03_knn/03_knn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbors (kNN) and the Iris Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://www.wpclipart.com/plants/diagrams/plant_parts/petal_sepal_label.png\" \n",
    " style=\"height: 170px;\"/ align=left>\n",
    "<img src=\"http://articles.concreteinteractive.com/wp-content/uploads/2015/03/irises.png\"\n",
    " style=\"height: 170px;\"/ align=right>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exercise involves Fisher's iris dataset. Although you could technically do your data mining without knowing what an iris is, it is definitely recommended that you'd make yourself a little familiar with the subject. \n",
    "(Recall that data science = math + coding + domain knowledge.) For now, though, looking at the above pictures is probably enough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('http://www.stat.cmu.edu/~rnugent/PUBLIC/teaching/CMU729/HW/iris.txt',sep=' ')\n",
    "data.columns =  ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "1           5.1          3.5           1.4          0.2  setosa\n",
       "2           4.9          3.0           1.4          0.2  setosa\n",
       "3           4.7          3.2           1.3          0.2  setosa\n",
       "4           4.6          3.1           1.5          0.2  setosa\n",
       "5           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setosa        50\n",
       "versicolor    50\n",
       "virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.species.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have three kinds of irises, and 50 samples of each."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We would like to classify the irises into one of these three species, based on the four given features.\n",
    "- First have a look at some basic statistics and visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = data.columns.tolist()[:-1]\n",
    "species = data.species.unique()\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.428</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                         \n",
       "setosa             5.006        3.428         1.462        0.246\n",
       "versicolor         5.936        2.770         4.260        1.326\n",
       "virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average sizes per feature per species\n",
    "data.groupby('species').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">sepal_length</th>\n",
       "      <th colspan=\"3\" halign=\"left\">sepal_width</th>\n",
       "      <th colspan=\"3\" halign=\"left\">petal_length</th>\n",
       "      <th colspan=\"3\" halign=\"left\">petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>setosa</th>\n",
       "      <td>5.006</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3.428</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.462</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>versicolor</th>\n",
       "      <td>5.936</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.770</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.260</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.326</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>virginica</th>\n",
       "      <td>6.588</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.974</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.552</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.026</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sepal_length           sepal_width           petal_length       \\\n",
       "                   mean  min  max        mean  min  max         mean  min   \n",
       "species                                                                     \n",
       "setosa            5.006  4.3  5.8       3.428  2.3  4.4        1.462  1.0   \n",
       "versicolor        5.936  4.9  7.0       2.770  2.0  3.4        4.260  3.0   \n",
       "virginica         6.588  4.9  7.9       2.974  2.2  3.8        5.552  4.5   \n",
       "\n",
       "                petal_width            \n",
       "            max        mean  min  max  \n",
       "species                                \n",
       "setosa      1.9       0.246  0.1  0.6  \n",
       "versicolor  5.1       1.326  1.0  1.8  \n",
       "virginica   6.9       2.026  1.4  2.5  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Good, but lets get some more stats\n",
    "data.groupby('species').agg(['mean','min','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's make some plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "setosa        y\n",
       "versicolor    r\n",
       "virginica     b\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Set up color labeling\n",
    "colors = pd.Series(['y', 'r', 'b'], index=species)\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'species' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-bc6601e72d49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Sepal: length vs width\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mx_feature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_feature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'sepal_length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal_width'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mspec\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mdata_spec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecies\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     plt.scatter(data_spec[x_feature], data_spec[y_feature], c=colors[spec], label=spec,\n",
      "\u001b[0;31mNameError\u001b[0m: name 'species' is not defined"
     ]
    }
   ],
   "source": [
    "# Sepal: length vs width\n",
    "x_feature, y_feature = 'sepal_length', 'sepal_width'\n",
    "for spec in species:\n",
    "    data_spec = data[data.species == spec]\n",
    "    plt.scatter(data_spec[x_feature], data_spec[y_feature], c=colors[spec], label=spec,\n",
    "                linewidths=0, s=50, alpha=.8)\n",
    "    plt.xlabel('sepal_length'), plt.ylabel('sepal_width')\n",
    "f = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KCyPPWVkJK1cGy/X3ivbuDX4edBD06dP+mNpAA/ilt2QM8Peb0M8nY7w0wJ8I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\niMSlJCEiInEpSYiISFz/H7X5W5rpwW37AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b69b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Petal: length vs width\n",
    "x_feature, y_feature = 'petal_length', 'petal_width'\n",
    "for spec in species:\n",
    "    data_spec = data[data.species == spec]\n",
    "    plt.scatter(data_spec[x_feature], data_spec[y_feature], c=colors[spec], label=spec,\n",
    "                linewidths=0, s=50, alpha=.8)\n",
    "    plt.xlabel('sepal_length'), plt.ylabel('sepal_width')\n",
    "f = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4HKxcuZKnn36a7OxsLBYLY8aMITExEVB3vD179iQhIYEHHniAL7/8krpgtxej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2ZBSUno9271ahu4sHWrVfYqKuz7nj3tcUZw33DfI6exnX4uSirwJxH5gYgMFJG+vleiF5XUeBPL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\njHTBaaS5uTndIoSlqakp3SIYRtelsRH27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hNPVH33XafNqlWqJ53Utu7KKx39GAumv8KW\nt1tX2ExbF6W8HN56ywlmO3Cgc4waBZs2Ob5hkc7zB8Ht3ds5Ro1qDYIbqd8VK+Cxx0L7fPRRZ6bq\no49C6z76CBYuDB88t6LCmWG7+7H+ofWP9qdiw45Y35K4ePll2OEyxP798PzzcOQIHDzofm51tfO4\nZg289FLbOq8Xbr21NRxLNIYPH84JJ5zAU089BcDq1avp3bs3EyZMCGk7ePBgLr/8cjweDzk5OTz5\n5JMsXLiQnj17cvTRRzN79my/oRCCiHDuuefyla98haysLC688ELeeuutkHZer5elS5dyxx13UFJS\ngsfjYeLEieTm5gLOHW/v3r3Jycnhxhtv5O233+ZwrBdrGEZCaGhw9LIbft9/v55yo7raWUm5+ebQ\niAGvvALPPhubHKa/EkdMRpuInOdLeHxIRA77jkPJFs6IH39A2h49HJ+2oqJW5/hIwXWjBcGN1K8/\nrpsb/qC8bvzpT+HrVq2C1Q9WhK9/IAEJVSPgZrAF1vXpA8OHu9cfc4zzGC40T10dvPpq7LLMmjWL\nx3xW8aOPPsqsWbNc2w0b1hoWbPfu3TQ1NbUpGxppmy9QXNyaZSkvL4+amtB4sXv27KGuro7Ro0eH\n1Hm9Xv7jP/6DMWPGUFBQwMiRIxER9vh3rqQB02FGd+Tgwdbc0MFU+qIF+vVUMB4PHH00vPlm+BvT\nNWtil8X0V2KIdabtVuAcVc1X1b6+wyXwg2F0LcaOjVwnApdcEhpeJTvb8X2DUKff4Hax8r3vfY+1\na9dSWVnJ008/HVbpSYAwAwcOJDs7m+3bW30AA5/HS1FRET179mTLli0hdY888gjPPPMMq1at4uDB\ng5SXlwcuA6YL02FGt6N///Ab0fy67bzznB3vwZx9NgwZYvqrk+ivFmI12j5T1feTKomRUOLdiOA/\nz43TTotc79/E4EbrTkoNOpyE8RHHvKg0fP3FI8OfnAAmToTjjgstHzHCceoF53HRIjjxREf5nXyy\nsxHBP/N/xhnufffpA1/7WuyyDBw4kLKyMubMmcOoUaMYN25c1HOysrI499xzWbBgAbW1tXzwwQc8\n/PDDbRRjILEqJo/Hw0UXXcT8+fOprq6mubmZV199lYaGBmpqaujRowf9+/fn888/5/rrr4/9IpOH\n6TCjSxFt4xY4BpfbLvbA8v79nT6+8x0YNMjZJDZ/Pvh/tiecEN7wC6fb3DD9lRgiGm2+JYXzgDdE\nZIWIzPSXici5KZLRiIORI+H44+GTT5ysCLt3O8/Hj48c3yxaINtI9eefD243T7NmwR13wNjiAyF1\nY4sPcOONUcY8ZRhXzNoXWj9rH6WTI0+VdxQRx99u1ixHcRUWwne/6+wYDYzFNnkyLF7s+Lndeaej\n6PycfHLo+9KzJ/z2t5CX1z55Zs2axapVqyLepQYrtLvvvpuDBw8yaNAgZs+ezcyZM1t8N/znRDo/\nuN7PH//4R4477ji++tWvMmDAAH7xi1+gqvzoRz9ixIgRDBkyhC996Ut87WtfC6tkk43pMKMrsmyZ\nkyXm3nudY8YMWL7cve33vw+/+Q2MG+fcKJ54ouMvHOhOVlICv/yl46P2+OOOvvLPsGVnOz5twSGe\nzjmnfUYbmP5KBBGD64rIMlqTG0vAcwBU9d+TJlk7sMCUofhTP7nlHo0l9VO0QLaR6jdsaE0ef/HF\nvtRWPoH+tPM8Fu26EICrj3qEy4v/0iJQ1DE37GjxYTvt4pFJMdiSmQZm61bHebd3bzj9dCgoSMow\nUbnuuuvYtWsXS5cuTY8A7aQjwSlNhxldjUhp/f75z+Tpr5oa+NvfnJBGJ53kGIHpoDvpL9f+YvmA\nRWSyqm6IVpYuTOGF4s+I4EYsmQsSTqcTyJ2umLvvww8/pL6+nuOOO45//OMffPOb32TJkiWcc845\n6RYtJhKh9EyHGV2FSKo0mUZbujD91ZZY3QjvBE6IoczoKpSXt24VnTbNWRdNRLf1g1l9+KtOt33/\nwcgeVQnpN6K8SbqWTOHw4cPMnDmTqqoqiouLufbaazNG4SUQ02FG16K+Hg77NkD3ze+yufNMf7Ul\n2vLo14CTgauB/x9neQGgL/BdVf23pEsYA3aXGkqkKfSoy6PLloUGTrviCpg9u0MCLTt1OXfv/n7b\nbo9awew1/96xRKKR5G3ntXTFmbZMp4PLo6bDjC5FeTnMmLYHdu1qW3HUUfyzYqDpr05Gomfaou0e\nzcVRblm+xz6+4xDwvfYOZqSOaBsKwlJeHjnSbZyUM5K75Weh3XIFFUQSKFrHEeRdvz4p12JkFKbD\njC7FSMq5grtCyq9oXJQGaYxUE3F5VFVfBl4WkaWq2s5siUa6mT0bpk6N7Nwfgn8Z0Y1Vq+L2PVu9\nGhhQ5OyGOOSb0s/Ph9weHek2sry+fHaudGhQI1MwHWZ0OVavZvaA55ja501WHXa2gJ7W93VKD7/D\nnPRKZqSAiEabiPxvwHOldWkBnLxZHVpYFpEHgW8Cu1Q1JBqWiJQB/wP4M77+RVVv6siY3Y3S0k5m\nm+T2gKKB6ZbC6CaYDjO6KqU9qpnb439aCzpHliUjyUTbiHCb7/G7wCDgv3CU3kxgZwLGXwrcBTwU\noc3LHVWsmUD5uu0t6ZqmXVTKyFOGRT4h5o7b6YQ/bVr4rUkBUXkjyusyZku3+/fBTp8vRvFR0K9/\na7fr1rXmu7roIjjllOjXEkneuXPh6qujXkswnSkmj9FhTIcZmUU0ne3XeVVVTgBOcCLk5uQApr+6\nOrGG/Pinqp4YrSwuAURKgf+NcJd6jaqeHaWPjHbiXXbxhpCE6FfM2sfs+yd3sONl8W0oWL7c8fsK\nc15EeSOMuXzc77jro7bRGK8Y+xKzP7zeCegWnG1+1iy4//7o1xJJ3ijXYmQu7Qz5YTrM6PzEqrML\nClrdTPx4PE6i6P37nYi4/hxTs2aBaot+Xdb0A+5uusQx9EaUsqfiMHL4EAOyWxOMJuL/x1RvZJId\np+194FuqutX3ehTwnKoe3W5JQ/suJbzCmwo8CewAKoFrVfU9l3YZq/DK121nxlnu89orX8yPP4Bs\nh7aPEjZ6bkR5l9ZQeutP3fubPRuuuooKHcEqnFmu01hFqWyDX/0K/vAH9/NefNFJgBftWiJF5o0W\ntdfISNpptJkOMzo3sersBQtg4UL3djk5TioDrxcGDnQMs4YG2LYNPB7KvSOY0fBfLc3rR4zlk519\nQL2M7n+A3OzmFl/jWP4momGqNzzJjtN2NbBGRPwZzkqBn7R3sDh4EximqkdE5CzgaSBCCu/Mw1li\nHOBat+qBcubGa7R1dENBGGe4iPLe8S5he73jDqdb2cZcHmxbd8894eV44AGYMiV8vf9aIjnvdTrH\nPiMNmA4zOjex6uxI+rKx0THcPB5npi0311lG9XrB42G1d2qb5od31QJ9QDwcyu7fJsdoIvZqmepN\nPDEZbar6VxEZC3wRJw3MB6pan1TJnHEPBzx/QUTuEZH+qhqSiHLBggUtz8vKyigrK0u2eIZhpJC1\na9eydu3auM41HWYYRjrpiP4KJFpw3dNUdZUv4XLgzisFUNUnOyyDBA6cAAAgAElEQVRA5KWFYpxd\nWSoiE4D/VtVSl3YZu7SQzuXRiP6uYSpb5PUqeJudek8WeKR1edQt4alveRSvt60sHk/r8qjX21rv\n8ThH4PJovIlUjS5JjMF1TYcZnYdomVsi6exPP3U2am3aBP/6l3u7rCznaG52dGT//vD557BvH2Rl\nUc5I9+VRYPRoZ2IucMhYVGs3TzgTN0nxaRORhap6Y1DS5RY6mmxZRB4DpgJFODu5bgRyfH0vFpHL\ngcuAJv5fe/ceX0d533n889PNFsi2DLYl2ziIcHGAQHEuQIMxrqFZTLtN08Zh47QxAdIsKQ4kDU3T\ndomb7W4u274gkAZYYrBTkpCYpm3SpSGEIoyhQMMlEIMJYDkxsuQL2GCKbEvWb/945ugcHZ3LSDpz\nLtL3/XrppXPmmZnn0Xjm52dmngu8CXza3R/JsZ+aDnjrP7aJG7+dQEeEAi1BC7Z3LdIYdv15t3Hj\npjOGJy9+ilUPXFq4Q0F7O+zM6rDX1ga9vWFAuU1Z00AuXgwPPBA+F+uoIJNOzEqbYphUhzidDPLF\n7AcfHB7/3nwzdx51dekbX7PQAaGlBaZODRW3hgbWs4obB/77UEeEV14Jqx+d0eolboeBJCbPmSwS\n7YhQ7SZCwNu26WXu+0ZobnP+5ceN/QnbiB1vG9EStOAN3XXb6fjU+/Mk3hWCwIoVbHtjFvftPj3s\ndvbTdLTsgeuuC0Ns5Hoids01YRiPgYHwA+keTrffDl/5CrzxRroL++zZIdhk5KknbZJprEGvGk2E\nGCYFjKZjWHbM/tWvYPnykdsdOgTNzdDXFypgZumprVLnkln4OfHE8H3PHli6lG0Xf5b7Xn/3UBYw\n+g4D4+3rNtkl2hHBzF4CHgEeBB50982jzUgK61h8zNg7HRTccceIlqAF27uu3Za/M0HqqgY6WvZw\nWUvWjlIzEEyZMnLy4htuCL8zu6KnRJ0UaGkJP3nyzLlfzWwgMSiGSUWNpmNYdszOaOs4TFMTnHZa\nuJmFUNnLHqMt9X3PnrDflhZYvpyOFe8eEedHG0YTmjxHiojbe/RU4CxgMfA3UYPeZ9z9dxMrmaQV\nG3S2kLE2OMj1VCvTq6+m7+rmzAl3eind3cOfmM2fn07L1W5NJHmKYZKcscTZVAz94hdh+/b0fMjv\nfCc8/nj686ZNIR7X1aUrYam4uXNneHMxdSocOJB+wiYTVtxx2hqAM4ElwLmEMR9+5u4fT7Z48Uzo\nVwvjacuVp8FB15JVhV+P/uG56UpXypw5cP/9ISicfnp4lZmppQXuvht+4zdCI9hMDQ1wxx3wkY+k\nX41mpv3934fXozkLlPF6NF+6nsNPSqMcp00xTJIRp2FX9rvELVtCB4FMqUrZ4cOhMwGEz5nt1FLr\npc6Vjo5wkzwwEJan4muqcpf6feKJ6bcYJYqZej06PmN9PRr3McfrwHVAF7DK3c+ulmA3oW3cOLLC\nBvDtb49stJ+tq2tkIAG48UaOs22sXj0yafVq6DhmYOQj9kyPPTYy2EBY9tWvjqywQQgkjzwSBm3M\nNn06nHkm+QvUEe5aC6WLFKcYJqVXIM4OPTmD4THs1Vdzx9DBwXT8PHw4/Tm7x/3gYKi01dcPTV01\ntDxV2YN0M5SjjkpX2EoYMxWWKyPuk7b3Ee5O3w30Aw8DG939J8kWL54Je5d6ySWwYUPutBUrwh1e\nPmvX5p+T84or4LLLco9Wndru0KH0NCnTp4f2E1dcEV7TPvLIyMfwZsPv9LK1tMDChbk7G0TlKTp8\ntobXlgyjfNKmGCalFyPODrNtG1xwAWzdGr6P5t881Ru0oSE9tEfqtWiq2UlLC7S2wr598La3hWGW\nUnE8oZipsDw2iXZEcPd/Bv7ZzN4GXARcDfwpMHW0GUr1KDhadVMTw4bHzjbWSYlzdTaIVaAY6SJ5\nKIZJVejoCMMcdUUTc4y20mYW2hdnb5dqHzxtWph/dObM0P453/vLElJYLq9Yr0fN7B+i3lc3AEcA\nfwjMTLJgQrjo8rn88sLbLluWPy3Vx3vjxvA075JLwufM7Q4eDD2O9uxJ9046/3z45CfDZ/fhPwDv\ne1/x8ubbr0iCFMMkEXHibKauLnj720PMzH7tWUjqKZt7GOYjtW32jfX06cNjbKpyKBNG3DZtXwIW\nuvt73f2v3f0Bd+9LJZrZbyZTvEluyZLQ6SDbypVh4NlCijU4uPzyMPbPhg3hZ/ly+NjHwnZnnBEe\n3+/eHX62boVFi8J2F18MJ5wwstJ2wgmh9Wlb28g829rCGG6F9iuSLMUwKb3RNOxaty48+Xr88eFT\nD6SkmphkSz1Fcw+/33wzVNqOOCI8WZszJ6w3Z054Fbp1a9jXhg0hv/Xrx/tXShUpyeC6Zvakuy8q\nQXnGmv/Ebg+yaVOYOB1CZatYhS1TrgYHGzfmHqwR0gPd5hvINtWTc+/e9OwGbW3hcXyqy9AXvpBu\nnHvllXDttemuRrnayqmrkYxBKQfXVQyTcSnWsCuzq+XBg6FilRpofOpUmDEjVMbMQrxNdVQ48sh0\nHD7qqHTnhFSnrg9/ODxtW7gw/D9x883puJpJMbbqJNqmTSps8eLRVdQy5WpwkBrzLZfUQLf5BrJN\nSbWbyE6/7LJQSbv22uFpqTGMcrWV00iMIlLLijXsyhyJNlUJSw00Pnt2iIl79oTl2fEx9fQtO96m\n1k3l+/zz+dshK8ZOGBrZVERERKQGqNI2EXR1ha7na9eObHiaq7NBoQ4OV12VP+3880ff8DZlrNuJ\niCShUNwsdR67d4fXogcPQn9/eC2a6kyQetU5bVrusSynTQs/uWTGTsXYSaFUr0fVRaVSskfjvumm\n9Gjc2bMpbNiQnk1h5cowSG+mlSvhgx8MExDfeOPwtMyGtatXF07PJdVgd7TbiZSHYthkUihuJpVH\nb29oz5sa9PbQodBOLdX+7DOfCb+zY2S+5dmxUzF2UijYEcHMfh9wIFdjOXf37ydVsNGYtI14C80j\ncs01+Z+o3XNPaCNXqINDUgPdaiRGKZE4DXkVw2SEcsy/lJ1HqvPB4GCoqDU2hjbDBw+GQXgvvjid\nb74YGTd2KsbWhLF2RChWaVtHCHg5uftHR5thEiZtwCs0GveUKfDUU7nTis2mIFIDYlba1qEYJplG\nO4tBKfLYs2f4TDCZHQZKlafUlER6j7r7JWMuUQxmdhvwW8Audz8tzzo3AMuBN4FL3P3JJMskIhOH\nYpiITCSx27SZ2W8Dp5Ax7Yu7f2Gc+d8O3Ah8M0+eFwEnuPuJZnYWcBNw9jjzTFZXV7p797JloZ3B\neLfLl7ZsWbib279/eHfxadPCzAX5Xo+mZicYS54iNUoxTIB03Mwlu8H+xo3pIZIuvTQMeF4srasr\nPFXbsyc9xuW0aeknbalOBamxMPfsCft64YV0+RRvJY+4E8bfAjQDy4BbgRXAo+4+7me6ZtYB/DDX\nXaqZ3Qzc7+7fjb5vAc5z951Z61XHq4XshqcQr3Froe2K7XPJEnjooeHpixfDAw+EGQ5ydTa49dbx\n5SlSBUY5YbximKStX5+7wX5mjMvuyAXp+Jkv7Zxz0rHzlVdg164wU8HRR4fvkP6cSnMPFbrUernK\nIhNOIm3aMnb+jLufZmZPu/vpZtYC/Mjdxzji67B9d5A/4P0Q+KK7Pxx9/wnwWXd/PGu9yge8sTZu\nLbTdddfBpz6Vf5+/+lWY2WBwMN19PDXlSaHOBuPJU41apUqMstKmGCbDFWqwX2jWmC99Cf7sz0Yu\nHxyEY4+Flpb0stQMMKnOBgB33hlmLkg9cdu6Nb3+8cene5Mq3k5oY620xR2nLTVH35tmNh8YANpH\nm9kYZf9R1RnZMke8zpY5k8Botlu7tvA+U4/m6+rSo2unRs9OVdQWLw5PztatS/cOHU+eIrVJMUyG\nS81icNllIytHcWaNyTY4mG6mkpKaAWbWrJBHR0e6I8KUKenZEVJSU/yB4q3kFLdN27+Y2Uzg/wCp\nO8RbkynSMN3Agozvx0TLRlizZs3Q56VLl7J06dIkyyUiZdbZ2UlnZ+dYN1cME5GKGWf8GhL39ehU\ndz+Q+kxoyHsgtWxcBSj8auEi4Ep3v8jMzgaud/cRjXir4tVCJV+P5pJ6PVrqPPW4XqrEKF+PKoZJ\nfKV6PZqSGTtzTR6fotejk0bSr0cfTn1w9wPuvi9z2ViZ2Xei/Sw0s+1mdqmZfdzMPh7ldTew1cxe\nBG4BPjHePBOTGo06W9yZAnJtd+65hfe5ZElo/Jpt5crCE8yPJ0+R2qQYJvEViq2rV+dO+4M/gM99\nbuTyfDMXQHhFOmdO+DxnTrrCpngreRQbXHcuMA/4FrCS0DbDgenAze7+tnIUspiquktNYqaAYvss\nNLNBUnmKVFjMwXUVw2TsCsXWfGljmblg4UJ4/vni28iEkdSMCKuAS4B3AT/NSNoPrNMUMCJSKTEr\nbYphIlJ1kh7y4wPufteYSlYGCngik88o27QpholI1Ui6TdsmM1trZj+KMjvFzDRZmojUCsUwEal5\ncStt64AfE9qGALwA5OliKCJSddahGCYiNS5upW1WNA3LYQB37ycMTikiUgsUw0Sk5sWttL1hZrNS\nX6Lxhl5LpkgiIiWnGCYiNS/ujAh/Avwz8FYzexiYDXwgsVKJiJSWYpiI1Ly4lbZngX8izN/3evT5\n+aQKJSJSYophIlLz4g75sYEQ6O4gDE65Epjh7nnmQiovdZcXmXxGOeSHYpiIVI2kx2l71t1PKbas\nUhTwRCafUVbaFMNEpGqMtdIW9/XoE2b26+7+71FmZwOPjzYzkWrT19fFvn3/BkBr6zKam4+rcIkk\nIYphCdO1JJK8uE/atgAnAdsJ8/a9hdAeZABwdz89yUIWo7tUGYuennV0d39t2LJjjllNe/uqCpVI\nRmOUT9oUwxKka0lkdJJ+PdpRKN3dt40241Kq9YAn5dfX18XmzbmbM5166l00N3eUt0AyaqOstHUU\nSlcMGztdSyKjl+jr0UoHNJFSS73GyZ12H83NmuFoIlEMS46uJZHyiTu4bmLM7EIz22JmL5jZZ3Ok\nLzWz18zsyejnLytRThGRbIpfIlJOFa20mVk98DXgQuAU4ENmdnKOVR9w90XRz1+XtZBSE/r6uujp\nWUtPz1r6+rqKrt/auqxA2vmlLJpMUIpfQa5ryf0g/f176O/fE+t6FJF4Kv2k7UzgRXffFs0FeCfw\nvhzrjfq9r0wePT3r2Lx5Bd3dN9HdfRObN6+gt3d9wW2am4/jmGNWj1h+zDGr1QZH4lL8YuS11N//\nCn19WwFj164Nsa5HEYkn7pAfSZlP6M2V8jJwVtY6DrzHzH4GdAOfcfdny1Q+qXJ9fV0jeq0BvPzy\njcyYcV7BClh7+ypmzDiPffvuA8ITNlXYZBQUvyKpa2nXrjvZseNmpk59K3V1U4bS41yPIlJcpStt\ncbpLPQEscPc3zWw5YfqZk5ItltSK8TaCbm7uUENpGSvFrwzNzR00Nc2msXFWznR1ShAZv0pX2rqB\nBRnfFxDuVoe4+/6Mz/9qZl83s6Pc/dXM9dasWTP0eenSpSxdujSJ8opIhXR2dtLZ2VnpYmQqWfwC\nxTCRiaxU8SvWOG1JMbMGwgCX5wM7gMeAD7n7cxnrtAG73N3N7Ezge+7ekbWfmh3jSMYnzhhRe/du\npLf3NgDa2y9l5swlJcs73wjwhdKSKs941OJo9mMd56iE+ZckfkXrVW0My3du9PZ+lx07bgBg3rxP\nMmPGmeza9V127LiZuropDA4eBKChYRpmUzj11LsAr7nzTCQJiQ6um6TolcH1QD2w1t2/aGYfB3D3\nW8zsj4ErCCOXvwl82t0fydpH1QY8SV5v73pefvnGYctSo7Fv2XI5u3Z9Z1haW9tKFi68dVx5FhoB\nvlBaUuUZj1odzb7SlbaoDOOOX9F+qjKG5Ts3duxYy+uvP5Sx1KmvP5Lm5oUcOPBLBgZexayBUK8N\n5/j06efU5HkmkoSarbSVQrUGPCmfvr5tIzoU7N27kWeeWZ5z/dNPv4fW1sVjzCv/073jj7+Ol176\nVM60BQuu4fnnLy15ecajlkezr4ZKW6lUYwzLd27097/KwYPbSHeKdVLN+5qaFjAw8AowiPsgjY2z\naWiYCYR/L7OmEfur9vNMJAljjV+VHvJDpCSamzuYO/cy5s69bOg/gNQryFx6er4x5rwKdX7o7V2b\nN627+4ZEyjMexTpyyOSV79zo79+Vd5v+/p3Rp7qhJ211dVM4fHg/AwOv58lH55lIXJXuiCAx1GJ7\no3wq87eEu34Aszoy71WSal92+PAb9PfvAaCxcRb19S0l2a9IuQ0M7GdgIJzLZk0MDr5J+umakbsT\nbbjmDh/eT339tPIVVmSC0+vRKler7Y1yKfffsnfvRp5++jdxHxi23KyBX/u1++jtXTem9mXFXo9u\n3vx7DAwM7xzY2HgUJ5zwNb0eLSG9Hk1WX18Xjz/+bgYG9kZLBotu09S0gP7+nUPXnFkTZnU0NMyk\nsfEovR4Viej16ARUaODYvr5t5S/QOFTib5k6dQH19dNHLK+vn86BA90jKmwAO3d+m337NhXcb6HZ\nFMA5fHjka6CBgdeZMmU+bW0rR6S1ta2sSIUNNDOE5HfgwHYOH06NWFK8wgZ11NcfMfQtvB4N/8XU\n1TXS1jby5kznmcjo6PVoFRvvwLHVpBJ/y759/8bUqccyMHDU0OudhoZZNDRMGxqqIJeenm8UrUTl\nm03huecuiXrM1ZH+j64Oszp6er7BySevo61t1VAbtrlzL69YhS1FM0NILr29t0UVr3oGBw+Qfyxh\nY8qU46ivP4KGhukMDh6grm5qtA00NEzHrInGxlZOPfUunWci46BKm0x4DQ3TaGgofbuaQrMpZLed\ny9TaurjiFbVsmhlC8iv8Bqeu7ggaG0MP0ebmE4fGZ8vVjlPnmcj4qNJWxVpbl9HdfVOetPPLXJr4\ncnU2SP0tAwN7OXQo9DBramqjoWHm0N+SPVhne/vFw/b70ktr6On5OgBz536C449fEyvPvr5fcvjw\nKwDU1x9Nc/OxzJv3SX7xi0txH8hofxN6u82de/nQfgt1VMiV1t5+Kbt3b4g6Pgx/0pa539EevyS3\nk8kr3zmzd+9GDh3qjSpg+TobBIOD/0lfXxd1dU309b3I4cNv0NAwk/r6IwGor59GXd0UWlvP1zkq\nMk7qiFDlCg0cW40KdTZ45JGFHDjwi2Fpzc0ncdZZz/PEE0uyBuuEGTMWs2jRAwBs2tTOwMDOYemN\njW2cc05vwTwfeKAZ9wPD0symct55fTz0UHvGEAXD9wkUHAi3UNqTT57Ha68NbxeX+bcUMtbOGhOp\nw0pc6ogwPvnOmX37Hhw6t0NP0bGrqwtt3DS4rshwGlx3Avwd+eQaOLYaFeqJ2Na2ipdeupqRd+zG\nvHmr2bHjxlybcfLJd/LGG5vZvv2vcqbPm3c1r7/+YM605ubT2L17Xc601tbl7Nt3DyMbWNdx8sl3\n0tTUlndg3uOO+zJdXZ/NmbZw4e1s3/6VnEN+FOslN9aenLXcA3Q8VGkbu3znzOHD+zl48FdAHYOD\nhwgTOYxdXd10mpuPxd01uK5IBvUencByDRxbjQp1Nuju/mr0ybJ+ig1Ie/3QK9FcenvzD0q7Z8/I\n3qHpst4TfarL+gl5FhqYt7v7+gJp4e+sr29h6tQOpk7tGGrbU2wQ0bEOdKsBcmW08g+cu2doTMPx\nVtggvDo1a9LguiIlokqbiIiISA1QpU1KprV1Wd60+fOvij454ZXkIKlXpe3tl2WkZf7A/PlXM3fu\nJ/Lut709f+P+WbM+VKCs/6VAWa+mvT0Mgus+ONRZIfUEYv78qwtse1XetGKdRwodv9S2fX1d9PSs\npadnLX19XbG3E8mU65xxP4hZI+lrdPz91Bob5wChM0JDw8gxE0NZdI6KxKVKm5RMoYFaFyxYTWPj\nbIa3aXMaG2dz0knX09x8AtmVtubmE2hrW8Hxx6+hsbFtxH4bG9s46aTr8uZ56qm35x1c94wz7mbG\njJHDbsyYsZi2thXMnLmEadPehfuhjErbIaZNexcLFlyZd5DctrYPjnmw2mID3fb0rGPz5hV0d99E\nd/dNbN68gt7e9RogV0Yt+5zp73+Fvr6t1NU1U18/g8HBQ0MD445dA1OmzAPgLW/5DAsW/MmINXSO\nioyOOiJIyeXqOLF370aeeWY5g4MDQH+0ZiN1dQ1Djff7+/cO9eZsbGyjsXHmsEbKW7d+gR07Qu+z\nefOu5K1vvbZgnim/+MWnhtq+tbdfzkknXTeUtnPnhqE2avPnX01b24pof6GhdqEOBfv2bco7SO54\nOo/k2jZOZ4Na6bBSKuqIMH59fdvYtetOduy4eWhoDkjPN9ra+hs0NS1gz57vcejQjgJ7Cm1C6+uP\nxKyJOXM+zLRpbweGn4uT7RwVyUe9RyfA3zGRPffcJezevSFnWkvLGUMDcmabP/8K5s4t/2CcPT1r\n846RV4kyVVt5qoEqbaUR59zKvH7DtXo4Y636ocre7NkrOPnkdckWWGQCqNneo2Z2oZltMbMXzCzn\nOApmdkOU/jMzW1TuMoqI5KL4JSLlVNFKm5nVA18DLgROAT5kZidnrXMRcIK7nwj8EZD7llCqWqph\nfy7jabyflGpr3F9t5ZGJE7/inFuZ12+YW5eM7/VDn+PO+iEiY1PpJ21nAi+6+zZ37wfuBN6Xtc7v\nAOsB3P1RoNXMRrZKl6o2c+aSRBrvJ6XaGvdXW3kEmCDxK865lXn9mtVnVNzSn9vaVlbdnLoiE02l\n5x6dD2zP+P4ycFaMdY4BdiI1ZeHCW2lrW5Wz8X57+ypmzDivqhopV1uZqq08MnHiV5xzK/v6bWl5\nN2+88R/AyI44IpKMSlfa4ra8zW6sN2K7NWvWDH1eunQpS5cuHXOhJDmtrYvzBvfm5g6am6urQX21\nlanaylNOnZ2ddHZ2VroYmUoWv6DyMSzOuVXo+hWR/EoVvyrae9TMzgbWuPuF0ffPAYPu/uWMdW4G\nOt39zuj7FuA8d9+ZsY56j4pMMpXuPVqq+BUtVwwTmURqtffoT4ETzazDwkzCFwM/yFrnB8BHYChI\n7ssOeCIiFaD4JSJlVdHXo+4+YGZXAvcA9cBad3/OzD4epd/i7neb2UVm9iLwn8BHK1hkERFA8UtE\nyk+D64pITar069FSUgwTmVxq9fWoiIiIiMSgSpuIiIhIDVClTURERKQGqNImIiIiUgNUaRMRERGp\nAaq0iYiIiNQAVdpEREREaoAqbSIiIiI1QJU2ERERkRqgSpuIiIhIDVClTURERKQGqNImIiIiUgNU\naRMRERGpAaq0iYiIiNSAhkplbGZHAd8FjgW2AR9093051tsGvA4cBvrd/cwyFlNEZATFLxGphEo+\nafsz4F53Pwm4L/qeiwNL3X1RLQW8zs7OShdhGJWnsGorD1RfmaqtPBU2oeJXJf9tK31eTeb8J/Pf\nXg35j0UlK22/A6yPPq8HfrfAupZ8cUqr2k4GlaewaisPVF+Zqq08FTah4pcqDpMz/8n8t1dD/mNR\nyUpbm7vvjD7vBNryrOfAT8zsp2b2sfIUTUSkIMUvESm7RNu0mdm9QHuOpL/I/OLubmaeZzfnuHuP\nmc0G7jWzLe7+YKnLKiKSSfFLRKqNueeLNQlnbLaF0Naj18zmAve7+9uKbPN54A13/9us5ZX5I0Sk\noty9Iq8eSxm/ojTFMJFJZizxq2K9R4EfAKuAL0e//yl7BTM7Aqh39/1mdiTwXuCvsterVOAWkUmr\nZPELFMNEJJ5KPmk7Cvge8BYyusyb2TzgVnf/LTN7K/D9aJMG4Fvu/sWKFFhEJKL4JSKVULFKm4iI\niIjEVzMzIpjZbWa208yeyZO+1MxeM7Mno5+/TLg8C8zsfjPbbGY/N7NP5lnvBjN7wcx+ZmaLKl2m\nch4nM5tqZo+a2VNRedbkWa8sxyhOecp9HkV51kd5/TBPetnOoThlqsC1ts3Mno7yeizPOmU/RqVg\nZhea2Zao7J8tc94FY2oZ8o8VQxPMP1Z8SrgMBa/9hPMuel0lnH+rmd1lZs+Z2bNmdnaZ8l2YEbue\njGJZuc+9T0Xn3DNm9m0zmxJ7Y3eviR/gXGAR8Eye9KXAD8pYnnbgjOhzC/A8cHLWOhcBd0efzwIe\nqYIylfs4HRH9bgAeAc6q8DEqVp6yHp8oz08D38qVb7mPT8wylfsc6gKOKpBekWNUgr+rHngR6AAa\ngaeyr9eE8y8YU8uQf9F4VYYyFIwHZcg/73VWhrwLXldlyH89cGnG8Z9RgTLUAT3AgjLmOR/YCkyJ\nvn8XWBV3+5p50uahm/zeIquVrTGvu/e6+1PR5zeA54B5WasNDcDp7o8CrWaWbzyncpUJynuc3ow+\nNhH+YxrMWqXcx6hYeaCMx8fMjiFUOr6RJ9+yHp+YZaLA8qQUyq/sx6hEzgRedPdt7t4P3Am8r1yZ\nx4ypSeYfN14lWYY48SARMa+zxItRkUzNZgDnuvttAO4+4O6vVaAoFwAvufv2MufbABxhZg3AEUB3\n3A1rptIWgwPviV6P3G1mp5QrYzPrINyxPpqVNB/IPBleBo6pcJnKepzMrM7MniIMQPpjd/+PrFXK\neoxilKfc59F1wDXk/8+iEudQsTKV+xgVG6C2YtfZOOUq9/wKlaWiCsSrpPMtFg+SVOw6S1olB34+\nDthtZreb2RNmdquF3tbl9t+Ab5czQ3fvBv4W+BWwA9jn7j+Ju/1EqrQ9QXjE+WvAjeTogp8EM2sB\n7gKuiu4WR6yS9T3xnh9FylTW4+Tug+5+BuE/0bPM7NRcRc7erILlKdvxMbPfBna5+5MUvuMt2/GJ\nWaZyX2vnuPsiYDnwx2Z2bo51yn6dlUAtlDFxMWJoYmLGp5IbxbWfpDjXVVIagHcAX3f3dwD/Sf75\nexNhZk3AfwU2lDnfmYS3Ax2EJ8stZvbhuNtPmEqbu+9PPYoHQyAAAAciSURBVOp2938FGi10y0+M\nmTUC/wDc4e65/uPqBhZkfD+GUTwGTaJMlThOUV6vAfcDF2Yllf0YFSpPmY/Pe4DfMbMu4DvAMjP7\nZtY65T4+RctU7nPI3Xui37uBfyS8VsxUkXOoBLLLvYDwtG3SiBFDy6JAfEpKnGs/UTGuqyS9DLyc\n8WTzLkIlrpyWA49Hf385XQB0ufsr7j5AGBboPXE3njCVNjNrMzOLPp9JGM7k1QTzM2At8Ky7X59n\ntR8AH4nWP5vwGHRnnnXLUqZyHiczm2VmrdHnZuA3Ce1WMpXtGMUpTzmPj7v/ubsvcPfjCI/p/83d\nP5K1WlnPoThlKvM5dISZTYs+pwaoze7tWNZjVEI/BU40s47orv9iwt8yKcSMoUnmHyc+JSLmtZ+Y\nmNdVYty9F9huZidFiy4ANpcr/8iHCBXmcvslcLaZNUfXwAXAs3E3ruSMCKNiZt8BzgNmmdl24POE\nhqO4+y3AB4ArzGwAeJNwISTpHOAPgKfN7Mlo2Z8TBtvE3W9x97vN7CIze5Hw+PejlS4T5T1Oc4H1\nZlZPuEH4bnRMPp4qT5mPUdHyUP7zKJMDVPD4xCoT5T1GbcA/RnXE1AC1P66yYzQm7j5gZlcC9xB6\nkq5197JUGmBYTD06iqnXuvvt5cqf3PHqc+7+ozLlnzMelCnvbOV+VZ7zuipzGVYD34puWF6ijNdt\nVFG9ACh3Wz7c/TEzu4vQzGQg+v1/426vwXVFREREasCEeT0qIiIiMpGp0iYiIiJSA1RpExEREakB\nqrSJiIiI1ABV2kRERERqgCptIiIiIjVAlTYRERGRGqBKm9QEM1tqZj8cx/bvNLOv5knbZmZHmdkM\nM7uiVHmKyMRkZqvMbG6M9daZ2e8XSO80s3eWuGyKYxOYKm0yKbj74+5+Vb7k6PdM4BNlKpKI1K5L\nCJN9F+MUnu2gWPpYKI5NYKq0ScmY2ZFm9v/M7Ckze8bMPhg94eo0s5+a2Y/MrD1at9PMrjezJ6N1\n3x0tP9PMHjazJ8zsoYy56Yrl/bSZTbfgFTP7w2j5N83sgsy7TTM72sx+bGY/N7NbAYt+vgQcH5Xp\nK4Rg2mJmG8zsOTO7o/RHTUQqLZr/dYuZ3WFmz0bXfHOu+GVmHwDeRZiC6Qkzm2pm15rZY1EsuyV7\n9zHL8N4o9j1uZt+LplpKvQlYEy1/2swWRstnm9m9qTgWrXc0imMTmiptUkoXAt3ufoa7nwb8CLgB\n+H13fxdwO/C/onUdaHb3RYS7wtui5c8B57r7Owjzy/7vmHk/BCwGTiXMY7c4Wn52lJbp88BGd387\n8I+EuVkd+Czwkrsvcvc/JQTbRcBVwCnAW83snJjlEZHachLwd+5+CvA6cCUhfn0gM365+13AT4GV\n7v4Odz8A3OjuZ0Zxr9nMfns0GZvZLOAvgPPd/Z3A48Cno2QHdkfLbwI+Ey3/PPCTKI7dheLYpFAz\nE8ZLTXga+Bsz+xLwL8A+4O3AT6KJieuBHRnrfwfA3R+MnpJNB2YA3zSzEwgBqDFm3g8CS4BfEgLb\nH5nZPGCvu/dF+aecC7w/yvtuM9sbLc91R/yYu+8AMLOngA5GVgJFpPZtd/d/jz7fQahEvR24N0/8\nyowXy8zsGuAI4Cjg54QYGIcRbi5PAR6O8moCHs5Y5/vR7yeA34s+nwP8LoC736M4Njmo0iYl4+4v\nmNki4LeAvwbuBza7+3tGsZv/Cdzn7u83s2OBzpjbbSTcGW8jBNv3Ax+IlucS65UFcDDj82F0zYhM\nVJlty4zwtK1Q/HIAM5sK/B3wTnfvNrPPA1PHkP+97r4yT1oqDmXHIMWxSUavR6Vkot5UB9z9W8Df\nAGcCs8zs7Ci90cxOydjk4mj5YmCfu78OTCd9N/vRuHm7+8vALOAEd+8CNhFeI+SqtG0EVkZ5Lyc0\n3AXYD0yLm6eITChvScUqQnx4BJidJ37tJ8QqSFfQXjGzFmDFKPP1KK9zzOz4KK8jzezEIts9BHww\nWv+9KI5NCqq0SSmdBjxqZk8C/yP6WQF8OXok/yTw6xnrHzCzJ4CvA5dFy74CfDFaXs/wu99ivawe\nAX4Rfd5E6N21KWPb1PZ/BSwxs58Tnsj9EsDdXwEeihoTf5ncPbtK3dNLRKrD88Afm9mzhGYaNxCe\n1ueKX+uAm6M4dQC4lfBK9EfAo1n7LRoz3H0PoUfqd8zsZ4RXowtzrcrwOPZeM3smKmcvsF9xbGIz\nd/3bSfmZ2f3An7j7E5Uui4hMbmbWAfww6khQE8ysCTjs7ofN7NcJnSjeUelySbL0XltERKT2nj69\nBfiemdUBh4CPVbg8UgZ60iY1xcwuIXRdz7TJ3VdXoDgiIkWZ2feB47IW/6m731uJ8kjtUqVNRERE\npAaoI4KIiIhIDVClTURERKQGqNImIiIiUgNUaRMRERGpAaq0iYiIiNSA/w/mFYZkU1gokwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b90f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hmm...lets try all the things!\n",
    "import itertools\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for idx, val in enumerate(itertools.combinations(features,2)):\n",
    "    x_feature, y_feature = val\n",
    "    plt.subplot(3,2,idx+1)\n",
    "    for spec in species:\n",
    "        data_spec = data[data.species == spec]\n",
    "        plt.scatter(data_spec[x_feature], data_spec[y_feature], c=colors[spec], label=spec,\n",
    "                linewidths=0, s=50, alpha=.8)\n",
    "        plt.xlabel(x_feature)\n",
    "        plt.ylabel(y_feature)\n",
    "    f = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier  # Import the KNN algorithm from sk-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=5)  # init kNN, with k=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = data[features]  # put our features in a separate matrix, this is the 'input'\n",
    "y = data.species  # these are the labels we would like to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_neighbors=5, p=2, weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)  # fit the model to the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take our first sample and see if it predicts that one correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\n",
       "1           5.1          3.5           1.4          0.2\n",
       "2           4.9          3.0           1.4          0.2\n",
       "3           4.7          3.2           1.3          0.2\n",
       "4           4.6          3.1           1.5          0.2\n",
       "5           5.0          3.6           1.4          0.2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the features of the first few samples?\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    setosa\n",
       "2    setosa\n",
       "3    setosa\n",
       "4    setosa\n",
       "5    setosa\n",
       "Name: species, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What was the label of our first sample?\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does our model predict it would be?\n",
    "model.predict(X.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'virginica', 'versicolor', 'virginica', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'virginica', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'versicolor', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'versicolor', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What does the model predict for all the samples?\n",
    "y_pred = model.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- What is the accuracy for the entire dataset? How many % of samples can we predict correctly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96666666666666667"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "97% Correct. Not bad...but is the above number correct? Let's try to calculate it ourselves.\n",
    "\n",
    "**1) First, predict y using `model.predict`, and save it to y_pred**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)\n",
    "y_pred = model.predict(X) \n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our quality measure, will be #Predictions correct / Total # Predictions.\n",
    "\n",
    "**2) Calculate this quality measure now. It may be beneficial to use np.sum. Check that the resulting number is the same**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum( y_pred == y) / np.float64(len(y)) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- However, this number is because we tested on the training set. The above method could be seen as cheating, since we had given all answers to the model to start with. \n",
    "\n",
    "**3) To illustrate this, try the process of fitting, predicting, and scoring using $k=1$ and see what happens.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=1)  # init kNN, with k=5\n",
    "model.fit(X,y) \n",
    "model.score(X,y) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train vs Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Wow, 100% accuracy! Why do you think that is?\n",
    "- Does this mean that our model performs better when we choose $k = 1$ than with the $k$ we chose earlier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- To avoid this problem, we split up the dataset in a **training set** for fitting the model, and a **test set** for testing the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 4)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.8)  # Use 80% of the data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 1.0\n",
      "Accuracy on test set:     0.966666666667\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=1)  # Let's do the extreme case of k=1 first\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print \"Accuracy on training set:\", model.score(X_train, y_train)\n",
    "\n",
    "print \"Accuracy on test set:    \", model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set 0.958333333333\n",
      "Accuracy on test set:    0.933333333333\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=20)  # Let's try with a lot more neighbors\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "print \"Accuracy on training set\", model.score(X_train, y_train)\n",
    "\n",
    "\n",
    "print \"Accuracy on test set:   \", model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Let's get a rough idea of how our training and test error changes as a function of $k$.\n",
    "\n",
    "**1) Create two arrays. Call them trainScore and testScore respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "trainScore = []\n",
    "testScore  = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) Split up the dataset into X_train, X_test, y_train, y_test as we did above using train_size = .7**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.7)  # Use 70% of the data for training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) For $k = 1, 2, 3, ..., 70$, fit a model using the particular value of k. Append the score on the training set to $trainScore$, and append the score on the test set to $testScore$ respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for k in range(1,71):\n",
    "    model = KNeighborsClassifier(n_neighbors=k)  # Let's try with a lot more neighbors\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    trainScore.append( model.score(X_train,y_train) )\n",
    "    testScore.append( model.score( X_test, y_test) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) Plot both trainScore and testScore on a single plot. You can call plt.plot and pass in your arrays twice**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10e2d19d0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6O2tuX0O3tt0CDzzGJCU5Y/BPPRVOO81ZLN1ESBQ8LKDG+ApksZWPNnykJ794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\nLNkbY0wzYMneGGOaAUv2xhjTDPx/pwBVN23tvQUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ddfd290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(trainScore)\n",
    "plt.plot(testScore)\n",
    "plt.legend(['train','test']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run the above sections of code a few times, you might see that your plots will change a lot. Generally, as you increase 'k', the training score will decrease, while test score will increase. At a certain point, they will both start to decrease. This is due to a bias-variance tradeoff that we will explore later on. \n",
    "\n",
    "Attempting to choose the best $k$ using our current setup will lead to bias in our results. Why is that? Imagine that we keep the train and test set constant and we only vary k. If we try to choose the best k based on what gives us the highest test score, then we've only really picked the best k for THIS split of data. We will also explore solutions to reducing this issue through cross-validation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying kNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the oenophiles out there, let's try to classify some wines based on some qualities about the wine. First download the following dataset, and use the column_names attribute to set the column names.\n",
    "```\n",
    "column_names = ['wine','alcohol','malic_acid','ash','alcalinity','magnesium','total_phenols','flavanoids','nonflavanoid_phenols','proanthocyanins','color_intensity','hue','od280','proline']\n",
    "\n",
    "x = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',names=column_names)\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many samples of each class are there?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Print out some descriptive stats (mean,min,max) of each column by each class.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Split out the wine dataset into an X train matrix, X test matrix, y train array, y test array. Use 80% of the dataset for training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Initialize knn on 5 neighbors, and generate the train and test scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try a few other values of k. How does your model compare?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try removing a few features. How does your model compare?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Exercise: Implementing kNN yourself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal of this exercise is to write the entire kNN algorithm yourself in python. This is a great exercise for those students who'd like to master the math *and* coding behind the algorithm.\n",
    "- The code below has defined a kNN class with empty functions.\n",
    "- Review the code. There are three methods that you need to complete: `fit`, `predict` and `score`.\n",
    "- Compare this implementation with the sklearn implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "class kNN(object):\n",
    "    \"\"\"\n",
    "    Class for simple k-Nearest Neighbors\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, k=5):\n",
    "        \"\"\"\n",
    "        Initialize model.\n",
    "        :param k: number of Neighbors (the 'k' in kNN)\n",
    "        \"\"\"\n",
    "        self.k = k\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model.\n",
    "        :param X: pandas dataframe or numpy ndarray with features\n",
    "        :param y: pandas series or numpy ndarray with classes\n",
    "        \"\"\"\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Predict classes of samples.\n",
    "        :param X: pandas dataframe or numpy ndarray with features\n",
    "        \"\"\"\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def score(self, X, y):\n",
    "        \"\"\"\n",
    "        Compute accuracy of predictions on X.\n",
    "        :param X: pandas dataframe or numpy ndarray with features\n",
    "        :param y: pandas series or numpy ndarray with true classes\n",
    "        \"\"\"\n",
    "        raise NotImplementedError\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Example Usage:\n",
    "model = knn.kNN(k=15)\n",
    "model.fit(X, y)\n",
    "model.score(X, y)"
   ]
  }
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