{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $k$-NN classifier using `scikit-learn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import metrics, model_selection as ms, neighbors\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "IRIS_URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/bezdekIris.data'\n",
    "var_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the Iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv(IRIS_URL, header=None, names=var_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic data exploration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\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>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['species'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for differences between species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>Iris-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>Iris-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>Iris-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",
       "Iris-setosa             5.006        3.428         1.462        0.246\n",
       "Iris-versicolor         5.936        2.770         4.260        1.326\n",
       "Iris-virginica          6.588        2.974         5.552        2.026"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby('species').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">petal_length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">petal_width</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sepal_length</th>\n",
       "      <th colspan=\"8\" halign=\"left\">sepal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</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",
       "      <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>Iris-setosa</th>\n",
       "      <td>50.0</td>\n",
       "      <td>1.462</td>\n",
       "      <td>0.173664</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.575</td>\n",
       "      <td>1.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.246</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.428</td>\n",
       "      <td>0.379064</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.200</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.675</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>50.0</td>\n",
       "      <td>4.260</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.600</td>\n",
       "      <td>5.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.326</td>\n",
       "      <td>...</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.770</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.525</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.000</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>50.0</td>\n",
       "      <td>5.552</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.55</td>\n",
       "      <td>5.875</td>\n",
       "      <td>6.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.026</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.974</td>\n",
       "      <td>0.322497</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.175</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                petal_length                                               \\\n",
       "                       count   mean       std  min  25%   50%    75%  max   \n",
       "species                                                                     \n",
       "Iris-setosa             50.0  1.462  0.173664  1.0  1.4  1.50  1.575  1.9   \n",
       "Iris-versicolor         50.0  4.260  0.469911  3.0  4.0  4.35  4.600  5.1   \n",
       "Iris-virginica          50.0  5.552  0.551895  4.5  5.1  5.55  5.875  6.9   \n",
       "\n",
       "                petal_width        ...  sepal_length      sepal_width         \\\n",
       "                      count   mean ...           75%  max       count   mean   \n",
       "species                            ...                                         \n",
       "Iris-setosa            50.0  0.246 ...           5.2  5.8        50.0  3.428   \n",
       "Iris-versicolor        50.0  1.326 ...           6.3  7.0        50.0  2.770   \n",
       "Iris-virginica         50.0  2.026 ...           6.9  7.9        50.0  2.974   \n",
       "\n",
       "                                                        \n",
       "                      std  min    25%  50%    75%  max  \n",
       "species                                                 \n",
       "Iris-setosa      0.379064  2.3  3.200  3.4  3.675  4.4  \n",
       "Iris-versicolor  0.313798  2.0  2.525  2.8  3.000  3.4  \n",
       "Iris-virginica   0.322497  2.2  2.800  3.0  3.175  3.8  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby('species').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot of all numeric columns grouped by species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fb00d2d9278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb0049924e0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fb0049cada0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fb004982710>]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00d2d9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.boxplot(by='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter matrix of all predictors coloured by species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb00483a5f8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00483a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot of petal length by species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb003f7f710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00401bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='species', y='petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb003b66cf8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb002102b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='species', y='petal_length', data=iris, jitter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0020b2630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb002071160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='species', y='petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb00204f940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XF8RyXrMOqAB2Au+703tFZJ2IzPQiOGO8sHLlSl555RU+XdLExHxviqpfPaaZ8wa38e//9hDHjx/3ZB/mTDfffDPDRwwntL6HAVLCENoQorikmC9+8YsJi89vsST8l3B66gxR1QKcC7ZPAbcDv/AiOGPira2tjYd+/iAjc5RPR3FzFcCjO7I6iqf9eE1uR9XM7gQEvlrWQFtLMw8//PC5hm2ilJGRwbfu+RZap8iuszftyE5BG5R7v3VvUo5sdTaxJPxZqvpy+y+q+idgvqquAjLiHpkxHli+fDkHDx3m+vENUbfb768L0hQO0BQOsL0mraNqZk+GZke4avQpXvnTn2zg8wSaO3cuM2fNJLg92HW9/BYI7gwyb948ZsyYkfD4/BRLwq8Wkb8TkWL38R3ghIgE8XZ0SWPiZtkf/sCwbOWCQi9GzjjT1WNOoaq89JLdb5hIf/P1v0Gb1amNcxp5X9AW5etf/7oPkfkrloT/RWA08CzwHDDGnRcEro9/aMbEV2NjIxvf28jsoac6RrDyWkGmMiG/jVUrVyRmhwaA8vJyZsyYQXBX8KPDIIYhuCfI3Llzk7LefU+iTviqWqmqd6rqDHcwk2+qaoWqtqjqLi+DNCYe9u7dSzgcobSHKpjxVjqwjd279xAOJ3a/qe7aa69FG/Uj/fLlsFMv59prr/UvMB9FfbVCRCYCfwuUdH6eql4R/7CMib+qqioACjIS2wJZkBGhta2N+vp6Bg4cmNB9p7KLL76Y3Lxc6iJ1HXXw5QNhUMEgZs5MzY6FsVyefhr4JfBfxKcYqTEJFYk4iT5RzTnt2vfXvn+TGKFQiPmXzufFP79I27Q2aIPAsQBXfOaKlLjJqiuxJPw2Vf0PzyIxxmN5eXkA1LcmNuO37y83Nzeh+zVw0UUX8cc//hGqgTBoWJkzZ47fYfkmlou2L4jI7SIyQkQGtz88i8yYOBsxYgQAx5piq6PQ1CZkZWVx3XXXkZWVRVNbbB8YxxoDDCkYRFqa3ZieaNOnTwdAKgWpEESEadOm+RyVf2I5w/+y+/PbneYpMC5+4RjjneHDh5OVmcH+uuhuuGrX2CYs/MRC7rrrLgDe+MOTMT1/f0Ma4yZPiOk5Jj7y8/MZPmI4R6qPQBiKS4rJzs72OyzfRJ3wVXWsl4EY47VAIEDZ5Mns2r0OiH44wuyQsmzZMgCWLVvG0FD0tXea2uBgfYArk3hQjb5u4oSJHNtwDAkLE6dN9DscX0X93VZEskXk+yKyyP19goh8wrvQjIm/adPO54O6AE0xjHOSFVKamppYvHgxTU1NZMWQ8N8/GUIVpk6d2otoTTwUFxejtUqkIZLyA83H0pj5a5werRe7vx8Efhz3iIzx0PTp04ko7KxJTP2UbSdCBIMBpkyZkpD9mTO1X7sBGDVqlI+R+C+WhD9eVf8ZtzqFqjYBCe7gZsy5mTJlCqFgkG0nEnMBdduJdCZNmpTS7cZ+GzZsWMd0so9o1ZNYEn6LiGTh3qgsIuOB3o8JZ4wPMjMzKS8vZ8uJdM/31dgGe2qDzJw5y/N9mbMbPHhwl9OpKJaE///hlEguEpHfA68C3/EkKmM8dOHs2eyrDVDb4u0X1K3VaUQULrzwQk/3Y7qXn5/f5XQqiqWWzivAZ4GvAI/jlEt+PZrnikhQRNaLyB96E6Qx8TRnzhwU2FjpbbPO+so0srOyOO+88zzdj+le+w13QMo3rfV45UpELjht1hH35xgRGaOq66LYz93ANmBAjPEZE3cTJ06kcEgB7x5v5dKRsYx4Hb22CKyrzGDupRen1AAbfVF6+ofNdyKpfdkxmv/Ef+lmmQLdFk8TkdHAQuAfgXujD80YbwQCAa68agFPP/kEJ5uFgRnxH2h8Y1UadS1w1VVXxX3bxvRWjwlfVT8WzYZEZIHb7HO6n+G09ed1scwYXyxcuJAnnniC1w9nRD3UYSz+fDCTgsGDUrpui+l7Yisq0r37T5/h3ph1XFXXnu1JInKriKwRkTUVFRVxDMeYsysuLmb2hRfy8sEsmuNc+3VfbZBNVSE+e+111pzTRwwdOpSiMUV+h+G7eCb8rhrH5gGfEpF9wBPAFSLyu84rqOoiVZ2lqrMKCwvjGI4x3bv5y1+mthleORDfIZkX78kiNyebz3zmM3Hdrum93/zmNyz6z0V+h+G7eCb8MxpCVfU+VR2tqiXADcBrqvqlOO7TmF6bNm0aF100h+f353TbRbM4L0xWMEJWMEJZfivFeWf/SrClOsSGyjRu/NJNH+kdYvyVm5tLTk6O32H4Lp4J35h+5/bb76A5Ijy9O+us69w0qYnivDDFeWG+P6uemyZ1XXitLQKPvp/LiOHDuO6667wK2Zhei2fC39fdQlV9XVWt2JrpU0pKSrj22ut4/VAGu0+e2yhIfz6YwcE64Zt33kVGRnybiYyJh2j64X+2u+WqutT92e16xvRVt9xyC6/++RUe2RnhB7NO9moIxJpmYcmeHGbPvpBLLrkk/kEaEwfRdCH4ZDfLFFgap1iM8UVOTg633X4HP/7xj3nrSDqX9eJmrKd2ZdGqAe6+++6Uv7nH9F3R9MO/JRGBGOOnBQsWsHTJEhbv2cZFw1rIiKF1Z39dkLeOZPD5Gz5HUZF1/TN9V0xt+CKyUES+IyL/u/3hVWDGJJKIcNvtt3PiFLx6MLb296V7ssjJyeamm27yKDpj4iOWEa9+CXweuBOnz/3ngGKP4jIm4c4//3wumDGDFw9k0xaJ7jmH6gOsrUjjc9d/3rphmj4vljP8i1X1ZuCEqv4QmAvY91eTVD5/ww2cOAVrjkdXSfPPBzNISwtxzTXXeByZMeculoTf3vm4UURG4ox8ZQObm6QyZ84chg0t5M0jPTfrtEZgxbEsLrvs8pSvs276h1gS/h9EJB/4v8A6nH73T3gRlDF+CQQCXLXg42yuTqO+tfveNluqQzS0KgsWLEhQdMacm1gS/j+rao2qLsFpuy/DBjE3SeiSSy4horCpqvtObOsr08jMyGDmzJkJisyYcxNLwl/ZPqGqzap6svM8Y5JFWVkZuTnZbKnuvh1/64kMZlww4yMDbBjTl0Vzp+1wYBSQJSIz+LAq5gAgtccLM0kpGAwy7fzp7Nj4DtDY5Tonm4UjDcJnps9IbHDGnINo7rT9K5xxbEcD/9ppfi3wPQ9iMsZ35513HitWrKCuRchLP3NErPdPhjrWM6a/iOZO20eAR0TkWrf93pikN2XKFAD21AY5f0jbGct31wYJBgNMnDgx0aEZ02uxtOG/IyK/EpEXAUSkXES+5lFcxvhq0qRJiAi7a7s+J9pdm8b4ceOsKqbpV2JJ+L8GXgZGur/vBO6Je0TG9AHZ2dkUjyli98kzE35EYV9dGmWTy32IzJjeiyXhD1HVp4AIgKq2AXEeDdSYvmNy+RT21qejpzXhH20M0NiqTJ482Z/AjOmlWBJ+g4gU4A5lKCIXASc9icqYPqCsrIzaZqXyVKBjxCug46zfEr7pb6LppdPuXuB5YJyIvAMUAjaOm0la7Ql9d23wI8Ma7qkNkpmRQXGx1Q40/UssZ/hbgWeA1cAx4GGcdnxjktL48eNJSwux57QLt3vq0igrKyMYPLchEY1JtFgS/m9xyin8E/AQMAF41IugjOkL0tLSKB0/nr2dEn5bBD6oCzKprMzHyIzpnViadCap6vmdfl8uIhvjHZAxfcmkssm8vHsnEYWAwKGGIK0Rp9umMf1NLGf4690LtQCIyBzgne6eICKZIvKuiGwUkS0i8sPeBmqMHyZOnEhTq3K8yXmr7KsLdsw3pr+J5Qx/DnCziHzg/j4G2CYimwBV1WldPKcZuEJV60UkDXhbRF5U1VXnFrYxiVFaWgo4zTjDsyN8UBckMyOdUaNG+RyZMbGLJeFfHevGVVWBevfXNPdxZmESY/qokpISRIQD9UFmD2vlQH2QkpISu2Br+qWoE76q7u/NDkQkCKwFSoF/V9W/9GY7xvghMzOTkSOGcajB+WJ7uCmduePG+xyVMb0TSxt+r6hqWFWn41TbnC0iHykvKCK3isgaEVlTUVHhdTjGxGxM8VgON6XR0CrUnFLrf2/6Lc8TfjtVrQFe57SmIVVdpKqzVHVWYWFhosIxJmpjxozhWINwpNF5uxQVFfkckTG942nCF5FCdxxcRCQLuArY7uU+jYm30aNH0xqBrSecFlBL+Ka/iuWibW+MwKmlH8T5cHlKVf/g8T6NiauRI50CsVvdIQ9HjBjhZzjG9JqnCV9V3wNsDDjTrw0fPhyAbSdCDM4faDXwTb+VsDZ8Y/qroUOHAhBWYeiwYT5HY0zvWcI3pgcZGRkMHJALQOFQS/im/7KEb0wUBg8uAKCgoMDnSIzpPUv4xkRhYP4gAPLz832OxJjes4RvTBREBICBAwf6HIkxvWcJ35gY5OXl+R2CMb1mCd+YGGRnZ/sdgjG9ZgnfmBhYH3zTn1nCNyYK8+fPBz7sk29MfyROyfq+YdasWbpmzRq/wzCmS01NTWRlZfkdhjFnEJG1qjqrp/XsDN+YKFmyN/2dJXxjjEkRlvCNMSZFWMI3xpgUYQnfGGNShCV8Y4xJEZbwjTEmRVjCN8aYFGEJ3xhjUoQlfGOMSRGW8I0xJkV4mvBFpEhElovINhHZIiJ3e7k/Y4wxZxfyePttwP9S1XUikgesFZFXVHWrx/vtk9oL1bWPnmSMMYnk6Rm+qh5R1XXudB2wDRjl5T77queee47LL7+caz77Werq6vwOxxiTghLWhi8iJcAM4C+J2mdfcfjwYX7xH79EVamuquLnP/85fakstTEmNSQk4YtILrAEuEdVa09bdquIrBGRNRUVFYkIJ6EqKyv51r33cqo1TOP519My6gJefvllfvWrX/kdmjEmxXie8EUkDSfZ/15Vl56+XFUXqeosVZ1VWFjodTgJVVdXxz33fIujxytonLAAzRxA66gZtBZO4re//S2PP/643yEaY1KI1710BPgVsE1V/9XLffVFDzzwMz44cIDG0gVE8oY5M0VoGTuPtsFj+eUvf8nWrSl5/doY4wOvz/DnATcBV4jIBvfx1x7vs0+orq7m1Vf/TMvw84gMHPnRhRKgedylEMpgyZIl/gRojEk5nnbLVNW3gZTsg7hp0yZUlfCg4q5XCKbTmjeC9Rs2oqrWVdMY4zm709YDJ0+e5NFHf4ekZRHJGXLW9cL5RVRWHGfp0qXWa8cY4zmvb7xKKdXV1SxbtozHn3iShoYGmsZfAYHgWddvG1JKqHovDz74IK+/8QZf/MIXmD17NsHg2Z9jjDG9JX3pzHLWrFm6Zs0av8OIydGjR1m1ahVvvvkW69atJRKJEB44muYxF6LZBT1vQCOEjm0j8+h7aHMD+YMGcflllzFv3jymT59ORkaG93+EMaZfE5G1qjqrx/Us4cemurqajRs3sn79elavWcOhgwedBVkDaRlUQtuQCWhW/hnPS9+/EoCW4rldbzgSJljzAaHK3aTVHkLDraSlpTNt2lRmzpzJ9OnTKSsrIxSyL2XGmI+KNuFb9uhGJBLhwIEDbN68mU2bNrHxvfc6ErwE02jNG054zBzC+UVo5kDo5sJroKGq+50FgoQHjyU8eCzNkTaCtUdoPXmItdv3snbtWgDSMzIoLy9n2tSpTJ06lfLycvLy8uL29xpjkpsl/E6qq6vZvn0727dvZ8uWrWzZuoXGhgYAJC2T1pxCIkUXEs4b4VyMDXh0zTsQIpxfRDi/iBaA1iaCtUdprTvK+vcPsmHDBnC/mY0uKuK8KVOYPHkyZWVljB8/nvT0dG/iMsb0aynbpFNVVcXOnTvZuXMnO3bsYNu27VRVVToLRSB7EK3ZhURyhxLOG9bjGXx30vevJFSxE4BIdgGRnIKzN+1EI9xKoL6CYP1xAvXHSGusRFuaAAgGg4wbN56ysklMmuQ8xo4dax8CxiQxa9JxqSrHjh3rSO47d+5k+46d1Jyo/nClrIG0ZQ8hPGY8kZxCIjkFEEyLWwyBhiok3ApAsO7ouW8wmEZk4MiOG7qaVZGWegL1FQQaKtl+rJJd+/7ECy+84Ow/GKR4TDFlZZOYOHEiEydOZPz48WRnZ597LMaYfiPpEn5dXR1btmxh69atzmMUelMdAAAOy0lEQVTbNurbyxG3n7lnDSYyZiKRnAIi2QUQ6udnvyJoRh7hjDzCBeNoBVBFmusINFQRaKzk/eoq9r36Bi+++KL7FKGoaAzl5ZMpLy+nvLyc8ePHW5dQY5JYv0/44XCYTZs28c477/Du6tXs27vXuYmpo1lmBJGCqURyhhDJHgwBH/7kcAtZWVksXLiQZcuWUR9u8X6fImjmAMKZAwgXjAXcbwKtjQQaKgk0VLKntpIDr73BSy+9BEBWVjbnn38+c+bM5tJLL2Xo0KHex2mMSZh+m/AjkQjPPvssj/7ud1RVVjq9XHKHER51AeHcYURyC+PaLHMupK2FhZ9ayF133QXAU8+/5FMggqbnEE7P6Sj50KyKNNcTqD9Ga91RVm3cwqpVK3nwwQeZPWcOd9x+O2PHjvUnXmNMXPXbhL9kyRIeeughInnDaSm9gnB+UZ9J8KfTUDrLli0DYNmyZWgoy+eIOhFBM/MIZ+YRHlJKCyBNJwlV7WL12g3cdtvtPP30U9b905gk0G9r6TQ2NgIQSc9F03P8aaqJVjCdpqYmFi9eTFNTEwT79jUDTctE03PRQIiWlmbC4bDfIRlj4qAPZ8nufeELX6CxsZGlS5+huWoXkp5Na94IwnnDiOQOI5I1yLt+8smm9RTBhgoCdccI1R0hUF8BGqG4pIR77r6b/Pwz7xw2xvQ//Tbhp6enc9ttt3HzzTezYsUKVq1axeo1a6jZtxsACYQIZw8mnD3Y6fuePdj5EOjvPXLORXv3zcZq59FQRdqparTJGXUyEAhQWjqB2bMXcMkllzB58mQr22xMEum3Cb9dTk4OCxYsYMGCBagqhw8fZuvWrWzfvt3pd//+LpqOb+9YXzLzaM3MJ5I1CM0aRCR7EJHMfAh691JEcgoINDqlFdpvvPJUe2+cxhNI0wkCTScINtUQPHUCbWvtWG3EiJFMOv8CJk2aRFlZGZMnT7a++cYksX6f8DsTEUaNGsWoUaNYsGAB8OGNV7t27WLv3r3s2bOH3Xv2cPDAVtra2j58btbAjg+CSPYgIlmD0ayBIOfeLNRSPLejls6p8k+c8/Y+ou0UgcYTzqOpmmDTCYKnatDW5o5V8gYMZHzpWMaNu4SxY8cyfvx4xo0bZ8ndmBSTVAm/KyLC8OHDGT58OJdccknH/La2Ng4dOsTevXvZt28fe/fuZdeu3Rw69B6RSMRZKRBEswfRluU2C+UUEsnxqS8/IC0NBOorCTRWftgcc6q+Y3l2Tg7jxo2jdPxcSkpKGDt2LGPHjrU2eGMMkAIJ/2xCoRDFxcUUF390CMKWlhY++OAD55vA7t28v2sXO3bspM6thUMggGYX0JY7jHDecMIDRkDIg5r1qkhTDcHawwTrjpLWWNGR3EWE0UVjmDTrIiZMmMC4ceMYN24cQ4YMsTZ3Y8xZpWzCP5v09HRKS0spLS3tmKeqHD9+3C2yto3Nmzezdes2Wo9uBhEiucNoHVRMuGCc00W0C1G126sSqD1CqHoP6ScPoM1Opc4hhUM5f94cysvLKSsrY8KECWRmZsbl7zXGpI6UrZZ5rlpbW9m6dSurV6/m7XdWsGf3LhChLb+Y1lHTux3L9gyRMKGKHWQc2wxNtaRnZHDRnDlcdNFFXHDBBYwcOdK7P8QY0+/1iRGvROS/gU8Ax1X1vJ7W708J/3QHDhzgj3/8I88++xwNDQ20Dp9CS9HsHu8FkMZqsncvh8YTTJ5czrXXfpb58+fbGbwxJmp9JeHPB+qB3yZ7wm9XX1/PokWLePbZZ2kbXEJz6ZVnraMfaKgie/sfGZiXzd995ztcfPHF1gZvjIlZtAnf01tRVfVNoLrHFZNIbm4u9957L3fccQeh6n2Ejm7qesVIG1m7X2PQwFweXrSIefPmWbI3xnjKag945Prrr+fiiy8m89B6pLn+jOVphzdC00n+4fvfZ9iwYT5EaIxJNb4nfBG5VUTWiMiaiooKv8OJGxHh7rvvJhQU0vev6BiDFkCaakg/8h5XXnklM2fO9DFKY0wq8T3hq+oiVZ2lqrMKCwv9DieuRowYwde/9jVCJz4gVLnLmRmJkLXnDXKzs7nzzjv9DdAYk1J8T/jJ7vrrr+e886aS+cEqpKWBtCPvIfUVfPvbf8vgwYP9Ds8Yk0I8Tfgi8jiwEpgkIgdF5Gte7q8vCgaD3HffdwkQJn3v22Qc2cD8+fP52Mc+5ndoxpgU43UvnS+o6ghVTVPV0ar6Ky/311cVFRXxyU98glDNATTcxje+8Q2/QzLGpCBr0kmQq6++GoAJEydSVFTkczTGmFRktXQSpLy8nCeffJKBAwf6HYoxJkVZwk+gESNG+B2CMSaFWZOOMcakCEv4xhiTIizhG2NMirCEb4wxKcISvjHGpAhL+MYYkyIs4RtjTIroU2PaikgFsN/vODw0BKj0OwjTa3b8+q9kP3bFqtpjueE+lfCTnYisiWYYMtM32fHrv+zYOaxJxxhjUoQlfGOMSRGW8BNrkd8BmHNix6//smOHteEbY0zKsDN8Y4xJEZbwOxGR+m6WrfBwv9/zatvJwq9jEy0R+aOI5PfieT8Qkb/1Iqa+yutjKSKfEpHv9uJ5Pe5bRP5LRMp7F5n/rEmnExGpV9Xc0+YFVTWc6P2aj/Lr2Jy2v5CqtsV5mz8A6lX1p37FkGg+vs/6/Wt3ruwMvwsicrmILBeRx4BN7rx69+cIEXlTRDaIyGYRubSL508RkXfddd4TkQnu/C91mv+fIhIUkZ8AWe6837vr3etue7OI3OPOyxGRZSKy0Z3/eXf+/xaR1e68RSIiiXmV/BGHY/MXEZnS6ffXRWSm+/r+t/tarheRT7vLvyIiT4vIC8CfzrYPEdknIkPc6Zvd475RRB515xWLyKvu/FdFZEwXsU0XkVXuOs+IyKBOMf6TiLwB3B3nl9Q3Hh7Lr4jIv7nzfiMi/yoiy4H7RaRQRF4RkXXue3B/p+NW3ymu10VksYhsF5Hft7+v3Pmz3Omr3e1sFJFX3XmzRWSF+z+0QkQmefkaxkxV7eE+cM60AC4HGoCxXSz7X8Dfu9NBIK+L7TwE3OhOpwNZwGTgBSDNnf8L4ObO23anZ+L88+cAucAWYAZwLfBwp/UGuj8Hd5r3KPBJv1/HPn5svgX80J0eAex0p/8J+JI7nQ/sdI/BV4CD7a/z2fYB7MO5m3MKsAMY0vn4uMf+y+70V4Fn3ekfAH/rTr8HXOZO/wj4mTv9OvALv49BPzqWXwH+zZ3+DfAHIOj+/m/Afe701YB2Olad4zoJjMY5KV4JXNLpWMwCCoED7bF3Os4DgJA7fRWwxO/Xu/PDzvDP7l1V3dvF/NXALeJ8FZ+qqnVdrLMS+J6I/B3OLc9NwJU4yXy1iGxwfx/XxXMvAZ5R1QZVrQeWApfifAhcJSL3i8ilqnrSXf9j7pnOJuAKnIST7M7l2DwFfM6dvh542p3+OPBd99i8DmQC7Wfhr6hqdZT7uAJYrKqVAJ2eNxd4zJ1+FOc4dxCRgUC+qr7hznoEmN9plSe7+FuSgRfH8nRP64fNRZcATwCo6kvAiW7iOqiqEWADUHLa8ouAN9tj73ScBwJPi8hm4AH62PvREv7ZNXQ1U1XfxHkjHgIedb++X+N+9dwgIrNU9THgU0AT8LKIXAEI8IiqTncfk1T1B13sossmGVXdyYdn//+/25STifNN4TpVnQo8jJOokt25HJtDQJWITAM+j/vmx3ndr+10fMao6rbT99fVPk4LQ3DOGnsS68WzLv/mJODFsexuH9E2eTZ3mg5z5vjfZzvO/wdYrqrnAZ+kj70fLeHHSESKgeOq+jDwK+ACVX2mU6JYIyLjgD2q+nPgeWAa8CpwnYgMdbcz2N0WQKuIpLnTbwKfEZFsEckBrgHeEpGRQKOq/g74KXABH/4zVYpILnCd5y9AHxbNsXFXfQL4Dk6z2CZ33svAnZ3aamdEu4/TVnkVuF5ECtz1B7vzVwA3uNM3Am93fpL7je1Ep7bqm4A3SFHneCy78zbOtwFE5OPAoF6GuBK4TETGuttqP84DcT6kwGla6lNO/9QyPbsc+LaItAL1wOlneOCcbXzJXeco8CNVrRaR7+Nc+AsArcAdONVBFwHvicg6Vb1RRH4DvOtu679Udb2I/BXwf0Uk4j73NlWtEZGHcc769+F8DU5ll9PzsQFYDDyIczbW7v8AP8M5DoLzen4i1n2o6hYR+UfgDREJA+tx3vh3Af8tIt8GKoBbutj2l4Ffikg2sOcs66SKy+n9sezOD4HHxen08AZwBOiquahbqlohIrcCS93383FgAfDPwCMici/wWqzb9Zp1yzTGpAwRyQDCqtomInOB/1DV6X7HlSh2hm+MSSVjgKfcs/IW4G98jieh7AzfGGNShF20NcaYFGEJ3xhjUoQlfGOMSRGW8I05B9LLKpnG+MEu2hpjTIqwM3yT9KSLSqPiVLe8X5zqpe+KSKm7bqGILBGnauZqEZnnzs8VkV+LyCZxqlle687vXCWzq2qoQXEqNm52n/st/14Jk+qsH75JBVcDh1V1IXQUKrsfqFXV2W49nJ/h3Fn7IPCAqr4tTgnjl3Eqnf4DcNKtWYS4pYvbichknDus56lqq4j8AqeEwhZglFtbBWv+MX6yhG9SwSbgpyJyP/AHVX3LLZnzuLv8cZzKhuCUtC2XD4cVGCAiee789lo4qOrpVRY7V0MFpyT2cZyyyONE5CFgGfCn+P5pxkTPEr5Jeqq6U0RmAn+NU2m0Pel2voDVPh0A5rolrTu49XW6u+DVXg31vjMWiJwP/BVO7aTrcerhG5Nw1oZvkt5ZKo2C0wTT/nOlO/0n4Judnjv9LPNPr7LYZTVUt30/oKpLcJqFTq+uaUzC2Bm+SQVTOa3SKE6VxQwR+QvOic8X3HXvAv5dRN7DeX+8CfxP4Mfu/M049dF/iDM4DQCquvUs1VCbgF+78wDO+AZgTKJYt0yTkkRkHzCrfWQqY1KBNekYY0yKsDN8Y4xJEXaGb4wxKcISvjHGpAhL+MYYkyIs4RtjTIqwhG+MMSnCEr4xxqSI/wd3EBFi41ithgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb00204a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='species', y='petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot of petal length versus petal width with density estimates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fb00462e2e8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb004640400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='petal_length', y='petal_width', data=iris, kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define new predictor (petal area)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['petal_area'] = iris['petal_length'] * iris['petal_width']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for differences in petal area between species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       species        \n",
       "count  Iris-setosa        50.000000\n",
       "       Iris-versicolor    50.000000\n",
       "       Iris-virginica     50.000000\n",
       "mean   Iris-setosa         0.365600\n",
       "       Iris-versicolor     5.720400\n",
       "       Iris-virginica     11.296200\n",
       "std    Iris-setosa         0.181155\n",
       "       Iris-versicolor     1.368403\n",
       "       Iris-virginica      2.157412\n",
       "min    Iris-setosa         0.110000\n",
       "       Iris-versicolor     3.300000\n",
       "       Iris-virginica      7.500000\n",
       "25%    Iris-setosa         0.280000\n",
       "       Iris-versicolor     4.860000\n",
       "       Iris-virginica      9.717500\n",
       "50%    Iris-setosa         0.300000\n",
       "       Iris-versicolor     5.615000\n",
       "       Iris-virginica     11.445000\n",
       "75%    Iris-setosa         0.420000\n",
       "       Iris-versicolor     6.750000\n",
       "       Iris-virginica     12.790000\n",
       "max    Iris-setosa         0.960000\n",
       "       Iris-versicolor     8.640000\n",
       "       Iris-virginica     15.870000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.groupby('species')['petal_area'].describe().unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot of petal area by species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb001beea90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb001e96978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='species', y='petal_area', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract observations that cannot be linearly separated by petal area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "      <th>petal_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>7.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width          species  \\\n",
       "119           6.0          2.2           5.0          1.5   Iris-virginica   \n",
       "56            6.3          3.3           4.7          1.6  Iris-versicolor   \n",
       "106           4.9          2.5           4.5          1.7   Iris-virginica   \n",
       "133           6.3          2.8           5.1          1.5   Iris-virginica   \n",
       "134           6.1          2.6           5.6          1.4   Iris-virginica   \n",
       "83            6.0          2.7           5.1          1.6  Iris-versicolor   \n",
       "77            6.7          3.0           5.0          1.7  Iris-versicolor   \n",
       "70            5.9          3.2           4.8          1.8  Iris-versicolor   \n",
       "126           6.2          2.8           4.8          1.8   Iris-virginica   \n",
       "138           6.0          3.0           4.8          1.8   Iris-virginica   \n",
       "\n",
       "     petal_area  \n",
       "119        7.50  \n",
       "56         7.52  \n",
       "106        7.65  \n",
       "133        7.65  \n",
       "134        7.84  \n",
       "83         8.16  \n",
       "77         8.50  \n",
       "70         8.64  \n",
       "126        8.64  \n",
       "138        8.64  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[(iris['petal_area'] >= 7.5) & (iris['petal_area'] <= 8.64)].sort_values('petal_area')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to predict the species based on petal area only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_iris(row):\n",
    "    if row['petal_area'] < 2:\n",
    "        return 'Iris-setosa'\n",
    "    elif row['petal_area'] < 7.5:\n",
    "        return 'Iris-versicolor'\n",
    "    else:\n",
    "        return 'Iris-virginica'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add predictions to DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris['prediction'] = iris.apply(classify_iris, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 46,  4],\n",
       "       [ 0,  0, 50]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(iris['species'], iris['prediction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>prediction</th>\n",
       "      <th>Iris-setosa</th>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <th>Iris-virginica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "prediction       Iris-setosa  Iris-versicolor  Iris-virginica\n",
       "species                                                      \n",
       "Iris-setosa               50                0               0\n",
       "Iris-versicolor            0               46               4\n",
       "Iris-virginica             0                0              50"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(iris['species'], iris['prediction'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $k$-nearest neighbours classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.loc[:,'sepal_length':'petal_width']\n",
    "y = iris.species.factorize()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All points in the neighbourhood are weighted equally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_uniform = neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform')\n",
    "knn_uniform.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96666666666666667"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y, knn_uniform.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96666666666666667"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(y == knn_uniform.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Points in the neighbourhood are weighted by the inverse of the distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_distance = neighbors.KNeighborsClassifier(n_neighbors=5, weights='distance')\n",
    "knn_distance.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y, knn_distance.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine 'optimal' number of neighbours and method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'weights': ['uniform', 'distance'], 'n_neighbors': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'n_neighbors': np.arange(2, 50)\n",
    "}\n",
    "five_fold_cv = ms.StratifiedKFold(n_splits=5, shuffle=True)\n",
    "gs = ms.GridSearchCV(\n",
    "    estimator=neighbors.KNeighborsClassifier(),\n",
    "    param_grid=params,\n",
    "    cv=five_fold_cv\n",
    ")\n",
    "gs.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Best' model accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97999999999999998"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Best' model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix of 'best' model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50,  0,  0],\n",
       "       [ 0, 47,  3],\n",
       "       [ 0,  2, 48]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y, gs.best_estimator_.predict(X))"
   ]
  }
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