Sessional 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afd2b803",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from nltk.tokenize import WordPunctTokenizer\n",
    "import time\n",
    "import re\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b23b318a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['black-beauty.txt',\n",
       " 'coral-island.txt',\n",
       " 'great expectations.txt',\n",
       " 'gullivers-travels.txt',\n",
       " 'hucleberry-finn.txt',\n",
       " 'jane-eyre.txt',\n",
       " 'kidnapped.txt',\n",
       " 'little-women.txt',\n",
       " 'oliver-twist.txt',\n",
       " 'robinson-crusoe.txt',\n",
       " 'swiss family robinson.txt',\n",
       " 'treasure-island.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH_TO_CORPUS=\"F:\\\\IR Lab\\\\classics\\\\\"\n",
    "filenames = os.listdir(PATH_TO_CORPUS)\n",
    "filenames\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3002daa",
   "metadata": {},
   "source": [
    "#### 1. Read in the Classic corpus into a list, where each text should be an entry into the list. Roughly how long does it take to read in the entire corpus. You can answer in absolute time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e991844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time :  54.3673038482666  ms \n"
     ]
    }
   ],
   "source": [
    "all = []\n",
    "\n",
    "start = time.time()\n",
    "for data in filenames:\n",
    "    try:\n",
    "        alltext = open(PATH_TO_CORPUS+data ,\"r\" , encoding = \"utf-8\").read()\n",
    "        all.append(alltext)\n",
    "    except:\n",
    "        print(\"read character encoding error\")\n",
    "end = time.time()\n",
    "print(\"time : \", (end - start)*1000, \" ms \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a9eb1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6799f14c",
   "metadata": {},
   "source": [
    "#### 2. Repeat the process using the bbc dataset. However, files inside the folders need to be added to the same list. How long did it take to read the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f14dd387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error\n",
      "time :  378.3278465270996  ms \n"
     ]
    }
   ],
   "source": [
    "path_to_bbc = \"F:\\\\IR Lab\\\\bbc\\\\\"\n",
    "\n",
    "start = time.time()\n",
    "bbc = []\n",
    "ans = 0\n",
    "subfolder_names = os.listdir(path_to_bbc)\n",
    "for subfolder in subfolder_names:\n",
    "    if os.path.isdir(path_to_bbc + subfolder):\n",
    "        textfiles = os.listdir(path_to_bbc + subfolder +\"\\\\\")\n",
    "        bbcfile = []\n",
    "        for data in textfiles:\n",
    "            #print(path_to_bbc + subfolder + \"\\\\\" + data)\n",
    "            try:\n",
    "                alltext_1 = open(path_to_bbc + subfolder + \"\\\\\" + data,\"r\",encoding=\"utf-8\").read()\n",
    "                all.append(alltext_1)\n",
    "                ans = ans + 1\n",
    "                bbcfile.append(alltext_1)\n",
    "            except:\n",
    "                print(\"error\")\n",
    "        if len(bbcfile) != 0:\n",
    "            bbc.append(bbcfile)\n",
    "            \n",
    "            \n",
    "end = time.time()\n",
    "print(\"time : \", (end - start)*1000, \" ms \")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f818b0",
   "metadata": {},
   "source": [
    "#### 3. Use the bbc dataset for this problem. How many raw tokens can you identify? A raw token is obtained by segmenting on a blank space. Any punctuations are retained. Multiple consecutive white spaces are to be collapsed into a single white space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "408a5f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = []\n",
    "for file in bbc:\n",
    "    for text in file:\n",
    "        tst = re.sub(r'\\s+', ' ', text)\n",
    "        tokens.extend(tst.split(\" \"))\n",
    "\n",
    "tokens = list(set(tokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d46fb6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of Token :  65539\n"
     ]
    }
   ],
   "source": [
    "print(\"Count of Token : \", len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751dd810",
   "metadata": {},
   "source": [
    "#### 4. Produce a list of better quality tokens for the documents in the bbc dataset. The tokens are now defined as follows:\n",
    "\n",
    "#### ”A Token is a collection of alphabets and/or digits with a (optional) single instance of an apostrophe or a hyphen.”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "01bbd132",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = re.compile(r\"^\\w*[-']?\\w*$\")\n",
    "better_tokens = list(filter(pattern.match, tokens))\n",
    "better_tokens = [i for i in better_tokens if len(i) != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0c753c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['incident',\n",
       " 'Wilmslow',\n",
       " '182',\n",
       " 'irritate',\n",
       " 'four-set',\n",
       " 'Helmut',\n",
       " 'disk',\n",
       " 'keys',\n",
       " '330m',\n",
       " 'Spirit',\n",
       " 'ill-equipped',\n",
       " 'city',\n",
       " 'male-orientated',\n",
       " 'skeletal',\n",
       " 'define',\n",
       " 'Blackburn',\n",
       " \"Hitchhiker's\",\n",
       " 'pave',\n",
       " 'underneath',\n",
       " 'grinds',\n",
       " 'clutches',\n",
       " 'Bristol',\n",
       " 'chord',\n",
       " 'Mardell',\n",
       " 'Marja',\n",
       " 'Grass',\n",
       " 'black',\n",
       " 'Cinematographer',\n",
       " 'Lundgren',\n",
       " 'far-flung',\n",
       " 'muddled',\n",
       " '4Children',\n",
       " 'Soccer',\n",
       " 'assassinated',\n",
       " \"You're\",\n",
       " 'must-have',\n",
       " 'Chia',\n",
       " 'wagging',\n",
       " 'duplicate',\n",
       " 'forcible',\n",
       " \"Kashyap's\",\n",
       " 'Dermot',\n",
       " 'clinches',\n",
       " 'fansite',\n",
       " 'Sarbanes',\n",
       " 'simulate',\n",
       " 'wider',\n",
       " 'conservative',\n",
       " 'UKA',\n",
       " 'aid-related',\n",
       " 'widened',\n",
       " 'Big',\n",
       " 'sometime',\n",
       " 'pace',\n",
       " 'Calvin',\n",
       " 'founding',\n",
       " 'polish',\n",
       " 'Wieting',\n",
       " 'mid-market',\n",
       " 'Abbas',\n",
       " '191',\n",
       " 'gospel',\n",
       " 'eyeing',\n",
       " \"U2's\",\n",
       " 'contemporary',\n",
       " 'apart',\n",
       " 'Clutterbuck',\n",
       " 'disciplines',\n",
       " 'procreative',\n",
       " 'ballerinas',\n",
       " \"'acts\",\n",
       " 'trawlers',\n",
       " 'repayments',\n",
       " 'out-thought',\n",
       " 'Boyle',\n",
       " 'Commentators',\n",
       " 'privacy',\n",
       " 'hard-line',\n",
       " 'exceeds',\n",
       " \"Robbies'\",\n",
       " 'procedure',\n",
       " 'Praising',\n",
       " 'Shawn',\n",
       " 'semi-professional',\n",
       " 'Osbournes',\n",
       " 'cartilage',\n",
       " 'Fightstar',\n",
       " 'teetered',\n",
       " 'Schweitzer',\n",
       " 'Limiting',\n",
       " 'persons',\n",
       " \"performer's\",\n",
       " 'Inanimate',\n",
       " 'DDoS',\n",
       " 'Remy',\n",
       " 'stealth',\n",
       " 'happens',\n",
       " 'sweatshops',\n",
       " 'budget',\n",
       " 'garbage',\n",
       " 'North',\n",
       " 'Spurlock',\n",
       " 'woods',\n",
       " 'Red',\n",
       " 'Portishead',\n",
       " 'Assistant',\n",
       " 'Welling',\n",
       " 'slipped',\n",
       " 'tens',\n",
       " 'ABN',\n",
       " 'Spanglish',\n",
       " 'Dougray',\n",
       " 'defeatism',\n",
       " 'blames',\n",
       " 'protecting',\n",
       " 'Futureheads',\n",
       " 'yellow',\n",
       " 'Vice',\n",
       " 'credibility',\n",
       " 'negatives',\n",
       " 'evaded',\n",
       " \"Thackeray's\",\n",
       " '1963',\n",
       " \"they'll\",\n",
       " 'over-complicated',\n",
       " 'Flamini',\n",
       " 'military',\n",
       " 'plugging',\n",
       " 'Buntes',\n",
       " 'Statistic',\n",
       " 'Duncan',\n",
       " 'SCH-V770',\n",
       " 'Kuwait',\n",
       " 'Anyone',\n",
       " 'signalling',\n",
       " \"Makarov's\",\n",
       " 'Lynn',\n",
       " 'has',\n",
       " 'units',\n",
       " 'Claims',\n",
       " 'stood',\n",
       " 'Side',\n",
       " 'Williams',\n",
       " 'bests',\n",
       " 'customary',\n",
       " 'bundling',\n",
       " 'ultra-wideband',\n",
       " 'Lamine',\n",
       " 'rulings',\n",
       " 'convert',\n",
       " 'omission',\n",
       " 'ex-boss',\n",
       " 'rattling',\n",
       " 'wide-angle',\n",
       " 'hood',\n",
       " 'un-British',\n",
       " 'resurrected',\n",
       " 'Vision',\n",
       " 'affects',\n",
       " 'execute',\n",
       " 'cinematography',\n",
       " 'Vickery',\n",
       " 'Zurich',\n",
       " 'senior',\n",
       " 'grow',\n",
       " 'Sachs',\n",
       " 'Dave',\n",
       " 'learners',\n",
       " 'happy',\n",
       " 'Loop',\n",
       " 'technology',\n",
       " 'buy-back',\n",
       " 'Lambert',\n",
       " \"radio's\",\n",
       " 'Lomana',\n",
       " 'irritated',\n",
       " 'werewolf',\n",
       " 'displace',\n",
       " 'road',\n",
       " 'Dec',\n",
       " 'Carwyn',\n",
       " 'Files',\n",
       " \"Blunkett's\",\n",
       " 'aspect',\n",
       " 'disseminated',\n",
       " 'Indian-administered',\n",
       " 'hijack',\n",
       " 'poisonous',\n",
       " 'Japanese',\n",
       " 'million',\n",
       " 'acknowledges',\n",
       " 'Is',\n",
       " 'stream',\n",
       " \"Zealand's\",\n",
       " 'Hertfordshire',\n",
       " 'incidence',\n",
       " \"didn't\",\n",
       " 'Morpeth',\n",
       " 'June',\n",
       " 'fairground',\n",
       " 'Canada',\n",
       " 'Anita',\n",
       " 'indeed',\n",
       " 'hike',\n",
       " 'Guardsman',\n",
       " 'Down',\n",
       " 'phenomenally',\n",
       " \"Rover's\",\n",
       " 'ousted',\n",
       " 'contribute',\n",
       " 'Wolong',\n",
       " 'Nicholson',\n",
       " 'Fletcher',\n",
       " 'residence',\n",
       " \"Telecom's\",\n",
       " 'stirred',\n",
       " \"Mousa's\",\n",
       " 'Darling',\n",
       " 'situations',\n",
       " 'juries',\n",
       " 'C4',\n",
       " \"eat'\",\n",
       " 'Iranian',\n",
       " 'Whenever',\n",
       " 'handling',\n",
       " 'Atlantic',\n",
       " '1815',\n",
       " 'punishment',\n",
       " 'disappearing',\n",
       " 'Aidan',\n",
       " 'laser-based',\n",
       " 'Tobacco',\n",
       " 'panicky',\n",
       " '24th',\n",
       " 'certainty',\n",
       " 'Tools',\n",
       " 'imaging',\n",
       " 'zombie',\n",
       " 'Alfonso',\n",
       " 'halls',\n",
       " 'Adopting',\n",
       " 'Supply',\n",
       " 'preview',\n",
       " 'Previously',\n",
       " 'researcher',\n",
       " \"Airway's\",\n",
       " 'unravelling',\n",
       " 'confirmation',\n",
       " 'boosting',\n",
       " 'outcry',\n",
       " 'centring',\n",
       " 'notebook',\n",
       " 'crisper',\n",
       " \"Springsteen's\",\n",
       " '45-6',\n",
       " 'Des',\n",
       " 'homophobic',\n",
       " 'Riccardo',\n",
       " 'UFJ-MTFG',\n",
       " 'vein',\n",
       " '16s',\n",
       " 'suspense-filled',\n",
       " 'Reidar',\n",
       " 'Guinness',\n",
       " 'savage',\n",
       " 'infects',\n",
       " 'setback',\n",
       " 'unaffected',\n",
       " 'Derval',\n",
       " 'enliven',\n",
       " 'Chennai-based',\n",
       " 'Portal',\n",
       " \"risk'\",\n",
       " 'Bless',\n",
       " 'Robbins',\n",
       " 'best-of',\n",
       " 'passing',\n",
       " 'Velimir',\n",
       " 'disadvantage',\n",
       " 'cocktail',\n",
       " 'Wholesale',\n",
       " 'squabbling',\n",
       " 'redemptions',\n",
       " 'hinting',\n",
       " 'J-Lo',\n",
       " 'compromised',\n",
       " 'Albanian',\n",
       " 'Serna',\n",
       " 'regretting',\n",
       " 'Hanover',\n",
       " 'Commerzbank',\n",
       " 'Brandon',\n",
       " 'Scott',\n",
       " '24-hour',\n",
       " 'cinemas',\n",
       " 'Together',\n",
       " 'holy',\n",
       " 'results',\n",
       " 'substituted',\n",
       " 'Tom',\n",
       " 'capitalising',\n",
       " 'Burrell',\n",
       " 'steady',\n",
       " 'dissatisfaction',\n",
       " 'Annies',\n",
       " 'vociferously',\n",
       " 'rallies',\n",
       " 'transplanted',\n",
       " 'retained',\n",
       " '142',\n",
       " 'Raj',\n",
       " 'Finn',\n",
       " 'hereditary',\n",
       " 'synthesised',\n",
       " 'incomes',\n",
       " 'Blizzard',\n",
       " 'frayed',\n",
       " 'evicted',\n",
       " 'funereal',\n",
       " 'derailed',\n",
       " 'I',\n",
       " '32nd',\n",
       " 'financings',\n",
       " 'misled',\n",
       " 'white-tail',\n",
       " 'merry',\n",
       " 'Arabic',\n",
       " \"viewer's\",\n",
       " 'Blige',\n",
       " 'LOL',\n",
       " 'Menatep',\n",
       " 'Cheating',\n",
       " 'Kristiansen',\n",
       " 'Kilo',\n",
       " 'owing',\n",
       " 'dithers',\n",
       " 'Hepatitis',\n",
       " 'Petr',\n",
       " 'Nasdaq',\n",
       " 'newest',\n",
       " 'Welsh-English',\n",
       " 'DSL',\n",
       " 'metres',\n",
       " 'expressing',\n",
       " \"sun's\",\n",
       " 'zone',\n",
       " 'appreciate',\n",
       " 'Macrovision',\n",
       " 'Andy',\n",
       " 'midstream',\n",
       " 'worn',\n",
       " 'interviewed',\n",
       " 'Laulala',\n",
       " 'Wyn',\n",
       " 'Justyna',\n",
       " 'Nemeth',\n",
       " 'pleasantly',\n",
       " 'workforce',\n",
       " 'save',\n",
       " 'haves',\n",
       " '140',\n",
       " 'Gielgud',\n",
       " 'Games',\n",
       " 'downturn',\n",
       " 'boardroom',\n",
       " \"'are\",\n",
       " 'outperformance',\n",
       " 'Porsche',\n",
       " 'halve',\n",
       " 'Hypertag',\n",
       " 'Kidney',\n",
       " 'covet',\n",
       " 'sells',\n",
       " \"airline's\",\n",
       " 'township',\n",
       " 'patchy',\n",
       " 'Scream',\n",
       " 'welcoming',\n",
       " 'Mississippi',\n",
       " 'editors',\n",
       " 'Looks',\n",
       " 'fetched',\n",
       " 'next',\n",
       " \"Government's\",\n",
       " 'joined-up',\n",
       " 'fascinate',\n",
       " 'Welshman',\n",
       " 'stain-resistant',\n",
       " 'Mosquito',\n",
       " 'turning',\n",
       " 'commanding',\n",
       " 'VAT',\n",
       " 'choked',\n",
       " \"'Golden\",\n",
       " \"5'\",\n",
       " 'Topics',\n",
       " '49-match',\n",
       " 'commercialised',\n",
       " 'bottle',\n",
       " 'sweet-natured',\n",
       " 'Ascenders',\n",
       " 'sell-offs',\n",
       " 'substitute',\n",
       " 'celebrate',\n",
       " 'Agency',\n",
       " 'gentlemen',\n",
       " 'disrupted',\n",
       " 'which',\n",
       " 'Raul',\n",
       " 'demolished',\n",
       " 'night',\n",
       " 'Names',\n",
       " 'jealous',\n",
       " 'HCL',\n",
       " 'soybean',\n",
       " 'Explaining',\n",
       " 'throughputs',\n",
       " 'eviction',\n",
       " 'sclerosis',\n",
       " 'school-leavers',\n",
       " 'Sittingbourne',\n",
       " 'stand-off',\n",
       " 'sending',\n",
       " 'Combining',\n",
       " 'emulators',\n",
       " \"Starbuck's\",\n",
       " 'deliberation',\n",
       " 'NHTCU',\n",
       " 'Parent',\n",
       " 'wonderfully',\n",
       " 'Whittingdale',\n",
       " \"maker's\",\n",
       " 'Hot',\n",
       " 'multi-cultural',\n",
       " 'detailed',\n",
       " 'Neumi',\n",
       " 'Survivors',\n",
       " 'proportionate',\n",
       " 'frighten',\n",
       " 'Finnan',\n",
       " 'ex-chatshow',\n",
       " 'Llwyd',\n",
       " 'Giants',\n",
       " 'legality',\n",
       " 'Walton',\n",
       " 'Swansea-born',\n",
       " 'really',\n",
       " 'NOT',\n",
       " 'Building',\n",
       " 'Cyprus',\n",
       " 'hush',\n",
       " 'domination',\n",
       " 'exists',\n",
       " 'bullying',\n",
       " 'with-profit',\n",
       " 'late',\n",
       " 'Nattrass',\n",
       " 'basking',\n",
       " 'water',\n",
       " 'Lakomskis',\n",
       " 'Fourth',\n",
       " 'Intellectual',\n",
       " 'rainy',\n",
       " 'Schnyder',\n",
       " 'accepted',\n",
       " 'lasers',\n",
       " 'placebo',\n",
       " \"Telegraaf's\",\n",
       " 'co-wrote',\n",
       " 'Vaccines',\n",
       " 'profile',\n",
       " 'furiously',\n",
       " 'Maldives',\n",
       " 'sweeps',\n",
       " 'Called',\n",
       " 'Envisional',\n",
       " 'Bebe',\n",
       " 'Solutions',\n",
       " 'reshape',\n",
       " 'Adult',\n",
       " 'illustrious',\n",
       " 'Notes',\n",
       " 'overstepping',\n",
       " 'Fisheries',\n",
       " 'delay',\n",
       " 'Bicester',\n",
       " 'Peugeot',\n",
       " \"tabloids'\",\n",
       " 'Carstairs',\n",
       " 'sting',\n",
       " 'transformers',\n",
       " 'hardliners',\n",
       " 'parade',\n",
       " 'pre-conditions',\n",
       " 'nearly',\n",
       " \"sides'\",\n",
       " 'got',\n",
       " 'Probably',\n",
       " 'outsiders',\n",
       " 'mansion',\n",
       " 'bookrunners',\n",
       " 'Scannell',\n",
       " \"Minister's\",\n",
       " 'degree',\n",
       " 'Minutes',\n",
       " 'revelation',\n",
       " 'Oaten',\n",
       " 'managing',\n",
       " 'Taran',\n",
       " 'When',\n",
       " 'Stability',\n",
       " 'sadly',\n",
       " 'Bird',\n",
       " \"sterling's\",\n",
       " 'Headlines',\n",
       " 'simulating',\n",
       " 'broadening',\n",
       " \"NAO's\",\n",
       " 'Apart',\n",
       " 'Conflict',\n",
       " 'apace',\n",
       " \"Hoon's\",\n",
       " 'Electrolux',\n",
       " 'secure',\n",
       " 'retreated',\n",
       " 'monitor',\n",
       " 'Jeannie',\n",
       " 'equivalent',\n",
       " 'threshold',\n",
       " '162',\n",
       " 'so-and',\n",
       " 'enveloping',\n",
       " 'Infrastructure',\n",
       " 'Peres',\n",
       " 'shooters',\n",
       " 'Revenues',\n",
       " 'archive',\n",
       " 'solicitor',\n",
       " \"Warriors'\",\n",
       " 'Gardener',\n",
       " \"Seagate's\",\n",
       " 'Milanesi',\n",
       " 'beige',\n",
       " 'come',\n",
       " 'posts',\n",
       " 'Clapton',\n",
       " \"Providers'\",\n",
       " 'fourfold',\n",
       " 'gadgets',\n",
       " \"Milan's\",\n",
       " 'conversations',\n",
       " 'Scare',\n",
       " \"BAA's\",\n",
       " 'onshore',\n",
       " 'Weaver',\n",
       " 'graduates',\n",
       " 'discounts',\n",
       " 'scenes',\n",
       " 'merger',\n",
       " 'PDAs',\n",
       " 'AirAsia',\n",
       " 'full-contact',\n",
       " 'Gun',\n",
       " 'Charlene',\n",
       " 'projectors',\n",
       " 'Gangs',\n",
       " '925p',\n",
       " 'track',\n",
       " 'parole',\n",
       " 'Ebay',\n",
       " 'pledging',\n",
       " 'Cosmetic',\n",
       " 'Worldcom',\n",
       " 'Broadcast',\n",
       " 'obsessively',\n",
       " 'Jurors',\n",
       " 'clamours',\n",
       " 'Tsutsumi',\n",
       " 'critic',\n",
       " 'McDonagh',\n",
       " 'Olivier',\n",
       " 'TW',\n",
       " \"Begg's\",\n",
       " 'AFP',\n",
       " 'standby',\n",
       " '6km',\n",
       " 'say-so',\n",
       " 'Fallon',\n",
       " 'Pendleton',\n",
       " 'accepting',\n",
       " 'versatile',\n",
       " 'able',\n",
       " 'Amstel',\n",
       " 'rugby',\n",
       " \"Andreas'\",\n",
       " 'solidity',\n",
       " 'legisation',\n",
       " 'quit',\n",
       " 'Tanzi',\n",
       " 'process',\n",
       " 'Rund',\n",
       " 'phonecam',\n",
       " 'marketers',\n",
       " \"moment's\",\n",
       " 'youths',\n",
       " 'Malian',\n",
       " 'Kiely',\n",
       " 'Allan',\n",
       " 'scrummaging',\n",
       " 'admired',\n",
       " 'blushes',\n",
       " 'Fenerbahce',\n",
       " 'welfare',\n",
       " 'Copper',\n",
       " \"'something\",\n",
       " 'taxi',\n",
       " 'Long',\n",
       " 'glimpse',\n",
       " 'supremely',\n",
       " 'disproportionate',\n",
       " 'McIlory',\n",
       " 'Tuidriva',\n",
       " 'or',\n",
       " \"Corelli's\",\n",
       " 'trading',\n",
       " 'policymakers',\n",
       " \"borrowers'\",\n",
       " 'scrapping',\n",
       " 'Kanye',\n",
       " 'Mansfield',\n",
       " 'embarrassed',\n",
       " 'mimicked',\n",
       " 'Dreamliner',\n",
       " 'thinly-disguised',\n",
       " 'Darth',\n",
       " 'earn',\n",
       " 'occasional',\n",
       " 'McCourt',\n",
       " \"England's\",\n",
       " 'put-upon',\n",
       " 'career-best',\n",
       " 'smaller',\n",
       " 'Catweazle',\n",
       " 'latest',\n",
       " 'calorie-heavy',\n",
       " 'Hi-Tech',\n",
       " 'Hewlett-Packard',\n",
       " 'Jordan',\n",
       " 'flow',\n",
       " \"Paterson's\",\n",
       " 'experiment',\n",
       " 'negotiations',\n",
       " 'bravado',\n",
       " 'autobiographical',\n",
       " 'knee',\n",
       " 'Abebe',\n",
       " 'Connacht',\n",
       " 'challenged',\n",
       " 'game-winning',\n",
       " 'misery',\n",
       " 'bribing',\n",
       " 'postgraduates',\n",
       " 'tremble',\n",
       " 'cheque',\n",
       " 'Goldman',\n",
       " \"Mandaric's\",\n",
       " 'triumphant',\n",
       " 'synergies',\n",
       " 'Guillermo',\n",
       " 'bringing',\n",
       " 'proven',\n",
       " 'receivable',\n",
       " 'Boris',\n",
       " 'pursue',\n",
       " 'Across',\n",
       " 'Ti',\n",
       " 'docu-drama',\n",
       " 'Alumnia',\n",
       " 'testimonies',\n",
       " 'Kronawitter',\n",
       " 'millennium',\n",
       " 'blurring',\n",
       " 'Katherine',\n",
       " 'outline',\n",
       " 'Fusiliers',\n",
       " 'sprinters',\n",
       " 'pits',\n",
       " 'predominately',\n",
       " 'Pullman',\n",
       " 'Candy',\n",
       " '52',\n",
       " 'indecision',\n",
       " 'Feature',\n",
       " 'tedious',\n",
       " 'Concern',\n",
       " 'Carolina',\n",
       " 'Stolichnaya',\n",
       " 'regulation',\n",
       " 'worthy',\n",
       " 'Kremlin',\n",
       " 'unsatisfying',\n",
       " 'Eyes',\n",
       " 'leeks',\n",
       " 'athletics-loving',\n",
       " 'heritage',\n",
       " 'garage',\n",
       " 'fertilizers',\n",
       " 'jersey',\n",
       " \"Gibson's\",\n",
       " 'demonstrated',\n",
       " 'silenced',\n",
       " 'Appleton',\n",
       " 'Muriel',\n",
       " 'unseen',\n",
       " 'Pompeii',\n",
       " 'Immigration',\n",
       " 'Lankans',\n",
       " 'Annuity',\n",
       " 'Paule',\n",
       " 'confined',\n",
       " 'presses',\n",
       " 'privatisations',\n",
       " 'Atomic',\n",
       " 'braking',\n",
       " 'Baa-Baas',\n",
       " 'trans-Atlantic',\n",
       " 'pooling',\n",
       " 'fisheries',\n",
       " 'KFB',\n",
       " \"Meldrum's\",\n",
       " 'Samuel',\n",
       " 'Highland',\n",
       " 'six-year',\n",
       " 'lovers',\n",
       " \"Hughes'\",\n",
       " 'stunned',\n",
       " 'trophy',\n",
       " 'error',\n",
       " 'gaffe',\n",
       " \"'le\",\n",
       " 'iron',\n",
       " 'Zomba',\n",
       " 'Sleepy',\n",
       " 'Collecting',\n",
       " 'nun',\n",
       " 'headmaster',\n",
       " 'Aaliyah',\n",
       " 'Josef',\n",
       " 'had',\n",
       " 'rehashing',\n",
       " \"Symbian's\",\n",
       " \"Britain's\",\n",
       " 'warns',\n",
       " \"Greens'\",\n",
       " 'War',\n",
       " 'Limits',\n",
       " 'theoretical',\n",
       " 'requirements',\n",
       " 'combat',\n",
       " \"Saturday's\",\n",
       " 'Riley',\n",
       " 'Antonio',\n",
       " 'ranking',\n",
       " 'Imagine',\n",
       " 'Diehard',\n",
       " \"Peru's\",\n",
       " 'Who',\n",
       " \"producer's\",\n",
       " 'fully-fit',\n",
       " 'Scare-mongering',\n",
       " 'unhappy',\n",
       " 'stalling',\n",
       " 'systematically',\n",
       " 'Scholls',\n",
       " 'Caribbean',\n",
       " '55th',\n",
       " 'Highlands',\n",
       " 'updates',\n",
       " 'dance',\n",
       " 'concept',\n",
       " \"McKee's\",\n",
       " 'smoked',\n",
       " 'correctly',\n",
       " 'breezes',\n",
       " 'hitches',\n",
       " 'First-choice',\n",
       " 'road-trip',\n",
       " 'headline',\n",
       " 'Sessions',\n",
       " 'consensus',\n",
       " 'Jarre',\n",
       " 'outset',\n",
       " 'approach',\n",
       " 'fixings',\n",
       " 'Prop',\n",
       " 'Lots',\n",
       " 'downward',\n",
       " 'slot',\n",
       " 'Jupiter',\n",
       " 'nutritional',\n",
       " 'resilience',\n",
       " 'calls',\n",
       " 'vague',\n",
       " 'entities',\n",
       " 'safeguards',\n",
       " 'star-studded',\n",
       " 'faith-based',\n",
       " 'Lil',\n",
       " 'Du',\n",
       " 'prevented',\n",
       " \"ITV's\",\n",
       " 'Regent',\n",
       " 'Faroe',\n",
       " 'divisions',\n",
       " 'powering',\n",
       " 'pumping',\n",
       " \"Motor's\",\n",
       " 'organs',\n",
       " 'apologises',\n",
       " 'Directors',\n",
       " 'Woolworths',\n",
       " 'entrepreneurs',\n",
       " 'Niyazov',\n",
       " 'entered',\n",
       " 'Laying',\n",
       " 'expression',\n",
       " 'Europe-wide',\n",
       " 'motorbike',\n",
       " '150th',\n",
       " 'again',\n",
       " 'rudimentary',\n",
       " 'routed',\n",
       " 'Curbishley',\n",
       " 'array',\n",
       " 'hesitant',\n",
       " 'animate',\n",
       " 'Standing',\n",
       " 'Ella',\n",
       " 'angrily',\n",
       " 'clashed',\n",
       " 'Skeens',\n",
       " 'toppling',\n",
       " 'spelling',\n",
       " 'Messenger',\n",
       " 'auditing',\n",
       " \"Hague's\",\n",
       " \"1995's\",\n",
       " 'scheduled',\n",
       " 'Blunt',\n",
       " 'Macaulay',\n",
       " 'Maniffesto',\n",
       " 'ears',\n",
       " 'projecting',\n",
       " 'Naoki',\n",
       " \"states'\",\n",
       " 'colliding',\n",
       " 'Results',\n",
       " 'BBC',\n",
       " 'During',\n",
       " 'fruits',\n",
       " 'Nachison',\n",
       " 'economist',\n",
       " \"military's\",\n",
       " 'demerged',\n",
       " \"Museum's\",\n",
       " 'recruit',\n",
       " 'NHS',\n",
       " 'undergone',\n",
       " 'Floor',\n",
       " 'twin-engined',\n",
       " 'shipments',\n",
       " 'rebounds',\n",
       " 'Douglas',\n",
       " 'disaffection',\n",
       " 'detecting',\n",
       " 'Pickering',\n",
       " 'collegiate',\n",
       " 're-spawning',\n",
       " 'Al-Walid',\n",
       " 'cyber',\n",
       " 'brewer',\n",
       " 'big-screen',\n",
       " 'Dawn',\n",
       " 'messages',\n",
       " 'TV',\n",
       " 'Slimfast',\n",
       " 'Nant',\n",
       " 'pre-selected',\n",
       " 'hazard',\n",
       " 'deletion',\n",
       " \"Canon's\",\n",
       " 'Harlequins',\n",
       " 'orchestrating',\n",
       " 'expenditure',\n",
       " 'confuse',\n",
       " 'Oriel',\n",
       " '831',\n",
       " \"America's\",\n",
       " 'barrel',\n",
       " 'iMP',\n",
       " 'swears',\n",
       " 'upward',\n",
       " 'Nanoscale',\n",
       " 'Garrow',\n",
       " 'Temple',\n",
       " 'race',\n",
       " 'Bailin',\n",
       " 'cartridge',\n",
       " \"Dragon's\",\n",
       " 'trimming',\n",
       " 'Euro-Africa',\n",
       " 'jackpot',\n",
       " 'unbiased',\n",
       " 'crop',\n",
       " 'caps',\n",
       " 'soy',\n",
       " 'reads',\n",
       " \"inmate's\",\n",
       " \"'confuse\",\n",
       " 'Parry',\n",
       " 'plantation',\n",
       " 'flawed',\n",
       " 'Lifting',\n",
       " 'Rev',\n",
       " 'universe',\n",
       " 'job',\n",
       " 'ducking',\n",
       " 'Toulouse',\n",
       " 'Created',\n",
       " 'T-Mobile',\n",
       " 'Danish',\n",
       " \"ATI's\",\n",
       " 'Literally',\n",
       " \"Halifax's\",\n",
       " 'mouthing',\n",
       " 'jeered',\n",
       " 'Dingolfing',\n",
       " 'bidders',\n",
       " 'contenders',\n",
       " 'trouble-shooting',\n",
       " 'Dollar',\n",
       " 'SAP',\n",
       " 'pre-school',\n",
       " 'clam',\n",
       " 'partnered',\n",
       " \"Israel's\",\n",
       " 'X5',\n",
       " 'snatching',\n",
       " \"departure'\",\n",
       " 'breweries',\n",
       " 'three-monthly',\n",
       " 'pronouncement',\n",
       " 'CEO',\n",
       " 'remain',\n",
       " 'Miles',\n",
       " 'widely',\n",
       " 'sandals',\n",
       " 'departed',\n",
       " 'Samaria',\n",
       " 'enquiry',\n",
       " 'immersion',\n",
       " 'simultaneously',\n",
       " 'salacious',\n",
       " 'Client',\n",
       " 'inpatients',\n",
       " 'intellectuals',\n",
       " 'Duchy',\n",
       " 'Schmaler',\n",
       " '287',\n",
       " \"council's\",\n",
       " 'They',\n",
       " 'exist',\n",
       " 'rockstars',\n",
       " \"commission's\",\n",
       " \"Sophos'\",\n",
       " 'Merchants',\n",
       " 'spats',\n",
       " 'Chassis',\n",
       " 'taste',\n",
       " 'Arabian',\n",
       " 'enjoyed',\n",
       " 'shortages',\n",
       " 'stellar',\n",
       " 'putting',\n",
       " 'Elements',\n",
       " 'materials',\n",
       " 'flautist',\n",
       " 'apologised',\n",
       " \"barriers'\",\n",
       " \"Ballantine's\",\n",
       " 'month-old',\n",
       " 'Keith',\n",
       " 'Wave',\n",
       " 'withdrawn',\n",
       " 'die',\n",
       " 'PCS',\n",
       " 'indicted',\n",
       " \"'a\",\n",
       " 'Hossam',\n",
       " 'amibition',\n",
       " ...]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba1357f",
   "metadata": {},
   "source": [
    "#### 5. Create a vocabulary for the bbc dataset containing the tokens found in the last exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "99a5c17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbc_vocab  = set(better_tokens)\n",
    "bbc_vocab = list(bbc_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f661185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33769"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bbc_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4245bf9",
   "metadata": {},
   "source": [
    "#### 6. Write a program that accepts a token and plots a bar graph showing the top 20 documents where the token is present. Top 20 here is defined as per frequency of occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "61e3daf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(file_text, limit = 3):\n",
    "    text = re.sub(r'\\s+', ' ', file_text)\n",
    "    pattern = re.compile(r\"^\\w*[-']?\\w*$\")\n",
    "    better_tokens = list(filter(pattern.match, text.split(\" \")))\n",
    "    token_list = [ele.lower() for ele in better_tokens if len(ele) > limit]\n",
    "    return Counter(token_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44030fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error\n"
     ]
    }
   ],
   "source": [
    "path_to_bbc = \"F:\\\\IR Lab\\\\bbc\\\\\"\n",
    "\n",
    "token_freq = {}\n",
    "file = 0\n",
    "subfolder_names = os.listdir(path_to_bbc)\n",
    "for subfolder in subfolder_names:\n",
    "    if os.path.isdir(path_to_bbc + subfolder):\n",
    "        textfiles = os.listdir(path_to_bbc + subfolder +\"\\\\\")\n",
    "        for data in textfiles:\n",
    "            try:\n",
    "                file_text = open(path_to_bbc + subfolder + \"\\\\\" + data,\"r\",encoding=\"utf-8\").read()\n",
    "                token_freq[file] = tokenize(file_text,3)\n",
    "                file = file + 1\n",
    "            except:\n",
    "                print(\"error\")\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f577f96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc20Max(freq):\n",
    "    doc_freq = {}\n",
    "    for key, val in freq.items():\n",
    "        for k,v in val.items():\n",
    "            if(k == user_token):\n",
    "                doc_freq[key] = v\n",
    "\n",
    "    sorted_freq = sorted(doc_freq.items(), key=lambda x:x[1], reverse=True)\n",
    "    sorted_freq = dict(sorted_freq)\n",
    "    # print(sorted_freq)\n",
    "\n",
    "    docs = list(sorted_freq.keys())\n",
    "    tfreq = list(sorted_freq.values())\n",
    "    if(len(sorted_freq) > 20):\n",
    "        docs = docs[1:21]\n",
    "        tfreq = tfreq[1:21]\n",
    "    \n",
    "    return docs , tfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3d048f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter token to graph : press\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_token = input(\"Enter token to graph : \")\n",
    "user_token = user_token.lower()\n",
    "docs , tfreq = calc20Max(token_freq)\n",
    "import seaborn as sns\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "sns.barplot(x = docs, y = tfreq, color ='maroon',width = 0.4)\n",
    "fig.autofmt_xdate()\n",
    "plt.xlabel(\"Documents\")\n",
    "plt.ylabel(\"Token Frequency\")\n",
    "plt.title(\"max Token Frequency in Documents\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "196f4ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot([len(ele) for ele in bbc_vocab])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}