Prediction_System.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from os import listdir\n",
    "import json\n",
    "from statistics import mean,median\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def app_name_split(s):\n",
    "    return(s.split(\";\"))\n",
    "def dic_json(dic,path):\n",
    "    with open(path+\".json\", \"w\") as outfile:  \n",
    "        json.dump(dic, outfile)\n",
    "def dic_json(dic,path):\n",
    "    with open(path+\".json\", \"w\") as outfile:  \n",
    "        json.dump(dic, outfile)\n",
    "#function to check weather the app pair is already exist or not\n",
    "def checker(app1,app2,cs):\n",
    "    if any(app1+\":\"+app2==app_pairs for app_pairs in cs.keys()):\n",
    "        return cs\n",
    "    else:\n",
    "        if any(app2+\":\"+app1==app_pairs for app_pairs in cs.keys()):\n",
    "            return cs\n",
    "        else:\n",
    "            cs[app1+\":\"+app2]=[]\n",
    "            cs[app2+\":\"+app1]=[]\n",
    "            return  cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open(\"info_frequency.csv\",\"r\", encoding=\"utf8\") as f:\n",
    "    reader = csv.reader(f)\n",
    "    num=0\n",
    "    for row in reader:\n",
    "        if num==0:\n",
    "            num+=1\n",
    "            continue\n",
    "        if num>20000:\n",
    "            break\n",
    "        num+=1\n",
    "        apps=app_name_split(row[2])\n",
    "        for a1 in range(len(apps)-1):\n",
    "            for a2 in range(a1+1,len(apps)):\n",
    "                flag=1\n",
    "                if apps[a1] in list(combined_score.keys()):\n",
    "                  \n",
    "                    if apps[a2] in list(combined_score[apps[a1]].keys()):    \n",
    "                        combined_score[apps[a1]][apps[a2]].append(row[0])\n",
    "                        continue\n",
    "                if apps[a2] in list(combined_score.keys()):\n",
    "                    flag=2\n",
    "                    if apps[a1] in list(combined_score[apps[a2]].keys()):                           \n",
    "                        combined_score[apps[a2]][apps[a1]].append(row[0])\n",
    "                        continue\n",
    "                if  flag==1:\n",
    "                    try:\n",
    "                        combined_score[apps[a1]][apps[a2]]=[]\n",
    "                        combined_score[apps[a1]][apps[a2]].append(row[0])\n",
    "                        continue\n",
    "                    except:\n",
    "                        combined_score[apps[a1]]={}\n",
    "                        combined_score[apps[a1]][apps[a2]]=[]\n",
    "                        combined_score[apps[a1]][apps[a2]].append(row[0])\n",
    "                        continue\n",
    "                else:\n",
    "                    \n",
    "                    combined_score[apps[a2]][apps[a1]]=[]\n",
    "                    combined_score[apps[a2]][apps[a1]].append(row[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-190-35c6eb8d2a28>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mauth\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mau_list\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m             \u001b[0map1_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msenti_frame_1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mauth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m             \u001b[0map2_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msenti_frame_2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mauth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0map1_score\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0map2_score\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0map1_score\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-128-d7de9900c093>\u001b[0m in \u001b[0;36mget_score\u001b[1;34m(senti_frame, auth)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msenti_frame\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mauth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msenti_frame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msenti_frame\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Author\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mauth\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0msumm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"compound\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0msumm\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other, axis)\u001b[0m\n\u001b[0;32m   1764\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1765\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1766\u001b[1;33m                 \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1767\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1768\u001b[0m                 raise TypeError('Could not compare {typ} type with Series'\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1624\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1625\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_comp_method_OBJECT_ARRAY\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1627\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_datetimelike_v_numeric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "not_found=[]\n",
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "num=0\n",
    "for app1 in combined_score.keys():\n",
    "    if num>2:\n",
    "        break\n",
    "    num+=1\n",
    "    try:\n",
    "        senti_frame_1=pd.read_csv(\"datasets_sentiment\\\\sentiment\\\\\"+\"Sentiment_Score_for_Reviews_of_\"+app1+\".csv\")#link to sentiment scores of app2\n",
    "    except:\n",
    "        not_found.append(app1)\n",
    "        continue\n",
    "    for app2 in combined_score[app1].keys():\n",
    "        au_list=set(combined_score[app1][app2])\n",
    "        try:\n",
    "            senti_frame_2=pd.read_csv(\"datasets_sentiment\\\\sentiment\\\\\"+\"Sentiment_Score_for_Reviews_of_\"+app2+\".csv\")#link to sentiment scores of app2\n",
    "        except:\n",
    "            not_found.append(app2)\n",
    "            continue\n",
    "        cs=checker(app1,app2,cs)\n",
    "        for auth in au_list:\n",
    "            ap1_score=get_score(senti_frame_1,auth)\n",
    "            ap2_score=get_score(senti_frame_2,auth)\n",
    "            if ap1_score==0 or ap2_score==0:\n",
    "                print(ap1_score)\n",
    "                print(ap2_score)\n",
    "                print(\"---------\")\n",
    "                continue\n",
    "            if ap2_score>ap1_score:\n",
    "                sub=ap2_score-ap1_score\n",
    "                cs[app1+\":\"+app2].append(sub)\n",
    "                cs[app2+\":\"+app1].append(sub*(-1))\n",
    "            else:\n",
    "                if ap1_score>ap2_score:\n",
    "                    sub=ap1_score-ap2_score\n",
    "                    cs[app2+\":\"+app1].append(sub)\n",
    "                    cs[app1+\":\"+app2].append(sub*(-1))\n",
    "                else:\n",
    "                    cs[app2+\":\"+app1].append(0)\n",
    "                    cs[app1+\":\"+app2].append(0)\n",
    "                \n",
    "finish_time=time.time()        \n",
    "print(\"--- %m Minutes ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"compound_score_wise_after_cal_20000.json\") as f:\n",
    "    cs= json.loads(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_score(senti_frame,auth):\n",
    "    f=senti_frame.loc[senti_frame[\"Author\"]==auth]\n",
    "    summ=0\n",
    "    for i in f[\"compound\"]:\n",
    "        summ+=i\n",
    "    return summ/len(f[\"compound\"])\n",
    "                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the edges\n",
    "edges={}\n",
    "edge_confidence={}\n",
    "for i in cs.keys():\n",
    "    try:\n",
    "        edges[i]=mean(cs[i])\n",
    "        edge_confidence[i]=len(cs[i])\n",
    "    except:\n",
    "        continue\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Given an author name returns the apps in all which they reviewed\n",
    "def get_apps(Author):\n",
    "    with open(\"info_frequency.csv\",\"r\", encoding=\"utf8\") as f:\n",
    "        reader = csv.reader(f)\n",
    "        num=0\n",
    "        for row in reader:\n",
    "            if num==0:\n",
    "                num+=1\n",
    "                continue\n",
    "            if row[0]==Author:\n",
    "                num+=1\n",
    "                apps=app_name_split(row[2])\n",
    "                break\n",
    "    return apps\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_s_score(Author,app):\n",
    "    try:\n",
    "        senti_frame=pd.read_csv(\"datasets_sentiment\\\\sentiment\\\\\"+\"Sentiment_Score_for_Reviews_of_\"+app+\".csv\")\n",
    "        return get_score(senti_frame,Author)\n",
    "    except:\n",
    "        print(\"prob_with_ge_s_score\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def author_scores(Author):\n",
    "    Author_scores={}\n",
    "    apps=get_apps(Author)\n",
    "    for app in apps:\n",
    "        t=get_s_score(Author,app)\n",
    "        if not(t==None):\n",
    "            Author_scores[app]=get_s_score(Author,app)\n",
    "        else:\n",
    "            continue\n",
    "    return Author_scores\n",
    "\n",
    "def sentiment_predictor(app1,s_score,app2):\n",
    "    try:\n",
    "        if edge_confidence[app1+\":\"+app2]>=5:#checking for confidence\n",
    "            diff=edges[app1+\":\"+app2]\n",
    "            return s_score+diff\n",
    "        else:\n",
    "            print(\"No enough confidence on------ \"+ app1+\":\"+app2 )\n",
    "            return None\n",
    "    except:\n",
    "        print(\"pair data not available\")\n",
    "        return None\n",
    "def get_pred_scores(aus):\n",
    "    split_index=int(len(aus.keys())/2)\n",
    "    train_apps=list(aus.keys())[:split_index]\n",
    "    test_apps=list(aus.keys())[split_index:]\n",
    "    pred_scores={}\n",
    "    for te_app in test_apps:\n",
    "        pred_scores[te_app]=[]\n",
    "        for tr_app in train_apps:\n",
    "            st=sentiment_predictor(tr_app,aus[tr_app],te_app)\n",
    "            if not(st==None):\n",
    "                pred_scores[te_app].append(st)   \n",
    "            else:\n",
    "                continue\n",
    "                \n",
    "    pred_mean={}\n",
    "    for i in pred_scores.keys():\n",
    "        try:\n",
    "             pred_mean[i]=mean(pred_scores[i])\n",
    "        except:\n",
    "            continue\n",
    "    return pred_mean\n",
    "def mean_sq_err(pred,aus):\n",
    "    ytrue=[]\n",
    "    for i in pred.keys():\n",
    "        ytrue.append(aus[i])\n",
    "        \n",
    "    return mean_squared_error(ytrue,list(pred.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No enough confidence on------ com.fungames.blockcraft:com.midasplayer.apps.papapearsaga\n",
      "No enough confidence on------ com.disney.wdpro.dlr:com.midasplayer.apps.papapearsaga\n",
      "pair data not available\n",
      "pair data not available\n",
      "pair data not available\n",
      "0.07809421735914482\n"
     ]
    }
   ],
   "source": [
    "#testing the prediction system for author Matt Arnold\n",
    "aus=author_scores('Matt Arnold')\n",
    "predicted=get_pred_scores(aus)\n",
    "if len(predicted)>0:\n",
    "    print(mean_sq_err(predicted,aus))\n",
    "else:\n",
    "    \"No confidence to predict any scores\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'com.google.android.apps.photos': 0.7843044994070658,\n",
       " 'com.microsoft.office.officelens': 1.082899074074074,\n",
       " 'com.midasplayer.apps.papapearsaga': 0.6635166666666666}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted#predicted score for three apps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'com.fungames.blockcraft': 0.9990000000000001,\n",
       " 'com.disney.wdpro.dlr': 0.5859,\n",
       " 'com.omgpop.dstfree': 0.743,\n",
       " 'com.google.android.apps.photos': 0.7506,\n",
       " 'com.microsoft.office.officelens': 0.6249,\n",
       " 'com.midasplayer.apps.papapearsaga': 0.5106,\n",
       " 'com.apalon.ringtones': 0.0}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aus#actual scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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