SMOTE--Entropy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SMOTE- Entropy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic Minority Oversampling Technique (SMOTE) with 'entropy' as criterion for best split. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing important libraries and modules. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split # to split the data\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the data into dataframe. Extracting the features and class varibales as well as fraud data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hpandya\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\hpandya\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('https://people.rit.edu/~hvp4259/project/data/creditcard.csv')\n",
    "print(data.shape)\n",
    "X = data.ix[:, data.columns != 'Class']\n",
    "y = data.ix[:, data.columns == 'Class']\n",
    "fraud_record = data[data.Class == 1]\n",
    "y_fraud_record = fraud_record.ix[:, fraud_record.columns == 'Class']\n",
    "x_fraud_record = fraud_record.ix[:, fraud_record.columns != 'Class']\n",
    "number_records_fraud = len(data[data.Class == 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining the utility function called plot_confusion_matrix for displaying the confusion matrix in a nice UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining the utility function called show_data for displaying precision, recall and accuracy from the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_data(cm, print_res = 0):\n",
    "    tp = cm[1,1]\n",
    "    fn = cm[1,0]\n",
    "    fp = cm[0,1]\n",
    "    tn = cm[0,0]\n",
    "    if print_res == 1:\n",
    "        print('Precision =     {:.5f}'.format(tp/(tp+fp)))\n",
    "        print('Recall (TPR) =  {:.5f}'.format(tp/(tp+fn)))\n",
    "        print('Accuracy =  {:.5f}'.format((tp+tn)/(tp+tn+fp+fn)))\n",
    "    return tp/(tp+fp), tp/(tp+fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definign the utility function called data_preparation for splitting the data into test and trainign datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def data_prepration(x):\n",
    "    x_features= x.ix[:,x.columns != \"Class\"]\n",
    "    x_labels=x.ix[:,x.columns==\"Class\"]\n",
    "    x_features_train,x_features_test,x_labels_train,x_labels_test = train_test_split(x_features,x_labels,test_size=0.3)\n",
    "    print(\"length of training data\")\n",
    "    print(len(x_features_train))\n",
    "    print(\"length of test data\")\n",
    "    print(len(x_features_test))\n",
    "    return(x_features_train,x_features_test,x_labels_train,x_labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preparing the data, splitting it into test and training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of training data\n",
      "199364\n",
      "length of test data\n",
      "85443\n",
      "Proportion of Normal data in training data is  0.9982995927048013\n",
      "Proportion of fraud data in training data is  0.001700407295198732\n"
     ]
    }
   ],
   "source": [
    "data_train_X,data_test_X,data_train_y,data_test_y=data_prepration(data)\n",
    "columns = data_train_X.columns\n",
    "print(\"Proportion of Normal data in training data is \",len(data_train_y[data_train_y[\"Class\"]==0])/len(data_train_X))\n",
    "print(\"Proportion of fraud data in training data is \",len(data_train_y[data_train_y[\"Class\"]==1])/len(data_train_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oversampling the highly disbalanced prepared data and balancing the training dataset with the nornal : fraud data ratio 0.5 : 0.5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  398050\n",
      "Number of normal transcation in oversampled data 199025\n",
      "Number of fraud transcation 199025\n",
      "Proportion of Normal data in oversampled data is  0.5\n",
      "Proportion of fraud data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "os = SMOTE(random_state = 0)\n",
    "os_data_X, os_data_y = os.fit_sample(data_train_X, data_train_y.values.ravel())\n",
    "os_data_X = pd.DataFrame(data = os_data_X, columns = columns )\n",
    "os_data_y = pd.DataFrame(data = os_data_y, columns = [\"Class\"])\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of normal transcation in oversampled data\",len(os_data_y[os_data_y[\"Class\"]==0]))\n",
    "print(\"Number of fraud transcation\",len(os_data_y[os_data_y[\"Class\"]==1]))\n",
    "print(\"Proportion of Normal data in oversampled data is \",len(os_data_y[os_data_y[\"Class\"]==0])/len(os_data_X))\n",
    "print(\"Proportion of fraud data in oversampled data is \",len(os_data_y[os_data_y[\"Class\"]==1])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model with RandomForestClassifier having 100 trees in the forest with criterion for best split as 'entropy'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=10000, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=1000,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf= RandomForestClassifier(n_estimators = 100, criterion = 'gini', max_features= 'sqrt', max_depth=10000, min_samples_split=1000)\n",
    "clf.fit(os_data_X, os_data_y.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving all 100 trees to the local drive in .dot format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#i_tree = 0\n",
    "#for tree_in_forest in clf.estimators_:\n",
    "#    with open('F:/RIT/Sem 2/AT/random_forest_entropy/tree_' + str(i_tree) + '.dot', 'w') as my_file:\n",
    "#        my_file = tree.export_graphviz(tree_in_forest, out_file = my_file)\n",
    "#    i_tree = i_tree + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the model on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85187   103]\n",
      " [   19   134]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAEmCAYAAAAnRIjxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfd9f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =     0.56540\n",
      "Recall (TPR) =  0.87582\n",
      "Accuracy =  0.99857\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(data_test_X)\n",
    "cm = confusion_matrix(data_test_y.values.ravel(), pred)\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm, ['0', '1'], title = 'Confusion Matrix- Test Data')\n",
    "pr, tpr = show_data(cm, print_res = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the model on the entire dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[284027    288]\n",
      " [    40    452]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAEmCAYAAAAqWvi2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6277f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =     0.61081\n",
      "Recall (TPR) =  0.91870\n",
      "Accuracy =  0.99885\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(X)\n",
    "cm = confusion_matrix(y, pred)\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm, ['0', '1'], title = 'Confusion Matrix- Entire Dataset')\n",
    "pr, tpr = show_data(cm, print_res = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the model on only the fraud data instances. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0]\n",
      " [ 40 452]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAEmCAYAAADr3bIaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc627128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =     1.00000\n",
      "Recall (TPR) =  0.91870\n",
      "Accuracy =  0.91870\n"
     ]
    }
   ],
   "source": [
    "pred = clf.predict(x_fraud_record)\n",
    "cm = confusion_matrix(y_fraud_record, pred)\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm, ['0', '1'], title = 'Confusion Matrix- Only fraud records')\n",
    "pr, tpr = show_data(cm, print_res = 1);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}