TF-IDF_my.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Document Retrieval using TF-IDF Weighted Rank and TF-IDF Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !unzip stories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from collections import Counter\n",
    "from num2words import num2words\n",
    "\n",
    "import nltk\n",
    "import os\n",
    "import string\n",
    "import numpy as np\n",
    "import copy\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import re\n",
    "import math\n",
    "\n",
    "# %load_ext autotime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   type                                              posts  \\\n",
      "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...   \n",
      "1  INFJ  'No, I can't draw on my own nails (haha). Thos...   \n",
      "2  INFJ  I'm not sure, that's a good question. The dist...   \n",
      "3  INFJ  'One time my parents were fighting over my dad...   \n",
      "4  INFJ  'Joe santagato - ENTP|||ENFJ or  ENTP?   I'm n...   \n",
      "5  INFJ  'some of these both excite and calm me:  BUTTS...   \n",
      "6  INFJ  'I fully believe in the power of being a prote...   \n",
      "7  INFJ  'It is very annoying to be misinterpreted. Esp...   \n",
      "8  INFJ  'I think that that can absolutely be true of i...   \n",
      "9  INFJ  it could be pyroluria.. you know.. it is an on...   \n",
      "\n",
      "                                        process_post  \n",
      "0  moment top ten play what ha life experience li...  \n",
      "1  no ca draw nail done professional nail yes gel...  \n",
      "2  sure good question distinction two perception ...  \n",
      "3  one time parent fighting dad affair dad fall b...  \n",
      "4  joe sure type yet you know re heavy fi doe mak...  \n",
      "5  some excite calm butt body brain community gar...  \n",
      "6  fully believe power protector give voice voice...  \n",
      "7  it annoying especially regard core intention d...  \n",
      "8  think absolutely true shamefully much potentia...  \n",
      "9  could know ongoing tension inside you love peo...  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>posts</th>\n",
       "      <th>process_post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INFJ</td>\n",
       "      <td>'http://www.youtube.com/watch?v=qsXHcwe3krw|||...</td>\n",
       "      <td>moment top ten play what ha life experience li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INFJ</td>\n",
       "      <td>'No, I can't draw on my own nails (haha). Thos...</td>\n",
       "      <td>no ca draw nail done professional nail yes gel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INFJ</td>\n",
       "      <td>I'm not sure, that's a good question. The dist...</td>\n",
       "      <td>sure good question distinction two perception ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INFJ</td>\n",
       "      <td>'One time my parents were fighting over my dad...</td>\n",
       "      <td>one time parent fighting dad affair dad fall b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INFJ</td>\n",
       "      <td>'Joe santagato - ENTP|||ENFJ or  ENTP?   I'm n...</td>\n",
       "      <td>joe sure type yet you know re heavy fi doe mak...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1413</th>\n",
       "      <td>ESFJ</td>\n",
       "      <td>'About to shower and then make grilled cheese....</td>\n",
       "      <td>about shower make grilled cheese anyone want j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>ESFJ</td>\n",
       "      <td>Cheers for the tag!  1. What is your favorite ...</td>\n",
       "      <td>cheer tag favorite food pancake crepe one what...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>ESFJ</td>\n",
       "      <td>Entj|||Esfp|||Entp|||Esfj|||Estp|||Infp|||Intj...</td>\n",
       "      <td>yeah you oh many thing cant tolerate id say to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>ESFJ</td>\n",
       "      <td>'54586  Adelaide Labille-Guiard is my favorite...</td>\n",
       "      <td>favorite painter coster next favorite artist f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>ESFJ</td>\n",
       "      <td>'Don't worry, I do that too when a car comes c...</td>\n",
       "      <td>do worry car come close behind least lane coul...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1418 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type                                              posts  \\\n",
       "0     INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...   \n",
       "1     INFJ  'No, I can't draw on my own nails (haha). Thos...   \n",
       "2     INFJ  I'm not sure, that's a good question. The dist...   \n",
       "3     INFJ  'One time my parents were fighting over my dad...   \n",
       "4     INFJ  'Joe santagato - ENTP|||ENFJ or  ENTP?   I'm n...   \n",
       "...    ...                                                ...   \n",
       "1413  ESFJ  'About to shower and then make grilled cheese....   \n",
       "1414  ESFJ  Cheers for the tag!  1. What is your favorite ...   \n",
       "1415  ESFJ  Entj|||Esfp|||Entp|||Esfj|||Estp|||Infp|||Intj...   \n",
       "1416  ESFJ  '54586  Adelaide Labille-Guiard is my favorite...   \n",
       "1417  ESFJ  'Don't worry, I do that too when a car comes c...   \n",
       "\n",
       "                                           process_post  \n",
       "0     moment top ten play what ha life experience li...  \n",
       "1     no ca draw nail done professional nail yes gel...  \n",
       "2     sure good question distinction two perception ...  \n",
       "3     one time parent fighting dad affair dad fall b...  \n",
       "4     joe sure type yet you know re heavy fi doe mak...  \n",
       "...                                                 ...  \n",
       "1413  about shower make grilled cheese anyone want j...  \n",
       "1414  cheer tag favorite food pancake crepe one what...  \n",
       "1415  yeah you oh many thing cant tolerate id say to...  \n",
       "1416  favorite painter coster next favorite artist f...  \n",
       "1417  do worry car come close behind least lane coul...  \n",
       "\n",
       "[1418 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "print(df.head(10))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   type                                              posts  \\\n",
      "0  INFJ  'http://www.youtube.com/watch?v=qsXHcwe3krw|||...   \n",
      "1  INFJ  'No, I can't draw on my own nails (haha). Thos...   \n",
      "2  INFJ  I'm not sure, that's a good question. The dist...   \n",
      "3  INFJ  'One time my parents were fighting over my dad...   \n",
      "4  INFJ  'Joe santagato - ENTP|||ENFJ or  ENTP?   I'm n...   \n",
      "5  INFJ  'some of these both excite and calm me:  BUTTS...   \n",
      "6  INFJ  'I fully believe in the power of being a prote...   \n",
      "7  INFJ  'It is very annoying to be misinterpreted. Esp...   \n",
      "8  INFJ  'I think that that can absolutely be true of i...   \n",
      "9  INFJ  it could be pyroluria.. you know.. it is an on...   \n",
      "\n",
      "                                        process_post  I-E  N-S  T-F  J-P  \n",
      "0  moment top ten play what ha life experience li...    0    0    1    0  \n",
      "1  no ca draw nail done professional nail yes gel...    0    0    1    0  \n",
      "2  sure good question distinction two perception ...    0    0    1    0  \n",
      "3  one time parent fighting dad affair dad fall b...    0    0    1    0  \n",
      "4  joe sure type yet you know re heavy fi doe mak...    0    0    1    0  \n",
      "5  some excite calm butt body brain community gar...    0    0    1    0  \n",
      "6  fully believe power protector give voice voice...    0    0    1    0  \n",
      "7  it annoying especially regard core intention d...    0    0    1    0  \n",
      "8  think absolutely true shamefully much potentia...    0    0    1    0  \n",
      "9  could know ongoing tension inside you love peo...    0    0    1    0  \n"
     ]
    }
   ],
   "source": [
    "map1 = {\"I\": 0, \"E\": 1}\n",
    "map2 = {\"N\": 0, \"S\": 1}\n",
    "map3 = {\"T\": 0, \"F\": 1}\n",
    "map4 = {\"J\": 0, \"P\": 1}\n",
    "df['I-E'] = df['type'].astype(str).str[0]\n",
    "df['I-E'] = df['I-E'].map(map1)\n",
    "df['N-S'] = df['type'].astype(str).str[1]\n",
    "df['N-S'] = df['N-S'].map(map2)\n",
    "df['T-F'] = df['type'].astype(str).str[2]\n",
    "df['T-F'] = df['T-F'].map(map3)\n",
    "df['J-P'] = df['type'].astype(str).str[3]\n",
    "df['J-P'] = df['J-P'].map(map4)\n",
    "print(df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "column = 'I-E'\n",
    "value =  0\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post\n",
    "\n",
    "\n",
    "column = 'I-E'\n",
    "value =  1\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# text[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "column = 'N-S'\n",
    "value =  0\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post\n",
    "    \n",
    "column = 'N-S'\n",
    "value =  1\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "column = 'T-F'\n",
    "value =  0\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post\n",
    "    \n",
    "column = 'N-S'\n",
    "value =  1\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "column = 'J-P'\n",
    "value =  0\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post\n",
    "    \n",
    "column = 'N-S'\n",
    "value =  1\n",
    "df_temp = df.loc[df[column] == value]\n",
    "df_t_Post = df_temp.process_post\n",
    "text.append(\"\")\n",
    "print(len(text))\n",
    "index = len(text)-1\n",
    "for post in df_t_Post:\n",
    "    text[index] += post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "document = ['I','E','N','S','T','F','J','P']   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating DF for all words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "N = len(document)\n",
    "DF = {}\n",
    "\n",
    "for i in range(N):\n",
    "    tokens = word_tokenize(text[i])\n",
    "    for w in tokens:\n",
    "        try:\n",
    "            DF[w].add(i)\n",
    "        except:\n",
    "            DF[w] = {i}\n",
    "for i in DF:\n",
    "    DF[i] = len(DF[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_vocab_size = len(DF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18710"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "total_vocab = [x for x in DF]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['moment', 'top', 'ten', 'play', 'what', 'ha', 'life', 'experience', 'repeat', 'today', 'may', 'immerse', 'you', 'the', 'last', 'thing', 'friend', 'posted', 'suicide', 'next']\n"
     ]
    }
   ],
   "source": [
    "print(total_vocab[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def doc_freq(word):\n",
    "    c = 0\n",
    "    try:\n",
    "        c = DF[word]\n",
    "    except:\n",
    "        pass\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating TF-IDF for body, we will consider this as the actual tf-idf as we will add the title weight to this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "doc = 0\n",
    "\n",
    "tf_idf = {}\n",
    "\n",
    "for i in range(N):\n",
    "    \n",
    "    tokens = word_tokenize(text[i])\n",
    "    \n",
    "    counter = Counter(tokens)\n",
    "    words_count = len(tokens)\n",
    "    \n",
    "    for token in np.unique(tokens):\n",
    "        \n",
    "        tf = counter[token]/words_count\n",
    "        dof = doc_freq(token)\n",
    "        idf = np.log((N+1)/(dof+1))\n",
    "        \n",
    "        tf_idf[doc, token] = tf*idf\n",
    "    doc += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 'aa'): 0.0,\n",
       " (0, 'aba'): 3.6552344722163795e-06,\n",
       " (0, 'aback'): 0.0,\n",
       " (0, 'abandon'): 0.0,\n",
       " (0, 'abandoned'): 0.0,\n",
       " (0, 'abandonment'): 1.324712063316541e-06,\n",
       " (0, 'abbreviation'): 0.0,\n",
       " (0, 'abhor'): 1.1327894105411707e-06,\n",
       " (0, 'abhorrent'): 5.309021708208914e-07,\n",
       " (0, 'abide'): 0.0,\n",
       " (0, 'abiding'): 9.138086180540949e-07,\n",
       " (0, 'ability'): 0.0,\n",
       " (0, 'able'): 0.0,\n",
       " (0, 'ablescience'): 1.324712063316541e-06,\n",
       " (0, 'abnormal'): 0.0,\n",
       " (0, 'abnormality'): 2.7414258541622844e-06,\n",
       " (0, 'abnormally'): 1.1327894105411707e-06,\n",
       " (0, 'aboard'): 0.0,\n",
       " (0, 'abomination'): 9.138086180540949e-07,\n",
       " (0, 'abortion'): 0.0,\n",
       " (0, 'about'): 0.0,\n",
       " (0, 'above'): 0.0,\n",
       " (0, 'abrasive'): 0.0,\n",
       " (0, 'abraxas'): 9.138086180540949e-07,\n",
       " (0, 'abroad'): 0.0,\n",
       " (0, 'abrupt'): 0.0,\n",
       " (0, 'abruptly'): 0.0,\n",
       " (0, 'absence'): 0.0,\n",
       " (0, 'absent'): 0.0,\n",
       " (0, 'absently'): 1.8276172361081898e-06,\n",
       " (0, 'absolute'): 0.0,\n",
       " (0, 'absolutely'): 0.0,\n",
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       " (0, 'authority'): 0.0,\n",
       " (0, 'autism'): 0.0,\n",
       " (0, 'autistic'): 0.0,\n",
       " (0, 'auto'): 0.0,\n",
       " (0, 'autobiography'): 1.3272554270522284e-06,\n",
       " (0, 'autograph'): 0.0,\n",
       " (0, 'automatic'): 0.0,\n",
       " (0, 'automatically'): 0.0,\n",
       " (0, 'automotive'): 0.0,\n",
       " (0, 'autonomous'): 9.138086180540949e-07,\n",
       " (0, 'autonomy'): 0.0,\n",
       " (0, 'autopilot'): 5.309021708208914e-07,\n",
       " (0, 'autopsy'): 1.324712063316541e-06,\n",
       " (0, 'autumn'): 0.0,\n",
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       " (0, 'available'): 0.0,\n",
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       " (0, 'aversion'): 0.0,\n",
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       " (0, 'avocado'): 7.963532562313372e-07,\n",
       " (0, 'avoid'): 0.0,\n",
       " (0, 'avoidance'): 0.0,\n",
       " (0, 'avoider'): 1.324712063316541e-06,\n",
       " (0, 'avow'): 1.324712063316541e-06,\n",
       " (0, 'aw'): 0.0,\n",
       " (0, 'await'): 9.138086180540949e-07,\n",
       " (0, 'awake'): 0.0,\n",
       " (0, 'awaken'): 0.0,\n",
       " (0, 'awakening'): 0.0,\n",
       " (0, 'award'): 0.0,\n",
       " (0, 'aware'): 0.0,\n",
       " (0, 'awareness'): 0.0,\n",
       " (0, 'away'): 0.0,\n",
       " (0, 'awe'): 0.0,\n",
       " (0, 'awesome'): 0.0,\n",
       " (0, 'awesomely'): 7.963532562313372e-07,\n",
       " (0, 'awesomeness'): 0.0,\n",
       " (0, 'awesomewow'): 1.8276172361081898e-06,\n",
       " (0, 'awful'): 0.0,\n",
       " (0, 'awfully'): 0.0,\n",
       " (0, 'awhile'): 0.0,\n",
       " (0, 'awkward'): 0.0,\n",
       " (0, 'awkwardly'): 0.0,\n",
       " (0, 'awkwardness'): 0.0,\n",
       " (0, 'awkwardnessno'): 2.475974768751839e-06,\n",
       " (0, 'awoke'): 1.8276172361081898e-06,\n",
       " (0, 'awry'): 9.138086180540949e-07,\n",
       " (0, 'ax'): 0.0,\n",
       " (0, 'axe'): 0.0,\n",
       " (0, 'axiomatic'): 1.324712063316541e-06,\n",
       " (0, 'axis'): 0.0,\n",
       " (0, 'axle'): 5.663947052705854e-07,\n",
       " (0, 'aye'): 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(tf_idf)\n",
    "tf_idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_individual_I = {}\n",
    "tfidf_individual_E = {}\n",
    "tfidf_individual_N = {}\n",
    "tfidf_individual_S = {}\n",
    "tfidf_individual_T = {}\n",
    "tfidf_individual_F = {}\n",
    "tfidf_individual_J = {}\n",
    "tfidf_individual_P = {}\n",
    "for key in tf_idf:\n",
    "    if key[0] == 0:\n",
    "        tfidf_individual_I[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 1:\n",
    "        tfidf_individual_E[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 2:\n",
    "        tfidf_individual_N[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 3:\n",
    "        tfidf_individual_S[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 4:\n",
    "        tfidf_individual_T[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 5:\n",
    "        tfidf_individual_F[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 6:\n",
    "        tfidf_individual_J[key[1]] = round(tf_idf[key],10)\n",
    "    elif key[0] == 7:\n",
    "        tfidf_individual_P[key[1]] = round(tf_idf[key],10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'awkwardnessno': 2.476e-06,\n",
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       " 'awry': 9.138e-07,\n",
       " 'ax': 0.0,\n",
       " 'axe': 0.0,\n",
       " 'axiomatic': 1.3247e-06,\n",
       " 'axis': 0.0,\n",
       " 'axle': 5.664e-07,\n",
       " 'aye': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'bari': 3.2872e-06,\n",
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       " 'barking': 0.0,\n",
       " 'barley': 7.52e-07,\n",
       " 'barnacle': 1.2132e-06,\n",
       " 'barney': 0.0,\n",
       " 'baron': 0.0,\n",
       " 'baroque': 0.0,\n",
       " 'barrage': 0.0,\n",
       " 'barred': 7.048e-07,\n",
       " 'barrel': 3.6396e-06,\n",
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       " 'barrier': 0.0,\n",
       " 'barring': 0.0,\n",
       " 'barrio': 1.7587e-06,\n",
       " 'barrowman': 7.52e-07,\n",
       " 'barry': 1.2132e-06,\n",
       " 'bartender': 0.0,\n",
       " 'barton': 3.2872e-06,\n",
       " 'bas': 2.4264e-06,\n",
       " 'base': 0.0,\n",
       " 'baseball': 0.0,\n",
       " 'based': 0.0,\n",
       " 'basedyes': 1.7587e-06,\n",
       " 'baseless': 2.4264e-06,\n",
       " 'basement': 0.0,\n",
       " 'bash': 7.048e-07,\n",
       " 'basic': 0.0,\n",
       " 'basically': 0.0,\n",
       " 'basicallybump': 1.7587e-06,\n",
       " 'basil': 1.4097e-06,\n",
       " 'basilisk': 1.2132e-06,\n",
       " 'basis': 0.0,\n",
       " 'bask': 3.524e-07,\n",
       " 'basket': 0.0,\n",
       " 'basketball': 0.0,\n",
       " 'bass': 0.0,\n",
       " 'bassist': 3.524e-07,\n",
       " 'bast': 3.524e-07,\n",
       " 'bastard': 0.0,\n",
       " 'bastion': 1.5039e-06,\n",
       " 'bat': 0.0,\n",
       " 'batch': 4.8528e-06,\n",
       " 'bath': 0.0,\n",
       " 'bathroom': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'awareness': 0.0,\n",
       " 'awaresorry': 1.817e-06,\n",
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       " 'awayat': 1.317e-06,\n",
       " 'awaydear': 1.317e-06,\n",
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       " 'awfully': 0.0,\n",
       " 'awhile': 0.0,\n",
       " 'awing': 1.817e-06,\n",
       " 'awkward': 0.0,\n",
       " 'awkwardly': 0.0,\n",
       " 'awkwardness': 0.0,\n",
       " 'awkwardnessno': 2.4616e-06,\n",
       " 'awoke': 1.817e-06,\n",
       " 'awry': 1.817e-06,\n",
       " 'ax': 0.0,\n",
       " 'axe': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'barefoot': 7.103e-07,\n",
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       " 'bargain': 7.103e-07,\n",
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       " 'barker': 1.5157e-06,\n",
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       " 'barley': 7.578e-07,\n",
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       " 'baron': 0.0,\n",
       " 'baroque': 0.0,\n",
       " 'barrage': 0.0,\n",
       " 'barred': 1.4207e-06,\n",
       " 'barren': 3.552e-07,\n",
       " 'barricade': 7.578e-07,\n",
       " 'barrier': 0.0,\n",
       " 'barring': 0.0,\n",
       " 'barrowman': 1.5157e-06,\n",
       " 'bartender': 0.0,\n",
       " 'bartending': 7.578e-07,\n",
       " 'base': 0.0,\n",
       " 'baseball': 0.0,\n",
       " 'based': 0.0,\n",
       " 'basedyes': 1.7725e-06,\n",
       " 'basement': 0.0,\n",
       " 'bases': 2.4453e-06,\n",
       " 'bash': 3.552e-07,\n",
       " 'basic': 0.0,\n",
       " 'basically': 0.0,\n",
       " 'basil': 1.0655e-06,\n",
       " 'basilisk': 1.2227e-06,\n",
       " 'basis': 0.0,\n",
       " 'bask': 3.552e-07,\n",
       " 'basket': 0.0,\n",
       " 'basketball': 0.0,\n",
       " 'bass': 0.0,\n",
       " 'bassist': 3.552e-07,\n",
       " 'bast': 3.552e-07,\n",
       " 'bastard': 0.0,\n",
       " 'bastion': 1.5157e-06,\n",
       " 'bat': 0.0,\n",
       " 'bath': 0.0,\n",
       " 'bathe': 1.2227e-06,\n",
       " 'bathroom': 0.0,\n",
       " 'bathtub': 1.2227e-06,\n",
       " 'batman': 0.0,\n",
       " 'battalion': 1.2227e-06,\n",
       " 'battered': 0.0,\n",
       " 'battery': 0.0,\n",
       " 'batting': 1.0655e-06,\n",
       " 'battle': 0.0,\n",
       " 'battlefield': 1.7759e-06,\n",
       " 'baxter': 1.2227e-06,\n",
       " 'bay': 0.0,\n",
       " 'baze': 2.4453e-06,\n",
       " 'be': 0.0,\n",
       " 'beach': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'axe': 0.0,\n",
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       " 'axiomatic': 1.4719e-06,\n",
       " 'axis': 0.0,\n",
       " 'axle': 6.293e-07,\n",
       " 'ay': 6.293e-07,\n",
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       " 'babbling': 2.949e-07,\n",
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       " 'bac': 6.293e-07,\n",
       " 'baccalaureate': 1.4719e-06,\n",
       " 'bach': 0.0,\n",
       " 'bachelor': 0.0,\n",
       " 'back': 0.0,\n",
       " 'backbone': 2.0307e-06,\n",
       " 'backdoor': 1.2586e-06,\n",
       " 'backed': 0.0,\n",
       " 'backfire': 0.0,\n",
       " 'background': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'batman': 0.0,\n",
       " 'battalion': 1.2227e-06,\n",
       " 'battered': 0.0,\n",
       " 'battery': 0.0,\n",
       " 'batting': 1.0655e-06,\n",
       " 'battle': 0.0,\n",
       " 'battlefield': 1.7759e-06,\n",
       " 'baxter': 1.2227e-06,\n",
       " 'bay': 0.0,\n",
       " 'baze': 2.4453e-06,\n",
       " 'be': 0.0,\n",
       " 'beach': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'balance': 0.0,\n",
       " 'balanced': 0.0,\n",
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       " 'balcony': 0.0,\n",
       " 'bald': 3.107e-07,\n",
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       " 'ball': 0.0,\n",
       " 'ballad': 3.101e-06,\n",
       " 'ballast': 0.0,\n",
       " 'balled': 1.0696e-06,\n",
       " 'baller': 3.101e-06,\n",
       " 'ballet': 6.629e-07,\n",
       " 'balloon': 3.107e-07,\n",
       " 'ballot': 6.629e-07,\n",
       " 'bam': 2.1391e-06,\n",
       " 'bamboo': 0.0,\n",
       " 'ban': 0.0,\n",
       " 'banana': 0.0,\n",
       " 'band': 0.0,\n",
       " 'bandage': 1.0696e-06,\n",
       " 'bane': 0.0,\n",
       " 'bang': 0.0,\n",
       " 'banger': 3.107e-07,\n",
       " 'banging': 0.0,\n",
       " 'banish': 1.5505e-06,\n",
       " 'banjo': 2.1391e-06,\n",
       " 'bank': 0.0,\n",
       " 'banker': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'abhorrent': 3.552e-07,\n",
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       " 'abiding': 1.2227e-06,\n",
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       " 'abnegation': 3.552e-07,\n",
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       " 'abnormality': 3.668e-06,\n",
       " 'abnormally': 1.5157e-06,\n",
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       " 'ballistics': 1.2227e-06,\n",
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       " 'banking': 0.0,\n",
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       " 'bare': 0.0,\n",
       " 'barefoot': 7.103e-07,\n",
       " 'barely': 0.0,\n",
       " 'bargain': 7.103e-07,\n",
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       " 'barker': 1.5157e-06,\n",
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       " 'barren': 3.552e-07,\n",
       " 'barricade': 7.578e-07,\n",
       " 'barrier': 0.0,\n",
       " 'barring': 0.0,\n",
       " 'barrowman': 1.5157e-06,\n",
       " 'bartender': 0.0,\n",
       " 'bartending': 7.578e-07,\n",
       " 'base': 0.0,\n",
       " 'baseball': 0.0,\n",
       " 'based': 0.0,\n",
       " 'basedyes': 1.7725e-06,\n",
       " 'basement': 0.0,\n",
       " 'bases': 2.4453e-06,\n",
       " 'bash': 3.552e-07,\n",
       " 'basic': 0.0,\n",
       " 'basically': 0.0,\n",
       " 'basil': 1.0655e-06,\n",
       " 'basilisk': 1.2227e-06,\n",
       " 'basis': 0.0,\n",
       " 'bask': 3.552e-07,\n",
       " 'basket': 0.0,\n",
       " 'basketball': 0.0,\n",
       " 'bass': 0.0,\n",
       " 'bassist': 3.552e-07,\n",
       " 'bast': 3.552e-07,\n",
       " 'bastard': 0.0,\n",
       " 'bastion': 1.5157e-06,\n",
       " 'bat': 0.0,\n",
       " 'bath': 0.0,\n",
       " 'bathe': 1.2227e-06,\n",
       " 'bathroom': 0.0,\n",
       " 'bathtub': 1.2227e-06,\n",
       " 'batman': 0.0,\n",
       " 'battalion': 1.2227e-06,\n",
       " 'battered': 0.0,\n",
       " 'battery': 0.0,\n",
       " 'batting': 1.0655e-06,\n",
       " 'battle': 0.0,\n",
       " 'battlefield': 1.7759e-06,\n",
       " 'baxter': 1.2227e-06,\n",
       " 'bay': 0.0,\n",
       " 'baze': 2.4453e-06,\n",
       " 'be': 0.0,\n",
       " 'beach': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual_P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual = [tfidf_individual_I,tfidf_individual_E,tfidf_individual_N,tfidf_individual_S,tfidf_individual_T,tfidf_individual_F,tfidf_individual_J,tfidf_individual_P]\n",
    "len(tfidf_individual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'axis': 0.0,\n",
       " 'axle': 5.664e-07,\n",
       " 'aye': 0.0,\n",
       " ...}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_individual[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "           Word    tf_idf\n",
       "0       thyself  0.000014\n",
       "1        govern  0.000013\n",
       "2         flute  0.000011\n",
       "3      migraine  0.000011\n",
       "4      misogyny  0.000011\n",
       "...         ...       ...\n",
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       "\n",
       "[15400 rows x 2 columns]"
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "sort_orders = sorted(tfidf_individual[0].items(), key=lambda x: x[1], reverse=True)\n",
    "df_single = pd.DataFrame (sort_orders,columns=['Word','tf_idf'])\n",
    "df_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word</th>\n",
       "      <th>tf_idf</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>thyself</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>govern</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>flute</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>migraine</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>misogyny</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15395</th>\n",
       "      <td>zodiac</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15396</th>\n",
       "      <td>zombie</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15397</th>\n",
       "      <td>zone</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15398</th>\n",
       "      <td>zoned</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15399</th>\n",
       "      <td>zoom</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Word    tf_idf  count\n",
       "0       thyself  0.000014     15\n",
       "1        govern  0.000013     10\n",
       "2         flute  0.000011     12\n",
       "3      migraine  0.000011      6\n",
       "4      misogyny  0.000011      6\n",
       "...         ...       ...    ...\n",
       "15395    zodiac  0.000000     10\n",
       "15396    zombie  0.000000     30\n",
       "15397      zone  0.000000     53\n",
       "15398     zoned  0.000000      3\n",
       "15399      zoom  0.000000      5\n",
       "\n",
       "[15400 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = word_tokenize(text[0])\n",
    "counter = Counter(tokens)\n",
    "df_single['count'] = df_single['Word'].apply(lambda x: counter[x])\n",
    "df_single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(N):\n",
    "    sort_orders = sorted(tfidf_individual[i].items(), key=lambda x: x[1], reverse=True)\n",
    "    df_single = pd.DataFrame (sort_orders,columns=['Word','tf_idf'])\n",
    "    compression_opts = dict(method='zip',archive_name=document[i]+'.csv')\n",
    "    df_single.to_csv(document[i]+'.zip', index=False,compression=compression_opts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF Matching Score Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def matching_score(k, query):\n",
    "#     preprocessed_query = preprocess(query)\n",
    "#     tokens = word_tokenize(str(preprocessed_query))\n",
    "\n",
    "#     print(\"Matching Score\")\n",
    "#     print(\"\\nQuery:\", query)\n",
    "#     print(\"\")\n",
    "#     print(tokens)\n",
    "    \n",
    "#     query_weights = {}\n",
    "\n",
    "#     for key in tf_idf:\n",
    "        \n",
    "#         if key[1] in tokens:\n",
    "#             try:\n",
    "#                 query_weights[key[0]] += tf_idf[key]\n",
    "#             except:\n",
    "#                 query_weights[key[0]] = tf_idf[key]\n",
    "    \n",
    "#     query_weights = sorted(query_weights.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "#     print(\"\")\n",
    "    \n",
    "#     l = []\n",
    "    \n",
    "#     for i in query_weights[:10]:\n",
    "#         l.append(i[0])\n",
    "    \n",
    "#     print(l)\n",
    "    \n",
    "\n",
    "# matching_score(10, \"Without the drive of Rebeccah's insistence, Kate lost her momentum. She stood next a slatted oak bench, canisters still clutched, surveying\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print_doc(2)"
   ]
  }
 ],
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