calculate similarity between words using word2vec.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word2vec implementation with Python to calculate similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "from nltk.corpus import gutenberg\n",
    "from multiprocessing import Pool\n",
    "from scipy import spatial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " # import the corpus and convert into a list\n",
    "sentences = list(gutenberg.sents('train.txt'))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert all letters into lowercase\n",
    "\n",
    "for i in range(len(sentences)):\n",
    "    sentences[i] = [word.lower() for word in sentences[i] if re.match('^[a-zA-Z]+', word)] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and train model\n",
    "\n",
    "    - Create a word2vec model and train it with raw_sentences corpus\n",
    "    - Key parameter:\n",
    "        * sentences: training data (has to be a list with tokenized sentences)\n",
    "        * size: dimension of embedding space\n",
    "        * sg: CBOW if 0, skip-gram if 1\n",
    "        * window: number of words accounted for each context (if the window size is 3, 3 word in the left neighorhood and 3 word in the right neighborhood are considered)\n",
    "        * min_count: minimum count of words to be included in the vocabulary\n",
    "        * iter: number of training iterations\n",
    "        * workers: number of worker threads to train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_model = Word2Vec(sentences = sentences, size = 100, sg = 1, window = 3, min_count = 1, iter = 10, workers = Pool()._processes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_model.init_sims(replace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save and load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_model.save('word2vec_model')\n",
    "our_model = Word2Vec.load('word2vec_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity calculation between words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('sound', 0.9857641458511353),\n",
       " ('image', 0.9848567247390747),\n",
       " ('impressive', 0.9837137460708618),\n",
       " ('rear', 0.9827864170074463),\n",
       " ('selfie', 0.9817068576812744),\n",
       " ('bright', 0.9798246622085571),\n",
       " ('loud', 0.9790486097335815),\n",
       " ('icon', 0.9778032302856445),\n",
       " ('outdoor', 0.9760921001434326),\n",
       " ('slim', 0.9758537411689758)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_model.most_similar('photo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('opens', 0.992725133895874),\n",
       " ('sleek', 0.9921761751174927),\n",
       " ('image', 0.9906978607177734),\n",
       " ('flawless', 0.9904495477676392),\n",
       " ('decent', 0.9903085231781006),\n",
       " ('loud', 0.989993691444397),\n",
       " ('superior', 0.9896432161331177),\n",
       " ('brilliant', 0.9895043969154358),\n",
       " ('slightly', 0.9893741607666016),\n",
       " ('thus', 0.9893368482589722)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_model.most_similar('pic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function that computes cosine similarity between two words\n",
    "def cosine_similarity(v1, v2):\n",
    "    return 1 - spatial.distance.cosine(v1, v2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\program files\\python36\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9456096291542053"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = our_model['pic']\n",
    "v2 = our_model['picture']\n",
    "cosine_similarity(v1, v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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