docword_nips_apriori.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F= 0.8\n",
      "K= 10\n",
      "Runtime is :3.256950855255127 seconds\n",
      "The Frequent sets are:\n",
      "[{'abstract'}, {'function'}, {'information'}, {'input'}, {'introduction'}, {'model'}, {'network'}, {'neural'}, {'number'}, {'problem'}, {'references'}, {'result'}, {'set'}, {'system'}]\n",
      "F= 0.7\n",
      "K= 10\n",
      "Runtime is :2.4340262413024902 seconds\n",
      "The Frequent sets are:\n",
      "[{'abstract'}, {'algorithm'}, {'case'}, {'data'}, {'form'}, {'function'}, {'information'}, {'input'}, {'introduction'}, {'large'}, {'learning'}, {'model'}, {'network'}, {'neural'}, {'number'}, {'order'}, {'output'}, {'paper'}, {'parameter'}, {'point'}, {'problem'}, {'processing'}, {'references'}, {'result'}, {'set'}, {'system'}, {'values'}]\n",
      "F= 0.6\n",
      "K= 10\n",
      "Runtime is :2.482834815979004 seconds\n",
      "The Frequent sets are:\n",
      "[{'abstract'}, {'algorithm'}, {'analysis'}, {'approach'}, {'case'}, {'computation'}, {'data'}, {'error'}, {'form'}, {'function'}, {'general'}, {'information'}, {'input'}, {'introduction'}, {'large'}, {'learning'}, {'linear'}, {'mean'}, {'method'}, {'model'}, {'network'}, {'neural'}, {'non'}, {'number'}, {'order'}, {'output'}, {'paper'}, {'parameter'}, {'performance'}, {'point'}, {'problem'}, {'processing'}, {'references'}, {'research'}, {'result'}, {'science'}, {'set'}, {'similar'}, {'simple'}, {'single'}, {'small'}, {'space'}, {'system'}, {'term'}, {'tion'}, {'training'}, {'unit'}, {'university'}, {'values'}, {'weight'}]\n",
      "F= 0.5\n",
      "K= 10\n",
      "Runtime is :3.269059658050537 seconds\n",
      "The Frequent sets are:\n",
      "[{'abstract'}, {'algorithm'}, {'allow'}, {'analysis'}, {'application'}, {'applied'}, {'approach'}, {'average'}, {'case'}, {'computation'}, {'computer'}, {'conclusion'}, {'consider'}, {'constant'}, {'current'}, {'data'}, {'defined'}, {'discussion'}, {'distribution'}, {'due'}, {'effect'}, {'equation'}, {'error'}, {'experiment'}, {'find'}, {'fixed'}, {'form'}, {'found'}, {'function'}, {'general'}, {'high'}, {'important'}, {'information'}, {'input'}, {'introduction'}, {'known'}, {'large'}, {'learning'}, {'level'}, {'linear'}, {'local'}, {'mean'}, {'method'}, {'model'}, {'network'}, {'neural'}, {'non'}, {'note'}, {'number'}, {'obtained'}, {'order'}, {'output'}, {'paper'}, {'parameter'}, {'part'}, {'pattern'}, {'performance'}, {'point'}, {'present'}, {'presented'}, {'press'}, {'probability'}, {'problem'}, {'process'}, {'processing'}, {'random'}, {'rate'}, {'real'}, {'references'}, {'represent'}, {'research'}, {'result'}, {'science'}, {'section'}, {'set'}, {'similar'}, {'simple'}, {'single'}, {'size'}, {'small'}, {'space'}, {'standard'}, {'step'}, {'system'}, {'task'}, {'term'}, {'test'}, {'theory'}, {'tion'}, {'training'}, {'unit'}, {'university'}, {'values'}, {'vector'}, {'weight'}, {'zero'}]\n",
      "F= 0.4\n",
      "K= 10\n",
      "Runtime is :6.692520380020142 seconds\n",
      "The Frequent sets are:\n",
      "[{'able'}, {'abstract'}, {'according'}, {'acknowledgement'}, {'algorithm'}, {'allow'}, {'analysis'}, {'application'}, {'applied'}, {'approach'}, {'approximation'}, {'architecture'}, {'assume'}, {'average'}, {'called'}, {'case'}, {'cases'}, {'chosen'}, {'compared'}, {'complex'}, {'component'}, {'computation'}, {'computational'}, {'computer'}, {'conclusion'}, {'condition'}, {'consider'}, {'consist'}, {'constant'}, {'control'}, {'correspond'}, {'current'}, {'data'}, {'defined'}, {'department'}, {'depend'}, {'determined'}, {'difference'}, {'dimensional'}, {'discussion'}, {'distribution'}, {'due'}, {'dynamic'}, {'effect'}, {'equation'}, {'error'}, {'examples'}, {'experiment'}, {'fact'}, {'find'}, {'fixed'}, {'follow'}, {'form'}, {'found'}, {'function'}, {'general'}, {'generated'}, {'hidden'}, {'high'}, {'higher'}, {'ieee'}, {'important'}, {'independent'}, {'information'}, {'ing'}, {'initial'}, {'input'}, {'introduction'}, {'known'}, {'large'}, {'layer'}, {'lead'}, {'learn'}, {'learning'}, {'left'}, {'level'}, {'line'}, {'linear'}, {'local'}, {'low'}, {'lower'}, {'maximum'}, {'mean'}, {'method'}, {'mit'}, {'model'}, {'need'}, {'net'}, {'network'}, {'neural'}, {'noise'}, {'non'}, {'note'}, {'number'}, {'obtained'}, {'optimal'}, {'order'}, {'output'}, {'paper'}, {'parameter'}, {'part'}, {'pattern'}, {'perform'}, {'performance'}, {'performed'}, {'point'}, {'present'}, {'presented'}, {'press'}, {'previous'}, {'probability'}, {'problem'}, {'process'}, {'processing'}, {'properties'}, {'provide'}, {'random'}, {'range'}, {'rate'}, {'real'}, {'references'}, {'report'}, {'represent'}, {'representation'}, {'required'}, {'research'}, {'result'}, {'resulting'}, {'science'}, {'section'}, {'set'}, {'signal'}, {'similar'}, {'simple'}, {'simulation'}, {'single'}, {'size'}, {'small'}, {'solution'}, {'space'}, {'standard'}, {'step'}, {'structure'}, {'sum'}, {'system'}, {'task'}, {'term'}, {'test'}, {'theory'}, {'tion'}, {'trained'}, {'training'}, {'unit'}, {'university'}, {'values'}, {'vector'}, {'weight'}, {'zero'}, {'abstract', 'according'}, {'abstract', 'learn'}]\n",
      "F= 0.3\n",
      "K= 10\n",
      "Runtime is :14.539158582687378 seconds\n",
      "The Frequent sets are:\n",
      "[{'able'}, {'abstract'}, {'according'}, {'achieved'}, {'acknowledgement'}, {'adaptive'}, {'addition'}, {'additional'}, {'advances'}, {'algorithm'}, {'allow'}, {'alway'}, {'amount'}, {'analysis'}, {'application'}, {'applied'}, {'approach'}, {'approaches'}, {'approximation'}, {'architecture'}, {'artificial'}, {'assume'}, {'assumption'}, {'average'}, {'basic'}, {'basis'}, {'behavior'}, {'called'}, {'cambridge'}, {'case'}, {'cases'}, {'center'}, {'change'}, {'choice'}, {'chosen'}, {'class'}, {'classification'}, {'close'}, {'com'}, {'combination'}, {'common'}, {'compared'}, {'comparison'}, {'complete'}, {'complex'}, {'complexity'}, {'component'}, {'computation'}, {'computational'}, {'compute'}, {'computed'}, {'computer'}, {'computing'}, {'con'}, {'conclusion'}, {'condition'}, {'conference'}, {'connection'}, {'consider'}, {'considered'}, {'consist'}, {'constant'}, {'constraint'}, {'continuous'}, {'control'}, {'correct'}, {'correspond'}, {'current'}, {'data'}, {'define'}, {'defined'}, {'department'}, {'depend'}, {'derived'}, {'describe'}, {'detail'}, {'determine'}, {'determined'}, {'developed'}, {'difference'}, {'difficult'}, {'dimension'}, {'dimensional'}, {'direction'}, {'directly'}, {'discussion'}, {'distance'}, {'distributed'}, {'distribution'}, {'due'}, {'dynamic'}, {'easily'}, {'edu'}, {'effect'}, {'efficient'}, {'element'}, {'equal'}, {'equation'}, {'equivalent'}, {'error'}, {'estimate'}, {'examples'}, {'exist'}, {'expected'}, {'experiment'}, {'experimental'}, {'fact'}, {'factor'}, {'feature'}, {'features'}, {'field'}, {'final'}, {'finally'}, {'find'}, {'finding'}, {'fixed'}, {'follow'}, {'form'}, {'found'}, {'full'}, {'function'}, {'gaussian'}, {'general'}, {'generalization'}, {'generated'}, {'give'}, {'goal'}, {'gradient'}, {'grant'}, {'hand'}, {'hidden'}, {'high'}, {'higher'}, {'ieee'}, {'implementation'}, {'important'}, {'including'}, {'increase'}, {'independent'}, {'individual'}, {'information'}, {'ing'}, {'initial'}, {'input'}, {'institute'}, {'interesting'}, {'introduction'}, {'knowledge'}, {'known'}, {'large'}, {'larger'}, {'layer'}, {'lead'}, {'learn'}, {'learning'}, {'left'}, {'level'}, {'limited'}, {'line'}, {'linear'}, {'local'}, {'log'}, {'low'}, {'lower'}, {'machine'}, {'main'}, {'makes'}, {'map'}, {'matrix'}, {'maximum'}, {'mean'}, {'measure'}, {'method'}, {'minimum'}, {'mit'}, {'model'}, {'morgan'}, {'multiple'}, {'natural'}, {'nature'}, {'need'}, {'negative'}, {'net'}, {'network'}, {'neural'}, {'neuron'}, {'noise'}, {'non'}, {'note'}, {'number'}, {'observed'}, {'obtain'}, {'obtained'}, {'occur'}, {'optimal'}, {'order'}, {'original'}, {'output'}, {'pair'}, {'paper'}, {'parallel'}, {'parameter'}, {'part'}, {'pattern'}, {'perform'}, {'performance'}, {'performed'}, {'point'}, {'position'}, {'positive'}, {'prediction'}, {'present'}, {'presented'}, {'press'}, {'previous'}, {'probability'}, {'problem'}, {'proc'}, {'procedure'}, {'proceeding'}, {'process'}, {'processing'}, {'produce'}, {'properties'}, {'proposed'}, {'provide'}, {'provided'}, {'random'}, {'range'}, {'rate'}, {'real'}, {'recognition'}, {'references'}, {'region'}, {'related'}, {'relative'}, {'report'}, {'represent'}, {'representation'}, {'represented'}, {'require'}, {'required'}, {'requires'}, {'research'}, {'respect'}, {'response'}, {'result'}, {'resulting'}, {'rule'}, {'run'}, {'sample'}, {'san'}, {'scale'}, {'science'}, {'section'}, {'seen'}, {'sequence'}, {'set'}, {'signal'}, {'significant'}, {'similar'}, {'simple'}, {'simply'}, {'simulation'}, {'single'}, {'size'}, {'small'}, {'solution'}, {'space'}, {'specific'}, {'standard'}, {'states'}, {'statistical'}, {'step'}, {'structure'}, {'study'}, {'suggest'}, {'sum'}, {'supported'}, {'system'}, {'table'}, {'target'}, {'task'}, {'technical'}, {'technique'}, {'techniques'}, {'term'}, {'test'}, {'tested'}, {'thank'}, {'theory'}, {'threshold'}, {'times'}, {'tion'}, {'total'}, {'train'}, {'trained'}, {'training'}, {'true'}, {'type'}, {'unit'}, {'university'}, {'values'}, {'variable'}, {'variables'}, {'vector'}, {'version'}, {'weight'}, {'yield'}, {'zero'}, {'abstract', 'able'}, {'abstract', 'according'}, {'achieved', 'abstract'}, {'considered', 'abstract'}, {'detail', 'abstract'}, {'dimensional', 'abstract'}, {'abstract', 'equal'}, {'abstract', 'learn'}, {'order', 'abstract'}, {'space', 'abstract'}]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F= 0.2\n",
      "K= 10\n",
      "Runtime is :49.95220422744751 seconds\n",
      "The Frequent sets are:\n",
      "[{'ability'}, {'able'}, {'abstract'}, {'according'}, {'account'}, {'accuracy'}, {'accurate'}, {'achieve'}, {'achieved'}, {'acknowledgement'}, {'action'}, {'activation'}, {'active'}, {'activity'}, {'actual'}, {'actually'}, {'adaptive'}, {'added'}, {'addition'}, {'additional'}, {'advances'}, {'advantage'}, {'algorithm'}, {'allow'}, {'alternative'}, {'alway'}, {'amount'}, {'analysis'}, {'application'}, {'applied'}, {'apply'}, {'applying'}, {'approach'}, {'approaches'}, {'approximate'}, {'approximately'}, {'approximation'}, {'arbitrary'}, {'architecture'}, {'area'}, {'artificial'}, {'aspect'}, {'assume'}, {'assumed'}, {'assuming'}, {'assumption'}, {'attempt'}, {'author'}, {'average'}, {'avoid'}, {'basic'}, {'basis'}, {'behavior'}, {'bias'}, {'binary'}, {'biological'}, {'bottom'}, {'bound'}, {'brain'}, {'calculated'}, {'california'}, {'call'}, {'called'}, {'cambridge'}, {'case'}, {'cases'}, {'cell'}, {'center'}, {'change'}, {'characteristic'}, {'choice'}, {'choose'}, {'chosen'}, {'class'}, {'classes'}, {'classification'}, {'clear'}, {'clearly'}, {'close'}, {'coefficient'}, {'cognitive'}, {'com'}, {'combination'}, {'combined'}, {'common'}, {'compare'}, {'compared'}, {'comparison'}, {'complete'}, {'complex'}, {'complexity'}, {'component'}, {'computation'}, {'computational'}, {'compute'}, {'computed'}, {'computer'}, {'computing'}, {'con'}, {'conclusion'}, {'condition'}, {'conference'}, {'connected'}, {'connection'}, {'consider'}, {'considered'}, {'consist'}, {'consistent'}, {'constant'}, {'constraint'}, {'construct'}, {'constructed'}, {'context'}, {'continuous'}, {'contrast'}, {'control'}, {'convergence'}, {'correct'}, {'correlation'}, {'correspond'}, {'cost'}, {'course'}, {'cross'}, {'current'}, {'curve'}, {'curves'}, {'data'}, {'decision'}, {'define'}, {'defined'}, {'definition'}, {'degree'}, {'demonstrate'}, {'demonstrated'}, {'denote'}, {'denotes'}, {'density'}, {'department'}, {'depend'}, {'dependent'}, {'depending'}, {'derived'}, {'descent'}, {'describe'}, {'describes'}, {'description'}, {'design'}, {'desired'}, {'detail'}, {'determine'}, {'determined'}, {'develop'}, {'developed'}, {'development'}, {'deviation'}, {'differ'}, {'difference'}, {'differences'}, {'difficult'}, {'dimension'}, {'dimensional'}, {'direct'}, {'direction'}, {'directly'}, {'discrete'}, {'discussed'}, {'discussion'}, {'distance'}, {'distributed'}, {'distribution'}, {'domain'}, {'due'}, {'dynamic'}, {'easily'}, {'editor'}, {'edu'}, {'effect'}, {'effective'}, {'efficient'}, {'element'}, {'end'}, {'engineering'}, {'entire'}, {'environment'}, {'equal'}, {'equation'}, {'equivalent'}, {'error'}, {'estimate'}, {'estimated'}, {'estimates'}, {'estimation'}, {'exactly'}, {'examples'}, {'exist'}, {'exp'}, {'expected'}, {'experiment'}, {'experimental'}, {'extended'}, {'extension'}, {'fact'}, {'factor'}, {'fast'}, {'feature'}, {'features'}, {'field'}, {'fig'}, {'final'}, {'finally'}, {'find'}, {'finding'}, {'finite'}, {'fit'}, {'fixed'}, {'follow'}, {'form'}, {'forward'}, {'found'}, {'foundation'}, {'framework'}, {'free'}, {'frequency'}, {'full'}, {'fully'}, {'function'}, {'functional'}, {'future'}, {'gain'}, {'gaussian'}, {'general'}, {'generalization'}, {'generally'}, {'generate'}, {'generated'}, {'give'}, {'global'}, {'goal'}, {'gradient'}, {'grant'}, {'greater'}, {'group'}, {'hand'}, {'hidden'}, {'high'}, {'higher'}, {'highly'}, {'hinton'}, {'hold'}, {'human'}, {'idea'}, {'identical'}, {'ieee'}, {'image'}, {'images'}, {'implement'}, {'implementation'}, {'implemented'}, {'important'}, {'improve'}, {'improved'}, {'improvement'}, {'include'}, {'including'}, {'increase'}, {'increased'}, {'increases'}, {'increasing'}, {'independent'}, {'independently'}, {'individual'}, {'information'}, {'ing'}, {'initial'}, {'input'}, {'instance'}, {'institute'}, {'interaction'}, {'interest'}, {'interesting'}, {'internal'}, {'international'}, {'interval'}, {'introduce'}, {'introduced'}, {'introduction'}, {'involves'}, {'iteration'}, {'john'}, {'joint'}, {'journal'}, {'kaufmann'}, {'knowledge'}, {'known'}, {'large'}, {'larger'}, {'layer'}, {'lead'}, {'learn'}, {'learned'}, {'learning'}, {'left'}, {'length'}, {'level'}, {'likelihood'}, {'likely'}, {'limit'}, {'limited'}, {'line'}, {'linear'}, {'lines'}, {'local'}, {'location'}, {'log'}, {'low'}, {'lower'}, {'machine'}, {'magnitude'}, {'main'}, {'makes'}, {'making'}, {'manner'}, {'map'}, {'mapping'}, {'mateo'}, {'matrix'}, {'maximum'}, {'mean'}, {'measure'}, {'measured'}, {'mechanism'}, {'memory'}, {'method'}, {'minimize'}, {'minimum'}, {'mit'}, {'model'}, {'modeling'}, {'modified'}, {'morgan'}, {'multi'}, {'multiple'}, {'national'}, {'natural'}, {'nature'}, {'need'}, {'needed'}, {'negative'}, {'net'}, {'network'}, {'neural'}, {'neuron'}, {'noise'}, {'non'}, {'nonlinear'}, {'normal'}, {'normalized'}, {'note'}, {'number'}, {'object'}, {'observation'}, {'observed'}, {'obtain'}, {'obtained'}, {'occur'}, {'operation'}, {'optimal'}, {'optimization'}, {'order'}, {'original'}, {'output'}, {'pages'}, {'pair'}, {'paper'}, {'parallel'}, {'parameter'}, {'part'}, {'pattern'}, {'perform'}, {'performance'}, {'performed'}, {'phase'}, {'plot'}, {'point'}, {'position'}, {'positive'}, {'potential'}, {'power'}, {'practical'}, {'practice'}, {'pre'}, {'predict'}, {'prediction'}, {'present'}, {'presented'}, {'press'}, {'previous'}, {'previously'}, {'principle'}, {'prior'}, {'pro'}, {'probabilities'}, {'probability'}, {'problem'}, {'proc'}, {'procedure'}, {'proceeding'}, {'process'}, {'processes'}, {'processing'}, {'produce'}, {'produced'}, {'produces'}, {'product'}, {'propagation'}, {'properties'}, {'property'}, {'propose'}, {'proposed'}, {'provide'}, {'provided'}, {'question'}, {'random'}, {'randomly'}, {'range'}, {'rate'}, {'rates'}, {'real'}, {'reason'}, {'recent'}, {'recently'}, {'recognition'}, {'reduce'}, {'reduced'}, {'references'}, {'region'}, {'related'}, {'relation'}, {'relationship'}, {'relative'}, {'relevant'}, {'remain'}, {'report'}, {'reported'}, {'represent'}, {'representation'}, {'represented'}, {'representing'}, {'require'}, {'required'}, {'requires'}, {'research'}, {'respect'}, {'response'}, {'result'}, {'resulting'}, {'role'}, {'rule'}, {'run'}, {'sample'}, {'samples'}, {'san'}, {'scale'}, {'scheme'}, {'science'}, {'search'}, {'section'}, {'seen'}, {'selected'}, {'selection'}, {'sense'}, {'separate'}, {'sequence'}, {'series'}, {'set'}, {'setting'}, {'short'}, {'signal'}, {'significant'}, {'significantly'}, {'similar'}, {'simple'}, {'simply'}, {'simulated'}, {'simulation'}, {'single'}, {'situation'}, {'size'}, {'slightly'}, {'small'}, {'smaller'}, {'solution'}, {'solve'}, {'space'}, {'spatial'}, {'special'}, {'specific'}, {'speech'}, {'speed'}, {'springer'}, {'square'}, {'standard'}, {'start'}, {'starting'}, {'states'}, {'statistic'}, {'statistical'}, {'step'}, {'stochastic'}, {'structure'}, {'studied'}, {'studies'}, {'study'}, {'subset'}, {'sufficient'}, {'suggest'}, {'sum'}, {'support'}, {'supported'}, {'system'}, {'table'}, {'takes'}, {'taking'}, {'target'}, {'task'}, {'technical'}, {'technique'}, {'techniques'}, {'technology'}, {'temporal'}, {'term'}, {'test'}, {'tested'}, {'testing'}, {'thank'}, {'theoretical'}, {'theory'}, {'threshold'}, {'times'}, {'tion'}, {'top'}, {'total'}, {'touretzky'}, {'train'}, {'trained'}, {'training'}, {'tran'}, {'transformation'}, {'transition'}, {'true'}, {'turn'}, {'type'}, {'types'}, {'typical'}, {'typically'}, {'underlying'}, {'uniform'}, {'unit'}, {'university'}, {'unknown'}, {'update'}, {'upper'}, {'values'}, {'variable'}, {'variables'}, {'variance'}, {'variation'}, {'variety'}, {'varying'}, {'vector'}, {'version'}, {'view'}, {'vision'}, {'visual'}, {'vol'}, {'volume'}, {'weight'}, {'weighted'}, {'wide'}, {'word'}, {'world'}, {'yield'}, {'york'}, {'zero'}, {'abstract', 'ability'}, {'abstract', 'able'}, {'applying', 'able'}, {'considered', 'able'}, {'editor', 'able'}, {'springer', 'able'}, {'abstract', 'according'}, {'account', 'abstract'}, {'accuracy', 'abstract'}, {'abstract', 'achieve'}, {'achieved', 'abstract'}, {'abstract', 'actual'}, {'application', 'abstract'}, {'applying', 'abstract'}, {'abstract', 'assumed'}, {'abstract', 'california'}, {'considered', 'abstract'}, {'abstract', 'constant'}, {'abstract', 'continuous'}, {'define', 'abstract'}, {'abstract', 'demonstrated'}, {'abstract', 'descent'}, {'detail', 'abstract'}, {'abstract', 'determined'}, {'dimensional', 'abstract'}, {'editor', 'abstract'}, {'environment', 'abstract'}, {'abstract', 'equal'}, {'abstract', 'fast'}, {'implementation', 'abstract'}, {'abstract', 'implemented'}, {'include', 'abstract'}, {'layer', 'abstract'}, {'abstract', 'learn'}, {'likely', 'abstract'}, {'magnitude', 'abstract'}, {'nature', 'abstract'}, {'abstract', 'observation'}, {'order', 'abstract'}, {'pages', 'abstract'}, {'position', 'abstract'}, {'practice', 'abstract'}, {'propagation', 'abstract'}, {'abstract', 'section'}, {'space', 'abstract'}, {'springer', 'abstract'}, {'applying', 'according'}, {'considered', 'according'}, {'editor', 'according'}, {'springer', 'according'}, {'applying', 'achieved'}, {'applying', 'assumed'}, {'applying', 'california'}, {'considered', 'applying'}, {'applying', 'descent'}, {'detail', 'applying'}, {'dimensional', 'applying'}, {'editor', 'applying'}, {'environment', 'applying'}, {'applying', 'equal'}, {'applying', 'fast'}, {'applying', 'layer'}, {'applying', 'learn'}, {'nature', 'applying'}, {'applying', 'observation'}, {'order', 'applying'}, {'pages', 'applying'}, {'practice', 'applying'}, {'propagation', 'applying'}, {'space', 'applying'}, {'springer', 'applying'}, {'considered', 'assumed'}, {'considered', 'detail'}, {'considered', 'editor'}, {'considered', 'environment'}, {'considered', 'learn'}, {'considered', 'observation'}, {'considered', 'propagation'}, {'springer', 'considered'}, {'detail', 'editor'}, {'springer', 'detail'}, {'dimensional', 'editor'}, {'environment', 'editor'}, {'editor', 'fast'}, {'editor', 'learn'}, {'editor', 'observation'}, {'propagation', 'editor'}, {'space', 'editor'}, {'springer', 'editor'}, {'springer', 'environment'}, {'springer', 'fast'}, {'springer', 'learn'}, {'springer', 'propagation'}, {'applying', 'able', 'abstract'}, {'considered', 'abstract', 'able'}, {'editor', 'able', 'abstract'}, {'springer', 'abstract', 'able'}, {'applying', 'according', 'abstract'}, {'considered', 'abstract', 'according'}, {'editor', 'according', 'abstract'}, {'springer', 'abstract', 'according'}, {'applying', 'achieved', 'abstract'}, {'applying', 'assumed', 'abstract'}, {'applying', 'abstract', 'california'}, {'considered', 'applying', 'abstract'}, {'applying', 'abstract', 'descent'}, {'detail', 'applying', 'abstract'}, {'dimensional', 'applying', 'abstract'}, {'editor', 'applying', 'abstract'}, {'environment', 'applying', 'abstract'}, {'applying', 'equal', 'abstract'}, {'applying', 'abstract', 'fast'}, {'applying', 'abstract', 'layer'}, {'applying', 'abstract', 'learn'}, {'applying', 'abstract', 'observation'}, {'order', 'applying', 'abstract'}, {'pages', 'applying', 'abstract'}, {'practice', 'applying', 'abstract'}, {'propagation', 'applying', 'abstract'}, {'space', 'applying', 'abstract'}, {'springer', 'applying', 'abstract'}, {'considered', 'abstract', 'assumed'}, {'considered', 'abstract', 'detail'}, {'considered', 'editor', 'abstract'}, {'considered', 'environment', 'abstract'}, {'considered', 'abstract', 'learn'}, {'considered', 'abstract', 'observation'}, {'considered', 'propagation', 'abstract'}, {'springer', 'considered', 'abstract'}, {'detail', 'editor', 'abstract'}, {'springer', 'abstract', 'detail'}, {'dimensional', 'editor', 'abstract'}, {'environment', 'editor', 'abstract'}, {'editor', 'abstract', 'fast'}, {'editor', 'abstract', 'learn'}, {'editor', 'abstract', 'observation'}, {'propagation', 'editor', 'abstract'}, {'space', 'editor', 'abstract'}, {'springer', 'editor', 'abstract'}, {'springer', 'environment', 'abstract'}, {'springer', 'abstract', 'fast'}, {'springer', 'abstract', 'learn'}, {'springer', 'propagation', 'abstract'}]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "input_file=\"docword.\"+\"nips\"+\".txt\"\n",
    "data =pd.read_csv(input_file,\n",
    "                  skiprows=3,\n",
    "                  header=None,sep= \" \",\n",
    "                  names=[\"docId\",\"wordId\",\"count\"])\n",
    "\n",
    "\n",
    "data_clean=data.pivot(index = \"docId\",\n",
    "           columns = \"wordId\",\n",
    "           values = \"count\")\n",
    "\n",
    "\n",
    "\n",
    "data_clean=data_clean.fillna(0)\n",
    "\n",
    "\n",
    "\n",
    "#data_clean=data.groupby([\"docId\",\"wordId\"])[\"count\"].sum().unstack().reset_index().fillna(0).set_index(\"docId\")\n",
    "data_clean[data_clean>0]=1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#set of all transactions\n",
    "Transactions=[]\n",
    "for id in data_clean.index:\n",
    "    ls=np.flatnonzero(data_clean.loc[id]).tolist()\n",
    "    ls1=[item+1 for item in ls]\n",
    "    ls2=set(ls1)\n",
    "    Transactions.append(ls2)\n",
    "\n",
    "#candidate-gen function\n",
    "def candidate_gen(f):\n",
    "    c_set=[]\n",
    "    c=set()\n",
    "    for f1 in f:\n",
    "        for f2 in f :\n",
    "            if (((f2.difference(f1)).union(f1.difference(f2))=={max(f1),max(f2)})&(max(f1)<max(f2))):\n",
    "                c =f1.union({max(f2)})\n",
    "                c_set.append(c)\n",
    "               \n",
    "                for i in c:\n",
    "                    if c.difference({i}) not in f:\n",
    "                        \n",
    "                        if (c_set != []) & (c in c_set):\n",
    "                           \n",
    "                            c_set.remove(c)\n",
    "    \n",
    "    return c_set\n",
    "\n",
    "#counts the frequency of a candidate set\n",
    "def support_count_of_sets(input_set):\n",
    "    count=0\n",
    "    for t in Transactions:\n",
    "        if input_set.issubset(t):\n",
    "            count =count+1\n",
    "    return count\n",
    "#converts the frequent itemset list to words vocubaliry file\n",
    "def to_word_conversion(word_index):\n",
    "    \n",
    "    vocabulary_file=open(\"vocab.\"+\"nips\"+\".txt\",\"r\")\n",
    "    vocabulary=vocabulary_file.read().split(\"\\n\")\n",
    "    frequent_set_words=[]\n",
    "    for frequent_set in word_index:\n",
    "        temp_word_set=set()\n",
    "        for index in frequent_set:\n",
    "            temp_word_set.add(vocabulary[index-1])\n",
    "        frequent_set_words.append(temp_word_set)\n",
    "    return frequent_set_words\n",
    "            \n",
    "\n",
    "# apriori function\n",
    "def apriori(data_clean,min_sup,k):\n",
    "    global F                           # defining global variabe as to be used in output_file function\n",
    "    global K\n",
    "    global total_time\n",
    "    global final_word_list\n",
    "    global final_f\n",
    "    c1=[]\n",
    "    f=[]\n",
    "    no_of_transactions=max(data_clean.index)\n",
    "    start_time=time.time()\n",
    "    for item in data_clean.columns:\n",
    "        c1.append(set([item]))\n",
    "        \n",
    "    for item in data_clean.columns:\n",
    "        if ((data_clean[item].sum())/no_of_transactions)>=min_sup:\n",
    "            f.append(set([item]))\n",
    "    final_f=[]\n",
    "    final_f=final_f+f\n",
    "   \n",
    "    \n",
    "    for i in range(2,k+1):\n",
    "        \n",
    "        c_set=candidate_gen(f)\n",
    "        \n",
    "        temp=len(f)\n",
    "        for item in c_set:\n",
    "            \n",
    "            if (support_count_of_sets(item) /no_of_transactions) >= min_sup:\n",
    "                f.append(item)                           #  now f contains k-1,k length frequent item sets\n",
    "                             \n",
    "        f=f[temp:]                                       # this removes k-1 length frequent item sets from f\n",
    "        final_f=final_f+f                                # appends all k length frequent items to f_final\n",
    "    end_time  =time.time() \n",
    "    total_time=end_time-start_time\n",
    "    F=min_sup\n",
    "    K=k\n",
    "    final_word_list=to_word_conversion(final_f)\n",
    "    \n",
    "    return final_f\n",
    "\n",
    "\n",
    "def output_file_apriori(min_sup,k):\n",
    "    apriori(data_clean,min_sup,k)\n",
    "    output_file=open(\"output_nips.txt\",\"a+\")\n",
    "    output=\"\"\n",
    "    output=output+\"\\n\\n\"+\"F=\"+str(F)+\"\\n\\n\"+\"K=\"+str(K)+\"\\n\\n\"+\"Runtime is :\"+str(total_time)+\" seconds\"+\"\\n\\n\"\n",
    "    output=output+\"The Frequent sets are:\"+\"\\n\\n\"+\"Word indices:\"+\"\\n\\n\"\n",
    "    output_file.write(output)\n",
    "    output_file.write(str(final_f))\n",
    "    output_file.write(\"\\n\\n\"+\"words:\"+\"\\n\\n\")\n",
    "    output_file.write(str(final_word_list))\n",
    "    print(\"F=\",min_sup)\n",
    "    print(\"K=\",k)\n",
    "    print(\"Runtime is :\"+str(total_time)+\" seconds\")\n",
    "    print(\"The Frequent sets are:\")\n",
    "    print(final_word_list)\n",
    "#############################################################################\n",
    "\n",
    "#Give input as minimum support and k,k being the maxmimum length frequent itemset\n",
    "\n",
    "output_file_apriori(0.8,10)\n",
    "\n",
    "output_file_apriori(0.7,10)\n",
    "\n",
    "output_file_apriori(0.6,10)\n",
    "\n",
    "output_file_apriori(0.5,10)\n",
    "\n",
    "output_file_apriori(0.4,10)\n",
    "\n",
    "output_file_apriori(0.3,10)\n",
    "\n",
    "output_file_apriori(0.2,10)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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