Word2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus=['king is a strong man', \n",
    "          'queen is a wise woman', \n",
    "          'boy is a young man',\n",
    "          'girl is a young woman',\n",
    "          'prince is a young king',\n",
    "          'princess is a young queen',\n",
    "          'man is strong', \n",
    "          'woman is pretty',\n",
    "          'prince is a boy will be king',\n",
    "          'princess is a girl will be queen']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stop_words(corpus):\n",
    "    stop_words=['a','is','will','be']\n",
    "    results=[]\n",
    "    for text in corpus:\n",
    "        tmp=text.split(' ')\n",
    "        for stop_word in stop_words:\n",
    "            if stop_word in tmp:\n",
    "                tmp.remove(stop_word)\n",
    "        results.append(\" \".join(tmp))\n",
    "    return results\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus=remove_stop_words(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['king strong man',\n",
       " 'queen wise woman',\n",
       " 'boy young man',\n",
       " 'girl young woman',\n",
       " 'prince young king',\n",
       " 'princess young queen',\n",
       " 'man strong',\n",
       " 'woman pretty',\n",
       " 'prince boy king',\n",
       " 'princess girl queen']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "words=[]\n",
    "for text in corpus:\n",
    "    for word in text.split(' '):\n",
    "        words.append(word)\n",
    "        \n",
    "words=set(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'boy',\n",
       " 'girl',\n",
       " 'king',\n",
       " 'man',\n",
       " 'pretty',\n",
       " 'prince',\n",
       " 'princess',\n",
       " 'queen',\n",
       " 'strong',\n",
       " 'wise',\n",
       " 'woman',\n",
       " 'young'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2int = {}\n",
    "\n",
    "for i,word in enumerate(words):\n",
    "    word2int[word] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wise': 0,\n",
       " 'young': 1,\n",
       " 'man': 2,\n",
       " 'girl': 3,\n",
       " 'prince': 4,\n",
       " 'princess': 5,\n",
       " 'king': 6,\n",
       " 'woman': 7,\n",
       " 'pretty': 8,\n",
       " 'queen': 9,\n",
       " 'strong': 10,\n",
       " 'boy': 11}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = []\n",
    "for sentence in corpus:\n",
    "    sentences.append(sentence.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['king', 'strong', 'man'],\n",
       " ['queen', 'wise', 'woman'],\n",
       " ['boy', 'young', 'man'],\n",
       " ['girl', 'young', 'woman'],\n",
       " ['prince', 'young', 'king'],\n",
       " ['princess', 'young', 'queen'],\n",
       " ['man', 'strong'],\n",
       " ['woman', 'pretty'],\n",
       " ['prince', 'boy', 'king'],\n",
       " ['princess', 'girl', 'queen']]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "WINDOW_SIZE = 2\n",
    "\n",
    "data = []\n",
    "for sentence in sentences:\n",
    "    for idx, word in enumerate(sentence):\n",
    "        for neighbor in sentence[max(idx - WINDOW_SIZE, 0) : min(idx + WINDOW_SIZE, len(sentence)) + 1] : \n",
    "            if neighbor != word:\n",
    "                data.append([word, neighbor])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "king strong man\n",
      "queen wise woman\n",
      "boy young man\n",
      "girl young woman\n",
      "prince young king\n",
      "princess young queen\n",
      "man strong\n",
      "woman pretty\n",
      "prince boy king\n",
      "princess girl queen\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pandas as pd\n",
    "for text in corpus:\n",
    "    print(text)\n",
    "\n",
    "df = pd.DataFrame(data, columns = ['input', 'label'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'wise': 0,\n",
       " 'young': 1,\n",
       " 'man': 2,\n",
       " 'girl': 3,\n",
       " 'prince': 4,\n",
       " 'princess': 5,\n",
       " 'king': 6,\n",
       " 'woman': 7,\n",
       " 'pretty': 8,\n",
       " 'queen': 9,\n",
       " 'strong': 10,\n",
       " 'boy': 11}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word2int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "ONE_HOT_DIM = len(words)\n"
   ]
  }
 ],
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