glueberry/tutorials/gensim_w2v.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec Tutorial\n",
    "[GENSIM Tutorial](https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html#visualising-word-embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 14:30:49,470 : INFO : loading projection weights from C:\\Users\\Francesco/gensim-data\\word2vec-google-news-300\\word2vec-google-news-300.gz\n",
      "2022-05-30 14:31:31,470 : INFO : KeyedVectors lifecycle event {'msg': 'loaded (3000000, 300) matrix of type float32 from C:\\\\Users\\\\Francesco/gensim-data\\\\word2vec-google-news-300\\\\word2vec-google-news-300.gz', 'binary': True, 'encoding': 'utf8', 'datetime': '2022-05-30T14:31:31.470006', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'load_word2vec_format'}\n"
     ]
    }
   ],
   "source": [
    "import gensim.downloader as api\n",
    "wv = api.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "word 5 / 3000000 is on\n",
      "word 6 / 3000000 is ##\n",
      "word 7 / 3000000 is The\n",
      "word 8 / 3000000 is with\n",
      "word 9 / 3000000 is said\n",
      "word 10 / 3000000 is was\n",
      "word 11 / 3000000 is the\n",
      "word 12 / 3000000 is at\n",
      "word 13 / 3000000 is not\n",
      "word 14 / 3000000 is as\n",
      "word 15 / 3000000 is it\n",
      "word 16 / 3000000 is be\n",
      "word 17 / 3000000 is from\n",
      "word 18 / 3000000 is by\n",
      "word 19 / 3000000 is are\n",
      "word 20 / 3000000 is I\n",
      "word 21 / 3000000 is have\n",
      "word 22 / 3000000 is he\n",
      "word 23 / 3000000 is will\n",
      "word 24 / 3000000 is has\n",
      "word 25 / 3000000 is ####\n",
      "word 26 / 3000000 is his\n",
      "word 27 / 3000000 is an\n",
      "word 28 / 3000000 is this\n",
      "word 29 / 3000000 is or\n",
      "word 30 / 3000000 is their\n",
      "word 31 / 3000000 is who\n",
      "word 32 / 3000000 is they\n",
      "word 33 / 3000000 is but\n",
      "word 34 / 3000000 is $\n",
      "word 35 / 3000000 is had\n",
      "word 36 / 3000000 is year\n",
      "word 37 / 3000000 is were\n",
      "word 38 / 3000000 is we\n",
      "word 39 / 3000000 is more\n",
      "word 40 / 3000000 is ###\n",
      "word 41 / 3000000 is up\n",
      "word 42 / 3000000 is been\n",
      "word 43 / 3000000 is you\n",
      "word 44 / 3000000 is its\n",
      "word 45 / 3000000 is one\n",
      "word 46 / 3000000 is about\n",
      "word 47 / 3000000 is would\n",
      "word 48 / 3000000 is which\n",
      "word 49 / 3000000 is out\n",
      "word 50 / 3000000 is can\n",
      "word 51 / 3000000 is It\n",
      "word 52 / 3000000 is all\n",
      "word 53 / 3000000 is also\n",
      "word 54 / 3000000 is two\n",
      "word 55 / 3000000 is after\n",
      "word 56 / 3000000 is first\n",
      "word 57 / 3000000 is He\n",
      "word 58 / 3000000 is do\n",
      "word 59 / 3000000 is time\n",
      "word 60 / 3000000 is than\n",
      "word 61 / 3000000 is when\n",
      "word 62 / 3000000 is We\n",
      "word 63 / 3000000 is over\n",
      "word 64 / 3000000 is last\n",
      "word 65 / 3000000 is new\n",
      "word 66 / 3000000 is other\n",
      "word 67 / 3000000 is her\n",
      "word 68 / 3000000 is people\n",
      "word 69 / 3000000 is into\n",
      "word 70 / 3000000 is In\n",
      "word 71 / 3000000 is our\n",
      "word 72 / 3000000 is there\n",
      "word 73 / 3000000 is A\n",
      "word 74 / 3000000 is she\n",
      "word 75 / 3000000 is could\n",
      "word 76 / 3000000 is just\n",
      "word 77 / 3000000 is years\n",
      "word 78 / 3000000 is some\n",
      "word 79 / 3000000 is U.S.\n",
      "word 80 / 3000000 is three\n",
      "word 81 / 3000000 is million\n",
      "word 82 / 3000000 is them\n",
      "word 83 / 3000000 is what\n",
      "word 84 / 3000000 is But\n",
      "word 85 / 3000000 is so\n",
      "word 86 / 3000000 is no\n",
      "word 87 / 3000000 is like\n",
      "word 88 / 3000000 is if\n",
      "word 89 / 3000000 is only\n",
      "word 90 / 3000000 is percent\n",
      "word 91 / 3000000 is get\n",
      "word 92 / 3000000 is did\n",
      "word 93 / 3000000 is him\n",
      "word 94 / 3000000 is game\n",
      "word 95 / 3000000 is back\n",
      "word 96 / 3000000 is because\n",
      "word 97 / 3000000 is now\n",
      "word 98 / 3000000 is #.#\n",
      "word 99 / 3000000 is before\n"
     ]
    }
   ],
   "source": [
    "for index, word in enumerate(wv.index_to_key):\n",
    "    if index==100:\n",
    "        break\n",
    "    print(f'word {index} / {len(wv.index_to_key)} is {word}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_king = wv['queen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The word 'cameroon' does not appear in this model\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    vec_cameroon = wv['cameroon']\n",
    "except KeyError:\n",
    "    print(\"The word 'cameroon' does not appear in this model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'car'\t'minivan'\t0.69\n",
      "'car'\t'bicycle'\t0.54\n",
      "'car'\t'airplane'\t0.42\n",
      "'car'\t'cereal'\t0.14\n",
      "'car'\t'communism'\t0.06\n"
     ]
    }
   ],
   "source": [
    "pairs = [\n",
    "    ('car', 'minivan'),\n",
    "    ('car', 'bicycle'),\n",
    "    ('car', 'airplane'),\n",
    "    ('car', 'cereal'),\n",
    "    ('car', 'communism')\n",
    "]\n",
    "\n",
    "for w1, w2 in pairs:\n",
    "    print('%r\\t%r\\t%.2f' % (w1, w2, wv.similarity(w1, w2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('bicycle', 0.7798016667366028), ('scooter', 0.7793240547180176), ('motorcycle', 0.7453441023826599), ('bikes', 0.7298908233642578), ('vehicle', 0.6923801302909851)]\n"
     ]
    }
   ],
   "source": [
    "print(wv.most_similar(positive=['car', 'bike'], topn=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jar\n"
     ]
    }
   ],
   "source": [
    "print(wv.doesnt_match(\n",
    "    ['fire', 'land', 'water', 'sea', 'air', 'jar']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train your own model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.test.utils import datapath\n",
    "from gensim import utils\n",
    "\n",
    "class MyCorpus:\n",
    "    \"\"\" An iterator that yields sentences (list of str)\"\"\"\n",
    "\n",
    "    def __iter__(self):\n",
    "        corpus_path = datapath('lee_background.cor')\n",
    "        for line in open(corpus_path):\n",
    "            # assume there's one document per line, tokens separated by whitespace\n",
    "            yield utils.simple_preprocess(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-05-30 14:47:56,513 : INFO : collecting all words and their counts\n",
      "2022-05-30 14:47:56,515 : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types\n",
      "2022-05-30 14:47:56,584 : INFO : collected 6981 word types from a corpus of 58152 raw words and 300 sentences\n",
      "2022-05-30 14:47:56,585 : INFO : Creating a fresh vocabulary\n",
      "2022-05-30 14:47:56,590 : INFO : Word2Vec lifecycle event {'msg': 'effective_min_count=5 retains 1750 unique words (25.07% of original 6981, drops 5231)', 'datetime': '2022-05-30T14:47:56.590672', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,591 : INFO : Word2Vec lifecycle event {'msg': 'effective_min_count=5 leaves 49335 word corpus (84.84% of original 58152, drops 8817)', 'datetime': '2022-05-30T14:47:56.591685', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,599 : INFO : deleting the raw counts dictionary of 6981 items\n",
      "2022-05-30 14:47:56,602 : INFO : sample=0.001 downsamples 51 most-common words\n",
      "2022-05-30 14:47:56,602 : INFO : Word2Vec lifecycle event {'msg': 'downsampling leaves estimated 35935.33721568072 word corpus (72.8%% of prior 49335)', 'datetime': '2022-05-30T14:47:56.602641', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'prepare_vocab'}\n",
      "2022-05-30 14:47:56,619 : INFO : estimated required memory for 1750 words and 100 dimensions: 2275000 bytes\n",
      "2022-05-30 14:47:56,621 : INFO : resetting layer weights\n",
      "2022-05-30 14:47:56,623 : INFO : Word2Vec lifecycle event {'update': False, 'trim_rule': 'None', 'datetime': '2022-05-30T14:47:56.623586', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'build_vocab'}\n",
      "2022-05-30 14:47:56,624 : INFO : Word2Vec lifecycle event {'msg': 'training model with 3 workers on 1750 vocabulary and 100 features, using sg=0 hs=0 sample=0.001 negative=5 window=5 shrink_windows=True', 'datetime': '2022-05-30T14:47:56.624583', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'train'}\n",
      "2022-05-30 14:47:56,706 : INFO : EPOCH 0: training on 58152 raw words (35868 effective words) took 0.1s, 453792 effective words/s\n",
      "2022-05-30 14:47:56,780 : INFO : EPOCH 1: training on 58152 raw words (35915 effective words) took 0.1s, 495146 effective words/s\n",
      "2022-05-30 14:47:56,860 : INFO : EPOCH 2: training on 58152 raw words (35942 effective words) took 0.1s, 461119 effective words/s\n",
      "2022-05-30 14:47:56,934 : INFO : EPOCH 3: training on 58152 raw words (35913 effective words) took 0.1s, 496536 effective words/s\n",
      "2022-05-30 14:47:57,009 : INFO : EPOCH 4: training on 58152 raw words (35893 effective words) took 0.1s, 489118 effective words/s\n",
      "2022-05-30 14:47:57,010 : INFO : Word2Vec lifecycle event {'msg': 'training on 290760 raw words (179531 effective words) took 0.4s, 465875 effective words/s', 'datetime': '2022-05-30T14:47:57.009845', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'train'}\n",
      "2022-05-30 14:47:57,010 : INFO : Word2Vec lifecycle event {'params': 'Word2Vec<vocab=1750, vector_size=100, alpha=0.025>', 'datetime': '2022-05-30T14:47:57.010844', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'created'}\n"
     ]
    }
   ],
   "source": [
    "import gensim.models\n",
    "\n",
    "sentences  = MyCorpus()\n",
    "model = gensim.models.Word2Vec(sentences=sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\t'the'\n",
      "1\t'to'\n",
      "2\t'of'\n",
      "3\t'in'\n",
      "4\t'and'\n",
      "5\t'he'\n",
      "6\t'is'\n",
      "7\t'for'\n",
      "8\t'on'\n",
      "9\t'said'\n"
     ]
    }
   ],
   "source": [
    "for index, word in enumerate(model.wv.index_to_key):\n",
    "    if index==10:\n",
    "        break\n",
    "    print('%r\\t%r' % (index, word))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Word Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Francesco\\Documents\\XPUB\\w2v\\venv\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:795: FutureWarning:\n",
      "\n",
      "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "\n",
      "c:\\Users\\Francesco\\Documents\\XPUB\\w2v\\venv\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:805: FutureWarning:\n",
      "\n",
      "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import IncrementalPCA # initial reduction\n",
    "from sklearn.manifold import TSNE # final reduction\n",
    "import numpy as np\n",
    "\n",
    "def reduce_dimensions(model):\n",
    "    num_dimensions = 2 # final num dimensions (2D, 3D, ecc)\n",
    "\n",
    "    # extract the words & their vectors, as numpy arrays\n",
    "    vectors = np.asarray(model.wv.vectors)\n",
    "    labels = np.asarray(model.wv.index_to_key)\n",
    "\n",
    "    # reducing using t-SNE\n",
    "    tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
    "    vectors = tsne.fit_transform(vectors)\n",
    "\n",
    "    x_vals = [v[0] for v in vectors]\n",
    "    y_vals = [v[1] for v in vectors]\n",
    "    return x_vals, y_vals, labels\n",
    "\n",
    "\n",
    "x_vals, y_vals, labels = reduce_dimensions(model)\n",
    "\n",
    "def plot_with_plotly(x_vals, y_vals, labels, plot_in_notebook=True):\n",
    "    from plotly.offline import init_notebook_mode, iplot, plot\n",
    "    import plotly.graph_objs as go\n",
    "\n",
    "    trace = go.Scatter(x=x_vals, y=y_vals, mode='text', text=labels)\n",
    "    data = [trace]\n",
    "\n",
    "    if plot_in_notebook:\n",
    "        init_notebook_mode(connected=True)\n",
    "        iplot(data, filename='word-embedding-plot')\n",
    "    else:\n",
    "        plot(data, filename='word-embedding-plot.html')\n",
    "\n",
    "def plot_with_matplotlib(x_vals, y_vals, labels):\n",
    "    import matplotlib.pyplot as plt\n",
    "    import random\n",
    "    \n",
    "    # random.seed(0)\n",
    "\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.scatter(x_vals, y_vals)\n",
    "\n",
    "    # label randomly subsampled 25 data points\n",
    "\n",
    "    indices = list(range(len(labels)))\n",
    "    selected_indices = random.sample(indices, 25)\n",
    "    for i in selected_indices:\n",
    "        plt.annotate(labels[i], (x_vals[i], y_vals[i]))\n",
    "\n",
    "try:\n",
    "    get_ipython()\n",
    "except Exception:\n",
    "    plot_function = plot_with_matplotlib\n",
    "else:\n",
    "    plot_function = plot_with_plotly\n",
    "\n",
    "# plot_function(x_vals, y_vals, labels)\n",
    "\n",
    "\n",
    "plot_with_matplotlib(x_vals, y_vals, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_with_matplotlib(x_vals, y_vals, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "mode": "text",
         "text": [
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