0/fr_core_news_md-2.3.0.tar.gz (46.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 46.1 MB 1.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: spacy<2.4.0,>=2.3.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from fr_core_news_md==2.3.0) (2.3.2)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (3.0.2)\n",
      "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (0.4.1)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (2.0.3)\n",
      "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (1.0.0)\n",
      "Requirement already satisfied: setuptools in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (49.2.0.post20200712)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (4.49.0)\n",
      "Requirement already satisfied: thinc==7.4.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (7.4.1)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (1.19.2)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (2.24.0)\n",
      "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (1.1.3)\n",
      "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (0.8.0)\n",
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      "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /srv/conda/envs/notebook/lib/python3.7/site-packages (from catalogue<1.1.0,>=0.0.7->spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (1.7.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (3.0.4)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from requests<3.0.0,>=2.13.0->spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (1.25.10)\n",
      "Requirement already satisfied: zipp>=0.5 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<2.4.0,>=2.3.0->fr_core_news_md==2.3.0) (3.1.0)\n",
      "Building wheels for collected packages: fr-core-news-md\n",
      "  Building wheel for fr-core-news-md (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for fr-core-news-md: filename=fr_core_news_md-2.3.0-py3-none-any.whl size=46100669 sha256=cbd80bde8ce21b71abac5e0fe22ef985130dd7101aca0341d2b02b1104de0803\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-8rresydw/wheels/62/a5/93/9c82d8ae84430ed80720262c11726d046d3444af301b7d2895\n",
      "Successfully built fr-core-news-md\n",
      "Installing collected packages: fr-core-news-md\n",
      "Successfully installed fr-core-news-md-2.3.0\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the model via spacy.load('fr_core_news_md')\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download fr_core_news_md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing avec spacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chargement de la librairie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Créer l'objet `nlp` en important le modèle `fr_core_news_md`\n",
    "- Créer un `doc`\n",
    "- Afficher son texte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ceci est une phrase narcissique puisqu'elle ne parle que d'elle-même.\n"
     ]
    }
   ],
   "source": [
    "#######################\n",
    "### 3 : Pas terrible parce qu'on ne sait pas quel modèle on charge\n",
    "# from spacy.lang.fr import French\n",
    "# nlp = French()\n",
    "\n",
    "### 2 : mieux mais plus verbeux, et on ne peut pas spécifier de chemin vers le modèle\n",
    "# import fr_core_news_md\n",
    "# nlp = fr_core_news_md.load()\n",
    "#######################\n",
    "\n",
    "### 3 : C'est la bonne\n",
    "# Créez l'objet nlp\n",
    "nlp = spacy.load('fr_core_news_md')\n",
    "\n",
    "# Traitement du texte\n",
    "doc = nlp(\"Ceci est une phrase narcissique puisqu'elle \"\n",
    "          \"ne parle que d'elle-même.\")\n",
    "\n",
    "# Affichez le texte du document\n",
    "print(doc.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**L'objet NLP**\n",
    "\n",
    "- contient le 'pipeline' des calculs\n",
    "- inclut des règles spécifiques au langage pour la tokenization, etc.\n",
    "\n",
    "**L'objet doc**\n",
    "\n",
    "Contient un document, c'est-à-dire un ensemble de *tokens*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manipulation d'un objet `Doc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'objet `Doc`se manipule comme une liste. \n",
    "\n",
    "A l'aide des `[]`:\n",
    "- Accedez au premier token de doc\n",
    "- Selectionnez les mots `'est une'` dans une slice\n",
    "- Selectionnez les mots entre `'est une phrase narcissique'` dans une slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "est une\n",
      "est une phrase narcissique\n"
     ]
    }
   ],
   "source": [
    "# A slice of the Doc for 'est une'\n",
    "est_une = doc[1:3]\n",
    "print(est_une.text)\n",
    "\n",
    "# A slice of the Doc for 'est une phrase narcissique'\n",
    "est_une_phrase_narcissique = doc[1:5]\n",
    "print(est_une_phrase_narcissique.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"L'apprentissage de la data science requiert de ne pas se décourager.\n",
    "Les défis et les revers ne sont pas des échecs, ils font simplement partie du voyage!\"\"\"\n",
    "\n",
    "# L'objet \"nlp\" est utilisé pour créer un document avec des annotations linguistiques\n",
    "doc = nlp(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"L'\", 'apprentissage', 'de', 'la', 'data', 'science', 'requiert', 'de', 'ne', 'pas', 'se', 'décourager', '.', '\\n', 'Les', 'défis', 'et', 'les', 'revers', 'ne', 'sont', 'pas', 'des', 'échecs', ',', 'ils', 'font', 'simplement', 'partie', 'du', 'voyage', '!']\n"
     ]
    }
   ],
   "source": [
    "# Tokenization de mots\n",
    "\n",
    "# Créer la liste des tokens de mots\n",
    "token_list = []\n",
    "for token in doc:\n",
    "    token_list.append(token.text)\n",
    "print(token_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans cet exemple, vous allez utiliser les objets `Doc` et `Token` de spaCy, et des attributs lexicaux pour identifier des pourcentages dans un texte. Vous devez chercher deux tokens qui se suivent: un nombre et un signe pourcentage.\n",
    "\n",
    "- Utiliser l'attribut de token `like_num` pour voir si le token dans le document ressemble à un nombre.\n",
    "- Lire le token qui suit immédiatement ce token. L'index du token suivant dans le `doc` est `token.i + 1`.\n",
    "- Regarder si l'attribut `text` du token suivant est un signe `”%“`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pourcentage trouvé: 60\n",
      "Pourcentage trouvé: 4\n"
     ]
    }
   ],
   "source": [
    "# Process the text\n",
    "doc = nlp(\n",
    "    \"En 1990, plus de 60% de la population d'Asie orientale vivait dans l'extrême pauvreté. \"\n",
    "    \"Cela représente désormais moins de 4%.\"\n",
    ")\n",
    "\n",
    "# Iterate over the tokens in the doc\n",
    "for token in doc:\n",
    "    # Check if the token resembles a number\n",
    "    if token.like_num:\n",
    "        # Get the next token in the document\n",
    "        next_token = doc[token.i + 1]\n",
    "        # Check if the next token's text equals \"%\"\n",
    "        if next_token.text == \"%\":\n",
    "            print(\"Pourcentage trouvé:\", token.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppression des stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rendre', 'remarquable', 'trente', 'particulière', 'lui', 'des', 'car', 'faisaient', 'unes', 'extenso']\n"
     ]
    }
   ],
   "source": [
    "from spacy.lang.fr.stop_words import STOP_WORDS\n",
    "\n",
    "print(list(STOP_WORDS)[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered Sentence: [apprentissage, data, science, requiert, décourager, \n",
      ", défis, revers, échecs, simplement, partie, voyage]\n"
     ]
    }
   ],
   "source": [
    "#Implémentation des stop words:\n",
    "filtered_sent=[]\n",
    "\n",
    "#  L'objet \"nlp\" est utilisé pour créer des documents avec annotations\n",
    "doc = nlp(text)\n",
    "\n",
    "# filtering stop words\n",
    "for word in doc:\n",
    "    if not word.is_stop and not word.is_punct:\n",
    "        filtered_sent.append(word)\n",
    "print(\"Filtered Sentence:\", filtered_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[L', apprentissage, data, science, requiert, décourager, \n",
      ", Les, défis, revers, échecs, simplement, partie, voyage]\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(text)\n",
    "tokens = [token for token in doc if not token.text.lower() in STOP_WORDS and not token.is_punct]\n",
    "print(tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enedis Enedis\n",
      "entretient entretenir\n",
      "plus plus\n",
      "d'1,34 d'1,34\n",
      "million million\n",
      "de de\n",
      "kilomètres kilomètre\n",
      "de de\n",
      "câbles câble\n",
      "électriques électrique\n",
      ". .\n"
     ]
    }
   ],
   "source": [
    "# Implémenter la lemmatization en anglais\n",
    "\n",
    "doc = nlp(\"Enedis entretient plus d'1,34 million de kilomètres de câbles électriques.\")\n",
    "\n",
    "for word in doc:\n",
    "    print(word, word.lemma_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:01:58,773 - spacy_lefff.downloader - INFO - downloading data for tagger...\n",
      "2020-09-24 10:01:58,780 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): github.com:443\n",
      "2020-09-24 10:01:58,946 - urllib3.connectionpool - DEBUG - https://github.com:443 \"GET /sammous/spacy-lefff-model/releases/latest/download/model.tar.gz HTTP/1.1\" 302 148\n",
      "2020-09-24 10:01:59,069 - urllib3.connectionpool - DEBUG - https://github.com:443 \"GET /sammous/spacy-lefff-model/releases/download/v0.1.1/model.tar.gz HTTP/1.1\" 302 638\n",
      "2020-09-24 10:01:59,084 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): github-production-release-asset-2e65be.s3.amazonaws.com:443\n",
      "2020-09-24 10:01:59,336 - urllib3.connectionpool - DEBUG - https://github-production-release-asset-2e65be.s3.amazonaws.com:443 \"GET /268203996/530ee880-beb7-11ea-8e6e-e7fbb6e591a5?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200924%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200924T100159Z&X-Amz-Expires=300&X-Amz-Signature=cf9b94ecd9f3eb31378a40c8845a8d0cde15518d00ce46d0a1520c6dbf8fa33d&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=268203996&response-content-disposition=attachment%3B%20filename%3Dmodel.tar.gz&response-content-type=application%2Foctet-stream HTTP/1.1\" 200 88864338\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 86.1M/88.9M [00:01<00:00, 39.1MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:03,047 - spacy_lefff.downloader - INFO - download complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "100%|██████████| 88.9M/88.9M [00:03<00:00, 24.0MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:03,050 - spacy_lefff.melt_tagger - INFO -   TAGGER: Loading lexicon...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:03,984 - spacy_lefff.melt_tagger - INFO -   TAGGER: Loading tags...\n",
      "2020-09-24 10:02:04,023 - spacy_lefff.melt_tagger - INFO -   TAGGER: Loading model from /srv/conda/envs/notebook/lib/python3.7/site-packages/spacy_lefff/data/tagger/models/fr...\n",
      "2020-09-24 10:02:05,153 - spacy_lefff.melt_tagger - INFO -   TAGGER: Loading model from /srv/conda/envs/notebook/lib/python3.7/site-packages/spacy_lefff/data/tagger/models/fr: done\n",
      "2020-09-24 10:02:05,155 - spacy_lefff.lefff - INFO - New LefffLemmatizer instantiated.\n",
      "2020-09-24 10:02:05,156 - spacy_lefff.lefff - INFO - Reading lefff data...\n",
      "2020-09-24 10:02:06,037 - spacy_lefff.lefff - INFO - Successfully loaded lefff lemmatizer\n",
      "2020-09-24 10:02:06,050 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "Enedis enedis\n",
      "entretient entretenir\n",
      "plus plus\n",
      "d'1,34 d'1,34\n",
      "million million\n",
      "de de\n",
      "kilomètres kilomètre\n",
      "de de\n",
      "câbles câble\n",
      "électriques électrique\n",
      ". .\n"
     ]
    }
   ],
   "source": [
    "# Implémenter la lemmatization en français\n",
    "from spacy_lefff import LefffLemmatizer, POSTagger\n",
    "\n",
    "## Sans POStagger\n",
    "# nlp = spacy.load('fr_core_news_md')\n",
    "# french_lemmatizer = LefffLemmatizer(default=True)\n",
    "# nlp.add_pipe(french_lemmatizer, name='lefff')\n",
    "\n",
    "## Avec POStagger\n",
    "pos = POSTagger()\n",
    "french_lemmatizer = LefffLemmatizer(after_melt = True, default = True)\n",
    "nlp.add_pipe(pos, name='pos')\n",
    "nlp.add_pipe(french_lemmatizer, name='lefff', after='pos')\n",
    "\n",
    "doc = nlp(\"Enedis entretient plus d'1,34 million de kilomètres de câbles électriques.\")\n",
    "\n",
    "for word in doc:\n",
    "    print(word.text, word._.lefff_lemma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Etiquetage morpho-syntaxique (POS tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Que sont les modèles statistiques ?**\n",
    "\n",
    "- Ils permettent à spaCy de prédir les attributs linguistiques dans un contexte\n",
    "    - Tags Part-of-speech (POS)\n",
    "    - Dépendances syntaxiques\n",
    "    - Entités nommées\n",
    "- Ils sont entraînés sur des textes labellisés\n",
    "- Ils peuvent être mis à jour avec des exemples additionnels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spacy import displacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,101 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "Elle PRON\n",
      "a AUX\n",
      "mangé VERB\n",
      "la DET\n",
      "pizza NOUN\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(\"Elle a mangé la pizza\")\n",
    "\n",
    "for token in doc:\n",
    "    # Afficher le texte et les 'part-of-speech' tags\n",
    "    print(token.text, token.pos_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pronoun'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"PRON\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse des dépendances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elle PRON nsubj mangé\n",
      "a AUX aux:tense mangé\n",
      "mangé VERB ROOT mangé\n",
      "la DET det pizza\n",
      "pizza NOUN obj mangé\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"fr\" id=\"f17f715687854bee9454445ccbf95d1f-0\" class=\"displacy\" width=\"925\" height=\"312.0\" direction=\"ltr\" style=\"max-width: none; height: 312.0px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">Elle</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">PRON</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">a</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">mangé</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">la</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"222.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">pizza</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-f17f715687854bee9454445ccbf95d1f-0-0\" stroke-width=\"2px\" d=\"M70,177.0 C70,2.0 400.0,2.0 400.0,177.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-f17f715687854bee9454445ccbf95d1f-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M70,179.0 L62,167.0 78,167.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-f17f715687854bee9454445ccbf95d1f-0-1\" stroke-width=\"2px\" d=\"M245,177.0 C245,89.5 395.0,89.5 395.0,177.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-f17f715687854bee9454445ccbf95d1f-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">aux:tense</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M245,179.0 L237,167.0 253,167.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-f17f715687854bee9454445ccbf95d1f-0-2\" stroke-width=\"2px\" d=\"M595,177.0 C595,89.5 745.0,89.5 745.0,177.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-f17f715687854bee9454445ccbf95d1f-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M595,179.0 L587,167.0 603,167.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-f17f715687854bee9454445ccbf95d1f-0-3\" stroke-width=\"2px\" d=\"M420,177.0 C420,2.0 750.0,2.0 750.0,177.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-f17f715687854bee9454445ccbf95d1f-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">obj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M750.0,179.0 L758.0,167.0 742.0,167.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "\n",
    "for token in doc:\n",
    "    print(token.text, token.pos_, token.dep_, token.head.text)\n",
    "\n",
    "displacy.render(doc, style=\"dep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nominal subject'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"nsubj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,211 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "En ADP case\n",
      "France PROPN obl:mod\n",
      "les DET det\n",
      "compteurs NOUN nsubj:pass\n",
      "intelligents ADJ amod\n",
      "seront AUX aux:pass\n",
      "bientôt ADV advmod\n",
      "entièrement ADV advmod\n",
      "déployés VERB ROOT\n",
      ". PUNCT punct\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"fr\" id=\"810b69c1b3e54c63b660f1f04415c797-0\" class=\"displacy\" width=\"1625\" height=\"574.5\" direction=\"ltr\" style=\"max-width: none; height: 574.5px; color: #000000; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">En</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">France</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">les</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">compteurs</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">intelligents</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"925\">seront</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">bientôt</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1275\">entièrement</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">déployés.</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-0\" stroke-width=\"2px\" d=\"M70,439.5 C70,352.0 205.0,352.0 205.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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       "    </text>\n",
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       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-1\" stroke-width=\"2px\" d=\"M245,439.5 C245,2.0 1450.0,2.0 1450.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">obl:mod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M245,441.5 L237,429.5 253,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-2\" stroke-width=\"2px\" d=\"M420,439.5 C420,352.0 555.0,352.0 555.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M420,441.5 L412,429.5 428,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-3\" stroke-width=\"2px\" d=\"M595,439.5 C595,89.5 1445.0,89.5 1445.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj:pass</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M595,441.5 L587,429.5 603,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-4\" stroke-width=\"2px\" d=\"M595,439.5 C595,352.0 730.0,352.0 730.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M730.0,441.5 L738.0,429.5 722.0,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-5\" stroke-width=\"2px\" d=\"M945,439.5 C945,177.0 1440.0,177.0 1440.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-5\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">aux:pass</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M945,441.5 L937,429.5 953,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-6\" stroke-width=\"2px\" d=\"M1120,439.5 C1120,264.5 1435.0,264.5 1435.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-6\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1120,441.5 L1112,429.5 1128,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-810b69c1b3e54c63b660f1f04415c797-0-7\" stroke-width=\"2px\" d=\"M1295,439.5 C1295,352.0 1430.0,352.0 1430.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-810b69c1b3e54c63b660f1f04415c797-0-7\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1295,441.5 L1287,429.5 1303,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choisir une phrase et demander à spaCy d'afficher les POS tags et les relations entre les mots\n",
    "\n",
    "doc = nlp(\"En France les compteurs intelligents seront bientôt entièrement déployés.\")\n",
    "\n",
    "for token in doc:\n",
    "    print(token.text, token.pos_, token.dep_)\n",
    "\n",
    "# Tester les styles 'dep' et 'ent'\n",
    "displacy.render(doc, style=\"dep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,521 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "En ADP case\n",
      "France PROPN obl:mod\n",
      "les DET det\n",
      "compteurs NOUN nsubj:pass\n",
      "intelligents ADJ amod\n",
      "seront AUX aux:pass\n",
      "bientôt ADV advmod\n",
      "entièrement ADV advmod\n",
      "déployés VERB ROOT\n",
      ". PUNCT punct\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xml:lang=\"fr\" id=\"4a431c2dc2e54f7d8741f1857b313968-0\" class=\"displacy\" width=\"1275\" height=\"574.5\" direction=\"ltr\" style=\"max-width: none; height: 574.5px; color: blue; background: #ffffff; font-family: Arial; direction: ltr\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">En</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">France</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">PROPN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">les compteurs intelligents</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">seront</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">bientôt</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"925\">entièrement</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">déployés.</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-0\" stroke-width=\"2px\" d=\"M70,439.5 C70,352.0 205.0,352.0 205.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">case</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M70,441.5 L62,429.5 78,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-1\" stroke-width=\"2px\" d=\"M245,439.5 C245,2.0 1100.0,2.0 1100.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">obl:mod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M245,441.5 L237,429.5 253,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-2\" stroke-width=\"2px\" d=\"M420,439.5 C420,89.5 1095.0,89.5 1095.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj:pass</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M420,441.5 L412,429.5 428,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-3\" stroke-width=\"2px\" d=\"M595,439.5 C595,177.0 1090.0,177.0 1090.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">aux:pass</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M595,441.5 L587,429.5 603,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-4\" stroke-width=\"2px\" d=\"M770,439.5 C770,264.5 1085.0,264.5 1085.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M770,441.5 L762,429.5 778,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-4a431c2dc2e54f7d8741f1857b313968-0-5\" stroke-width=\"2px\" d=\"M945,439.5 C945,352.0 1080.0,352.0 1080.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-4a431c2dc2e54f7d8741f1857b313968-0-5\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M945,441.5 L937,429.5 953,429.5\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choisir une phrase et demander à spaCy d'afficher les POS tags et les relations entre les mots\n",
    "\n",
    "doc = nlp(\"En France les compteurs intelligents seront bientôt entièrement déployés.\")\n",
    "\n",
    "for token in doc:\n",
    "    print(token.text, token.pos_, token.dep_)\n",
    "\n",
    "# Changer la couleur en bleu et demander à spaCy d'afficher les groupes nominaux comme un même ensemble\n",
    "displacy.render(doc, style=\"dep\", options = {\"color\": \"blue\", \"collapse_phrases\" : True})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Détection d'entités"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,587 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "Enedis ORG\n",
      "Vaison-La-Romaine LOC\n",
      "François Cordel PER\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Enedis\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " cherche de nouveaux bureaux à \n",
       "<mark class=\"entity\" style=\"background: #ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Vaison-La-Romaine\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">LOC</span>\n",
       "</mark>\n",
       " avec l'aide de \n",
       "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    François Cordel\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; text-transform: uppercase; vertical-align: middle; margin-left: 0.5rem\">PER</span>\n",
       "</mark>\n",
       ".</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Process a text\n",
    "doc = nlp(\"Enedis cherche de nouveaux bureaux à Vaison-La-Romaine \"\n",
    "          \"avec l'aide de François Cordel.\")\n",
    "\n",
    "# Iterate over the predicted entities\n",
    "for ent in doc.ents:\n",
    "    # Print the entity text and its label\n",
    "    print(ent.text, ent.label_)\n",
    "    \n",
    "displacy.render(doc, style=\"ent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Companies, agencies, institutions, etc.'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"ORG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Non-GPE locations, mountain ranges, bodies of water'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"LOC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Named person or family.'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spacy.explain(\"PER\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunking : noun chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,692 - spacy_lefff.melt_tagger - INFO -   TAGGER: POS Tagging...\n",
      "Groupe nominal : de véhicules électriques ; Nom :  véhicules \n",
      "\n",
      "Groupe nominal : de recharge pratiques ; Nom :  recharge \n",
      "\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(\"Il est important pour les utilisateurs de véhicules électriques de disposer facilement \"\n",
    "          \"d'infrastructures de recharge pratiques.\")\n",
    "\n",
    "for chunk in doc.noun_chunks:\n",
    "    print(\"Groupe nominal :\", chunk.text, \"; Nom : \", chunk.root.text, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nuage de mots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charger les données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q6UZFmRZlG2a"
   },
   "source": [
    "Nous allons scrapper la page wikipedia d'Enedis et créer un nuage de mots pour comprendre ce qu'il ressort de l'article. \n",
    "\n",
    "- Utilisez le code suivant pour charger la page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wfgPrBkwxqD2",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:06,801 - urllib3.connectionpool - DEBUG - Starting new HTTPS connection (1): fr.wikipedia.org:443\n",
      "2020-09-24 10:02:07,008 - urllib3.connectionpool - DEBUG - https://fr.wikipedia.org:443 \"GET /wiki/Enedis HTTP/1.1\" 200 None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Enedis, anciennement ERDF (pour Électricité Réseau Distribution France...'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "r = requests.get(\"https://fr.wikipedia.org/wiki/Enedis\")\n",
    "html_doc = r.text\n",
    "soup = BeautifulSoup(html_doc, 'html.parser')\n",
    "\n",
    "corpus = []\n",
    "for p in soup.find_all(\"p\"):\n",
    "    corpus.append(p.text)\n",
    "\n",
    "join_corpus = \" \".join(corpus)\n",
    "clean_corpus = join_corpus.strip().encode(\"utf-8\").decode(\"utf-8\")\n",
    "clean_corpus[:70]+\"...\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comme vu précédemment :\n",
    "- Chargez le parser nlp francais\n",
    "- Créez un objet doc en appliquant le parser sur la variable `clean_corpus`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('fr_core_news_md')\n",
    "nlp.add_pipe(french_lemmatizer, name='lefff')\n",
    "\n",
    "doc = nlp(clean_corpus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracer un wordcloud brut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zYxAusV8_Igf"
   },
   "source": [
    "* Utilisez le code suivant pour générer un premier wordcloud avec le texte brut "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1726,
     "status": "ok",
     "timestamp": 1582107372818,
     "user": {
      "displayName": "Antoine Krajnc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mC4XzNDVGvURzl4T5duDbMr6bUdhkYkDul_37G0OA=s64",
      "userId": "08465960390418158788"
     },
     "user_tz": -60
    },
    "id": "23x9RYa-8isd",
    "outputId": "8b5ffd2c-308f-473b-804f-7328eec81f0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:12,825 - matplotlib - DEBUG - (private) matplotlib data path: /srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data\n",
      "2020-09-24 10:02:12,826 - matplotlib - DEBUG - matplotlib data path: /srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data\n",
      "2020-09-24 10:02:12,831 - matplotlib - DEBUG - CONFIGDIR=/home/jovyan/.config/matplotlib\n",
      "2020-09-24 10:02:12,838 - matplotlib - DEBUG - matplotlib version 3.3.2\n",
      "2020-09-24 10:02:12,846 - matplotlib - DEBUG - interactive is False\n",
      "2020-09-24 10:02:12,847 - matplotlib - DEBUG - platform is linux\n",
      "2020-09-24 10:02:12,848 - matplotlib - DEBUG - loaded modules: ['sys', 'builtins', '_frozen_importlib', '_imp', '_thread', '_warnings', '_weakref', 'zipimport', '_frozen_importlib_external', '_io', 'marshal', 'posix', 'encodings', 'codecs', '_codecs', 'encodings.aliases', 'encodings.utf_8', '_signal', '__main__', 'encodings.latin_1', 'io', 'abc', '_abc', '_bootlocale', '_locale', 'site', 'os', 'stat', '_stat', '_collections_abc', 'posixpath', 'genericpath', 'os.path', '_sitebuiltins', 'types', 'importlib', 'importlib._bootstrap', 'importlib._bootstrap_external', 'warnings', 'importlib.util', 'importlib.abc', 'importlib.machinery', 'contextlib', 'collections', 'operator', '_operator', 'keyword', 'heapq', '_heapq', 'itertools', 'reprlib', '_collections', 'functools', '_functools', 'mpl_toolkits', 'ruamel', 'runpy', 'pkgutil', 'weakref', '_weakrefset', 'ipykernel', 'ipykernel._version', 'ipykernel.connect', '__future__', 'json', 'json.decoder', 're', 'enum', 'sre_compile', '_sre', 'sre_parse', 'sre_constants', 'copyreg', 'json.scanner', '_json', 'json.encoder', 'subprocess', 'time', 'signal', 'errno', '_posixsubprocess', 'select', 'selectors', 'collections.abc', 'math', 'threading', 'traceback', 'linecache', 'tokenize', 'token', 'IPython', 'IPython.core', 'IPython.core.getipython', 'IPython.core.release', 'IPython.core.application', 'atexit', 'copy', 'glob', 'fnmatch', 'logging', 'string', '_string', 'shutil', 'zlib', 'bz2', '_compression', '_bz2', 'lzma', '_lzma', 'pwd', 'grp', 'traitlets', 'traitlets.traitlets', 'inspect', 'dis', 'opcode', '_opcode', 'six', 'struct', '_struct', 'traitlets.utils', 'traitlets.utils.getargspec', 'traitlets.utils.importstring', 'ipython_genutils', 'ipython_genutils._version', 'ipython_genutils.py3compat', 'ipython_genutils.encoding', 'locale', 'platform', 'traitlets.utils.sentinel', 'traitlets.utils.bunch', 'traitlets._version', 'traitlets.config', 'traitlets.config.application', 'decorator', 'traitlets.config.configurable', 'traitlets.config.loader', 'argparse', 'gettext', 'ast', '_ast', 'ipython_genutils.path', 'random', 'hashlib', '_hashlib', '_blake2', '_sha3', 'bisect', '_bisect', '_random', 'ipython_genutils.text', 'textwrap', 'ipython_genutils.importstring', 'IPython.core.crashhandler', 'pprint', 'IPython.core.ultratb', 'pydoc', 'sysconfig', 'urllib', 'urllib.parse', '_sysconfigdata_m_linux_x86_64-linux-gnu', 'IPython.core.debugger', 'bdb', 'IPython.utils', 'IPython.utils.PyColorize', 'IPython.utils.coloransi', 'IPython.utils.ipstruct', 'IPython.utils.colorable', 'pygments', 'IPython.utils.py3compat', 'IPython.utils.encoding', 'IPython.core.excolors', 'IPython.testing', 'IPython.testing.skipdoctest', 'pdb', 'cmd', 'code', 'codeop', 'IPython.core.display_trap', 'IPython.utils.path', 'IPython.utils.process', 'IPython.utils._process_posix', 'pexpect', 'pexpect.exceptions', 'pexpect.utils', 'pexpect.expect', 'pexpect.pty_spawn', 'pty', 'tty', 'termios', 'ptyprocess', 'ptyprocess.ptyprocess', 'fcntl', 'resource', 'ptyprocess.util', 'pexpect.spawnbase', 'pexpect.run', 'IPython.utils._process_common', 'shlex', 'IPython.utils.decorators', 'IPython.utils.data', 'IPython.utils.terminal', 'IPython.utils.sysinfo', 'IPython.utils._sysinfo', 'IPython.core.profiledir', 'IPython.paths', 'tempfile', 'IPython.utils.importstring', 'IPython.terminal', 'IPython.terminal.embed', 'IPython.core.compilerop', 'IPython.core.magic_arguments', 'IPython.core.error', 'IPython.utils.text', 'pathlib', 'ntpath', 'IPython.core.magic', 'getopt', 'IPython.core.oinspect', 'typing', 'typing.io', 'typing.re', 'IPython.core.page', 'IPython.core.display', 'binascii', 'mimetypes', 'IPython.lib', 'IPython.lib.security', 'getpass', 'IPython.lib.pretty', 'datetime', '_datetime', 'IPython.utils.openpy', 'IPython.utils.dir2', 'IPython.utils.wildcard', 'pygments.lexers', 'pygments.lexers._mapping', 'pygments.modeline', 'pygments.plugin', 'pygments.util', 'pygments.lexers.python', 'pygments.lexer', 'pygments.filter', 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'thinc.neural._classes.difference', 'spacy.cli', 'spacy.cli.download', 'plac', 'plac_core', 'plac_ext', 'asyncore', 'asynchat', 'requests', 'urllib3', 'urllib3.connectionpool', 'urllib3.exceptions', 'urllib3.packages', 'urllib3.packages.ssl_match_hostname', 'urllib3.packages.six', 'urllib3.packages.six.moves', 'urllib3.packages.six.moves.http_client', 'urllib3.connection', 'urllib3.util', 'urllib3.util.connection', 'urllib3.util.wait', 'urllib3.contrib', 'urllib3.contrib._appengine_environ', 'urllib3.util.request', 'brotli', 'brotli.brotli', '_cffi_backend', '_brotli.lib', '_brotli', 'brotli._brotli', 'urllib3.util.response', 'urllib3.util.ssl_', 'urllib3.util.url', 'urllib3.util.timeout', 'urllib3.util.retry', 'urllib3._collections', 'urllib3.request', 'urllib3.filepost', 'urllib3.fields', 'urllib3.packages.six.moves.urllib', 'urllib3.packages.six.moves.urllib.parse', 'urllib3.response', 'urllib3.util.queue', 'urllib3.poolmanager', 'urllib3._version', 'chardet', 'chardet.compat', 'chardet.universaldetector', 'chardet.charsetgroupprober', 'chardet.enums', 'chardet.charsetprober', 'chardet.escprober', 'chardet.codingstatemachine', 'chardet.escsm', 'chardet.latin1prober', 'chardet.mbcsgroupprober', 'chardet.utf8prober', 'chardet.mbcssm', 'chardet.sjisprober', 'chardet.mbcharsetprober', 'chardet.chardistribution', 'chardet.euctwfreq', 'chardet.euckrfreq', 'chardet.gb2312freq', 'chardet.big5freq', 'chardet.jisfreq', 'chardet.jpcntx', 'chardet.eucjpprober', 'chardet.gb2312prober', 'chardet.euckrprober', 'chardet.cp949prober', 'chardet.big5prober', 'chardet.euctwprober', 'chardet.sbcsgroupprober', 'chardet.sbcharsetprober', 'chardet.langcyrillicmodel', 'chardet.langgreekmodel', 'chardet.langbulgarianmodel', 'chardet.langthaimodel', 'chardet.langhebrewmodel', 'chardet.hebrewprober', 'chardet.langturkishmodel', 'chardet.version', 'requests.exceptions', 'requests.__version__', 'requests.utils', 'requests.certs', 'certifi', 'certifi.core', 'importlib.resources', 'requests._internal_utils', 'requests.compat', 'http.cookiejar', 'http.cookies', 'requests.cookies', 'requests.structures', 'requests.packages', 'requests.packages.urllib3', 'requests.packages.urllib3.connectionpool', 'requests.packages.urllib3.exceptions', 'requests.packages.urllib3.packages', 'requests.packages.urllib3.packages.ssl_match_hostname', 'requests.packages.urllib3.packages.six', 'requests.packages.urllib3.packages.six.moves', 'requests.packages.urllib3.packages.six.moves.http_client', 'requests.packages.urllib3.connection', 'requests.packages.urllib3.util', 'requests.packages.urllib3.util.connection', 'requests.packages.urllib3.util.wait', 'requests.packages.urllib3.contrib', 'requests.packages.urllib3.contrib._appengine_environ', 'requests.packages.urllib3.util.request', 'requests.packages.urllib3.util.response', 'requests.packages.urllib3.util.ssl_', 'requests.packages.urllib3.util.url', 'requests.packages.urllib3.util.timeout', 'requests.packages.urllib3.util.retry', 'requests.packages.urllib3._collections', 'requests.packages.urllib3.request', 'requests.packages.urllib3.filepost', 'requests.packages.urllib3.fields', 'requests.packages.urllib3.packages.six.moves.urllib', 'requests.packages.urllib3.packages.six.moves.urllib.parse', 'requests.packages.urllib3.response', 'requests.packages.urllib3.util.queue', 'requests.packages.urllib3.poolmanager', 'requests.packages.urllib3._version', 'requests.packages.idna', 'requests.packages.idna.package_data', 'requests.packages.idna.core', 'requests.packages.idna.idnadata', 'requests.packages.idna.intranges', 'requests.packages.chardet', 'requests.packages.chardet.compat', 'requests.packages.chardet.universaldetector', 'requests.packages.chardet.charsetgroupprober', 'requests.packages.chardet.enums', 'requests.packages.chardet.charsetprober', 'requests.packages.chardet.escprober', 'requests.packages.chardet.codingstatemachine', 'requests.packages.chardet.escsm', 'requests.packages.chardet.latin1prober', 'requests.packages.chardet.mbcsgroupprober', 'requests.packages.chardet.utf8prober', 'requests.packages.chardet.mbcssm', 'requests.packages.chardet.sjisprober', 'requests.packages.chardet.mbcharsetprober', 'requests.packages.chardet.chardistribution', 'requests.packages.chardet.euctwfreq', 'requests.packages.chardet.euckrfreq', 'requests.packages.chardet.gb2312freq', 'requests.packages.chardet.big5freq', 'requests.packages.chardet.jisfreq', 'requests.packages.chardet.jpcntx', 'requests.packages.chardet.eucjpprober', 'requests.packages.chardet.gb2312prober', 'requests.packages.chardet.euckrprober', 'requests.packages.chardet.cp949prober', 'requests.packages.chardet.big5prober', 'requests.packages.chardet.euctwprober', 'requests.packages.chardet.sbcsgroupprober', 'requests.packages.chardet.sbcharsetprober', 'requests.packages.chardet.langcyrillicmodel', 'requests.packages.chardet.langgreekmodel', 'requests.packages.chardet.langbulgarianmodel', 'requests.packages.chardet.langthaimodel', 'requests.packages.chardet.langhebrewmodel', 'requests.packages.chardet.hebrewprober', 'requests.packages.chardet.langturkishmodel', 'requests.packages.chardet.version', 'requests.models', 'requests.hooks', 'requests.auth', 'requests.status_codes', 'requests.api', 'requests.sessions', 'requests.adapters', 'urllib3.contrib.socks', 'socks', 'spacy.cli.link', 'spacy.cli.info', 'spacy.cli.package', 'spacy.cli.profile', 'thinc.extra.datasets', 'thinc.extra._vendorized', 'thinc.extra._vendorized.keras_data_utils', 'tarfile', 'thinc.extra._vendorized.keras_generic_utils', 'spacy.cli.train', 'spacy.cli.pretrain', 'spacy.cli.debug_data', 'spacy.cli.evaluate', 'spacy.displacy', 'spacy.displacy.render', 'spacy.displacy.templates', 'spacy.cli.convert', 'spacy.cli.converters', 'spacy.cli.converters.conllu2json', 'spacy.cli.converters.iob2json', 'spacy.cli.converters.conll_ner2json', 'spacy.lang.xx', 'spacy.cli.converters.jsonl2json', 'spacy.cli.init_model', 'preshed.counter', 'spacy.cli.validate', 'spacy.glossary', 'pkg_resources', 'plistlib', 'xml.parsers', 'xml.parsers.expat', 'pyexpat.errors', 'pyexpat.model', 'pyexpat', 'xml.parsers.expat.model', 'xml.parsers.expat.errors', 'pkg_resources.extern', 'pkg_resources._vendor', 'pkg_resources._vendor.six', 'pkg_resources.extern.six', 'pkg_resources._vendor.six.moves', 'pkg_resources.extern.six.moves', 'pkg_resources._vendor.appdirs', 'pkg_resources.extern.appdirs', 'pkg_resources._vendor.packaging', 'pkg_resources._vendor.packaging.__about__', 'pkg_resources.extern.packaging', 'pkg_resources.extern.packaging.version', 'pkg_resources.extern.packaging._structures', 'pkg_resources.extern.packaging.specifiers', 'pkg_resources.extern.packaging._compat', 'pkg_resources.extern.packaging.requirements', 'pkg_resources._vendor.pyparsing', 'pkg_resources.extern.pyparsing', 'pkg_resources._vendor.six.moves.urllib', 'pkg_resources.extern.six.moves.urllib', 'pkg_resources.extern.packaging.markers', 'fr_core_news_md', 'spacy.lang.fr', 'spacy.lang.fr.tokenizer_exceptions', 'spacy.lang.fr.punctuation', 'spacy.lang.fr.tag_map', 'spacy.lang.fr.stop_words', 'spacy.lang.fr.lex_attrs', 'spacy.lang.fr.lemmatizer', 'spacy.lang.fr.syntax_iterators', 'spacy_lefff', 'spacy_lefff.lefff', 'spacy_lefff.mappings', 'spacy_lefff.melt_tagger', 'optparse', 'spacy_lefff.downloader', 'netrc', 'multiprocessing.synchronize', '_multiprocessing', 'multiprocessing.util', 'bs4', 'bs4.builder', 'bs4.element', 'soupsieve', 'soupsieve.__meta__', 'soupsieve.css_parser', 'soupsieve.util', 'soupsieve.css_match', 'soupsieve.css_types', 'bs4.formatter', 'bs4.dammit', 'bs4.builder._htmlparser', 'html.parser', '_markupbase', 'encodings.cp1252', 'wordcloud', 'wordcloud.wordcloud', 'matplotlib', 'matplotlib.cbook', 'matplotlib.cbook.deprecation', 'matplotlib.rcsetup', 'matplotlib.animation', 'matplotlib._animation_data', 'matplotlib.fontconfig_pattern', 'pyparsing', 'matplotlib.colors', 'matplotlib.docstring', 'matplotlib._color_data', 'cycler', 'matplotlib._version', 'matplotlib.ft2font', 'kiwisolver']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:13,197 - matplotlib - DEBUG - CACHEDIR=/home/jovyan/.cache/matplotlib\n",
      "2020-09-24 10:02:13,198 - matplotlib.font_manager - INFO - Generating new fontManager, this may take some time...\n",
      "2020-09-24 10:02:13,199 - matplotlib.font_manager - DEBUG - font search path [PosixPath('/srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf'), PosixPath('/srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/afm'), PosixPath('/srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts')]\n",
      "2020-09-24 10:02:13,738 - matplotlib.pyplot - DEBUG - Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n",
      "2020-09-24 10:02:13,741 - matplotlib.pyplot - DEBUG - Loaded backend module://ipykernel.pylab.backend_inline version unknown.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe0689cb410>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-09-24 10:02:14,197 - matplotlib.font_manager - DEBUG - findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
      "2020-09-24 10:02:14,198 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,199 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
      "2020-09-24 10:02:14,200 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
      "2020-09-24 10:02:14,201 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,201 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
      "2020-09-24 10:02:14,202 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
      "2020-09-24 10:02:14,203 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
      "2020-09-24 10:02:14,204 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,204 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,205 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,206 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,206 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,207 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,208 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,208 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,209 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,210 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,211 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,211 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,212 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
      "2020-09-24 10:02:14,213 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,214 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
      "2020-09-24 10:02:14,214 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
      "2020-09-24 10:02:14,215 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,216 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
      "2020-09-24 10:02:14,217 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,217 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
      "2020-09-24 10:02:14,218 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,219 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,219 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,220 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,221 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
      "2020-09-24 10:02:14,222 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
      "2020-09-24 10:02:14,222 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,223 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,224 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,224 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,225 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,268 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,269 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,270 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
      "2020-09-24 10:02:14,271 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
      "2020-09-24 10:02:14,271 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
      "2020-09-24 10:02:14,272 - matplotlib.font_manager - DEBUG - findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
      "2020-09-24 10:02:14,273 - matplotlib.font_manager - DEBUG - findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/srv/conda/envs/notebook/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "wd = WordCloud()\n",
    "cloud = wd.generate(doc.text)\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtrer les stop words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XZyM_xpj_NHs"
   },
   "source": [
    "* Importez maintenant les stop-words présents dans `spacy`\n",
    "\n",
    "* Faites à nouveau un nuage de mots sans les stop words grâce à l'option `stopwords` du constructeur `WordCloud`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe068843090>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import des Stop words\n",
    "from spacy.lang.fr.stop_words import STOP_WORDS\n",
    "\n",
    "wd = WordCloud(stopwords=STOP_WORDS)\n",
    "cloud = wd.generate(doc.text)\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatiser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Créez une liste `lemma` qui contient les tokens lemmatisés\n",
    "- Utilisez la fonction `join` pour créer une chaîne de caractère contenant tous les tokens séparés par un espace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma = [token.lemma_ for token in doc]\n",
    "corpus_lemma = \" \".join(lemma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Affichez le wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2032,
     "status": "ok",
     "timestamp": 1582110215852,
     "user": {
      "displayName": "Antoine Krajnc",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mC4XzNDVGvURzl4T5duDbMr6bUdhkYkDul_37G0OA=s64",
      "userId": "08465960390418158788"
     },
     "user_tz": -60
    },
    "id": "ADnFJsRjG-4f",
    "outputId": "ad82bb24-2462-4fc3-99db-456e0b112384",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe0687e61d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cloud = wd.generate(corpus_lemma)\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatisation en français"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Même exercice en utilisant le lemmatiseur français"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe068778f50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cloud = wd.generate(\" \".join([token._.lefff_lemma for token in doc]))\n",
    "plt.imshow(cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp(\"EDF\")[0]._.lefff_lemma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remarque**\n",
    "\n",
    "La lemmatisation est très puissante pour certaines tâches (extraction de sens) mais attention, il y a une perte d'information importante. Pour rechercher une forme par exemple, il vaut mieux utiliser une distance de levenshtein."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification de texte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans cette partie, nous allons prendre un peu d'avance sur les formations python et utiliser les librairies `pandas` et `scikit learn` pour faire un exemple simple de classification de texte. Nous utiliserons un jeu de données de commentaires de restaurants en français, l'objectif sera de déterminer si le commentaire est négatif ou positif. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Importer la librairie `pandas` sous l'allias `pd`\n",
    "- Utiliser la fonction `pd.read_csv()` pour charger le fichier `restaurant.csv` dans un dataframe `df` (on pourra utiliser l'option `index_col=0` pour indiquer que la première colonne du csv est l'index)\n",
    "- Afficher les premières  lignes du dataframe en utilisant la fonction `<dataframe>.head()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>texte</th>\n",
       "      <th>label_2</th>\n",
       "      <th>label_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Un service passable .. Des plats surcuits, des...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nous avons bien aimé l'ambiance, sur la promen...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>on s'attendait à mieux (attente, qualité moyen...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Une adresse à garder pour soi vue imprenable s...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Une petite dame nous accueille sans le sourire...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               texte  label_2  label_3\n",
       "0  Un service passable .. Des plats surcuits, des...        0        0\n",
       "1  Nous avons bien aimé l'ambiance, sur la promen...        1        2\n",
       "2  on s'attendait à mieux (attente, qualité moyen...        0        0\n",
       "3  Une adresse à garder pour soi vue imprenable s...        1        2\n",
       "4  Une petite dame nous accueille sans le sourire...        0        0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('restaurants.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notre dataframe contient 3 colonnes : \n",
    "- `texte` : le texte du commentaire \n",
    "- `label_2` : le label du commentaire : 0 si négatif, 1 si positif\n",
    "- `label_3` : idem : 0 si négatif, 1 si neutre, 2 si positif\n",
    "\n",
    "Pour l'exemple de classification, nous n'utiliserons que les colonnes `texte` et `label_2`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comme vu dans la partie précédente, importez le parser `French` en utilisant la fonction `load('fr_core_news_md')`\n",
    "- Ajoutez le lemmatiseur français au parser en utilisant `add_pipe`\n",
    "- Créez un exemple de document tokenisé `doc` en appliquant le parser sur le premier texte `df.texte[0]`\n",
    "- Affichez l'exemple `doc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Un service passable .. Des plats surcuits, des sauces figées dans leur pot (on se demande si elles n'étaient pas sur d'autres tables avant de nous être servies) , le prix est correct mais le goût n'est pas au rendez vous ... très déçu par ce resto\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "from spacy_lefff import LefffLemmatizer, root\n",
    "\n",
    "root.setLevel(50)\n",
    "\n",
    "nlp = spacy.load('fr_core_news_md')\n",
    "french_lemmatizer = LefffLemmatizer(default=True)\n",
    "nlp.add_pipe(french_lemmatizer, name='lefff')\n",
    "\n",
    "doc = nlp(df.texte[0])\n",
    "print(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comme vu dans la première partie, supprimez les stop words et la ponctuation en utilisant une list comprehension.\n",
    "- Stockez le résultat dans une liste `mytokens`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[service, passable, plats, surcuits, sauces, figées, pot, demande, tables, servies, prix, correct, goût, rendez, déçu, resto]\n"
     ]
    }
   ],
   "source": [
    "mytokens = [token for token in doc if not token.is_stop and not token.is_punct]\n",
    "print(mytokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Appliquez à `mytokens`, dans une list comprehension :\n",
    "    - La lemmatisation via lefff `_.lefff_lemma`\n",
    "    - Le passage en minuscule en minuscule avec `.lower()`\n",
    "    - La suppression des espaces avec `.strip()`\n",
    "- Stockez le résultat dans la même variable `mytokens` et affichez la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['service', 'passable', 'plat', 'surcuits', 'sauce', 'figer', 'pot', 'demander', 'table', 'servir', 'prix', 'correct', 'goût', 'rendez', 'décevoir', 'resto']\n"
     ]
    }
   ],
   "source": [
    "mytokens = [word._.lefff_lemma.lower().strip() for word in mytokens ]\n",
    "print(mytokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Créer la function `spacy_tokenizer` qui prend en entrée un texte et qui effectue les étapes précédentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création de la fonction de tokenization\n",
    "def spacy_tokenizer(text):\n",
    "    # Création du token, utilisé pour créer des documents avec des annotations linguistiques\n",
    "    doc = nlp(text)\n",
    "    \n",
    "    # Suppression des stop words\n",
    "    mytokens = [token for token in doc if not token.is_stop and not token.is_punct]\n",
    "    \n",
    "    # Lemmatisation de chaque token et conversion en caractères minuscules\n",
    "    mytokens = [word._.lefff_lemma.lower().strip() for word in mytokens ]\n",
    "\n",
    "    # On retourne la liste des tokens préparés\n",
    "    return mytokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour utiliser des algorithmes de machine learning sur du texte, il est nécessaire de représenter le texte par des valeurs numériques. C'est ce qu'on appelle la vectorisation. La technique la plus simple est le *bag of words* :\n",
    "\n",
    "**Exemple de Bag of Words**\n",
    "- Texte 1 : \"enedis innovation\"\n",
    "- Texte 2 : \"Cordel croissants\"\n",
    "- Texte 3 : \"Cordel innovation innovation\"\n",
    "\n",
    "|          | enedis | innovation | Cordel | croissants |\n",
    "| -------- |: ----: | ---------: |: ----: |: --------: |\n",
    "| Texte 1  |    1   |          1 |      0 |          0 |\n",
    "| Texte 2  |    0   |          0 |      1 |          1 |\n",
    "| Texte 3  |    0   |          2 |      1 |          0 |\n",
    "\n",
    "\n",
    "Pour enrichir un peu cette représentation, on peut utiliser la TF IDF (Term Frequency - Inverse Document Frequency) à la place d'un simple décompte dans la matrice. Cette technique, bien qu'assez ancienne, est légère et très efficace pour de l'extraction de sens. \n",
    "\n",
    "Aujourd'hui, les plus gros progrès en NLP concernent cette étape de vectorisation qui peut être améliorée via l'usage du deep learning notamment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Création du bag of words\n",
    "bow_vector = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1), max_features=None)\n",
    "\n",
    "# Création de la représentation TF-IDF\n",
    "tfidf_vector = TfidfVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1), max_features=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant préparer les données pour l'entrainement en selectionnant les colonnes correspondantes et en faisant un split des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['texte'] # les données que l'on veut analyser\n",
    "ylabels = df['label_2'] # les labels, ou réponses, pour lesquels on veut tester notre modèle\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, ylabels, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('vectorizer',\n",
       "                 CountVectorizer(tokenizer=<function spacy_tokenizer at 0x7fe03387af80>)),\n",
       "                ('classifier', LogisticRegression())])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression Classifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "classifier = LogisticRegression()\n",
    "\n",
    "# Création du pipeline en utilisant des Bag of Words\n",
    "pipe = Pipeline([\n",
    "#     ('cleaner', predictors()),\n",
    "    ('vectorizer', bow_vector),\n",
    "    ('classifier', classifier)\n",
    "])\n",
    "\n",
    "# Génération du modèle\n",
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      " [[51  9]\n",
      " [ 7 34]]\n",
      "\n",
      "Logistic Regression Accuracy: 0.8415841584158416\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Predicting avec un dataset de test\n",
    "predicted = pipe.predict(X_test)\n",
    "\n",
    "# Précision du modèle\n",
    "print(\"Confusion matrix:\\n\",metrics.confusion_matrix(y_test, predicted))\n",
    "print(\"\\nLogistic Regression Accuracy:\",metrics.accuracy_score(y_test, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remarque**\n",
    "\n",
    "La qualité des données initiales a un rôle fondamental.\n",
    "\n",
    "*Exemple*\n",
    "- `' '.join(sentences)` --> 75% accuracy\n",
    "- `'. '.join(sentences)` --> 80% accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-SNE sur TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                 texte  label_3\n",
      "0    Un service passable .. Des plats surcuits, des...        0\n",
      "1    Nous avons bien aimé l'ambiance, sur la promen...        2\n",
      "2    on s'attendait à mieux (attente, qualité moyen...        0\n",
      "3    Une adresse à garder pour soi vue imprenable s...        2\n",
      "4    Une petite dame nous accueille sans le sourire...        0\n",
      "..                                                 ...      ...\n",
      "330  des problèmes digestifs aprés le repas!. j'ai ...        0\n",
      "331  Alors là, si vous voulez un resto classe, une ...        2\n",
      "332  Les amis voulaient manger des crêpes mais ... ...        2\n",
      "333  Dés que nous sommes rentrés dans l’établissent...        0\n",
      "334  la salle de restaurant fait cantine,la déco es...        0\n",
      "\n",
      "[335 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "X = df['texte'] # les données que l'on veut analyser\n",
    "ylabels = df['label_3'] # les labels, ou réponses, pour lesquels on veut tester notre modèle\n",
    "print(df.loc[:, ['texte','label_3']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 2108)\t0.22621462074436308\n",
      "  (0, 841)\t0.22886025288552864\n",
      "  (0, 2072)\t0.33759571537509775\n",
      "  (0, 1200)\t0.21234335429816198\n",
      "  (0, 671)\t0.19142723990259058\n",
      "  (0, 1914)\t0.14405034879818315\n",
      "  (0, 2254)\t0.19142723990259058\n",
      "  (0, 2380)\t0.19142723990259058\n",
      "  (0, 757)\t0.21234335429816198\n",
      "  (0, 1884)\t0.3237244489288966\n",
      "  (0, 1076)\t0.3554788881366306\n",
      "  (0, 2214)\t0.22886025288552864\n",
      "  (0, 2352)\t0.3806838045058536\n",
      "  (0, 1843)\t0.11884543242896015\n",
      "  (0, 1767)\t0.3237244489288966\n",
      "  (0, 2250)\t0.11930761279229458\n"
     ]
    }
   ],
   "source": [
    "# On calcule le tf-idf pour chaque mot de chaque texte\n",
    "tfidf = tfidf_vector.fit_transform(X)\n",
    "print(tfidf[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Un service passable .. Des plats surcuits, des sauces figées dans leur pot (on se demande si elles n'étaient pas sur d'autres tables avant de nous être servies) , le prix est correct mais le goût n'est pas au rendez vous ... très déçu par ce resto\n"
     ]
    },
    {
     "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>tf_idf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>surcuits</th>\n",
       "      <td>0.380684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figer</th>\n",
       "      <td>0.355479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rendez</th>\n",
       "      <td>0.337596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pot</th>\n",
       "      <td>0.323724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>passable</th>\n",
       "      <td>0.323724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>démarquer</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>démonter</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>départ</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dépasser</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>€</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2658 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tf_idf\n",
       "surcuits   0.380684\n",
       "figer      0.355479\n",
       "rendez     0.337596\n",
       "pot        0.323724\n",
       "passable   0.323724\n",
       "...             ...\n",
       "démarquer  0.000000\n",
       "démonter   0.000000\n",
       "départ     0.000000\n",
       "dépasser   0.000000\n",
       "€          0.000000\n",
       "\n",
       "[2658 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Affichage des résultats pour le premier commentaire\n",
    "\n",
    "# Premier commentaire\n",
    "print(df.loc[0, 'texte'])\n",
    "\n",
    "# TF-IDF pour le premier commentaire\n",
    "df_tfidf = pd.DataFrame(tfidf[0].T.todense(), index = tfidf_vector.get_feature_names(), columns=[\"tf_idf\"])\n",
    "df_tfidf.sort_values(by=[\"tf_idf\"],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       +  -en  -là  -plat   -t  .repas    0  0,50   00    1  ...  éviter!!!!.  \\\n",
      "0    0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "1    0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "2    0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "3    0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "4    0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "..   ...  ...  ...    ...  ...     ...  ...   ...  ...  ...  ...          ...   \n",
      "330  0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "331  0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "332  0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "333  0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "334  0.0  0.0  0.0    0.0  0.0     0.0  0.0   0.0  0.0  0.0  ...          0.0   \n",
      "\n",
      "     évolution  évoquer  êtes      être  île  œuf  œufs    €  label_3  \n",
      "0          0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "1          0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        2  \n",
      "2          0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "3          0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        2  \n",
      "4          0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "..         ...      ...   ...       ...  ...  ...   ...  ...      ...  \n",
      "330        0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "331        0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        2  \n",
      "332        0.0      0.0   0.0  0.169101  0.0  0.0   0.0  0.0        2  \n",
      "333        0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "334        0.0      0.0   0.0  0.000000  0.0  0.0   0.0  0.0        0  \n",
      "\n",
      "[335 rows x 2659 columns]\n"
     ]
    }
   ],
   "source": [
    "# Création d'un dataframe regroupant les tf-idf et les labels\n",
    "df_tfidf = pd.DataFrame(tfidf.todense(), columns = tfidf_vector.get_feature_names())\n",
    "df_tfidf = pd.concat([df_tfidf, ylabels], axis=1)\n",
    "print(df_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 31 nearest neighbors...\n",
      "[t-SNE] Indexed 335 samples in 0.003s...\n",
      "[t-SNE] Computed neighbors for 335 samples in 0.096s...\n",
      "[t-SNE] Computed conditional probabilities for sample 335 / 335\n",
      "[t-SNE] Mean sigma: 0.168311\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 84.418800\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.841301\n"
     ]
    }
   ],
   "source": [
    "# t-SNE sur les données\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tsvd = TruncatedSVD(n_components=50)\n",
    "tsne = TSNE(n_components=2, init = 'random', verbose=1, perplexity=10, n_iter=1000)\n",
    "\n",
    "tmp = tsvd.fit_transform(df_tfidf.drop(['label_3'], axis=1))\n",
    "tsne_results = tsne.fit_transform(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dim 1', ylabel='Dim 2'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# On considère les deux dimensions les plus importantes\n",
    "df_tfidf['Dim 1'] = tsne_results[:,0]\n",
    "df_tfidf['Dim 2'] = tsne_results[:,1]\n",
    "\n",
    "# On projette les données sur les deux axes, en colorant les points avec les labels\n",
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(\n",
    "    x = \"Dim 1\", y = \"Dim 2\",\n",
    "    hue = \"label_3\",\n",
    "    palette = sns.color_palette(\"hls\", 3),\n",
    "    data = df_tfidf,\n",
    "    legend = \"full\",\n",
    "    alpha = 1.0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minisom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from minisom import MiniSom \n",
    "import numpy as np\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/minisom.py:154: UserWarning: Warning: sigma is too high for the dimension of the map.\n",
      "  warn('Warning: sigma is too high for the dimension of the map.')\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/minisom.py:370: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  self._weights[i, j] = c1*pc[pc_order[0]] + c2*pc[pc_order[1]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [ 3000 / 3000 ] 100% - 0:00:00 left \n",
      " quantization error: 9.214460624296438\n"
     ]
    }
   ],
   "source": [
    "X = df_tfidf.drop(['label_3'], axis=1)\n",
    "\n",
    "som = MiniSom(4,4,len(X.columns), sigma= 5, neighborhood_function='mexican_hat')\n",
    "som.pca_weights_init(X.values)\n",
    "som.train_batch(X.values, 3000, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "taille_map = 4\n",
    "\n",
    "labels_map = som.labels_map(X.values, df_tfidf.label_3)\n",
    "label_names = np.unique(df_tfidf.label_3)\n",
    "plt.figure(figsize=(5, 5))\n",
    "the_grid = GridSpec(taille_map, taille_map)\n",
    "for position in labels_map.keys():\n",
    "    label_fracs = [labels_map[position][l] for l in label_names]\n",
    "    plt.subplot(the_grid[taille_map-1-position[1], position[0]], aspect=1)\n",
    "    patches, texts = plt.pie(label_fracs)\n",
    "plt.legend(patches, label_names, bbox_to_anchor=(0, 1.5))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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