Jenny-Models (pCTR).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from itertools import cycle\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_all = pd.read_csv('train1090.csv')\n",
    "#train = pd.read_csv('train1040.csv')\n",
    "#train = pd.read_csv('train100900.csv')\n",
    "#train_all = pd.read_csv('train005995.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>click</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>adexchange</th>\n",
       "      <th>slotwidth</th>\n",
       "      <th>...</th>\n",
       "      <th>browser_other</th>\n",
       "      <th>browser_safari</th>\n",
       "      <th>browser_sogou</th>\n",
       "      <th>browser_theworld</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>payprice</th>\n",
       "      <th>counts_c</th>\n",
       "      <th>freq_c</th>\n",
       "      <th>counts_r</th>\n",
       "      <th>freq_r</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1907</td>\n",
       "      <td>314</td>\n",
       "      <td>314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>146</td>\n",
       "      <td>162</td>\n",
       "      <td>3</td>\n",
       "      <td>960</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
       "      <td>5081</td>\n",
       "      <td>0.002090</td>\n",
       "      <td>160464</td>\n",
       "      <td>0.066008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3234</td>\n",
       "      <td>579</td>\n",
       "      <td>579</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>950</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>23899</td>\n",
       "      <td>0.009831</td>\n",
       "      <td>108509</td>\n",
       "      <td>0.044636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5886</td>\n",
       "      <td>1069</td>\n",
       "      <td>1069</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>960</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>20</td>\n",
       "      <td>11572</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>108509</td>\n",
       "      <td>0.044636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7172</td>\n",
       "      <td>1104</td>\n",
       "      <td>1104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>80</td>\n",
       "      <td>63513</td>\n",
       "      <td>0.026126</td>\n",
       "      <td>63513</td>\n",
       "      <td>0.026126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4143</td>\n",
       "      <td>1547</td>\n",
       "      <td>1547</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>368</td>\n",
       "      <td>373</td>\n",
       "      <td>2</td>\n",
       "      <td>728</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>184</td>\n",
       "      <td>723</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>8991</td>\n",
       "      <td>0.003699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0  Unnamed: 0  index  click  weekday  hour  region  city  adexchange  \\\n",
       "0     1907         314    314      0        0     9     146   162           3   \n",
       "1     3234         579    579      0        4    18       3     4           1   \n",
       "2     5886        1069   1069      0        0    14       3     5           3   \n",
       "3     7172        1104   1104      1        1    19      79    79           3   \n",
       "4     4143        1547   1547      0        2    12     368   373           2   \n",
       "\n",
       "   slotwidth    ...     browser_other  browser_safari  browser_sogou  \\\n",
       "0        960    ...                 0               0              0   \n",
       "1        950    ...                 0               0              0   \n",
       "2        960    ...                 0               0              0   \n",
       "3       1000    ...                 0               0              0   \n",
       "4        728    ...                 0               0              0   \n",
       "\n",
       "   browser_theworld  bidprice  payprice  counts_c    freq_c  counts_r  \\\n",
       "0                 0       300        50      5081  0.002090    160464   \n",
       "1                 0       238         2     23899  0.009831    108509   \n",
       "2                 0       300        20     11572  0.004760    108509   \n",
       "3                 0       300        80     63513  0.026126     63513   \n",
       "4                 0       300       184       723  0.000297      8991   \n",
       "\n",
       "     freq_r  \n",
       "0  0.066008  \n",
       "1  0.044636  \n",
       "2  0.044636  \n",
       "3  0.026126  \n",
       "4  0.003699  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.sort_values(by=['index']).reset_index()\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>click</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>region</th>\n",
       "      <th>city</th>\n",
       "      <th>adexchange</th>\n",
       "      <th>slotwidth</th>\n",
       "      <th>slotheight</th>\n",
       "      <th>slotprice</th>\n",
       "      <th>...</th>\n",
       "      <th>browser_other</th>\n",
       "      <th>browser_safari</th>\n",
       "      <th>browser_sogou</th>\n",
       "      <th>browser_theworld</th>\n",
       "      <th>bidprice</th>\n",
       "      <th>payprice</th>\n",
       "      <th>counts_c</th>\n",
       "      <th>freq_c</th>\n",
       "      <th>counts_r</th>\n",
       "      <th>freq_r</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>314</td>\n",
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       "      <td>162</td>\n",
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       "      <td>960</td>\n",
       "      <td>90</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>50</td>\n",
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       "      <td>0.002090</td>\n",
       "      <td>160464</td>\n",
       "      <td>0.066008</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>579</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>950</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>23899</td>\n",
       "      <td>0.009831</td>\n",
       "      <td>108509</td>\n",
       "      <td>0.044636</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1069</td>\n",
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       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>960</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>20</td>\n",
       "      <td>11572</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>108509</td>\n",
       "      <td>0.044636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>1000</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>80</td>\n",
       "      <td>63513</td>\n",
       "      <td>0.026126</td>\n",
       "      <td>63513</td>\n",
       "      <td>0.026126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1547</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>368</td>\n",
       "      <td>373</td>\n",
       "      <td>2</td>\n",
       "      <td>728</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>184</td>\n",
       "      <td>723</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>8991</td>\n",
       "      <td>0.003699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  click  weekday  hour  region  city  adexchange  slotwidth  \\\n",
       "0    314      0        0     9     146   162           3        960   \n",
       "1    579      0        4    18       3     4           1        950   \n",
       "2   1069      0        0    14       3     5           3        960   \n",
       "3   1104      1        1    19      79    79           3       1000   \n",
       "4   1547      0        2    12     368   373           2        728   \n",
       "\n",
       "   slotheight  slotprice    ...     browser_other  browser_safari  \\\n",
       "0          90         50    ...                 0               0   \n",
       "1          90          0    ...                 0               0   \n",
       "2          90         20    ...                 0               0   \n",
       "3          90         80    ...                 0               0   \n",
       "4          90          5    ...                 0               0   \n",
       "\n",
       "   browser_sogou  browser_theworld  bidprice  payprice  counts_c    freq_c  \\\n",
       "0              0                 0       300        50      5081  0.002090   \n",
       "1              0                 0       238         2     23899  0.009831   \n",
       "2              0                 0       300        20     11572  0.004760   \n",
       "3              0                 0       300        80     63513  0.026126   \n",
       "4              0                 0       300       184       723  0.000297   \n",
       "\n",
       "   counts_r    freq_r  \n",
       "0    160464  0.066008  \n",
       "1    108509  0.044636  \n",
       "2    108509  0.044636  \n",
       "3     63513  0.026126  \n",
       "4      8991  0.003699  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.drop(train.columns[[0,1]],axis =1)\n",
    "#test= test.drop(test.columns[[0]],axis = 1)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8965,)\n",
      "(8965, 100)\n"
     ]
    }
   ],
   "source": [
    "#100 features\n",
    "y_ctr = train['click']\n",
    "x_ctr = train.drop(['index','region','city','counts_r','counts_c','click','payprice','bidprice'],axis =1)\n",
    "print(y_ctr.shape)\n",
    "print(x_ctr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7172, 100)\n",
      "(7172,)\n",
      "(1793, 100)\n",
      "(1793,)\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(x_ctr, y_ctr, test_size=0.2)\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing out different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 1 - Decision Tree Classifier\n",
      "score = 0.846625766871\n",
      "auc score = 0.743342814114\n",
      "error_model_1 =  0.153374233129\n",
      "Running time = 0.053954124450683594\n",
      "pCTR: \n",
      "[ 0.08076923  0.          0.7037037  ...,  0.          0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 1 - Decision Tree Classifier\"\n",
    "\n",
    "start = time.time()\n",
    "clf = DecisionTreeClassifier(max_depth = 15, random_state = 0)\n",
    "clf.fit(train_x, train_y)\n",
    "prediction1 = np.array(clf.predict(test_x))\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction1)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "score = accuracy_score(test_y, prediction1)\n",
    "end = time.time()\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m1 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 1 - Decision Tree Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc score =\", auc)\n",
    "print (\"error_model_1 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m1[:,1])\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1793, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_pctr=pd.DataFrame(data =results, columns=['pCTR'])\n",
    "r.head(5)\n",
    "r.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 2 - Extra Tree Classifier\n",
      "score = 0.876185164529\n",
      "auc score = 0.726471152194\n",
      "error_model_2 =  0.123814835471\n",
      "Running time = 0.07992911338806152\n",
      "pCTR: \n",
      "[ 0.13458065  0.04935949  0.47238114 ...,  0.05053282  0.10428407\n",
      "  0.09057756]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 2 - Extra Tree Classifier\"\n",
    "\n",
    "start = time.time()\n",
    "clf = ExtraTreesClassifier(max_depth = 15, random_state=0)\n",
    "#class_weight={0:0.8,1:0.2}, max_depth = 10, n_estimators=10,min_samples_leaf=10, \n",
    "clf.fit(train_x, train_y)\n",
    "prediction2 = np.array(clf.predict(test_x))\n",
    "score = accuracy_score(test_y, prediction2)\n",
    "end = time.time()\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction2)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m2 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 2 - Extra Tree Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc score =\", auc)\n",
    "print (\"error_model_2 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m2[:,1])\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 3 - Random Forest Classifier\n",
      "score = 0.87228109314\n",
      "auc score = 0.713056912648\n",
      "error_model_3 =  0.12771890686\n",
      "Running time = 0.07909703254699707\n",
      "pCTR: \n",
      "[ 0.28027405  0.0601844   0.47911117 ...,  0.02190687  0.082931    0.15446418]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 3 - Random Forest Classifier\"\n",
    "\n",
    "start = time.time()\n",
    "clf = RandomForestClassifier(max_depth = 15)\n",
    "#min_samples_leaf=20\n",
    "clf.fit(train_x, train_y)\n",
    "prediction3 = np.array(clf.predict(test_x))\n",
    "score = accuracy_score(test_y, prediction3)\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction3)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m3 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 3 - Random Forest Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc score =\", auc)\n",
    "print (\"error_model_3 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m3[:,1])\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 4 - Adaboost Classifier\n",
      "score = 0.817066369214\n",
      "auc score = 0.725029061523\n",
      "error_model_4 =  0.182933630786\n",
      "Running time = 0.7671270370483398\n",
      "pCTR: \n",
      "[ 0.0250941   0.05013313  0.18827188 ...,  0.02517506  0.04887171\n",
      "  0.04934203]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 4 - Adaboost Classifier\"\n",
    "\n",
    "start = time.time()\n",
    "clf_base = DecisionTreeClassifier()\n",
    "clf = AdaBoostClassifier(n_estimators=50, base_estimator=clf_base,learning_rate=0.5)\n",
    "clf.fit(train_x, train_y)\n",
    "prediction4 = np.array(clf.predict(test_x))\n",
    "score = accuracy_score(test_y, prediction4)\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction4)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "end = time.time()\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m4 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 4 - Adaboost Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc score =\", auc)\n",
    "print (\"error_model_4 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n",
    "pCTR_1_m4 = pCTR_m4[:,1]\n",
    "print(pCTR_1_m4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 6 - Logistic Classifier\n",
      "score = 0.87228109314\n",
      "auc = 0.718554633665\n",
      "error_model_6 =  0.12771890686\n",
      "Running time = 0.26584506034851074\n",
      "pCTR: \n",
      "[ 0.02788685  0.09790855  0.52887312 ...,  0.02149759  0.12991526\n",
      "  0.08619453]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 6 - Logistic Regression Classifier\"\n",
    "\n",
    "start = time.time()\n",
    "clf = LogisticRegression()\n",
    "clf.fit(train_x, train_y)\n",
    "prediction6 = np.array(clf.predict(test_x))\n",
    "score = accuracy_score(test_y, prediction6)\n",
    "end = time.time()\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction6)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m6 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 6 - Logistic Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc =\", auc)\n",
    "print (\"error_model_6 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m6[:,1])\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL 7 - Xgboost Classifier\n",
      "score = 0.887339654211\n",
      "auc = 0.738879723246\n",
      "error_model_7 =  0.112660345789\n",
      "Running time = 1.1500837802886963\n",
      "pCTR: \n",
      "[ 0.05912987  0.07069881  0.52397525 ...,  0.03444372  0.07563879\n",
      "  0.04898234]\n"
     ]
    }
   ],
   "source": [
    "#\"MODEL 7 - Xgboost Classifier\"\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "start = time.time()\n",
    "clf = XGBClassifier()\n",
    "clf.fit(train_x, train_y)\n",
    "prediction7 = np.array(clf.predict(test_x))\n",
    "\n",
    "fpr, tpr, thresholds = metrics.roc_curve(test_y, prediction7)\n",
    "auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "score = accuracy_score(test_y, prediction7)\n",
    "end = time.time()\n",
    "\n",
    "#for row in test_x.iterrows():\n",
    "#    test_x['pCTR'] = clf.predict_proba(test_x[row])\n",
    "pCTR_m7 = clf.predict_proba(test_x)  \n",
    "    \n",
    "print(\"MODEL 7 - Xgboost Classifier\")\n",
    "print(\"score =\", score)\n",
    "print(\"auc =\", auc)\n",
    "print (\"error_model_7 = \", 1-score)\n",
    "print(\"Running time =\", end - start)\n",
    "print(\"pCTR: \")\n",
    "print(pCTR_m7[:,1])\n",
    "#print(pCTR_m1[1],axis =1)\n",
    "#test_x.assign('pCTR')\n",
    "#test_x['pCTR'] = pCTR_m1[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pCTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.059130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.070699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.523975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.090823</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.131108</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       pCTR\n",
       "0  0.059130\n",
       "1  0.070699\n",
       "2  0.523975\n",
       "3  0.090823\n",
       "4  0.131108"
      ]
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     "execution_count": 45,
     "metadata": {},
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   ],
   "source": [
    "result_m7 = pd.DataFrame(data = pCTR_m7[:,1], columns=['pCTR'])\n",
    "result_m7.head(5)\n"
   ]
  },
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