{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задача 12. Предсказать сорт винограда из которого сделано вино, используя результаты химических анализов, c помощью KNN - метода k ближайших соседей с тремя различными метриками. Построить график зависимости величины ошибки от числа соседей k.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.neighbors\n",
    "import sklearn.model_selection\n",
    "import sklearn.metrics\n",
    "import sklearn.preprocessing\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Считываем данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Class</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD/OD of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.75</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.20</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.22</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0      1    13.20        1.78  2.14               11.2        100   \n",
       "1      1    13.16        2.36  2.67               18.6        101   \n",
       "2      1    14.37        1.95  2.50               16.8        113   \n",
       "3      1    13.24        2.59  2.87               21.0        118   \n",
       "4      1    14.20        1.76  2.45               15.2        112   \n",
       "5      1    14.39        1.87  2.45               14.6         96   \n",
       "6      1    14.06        2.15  2.61               17.6        121   \n",
       "7      1    14.83        1.64  2.17               14.0         97   \n",
       "8      1    13.86        1.35  2.27               16.0         98   \n",
       "9      1    14.10        2.16  2.30               18.0        105   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.65        2.76                  0.26             1.28   \n",
       "1           2.80        3.24                  0.30             2.81   \n",
       "2           3.85        3.49                  0.24             2.18   \n",
       "3           2.80        2.69                  0.39             1.82   \n",
       "4           3.27        3.39                  0.34             1.97   \n",
       "5           2.50        2.52                  0.30             1.98   \n",
       "6           2.60        2.51                  0.31             1.25   \n",
       "7           2.80        2.98                  0.29             1.98   \n",
       "8           2.98        3.15                  0.22             1.85   \n",
       "9           2.95        3.32                  0.22             2.38   \n",
       "\n",
       "   Color intensity   Hue  OD/OD of diluted wines  Proline   \n",
       "0             4.38  1.05                    3.40      1050  \n",
       "1             5.68  1.03                    3.17      1185  \n",
       "2             7.80  0.86                    3.45      1480  \n",
       "3             4.32  1.04                    2.93       735  \n",
       "4             6.75  1.05                    2.85      1450  \n",
       "5             5.25  1.02                    3.58      1290  \n",
       "6             5.05  1.06                    3.58      1295  \n",
       "7             5.20  1.08                    2.85      1045  \n",
       "8             7.22  1.01                    3.55      1045  \n",
       "9             5.75  1.25                    3.17      1510  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('wine.data')\n",
    "data.columns = ['Class', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols'\n",
    "                , 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD/OD of diluted wines',\n",
    "                'Proline ']\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Обрабатываем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_data=data[data.columns[data.columns!='Class']]\n",
    "wine_class=data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Обучение и подсчет ошибки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbours=range(1,20)\n",
    "metrics=['euclidean', 'manhattan', 'chebyshev']\n",
    "X_train,X_test,y_train,y_test=sklearn.model_selection.train_test_split(wine_data, wine_class,test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Построение графика зависимости ошибки от количества соседей для 3 различных метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "error=[]\n",
    "for i in range(3):\n",
    "    error.append([])\n",
    "    for k in range(1,20):\n",
    "        neighbor = sklearn.neighbors.KNeighborsClassifier(n_neighbors = k, metric= metrics[i])\n",
    "        neighbor.fit(X_train,y_train)\n",
    "        predictions = neighbor.predict(X_test)\n",
    "        accuracy = sklearn.metrics.accuracy_score(y_test, predictions)\n",
    "        error[i].append(1-accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Построение графика зависимости ошибки от количества соседей для 3 различных метрик"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i in range(3):\n",
    "    plt.plot(neighbours,error[i], label=metrics[i])\n",
    "plt.legend()\n",
    "plt.xlabel('Количество соседей')\n",
    "plt.ylabel('Ошибка')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}