mnist_knn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First lets import matplotlib(for ploting the images), MNIST dataset, and KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pt\n",
    "from sklearn import datasets, metrics\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading MNIST digit dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnist= datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = list(zip(mnist.images, mnist.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the dataset flat for feeding into the machine learning model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_samples = len(mnist.images)\n",
    "images = mnist.images.reshape((n_samples,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now building the classifier. Here the number of neighbors is set to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = NearestNeighbors(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model with MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "         metric_params=None, n_jobs=1, n_neighbors=1, p=2, radius=1.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(images[:n_samples//2],mnist.target[:n_samples//2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now making predictions with test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "distances, indices = classifier.kneighbors(images[n_samples//2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets calculate the accuracy of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy =  99.9877641824 %\n"
     ]
    }
   ],
   "source": [
    "acc = metrics.accuracy_score(mnist.target[n_samples//2:],indices)\n",
    "print(\"accuracy = \",100-acc,\"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now print first eight actual images and predictions of test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10996b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109eeaf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097f5a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109599ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10942e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096cd898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ad2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YCXwDkKRTgMXAw3Wcy3GmKsXrpEj6mmgF6rJP0sckbQSuBT4xSbYVqfc1PF/Sk5K+LWlB\nlfKJou73WNIi6vR7E3Fj7hbCp+gKwhLJ24Bvm9ngBJzLcZwmYmbXm9lRwKeAK/O2pwrfB44wsxOA\nhwiDvVbkQur0e/U44T7CWvM+4ABgHmFKYh4wGPMPjyHAHjPbRxiqH0v4NPCpCKfdKV4nRdLXRCsw\nVvtWE+7zTCaj2mhmO6N/AbgJeOsk2QZjew0vpE6/V48TXgMcTZgPPpQwFXEfsCjm3x9PeC8wAzgo\ntjsL2AUMAI/VY4zjTGHWAEdLWiypm+FrolUY1T5JR6eSZxHu+Uwm9dg4N5U8B3i6lewDkHQMMIt6\n/V6ddwWXA+uBF4BXCE9IvA7sBl4mvFk7CCPjoXjsIcyZrMrjTqYffkz2kbpONgKfiXn/CJwT42+L\n18Rewm5j61rMvq8A64C1wI+B41rwNbwm2vhEtPGYVrIvpq8CvlRvn75iznEcJ0d8xZzjOE6OuBN2\nHMfJEXfCjuM4OeJO2HEcJ0fcCTuO4+SIO2HHcZwccSfsOI6TI+6EHcdxcuT/ARO5vMLCamWQAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109420358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = dataset[n_samples//2:]\n",
    "for index, (image, label) in enumerate(data[:8]):\n",
    "    fig = pt.figure()\n",
    "    a = fig.add_subplot(4,2,1)\n",
    "    imgplot = pt.imshow(image)\n",
    "    a.set_title(\"actual :\"+str(label))\n",
    "    pt.colorbar(ticks = [0.1,0.3,0.5,0.7],orientation = 'horizontal')\n",
    "    a = fig.add_subplot(4,2,2)\n",
    "    current = indices[index][0]\n",
    "    \n",
    "    imgplot = pt.imshow(dataset[current][0])\n",
    "    imgplot.set_clim(0.0,0.7)\n",
    "    a.set_title(\"prediction :\"+str(dataset[current][1]))\n",
    "    pt.colorbar(ticks = [0.1,0.3,0.5,0.7], orientation = 'horizontal')\n",
    "    pt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}