code1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler,normalize\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "%matplotlib inline\n",
    "from scipy.stats import mode\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from collections import namedtuple, Counter, defaultdict\n",
    "from math import log,ceil,sqrt\n",
    "\n",
    "from sklearn import metrics\n",
    "from math import exp,log10\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(Y_actual,Y_predicted):\n",
    "    conf_matrix =  metrics.confusion_matrix(Y_actual,Y_predicted)\n",
    "    sns.heatmap(conf_matrix, annot=True,  fmt='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "####function to normalize the data##\n",
    "\n",
    "def norm(Z):\n",
    "    mean = np.sum(Z,axis=0)/len(Z)\n",
    "    var = []\n",
    "    for i in range(Z.shape[1]):\n",
    "        sum1 = 0\n",
    "        for j in range(Z.shape[0]):\n",
    "            sum1 += (Z[j,i] - mean[i])**2\n",
    "        var.append(1. *sum1/len(Z))\n",
    "\n",
    "    for i in range(Z.shape[1]):\n",
    "        for j in range(Z.shape[0]):\n",
    "            Z[j,i] = (Z[j,i] - mean[i])/sqrt(var[i])\n",
    "    \n",
    "    \n",
    "    \n",
    "            \n",
    "    return Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def accuracy(Y,Y_hat):\n",
    "                \n",
    "    sum1 = sum([1 for i in range(len(Y)) if Y[i] == Y_hat[i]])\n",
    "    return (1.0*sum1/len(Y))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[ -2.86412480e-01   4.18085780e-01  -1.11026312e-03   1.01863307e-01\n",
      "    6.06415114e-01  -5.56291881e-04   1.02764931e-01  -9.81055026e-02\n",
      "   -3.70475535e-01  -2.69517624e-01   2.97319448e-01  -2.15685035e-01\n",
      "    7.68931624e-03]\n",
      " [ -1.11851050e-01  -4.52169817e-01   4.62665878e-01   2.61633515e-01\n",
      "   -2.72616620e-02  -1.31563296e-01   5.49455212e-01   4.85384408e-02\n",
      "    1.76472086e-01  -2.35707305e-01   1.29797785e-01  -2.72166933e-01\n",
      "    2.42537421e-02]\n",
      " [ -2.85150133e-01  -1.46087187e-01  -4.13561677e-01   3.32325555e-01\n",
      "    1.07426511e-01  -5.01950906e-02   3.59196082e-01  -4.40445294e-01\n",
      "    5.12373485e-02   4.41091076e-01  -4.63254265e-03   2.80753552e-01\n",
      "   -5.26297760e-02]\n",
      " [ -1.65671403e-01   3.77153651e-01   3.54447382e-01  -1.76899460e-01\n",
      "   -2.62484985e-01  -1.24312760e-01   1.30494274e-01   2.66645392e-02\n",
      "    8.31897891e-02   3.54143809e-01   4.50391856e-01   2.28792154e-01\n",
      "    4.28664099e-01]\n",
      " [ -9.10332303e-02   4.38510899e-01  -2.27631512e-01   2.03764802e-01\n",
      "   -1.70554159e-01  -2.88846061e-02   1.87808030e-01  -2.63500789e-02\n",
      "    3.96336918e-01  -4.11238228e-01  -4.06398787e-01  -2.30639220e-02\n",
      "    3.85102540e-01]\n",
      " [ -8.12418102e-02   1.94138600e-01   5.22635007e-01   1.70048002e-01\n",
      "    3.42975189e-04   1.33576705e-01   4.77038639e-02  -8.40022409e-02\n",
      "   -1.70156744e-01  -1.31281818e-01  -3.49345351e-01   5.86054110e-01\n",
      "   -3.38794308e-01]\n",
      " [ -1.50128304e-01   2.68612479e-01   7.63357354e-02   3.05239547e-02\n",
      "    1.38200689e-02   9.16065630e-02  -1.58316207e-01  -7.74643760e-02\n",
      "    6.50223860e-01   8.41507556e-02   2.24730725e-01  -2.00485202e-01\n",
      "   -5.81662989e-01]\n",
      " [  3.89367773e-01   4.60082176e-02   2.33898557e-01   3.87614993e-02\n",
      "    6.36890379e-01  -1.86105180e-01  -6.36965001e-03   8.21744053e-02\n",
      "    3.00094521e-01   3.59023401e-01  -2.82380147e-01  -5.88107262e-03\n",
      "    2.10427269e-01]\n",
      " [ -3.33395898e-01  -2.09857957e-01  -1.89475654e-01   1.32782343e-01\n",
      "    2.25162091e-01  -7.85062275e-02  -1.63134291e-01   6.46598675e-01\n",
      "    2.04078719e-01  -1.43664567e-01   1.79526934e-01   4.39208966e-01\n",
      "    4.78407149e-02]\n",
      " [ -3.97011316e-01  -5.97919219e-02   4.15865994e-02  -4.10620797e-01\n",
      "    1.19133688e-01   6.26549560e-01   2.37744512e-01   1.89795518e-01\n",
      "    3.54504037e-02   2.14957916e-01  -2.98709872e-01  -1.60938652e-01\n",
      "    8.10777740e-02]\n",
      " [ -3.54125244e-01  -8.08147501e-02   9.47646764e-03  -5.81650571e-01\n",
      "    6.51279624e-02  -6.39141866e-01   1.16667815e-02  -1.62408261e-01\n",
      "    4.14314310e-02  -1.08891517e-01  -2.43880615e-01   2.71627642e-02\n",
      "   -1.28883929e-01]\n",
      " [ -3.07018147e-01   1.57048497e-01   8.02851173e-02   3.90991888e-01\n",
      "   -2.07418632e-01  -2.80435606e-01  -1.58154155e-01   3.46860367e-01\n",
      "   -2.58384729e-01   3.83003091e-01  -3.03444573e-01  -3.75515968e-01\n",
      "   -9.06582298e-02]\n",
      " [ -3.45298321e-01  -2.72210728e-01   2.44146961e-01   1.70689371e-01\n",
      "    6.33605366e-02   1.30918259e-01  -6.08729455e-01  -4.14742651e-01\n",
      "    1.08588801e-01  -6.40082001e-02  -3.76952292e-02  -5.88271105e-02\n",
      "    3.65295071e-01]]\n",
      "[ -2.86412480e-01   4.18085780e-01  -1.11026312e-03   1.01863307e-01\n",
      "   6.06415114e-01  -5.56291881e-04   1.02764931e-01  -9.81055026e-02\n",
      "  -3.70475535e-01  -2.69517624e-01   2.97319448e-01  -2.15685035e-01\n",
      "   7.68931624e-03]\n"
     ]
    },
    {
     "data": {
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TwJuAtyWZYW7Gvmcpm5YkXVmX0zJU1TQwPbDtYN/rbwHPHG9rI1uS0z3LzGNYHVo4Bmjj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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X's shape:  (303, 13)\n",
      "Z's shape:  (303, 13)\n",
      "Y's shape:  (303,)\n",
      "[[-1.16272226  0.92962183  3.21234102 ..., -0.59612792  0.16422075\n",
      "  -0.96657115]\n",
      " [-3.21783047  1.45102084 -0.48153238 ...,  1.05859135 -0.63134011\n",
      "  -0.40367654]\n",
      " [-3.14360376 -0.63656877 -0.36430077 ...,  0.18565038 -1.00393091\n",
      "  -0.68150469]\n",
      " ..., \n",
      " [-1.87570429 -2.38177479 -0.16316278 ...,  1.28966971  0.41322839\n",
      "  -0.1347603 ]\n",
      " [ 1.31332548  1.51469484 -0.14011541 ..., -0.07706784 -0.70312145\n",
      "  -0.86318064]\n",
      " [ 2.60110963 -1.41284182  0.5870924  ...,  0.53654327  0.35423456\n",
      "   0.32703919]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data_txt =open(\"processed.cleveland.data1.txt\",\"r\") \n",
    "data = data_txt.read()\n",
    "data = data.split('\\n')[:-1]\n",
    "\n",
    "\n",
    "X = []\n",
    "Y = []\n",
    "for i in data:\n",
    "    \n",
    "    x_1 = i.split(',')\n",
    "    temp=map(float,x_1)\n",
    "    x=tuple(temp)\n",
    "   \n",
    "    \n",
    "    Y.append(float(x[len(x)-1]))\n",
    "    x = x[0:len(x)-1]   \n",
    "    \n",
    "        \n",
    "    #print type(i)\n",
    "    X.append(x)\n",
    "    \n",
    "X = np.array(X)\n",
    "\n",
    "Y = np.reshape(np.array(Y),(len(Y),))\n",
    "Y[Y > 0] = 1 ###changing the lables to 0 and 1\n",
    "\n",
    "for i in range(X.shape[1]): #####imputing the missing values with mode of the feature\n",
    "    m =  mode(X[:,i])[0][0]\n",
    "    for j in range(X.shape[0]):\n",
    "        if X[j][i] == -9:\n",
    "            X[j][i] = m\n",
    "\n",
    "\n",
    "X_norm = norm(X.copy())##normalizing the data\n",
    "\n",
    "cov_matrix = np.dot(np.transpose(X_norm),X_norm)\n",
    "eigen= np.linalg.eig(cov_matrix)\n",
    "eigen_val = eigen[0]\n",
    "#print eigen_val\n",
    "eigen_vec = eigen[1]\n",
    "print( type(eigen_vec))\n",
    "print (eigen_vec)\n",
    "print (eigen_vec[0])\n",
    "total_eigen_val = np.sum(eigen_val)\n",
    "sum_d = []\n",
    "for i in range(len(eigen_val)):\n",
    "    sum_d.append(1. *np.sum(eigen_val[i+1:])/total_eigen_val)\n",
    "width = 0.35\n",
    "plt.bar(range(len(eigen_val)),sum_d,width)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "W = eigen_vec[:,:]\n",
    "\n",
    "Z = []\n",
    "for i in X_norm:\n",
    "    Z.append(np.dot(np.transpose(W),i))\n",
    "Z = np.array(Z)\n",
    "\n",
    "print (\"X's shape: \", X.shape)\n",
    "print (\"Z's shape: \", Z.shape)\n",
    "print (\"Y's shape: \", Y.shape)\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.438944</td>\n",
       "      <td>0.679868</td>\n",
       "      <td>3.158416</td>\n",
       "      <td>131.689769</td>\n",
       "      <td>246.693069</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.990099</td>\n",
       "      <td>149.607261</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.600660</td>\n",
       "      <td>0.663366</td>\n",
       "      <td>4.722772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.038662</td>\n",
       "      <td>0.467299</td>\n",
       "      <td>0.960126</td>\n",
       "      <td>17.599748</td>\n",
       "      <td>51.776918</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.994971</td>\n",
       "      <td>22.875003</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.934375</td>\n",
       "      <td>1.938383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.438944    0.679868    3.158416  131.689769  246.693069    0.148515   \n",
       "std      9.038662    0.467299    0.960126   17.599748   51.776918    0.356198   \n",
       "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
       "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
       "50%     56.000000    1.000000    3.000000  130.000000  241.000000    0.000000   \n",
       "75%     61.000000    1.000000    4.000000  140.000000  275.000000    0.000000   \n",
       "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.990099  149.607261    0.326733    1.039604    1.600660    0.663366   \n",
       "std      0.994971   22.875003    0.469794    1.161075    0.616226    0.934375   \n",
       "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
       "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
       "\n",
       "             thal  \n",
       "count  303.000000  \n",
       "mean     4.722772  \n",
       "std      1.938383  \n",
       "min      3.000000  \n",
       "25%      3.000000  \n",
       "50%      3.000000  \n",
       "75%      7.000000  \n",
       "max      7.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs','restecg', 'thalach', 'exang','oldpeak','slope','ca','thal']\n",
    "df = pd.DataFrame(data=X,columns=colnames)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<memory at 0x000002470CFBBDC8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.77      0.85      0.81        81\n",
      "        1.0       0.81      0.71      0.76        72\n",
      "\n",
      "avg / total       0.79      0.78      0.78       153\n",
      "\n",
      "[[69 12]\n",
      " [21 51]]\n",
      "Accuracy of the Decision Tree is: 78.43137254901961\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# load the iris datasets\n",
    "\n",
    "# fit a CART model to the data\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(Z[:170], Y[:170])\n",
    "print(model)\n",
    "# make predictions\n",
    "expected = Y[150:]\n",
    "predicted = model.predict(Z[150:])\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))\n",
    "print (\"Accuracy of the Decision Tree is: %s\" %(accuracy(Y[150:]\n",
    "                                                         ,predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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