probability_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "%matplotlib inline\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import datetime\n",
    "from sklearn.utils import resample\n",
    "from sklearn.cross_validation import train_test_split, StratifiedKFold\n",
    "from patsy import dmatrices\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.isotonic import IsotonicRegression\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report, recall_score, precision_recall_fscore_support\n",
    "from scipy import interp\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def strictly_decreasing(L):\n",
    "    return all(x>y for x, y in zip(L, L[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = 'data/ATA_data.csv'\n",
    "random_seed = 0\n",
    "num_samp = 1000000\n",
    "test_percentage = 0.2\n",
    "charting = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "    # need to add seed options, test_percentage, totalData,\n",
    "    '''\n",
    "    This function is used to train the probability model, returns pickled model\n",
    "    '''\n",
    "    # Setting random seed\n",
    "\n",
    "    # Loading Dataset and Reordering Data\n",
    "    df = pd.read_csv(filename)\n",
    "    df = df.drop('Unnamed: 0', 1)\n",
    "\n",
    "    ''' Downsampling by a target total samples, due to the large number of\n",
    "    categorical variables, however the outcome ratio is preserved.'''\n",
    "\n",
    "    # Dummy variables are created\n",
    "    y_com, X_com = dmatrices('installed ~ C(hours) + C(device) + C(weekday) +'\n",
    "                             'C(app_type) + C(country) +C(publisher_name) + C(ad_id)',\n",
    "                             df, return_type='dataframe')\n",
    "    y_com = np.ravel(y_com.iloc[:, 1:])\n",
    "\n",
    "    ''' Downsampling by a target total samples, due to the large number of\n",
    "    categorical variables, however the outcome ratio is preserved.'''\n",
    "\n",
    "    X_rsampled, y_rsampled = resample(X_com, y_com, n_samples=num_samp, random_state=random_seed)\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_rsampled, y_rsampled,\n",
    "                                            test_size=test_percentage,\n",
    "                                            random_state=random_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "    ProbModel = LogisticRegression(class_weight='balanced', fit_intercept=False)\n",
    "    ProbModel.fit(X_train, y_train)\n",
    "    probLR2 = ProbModel.predict_proba(X_test)\n",
    "    pranks = range(0, 100, 10)\n",
    "    pranks = pranks[::-1]\n",
    "    probability_to_install = probLR2[:, 1]\n",
    "    Test_df = pd.DataFrame(probability_to_install, columns=['Prob'])\n",
    "    Test_df['Outcome'] = y_test\n",
    "    num_of_customers = np.empty((10))\n",
    "    num_response = np.empty((10))\n",
    "    response_rate = np.empty((10))\n",
    "    total_pos_outcomes = Test_df['Outcome'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = ProbModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.64      0.78    199898\n",
      "        1.0       0.00      0.81      0.00       102\n",
      "\n",
      "avg / total       1.00      0.64      0.78    200000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  confusion_matrix(y_test,Z, labels=[True, False]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    83  71841]\n",
      " [    19 128057]]\n"
     ]
    }
   ],
   "source": [
    "print cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title, fontsize=25)\n",
    "    plt.colorbar()\n",
    "    tick_marks = [0, 1]\n",
    "    plt.xticks(tick_marks, ['T', 'F'], rotation=45, fontsize=25)\n",
    "    plt.yticks(tick_marks, ['T', 'F'], fontsize=25)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('Truth', fontsize=25)\n",
    "    plt.xlabel('Prediction', fontsize=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Iz8+nSZMmrrvxFKvs3+JRfK7wf//73zkfPlauXMkjjzzier9/PwxePBR94MABryYJlfbe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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110148990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\", {'axes.grid' : False})\n",
    "\n",
    "plot_confusion_matrix(cm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = np.array(X_rsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = StratifiedKFold(y_com, n_folds=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JMLYUCATVgsGDB7NlyxaioqLo378/devWxcvLi7S0NK5du4YkSfTq1euOz1gQ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P2LBhA25ubnTr1o07d+5w8uRJWrZsqbXyCGDLli08fvyYcePGFViNExUVVexD6IYNG8aJEyc4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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3ef5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = StratifiedKFold(y_rsampled, n_folds=6)\n",
    "classifier = LogisticRegression(class_weight='balanced', fit_intercept=False)\n",
    "\n",
    "mean_tpr = 0.0\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "all_tpr = []\n",
    "\n",
    "for i, (train, test) in enumerate(cv):\n",
    "    probas_ = classifier.fit(aa[train], y_rsampled[train]).predict_proba(aa[test])\n",
    "    # Compute ROC curve and area the curve\n",
    "    fpr, tpr, thresholds = roc_curve(y_rsampled[test], probas_[:, 1])\n",
    "    mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "    mean_tpr[0] = 0.0\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i, roc_auc))\n",
    "\n",
    "plt.plot([0, 1], [0, 1], '--', color=(0.6, 0.6, 0.6), label='Random')\n",
    "\n",
    "mean_tpr /= len(cv)\n",
    "mean_tpr[-1] = 1.0\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "plt.plot(mean_fpr, mean_tpr, 'k--',\n",
    "         label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)\n",
    "\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=25)\n",
    "plt.ylabel('True Positive Rate', fontsize=25)\n",
    "plt.xticks(fontsize=25)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.title('Receiver operating characteristic', fontsize=25)\n",
    "plt.legend(loc=\"lower right\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0011786198361718428"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "80.0/(67796+80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11800106200955808"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "80.0/(67796)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JSYlb3Wq1UlNTQ2lpKdu2bcPlchEdHe2275KSEgzDOL2jFhERETdNBoJdu3bR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\ni/MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3fa2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(probLR2[:,1], bins = 50)\n",
    "plt.xlabel('Probabilities', fontsize = 18)\n",
    "plt.title('Histogram of Outputted Probabilities', fontsize = 18)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.yticks(fontsize = 14)\n",
    "plt.savefig('img/probabilities.png', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_percent = (np.array(range(0,100,20))/100.0)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rank_percent = np.insert(rank_percent,0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  0.8,  0.6,  0.4,  0.2,  0. ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binning(dataframe):\n",
    "    Num_per_bins = []\n",
    "    response_per_bins = []\n",
    "    for i in range(1,len(rank_percent)):\n",
    "        Num_per_bins.append(dataframe[np.logical_and(dataframe['Prob']< rank_percent[i-1], dataframe['Prob'] > rank_percent[i])].count()['Outcome'])\n",
    "        response_per_bins.append(dataframe[np.logical_and(dataframe['Prob']< rank_percent[i-1], dataframe['Prob'] > rank_percent[i])].sum()['Outcome'])\n",
    "    return Num_per_bins, response_per_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Num_per_bins = []\n",
    "response_per_bins = []\n",
    "for i in range(1,len(rank_percent)):\n",
    "    Num_per_bins.append(Test_df[np.logical_and(Test_df['Prob']< rank_percent[i-1], Test_df['Prob'] > rank_percent[i])].count()['Outcome'])\n",
    "    response_per_bins.append(Test_df[np.logical_and(Test_df['Prob']< rank_percent[i-1], Test_df['Prob'] > rank_percent[i])].sum()['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_bins, response_bins = binning(Test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4981, 53777, 30228, 34846, 76168]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 69.0, 13.0, 6.0, 9.0]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4981, 53777, 30228, 34846, 76168]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Num_per_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 69.0, 13.0, 6.0, 9.0]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_per_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "factor = np.array(response_per_bins) / np.array(Num_per_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00100381,  0.00128308,  0.00043006,  0.00017219,  0.00011816])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = []\n",
    "for i in range(1,len(rank_percent)):\n",
    "    T = (Test_df[np.logical_and(Test_df['Prob']< rank_percent[i-1], Test_df['Prob'] > rank_percent[i])])\n",
    "    alphas.append((rank_percent[i-1] - T['Prob'])/(rank_percent[0]- rank_percent[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting monotonically\n",
    "fit_reg = IsotonicRegression(y_min= factor[0])\n",
    "\n",
    "Tfit = (Test_df[np.logical_and(Test_df['Prob']< 1, Test_df['Prob'] > 0.8)])\n",
    "X_fit = rank_percent[1]- Tfit['Prob']\n",
    "\n",
    "y_fit = (alphas[0]*factor[1]+(1-alphas[0])*factor[0])\n",
    "\n",
    "fit_reg.fit(X_fit,y_fit)\n",
    "\n",
    "a1 = fit_reg.predict(X_fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strictly_decreasing(factor)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 69.0, 13.0, 6.0, 9.0]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_per_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = (alphas[0]*factor[1]+(1-alphas[0])*factor[0])\n",
    "\n",
    "a2 = (alphas[1]*factor[2]+(1-alphas[1])*factor[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "a3 = (alphas[2]*factor[3]+(1-alphas[2])*factor[2])\n",
    "\n",
    "a4 = (alphas[3]*factor[4]+(1-alphas[3])*factor[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a5 = ((1-alphas[4])*factor[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "adjusted_prob = np.concatenate((a1,a2,a3,a4,a5))[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(adjusted_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(probLR2[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAiGEEBMnThQvv/yyKCwsFBs3bhSBgYHi559/FkIIUV5eLoKCgsQHH3wgCgsLxUsvvSTG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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c3fa5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.hist(probLR2[:,1], bins=50, alpha=0.5, label='Logistic Scores' )\n",
    "plt.hist(adjusted_prob, bins=50, color='m' )\n",
    "\n",
    "plt.xlabel('Probabilities', fontsize = 18)\n",
    "plt.title('Adjusted Probabilities', fontsize = 18)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.yticks(fontsize = 14)\n",
    "plt.savefig('img/Adjustedprob.png', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "total_response = []\n",
    "difference = []\n",
    "pdiff = []\n",
    "for o in range(0,1000):        \n",
    "    X_rsampled, y_rsampled = resample(X_com, y_com, n_samples=500000)\n",
    "    probLR2 = ProbModel.predict_proba(X_rsampled)\n",
    "    probability_to_install = probLR2[:, 1]\n",
    "    Test_df2 = pd.DataFrame(probability_to_install, columns=['Prob'])\n",
    "    Test_df2['Outcome'] = y_rsampled\n",
    "\n",
    "    alphas2 = []\n",
    "    for i in range(1,len(rank_percent)):\n",
    "        T = (Test_df2[np.logical_and(Test_df2['Prob']< rank_percent[i-1], Test_df2['Prob'] > rank_percent[i])])\n",
    "        alphas2.append((rank_percent[i-1] - T['Prob'])/(rank_percent[0]- rank_percent[1]))\n",
    "\n",
    "    Num_per_bins2 = []\n",
    "    response_per_bins2 = []\n",
    "    for i in range(1,len(rank_percent)):\n",
    "        Num_per_bins2.append(Test_df2[np.logical_and(Test_df2['Prob']< rank_percent[i-1], Test_df2['Prob'] > rank_percent[i])].count()['Outcome'])\n",
    "        response_per_bins2.append(Test_df2[np.logical_and(Test_df2['Prob']< rank_percent[i-1], Test_df2['Prob'] > rank_percent[i])].sum()['Outcome'])\n",
    "    \n",
    "    a1 = (alphas2[0]*factor[1]+(1-alphas2[0])*factor[0]).sum()\n",
    "    a2 = (alphas2[1]*factor[2]+(1-alphas2[1])*factor[1]).sum()\n",
    "    a3 = (alphas2[2]*factor[3]+(1-alphas2[2])*factor[2]).sum()\n",
    "    a4 = (alphas2[3]*factor[4]+(1-alphas2[3])*factor[3]).sum()\n",
    "    a5 = ((1-alphas2[4])*factor[4]).sum()\n",
    "    \n",
    "    total_response.append(np.array(response_per_bins2).sum())\n",
    "    difference.append(np.array(response_per_bins2).sum() - (a1+a2+a3+a4+a5)*1.30)\n",
    "    pdiff.append(difference[o]/total_response[o])\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110676150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pdiff, bins= 20 )\n",
    "plt.savefig('img/scaled.png', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTlVmZqbmzp2rgoICubm5SZJSUlIUHBzsWMUAAKAMhwL9nnvusft81113qWbN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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1123a1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pdiff, bins= 20 )\n",
    "plt.xlabel('Mean difference ratio', fontsize = 18)\n",
    "plt.title('Difference Ratio', fontsize = 18)\n",
    "plt.xticks(fontsize = 14)\n",
    "plt.yticks(fontsize = 14)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('img/imMeandiff.png', dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10118700400518557"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057200620570103118"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 1, 6, 1, 6])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([1, 6])\n",
    "c = np.array([1, 6])\n",
    "np.concatenate((a, b,c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime          object\n",
       "ad_id              int64\n",
       "app_name          object\n",
       "app_categories    object\n",
       "publisher_name    object\n",
       "os                object\n",
       "osv                int64\n",
       "make              object\n",
       "model             object\n",
       "country           object\n",
       "city              object\n",
       "language          object\n",
       "impression          bool\n",
       "clicked             bool\n",
       "installed           bool\n",
       "device            object\n",
       "app_type          object\n",
       "weekday            int64\n",
       "hours              int64\n",
       "week               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4166666666666667"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20.0/48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10118700400518557"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057200620570103118"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27009332705088485"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.10320465648424103"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(pdiff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "factor2 = factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "factoring = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "factoring.append(factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "factoring.append(factor2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167.28843098303074"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1+a2+a3+a4+a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "494"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "34+268+106+62+24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "T1 = Test_df[np.logical_and(Test_df['Prob']< 1, Test_df['Prob'] > 0.80)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = (1 - T1['Prob'])/(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "T2 = Test_df[np.logical_and(Test_df['Prob']< 0.8, Test_df['Prob'] > 0.60)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0010038144950813091"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "alpha = (0.8 - T2['Prob'])/(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         0.663556\n",
       "6         0.352049\n",
       "9         0.488615\n",
       "20        0.552659\n",
       "21        0.923049\n",
       "22        0.549388\n",
       "26        0.920506\n",
       "27        0.301055\n",
       "30        0.743276\n",
       "33        0.594172\n",
       "36        0.586343\n",
       "39        0.620679\n",
       "58        0.238821\n",
       "60        0.463960\n",
       "61        0.342011\n",
       "65        0.256168\n",
       "66        0.521891\n",
       "68        0.905775\n",
       "69        0.665055\n",
       "71        0.375048\n",
       "75        0.822300\n",
       "76        0.607285\n",
       "79        0.661941\n",
       "87        0.709092\n",
       "88        0.133216\n",
       "90        0.810978\n",
       "94        0.689408\n",
       "99        0.004514\n",
       "102       0.540065\n",
       "106       0.908533\n",
       "            ...   \n",
       "199879    0.505963\n",
       "199880    0.916532\n",
       "199887    0.571689\n",
       "199893    0.634649\n",
       "199894    0.414838\n",
       "199896    0.321079\n",
       "199901    0.946992\n",
       "199905    0.690692\n",
       "199906    0.787667\n",
       "199910    0.299368\n",
       "199914    0.176658\n",
       "199915    0.846251\n",
       "199918    0.259854\n",
       "199935    0.281762\n",
       "199942    0.842547\n",
       "199945    0.692599\n",
       "199948    0.387291\n",
       "199949    0.558034\n",
       "199956    0.530421\n",
       "199959    0.414177\n",
       "199961    0.308114\n",
       "199965    0.693841\n",
       "199967    0.156983\n",
       "199968    0.471471\n",
       "199971    0.849370\n",
       "199978    0.293903\n",
       "199979    0.775683\n",
       "199981    0.551790\n",
       "199993    0.732611\n",
       "199998    0.191885\n",
       "Name: Prob, Length: 53777, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.627458148192865"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(alpha*factor[1] + (1-alpha)*factor[0]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in divide\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "impressions_percent = range(0, 110, 10)\n",
    "gains_baseline = impressions_percent\n",
    "\n",
    "Gain_df = pd.DataFrame(num_of_customers, columns=['No. of Impressions'])\n",
    "Gain_df['No. of Installs'] = num_response\n",
    "Gain_df['Response Rate'] = response_rate\n",
    "bin_response = num_response[::-1] - np.roll(num_response[::-1],-1)\n",
    "bin_response[0] = num_response[0]\n",
    "Gain_df['bin response'] = bin_response\n",
    "gains_model = [0]+(list(Gain_df['Response Rate']))\n",
    "lift = np.round(list(np.array(gains_model)/np.array(impressions_percent)), 1)\n",
    "lift[0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hhCheYKCxEwgTIHMWhBCioktJ0YwgVKzu/+IJSLEghBAV1e3bEBYG7u4wYQLs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\naj34mvRGT4sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110481a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r16/HxYsXsWrVKgDAoUOHEBQUhEmTJmHnzp1wd3eHr68vEhIS8s3y22+/wcbGBrt370b7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\nIYQwJEGCEEIIIQz9BTjxqzDgJCZiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110aeb490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gains plot\n",
    "plt.plot(impressions_percent, gains_model, '--', color=(0, 0, 0),\n",
    "         label='Mean')\n",
    "plt.plot(impressions_percent, gains_baseline, 'rs--')\n",
    "plt.xlim([0, 100.5])\n",
    "plt.xlabel('% of Impressions Sampled', fontsize=18)\n",
    "plt.ylabel('% of Installs Found', fontsize=18)\n",
    "plt.title('Gains Chart', fontsize=18)\n",
    "plt.xticks(impressions_percent, impressions_percent, fontsize=14)\n",
    "plt.yticks(gains_baseline, gains_baseline, fontsize=14)\n",
    "plt.legend(['Model', 'Baseline'], fontsize=14, loc=0)\n",
    "plt.show()\n",
    "\n",
    "# Lift plot\n",
    "plt.plot(gains_baseline, lift, '--', color=(0, 0, 0),\n",
    "         label='Mean')\n",
    "plt.plot(gains_baseline, np.ones(11), 'rs--')\n",
    "plt.xlabel('% of Impressions Sampled', fontsize=18)\n",
    "plt.ylabel('Lift', fontsize=18)\n",
    "plt.title('Lift Chart', fontsize=18)\n",
    "plt.xticks(impressions_percent, impressions_percent, fontsize=14)\n",
    "plt.yticks(np.arange(0, max(lift)+0.5, 0.5), np.arange(0, max(lift)+0.5,\n",
    "           0.5), fontsize=14)\n",
    "plt.legend(['Model', 'baseline'], fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
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       "      <th></th>\n",
       "      <th>No. of Impressions</th>\n",
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       "      <th>Response Rate</th>\n",
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      "text/plain": [
       "   No. of Impressions  No. of Installs  Response Rate   bin response\n",
       "0       -2.000000e+00    -2.000000e+00  -2.000000e+00  -2.000000e+00\n",
       "1       -1.730597e-77    -2.000000e+00  -2.000000e+00  2.228088e-314\n",
       "2       2.205145e-314    2.205145e-314  2.205145e-314 -6.932840e-310\n",
       "3       6.932837e-310    6.932837e-310  6.932837e-310   2.000000e+00\n",
       "4       -2.000000e+00    -2.000000e+00  -2.000000e+00   0.000000e+00\n",
       "5       -2.000000e+00    -2.000000e+00  -2.000000e+00  -2.000000e+00\n",
       "6       -2.000000e+00    6.932840e-310  2.425550e-314  6.932617e-310\n",
       "7       -1.730597e-77     0.000000e+00  2.255296e-314   2.000000e+00\n",
       "8      -3.111083e+231    2.228088e-314  2.255444e-314   0.000000e+00\n",
       "9       6.953356e-309    6.953356e-309  2.392118e-314  -2.000000e+00"
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     "execution_count": 68,
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