{
 "cells": [
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    {
     "ename": "NameError",
     "evalue": "name 'text_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-d179e01d96de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mw2v_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgensim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWord2Vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mw2v_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'word'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'text_data' is not defined"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "import warnings\n",
    "  \n",
    "warnings.filterwarnings(action = 'ignore')\n",
    "  \n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "sample = open(\"/train/in.tsv\", \"r\")\n",
    "s = sample.read()\n",
    "  \n",
    "# Replaces escape character with space\n",
    "f = s.replace(\"\\n\", \" \")\n",
    "  \n",
    "data = []\n",
    "  \n",
    "# iterate through each sentence in the file\n",
    "for i in sent_tokenize(f):\n",
    "    temp = []\n",
    "      \n",
    "    # tokenize the sentence into words\n",
    "    for j in word_tokenize(i):\n",
    "        temp.append(j.lower())\n",
    "  \n",
    "    data.append(temp)\n",
    "  \n",
    "# Create CBOW model\n",
    "model1 = gensim.models.Word2Vec(data, min_count = 1, \n",
    "                              size = 100, window = 5)\n",
    "  \n",
    "w2v_model = gensim.models.Word2Vec(text_data, size=300, min_count=1, window=5, iter=50)\n",
    "w2v_model.wv['word']\n",
    "\n",
    "with open(\"train/in.tsv\") as f:\n",
    "    content = f.readlines()\n",
    "    with open(\"train/expected.tsv\") as ff:\n",
    "        y = ff.readlines()\n",
    "        vectorizer = TfidfVectorizer(ngram_range=(1,2), use_idf = False)\n",
    "        vectorizer = TfidfVectorizer()\n",
    "        x = vectorizer.fit_transform(content)\n",
    "        x=x.toarray()\n",
    "        y=y.toarray()\n",
    "        model = GaussianNB()\n",
    "        model.fit(x,y)\n",
    "        y_pred = model.predict([[0,1]])\n",
    "        print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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