keyWordCloud.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Task 2 -Recommend Relevant Keywords with Page Rank Algorithm \n",
    "### MICROSOFT ACADEMIC GRAPH PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This notebook walks you through the analysis done for keywords match and script used to visualize our output pertaining to predicting keywords. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total no. of papers found in our subset:                117003  papers\n",
      "Total no. of unique keywords in our subset              17124  keywords\n",
      "Total no. of unique authors present in our subset       224343  authors\n",
      "Please enter the paperID to compute page rank: 5D03E53B\n",
      "\n",
      "List of related topics found for papers with pid:                \n",
      "data mining\n",
      "matrix decomposition\n",
      "noise\n",
      "nonnegative matrix factorization\n",
      "multi document summarization\n",
      "k means\n",
      "non negative matrix factorization\n",
      "feature extraction\n",
      "text analysis\n",
      "coherence\n",
      "Total no. of related papers(with above)found prior to applying our Page Rank:   5973\n",
      "\n",
      "\n",
      "We have to filter out papers that appeared after a paper was published\n",
      "\n",
      "Done with filtering out papers published after the year:   2008\n",
      "So now the total number of recommended papers in hand is:  3738\n",
      " ------------------- Filtered Out Papers -------------------- [1]\n",
      "\n",
      "Total number of related authors found from related papers: 15654\n",
      "The author who wrote max papers in this recomended list is :1227C55A\n",
      "and he wrote                                238 papers\n",
      "\n",
      "\n",
      "List of 20 authors with maximum number of papers under them in this field with their assigned weights are:\n",
      "\n",
      "[('1227C55A', 0.2), ('1051ADFF', 0.1613445378151261), ('145F0C2D', 0.1445378151260504), ('0EEBCCBF', 0.12268907563025211), ('11A748DF', 0.12268907563025211), ('1359625A', 0.10672268907563026), ('0D0644F2', 0.10588235294117648), ('10BE6E3A', 0.10252100840336136), ('12FF39DC', 0.09411764705882354), ('111C6D4B', 0.08067226890756304)]\n",
      "\n",
      "Next step is to assign these weights to the paperIds which are written by the above authors!\n",
      "\n",
      "The number of paper weights are assigned from the author weights are:   1195\n",
      " ----------- Author popularity assigned for Papers ------------ [2]\n",
      "\n",
      " ----------- Paper Similarity assigned for Keywords ------------ [3]\n",
      "\n",
      "Recomended Keywords are:\n",
      "\n",
      "['key', 'feature extraction', 'data mining', 'data mining', 'data mining', 'data mining', 'feature extraction', 'feature extraction', 'data mining', 'multi document summarization', 'data mining', 'feature extraction', 'feature extraction', 'non negative matrix factorization', 'data mining', 'data mining', 'data mining', 'data mining', 'data mining', 'data mining', 'data mining', 'data mining', 'feature extraction', 'noise', 'feature extraction', 'text analysis', 'data mining', 'feature extraction', 'data mining', 'data mining', 'feature extraction', 'feature extraction', 'feature extraction', 'data mining', 'multi document summarization', 'data mining', 'data mining', 'feature extraction', 'text analysis', 'feature extraction', 'feature extraction', 'feature extraction', 'data mining', 'data mining', 'matrix decomposition', 'noise', 'nonnegative matrix factorization', 'multi document summarization', 'k means', 'non negative matrix factorization', 'feature extraction', 'text analysis', 'coherence', 'feature extraction', 'data mining', 'text analysis', 'data mining', 'data mining', 'feature extraction', 'k means', 'feature extraction', 'feature extraction', 'data mining', 'feature extraction', 'feature extraction', 'non negative matrix factorization', 'feature extraction', 'feature extraction', 'data mining', 'data mining', 'feature extraction', 'data mining', 'non negative matrix factorization', 'matrix decomposition', 'data mining', 'non negative matrix factorization', 'data mining', 'feature extraction', 'feature extraction', 'data mining', 'feature extraction', 'data mining', 'text analysis', 'feature extraction', 'data mining', 'data mining', 'data mining', 'feature extraction', 'data mining', 'classification algorithms', 'information retrieval', 'data mining', 'text analysis', 'non negative matrix factorization', 'clustering', 'nonnegative matrix factorization', 'data mining', 'machine learning', 'multi document summarization', 'feature extraction', 'spatial database', 'data mining', 'apriori algorithm', 'data mining', 'data mining', 'decision tree', 'data mining', 'formal concept analysis', 'data mining', 'market value', 'rough set', 'data mining', 'data mining', 'sport', 'rough set theory', 'data mining', 'formal concept analysis', 'data mining', 'data mining', 'data cube', 'search space', 'data mining', 'data mining', 'hot spot', 'data mining', 'prediction model', 'pattern recognition', 'feature extraction', 'front end', 'feature extraction', 'feature extraction', 'first principle', 'fuzzy systems', 'data mining', 'knowledge base', 'feature extraction', 'decision support', 'data mining', 'face recognition', 'feature extraction', 'text analysis', 'electronic publishing', 'data mining', 'rough set theory', 'data mining', 'web service', 'association rules', 'data mining', 'feature extraction', 'time series', 'data mining', 'educational software', 'data mining', 'association rule', 'feature extraction', 'speech synthesis', 'detectors', 'radiation detectors', 'noise', 'feature extraction', 'feature extraction', 'scale invariant feature transform', 'automatic speech recognition', 'feature extraction', 'data mining', 'data structure', 'decision tree', 'data mining', 'coherence', 'computer mediated communication', 'electronic commerce', 'data mining', 'association rule mining', 'data mining', 'context free grammars', 'feature extraction', 'data mining', 'information retrieval', 'object recognition', 'data mining', 'histograms', 'feature extraction', 'text analysis', 'knowledge management', 'data mining', 'support vector machines', 'domain knowledge', 'data mining', 'data mining', 'life cycle', 'k means', 'web usage mining', 'data mining', 'sampling methods', 'feature selection', 'data mining', 'feature extraction', 'genetic algorithm', 'feature extraction', 'radiography', 'feature extraction', 'low frequency', 'wavelet transform', 'face recognition', 'non negative matrix factorization', 'matrix decomposition', 'data mining', 'granular computing', 'meta data', 'search engines', 'data mining', 'feature extraction', 'internet', 'text analysis', 'data mining', 'active network', 'multi document summarization', 'document structure', 'probabilistic model', 'data mining', 'support vector machines', 'text analysis', 'data mining', 'data transformation', 'filter bank', 'feature extraction', 'clustering', 'k means', 'feature extraction', 'context free grammars', 'feature extraction', 'image segmentation', 'data mining', 'knowledge discovery', 'feature extraction', 'discriminant analysis', 'text to speech', 'text analysis', 'cumulant', 'data mining', 'data mining', 'greedy algorithm', 'data mining', 'bismuth', 'hidden markov model', 'feature extraction', 'membership function', 'feature extraction', 'data compression', 'noise', 'data mining', 'termination analysis', 'data mining', 'machine learning', 'satisfiability', 'k means', 'multi document summarization', 'document retrieval', 'data mining', 'independent component analysis', 'transaction data', 'data mining', 'bioinformatics', 'data mining', 'relational database', 'data mining', 'association rules', 'data mining', 'speech recognition', 'feature extraction', 'data mining', 'association rule', 'machine learning', 'data mining', 'neuro fuzzy', 'data mining', 'complex system', 'data mining', 'feature extraction', 'image processing', 'feature extraction', 'digital video broadcasting', 'neural network', 'feature extraction', 'learning artificial intelligence', 'text analysis', 'human factors', 'text analysis', 'data mining', 'hot spot', 'feature extraction', 'image retrieval', 'feature extraction', 'autonomous agent', 'rough set', 'data mining', 'information retrieval', 'data mining', 'feature extraction', 'speech synthesis', 'nonnegative matrix factorization', 'higher order', 'feature extraction', 'feature selection', 'knowledge extraction', 'data mining', 'multi layer perceptron', 'feature extraction', 'feature extraction', 'neural net', 'feature extraction', 'jitter', 'data mining', 'confidence level', 'data mining', 'information retrieval', 'text analysis', 'internet', 'data mining', 'machine learning', 'feature extraction', 'independent component analysis', 'feature extraction', 'autoregressive model', 'data mining', 'classification rule', 'speaker recognition', 'feature extraction', 'filter bank', 'feature extraction', 'data mining', 'association rule mining', 'data processing', 'data mining', 'k means', 'k means clustering', 'data mining', 'genetic algorithm', 'data mining', 'knowledge discovery', 'face recognition', 'feature extraction', 'association rule mining', 'data mining', 'reliability', 'feature extraction', 'sequential pattern mining', 'data mining', 'feature extraction', 'segmentation', 'association rule mining', 'data mining', 'noise', 'switches', 'question answering', 'text analysis']\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Tue April  23 19:17:59 2016\n",
    "\n",
    "@author: Santhosh\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import division\n",
    "import operator, re\n",
    "\n",
    "input_file = '/Users/Santhosh/PaperCollection5.json'\n",
    "\n",
    "# list that stores all papers\n",
    "papers = []\n",
    "\n",
    "# dict to store keyword and associated pid as {{kw1:[pid1, pid2...]}, {kw2:..}...}\n",
    "keyword_dict = {}\n",
    "\n",
    "# dict to store {{author1: [pid1, pid2...]}, {a2}...)\n",
    "author_dict_pid = {}\n",
    "\n",
    "# dict to store pids and associated keywords {{pid1:[kw1, kw2...]}, {pid2:..}...}\n",
    "pidKw_dict = {}\n",
    "\n",
    "# dict to store pids and associated authors {{pid1:[auth1, auth2...]}, {pid2:..}...}\n",
    "paperAuth_dict = {}   \n",
    "  \n",
    "# dict to store pids and associated published years ({pid1:yr, {pid2:yr}...}\n",
    "paper_pubYr = {}\n",
    "\n",
    "# dict for actual reference paper ids\n",
    "actual_refPids = {}\n",
    "\n",
    "\n",
    "\n",
    "######################## Code Section that reads and stores data using regex #################### \n",
    "\n",
    "f_obj = open(input_file)\n",
    "\n",
    "for line in f_obj:\n",
    "    try:\n",
    "        kw = re.findall('\"KW\":\"([^\"]*)', line)\n",
    "        authIds = re.findall('\"AuthIds\":\"([^\"]*)',line)\n",
    "        kw = kw[0].split(\"|\")\n",
    "        authIds = authIds[0].split(\"|\")\n",
    "        pap_id = re.findall('\"PId\":\"([^\"]*)', line)[0] \n",
    "        papers.append(pap_id)        #add(pap_id)\n",
    "        for i in kw:\n",
    "            if i in keyword_dict:\n",
    "                keyword_dict[i].append(pap_id)\n",
    "            else:\n",
    "                keyword_dict[i] = [pap_id]\n",
    "    \n",
    "        for i in authIds:\n",
    "            if i in author_dict_pid:\n",
    "                author_dict_pid[i].append(pap_id)\n",
    "            else:\n",
    "                author_dict_pid[i] = [pap_id]\n",
    "\n",
    "        paperAuth_dict[pap_id] = authIds;\n",
    "        pub_yr = re.findall('\"PublYear\":([^,]*)', line)[0]\n",
    "        paper_pubYr[pap_id] = pub_yr;\n",
    "        \n",
    "        refPid = re.findall('\"PRefIds\":\"([^\"]*)', line)\n",
    "        refPid = refPid[0].split(\"|\")\n",
    "        for i in refPid:\n",
    "            if pap_id in actual_refPids:\n",
    "                actual_refPids[pap_id].append(i)\n",
    "            else:\n",
    "                actual_refPids[pap_id] = [i]\n",
    "        actual_refPids[pap_id].remove('')\n",
    "        \n",
    "        for i in kw:\n",
    "            if pap_id in pidKw_dict:\n",
    "                pidKw_dict[pap_id].append(i)\n",
    "            else:\n",
    "                pidKw_dict[pap_id] = [i]\n",
    "        pidKw_dict[pap_id].remove('')\n",
    "        \n",
    "\n",
    "    except Exception, e:\n",
    "        pass\n",
    "\n",
    "print \"Total no. of papers found in our subset:                \" + str(len(papers)) +\"  papers\"\n",
    "print \"Total no. of unique keywords in our subset              \" + str(len(keyword_dict)) +\"  keywords\"\n",
    "print \"Total no. of unique authors present in our subset       \" + str(len(author_dict_pid)) +\"  authors\"\n",
    "\n",
    "\n",
    "#################### End Regex section  ################################################\n",
    "\n",
    "RecomendedPids = []\n",
    "searchPid = raw_input(\"Please enter the paperID to compute page rank: \")\n",
    "print \n",
    "\n",
    "\n",
    "relKeyWords = pidKw_dict[searchPid]\n",
    "print \"List of related topics found for papers with pid:                \"  \n",
    "\n",
    "for rkw in relKeyWords:\n",
    "    print(rkw)\n",
    "    if rkw:\n",
    "        RecomendedPids.extend(keyword_dict[rkw])\n",
    "    \n",
    "print \"Total no. of related papers(with above)found prior to applying our Page Rank:   \" +str(len(RecomendedPids)) + '\\n' '\\n'\n",
    "\n",
    "print \"We have to filter out papers that appeared after a paper was published\" + '\\n'\n",
    "ourPprYr = paper_pubYr[searchPid]\n",
    "for p in RecomendedPids:\n",
    "    if paper_pubYr[p] > ourPprYr:\n",
    "        RecomendedPids.remove(p)\n",
    "\n",
    "print \"Done with filtering out papers published after the year:   \" + str(ourPprYr)\n",
    "print \"So now the total number of recommended papers in hand is:  \" +str(len(RecomendedPids))\n",
    "print \" ------------------- Filtered Out Papers -------------------- [1]\" + '\\n'\n",
    "\n",
    "RecomendedAuthors = []\n",
    "for recPid in RecomendedPids:\n",
    "    RecomendedAuthors.extend(paperAuth_dict[recPid])\n",
    "\n",
    "print \"Total number of related authors found from related papers: \" +str(len(RecomendedAuthors))\n",
    "\n",
    "authID_count = {}\n",
    "\n",
    "for a in RecomendedAuthors:\n",
    "            if a in authID_count:\n",
    "                authID_count[a].append(len(author_dict_pid[a]))\n",
    "            else:\n",
    "                authID_count[a] = [len(author_dict_pid[a])]\n",
    "\n",
    "\n",
    "if '' in authID_count:\n",
    "    del authID_count['']\n",
    "   \n",
    "keyMax = []\n",
    "\n",
    "# top authors dict with their weight \n",
    "topAuth = {}\n",
    "\n",
    "def cal_weight(max_papers_count, max_weight, curr_paper):\n",
    "    \"\"\"\n",
    "    Function to compute the weight for the papers based on author popularity. \n",
    "    \"\"\"\n",
    "    return max_weight*curr_paper/max_papers_count\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Computing overall Max_Weight of authors\n",
    "\"\"\"\n",
    "try:\n",
    "    authID_count.pop('0.0')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "tempMax = max(authID_count, key=lambda k: authID_count[k])\n",
    "print \"The author who wrote max papers in this recomended list is :\" + str(tempMax)\n",
    "print \"and he wrote                                \" + str(len(author_dict_pid[tempMax])) + \" papers\" + '\\n' '\\n'\n",
    "allmax_AuthAppear = len(author_dict_pid[tempMax])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for items in xrange(10):\n",
    "    tempKey = max(authID_count.iterkeys(), key=(lambda key: authID_count[key]))\n",
    "    keyMax.append(tempKey)\n",
    "    topAuth[tempKey] = cal_weight(allmax_AuthAppear, 0.2, len(author_dict_pid[tempKey]))\n",
    "    del authID_count[tempKey]\n",
    "    cal_weight\n",
    "    \n",
    "\n",
    "print \"List of 20 authors with maximum number of papers under them in this field with their assigned weights are:\" +'\\n'\n",
    "sorted_topAuth = sorted(topAuth.items(), key=operator.itemgetter(1) , reverse = True)\n",
    "print sorted_topAuth\n",
    "print\n",
    "print \"Next step is to assign these weights to the paperIds which are written by the above authors!\" + '\\n'\n",
    "weight_dict = {\"kw\":0.6, \"ap\":0.2, \"pRank\":0.2}\n",
    "\n",
    "\n",
    "\n",
    "Paper_toAuthWeight = {}\n",
    "# After the author weight is computed, we assign it to the Paper_toAuthWeight dict\n",
    "for aids in topAuth.keys():\n",
    "    for pap in author_dict_pid[aids]:\n",
    "        Paper_toAuthWeight[pap]= topAuth[aids]\n",
    "\n",
    "#Paper_toAuthWeight = sorted(Paper_toAuthWeight.items(), key=operator.itemgetter(1) , reverse = True)\n",
    "print 'The number of paper weights are assigned from the author weights are:   ' + str(len(Paper_toAuthWeight.keys()))\n",
    "print \" ----------- Author popularity assigned for Papers ------------ [2]\" + '\\n'\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "recPapers_toKw = {}\n",
    "referenceKw = pidKw_dict[searchPid]\n",
    "\n",
    "\n",
    "def compute_paperWt(referenceKw, RecomendedPids, recPapers_toKw, maxPaperWt):\n",
    "    for k in RecomendedPids:\n",
    "        noMatch = 0\n",
    "        for i in pidKw_dict[k]:\n",
    "            for j in referenceKw:\n",
    "                if i==j:\n",
    "                    noMatch +=1\n",
    "                    \n",
    "        tempWt = maxPaperWt*noMatch/len(pidKw_dict[k])\n",
    "\n",
    "        recPapers_toKw[k] = tempWt\n",
    "        \n",
    "    \n",
    "compute_paperWt(referenceKw, RecomendedPids, recPapers_toKw, 0.6)\n",
    "\n",
    "\n",
    "tempPw = recPapers_toKw.copy()\n",
    "maxMatchPapers = []\n",
    "for items in xrange(200):\n",
    "    tempKey = max(tempPw.iterkeys(), key=(lambda key: tempPw[key]))\n",
    "    \n",
    "    maxMatchPapers.append(tempKey)\n",
    "    #Paper_toAuthWeight[tempKey] = cal_weight(allmax_AuthAppear, 0.4, len(author_dict_pid[tempKey]))\n",
    "    del tempPw[tempKey]\n",
    "    \n",
    "print \" ----------- Paper Similarity assigned for Keywords ------------ [3]\" + '\\n'\n",
    "print \"Recomended Keywords are:\"\n",
    "    \n",
    "\n",
    "kwList = ['key']\n",
    "\n",
    "for i in maxMatchPapers:\n",
    "    kwList.extend(pidKw_dict[i])\n",
    "\n",
    "print \n",
    "print kwList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rU/9A4Rc/xpwyo9IjHcQzKPmBjxNu3UKw+nk6r7myctCycJYtp+Z7Pz8mz5h8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\nyCgJ/hIlSpQ4yPj/AQCME9D9+U4NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12468fd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ae92d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from os import path\n",
    "from wordcloud import WordCloud\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "text=json.dumps(kwList)\n",
    "\n",
    "wc = WordCloud(background_color=\"white\", max_words=200)\n",
    "# generate word cloud\n",
    "wc.generate(text)\n",
    "\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.figure()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanations for the above plot is mentioned in the report. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}