Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from scipy import interp\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import confusion_matrix, classification_report, recall_score, precision_recall_curve, roc_curve, auc\n",
    "from sklearn.cross_validation import train_test_split, cross_val_score, KFold, StratifiedKFold\n",
    "from patsy import dmatrices\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "#import statsmodels.api as sm\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prob_adjusted_outcome(model_probabilities,threshold):\n",
    "    y_adjusted=[]\n",
    "    for prob in model_probabilities:\n",
    "        if prob[1] > threshold:\n",
    "            y_adjusted.append(True)\n",
    "        else:\n",
    "            y_adjusted.append(False)\n",
    "    return y_adjusted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Dataset and Reordering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename='ATA_data.csv'\n",
    "filename2='ATA2_data.csv'\n",
    "\n",
    "\n",
    "df = pd.read_csv(filename)\n",
    "df=df.drop('Unnamed: 0',1)\n",
    "\n",
    "df2 = pd.read_csv(filename2)\n",
    "df2 = df2.drop('Unnamed: 0',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_com=pd.concat([df,df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00045545725525535932"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df_com['installed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "cats_to_keep=['entertainment', 'games', 'lifestyle', 'music', 'photography', 'productivity', \n",
    "             'social_networking', 'sports', 'travel', 'utilities']\n",
    "\n",
    "df_com=df_com[df_com.app_type.isin(cats_to_keep)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking interesting features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols_to_keep=['installed', 'creative_id','clicked','impression','app_type','weekday','hours','model','country','publisher_name','language','app_name']\n",
    "filtered_df=df_com[cols_to_keep]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ratio=np.mean(df_com['installed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Downsampling by target downsample rate\n",
    "TotalData=100000\n",
    "Fsamples=int(np.round(TotalData*(1-ratio)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "False_df=filtered_df[filtered_df['installed'] == False]\n",
    "Truth_df=filtered_df[filtered_df['installed'] == True]\n",
    "\n",
    "df2_false=resample(False_df, n_samples=Fsamples, random_state=0, replace = False)\n",
    "df2_truth=resample(Truth_df, n_samples=TotalData-Fsamples, random_state=0, replace = False)\n",
    "# Merge the downsampled False with total True outcomes\n",
    "less_imbalance=pd.concat([df2_false,df2_truth])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 12)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_imbalance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_com,X_com = dmatrices('installed ~ hours + C(weekday) + C(app_type) + C(country) + C(model) +C(publisher_name) + C(language) + C(app_name)'\n",
    "               ,less_imbalance, return_type='dataframe'\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_com=np.ravel(y_com.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yy=y_com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XX=np.array(X_com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0., ...,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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506xZC1JTvdwmxeFQIaIKhaJhsmLFar/mDwXK4v5dI3+8dgBlKCegUCgU4cfp0ydZu/bb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cff0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run classifier with cross-validation and plot ROC curves\n",
    "cv = StratifiedKFold(yy, n_folds=5)\n",
    "classifier = LogisticRegression(fit_intercept=False, class_weight='balanced')\n",
    "\n",
    "mean_tpr = 0.0\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "all_tpr = []\n",
    "class_score = 0\n",
    "for i, (train, test) in enumerate(cv):\n",
    "    probas_ = classifier.fit(XX[train], yy[train]).predict_proba(XX[test])\n",
    "    # Compute ROC curve and area the curve\n",
    "    fpr, tpr, thresholds = roc_curve(yy[test], probas_[:, 1])\n",
    "    mean_tpr += interp(mean_fpr, fpr, tpr)\n",
    "    mean_tpr[0] = 0.0\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    if roc_auc - class_score > 0:\n",
    "        class_score = roc_auc\n",
    "        better_classifier = classifier\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='Luck')\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')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='auto', dual=False,\n",
       "          fit_intercept=False, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KSqJHjx4MGTKES5cuERFxVxv5U6wYLy1air2Dtf9J5x8FaT/nFmv02drfmL1A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\nQgjhwCQICCGEA5MgIIQQDuz/AftOGui+HHeWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d182810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run classifier with cross-validation and plot ROC curves\n",
    "cv = StratifiedKFold(yy, n_folds=5)\n",
    "classifier = RandomForestClassifier(n_jobs = 2, class_weight = 'auto', n_estimators=100)\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(XX[train], yy[train]).predict_proba(XX[test])\n",
    "    # Compute ROC curve and area the curve\n",
    "    fpr, tpr, thresholds = roc_curve(yy[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='Luck')\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')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_com,y_com, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "modelLogR2 = LogisticRegression(class_weight='balanced',fit_intercept=False);\n",
    "modelLogR2.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use Training Set to verify if model is learning anything\n",
    "verify_train = modelLogR2.predict(X_train)\n",
    "prob_train = modelLogR2.predict_proba(X_train)\n",
    "recall_score(y_train,verify_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   36  9640]\n",
      " [    0 60324]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.86      0.93     69964\n",
      "        1.0       0.00      1.00      0.01        36\n",
      "\n",
      "avg / total       1.00      0.86      0.93     70000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#metrics for recall and confusion matrix\n",
    "\n",
    "print confusion_matrix(y_train, verify_train, labels=[True, False]).transpose()\n",
    "print classification_report(y_train, verify_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29999999999999999"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction using test set\n",
    "predictedLR2 = modelLogR2.predict(X_test)\n",
    "probLR2 = modelLogR2.predict_proba(X_test)\n",
    "recall_score(y_test,predictedLR2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.99210336e-01,   7.89663770e-04],\n",
       "       [  9.93295218e-01,   6.70478227e-03],\n",
       "       [  3.65649258e-01,   6.34350742e-01],\n",
       "       ..., \n",
       "       [  9.98680821e-01,   1.31917897e-03],\n",
       "       [  9.99999710e-01,   2.89547649e-07],\n",
       "       [  9.99989311e-01,   1.06885853e-05]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probLR2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    3  4247]\n",
      " [    7 25743]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      0.86      0.92     29990\n",
      "        1.0       0.00      0.30      0.00        10\n",
      "\n",
      "avg / total       1.00      0.86      0.92     30000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#metrics for recall and confusion matrix\n",
    "\n",
    "cm = confusion_matrix(y_test, predictedLR2, labels=[True, False]).transpose()\n",
    "print cm\n",
    "print classification_report(y_test, predictedLR2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418.22310911807369"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probLR2[:,1].sum()/y_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probLR2.sum()/y_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 1507)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_com.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm=[LogisticRegression(fit_intercept=False)]\n",
    "cvs=5\n",
    "model_aucs=np.empty((len(rm),cvs))\n",
    "model_recalls=np.empty((len(rm),cvs))\n",
    "model_precision=np.empty((len(rm),cvs))\n",
    "\n",
    "for i,models in enumerate(rm):\n",
    "    \n",
    "    model_recalls[i,:] = cross_val_score(models, X_com, y_com, scoring = 'recall', cv = cvs)\n",
    "    model_precision[i,:] = cross_val_score(models, X_com, y_com, scoring = 'precision', cv = cvs)\n",
    "    model_aucs[i,:] = cross_val_score(models, X_com, y_com, scoring = 'roc_auc', cv = cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.        ]\n",
      " [ 0.        ]\n",
      " [ 0.        ]\n",
      " [ 0.        ]\n",
      " [ 0.66932104]\n",
      " [ 0.06348148]]\n"
     ]
    }
   ],
   "source": [
    "summary_matrices = np.empty((6,len(rm)))\n",
    "summary_matrices[0, :] = np.mean(model_recalls, axis=1)\n",
    "summary_matrices[1, :] = np.std(model_recalls, axis = 1)\n",
    "summary_matrices[2, :] = np.mean(model_precision, axis=1)\n",
    "summary_matrices[3, :] = np.std(model_precision, axis = 1)\n",
    "summary_matrices[4, :] = np.mean(model_aucs, axis=1)\n",
    "summary_matrices[5, :] = np.std(model_aucs, axis = 1)\n",
    "print summary_matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "summary_df=pd.DataFrame(summary_matrices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.669321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.063481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.000000\n",
       "1  0.000000\n",
       "2  0.000000\n",
       "3  0.000000\n",
       "4  0.669321\n",
       "5  0.063481"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "            criterion='gini', max_depth=None, max_features='auto',\n",
       "            max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelRandomForest= RandomForestClassifier(class_weight='balanced')\n",
    "modelRandomForest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1507,)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = modelRandomForest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in modelRandomForest.estimators_], axis=0)\n",
    "indices = np.argsort(importances)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1507,)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(range(400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.01585746,  0.02383725, ...,  0.        ,\n",
       "        0.        ,  0.11744173])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelRandomForest.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(importances[indices][0:400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1507"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelRandomForest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gT+DGWoTKzPYBDgc2NrP1mjnu78B/gKPNrMOMMjp99/z4RPczgSWAvZsSKjNb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\nIiIiIqJhEZVURERERETD4v8DjHDyx/PRkHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111608310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_limit = 25\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(index_limit), importances[indices][0:index_limit],\n",
    "       color=\"r\", yerr=std[indices][0:index_limit], align=\"center\")\n",
    "plt.xticks(range(index_limit), feature_names[0:index_limit], rotation=45)\n",
    "plt.xlim([-1, index_limit])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Random Forest Predictions\n",
    "predictedRF = modelRandomForest.predict(X_test)\n",
    "probsRF = modelRandomForest.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.],\n",
       "       [ 1.,  0.],\n",
       "       [ 1.,  0.],\n",
       "       ..., \n",
       "       [ 1.,  0.],\n",
       "       [ 1.,  0.],\n",
       "       [ 1.,  0.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probsRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0    12]\n",
      " [   10 29978]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00     29990\n",
      "        1.0       0.00      0.00      0.00        10\n",
      "\n",
      "avg / total       1.00      1.00      1.00     30000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Look at precision/recall and confusion matrix\n",
    "\n",
    "print confusion_matrix(y_test,predictedRF, labels=[True, False]).transpose()\n",
    "print classification_report(y_test,predictedRF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'probs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-88-b0fa1e5eec72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprobs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'probs' is not defined"
     ]
    }
   ],
   "source": [
    "probs.sum()/y_test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prob_class_outcome(model_probabilities,select_class):\n",
    "    prob_class=[]\n",
    "    for prob in model_probabilities:\n",
    "            prob_class.append(prob[select_class])\n",
    "    return prob_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "probRF1=prob_class_outcome(probsRF,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.98780000e+04,   6.80000000e+01,   1.60000000e+01,\n",
       "          2.00000000e+00,   1.20000000e+01,   1.20000000e+01,\n",
       "          7.00000000e+00,   3.00000000e+00,   1.00000000e+00,\n",
       "          1.00000000e+00]),\n",
       " array([ 0.        ,  0.08982547,  0.17965094,  0.26947641,  0.35930188,\n",
       "         0.44912734,  0.53895281,  0.62877828,  0.71860375,  0.80842922,\n",
       "         0.89825469]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113fe2110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(probRF1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(probRF1 == 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}