{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задача 12\n",
    "\n",
    "Предсказать сорт винограда из которого сделано вино, используя результаты химических анализов (описание данных), c помощью KNN - метода k ближайших соседей с тремя различными метриками. Построить график зависимости величины ошибки от числа соседей k. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn.model_selection\n",
    "import sklearn.neighbors\n",
    "import sklearn.metrics\n",
    "import sklearn.preprocessing\n",
    "import sklearn.pipeline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"always\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Удобная обертка над tqdm для возможности итерации по декартовому произведению некоторых множеств\n",
    "import tqdm\n",
    "import itertools\n",
    "import operator\n",
    "import functools\n",
    "\n",
    "def tqdm_cross(*args):\n",
    "    total_length = functools.reduce(operator.mul, map(len, args), 1)\n",
    "    return tqdm.tqdm(itertools.product(*args), total=total_length)    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Считаем данные, подпишем столбцы согласно описанию набора данных (http://archive.ics.uci.edu/ml/datasets/Wine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Y</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>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Y  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  Total phenols  \\\n",
       "0  1    14.23        1.71  2.43               15.6        127           2.80   \n",
       "1  1    13.20        1.78  2.14               11.2        100           2.65   \n",
       "2  1    13.16        2.36  2.67               18.6        101           2.80   \n",
       "3  1    14.37        1.95  2.50               16.8        113           3.85   \n",
       "4  1    13.24        2.59  2.87               21.0        118           2.80   \n",
       "\n",
       "   Flavanoids  Nonflavanoid phenols  Proanthocyanins  Color intensity   Hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   OD/OD of diluted wines  Proline   \n",
       "0                    3.92      1065  \n",
       "1                    3.40      1050  \n",
       "2                    3.17      1185  \n",
       "3                    3.45      1480  \n",
       "4                    2.93       735  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)\n",
    "dataframe.columns = ['Y', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD/OD of diluted wines', 'Proline ']\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим данные, определять точность моделей будем с помощью кросс валидации. От выборки отделяется в виде тестовой одна точка. Также учтем, что для использования методов ближайших соседей данные необходимо нормировать."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = dataframe.drop('Y', axis=1), dataframe['Y']\n",
    "\n",
    "n_neighbors_list = list(range(1, 20))\n",
    "metrics_list = ['euclidean', 'manhattan', 'chebyshev']\n",
    "\n",
    "result_format = {'n_neighbors' : np.int32, 'metric' : 'str', 'mean_error_cv' : np.float32, 'std_err_cv' : np.float32}\n",
    "results = pd.DataFrame(columns=['n_neighbors', 'metric', 'mean_error_cv', 'std_err_cv']).astype(result_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 57/57 [00:00<00:00, 106.68it/s]\n"
     ]
    },
    {
     "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>n_neighbors</th>\n",
       "      <th>metric</th>\n",
       "      <th>mean_accuracy_cv</th>\n",
       "      <th>std_err_cv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.932950</td>\n",
       "      <td>0.026749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.949617</td>\n",
       "      <td>0.023304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>chebyshev</td>\n",
       "      <td>0.910345</td>\n",
       "      <td>0.030899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>0.915900</td>\n",
       "      <td>0.035664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>manhattan</td>\n",
       "      <td>0.960920</td>\n",
       "      <td>0.043648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_neighbors     metric  mean_accuracy_cv  std_err_cv\n",
       "0            1  euclidean          0.932950    0.026749\n",
       "1            1  manhattan          0.949617    0.023304\n",
       "2            1  chebyshev          0.910345    0.030899\n",
       "3            2  euclidean          0.915900    0.035664\n",
       "4            2  manhattan          0.960920    0.043648"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = []\n",
    "\n",
    "kfold = sklearn.model_selection.KFold(n_splits=len(dataframe), shuffle=True)\n",
    "\n",
    "for n_neighbors, metric in tqdm_cross(n_neighbors_list, metrics_list):\n",
    "        cv_pipeline = sklearn.pipeline.make_pipeline(sklearn.preprocessing.StandardScaler(),\n",
    "                                                     sklearn.neighbors.KNeighborsClassifier(\n",
    "                                                         n_neighbors=n_neighbors,\n",
    "                                                         metric=metric\n",
    "                                                     ))\n",
    "        current_result = sklearn.model_selection.cross_val_score(cv_pipeline, X, Y, scoring='accuracy')\n",
    "        result.append([n_neighbors, metric, np.mean(current_result), np.std(current_result)])\n",
    "\n",
    "result = pd.DataFrame(result, columns=['n_neighbors', 'metric', 'mean_accuracy_cv', 'std_err_cv'])\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проведем визуализацию полученных результатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f80403b0668>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 7))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.grid()\n",
    "ax.set_xticks(np.arange(0, 20))\n",
    "ax.set_yticks(np.arange(0.89, 1, 0.01))\n",
    "for m in metrics_list:\n",
    "    ds = result[result['metric'] == m]\n",
    "    ax.plot(ds.n_neighbors, ds.mean_accuracy_cv, label=m)\n",
    "    \n",
    "ax.set_xlabel('n_neighbors')\n",
    "ax.set_ylabel('mean_accuracy_cv')\n",
    "ax.set_title('Accuracy score')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/3 [00:00<?, ?it/s]\u001b[A\n",
      "100%|██████████| 3/3 [00:00<00:00, 2685.79it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1,)\n",
      "(2,)\n",
      "(3,)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}