{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Classification\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature dimensions:  (1797, 64)\n",
      "Label dimensions:    (1797,)\n"
     ]
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, neighbors, linear_model\n",
    "\n",
    "# load data\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data\n",
    "y_digits = digits.target\n",
    "\n",
    "print(\"Feature dimensions: \", X_digits.shape)\n",
    "print(\"Label dimensions:   \", y_digits.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot sample images\n",
    "nplot = 10\n",
    "fig, axes = plt.subplots(nrows=1, ncols=nplot)\n",
    "\n",
    "for i in range(nplot):\n",
    "    img = X_digits[i].reshape(8, 8)\n",
    "    axes[i].imshow(img)\n",
    "    axes[i].set_title(y_digits[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split train / test data\n",
    "n_samples = len(X_digits)\n",
    "n_train = int(0.4 * n_samples)\n",
    "\n",
    "X_train = X_digits[:n_train]\n",
    "y_train = y_digits[:n_train]\n",
    "X_test = X_digits[n_train:]\n",
    "y_test = y_digits[n_train:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN score: 0.953661\n",
      "LogisticRegression score: 0.908248\n"
     ]
    }
   ],
   "source": [
    "# do KNN classification\n",
    "knn = neighbors.KNeighborsClassifier()\n",
    "logistic = linear_model.LogisticRegression()\n",
    "\n",
    "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n",
    "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}