sbos/baseline.ipynb

## baseline.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10ad75d50>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107456e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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e+z3ezwcAAwcONPPm5mYz98bkT5kyxcw//PBDM583b56ZP/nkk2a+cuVKM3/44bOmcTyD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\nkP8Hrlc8fZYqyd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a52ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = np.load('train_data.npz')\n",
    "validation_data = np.load('validation_data.npz')\n",
    "\n",
    "plt.matshow(train_data['X'][2224].reshape(28, 28), cmap=plt.get_cmap('Greys'))\n",
    "plt.matshow(train_data['X'][2225].reshape(28, 28), cmap=plt.get_cmap('Greys'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_classifier(cl, train_x, train_y):\n",
    "    print 'Train accuracy: ', cl.score(train_x, train_y)\n",
    "    print 'Cross-validation accuracy: ', cross_val_score(cl, train_x, train_y, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.860515021459\n",
      "Cross-validation accuracy:  0.824034334764\n"
     ]
    }
   ],
   "source": [
    "cl = KNeighborsClassifier()\n",
    "cl.fit(train_data['X'], train_data['y'])\n",
    "test_classifier(cl, train_data['X'], train_data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x = train_data['X'][1950:].copy()\n",
    "train_y = train_data['y'][1950:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.505263157895\n",
      "Cross-validation accuracy:  0.305263157895\n"
     ]
    }
   ],
   "source": [
    "cl = KNeighborsClassifier()\n",
    "cl.fit(train_x, train_y)\n",
    "test_classifier(cl, train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10f148490>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10623c7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAD7CAYAAABOrvnfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b6435d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def binarize(x):\n",
    "    x[x > 0] = 255\n",
    "    return x\n",
    "\n",
    "train_x = binarize(train_x)\n",
    "validation_x = binarize(validation_data['X']) # not forgetting about preprocessing validation data\n",
    "\n",
    "plt.matshow(train_x[2224 - 1950].reshape(28, 28), cmap=plt.get_cmap('Greys'))\n",
    "plt.matshow(train_x[2225 - 1950].reshape(28, 28), cmap=plt.get_cmap('Greys'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy:  0.823684210526\n",
      "Cross-validation accuracy:  0.726315789474\n"
     ]
    }
   ],
   "source": [
    "cl = KNeighborsClassifier()\n",
    "cl.fit(train_x, train_y)\n",
    "test_classifier(cl, train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fout = open('answer.csv', 'w')\n",
    "fout.write('id,answer\\n')\n",
    "\n",
    "i = 0\n",
    "for y in cl.predict(validation_x):\n",
    "    fout.write('%d,%d\\n' % (i, y))\n",
    "    i += 1\n",
    "\n",
    "fout.close()"
   ]
  },
  {
   "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": "2.7.11"
  }
 },
 "nbformat": 4,
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