Nearest_Neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import neighbors,datasets\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=15, p=2, weights='distance')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = neighbors.KNeighborsClassifier(n_neighbors=15,weights='distance')\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13  0  0]\n",
      " [ 0 15  1]\n",
      " [ 0  0  9]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109698a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(cm)\n",
    "print(cm)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Je0p6akRsadTGicnMkmaJaelo2iZcPmVJZ9sqKZKeAdxd1JU7FqBZUgInJjOb0GQpQDsl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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db6bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_conf = []\n",
    "for i in cm:\n",
    "    a = 0\n",
    "    tmp_arr = []\n",
    "    a = sum(i, 0)\n",
    "    for j in i:\n",
    "        tmp_arr.append(float(j)/float(a))\n",
    "    norm_conf.append(tmp_arr)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.clf()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_aspect(1)\n",
    "res = ax.imshow(np.array(norm_conf), cmap=plt.cm.jet, \n",
    "                interpolation='nearest')\n",
    "\n",
    "width = len(cm)\n",
    "height = len(cm[0])\n",
    "\n",
    "for x in xrange(width):\n",
    "    for y in xrange(height):\n",
    "        ax.annotate(str(cm[x][y]), xy=(y, x), \n",
    "                    horizontalalignment='center',\n",
    "                    verticalalignment='center')\n",
    "\n",
    "cb = fig.colorbar(res)\n",
    "alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
    "plt.xticks(range(width), alphabet[:width])\n",
    "plt.yticks(range(height), alphabet[:height])\n",
    "plt.savefig('confusion_matrix.png', format='png')"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}