collaborative filtering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\python35.zip', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\DLLs', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\lib', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\lib\\\\site-packages', 'c:\\\\users\\\\tanmaya\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\Tanmaya\\\\.ipython']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sys\n",
    "print(sys.path)\n",
    "import time\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tanmaya\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       advertiserurl  \\\n",
      "0  https://www.dice.com/jobs/detail/AUTOMATION-TE...   \n",
      "1  https://www.dice.com/jobs/detail/Information-S...   \n",
      "2  https://www.dice.com/jobs/detail/Business-Solu...   \n",
      "3  https://www.dice.com/jobs/detail/Java-Develope...   \n",
      "4  https://www.dice.com/jobs/detail/DevOps-Engine...   \n",
      "\n",
      "                             company  \\\n",
      "0  Digital Intelligence Systems, LLC   \n",
      "1  University of Chicago/IT Services   \n",
      "2               Galaxy Systems, Inc.   \n",
      "3                      TransTech LLC   \n",
      "4                   Matrix Resources   \n",
      "\n",
      "                            employmenttype_jobstatus  \\\n",
      "0  C2H Corp-To-Corp, C2H Independent, C2H W2, 3 M...   \n",
      "1                                          Full Time   \n",
      "2                                          Full Time   \n",
      "3                                          Full Time   \n",
      "4                                          Full Time   \n",
      "\n",
      "                                      jobdescription               jobid  \\\n",
      "0  Looking for Selenium engineers...must have sol...  Dice Id : 10110693   \n",
      "1  The University of Chicago has a rapidly growin...  Dice Id : 10114469   \n",
      "2  GalaxE.SolutionsEvery day, our solutions affec...  Dice Id : CXGALXYS   \n",
      "3  Java DeveloperFull-time/direct-hireBolingbrook...  Dice Id : 10113627   \n",
      "4  Midtown based high tech firm has an immediate ...  Dice Id : matrixga   \n",
      "\n",
      "  joblocation_address                                           jobtitle  \\\n",
      "0         Atlanta, GA                           AUTOMATION TEST ENGINEER   \n",
      "1         Chicago, IL                      Information Security Engineer   \n",
      "2      Schaumburg, IL                       Business Solutions Architect   \n",
      "3     Bolingbrook, IL  Java Developer (mid level)- FT- GREAT culture,...   \n",
      "4         Atlanta, GA                                    DevOps Engineer   \n",
      "\n",
      "         postdate                                            shift site_name  \\\n",
      "0      1 hour ago  Telecommuting not available|Travel not required       NaN   \n",
      "1      1 week ago  Telecommuting not available|Travel not required       NaN   \n",
      "2     2 weeks ago  Telecommuting not available|Travel not required       NaN   \n",
      "3     2 weeks ago  Telecommuting not available|Travel not required       NaN   \n",
      "4  48 minutes ago  Telecommuting not available|Travel not required       NaN   \n",
      "\n",
      "                                              skills  \\\n",
      "0                                          SEE BELOW   \n",
      "1  linux/unix, network monitoring, incident respo...   \n",
      "2  Enterprise Solutions Architecture, business in...   \n",
      "3                         Please see job description   \n",
      "4  Configuration Management, Developer, Linux, Ma...   \n",
      "\n",
      "                            uniq_id  \n",
      "0  418ff92580b270ef4e7c14f0ddfc36b4  \n",
      "1  8aec88cba08d53da65ab99cf20f6f9d9  \n",
      "2  46baa1f69ac07779274bcd90b85d9a72  \n",
      "3  3941b2f206ae0f900c4fba4ac0b18719  \n",
      "4  45efa1f6bc65acc32bbbb953a1ed13b7  \n",
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"./data/collaborative filtering/dice_com-job_us_sample.csv\")\n",
    "df1=pd.read_csv(\"./data/collaborative filtering/survey_results_public.csv\")\n",
    "print(df.head())\n",
    "l=list(df[\"company\"])\n",
    "print(type(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4292\n",
      "Guidewire Software\n"
     ]
    }
   ],
   "source": [
    "companies=set(l)\n",
    "print(len(companies))\n",
    "#print(companies)\n",
    "companies=list(companies)\n",
    "len(companies)\n",
    "print(companies[1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "602.791433095932\n",
      "   Respondent                       company\n",
      "0           1         Softpath System, LLC.\n",
      "1           3      Amzur Technologies, Inc.\n",
      "2           4             AGS International\n",
      "3           5                       Experis\n",
      "4           7  New York Technology Partners\n"
     ]
    }
   ],
   "source": [
    "#Assign random companies to each respondent\n",
    "cbf = pd.DataFrame(df1['Respondent'])\n",
    "import time\n",
    "start=time.time()\n",
    "for i in range(98855):\n",
    "    r=random.randint(1,4291)\n",
    "    cbf.loc[i,\"company\"]=l[r]\n",
    "end=time.time()\n",
    "print(end-start)\n",
    "print(cbf.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cbf.to_csv(\"./data/collaborative filtering/colabdata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#merge all the dataset having skills from user profile.\n",
    "dflang=pd.read_csv(\"./data/collaborative filtering/LanguageWorkedwith.csv\")\n",
    "dfdata=pd.read_csv(\"./data/collaborative filtering/DatabaseWorkedWith.csv\")\n",
    "dfplat=pd.read_csv(\"./data/collaborative filtering/PlatformWorkedWith.csv\")\n",
    "dfframe=pd.read_csv(\"./data/collaborative filtering/FrameworkWorkedWith.csv\")\n",
    "dfdev=pd.read_csv(\"./data/collaborative filtering/DevType.csv\")\n",
    "dfmerge=pd.merge(dflang,dfdata,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfplat,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfframe,on=\"Respondent\")\n",
    "dfmerge=pd.merge(dfmerge,dfdev,on=\"Respondent\")\n",
    "#dfmerge.to_csv(\"./data/collaborative filtering/userskills.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101592\n",
      "<class 'int'>\n",
      "[ 1.  3.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  0.  1.\n",
      "  0.  0.  0.  0.  1.  0.  0.  0.  1.  0.  0.  0.  1.  0.  0.  0.  0.  1.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  0.  0.  1.]\n",
      "17.259256839752197\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import array\n",
    "dfuser=pd.read_csv(\"./data/collaborative filtering/userskills.csv\")\n",
    "#remove duplicate columns during merge\n",
    "dfuser = dfuser.loc[:,~dfuser.columns.duplicated()]\n",
    "dfuser=dfuser.fillna(0)\n",
    "dfuser.shape\n",
    "dfuser.head()\n",
    "#remove rows with less than 5 skills\n",
    "dfuser=dfuser.dropna(thresh=5)\n",
    "dfuser.shape\n",
    "m=(np.asscalar(np.int32(max(dfuser[\"Respondent\"]))))\n",
    "print(m)\n",
    "print(type(m))\n",
    "#Build  a dictionary of respondent id's as keys  and thier skills as values\n",
    "temp=[0]*m\n",
    "vector=np.array(temp)\n",
    "count=1\n",
    "star=time.time()\n",
    "d=dict()\n",
    "for row in dfuser.iterrows():\n",
    "        index,data=row\n",
    "        l=list()\n",
    "        #l=[data.values[0],list(data.values[1:])]\n",
    "        s=np.asscalar(np.int32(data.values[0]))\n",
    "        d[s]=np.array(list(data.values[1:]))\n",
    "        #print(type(data))\n",
    "end=time.time()\n",
    "print(d[1])\n",
    "print(end-star)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4771935939788818\n"
     ]
    }
   ],
   "source": [
    "#Build the user user similarity matrix based on thier skill vectors\n",
    "#initialise a user user matrix uptill respondent number 5000\n",
    "sim=list()\n",
    "s=time.time()\n",
    "for i in range(5000):\n",
    "        l=list()\n",
    "        l=[0]*5000\n",
    "        sim.append(l)\n",
    "e=time.time()\n",
    "print(e-s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
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     "name": "stderr",
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     "text": [
      "c:\\users\\tanmaya\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  if sys.path[0] == '':\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
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      "total time for building similarity matrix \n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'pirnt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-f81790909938>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"total time for building similarity matrix \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mpirnt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pirnt' is not defined"
     ]
    }
   ],
   "source": [
    "count1=1\n",
    "count2=1\n",
    "t=time.time()\n",
    "for key, value in d.items():\n",
    "        if(key<5000):\n",
    "            print(key)\n",
    "            b=(np.linalg.norm(value))\n",
    "            for key2,value2 in d.items():\n",
    "                if(key2<5000):\n",
    "                    #print(key2)\n",
    "                    a=np.dot(d[key],d[key2])\n",
    "                    ans=a/(np.linalg.norm(value2)*b)\n",
    "                    sim[key][key2]=ans\n",
    "                    #count2+=1\n",
    "            #count1+=1\n",
    "e=time.time()\n",
    "print(\"total time for building similarity matrix \")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1542636210.859989\n",
      "[0, 1.0, 0.0, 0.53813823519705006, 0.17149858514250882, 0.51856297884173153, 0, 0.1143323900950059, 0.38892223413129867, 0.43876345447627835]\n",
      "0.713024095907\n",
      "4653\n"
     ]
    }
   ],
   "source": [
    "print(e-s)\n",
    "print(sim[1][:10])\n",
    "# the similarity of user 1 with others \n",
    "print((max(sim[1][2:])))\n",
    "print(sim[1].index(max(sim[1][2:])))\n",
    "#user 1 is most similar to user 4653 with similarity 0.71 in skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent 3 is working in \n",
      "['Birlasoft']\n",
      "Respondent  3265 is working in\n",
      "['Pioneer IT Associates']\n"
     ]
    }
   ],
   "source": [
    "#Lets check with the random jobs given to the users .\n",
    "dfjob=pd.read_csv(\"./data/collaborative filtering/colabdata.csv\")\n",
    "#Recommend user 3 a job based on another user who has almost the same skill as him.\n",
    "print(\"Respondent 3 is working in \")\n",
    "print(dfjob.loc[dfjob.Respondent==3][\"company\"].values)\n",
    "\n",
    "m1=max(sim[3][:3])\n",
    "m2=max(sim[3][4:])\n",
    "ma=max(m1,m2)\n",
    "suser=sim[3].index(ma)\n",
    "#print(suser) #user 3265 is very similar to user 3 and hence we can recommend user 3265 job to user 3.\n",
    "print(\"Respondent \",suser,\"is working in\")\n",
    "print(dfjob.loc[dfjob.Respondent==suser][\"company\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.002000570297241211\n",
      "Building the similarity matrix....\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#For buiding  collaborative filtering based on The Content based recommendations for the first 200 Respondents. \n",
    "dfcont=pd.read_csv(\"./data/collaborative filtering/recommendations.csv\")\n",
    "sim=list()\n",
    "s=time.time()\n",
    "for i in range(200):\n",
    "        l=list()\n",
    "        l=[0]*200\n",
    "        sim.append(l)\n",
    "e=time.time()\n",
    "print(e-s)\n",
    "t=time.time()\n",
    "print(\"Building the similarity matrix....\\n\")\n",
    "for key, value in d.items():\n",
    "        if(key<200):\n",
    "            #print(key)\n",
    "            b=(np.linalg.norm(value))\n",
    "            for key2,value2 in d.items():\n",
    "                if(key2<200):\n",
    "                    #print(key2)\n",
    "                    a=np.dot(d[key],d[key2])\n",
    "                    ans=a/(np.linalg.norm(value2)*b)\n",
    "                    sim[key][key2]=ans\n",
    "                    #count2+=1\n",
    "            #count1+=1\n",
    "e=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent 3 was recommended jobs from content based filtering in \n",
      "['ICONMA' 'Cypress Group' 'SolTech, Inc' 'Saicon Consultants Inc.'\n",
      " 'Samsung SDS America Inc' 'Strivector' 'Aria Systems' 'Aria Systems'\n",
      " 'Strivector' 'Universal Software Corporation']\n",
      "Respondent 3 was recommended job titles from content based filtering \n",
      "['Python Developer' 'Full-Stack Python Developer'\n",
      " 'Full Stack Javascript Engineer' 'Full Stack Developer'\n",
      " 'System Administrator' 'System Administrator' 'Application Administrator'\n",
      " 'Application Administrator' 'System Administrator' 'Full Stack Engineer']\n",
      "\n",
      "\n",
      "Respondent  7 was most similiar to respondent 3\n",
      "\n",
      "Based on respondent  7  the jobs recommended to 3 are \n",
      "['Levanture' 'Avesta Computer Services' 'Confidential Company'\n",
      " 'MatchPoint Consulting Group' 'Genesis TechSystems' 'Collabera'\n",
      " 'Cox Communications' 'Celestos, LLC' 'Capgemini' 'iconectiv']\n",
      "The recommended job titles are \n",
      "['NMS Solution Architect' 'Sr. Systems Test Engineer (PERM)'\n",
      " 'Oracle iStore Technical Resource' 'Sr. SQL DBA' 'Health Care ETL Tester'\n",
      " 'ETL Production Support' 'Senior DBA'\n",
      " 'Technology Lead | Telecom | OSS/BSS' 'Oracle PL/SQL' 'Director']\n"
     ]
    }
   ],
   "source": [
    "#Recommend user 3 a job based on another user who has almost the same skill as him.\n",
    "print(\"Respondent 3 was recommended jobs from content based filtering in \")\n",
    "print(dfcont.loc[dfcont.Respondent==3][\"company\"].values)\n",
    "print(\"Respondent 3 was recommended job titles from content based filtering \")\n",
    "print(dfcont.loc[dfcont.Respondent==3][\"jobtitle\"].values)\n",
    "print(\"\\n\")\n",
    "m1=max(sim[3][:3])\n",
    "m2=max(sim[3][4:])\n",
    "ma=max(m1,m2)\n",
    "suser=sim[3].index(ma)\n",
    "#print(suser) #user 3265 is very similar to user 3 and hence we can recommend user 3265 job to user 3.\n",
    "print(\"Respondent \",suser,\"was most similiar to respondent 3\\n\")\n",
    "print(\"Based on respondent \",suser,\" the jobs recommended to 3 are \")\n",
    "print(dfcont.loc[dfcont.Respondent==suser][\"company\"].values)\n",
    "print(\"The recommended job titles are \")\n",
    "print(dfcont.loc[dfcont.Respondent==suser][\"jobtitle\"].values)\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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