sim_questions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "memory = '10g'\n",
    "pyspark_submit_args = ' --driver-memory ' + memory + ' pyspark-shell'\n",
    "os.environ[\"PYSPARK_SUBMIT_ARGS\"] = pyspark_submit_args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import Word2Vec\n",
    "from pyspark.context import SparkContext\n",
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.sql import SQLContext\n",
    "import pandas as pd\n",
    "import nltk\n",
    "from numpy.linalg import norm\n",
    "import numpy as np\n",
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What is the step by step guide to invest in share market in india?'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['question1'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              0\n",
       "qid1            0\n",
       "qid2            0\n",
       "question1       0\n",
       "question2       2\n",
       "is_duplicate    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check for some null values..\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(how='any',axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              0\n",
       "qid1            0\n",
       "qid2            0\n",
       "question1       0\n",
       "question2       0\n",
       "is_duplicate    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of questions 404288\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of questions\",len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_analysis = pd.Series(df['question1'].tolist() + df['question2'].tolist()).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    What is the step by step guide to invest in sh...\n",
       "1    What is the story of Kohinoor (Koh-i-Noor) Dia...\n",
       "2    How can I increase the speed of my internet co...\n",
       "3    Why am I mentally very lonely? How can I solve...\n",
       "4    Which one dissolve in water quikly sugar, salt...\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_analysis[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAHwCAYAAADwwkrGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a9bd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = data_analysis.apply(len)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title(\"Histogram of number of words in a question\")\n",
    "plt.xlabel('Number of words in a question')\n",
    "plt.ylabel('Frequency')\n",
    "plt.hist(dist,range=[0,200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 1299.5, 999.5, -0.5)"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAImCAYAAACsOWf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5P8BPSlcET84EQsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7463bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "cloud = WordCloud(width=1300, height=1000).generate(\" \".join(data_analysis.astype(str)))\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.imshow(cloud)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Semantics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of questions having '?' = 99.87%\n",
      "Number of questions having '.' = 6.31%\n",
      "Number of questions having a number = 11.83%\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of questions having '?' = {:.2f}%\".format(np.mean(data_analysis.apply(lambda x: '?' in x))*100))\n",
    "print(\"Number of questions having '.' = {:.2f}%\".format(np.mean(data_analysis.apply(lambda x: '.' in x))*100))\n",
    "print(\"Number of questions having a number = {:.2f}%\".format(np.mean(data_analysis.apply(lambda x: max([y.isdigit() for y in x])))*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data preprocessing done in python to make it easier for pyspark do work on :)\n",
    "stopwords = nltk.corpus.stopwords.words()\n",
    "import re\n",
    "def clean(col):\n",
    "    sent = re.compile('([^\\s\\w]|_)+').sub('', col).lower()\n",
    "    sent = sent.split(\" \")\n",
    "\n",
    "    for w in sent:\n",
    "        if w in stopwords:\n",
    "            sent.remove(w)\n",
    "\n",
    "    sent = \" \".join(sent)\n",
    "    return sent\n",
    "\n",
    "\n",
    "df['question1'] = df['question1'].apply(clean)\n",
    "df['question2'] = df['question2'].apply(clean)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>is step step guide invest share market india</td>\n",
       "      <td>is step step guide invest share market</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>is story kohinoor kohinoor diamond</td>\n",
       "      <td>would happen indian government stole the kohin...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>can increase speed my internet connection usin...</td>\n",
       "      <td>can internet speed increased hacking dns</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>am mentally lonely can solve</td>\n",
       "      <td>find remainder math2324math divided 2423</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2       is step step guide invest share market india   \n",
       "1   1     3     4                 is story kohinoor kohinoor diamond   \n",
       "2   2     5     6  can increase speed my internet connection usin...   \n",
       "3   3     7     8                       am mentally lonely can solve   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0             is step step guide invest share market             0  \n",
       "1  would happen indian government stole the kohin...             0  \n",
       "2           can internet speed increased hacking dns             0  \n",
       "3           find remainder math2324math divided 2423             0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# made the document file in python(using df dataframe created before) \n",
    "# ( to save time :) )\n",
    "documents = []\n",
    "\n",
    "for i in range(0,len(df)):\n",
    "    documents.append(df.iloc[i]['question1'])\n",
    "    if(df.iloc[i]['is_duplicate']==0):        # using unique questions to not make training redundant on same questions\n",
    "        documents.append(df.iloc[i]['question2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['is_duplicate'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# documents[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['can', 'internet', 'speed', 'increased', 'hacking', 'dns']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents = [x.split() for x in documents]\n",
    "documents[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can use this to read the training file\n",
    "# train = sqlContext.read.csv('train.csv',header = True,inferSchema = True)\n",
    "sc = SparkContext('local')\n",
    "sqlContext = SQLContext(sc)\n",
    "train = sqlContext.createDataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----+----+--------------------+--------------------+\n",
      "| id|qid1|qid2|           question1|           question2|\n",
      "+---+----+----+--------------------+--------------------+\n",
      "|  0|   1|   2|is step step guid...|is step step guid...|\n",
      "|  1|   3|   4|is story kohinoor...|would happen indi...|\n",
      "|  2|   5|   6|can increase spee...|can internet spee...|\n",
      "|  3|   7|   8|am mentally lonel...|find remainder ma...|\n",
      "+---+----+----+--------------------+--------------------+\n",
      "only showing top 4 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train.show(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+----+----+---------+---------+\n",
      "| id|qid1|qid2|question1|question2|\n",
      "+---+----+----+---------+---------+\n",
      "|  0|   0|   0|        0|        0|\n",
      "+---+----+----+---------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import isnan,count,when\n",
    "train.select([count(when(isnan(c), c)).alias(c) for c in train.columns]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can do this step but this takes a lot of time (using pyspark syntax)\n",
    "\"\"\"\n",
    "documents = []\n",
    "\n",
    "for i in range(0,train.select('question1').count()):\n",
    "    documents.append(train.collect()[i]['question1'])\n",
    "    if(train.collect()[i]['is_duplicate']=='0'):\n",
    "        documents.append(train.collect()[i]['question2'])\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "from pyspark.sql import Row\n",
    "rdd1 = sqlContext.parallelize(documents)\n",
    "row_rdd = rdd1.map(lambda x: Row(x))\n",
    "\"\"\"\n",
    "\n",
    "pd.DataFrame({'documents':documents})\n",
    "df2=sqlContext.createDataFrame(pd.DataFrame({'documents':documents}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|           documents|\n",
      "+--------------------+\n",
      "|[is, step, step, ...|\n",
      "|[is, step, step, ...|\n",
      "|[is, story, kohin...|\n",
      "|[would, happen, i...|\n",
      "|[can, increase, s...|\n",
      "+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets use Word2Vec !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets apply Word2Vec\n",
    "word2Vec = Word2Vec(vectorSize=100,windowSize=10, minCount=200, inputCol=\"documents\", outputCol=\"result\")\n",
    "model = word2Vec.fit(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.transform(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <object repr() failed>\n",
      "Traceback (most recent call last):\n",
      "  File \"/anaconda3/lib/python3.6/site-packages/pyspark/ml/wrapper.py\", line 40, in __del__\n",
      "    if SparkContext._active_spark_context and self._java_obj is not None:\n",
      "AttributeError: 'Word2Vec' object has no attribute '_java_obj'\n",
      "Exception ignored in: <object repr() failed>\n",
      "Traceback (most recent call last):\n",
      "  File \"/anaconda3/lib/python3.6/site-packages/pyspark/ml/wrapper.py\", line 40, in __del__\n",
      "    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence: [is, step, step, guide, invest, share, market, india] => \n",
      "Vector: [0.0818742960691452,0.1352273221127689,-0.06247943494236097,-0.10914855170994997,-0.19595806277357042,-0.19648136314935982,-0.10534135671332479,0.35443341435166076,0.12426456203684211,0.07455665752058849,-0.02792829438112676,0.16796494089066982,0.2093145006801933,0.039547330365167,0.19630261091515422,0.12626834865659475,-0.004686599597334862,-0.011669308529235423,-0.167632139287889,-0.04216541023924947,0.15394546458264813,-0.07585535943508148,-0.022493957309052348,0.0891863374854438,-0.09348013438284397,0.09886524238390848,-0.09733884315937757,-0.3494789395481348,0.1382706486620009,0.13007311522960663,-0.016432275297120214,-0.05879818648099899,0.10043670138111338,-0.03908898262307048,-0.040998848620802164,0.051089386688545346,-0.04075429419754073,-0.039847508538514376,0.13410601659779786,-0.09756129141896963,0.017554214224219322,-0.09717499790713191,-0.10871897265315056,-0.06651860103011131,0.009830094873905182,-0.12517384067177773,0.08597551833372563,-0.0708354040980339,-0.036159688606858253,0.23676703521050513,0.03809489286504686,-0.06767000211402774,0.18810617364943027,0.04110631160438061,-0.2589869014918804,-0.019085676409304142,0.03125430457293987,-0.09794878540560603,0.06736715463921428,-0.1702833492308855,-0.0020918394438922405,0.06288546789437532,0.2677227193489671,-0.348203667672351,-0.08292462956160307,-0.19050835794769228,-0.006542259361594915,0.2677703509107232,-0.0960806761868298,0.08501499705016613,-0.21289406437426805,-0.12631284445524216,-0.18204959109425545,-0.21439007204025984,0.052227078238502145,-0.20022334880195558,0.061703042418230325,0.15548606729134917,0.026459958753548563,-0.04915240127593279,-0.06089052790775895,-0.23528498224914074,0.08998355711810291,-0.18662253947695717,0.09256764315068722,-0.07292021159082651,-0.06141416262835264,-0.08657761570066214,0.14738559257239103,-0.06985444424208254,0.22887981310486794,-0.15855229925364256,-0.03344300715252757,-0.02653784421272576,0.20247018244117498,0.08734629140235484,0.17863528849557042,0.00258101848885417,0.02602393919005408,0.058453201316297054]\n",
      "\n",
      "Sentence: [is, step, step, guide, invest, share, market] => \n",
      "Vector: [0.09756376594305038,0.12459486669727733,-0.07811195928869502,-0.14928663947752543,-0.2129253561475447,-0.1883546467870474,-0.1393838594002383,0.3595843196048268,0.1608237661421299,0.04490632823269282,-0.007422761991620064,0.2032479356442179,0.241675145391907,0.08508607600482979,0.19784923057471002,0.15451085780348095,0.026004422988210405,-0.06713908977274384,-0.17802672939641134,-0.006273891776800156,0.17172252380156092,-0.053390326244490485,-0.028390997222491672,0.09770153669108236,-0.09926889996443475,0.10450822347775102,-0.08335471046822411,-0.39211683188165936,0.17232859932950564,0.16320958733558655,-0.04390520629073892,-0.10842008250100271,0.10624951011102114,-0.029555461768593104,-0.05349529268486159,0.07676761411130428,-0.015566964434193713,-0.07196002826094627,0.14803965961410928,-0.09091571292706897,0.024487656142030443,-0.06300927006772586,-0.1774589589663914,-0.04498144558497837,-0.010957681706973484,-0.10333935277802603,0.1232595740418349,-0.06612847638981682,-0.02399724828345435,0.273695313504764,-0.010333576638783726,-0.08067140302487781,0.1782223688704627,0.03281909227371216,-0.2497620369706835,-0.01090781284230096,0.01984434574842453,-0.09519558772444725,0.04991081303783825,-0.17269807628222875,-0.021872845611401966,0.05564303802592414,0.2582316835011755,-0.3957751430571079,-0.07976155089480536,-0.18443050953958715,0.010245733495269502,0.2938015716416495,-0.04120107261197908,0.07223749586514064,-0.21146767373595918,-0.1572779853429113,-0.22484450255121502,-0.21829035133123398,0.05700039224965232,-0.2551264909229108,0.034845826149519,0.18306250018732886,0.01756660041532346,-0.0840873856629644,-0.05557732869471822,-0.2408322351319449,0.1348086390644312,-0.2128936190690313,0.08955461106130054,-0.06259339089904513,-0.05050082185438701,-0.13353912638766424,0.1384366846510342,-0.04082080123147794,0.23809156886168886,-0.11337447060006005,-0.05441108824951308,-0.027818866074085236,0.18656974498714718,0.10763193533888885,0.20688163595540182,-0.015354625348533902,0.07446703307713115,0.07986910534756524]\n",
      "\n",
      "Sentence: [is, story, kohinoor, kohinoor, diamond] => \n",
      "Vector: [0.056736855208873754,0.019540347903966904,-0.07073322525247931,0.020108393579721454,0.04929514825344086,-0.13030049204826355,0.0458024337887764,-0.02941645858809352,0.06692120134830475,-0.019265588745474817,0.027310416102409363,-0.060032182931900026,0.034833341091871264,0.02539464831352234,-0.08506983891129494,-0.023427967727184296,0.03376547396183014,-0.01259149331599474,-0.1491429924964905,-0.04406237378716469,0.0966399610042572,-0.008033347129821778,0.01986655592918396,0.04074452621862293,-0.06756211519241333,0.056092054676264526,0.0014054641127586366,-0.05808386504650116,0.1151466503739357,0.09174046069383622,-0.0704556055366993,0.021713068708777428,-0.01338692456483841,0.0009731709957122803,0.04045076593756676,-0.05668286979198456,0.03886393010616303,-0.01786950696259737,0.016151148080825805,-0.0492528647184372,0.020407897233963013,-0.056564816087484364,0.016587388515472413,0.12402591109275818,-0.006961101293563843,0.04826359152793885,-0.06760267186909914,-0.05960587859153748,-0.04924715012311936,0.08069855868816377,-0.0013984749093651772,0.04003364741802216,-0.10265691578388214,-0.08400896191596985,-0.049929638206958775,0.06068396717309952,-0.025098206102848054,0.032952123880386354,-0.017742492258548737,-0.031370654702186584,-0.048210741579532625,-0.03560532629489899,0.0044924944639205934,0.029704257100820544,0.01705635040998459,0.08129816055297852,-0.11421847343444824,0.024688714742660524,-0.028646785765886307,-0.10522348582744599,-0.028178301081061365,-0.1344847857952118,0.11813287138938905,0.027003374695777894,0.0959862232208252,-0.07910044193267823,0.03270726799964905,0.005038601160049439,0.01049046814441681,-0.0406401738524437,0.05011324286460877,0.04230640381574631,0.11732442528009415,-0.03996306508779526,-0.037818689644336705,0.013662107288837433,-0.011525992676615715,-0.03227676097303629,-0.01936519518494606,0.13314522802829742,0.09721360802650453,0.013337662816047669,-0.006963615864515305,-0.009632652252912523,-0.08715629130601883,0.0466606080532074,0.0978906273841858,0.040095800161361696,-0.006451718509197235,0.11431633234024048]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "if SparkContext._active_spark_context and self._java_obj is not None:\n",
      "AttributeError: 'Word2Vec' object has no attribute '_java_obj'\n"
     ]
    }
   ],
   "source": [
    "for row in result.collect()[0:3]:\n",
    "    sentence, vector = row\n",
    "    print(\"Sentence: [%s] => \\nVector: %s\\n\" % (\", \".join(sentence), str(vector)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+\n",
      "|           documents|              result|\n",
      "+--------------------+--------------------+\n",
      "|[is, step, step, ...|[-0.0258020996116...|\n",
      "|[is, step, step, ...|[-0.0443133823573...|\n",
      "|[is, story, kohin...|[-0.0244293399155...|\n",
      "|[would, happen, i...|[-0.0394877173006...|\n",
      "|[can, increase, s...|[-0.1989977355115...|\n",
      "|[can, internet, s...|[-0.1959825431307...|\n",
      "+--------------------+--------------------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result.show(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------------------+\n",
      "|         word|              vector|\n",
      "+-------------+--------------------+\n",
      "|     incident|[0.15706565976142...|\n",
      "|      serious|[0.02947017177939...|\n",
      "|     embedded|[-0.0043854531832...|\n",
      "|      speaker|[-0.1458204835653...|\n",
      "|         rate|[0.32507535815238...|\n",
      "|         2014|[0.21890351176261...|\n",
      "|           45|[-0.1518665105104...|\n",
      "|         snow|[0.06463630497455...|\n",
      "|      michael|[0.02391052804887...|\n",
      "|        looks|[-0.0521396286785...|\n",
      "|          4th|[-0.5105449557304...|\n",
      "|   california|[-0.0821165964007...|\n",
      "|     accident|[0.03092468343675...|\n",
      "|      muscles|[-0.0798977687954...|\n",
      "|   physically|[-0.2371650636196...|\n",
      "|conversations|[0.19810788333415...|\n",
      "|        ideas|[-0.1504241228103...|\n",
      "|       liquid|[0.08924348652362...|\n",
      "|      joining|[-0.2891990244388...|\n",
      "| particularly|[-0.1773614585399...|\n",
      "|         used|[-0.1108705326914...|\n",
      "|          eye|[0.06833478808403...|\n",
      "|      allowed|[-0.0021459464915...|\n",
      "|    reference|[-0.0927777811884...|\n",
      "|       widely|[-0.1644726395606...|\n",
      "+-------------+--------------------+\n",
      "only showing top 25 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.getVectors().show(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "filename = 'stored_model.wv' # Can be any arbitrary filename\n",
    "model.save(filename) \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word = printer \n",
      "Most Similar Word = hp\n"
     ]
    }
   ],
   "source": [
    "ind = np.random.randint(0,model.getVectors().count()+1)\n",
    "print('Word = {} '.format(model.getVectors().collect()[ind]['word']))\n",
    "max_test = -100000000000000\n",
    "\n",
    "for i in range(0,len(model.getVectors().collect())):\n",
    "    if(i!=ind):\n",
    "        cosine_sim = np.dot(model.getVectors().collect()[i]['vector'],model.getVectors().collect()[ind]['vector'])/((norm(model.getVectors().collect()[i]['vector']))*(norm(model.getVectors().collect()[ind]['vector'])))        \n",
    "        if(cosine_sim>max_test):\n",
    "            max_test = cosine_sim\n",
    "            sim_word_test = model.getVectors().collect()[i]['word']\n",
    "            \n",
    "print('Most Similar Word = {}'.format(sim_word_test))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------------------+\n",
      "|     word|        similarity|\n",
      "+---------+------------------+\n",
      "|   moment|0.6596916317939758|\n",
      "|  moments|0.5998780131340027|\n",
      "|childhood|0.5727454423904419|\n",
      "+---------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = model.findSynonyms('incident',3) # return 3 \"synonyms\"\n",
    "res.show(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+------------------+\n",
      "|      word|        similarity|\n",
      "+----------+------------------+\n",
      "|attractive|0.5898951888084412|\n",
      "|     funny|0.5681017637252808|\n",
      "|    beauty|0.5634511709213257|\n",
      "|underrated|0.5537212491035461|\n",
      "|   awesome|0.5526648163795471|\n",
      "+----------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = model.findSynonyms('beautiful',5) # return 5 \"synonyms\"\n",
    "res.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|   word|        similarity|\n",
      "+-------+------------------+\n",
      "|   cars|0.7503092288970947|\n",
      "|  wheel|0.6885342597961426|\n",
      "|   ride|0.6587249040603638|\n",
      "|   bike| 0.658012330532074|\n",
      "|vehicle| 0.654242753982544|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = model.findSynonyms('car',5) # return 5 \"synonyms\"\n",
    "res.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So you can actually tag the questions with a word and find similar questions by finding the questions which have\n",
    "# the synonymous tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|              vector|\n",
      "+--------------------+\n",
      "|[0.16988040506839...|\n",
      "|[-0.1837505251169...|\n",
      "+--------------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(model.getVectors().select('vector'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.07466133683919907,0.2635296583175659,0.03382672742009163,0.2153698205947876,0.26321572065353394,0.19767826795578003,0.024400832131505013,0.10512962937355042,-0.07421072572469711,0.0075380741618573666,0.34349945187568665,-0.36908528208732605,-0.27458861470222473,-0.05353272706270218,0.4964402914047241,0.010587530210614204,-0.16596823930740356,-0.23092319071292877,0.06271348148584366,-0.1126525029540062,-0.21491681039333344,0.08903124183416367,0.02798421122133732,-0.03838581591844559,0.045961763709783554,-0.2182745784521103,-0.12445288151502609,-0.3983164131641388,0.05603325366973877,-0.29980817437171936,0.004810095764696598,-0.11516553908586502,-0.17039737105369568,0.1774015575647354,0.012232450768351555,0.4874473810195923,0.06703566014766693,0.28657349944114685,0.5582836270332336,-0.13123057782649994,-0.34068942070007324,-0.13045968115329742,-0.1780751794576645,-0.040764763951301575,0.495950847864151,0.04393470659852028,-0.13326537609100342,-0.261738121509552,-0.39681142568588257,0.12589968740940094,-0.17206084728240967,-0.001695960876531899,-0.16348806023597717,-0.172096848487854,0.2335243672132492,-0.0843443050980568,0.2434368133544922,-0.033983562141656876,0.26221466064453125,-0.36393824219703674,0.4633180499076843,0.04464700445532799,-0.24347072839736938,0.2739693820476532,0.49463051557540894,-0.3997391164302826,-0.01052616722881794,0.19073763489723206,0.42001327872276306,0.12334028631448746,-0.4404665231704712,0.18565456569194794,-0.2977607846260071,-0.19798146188259125,0.39661574363708496,-0.36149120330810547,-0.2742150127887726,0.4724181294441223,-0.3978215157985687,-0.0330815427005291,-0.45093104243278503,0.21031855046749115,0.0663648247718811,-0.1273074597120285,0.23441307246685028,-0.48055580258369446,-0.3024572432041168,0.25884997844696045,0.20681771636009216,-0.20081505179405212,-0.017281025648117065,0.04755428433418274,0.008608594536781311,-0.3638182282447815,-0.2220231145620346,0.083513043820858,-0.04136369377374649,0.5736882090568542,0.15353970229625702,0.36294040083885193]\n"
     ]
    }
   ],
   "source": [
    "# .transform method\n",
    "p = [['snow']]\n",
    "print(model.transform(sqlContext.createDataFrame(pd.DataFrame([res_word],columns=['documents']))).collect()[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using PCA to decrease the dimensionality of vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import PCA\n",
    "pca = PCA(k=3, inputCol=\"vector\", outputCol=\"pcaFeatures\")\n",
    "model_pca = pca.fit(model.getVectors().select('vector'))\n",
    "\n",
    "result_pca = model_pca.transform(model.getVectors().select('vector')).select(\"pcaFeatures\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word = incident and its representation = [0.15706565976142883,0.06711146235466003,-0.08294181525707245,-0.3025716245174408,-0.28888267278671265,-0.616371214389801,-0.30569466948509216,-0.024195021018385887,-0.11543864011764526,-0.06305581331253052,0.25957098603248596,-0.10166414082050323,0.13632053136825562,0.030745381489396095,0.0792156234383583,0.5676795244216919,0.08982932567596436,-0.06298629939556122,-0.3663630187511444,-0.20902207493782043,-0.17246180772781372,0.29024043679237366,0.1455729603767395,-0.48097142577171326,-0.1816185563802719,0.03555629774928093,-0.18250221014022827,0.07684614509344101,0.07048959285020828,-0.0719909518957138,-0.2356393188238144,-0.44632744789123535,0.020875204354524612,0.17501342296600342,0.4831019937992096,-0.22527357935905457,-0.3422291874885559,-0.09828032553195953,0.2611093819141388,0.010137075558304787,-0.11620756983757019,-0.014132685959339142,-0.017760325223207474,0.11344199627637863,0.1371573507785797,-0.32587286829948425,0.3763252794742584,0.5244693160057068,0.3212750554084778,-0.10587139427661896,0.07107733190059662,-0.17041322588920593,-0.27495715022087097,0.012914640828967094,0.30650538206100464,0.1509355902671814,-0.2370557337999344,0.03009583242237568,0.0023966378066688776,0.17894414067268372,-0.4510911703109741,0.0018660633359104395,0.04919935017824173,-0.1796177178621292,-0.0840921401977539,-0.12427713721990585,0.02034963294863701,-0.3964037597179413,-0.09378756582736969,-0.01275875885039568,-0.03750620409846306,0.10750831663608551,-0.2803174555301666,0.14276160299777985,-0.1561713069677353,-0.22551226615905762,-0.20527324080467224,-0.07285653054714203,-0.2403394877910614,0.16581407189369202,0.15231916308403015,0.18924736976623535,-0.11313699185848236,-0.016882894560694695,0.09480397403240204,0.10643219202756882,-0.08260707557201385,-0.09781509637832642,0.16527652740478516,0.024799954146146774,-0.25809144973754883,0.3052951991558075,-0.39440250396728516,0.04411798343062401,0.13215982913970947,-0.26995617151260376,0.09567604213953018,0.25726285576820374,-0.26327913999557495,-0.1699831634759903]\n",
      "\n",
      "Word = incident and its PCA representation = [0.659770885682689,0.12830893928945666,-0.30970282221223]\n"
     ]
    }
   ],
   "source": [
    "print(\"Word = {0} and its representation = {1}\".format(model.getVectors().collect()[0][0],model.getVectors().collect()[0]['vector']))\n",
    "print()\n",
    "print(\"Word = {0} and its PCA representation = {1}\".format(model.getVectors().collect()[0][0],result_pca.collect()[0]['pcaFeatures']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|         pcaFeatures|\n",
      "+--------------------+\n",
      "|[0.65977088568268...|\n",
      "+--------------------+\n",
      "only showing top 1 row\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result_pca.show(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseVector([0.6446, 0.1481, 0.2543])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_pca.collect()[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3074"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.getVectors().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3074"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_pca.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+------------------+\n",
      "|      word|        similarity|\n",
      "+----------+------------------+\n",
      "|    mobile|0.7708603739738464|\n",
      "|    phones|0.7286869287490845|\n",
      "|    device|0.6977419257164001|\n",
      "|smartphone|0.6695911288261414|\n",
      "| antivirus|0.6474941968917847|\n",
      "+----------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = model.findSynonyms('phone',5) # return 5 \"synonyms\"\n",
    "res.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca2 = PCA(k=2, inputCol=\"vector\", outputCol=\"pcaFeatures2D\")\n",
    "model_pca2 = pca2.fit(model.getVectors().select('vector'))\n",
    "\n",
    "result_pca2 = model_pca2.transform(model.getVectors().select('vector')).select(\"pcaFeatures2D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|       pcaFeatures2D|\n",
      "+--------------------+\n",
      "|[0.65977088568268...|\n",
      "|[0.53021994564132...|\n",
      "|[-1.1410495880768...|\n",
      "|[-0.0649132110454...|\n",
      "|[-0.3632829014518...|\n",
      "+--------------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result_pca2.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = []\n",
    "word_labels = []\n",
    "close_word = 'phone'\n",
    "for j in range(0,model.getVectors().count()):\n",
    "    if((model.getVectors().collect()[j][0])==close_word):\n",
    "        word_labels.append(np.asarray(model.getVectors().collect()[j][0]))\n",
    "        arr.append(np.asarray(result_pca2.collect()[j][0]))\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfor i in range(0,res.count()):\\n    res_word = [[res.collect()[i][0]]]\\n    print(model.transform(sqlContext.createDataFrame(pd.DataFrame([res_word],columns=['documents']))).collect()[0][1])\\n    word_labels.append(np.asarray(res_word))\\n    arr.append(np.asarray(model.transform(sqlContext.createDataFrame(pd.DataFrame([res_word],columns=['documents']))).collect()[0][1]))\\n \\n\""
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,res.count()):\n",
    "    close_word = res.collect()[i][0]\n",
    "    for j in range(0,model.getVectors().count()):\n",
    "        if((model.getVectors().collect()[j][0])==close_word):\n",
    "            word_labels.append(np.asarray(model.getVectors().collect()[j][0]))\n",
    "            arr.append(np.asarray(result_pca2.collect()[j][0]))\n",
    "            break\n",
    "            \n",
    "            \n",
    "# or you can also use .transform method\n",
    "\"\"\"\n",
    "for i in range(0,res.count()):\n",
    "    res_word = [[res.collect()[i][0]]]\n",
    "    print(model.transform(sqlContext.createDataFrame(pd.DataFrame([res_word],columns=['documents']))).collect()[0][1])\n",
    "    word_labels.append(np.asarray(res_word))\n",
    "    arr.append(np.asarray(model.transform(sqlContext.createDataFrame(pd.DataFrame([res_word],columns=['documents']))).collect()[0][1]))\n",
    " \n",
    "\"\"\"                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntsne = TSNE(n_components=2, random_state=0)\\nnp.set_printoptions(suppress=True)\\nY = tsne.fit_transform(arr)\\n'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "np.set_printoptions(suppress=True)\n",
    "Y = tsne.fit_transform(arr)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcoordinate = []\n",
    "ycoordinate = []\n",
    "for i in range(0,len(arr)):\n",
    "    xcoordinate.append(arr[i][0])\n",
    "    ycoordinate.append(arr[i][1])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10b6cdf98>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11b7b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\"\"\"\n",
    "x_coords = Y[:, 0]\n",
    "y_coords = Y[:, 1]\n",
    "plt.scatter(x_coords, y_coords)\n",
    "\"\"\"\n",
    "# display scatter plot\n",
    "plt.scatter(xcoordinate, ycoordinate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "xcoordinate = np.asarray(xcoordinate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "ycoordinate = np.asarray(ycoordinate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbUAAAERCAYAAAD8JzklAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a84fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "for label, x, y in zip(word_labels, x_coords, y_coords):\n",
    "    plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "    plt.scatter(x, y)\n",
    "\n",
    "plt.xlim(x_coords.min()+0.005, x_coords.max()+0.005)\n",
    "plt.ylim(y_coords.min()+0.005, y_coords.max()+0.005)\n",
    "plt.show()\n",
    "\"\"\"\n",
    "\n",
    "for label, x, y in zip(word_labels, xcoordinate, ycoordinate):\n",
    "    plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "    plt.scatter(x, y)\n",
    "\n",
    "plt.xlim(xcoordinate.min()+0.007, xcoordinate.max()+0.005)\n",
    "plt.ylim(ycoordinate.min()-0.005, ycoordinate.max()+0.005)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting some Analogies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do we see some relation between the maths of the word vectors ??\n",
    "p = [['mobile']]\n",
    "q = [['phone']]\n",
    "r = [['student']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qry = wordvector(q) - wordvector(p) - wordvector(r)\n",
    "qry = model.transform(sqlContext.createDataFrame(pd.DataFrame([q],columns=['documents']))).collect()[0][1] - model.transform(sqlContext.createDataFrame(pd.DataFrame([p],columns=['documents']))).collect()[0][1] - model.transform(sqlContext.createDataFrame(pd.DataFrame([r],columns=['documents']))).collect()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analogies = ['accidentally', 'lying', 'dead', 'died']\n"
     ]
    }
   ],
   "source": [
    "analogy_result = model.findSynonyms(qry,num=5)\n",
    "word_list = []\n",
    "for i in range(0,analogy_result.count()):\n",
    "    word_list.append(analogy_result.collect()[i][0])\n",
    "\n",
    "word_list_copy = word_list.copy()    \n",
    "for i in range(0,len(word_list_copy)):\n",
    "    if word_list_copy[i]==p[0][0] or word_list_copy[i]==q[0][0] or word_list_copy[i]==r[0][0]:\n",
    "        word_list.remove(word_list[i])\n",
    "\n",
    "print('Analogies =',word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Probably some relation with students meeting with accident using phone while driving\n",
    "# accidently could refer to accidently dropping the phone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "#from numpy.linalg import norm\n",
    "#norm(result_pca.collect()[0][0] - result_pca.collect()[1][0])\n",
    "\n",
    "# finding most similar question to question1 using cosine similarity score (in result_pca)\n",
    "import sys\n",
    "m = float('-inf')\n",
    "for i in range(0,result_pca.count()):\n",
    "    cosine_sim = np.dot(result_pca.collect()[0][0],result_pca.collect()[i][0])/((norm(result_pca.collect()[0][0]))*(norm(result_pca.collect()[i][0])))\n",
    "    if(cosine_sim>m and i!=0):\n",
    "        m = cosine_sim\n",
    "        similar_question = result.select('documents').collect()[i]\n",
    "\"\"\"        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question Row(documents=['is', 'step', 'step', 'guide', 'invest', 'share', 'market', 'india'])\n",
      "Similar Question Row(documents=['is', 'meant', 'c', 'language'])\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "print('Question',result.select('documents').collect()[0])\n",
    "print('Similar Question',similar_question)\n",
    "\n",
    "# probably we don't have enough similar questions in the dataset other than the provided similar question\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets Test !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_id</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>How does the Surface Pro himself 4 compare wit...</td>\n",
       "      <td>Why did Microsoft choose core m3 and not core ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Should I have a hair transplant at age 24? How...</td>\n",
       "      <td>How much cost does hair transplant require?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>What but is the best way to send money from Ch...</td>\n",
       "      <td>What you send money to China?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_id                                          question1  \\\n",
       "0        0  How does the Surface Pro himself 4 compare wit...   \n",
       "1        1  Should I have a hair transplant at age 24? How...   \n",
       "2        2  What but is the best way to send money from Ch...   \n",
       "\n",
       "                                           question2  \n",
       "0  Why did Microsoft choose core m3 and not core ...  \n",
       "1        How much cost does hair transplant require?  \n",
       "2                      What you send money to China?  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test_id      0\n",
       "question1    2\n",
       "question2    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.dropna(how='any',axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test_id      0\n",
       "question1    0\n",
       "question2    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test[0:15] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['question1'] = df_test['question1'].apply(clean)\n",
    "df_test['question2'] = df_test['question2'].apply(clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_id</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>does surface pro 4 compare ipad pro</td>\n",
       "      <td>did microsoft choose core m3 not core i3 home ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>i a hair transplant age 24 much would cost</td>\n",
       "      <td>much cost hair transplant require</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>but the best way send money china the us</td>\n",
       "      <td>you send money china</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_id                                   question1  \\\n",
       "0        0         does surface pro 4 compare ipad pro   \n",
       "1        1  i a hair transplant age 24 much would cost   \n",
       "2        2    but the best way send money china the us   \n",
       "\n",
       "                                           question2  \n",
       "0  did microsoft choose core m3 not core i3 home ...  \n",
       "1                  much cost hair transplant require  \n",
       "2                               you send money china  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "documents_test = []\n",
    "\n",
    "for i in range(0,len(df_test)):\n",
    "    documents_test.append(df_test.iloc[i]['question1'])\n",
    "    documents_test.append(df_test.iloc[i]['question2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['does surface pro 4 compare ipad pro',\n",
       " 'did microsoft choose core m3 not core i3 home surface pro 4',\n",
       " 'i a hair transplant age 24 much would cost',\n",
       " 'much cost hair transplant require',\n",
       " 'but the best way send money china the us']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['does', 'surface', 'pro', '4', 'compare', 'ipad', 'pro'],\n",
       " ['did',\n",
       "  'microsoft',\n",
       "  'choose',\n",
       "  'core',\n",
       "  'm3',\n",
       "  'not',\n",
       "  'core',\n",
       "  'i3',\n",
       "  'home',\n",
       "  'surface',\n",
       "  'pro',\n",
       "  '4'],\n",
       " ['i', 'a', 'hair', 'transplant', 'age', '24', 'much', 'would', 'cost'],\n",
       " ['much', 'cost', 'hair', 'transplant', 'require'],\n",
       " ['but', 'the', 'best', 'way', 'send', 'money', 'china', 'the', 'us'],\n",
       " ['you', 'send', 'money', 'china'],\n",
       " ['food', 'emulsifiers'],\n",
       " ['foods', 'fibre'],\n",
       " ['aberystwyth', 'start', 'reading'],\n",
       " ['their', 'i', 'start', 'reading'],\n",
       " ['are', 'two', 'wheeler', 'insurance', 'bharti', 'axa', 'insurance'],\n",
       " ['admire', 'am', 'considering', 'buying', 'insurance', 'them'],\n",
       " ['can', 'reduce', 'belly', 'fat', 'a', 'diet'],\n",
       " ['can', 'reduce', 'lower', 'belly', 'fat', 'one', 'month'],\n",
       " ['scrapping',\n",
       "  '500',\n",
       "  '1000',\n",
       "  'rupee',\n",
       "  'notes',\n",
       "  'is',\n",
       "  'rbi',\n",
       "  'planning',\n",
       "  'fight',\n",
       "  'issue',\n",
       "  'black',\n",
       "  'money'],\n",
       " ['recent',\n",
       "  'move',\n",
       "  'declare',\n",
       "  '500',\n",
       "  '1000',\n",
       "  'denomination',\n",
       "  'lewin',\n",
       "  'illegal',\n",
       "  'will',\n",
       "  'curb',\n",
       "  'black',\n",
       "  'money'],\n",
       " ['are', 'how', 'best', 'books', 'all', 'time'],\n",
       " ['are', 'military', 'history', 'books', 'of', 'all', 'time'],\n",
       " ['12th',\n",
       "  'years',\n",
       "  'old',\n",
       "  'boy',\n",
       "  'i',\n",
       "  'sex',\n",
       "  'a',\n",
       "  '12',\n",
       "  'years',\n",
       "  'old',\n",
       "  'girl',\n",
       "  'her',\n",
       "  'consent',\n",
       "  'there',\n",
       "  'anything',\n",
       "  'wrong'],\n",
       " ['14', 'old', 'guy', 'date', 'a', '12', 'year', 'old', 'girl'],\n",
       " ['is', 'best', 'slideshow', 'app', 'android'],\n",
       " ['are', 'best', 'app', 'android'],\n",
       " ['services', 'from', 'google', 'facebook', 'youtube', 'betray', 'twitter'],\n",
       " ['social',\n",
       "  'network',\n",
       "  'like',\n",
       "  'google',\n",
       "  'facebook',\n",
       "  'whatsapp',\n",
       "  'viber',\n",
       "  'twitter',\n",
       "  'youtube',\n",
       "  'instagram',\n",
       "  'skype',\n",
       "  'wiki',\n",
       "  'etc',\n",
       "  'made',\n",
       "  'huge',\n",
       "  'impact',\n",
       "  'people',\n",
       "  'lifestyles'],\n",
       " ['if',\n",
       "  'cricket',\n",
       "  'hits',\n",
       "  'batsmans',\n",
       "  'helmet',\n",
       "  'then',\n",
       "  'goes',\n",
       "  'the',\n",
       "  'boundary'],\n",
       " ['carbonated',\n",
       "  'red',\n",
       "  'balls',\n",
       "  '8',\n",
       "  'yellow',\n",
       "  'balls',\n",
       "  '5',\n",
       "  'balls',\n",
       "  'drawn',\n",
       "  'is',\n",
       "  'probability',\n",
       "  'getting',\n",
       "  '2',\n",
       "  'red',\n",
       "  'balls',\n",
       "  '3',\n",
       "  'yellow',\n",
       "  'balls'],\n",
       " ['how', 'you', 'learn', 'fruity', 'loops'],\n",
       " ['do', 'fruity', 'wrappers', 'work'],\n",
       " ['does', 'batman', 'get', 'kill', 'batman', 'superman'],\n",
       " ['batman',\n",
       "  'superman',\n",
       "  'reduce',\n",
       "  'lex',\n",
       "  'luthor',\n",
       "  'pit',\n",
       "  'superman',\n",
       "  'batman']]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents_test = [x.split() for x in documents_test]\n",
    "documents_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({'documents':documents_test})\n",
    "df2_test=sqlContext.createDataFrame(pd.DataFrame({'documents':documents_test}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|           documents|\n",
      "+--------------------+\n",
      "|[does, surface, p...|\n",
      "|[did, microsoft, ...|\n",
      "+--------------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df2_test.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_test = model.transform(df2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseVector([-0.1251, -0.3238, 0.1354, 0.2108, 0.0398, 0.0207, -0.0609, 0.2348, 0.1606, -0.2812, 0.0501, -0.4497, -0.0583, 0.2359, 0.228, -0.1523, 0.2774, -0.3083, -0.0459, 0.0256, -0.0948, 0.0821, 0.1753, -0.0255, -0.0102, -0.0984, 0.1838, -0.2003, 0.1764, 0.044, 0.0633, -0.1103, 0.0138, 0.0797, 0.2571, 0.3594, -0.1814, -0.4786, -0.2406, 0.1439, 0.1895, 0.1242, 0.2592, -0.3771, 0.0786, -0.0546, -0.1488, -0.3846, -0.0524, 0.1423, 0.1451, -0.2733, -0.37, -0.258, -0.0186, -0.0811, -0.0789, 0.0839, -0.1019, 0.041, -0.1217, 0.3008, -0.0384, 0.0791, -0.1755, 0.0148, -0.16, -0.0487, 0.2047, -0.1129, -0.074, -0.3963, -0.1185, 0.0606, -0.2108, 0.3314, 0.0245, -0.3209, 0.0805, 0.0989, 0.1283, 0.0397, 0.2733, -0.303, -0.1997, -0.1102, -0.0855, -0.2114, 0.0133, -0.3569, -0.0784, 0.1167, 0.2585, -0.0391, 0.244, -0.2385, 0.0806, -0.1314, 0.2557, -0.0377])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_test.collect()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 1 How does the Surface Pro himself 4 compare with iPad Pro?\n",
      "Question 2 Why did Microsoft choose core m3 and not core i3 home Surface Pro 4?\n",
      "Cosine_Sim 0.7359112922209852\n",
      "Similar\n",
      "\n",
      "Question 1 Should I have a hair transplant at age 24? How much would it cost?\n",
      "Question 2 How much cost does hair transplant require?\n",
      "Cosine_Sim 0.7938762579683857\n",
      "Similar\n",
      "\n",
      "Question 1 What but is the best way to send money from China to the US?\n",
      "Question 2 What you send money to China?\n",
      "Cosine_Sim 0.7751495033656934\n",
      "Similar\n",
      "\n",
      "Question 1 Which food not emulsifiers?\n",
      "Question 2 What foods fibre?\n",
      "Cosine_Sim 0.5068020237049685\n",
      "Not Similar\n",
      "\n",
      "Question 1 How \"aberystwyth\" start reading?\n",
      "Question 2 How their can I start reading?\n",
      "Cosine_Sim 0.797250456521179\n",
      "Similar\n",
      "\n",
      "Question 1 How are the two wheeler insurance from Bharti Axa insurance?\n",
      "Question 2 I admire I am considering of buying insurance from them\n",
      "Cosine_Sim 0.6301503770157117\n",
      "Not Similar\n",
      "\n",
      "Question 1 How can I reduce my belly fat through a diet?\n",
      "Question 2 How can I reduce my lower belly fat in one month?\n",
      "Cosine_Sim 0.9297584931492567\n",
      "Similar\n",
      "\n",
      "Question 1 By scrapping the 500 and 1000 rupee notes, how is RBI planning to fight against issue black money?\n",
      "Question 2 How will the recent move to declare 500 and 1000 denomination lewin illegal will curb black money?\n",
      "Cosine_Sim 0.9635469118263122\n",
      "Similar\n",
      "\n",
      "Question 1 What are the how best books of all time?\n",
      "Question 2 What are some of the military history books of all time?\n",
      "Cosine_Sim 0.5360237821990445\n",
      "Not Similar\n",
      "\n",
      "Question 1 After 12th years old boy and I had sex with a 12 years old girl, with her consent. Is there anything wrong?\n",
      "Question 2 Can a 14 old guy date a 12 year old girl?\n",
      "Cosine_Sim 0.9086759523693912\n",
      "Similar\n",
      "\n",
      "Question 1 What is the best slideshow app for Android?\n",
      "Question 2 What are the best app for android?\n",
      "Cosine_Sim 0.9412422223416231\n",
      "Similar\n",
      "\n",
      "Question 1 What services are from Google: Facebook, YouTube betray Twitter?\n",
      "Question 2 What social network (like Google, Facebook, WhatsApp, Viber, Twitter, YouTube, Instagram, Skype, Wiki, etc.) made huge impact on people and lifestyles?\n",
      "Cosine_Sim 0.8493537734166448\n",
      "Similar\n",
      "\n",
      "Question 1 What if a cricket hits a batsman’s helmet and then goes to the boundary?\n",
      "Question 2 Should carbonated red balls and 8 yellow balls. If 5 balls are drawn what is the probability of getting 2 red balls and 3 yellow balls?\n",
      "Cosine_Sim 0.24874441132974043\n",
      "Not Similar\n",
      "\n",
      "Question 1 Just how do you learn fruity loops?\n",
      "Question 2 How do Fruity Wrappers work?\n",
      "Cosine_Sim 0.1601729706209288\n",
      "Not Similar\n",
      "\n",
      "Question 1 Why does Batman get kill in Batman v Superman?\n",
      "Question 2 In Batman v Superman, why reduce Lex Luthor pit Superman against Batman?\n",
      "Cosine_Sim 0.9281267492692529\n",
      "Similar\n",
      "\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "for i in range(0,result_test.count()-1,2):\n",
    "    cosine_sim = np.dot(result_test.collect()[i][1],result_test.collect()[i+1][1])/((norm(result_test.collect()[i][1]))*(norm(result_test.collect()[i+1][1])))        \n",
    "    print('Question 1',test_report['question1'].iloc[k])\n",
    "    print('Question 2',test_report['question2'].iloc[k])  \n",
    "    print('Cosine_Sim',cosine_sim)\n",
    "    k+=1\n",
    "    if(cosine_sim>0.7):\n",
    "        print('Similar')\n",
    "    else:\n",
    "        print('Not Similar')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}