07 D_Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('00 df.csv')\n",
    "train = df[df['flag']=='train']\n",
    "test = df[df['flag']=='test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_feats = ['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']\n",
    "\n",
    "y_train = train['y']\n",
    "x_train = train[['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']]\n",
    "x_train = pd.get_dummies(x_train,columns=cat_feats,drop_first=True)\n",
    "\n",
    "y_test = test['y']\n",
    "x_test = test[['age_bin','capital_gl_bin','education_bin','hours_per_week_bin','msr_bin','occupation_bin','race_sex_bin']]\n",
    "x_test = pd.get_dummies(x_test,columns=cat_feats,drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20703c75630>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiID27inwly9fhpubGwAgJycHubm52LRpEwYNGoSFCxfClu9/tGrFly5hR2goLh84gCGv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20703c416a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_depth_options = [2,4,6,8,10,12,14,16,18,20]\n",
    "for trees in max_depth_options:\n",
    "    model = DecisionTreeClassifier(max_depth=trees, random_state=101)\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    accuracy = np.mean(y_test==y_pred)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_depth_options).plot(color=\"darkred\",marker=\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20703c75e10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l7XA4yMjIAGDatGm0tLR4t8XExDB+/Hi6urro6uoi7P+u7vLH+2RmZnL48GGOHz9OQkIC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\nJp47ujzlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20704b372b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "max_features_options = ['auto',None,'sqrt',0.95,0.75,0.5,0.25,0.10]\n",
    "for trees in max_features_options:\n",
    "    model = DecisionTreeClassifier(max_depth=10, random_state=101, max_features = trees)\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    accuracy = np.mean(y_test==y_pred)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, max_features_options).plot(kind=\"bar\",color=\"darkred\",ylim=(0.7,0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20703fc07b8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1a5D10EP4ads2PJGYyDHZZBTMbWzw/ObNaLh0CXtjYqBWq4UuiYjawOC+Bxf+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\nIgPC4CYiIjIgDG4iIiID8v8BZZMUEepbpOUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20703f6da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = []\n",
    "min_samples_leaf_options = [5,10,15,20,25,30,35,40,45,50]\n",
    "for trees in min_samples_leaf_options:\n",
    "    model = DecisionTreeClassifier(max_depth=10, random_state=101, max_features = None, min_samples_leaf = trees)\n",
    "    model.fit(x_train, y_train)\n",
    "    y_pred = model.predict(x_test)\n",
    "    accuracy = np.mean(y_test==y_pred)\n",
    "    results.append(accuracy)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "pd.Series(results, min_samples_leaf_options).plot(color=\"darkred\",marker=\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(max_depth=10, random_state=101, max_features = None, min_samples_leaf = 15)\n",
    "dtree.fit(x_train, y_train)\n",
    "y_pred=dtree.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11521   914]\n",
      " [ 1653  2193]]\n",
      "accuracy: 0.842331552116\n",
      "precision: 0.705825555198\n",
      "recall: 0.570202808112\n",
      "f1 score:  0.630806845966\n"
     ]
    }
   ],
   "source": [
    "test_calc = pd.concat([pd.DataFrame(y_test).reset_index(drop=True),pd.DataFrame(y_pred).reset_index(drop=True)],axis=1)\n",
    "test_calc.rename(columns={0: 'predicted'}, inplace=True)\n",
    "\n",
    "test_calc['predicted'] = test_calc['predicted'].apply(lambda x: 1 if x > 0.5 else 0)\n",
    "df_table = confusion_matrix(test_calc['y'],test_calc['predicted'])\n",
    "print (df_table)\n",
    "\n",
    "print('accuracy:', (df_table[0,0] + df_table[1,1]) / (df_table[0,0] + df_table[0,1] + df_table[1,0] + df_table[1,1]))\n",
    "print ('precision:', df_table[1,1] / (df_table[1,1] + df_table[0,1]))\n",
    "print('recall:', df_table[1,1] / (df_table[1,1] + df_table[1,0]))\n",
    "\n",
    "p = df_table[1,1] / (df_table[1,1] + df_table[0,1])\n",
    "r = df_table[1,1] / (df_table[1,1] + df_table[1,0])\n",
    "print('f1 score: ', (2*p*r)/(p+r))"
   ]
  }
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