{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
     "text": [
      "[ 1.25976562e-01  2.97851562e-02  8.60595703e-03  1.39648438e-01\n",
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      "  5.12695312e-02  3.63281250e-01 -2.42187500e-01 -3.02734375e-01\n",
      " -1.77734375e-01 -2.49023438e-02 -1.67968750e-01 -1.69921875e-01\n",
      "  3.46679688e-02  5.21850586e-03  4.63867188e-02  1.28906250e-01\n",
      "  1.36718750e-01  1.12792969e-01  5.95703125e-02  1.36718750e-01\n",
      "  1.01074219e-01 -1.76757812e-01 -2.51953125e-01  5.98144531e-02\n",
      "  3.41796875e-01 -3.11279297e-02  1.04492188e-01  6.17675781e-02\n",
      "  1.24511719e-01  4.00390625e-01 -3.22265625e-01  8.39843750e-02\n",
      "  3.90625000e-02  5.85937500e-03  7.03125000e-02  1.72851562e-01\n",
      "  1.38671875e-01 -2.31445312e-01  2.83203125e-01  1.42578125e-01\n",
      "  3.41796875e-01 -2.39257812e-02 -1.09863281e-01  3.32031250e-02\n",
      " -5.46875000e-02  1.53198242e-02 -1.62109375e-01  1.58203125e-01\n",
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      "  1.85546875e-01  4.47265625e-01  9.58251953e-03  1.31835938e-01\n",
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      "  1.38671875e-01  8.88671875e-02 -7.51953125e-02 -2.13623047e-02\n",
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      " -3.95507812e-02 -8.98437500e-02  6.34765625e-03  2.03125000e-01\n",
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      " -9.81445312e-02  1.38671875e-01  1.82617188e-01  1.73828125e-01\n",
      "  1.73828125e-01 -2.37304688e-01  1.78710938e-01  6.34765625e-02\n",
      "  2.36328125e-01 -2.08984375e-01  8.74023438e-02 -1.66015625e-01\n",
      " -7.91015625e-02  2.43164062e-01 -8.88671875e-02  1.26953125e-01\n",
      " -2.16796875e-01 -1.73828125e-01 -3.59375000e-01 -8.25195312e-02\n",
      " -6.49414062e-02  5.07812500e-02  1.35742188e-01 -7.47070312e-02\n",
      " -1.64062500e-01  1.15356445e-02  4.45312500e-01 -2.15820312e-01\n",
      " -1.11328125e-01 -1.92382812e-01  1.70898438e-01 -1.25000000e-01\n",
      "  2.65502930e-03  1.92382812e-01 -1.74804688e-01  1.39648438e-01\n",
      "  2.92968750e-01  1.13281250e-01  5.95703125e-02 -6.39648438e-02\n",
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      "  2.89916992e-03  8.20312500e-02  3.41796875e-01  4.32128906e-02\n",
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      "  1.31835938e-01  1.66992188e-01  2.35351562e-01  1.04980469e-01\n",
      " -4.96093750e-01 -1.64062500e-01 -1.56250000e-01 -5.22460938e-02\n",
      "  1.03027344e-01  2.43164062e-01 -1.88476562e-01  5.07812500e-02\n",
      " -9.37500000e-02 -6.68945312e-02  2.27050781e-02  7.61718750e-02\n",
      "  2.89062500e-01  3.10546875e-01 -5.37109375e-02  2.28515625e-01\n",
      "  2.51464844e-02  6.78710938e-02 -1.21093750e-01 -2.15820312e-01\n",
      " -2.73437500e-01 -3.07617188e-02 -3.37890625e-01  1.53320312e-01\n",
      "  2.33398438e-01 -2.08007812e-01  3.73046875e-01  8.20312500e-02\n",
      "  2.51953125e-01 -7.61718750e-02 -4.66308594e-02 -2.23388672e-02\n",
      "  2.99072266e-02 -5.93261719e-02 -4.66918945e-03 -2.44140625e-01\n",
      " -2.09960938e-01 -2.87109375e-01 -4.54101562e-02 -1.77734375e-01\n",
      " -2.79296875e-01 -8.59375000e-02  9.13085938e-02  2.51953125e-01]\n"
     ]
    }
   ],
   "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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