word2vec_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# getting corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus_raw = 'He is the king . The king is royal . She is the royal  queen '\n",
    "corpus_raw = corpus_raw.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating word index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words = []\n",
    "for word in corpus_raw.split():\n",
    "    if word != '.':\n",
    "        words.append(word)\n",
    "words = set(words)\n",
    "word2int = {}\n",
    "int2word = {}\n",
    "vocab_size = len(words)\n",
    "for i,word in enumerate(words):\n",
    "    word2int[word] = i\n",
    "    int2word[i] = word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# list the sentences as a list of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_sentences = corpus_raw.split('.')\n",
    "sentences = []\n",
    "for sentence in raw_sentences:\n",
    "    sentences.append(sentence.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating i/p o/p pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "WINDOW_SIZE = 2\n",
    "for sentence in sentences:\n",
    "    for word_index, word in enumerate(sentence):\n",
    "        for nb_word in sentence[max(word_index - WINDOW_SIZE, 0) : min(word_index + WINDOW_SIZE, len(sentence)) + 1] : \n",
    "            if nb_word != word:\n",
    "                data.append([word, nb_word])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['he', 'is'],\n",
       " ['he', 'the'],\n",
       " ['is', 'he'],\n",
       " ['is', 'the'],\n",
       " ['is', 'king'],\n",
       " ['the', 'he'],\n",
       " ['the', 'is'],\n",
       " ['the', 'king'],\n",
       " ['king', 'is'],\n",
       " ['king', 'the'],\n",
       " ['the', 'king'],\n",
       " ['the', 'is'],\n",
       " ['king', 'the'],\n",
       " ['king', 'is'],\n",
       " ['king', 'royal'],\n",
       " ['is', 'the'],\n",
       " ['is', 'king'],\n",
       " ['is', 'royal'],\n",
       " ['royal', 'king'],\n",
       " ['royal', 'is'],\n",
       " ['she', 'is'],\n",
       " ['she', 'the'],\n",
       " ['is', 'she'],\n",
       " ['is', 'the'],\n",
       " ['is', 'royal'],\n",
       " ['the', 'she'],\n",
       " ['the', 'is'],\n",
       " ['the', 'royal'],\n",
       " ['the', 'queen'],\n",
       " ['royal', 'is'],\n",
       " ['royal', 'the'],\n",
       " ['royal', 'queen'],\n",
       " ['queen', 'the'],\n",
       " ['queen', 'royal']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating one hot encoding vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "def to_one_hot(data_point_index, vocab_size):\n",
    "    temp = np.zeros(vocab_size)\n",
    "    temp[data_point_index] = 1\n",
    "    return temp\n",
    "x_train = [] \n",
    "y_train = []\n",
    "for data_word in data:\n",
    "    x_train.append(to_one_hot(word2int[ data_word[0] ], vocab_size))\n",
    "    y_train.append(to_one_hot(word2int[ data_word[1] ], vocab_size))\n",
    "x_train = np.asarray(x_train)\n",
    "y_train = np.asarray(y_train)\n",
    "print y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fixing i/p o/p vector size through place holder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(None, vocab_size))\n",
    "y_label = tf.placeholder(tf.float32, shape=(None, vocab_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fixing size of w1 (weight matrix) through place holder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EMBEDDING_DIM = 5\n",
    "W1 = tf.Variable(tf.random_normal([vocab_size, EMBEDDING_DIM]))\n",
    "b1 = tf.Variable(tf.random_normal([EMBEDDING_DIM]))\n",
    "hidden_representation = tf.add(tf.matmul(x,W1), b1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fixing size of the w2 (weight matrix) through place holder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, vocab_size]))\n",
    "b2 = tf.Variable(tf.random_normal([vocab_size]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fixing output softmax function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction = tf.nn.softmax(tf.add( tf.matmul(hidden_representation, W2), b2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy_loss = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(prediction), reduction_indices=[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy_loss)\n",
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "n_iters = 10000\n",
    "for i in range(n_iters):\n",
    "    sess.run(train_step, feed_dict={x: x_train, y_label: y_train})\n",
    "    print (sess.run(cross_entropy_loss, feed_dict={x: x_train, y_label: y_train}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# getting word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.91941857 -1.46123028 -0.38772389  0.65439284  2.92436075]\n"
     ]
    }
   ],
   "source": [
    "vectors = sess.run(W1 + b1)\n",
    "print(vectors[ word2int['queen'] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# finding closest words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def euclidean_dist(vec1, vec2):\n",
    "    return np.sqrt(np.sum((vec1-vec2)**2))\n",
    "\n",
    "def find_closest(word_index, vectors):\n",
    "    min_dist = 10000 # to act like positive infinity\n",
    "    min_index = -1\n",
    "    query_vector = vectors[word_index]\n",
    "    for index, vector in enumerate(vectors):\n",
    "        if euclidean_dist(vector, query_vector) < min_dist and not np.array_equal(vector, query_vector):\n",
    "            min_dist = euclidean_dist(vector, query_vector)\n",
    "            min_index = index\n",
    "    return min_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "royal\n",
      "king\n",
      "he\n"
     ]
    }
   ],
   "source": [
    "print(int2word[find_closest(word2int['he'], vectors)])\n",
    "print(int2word[find_closest(word2int['queen'], vectors)])\n",
    "print(int2word[find_closest(word2int['royal'], vectors)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('king', 0.4571884100581789)\n",
      "('is', 0.80000000000000004)\n",
      "('queen', 0.20000000000000001)\n",
      "('royal', 0.35374497056905951)\n",
      "('she', 0.45848076356042794)\n",
      "('the', 0.65337616226065354)\n",
      "('he', 0.40497817520445861)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8348803b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "model = TSNE(n_components=2, random_state=0)\n",
    "np.set_printoptions(suppress=True)\n",
    "vectors = model.fit_transform(vectors)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0.2, 0.8))\n",
    "scaler.fit(vectors)\n",
    "vectors = scaler.transform(vectors)\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "for word in words:\n",
    "    print(word, vectors[word2int[word]][1])\n",
    "    ax.annotate(word, (vectors[word2int[word]][0],vectors[word2int[word]][1]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}