pythonDetails.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class pdfToText:\n",
    "    def __init__(self,fileName):\n",
    "        self.fileName=fileName\n",
    "        #holds regrexed text\n",
    "        self.text=\"\"\n",
    "        #holds string of pdf data directly\n",
    "        self.rawtext=\"\"\n",
    "        self.refinedText=\"\"\n",
    "        self.refinedWordWholeList=[]\n",
    "        self.lineList=[]\n",
    "        self.wordListEachLine=[]\n",
    "        self.totalWordList=[]\n",
    "        self.tagList=[]\n",
    "    def convertPDFtoText(self):\n",
    "        import PyPDF2\n",
    "        pdfFileObj = open(self.fileName,'rb')     #'rb' for read binary mode\n",
    "        pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\n",
    "        noOfPages=pdfReader.numPages\n",
    "        for pages in range(0,pdfReader.numPages):\n",
    "            pageObj = pdfReader.getPage(pages)          #'9' is the page number\n",
    "            self.rawtext+=pageObj.extractText()\n",
    "        self.normalizeText()\n",
    "        self.sizeOfText()\n",
    "    def normalizeText(self):\n",
    "        import nltk\n",
    "        self.removeSpecialrefinedTextCharacterFromString()\n",
    "        self.tokenizeToLine()\n",
    "        self.tokenizeToWords()\n",
    "        self.tokenizeToWordsEachLine()\n",
    "        stopwords=nltk.corpus.stopwords.words('english')\n",
    "        self.refinedWordWholeList=[word for word in self.totalWordList if word not in stopwords]\n",
    "        wnl = nltk.WordNetLemmatizer()\n",
    "        self.refinedWordWholeList=[wnl.lemmatize(word) for word in self.refinedWordWholeList]\n",
    "        self.refinedText=' '.join(self.refinedWordWholeList)\n",
    "    def removeSpecialrefinedTextCharacterFromString(self):\n",
    "        import re\n",
    "        self.text+= re.sub(r'[^a-zA-Z ]',r'',self.rawtext)\n",
    "        \n",
    "    def printAllVariables(self):\n",
    "        print(\"\\nself.text:\\n\"+self.text)\n",
    "        print(\"\\nself.rawtext:\\n\"+self.rawtext)\n",
    "        print(\"\\nself.lineList:\\n\")\n",
    "        for line in self.lineList:\n",
    "            print(line)\n",
    "        print(\"\\nself.wordListEachLine:\\n\")\n",
    "        for line in self.wordListEachLine:\n",
    "            print(line)\n",
    "        print(\"\\nself.tagList:\\n\")\n",
    "        for line in self.tagList:\n",
    "            print(line)\n",
    "        print(\"\\nself.refinedWordWholeList:\\n\")\n",
    "        print(self.refinedWordWholeList)\n",
    "        print(\"\\nself.totalWordList:\\n\")\n",
    "        print(self.totalWordList)\n",
    "       \n",
    "      \n",
    "    def sizeOfText(self):\n",
    "        return len(self.text)\n",
    "    \n",
    "    def tokenizeToLine(self):\n",
    "        import nltk\n",
    "        self.lineList=nltk.sent_tokenize(self.rawtext)\n",
    "        \n",
    "    def tokenizeToWordsEachLine(self):\n",
    "        import nltk\n",
    "        for line in self.lineList:\n",
    "            self.wordListEachLine.append(nltk.word_tokenize(line))\n",
    "            \n",
    "    def tokenizeToWords(self):\n",
    "        import nltk\n",
    "        self.totalWordList=nltk.word_tokenize(self.text)\n",
    "            \n",
    "    def textVsWordGraph(self,listName=None):\n",
    "        import nltk\n",
    "        if listName is None:\n",
    "            listName=self.totalWordList\n",
    "        FDIST=nltk.FreqDist(listName)\n",
    "        FDIST.plot(10,linewidth=1.0)\n",
    "        print(\"comparing top 20 word occurance in whole text\")\n",
    "        \n",
    "   \n",
    "    def categorizePOSTag(self,listName=None):\n",
    "        import nltk\n",
    "        if listName is None:\n",
    "            listName=self.totalWordList\n",
    "        self.tagList=nltk.pos_tag(listName)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class databaseHandler:\n",
    "    def __init__(self,password=\"ST.xavier51\",databaseName=\"cvRanking\"):\n",
    "        import pymysql\n",
    "        try:\n",
    "            self.database=pymysql.connect(\"localhost\",\"root\",\"ST.xavier51\",\"cvRanking\")\n",
    "            self.cursor = self.database.cursor()\n",
    "        except Exception as e:\n",
    "            print(\"error info %s\"%e)\n",
    "    def entryTheCV(self,data_CV):\n",
    "        try:\n",
    "            dataReturned=self.checkDuplication(data_CV[1])\n",
    "            if(len(dataReturned)==0):\n",
    "                add_CV=\"\"\"insert into recordentry1\n",
    "                    (checkRecord,recordname)\n",
    "                    values(%s,%s)\"\"\"\n",
    "    #             add_CV=\"\"\"insert into recordentry\n",
    "    #                 (idrecordEntry,checkRecord,recordname)\n",
    "    #                 values(%s,%s,%s)\"\"\"\n",
    "                #data_CV=(\"yes\",\"ram\")\n",
    "                print(data_CV)\n",
    "                self.cursor.execute(add_CV,data_CV)\n",
    "                self.database.commit()\n",
    "                print(\"CV is entered\")\n",
    "                return True;\n",
    "            else:\n",
    "                print(\"CV already present\\n location at %s\"%str(dataReturned))\n",
    "                return False\n",
    "        except Exception as e:\n",
    "            print(\"in entryTheCV\\t\"+str(e))\n",
    "            return False\n",
    "    def checkDuplication(self,name_CV):\n",
    "        try:\n",
    "            check_CV=\"\"\"select* from recordentry as re where re.recordname=%s\"\"\"\n",
    "            self.cursor.execute(check_CV,name_CV)\n",
    "            result = self.cursor.fetchall()\n",
    "        except Exception as e:\n",
    "            print(\"in process checkDuplication\\t\"+str(e))\n",
    "        return result\n",
    "            \n",
    "\n",
    "        \n",
    "    def closeConnection(self):\n",
    "        self.database.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class CV_manager:\n",
    "    def __init__(self):\n",
    "        self.database=databaseHandler()\n",
    "        from sklearn.feature_extraction.text import CountVectorizer\n",
    "        self.vectorizer=CountVectorizer()\n",
    "        self.CVList=[]\n",
    "        #refined word coll\n",
    "        self.textCollectionForDM=[]\n",
    "        self.wordCollection=[]\n",
    "        self.tagCollection=[]\n",
    "        self.documentMatrix=[]\n",
    "        self.featureVectorDict={}\n",
    "        self.normalizedVectorValue=[]\n",
    "        self.fileNamesWithPath=[]\n",
    "\n",
    "    def list_CVs(self,rootPath):\n",
    "        import os\n",
    "        for root, dirs, files in os.walk(rootPath):\n",
    "            for name in files:\n",
    "                self.fileNamesWithPath.append(os.path.join(root, name))\n",
    "    def addCVFromSavedList(self):\n",
    "        for file in self.fileNamesWithPath:\n",
    "            if(self.database.entryTheCV((\"yes\",file))):\n",
    "                pdf=pdfToText(file)\n",
    "                self.CVList.append(pdf)\n",
    "    def doConversion(self):\n",
    "        for cv in self.CVList:\n",
    "            cv.convertPDFtoText();\n",
    "    def size(self):\n",
    "        return len(self.CVList)\n",
    "    def compareTotalLines(self):\n",
    "        import matplotlib.pyplot as plt\n",
    "        for cvNum,cv in enumerate(self.CVList):\n",
    "            if not cv.text:\n",
    "                #conversion not done\n",
    "                cv.convertPDFtoText()\n",
    "            #conversion handles all the tokenization tasks\n",
    "            plt.plot(cvNum+1,len(cv.lineList),'g^',linewidth=2.0)\n",
    "            plt.xlabel('CV')\n",
    "            plt.ylabel('totalLines')\n",
    "        plt.show()\n",
    "    def compareTotalWords(self):\n",
    "        import matplotlib.pyplot as plt\n",
    "        for cvNum,cv in enumerate(self.CVList):\n",
    "            if not cv.text:\n",
    "                #conversion not done\n",
    "                cv.convertPDFtoText()\n",
    "            #conversion handles all the tokenization tasks\n",
    "            plt.plot(cvNum+1,len(cv.refinedWordWholeList),'g^',linewidth=2.0)\n",
    "            plt.xlabel('CV')\n",
    "            plt.ylabel('totalLines')\n",
    "        plt.show()\n",
    "    def PopualarWords(self,number=20):\n",
    "        import nltk\n",
    "        for cvNum,cv in enumerate(self.CVList):\n",
    "            if not cv.text:\n",
    "                #conversion not done\n",
    "                cv.convertPDFtoText()\n",
    "            FDIST=nltk.FreqDist(cv.refinedWordWholeList)\n",
    "            FDIST.plot(number,linewidth=1.0)\n",
    "            print(\"comparing top %d word occurance in whole text of %d\"%(number,cvNum+1))\n",
    "    def collectAllWordsFromCVList(self):\n",
    "        for cv in self.CVList:\n",
    "            for word in cv.refinedWordWholeList:\n",
    "                self.wordCollection.append(word)\n",
    "    def frequentWordsGraph(self,wordList=None,number=20):\n",
    "        import nltk\n",
    "        if wordList is None:\n",
    "            wordList=self.wordCollection\n",
    "        FDIST=nltk.FreqDist(wordList)\n",
    "        FDIST.plot(number,linewidth=1.0)\n",
    "        print(\"comparing top %d word occurance in whole CV collection\"%(number))\n",
    "    def tagAllWordsFromCVList(self):\n",
    "        import nltk\n",
    "        if self.wordCollection is None:\n",
    "            self.collectAllWordsFromCVList()\n",
    "        self.tagCollection=(nltk.pos_tag(self.wordCollection))\n",
    "    def getRequierdPOSGraph(self,selectionList):\n",
    "        import nltk\n",
    "        NNPList=[]\n",
    "        if self.tagCollection is None:\n",
    "            self.tagAllWordsFromCVList()\n",
    "        for word in self.tagCollection:\n",
    "            if word[-1] in selectionList:\n",
    "                NNPList.append(word)\n",
    "        self.frequentWordsGraph(NNPList)\n",
    "    def convertToDocumentMatrix(self,frequency):\n",
    "        for cv in self.CVList:\n",
    "            self.textCollectionForDM.append(cv.refinedText)\n",
    "        self.documentMatrix=self.vectorizer.fit_transform(self.textCollectionForDM)\n",
    "        print (self.vectorizer.vocabulary_)\n",
    "        #print(self.vectorizer.vocabulary_)\n",
    "        #self.getFrequencyTableOfWords(frequency)\n",
    "        #self.normalizeFeatureVectors()\n",
    "            #to comment or uncomment ctrl+/\n",
    "    def getFrequencyTableOfWords(self,frequency):\n",
    "        import numpy as np\n",
    "        import json\n",
    "        import matplotlib.pyplot as plt\n",
    "        vocab=self.vectorizer.get_feature_names()\n",
    "        dist = np.sum(self.documentMatrix.toarray(), axis=0)\n",
    "        for tag, countTF in zip(vocab, dist):\n",
    "            if(countTF > frequency):\n",
    "                self.featureVectorDict.update({tag:int(countTF)})\n",
    "        print(self.featureVectorDict)\n",
    "        with open(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\documentMatrixWord.txt\",\"w\") as file:\n",
    "            json.dump(self.featureVectorDict, file)\n",
    "        file.close()\n",
    "    def normalizeFeatureVectors(self):\n",
    "        from sklearn.preprocessing import normalize\n",
    "        import numpy as np\n",
    "        vectorValue=[]\n",
    "        for key in self.featureVectorDict:\n",
    "            vectorValue.append(self.featureVectorDict[key])\n",
    "        datas = np.asarray(vectorValue, dtype=np.float)\n",
    "        datas =normalize(datas, norm='l2')\n",
    "        #converting numpy.array to list\n",
    "        for r in datas:\n",
    "            for column in r:\n",
    "                self.normalizedVectorValue.append(column)\n",
    "        print(self.normalizedVectorValue)\n",
    "                \n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Agnes-Szelag.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Alison-Haller.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Brad-Blackburn.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Bruce-Jones.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Carolyn-Carmel.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Christina-Le-Sesne.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Christopher-Mackey.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Cosmo-Nardozza.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Craig-Thacker.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Cristopher-Schafer.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Daniel-Montgomery.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\David-Cordon.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\David-Hindelang.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\David-Hunter.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\David-Moshier.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Denise-Sucik.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Destiny-Washington.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Devan-Brush.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Eric-Bronk.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Hannah-Nichols.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Jack-Siler.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Jayne-McELHENY.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Jessica-Yen.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\John-C-Clisham.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Jonathan-Eaton.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Joseph-Adrien-Munoz.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Josh-Becker.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\July-Lopez.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Karena-Taveras-Lugo.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Kevin-Kolbe.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Kristin-Dunn.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Lauren-Milewski.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Luis-Mata-R.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Marc-Steffensen.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Marcus-Bickler.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Melanie-Mikel.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Michael-Kunhenn.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Michael-Lay.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Michael-Showalter.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Michelle-Collins.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Nashona-Haldane.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Patricia-McNair.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Philip-Dirlam.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Ryan-Flynn.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Sara-Chamberlain.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Stephanie-Kemp.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Stephanie-Valderrama.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Tammy-Morse.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Timothy-Rabb.pdf')\n",
      "CV is entered\n",
      "('yes', 'C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\\\\Tracy-Jones.pdf')\n",
      "CV is entered\n"
     ]
    }
   ],
   "source": [
    "cmp=CV_manager()\n",
    "cmp.list_CVs(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\\\\creative director\")\n",
    "cmp.addCVFromSavedList()\n",
    "cmp.doConversion()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'coached': 891, 'programeducationmasters': 3942, 'secondary': 4362, 'teachinggrand': 4781, 'canyon': 727, 'masters': 3034, 'leadershipgrand': 2776, 'englishuniversity': 1776, 'colorado': 928, 'yearslinkshttpbysleighcomhttpwwwlinkedincominstephaniekemp': 5348, 'valderramacreative': 5113, 'indeedcomrstephanievalderramaeecwork': 2476, 'directorlimbitless': 1534, 'solutionsaugust': 4526, 'campaignsdigital': 715, 'eventsdirected': 1834, 'strategizing': 4645, 'gather': 2122, 'educationuniversity': 1702, 'floridaseptember': 1996, 'raised': 4061, 'crowd': 1185, 'bionic': 561, 'limb': 2823, 'childrennationally': 819, 'globally': 2149, 'bfa': 548, 'designillustratordigital': 1386, 'marketingjune': 3008, 'mondelez': 3214, 'miracle': 3178, 'whip': 5245, 'oreo': 3494, 'ritz': 4266, 'crackers': 1144, 'brandingafter': 622, 'effectswon': 1714, 'peoples': 3623, 'internship': 2607, 'americas': 181, 'photoshopmaip': 3678, 'internkeynote': 2605, 'momentum': 3213, 'worldwidelimbitless': 5303, 'solutions': 4525, 'representativewhite': 4195, 'attendee': 428, 'studies': 4671, 'limbitless': 2824, 'representativefellowmaip': 4190, 'multicultural': 3260, 'programhead': 3943, 'designerdigital': 1338, 'experiential': 1881, 'nonprofit': 3362, 'diems': 1457, 'against': 131, 'poverty': 3766, 'prophoto': 3987, 'retouchinghead': 4245, 'ucf': 5022, 'aug': 443, 'aiga': 145, 'orlandos': 3508, 'mentorship': 3117, 'educationfreelance': 1697, 'animatoramerican': 272, 'artsfebruary': 371, 'clinton': 876, 'intiative': 2617, 'universitylinkshttpsimplystephco': 5053, 'tammy': 4756, 'morseexeter': 3235, 'nhemail': 3346, 'indeedcomrtammymorseedbfadcbauthorized': 2477, 'directormorse': 1545, 'exeter': 1859, 'nh': 3344, 'strategistbroadcast': 4644, 'bcupdated': 519, 'brandguidelines': 620, 'centergenerating': 776, 'socialmedia': 4510, 'interaction': 2580, 'previous': 3851, 'massachusettsbased': 3032, 'churchills': 829, 'simon': 4467, 'diagnostics': 1451, 'nevins': 3326, 'benefitaudits': 537, 'cba': 766, 'littlefield': 2848, 'tennis': 4819, 'seacoast': 4357, 'power': 3767, 'tracey': 4934, 'miller': 3162, 'forums': 2045, 'dianakirkpatrick': 1452, 'artsart': 369, 'designercreative': 1336, 'coop': 1085, 'nhfebruary': 3347, 'fullyintegrated': 2086, 'heatingenergy': 2254, 'sectorsclients': 4365, 'pepco': 3624, 'optima': 3482, 'trust': 4990, 'merrimack': 3128, 'savings': 4322, 'millriver': 3164, 'wealth': 5206, 'managementclaremont': 2940, 'bridgescreative': 632, 'executivesean': 1855, 'portsmouth': 3747, 'stas': 4603, 'varied': 5123, 'createdmanaged': 1155, 'analyzed': 197, 'engaged': 1764, 'thought': 4873, 'oncamera': 3446, 'auditioned': 442, 'culled': 1194, 'challenged': 791, 'self': 4379, 'coordinator': 1091, 'ontime': 3465, 'efficientand': 1717, 'digitalweb': 1465, 'tvclients': 5000, 'bigelow': 553, 'tea': 4775, 'dan': 1229, 'brown': 651, 'blythe': 582, 'androscoggin': 257, 'wentworthdouglass': 5236, 'collaborations': 910, 'massachusetts': 3031, 'woodsville': 5274, 'guaranty': 2208, 'savingsbank': 4323, 'members': 3106, 'sugar': 4693, 'hill': 2285, 'episcopal': 1796, 'deadrivercompanycreative': 1250, 'designereisenberg': 1340, 'vital': 5177, 'ryze': 4294, 'manchester': 2973, 'strategist': 4643, 'hampshire': 2226, 'liquor': 2836, 'commission': 937, 'campaignthat': 718, 'profitsan': 3939, 'yearexceeding': 5331, 'translating': 4967, 'billion': 556, 'nexteight': 3342, 'taxpayersclients': 4772, 'dartmouth': 1233, 'hitchcock': 2295, 'chad': 787, 'catholic': 765, 'medicalcenter': 3094, 'elliot': 1731, 'bellwether': 532, 'newhampshire': 3328, 'lawcraig': 2763, 'deachman': 1247, 'amann': 175, 'pllc': 3727, 'blackinvestment': 569, 'wells': 5234, 'fargo': 1915, 'yankee': 5326, 'equipmentart': 1800, 'east': 1658, 'longmeadow': 2874, 'maapril': 2897, 'systemsphysicianowned': 4738, 'healthcare': 2251, 'hightech': 2284, 'healthrelated': 2253, 'delivered': 1271, 'thatraised': 4833, 'ofand': 3411, 'referral': 4132, 'tonationally': 4911, 'opportunitiesclients': 3479, 'mercy': 3126, 'weldon': 5230, 'rehabilitation': 4145, 'providence': 4000, 'behavioral': 531, 'riverbendmedical': 4269, 'hollingsworth': 2297, 'vose': 5182, 'castion': 757, 'marox': 3027, 'litron': 2846, 'curtis': 1212, 'universal': 5047, 'jointsr': 2670, 'designjanlynn': 1389, 'indian': 2486, 'orchard': 3487, 'majanuary': 2927, 'realestate': 4092, 'janlynn': 2654, 'skus': 4495, 'michaels': 3139, 'hobby': 2296, 'lobbywalmart': 2862, 'joann': 2663, 'salessupport': 4308, 'multilanguage': 3263, 'licensesthat': 2809, 'disney': 1591, 'sesame': 4407, 'cherished': 813, 'teddies': 4807, 'precious': 3780, 'momentsdesigner': 3212, 'editorpaulson': 1684, 'maoctober': 2986, 'localretailers': 2866, 'maximized': 3053, 'rather': 4077, 'renting': 4176, 'expensive': 1872, 'analog': 186, 'inline': 2535, 'balise': 489, 'motor': 3241, 'mannys': 2977, 'tv': 4999, 'appliances': 302, 'ace': 29, 'kavanaugh': 2691, 'yale': 5324, 'genton': 2131, 'mens': 3111, 'clothingeducationmfa': 883, 'writingsouthern': 5313, 'skillscreative': 4489, 'mediaintegrated': 3084, 'creaative': 1150, 'yearslinkshttptammymorsecomhttpswwwlinkedincominmorsecreativeawardsseveral': 5357, 'kudosdavey': 2724, 'telly': 4814, 'hermes': 2270, 'lamp': 2737, 'lighters': 2817, 'timothy': 4889, 'rabbatlanta': 4054, 'indeedcomrtimothyrabbceffdbfauthorized': 2478, 'zell': 5383, 'ganovember': 2116, 'presentleading': 3828, 'cocollaborated': 898, 'webmobile': 5215, 'visualization': 5173, 'develops': 1446, 'remain': 4160, 'standardof': 4594, 'excellencecontributes': 1842, 'ital': 2640, 'transforms': 4961, 'stunning': 4678, 'andengaging': 220, 'graphicscreative': 2185, 'manageruniversoul': 2970, 'circus': 836, 'gajanuary': 2103, 'internsprovided': 2609, 'productcreated': 3905, 'productionsenior': 3916, 'designerbrandmoversaugust': 1327, 'mediaapplicationsproduced': 3078, 'gamesmanaged': 2111, 'motivated': 3240, 'contentkey': 1052, 'clientstrategy': 873, 'sessionseducationbfa': 4411, 'businessthe': 682, 'atlantalinkshttptimrabbcom': 421, 'tracy': 4940, 'jonesproject': 2674, 'communicatorclark': 960, 'indeedcomrtracyjonesbaffbfbdderare': 2479, 'mind': 3166, 'acumen': 52, 'blend': 575, 'marketingresulting': 3016, 'applying': 309, 'deploys': 1298, 'corporateintent': 1108, 'learner': 2783, 'versatile': 5147, 'teamwork': 4792, 'commitment': 939, 'andindustriousness': 233, 'demanding': 1279, 'rolesintuitive': 4278, 'designentrepreneurial': 1319, 'approachcustomer': 316, 'centric': 781, 'mindsetcollaborativeresults': 3169, 'orientededucator': 3505, 'trainerteam': 4952, 'mentorrelationship': 3116, 'builderrole': 666, 'modelgoalorientedunderstands': 3202, 'assetcontributes': 390, 'othersbb': 3518, 'bcdomestic': 518, 'internationalorganizational': 2602, 'developmentconceptual': 1442, 'visionarystrong': 5166, 'orientationcomplete': 3504, 'oftechnical': 3432, 'setswilling': 4413, 'directorempath': 1514, 'consultantcohesive': 1040, 'entitiesfocused': 1790, 'educating': 1690, 'exceptional': 1846, 'clientsmarketing': 868, 'temporary': 4817, 'contractajax': 1064, 'tocco': 4898, 'magnethermic': 2914, 'corp': 1103, 'warren': 5193, 'ohjanuary': 3436, 'internationally': 2601, 'budgeted': 658, 'launched': 2760, 'organizationsdatabases': 3499, 'curate': 1199, 'install': 2554, 'revitalizing': 4256, 'employeeengagement': 1741, 'confidence': 1015, 'printingservices': 3870, 'andlayout': 239, 'buy': 683, 'ajax': 149, 'toccosister': 4899, 'companiesproduction': 963, 'artistthe': 360, 'kroger': 2723, 'comay': 930, 'expediting': 1870, 'trainingprocess': 4956, 'resulting': 4234, 'contribution': 1076, 'attaining': 424, 'circular': 834, 'multidepartment': 3261, 'dailydeadlines': 1224, 'adapt': 54, 'effectiveness': 1710, 'basedapplicationscreative': 504, 'directorpolaris': 1556, 'window': 5252, 'youngstown': 5377, 'concurrent': 1010, 'budgetof': 660, 'negotiating': 3317, 'assigned': 394, 'illustrationphoto': 2370, 'extensively': 1892, 'bbbc': 514, 'form': 2024, 'promotionalmaterials': 3972, 'introduce': 2621, 'intranetbased': 2620, 'ordering': 3493, 'marketingmaterialseducationbs': 3011, 'cum': 1198, 'laude': 2756, 'graduate': 2171, 'akron': 150, 'ohaa': 3435, 'honors': 2307, 'communicationart': 950, 'paskillsproject': 3597, 'adobephotoshop': 83, 'mailprint': 2920, 'yearslinkshttpcoroflotcomtajonesportfoliohttplinkedincomintajones': 5349}\n"
     ]
    }
   ],
   "source": [
    " cmp.convertToDocumentMatrix(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# class databaseHandler:\n",
    "#     def __init__(self,password=\"ST.xavier51\",databaseName=\"cvRanking\"):\n",
    "#         import pymysql\n",
    "#         try:\n",
    "#             self.database=pymysql.connect(\"localhost\",\"root\",\"ST.xavier51\",\"cvRanking\")\n",
    "#             self.cursor = self.database.cursor()\n",
    "#         except Exception as e:\n",
    "#             print(\"error info %s\"%e)\n",
    "#     def entryTheCV(self,data_CV):\n",
    "#         try:\n",
    "# #             dataReturned=self.checkDuplication(data_CV[1])\n",
    "# #             if(len(dataReturned)==0):\n",
    "#                 add_CV=\"\"\"insert into recordentry\n",
    "#                     (checkRecord,recordname)\n",
    "#                     values(%s,%s)\"\"\"\n",
    "#     #             add_CV=\"\"\"insert into recordentry\n",
    "#     #                 (idrecordEntry,checkRecord,recordname)\n",
    "#     #                 values(%s,%s,%s)\"\"\"\n",
    "#                 #data_CV=(\"yes\",\"ram\")\n",
    "#                 print(data_CV)\n",
    "#                 self.cursor.execute(add_CV,data_CV)\n",
    "#                 self.database.commit()\n",
    "#                 print(\"CV name is entered in database\")\n",
    "#         except Exception as e:\n",
    "#             print(\"in entryTheCV\\t\"+str(e))\n",
    "#     def checkDuplication(self,name_CV):\n",
    "#         try:\n",
    "#             check_CV=\"\"\"select* from recordentry as re where re.recordname=%s\"\"\"\n",
    "#             self.cursor.execute(check_CV,name_CV)\n",
    "#             result = self.cursor.fetchall()\n",
    "#         except Exception as e:\n",
    "#             print(\"in process checkDuplication\\t\"+str(e))\n",
    "#         if(len(result)==0):  \n",
    "#             return False\n",
    "#         else:\n",
    "#             return True  \n",
    "#     def closeConnection(self):\n",
    "#         self.database.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cmp.compareTotalLines()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cmp.compareTotalWords()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 2]\n",
      " [0 1 1]\n",
      " [1 0 1]]\n",
      "{'sagar': 2, 'hero': 1, 'ass': 0}\n",
      "<class 'scipy.sparse.csr.csr_matrix'>\n"
     ]
    }
   ],
   "source": [
    "textCollectionForDM=['i am sagar sagar','i is hero sagar','i ass sagar']\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "documentMatrix=[]\n",
    "vectorizer=CountVectorizer(stop_words='english',min_df=1)\n",
    "documentMatrix=vectorizer.fit_transform(textCollectionForDM)\n",
    "tfidf = TfidfTransformer(norm=\"l2\")\n",
    "tfidf.fit(documentMatrix)\n",
    "documentMatrixNormalized=tfidf.transform(documentMatrix)\n",
    "print(documentMatrix.todense())\n",
    "print (vectorizer.vocabulary_)\n",
    "print(type(documentMatrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "with open(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\documentMatrixsaas.txt\",\"w\") as file:\n",
    "#Save\n",
    "sparse.save_npz('data_sparse.npz', file)\n",
    "\n",
    "#Load\n",
    "data = sparse.load_npz(\"data_sparse.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# import numpy as np\n",
    "# # print(documentMatrix.toarray())\n",
    "# # print(documentMatrixNormalized.toarray())\n",
    "# # x=[1,2,3,4]\n",
    "# # y=[5,6,7,8]\n",
    "# # vocab=vectorizer.get_feature_names()\n",
    "# # dist = documentMatrix.toarray()\n",
    "# print(type(documentMatrix))\n",
    "# # for vectors in dist:\n",
    "# #     print(vectors)\n",
    "# from sklearn.preprocessing import normalize\n",
    "# data = np.array(documentMatrix.toarray())\n",
    "# print(data)\n",
    "# data =normalize(data, norm='l2')\n",
    "# print(data)\n",
    "\n",
    "# # for item,freqn in zip(vocab, dist):\n",
    "# #         if(freqn>2):\n",
    "# #             print(item,freqn)\n",
    "# #print(count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# import numpy as np\n",
    "# import matplotlib.pyplot as plt\n",
    "# vocab=vectorizer.get_feature_names()\n",
    "# dist = np.sum(documentMatrix.toarray(), axis=0)\n",
    "# print(dist)\n",
    "# yLocation=np.arange(len(dist))\n",
    "# width = 0.35\n",
    "# fig, ax = plt.subplots()\n",
    "# for tag, count in zip(vocab, dist):\n",
    "#     print(count, tag) \n",
    "# plt.bar(yLocation, dist, align='center', alpha=0.5)\n",
    "# plt.xticks(yLocation, vocab)\n",
    "# plt.ylabel('words')\n",
    "# plt.title('comp')\n",
    " \n",
    "# print(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print(vectorizer.vocabulary_)\n",
    "# for x in documentMatrix:\n",
    "#     print(x[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from sklearn.preprocessing import normalize\n",
    "# vectorValue=[1,2,3,4,5]\n",
    "# data = np.asarray(vectorValue,dtype=np.float)\n",
    "# data\n",
    "# data1 =normalize(data, norm='l2')\n",
    "# x=[]\n",
    "# for r in data1:\n",
    "#     print(data1[0])\n",
    "#     for column in r:\n",
    "#         print(column)\n",
    "#         x.append(column)\n",
    "#         #print(data1[0][column])\n",
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#len(cmp.normalizedVectorValue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from sklearn.cluster import MeanShift\n",
    "# from sklearn.datasets.samples_generator import make_blobs\n",
    "# import matplotlib.pyplot as plt\n",
    "# from matplotlib import style\n",
    "# style.use(\"ggplot\")\n",
    "# #plt.scatter(cmp.normalizedVectorValue)\n",
    "# #plt.show()\n",
    "# ms = MeanShift()\n",
    "# ms.fit(cmp.normalizedVectorValue)\n",
    "# labels = ms.labels_\n",
    "# cluster_centers = ms.cluster_centers_\n",
    "# print(cluster_centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filepath=(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\")\n",
    "# import os\n",
    "# try:\n",
    "#     if(os.path.exists(filepath)):\n",
    "#         for filename in os.listdir(filepath):\n",
    "#             print(filename)\n",
    "#             print(os.path.isfile(filename))\n",
    "#     else:\n",
    "#         print(\"path not found\")\n",
    "#             #print(\"directory\")\n",
    "# #         if filename.endswith(\".pdf\"):\n",
    "# #                 print(filename)\n",
    "# #         else:\n",
    "# #             print(\"files not found in required format\")\n",
    "# except:\n",
    "#     print(\"location not valid\")\n",
    "# #pdfFileObj = open(filepath+'.pdf','rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filepath=(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\kailesh\")\n",
    "# import os\n",
    "\n",
    "# def list_files(dir):\n",
    "#     r = []\n",
    "#     for root, dirs, files in os.walk(dir):\n",
    "#         for name in files:\n",
    "#             print(name)\n",
    "#             r.append(os.path.join(root, name))\n",
    "#     return r\n",
    "# list_files(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eats\n",
      "eats\n",
      "eats\n"
     ]
    }
   ],
   "source": [
    "words=['eat','eats','eating']\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "x=''\n",
    "wnl = nltk.WordNetLemmatizer()\n",
    "for word in words:\n",
    "    x=wnl.lemmatize(\"eats\")\n",
    "    print(x)\n",
    "#refinedText=' '.join(self.refinedWordWholeList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eat', 'eats', 'eating']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refinedWordWholeList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'happier'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "wordnet_lemmatizer.lemmatize('happier')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stemmer = PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "singles = [stemmer.stem(plural) for plural in words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['eat', 'eat', 'eat']\n",
      "['eat']\n"
     ]
    }
   ],
   "source": [
    "print(singles)\n",
    "print(list(set(singles)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n",
      "cactus\n",
      "goose\n",
      "rock\n",
      "python\n",
      "good\n",
      "best\n",
      "run\n",
      "run\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "print(lemmatizer.lemmatize(\"cats\"))\n",
    "print(lemmatizer.lemmatize(\"cacti\"))\n",
    "print(lemmatizer.lemmatize(\"geese\"))\n",
    "print(lemmatizer.lemmatize(\"rocks\"))\n",
    "print(lemmatizer.lemmatize(\"python\"))\n",
    "print(lemmatizer.lemmatize(\"better\", pos=\"a\"))\n",
    "print(lemmatizer.lemmatize(\"best\", pos=\"a\"))\n",
    "print(lemmatizer.lemmatize(\"run\"))\n",
    "print(lemmatizer.lemmatize(\"run\",'v'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'nltk' has no attribute 'word_tokenizer'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-b67b215be87b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m#words=['eat','eats','eating','happy','happying','cry','crying']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mwords\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'i am eating apple'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'i am eat orange'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mword\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mword_tokenizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfindDocumentMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtotalCVText\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'nltk' has no attribute 'word_tokenizer'"
     ]
    }
   ],
   "source": [
    "#words=['eat','eats','eating','happy','happying','cry','crying']\n",
    "words=['i am eating apple','i am eat orange']\n",
    "word=nltk.word_tokenize(words)\n",
    "print(word)\n",
    "def findDocumentMatrix(totalCVText):\n",
    "        import nltk\n",
    "        from sklearn.feature_extraction.text import CountVectorizer\n",
    "        textList=[]\n",
    "        word=nltk.word_tokenizer(words)\n",
    "        print(word)\n",
    "        textList=lemmatize(totalCVText)\n",
    "        textList=Stemmer(totalCVText)\n",
    "        vectorizer=CountVectorizer(stop_words='english')\n",
    "        documentMatrix=vectorizer.fit_transform(textList)\n",
    "        vocabulary=vectorizer.vocabulary_\n",
    "        print(documentMatrix)\n",
    "        print(vocabulary)\n",
    "        return documentMatrix,vocabulary\n",
    "def Stemmer(totalCVText):\n",
    "        from nltk.stem import PorterStemmer\n",
    "        stemmer = PorterStemmer()\n",
    "        singles = [stemmer.stem(plural) for plural in totalCVText]\n",
    "        print(singles)\n",
    "        singles=list(set(singles))\n",
    "        print(singles)\n",
    "        return singles\n",
    "def lemmatize(totalCVText):\n",
    "        from nltk.stem import WordNetLemmatizer\n",
    "        stemmer = WordNetLemmatizer()\n",
    "        singles = [stemmer.lemmatize(plural) for plural in totalCVText]\n",
    "        print(singles)\n",
    "        singles=list(set(singles))\n",
    "        print(singles)\n",
    "        return singles\n",
    "findDocumentMatrix(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['i', 'am', 'eating', 'apple'], ['i', 'am', 'eat', 'orange']]\n"
     ]
    }
   ],
   "source": [
    "#words='i am eating apple i am eat orange'\n",
    "words=['i am eating apple','i am eat orange']\n",
    "wordList=[]\n",
    "for word in words:\n",
    "    word=nltk.word_tokenize(word)\n",
    "    wordList.append(word)\n",
    "print(wordList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "words=['i am eating apple','i am eat orange']\n",
    "text=' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i am eating apple i am eat orange'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "CVTextColl=[\"hari\",\"ram\"]\n",
    "with open(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\wholeCVTextsss.json\",\"w\") as file:\n",
    "    json.dump(CVTextColl,file)\n",
    "file.close()\n",
    "CVTextColl=[]\n",
    "with open(\"C:\\\\Users\\\\sagar\\\\Desktop\\\\CV coll\\\\wholeCVTextsss.json\",\"r\") as file:\n",
    "    CVTextColl=json.load(file)\n",
    "file.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hari'"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CVTextColl[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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