{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=DeprecationWarning)"
   ]
  },
  {
   "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": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_names = ['Sort','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium', 'Total phenols','Flavanoids', 'Nonflavanoid phenols','Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sort</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>OD280/OD315 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": [
       "   Sort  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0     1    14.23        1.71  2.43               15.6        127   \n",
       "1     1    13.20        1.78  2.14               11.2        100   \n",
       "2     1    13.16        2.36  2.67               18.6        101   \n",
       "3     1    14.37        1.95  2.50               16.8        113   \n",
       "4     1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('wine.csv',header=None,names=col_names)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Мысль1 : все признаки вещественные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sort</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>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.938202</td>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.775035</td>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sort     Alcohol  Malic acid         Ash  Alcalinity of ash  \\\n",
       "count  178.000000  178.000000  178.000000  178.000000         178.000000   \n",
       "mean     1.938202   13.000618    2.336348    2.366517          19.494944   \n",
       "std      0.775035    0.811827    1.117146    0.274344           3.339564   \n",
       "min      1.000000   11.030000    0.740000    1.360000          10.600000   \n",
       "25%      1.000000   12.362500    1.602500    2.210000          17.200000   \n",
       "50%      2.000000   13.050000    1.865000    2.360000          19.500000   \n",
       "75%      3.000000   13.677500    3.082500    2.557500          21.500000   \n",
       "max      3.000000   14.830000    5.800000    3.230000          30.000000   \n",
       "\n",
       "        Magnesium  Total phenols  Flavanoids  Nonflavanoid phenols  \\\n",
       "count  178.000000     178.000000  178.000000            178.000000   \n",
       "mean    99.741573       2.295112    2.029270              0.361854   \n",
       "std     14.282484       0.625851    0.998859              0.124453   \n",
       "min     70.000000       0.980000    0.340000              0.130000   \n",
       "25%     88.000000       1.742500    1.205000              0.270000   \n",
       "50%     98.000000       2.355000    2.135000              0.340000   \n",
       "75%    107.000000       2.800000    2.875000              0.437500   \n",
       "max    162.000000       3.880000    5.080000              0.660000   \n",
       "\n",
       "       Proanthocyanins  Color intensity         Hue  \\\n",
       "count       178.000000       178.000000  178.000000   \n",
       "mean          1.590899         5.058090    0.957449   \n",
       "std           0.572359         2.318286    0.228572   \n",
       "min           0.410000         1.280000    0.480000   \n",
       "25%           1.250000         3.220000    0.782500   \n",
       "50%           1.555000         4.690000    0.965000   \n",
       "75%           1.950000         6.200000    1.120000   \n",
       "max           3.580000        13.000000    1.710000   \n",
       "\n",
       "       OD280/OD315 of diluted wines      Proline  \n",
       "count                    178.000000   178.000000  \n",
       "mean                       2.611685   746.893258  \n",
       "std                        0.709990   314.907474  \n",
       "min                        1.270000   278.000000  \n",
       "25%                        1.937500   500.500000  \n",
       "50%                        2.780000   673.500000  \n",
       "75%                        3.170000   985.000000  \n",
       "max                        4.000000  1680.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\frizman04\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Z682ZEXF6RFwRESe1Ld9IdakfnW14c0TcGxEvGMa8Hj82hzHN4RHxJ0OdR5tl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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x227f99f16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.boxplot(data=data[ col_names[0:len(col_names)] ])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Мысль2 :  В данных отсутствуют пропуски"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier,BaggingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Мысль3 : Признаки отличаются на порядки, по скольку KNN метрический алгоритм - признаки нужно отнормировать"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178,)\n",
      "(178, 13)\n"
     ]
    }
   ],
   "source": [
    "y = data[ col_names[0] ].values\n",
    "X = data[ col_names[1:]].values\n",
    "\n",
    "X = preprocessing.scale(X,axis=0)\n",
    "\n",
    "print(y.shape)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Мысль4 : асамблируем с 3мя разными метрками близости"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "matplotlib.rcParams.update({'font.size': 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min error : 0.0224, it was with 16 neighbors\n"
     ]
    },
    {
     "data": {
      "image/png": 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dJLXWzyf5/ADb3pDkhkE+f9DvAQCATrJqS0/mzpicE2ZNaXYp0FZaaQ00AAAw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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22785b44630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_split = 5\n",
    "metric_names = ['euclidean','chebyshev','manhattan']  #евклид, максм модулей, сумма модулей\n",
    "max_neighbors_cnt = int(len(X) - len(X) / n_split)\n",
    "\n",
    "err_list = []\n",
    "for neighbors_cnt in range(1,max_neighbors_cnt) :    \n",
    "    \n",
    "    clf = VotingClassifier(estimators=[('knn_' + metric_name,\n",
    "                                        KNeighborsClassifier(n_neighbors=neighbors_cnt,\n",
    "                                                             metric=metric_name,\n",
    "                                                             weights='uniform'))\n",
    "                                        for metric_name in metric_names],\n",
    "                           voting='soft')\n",
    "    \n",
    "    acc = cross_val_score(clf,X,y,cv=n_split,scoring='accuracy')\n",
    "    acc = acc.mean()\n",
    "    \n",
    "    err_list.append(1 - acc)\n",
    "\n",
    "best_error = np.min(err_list)\n",
    "best_neighbors_cnt = np.argmin(err_list) + 1\n",
    "print('Min error : %.4f, it was with %.f neighbors' % (best_error,best_neighbors_cnt))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,12))\n",
    "plt.grid(True)\n",
    "\n",
    "plt.plot(list(range(1,max_neighbors_cnt)),err_list)\n",
    "plt.xlabel('Neighbors count')\n",
    "plt.ylabel('Error')\n",
    "\n",
    "#plt.savefig(fname='Error_from_Neighborscnt_withSTD.png',format='png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 2\n",
    "\n",
    "Зависимость значения ошибки и его стандартного отклонения от сложности (числа признаков) на обучении и контроле."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Lz9cuMjVdr9+1pYVcx99Pl95fe9LtixwdOMDzwPwkgOn9bX1VlmnT9gyTWVH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2278570e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_list = []\n",
    "std_list = []\n",
    "best_n_neighbors = 16\n",
    "\n",
    "random_order = list(range(16,X.shape[0]))\n",
    "np.random.shuffle(random_order)\n",
    "\n",
    "for i in range(best_n_neighbors,X.shape[0]) :\n",
    "    X_subset = X[random_order[:i]]\n",
    "    y_subset = y[random_order[:i]]\n",
    "    \n",
    "    clf = VotingClassifier(estimators=[('knn_' + metric_name,\n",
    "                                    KNeighborsClassifier(n_neighbors=16,\n",
    "                                                         metric=metric_name,\n",
    "                                                         weights='uniform'))\n",
    "                                    for metric_name in metric_names],\n",
    "                       voting='soft')\n",
    "    \n",
    "    clf.fit(X_subset,y_subset)\n",
    "    y_head_subset = clf.predict(X_subset)\n",
    "    \n",
    "    std = np.std(y_head_subset == y_subset)\n",
    "    acc = np.mean(y_head_subset == y_subset)   \n",
    "    \n",
    "    std_list.append(std)\n",
    "    acc_list.append(acc)\n",
    "    \n",
    "    \n",
    "plt.figure(figsize=(16,12))\n",
    "plt.grid(True)\n",
    "\n",
    "plt.errorbar(list(range(best_n_neighbors,X.shape[0])),acc_list, yerr=std_list) \n",
    "plt.xlabel('Items in ')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.savefig(fname='Error_from_Neighborscnt_withSTD.png',format='png')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}