TP_WordEmbedding/wordvec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader as api\n",
    "wv = api.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Affichage des premiers mots du dictionnaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word #0/3000000 is </s>\n",
      "word #1/3000000 is in\n",
      "word #2/3000000 is for\n",
      "word #3/3000000 is that\n",
      "word #4/3000000 is is\n"
     ]
    }
   ],
   "source": [
    "# Affichage de quelques mots du vocabulaire\n",
    "for index, word in enumerate(wv.index_to_key):\n",
    "    if index == 5:\n",
    "        break\n",
    "    print(f\"word #{index}/{len(wv.index_to_key)} is {word}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Conversion du mot en vecteur\n",
    "vec_king = wv['king']\n",
    "print(vec_king)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarités entre les mots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('lumber', 0.678311824798584), ('timber', 0.6622092127799988), ('softwoods', 0.649677038192749)]\n",
      "[('fleur', 0.6820620894432068), ('jardin', 0.6616703271865845), (\"c'est_le\", 0.6514866352081299)]\n"
     ]
    }
   ],
   "source": [
    "# N mots les plus proches du mot cible\n",
    "print(wv.most_similar(positive=['wood'], topn=3))\n",
    "print(wv.most_similar(positive=['bois'], topn=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'wood'\t'pine'\tSimilarité : 0.59\n",
      "'wood'\t'leaf'\tSimilarité : 0.30\n",
      "'wood'\t'plank'\tSimilarité : 0.26\n"
     ]
    }
   ],
   "source": [
    "# Similarité entre des paires de mots\n",
    "pairs = [\n",
    "    ('wood', 'pine'), \n",
    "    ('wood', 'leaf'),\n",
    "    ('wood', 'plank') \n",
    "]\n",
    "for w1, w2 in pairs:\n",
    "    print('%r\\t%r\\tSimilarité : %.2f' % (w1, w2, wv.similarity(w1, w2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le modèle a été entrainé en anglais donc il ne fonctionne pas dans les autres langues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemple d'opération sur les mots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('mother', 0.8671472072601318)]\n"
     ]
    }
   ],
   "source": [
    "# Opérations sur les mots\n",
    "vec_father = wv['father']\n",
    "vec_man = wv['man']\n",
    "vec_woman = wv['woman']\n",
    "\n",
    "result = wv.most_similar(positive=(vec_father - vec_man + vec_woman), topn=1)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On obtient bien le mot \"maman\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut également jouer au jeu de l'intru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iron\n"
     ]
    }
   ],
   "source": [
    "print(wv.doesnt_match(['wood', 'oak', 'tree', 'iron', 'leaf']))"
   ]
  }
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