CH2/templates/07_sklearn.case_part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bike Sharing Demand\n",
    "Задача на kaggle: https://www.kaggle.com/c/bike-sharing-demand\n",
    "\n",
    "По историческим данным о прокате велосипедов и погодным условиям необходимо оценить спрос на прокат велосипедов.\n",
    "\n",
    "В исходной постановке задачи доступно 11 признаков: https://www.kaggle.com/c/prudential-life-insurance-assessment/data\n",
    "\n",
    "В наборе признаков присутсвуют вещественные, категориальные, и бинарные данные. \n",
    "\n",
    "Для демонстрации используется обучающая выборка из исходных данных train.csv, файлы для работы прилагаются."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation, grid_search, linear_model, metrics\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv('bike_sharing_demand.csv', header = 0, sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2  2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3  2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  \n",
       "0        81        0.0       3          13     16  \n",
       "1        80        0.0       8          32     40  \n",
       "2        80        0.0       5          27     32  \n",
       "3        75        0.0       3          10     13  \n",
       "4        75        0.0       0           1      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***datetime*** - hourly date + timestamp  \n",
    "\n",
    "***season*** -  1 = spring, 2 = summer, 3 = fall, 4 = winter \n",
    "\n",
    "***holiday*** - whether the day is considered a holiday\n",
    "\n",
    "***workingday*** - whether the day is neither a weekend nor holiday\n",
    "\n",
    "***weather*** - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog \n",
    "    \n",
    "***temp*** - temperature in Celsius\n",
    "\n",
    "***atemp*** - \"feels like\" temperature in Celsius\n",
    "\n",
    "***humidity*** - relative humidity\n",
    "\n",
    "***windspeed*** - wind speed\n",
    "\n",
    "***casual*** - number of non-registered user rentals initiated\n",
    "\n",
    "***registered*** - number of registered user rentals initiated\n",
    "\n",
    "***count*** - number of total rentals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 12)\n"
     ]
    }
   ],
   "source": [
    "print raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Типы признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10886 entries, 0 to 10885\n",
      "Data columns (total 12 columns):\n",
      "datetime      10886 non-null object\n",
      "season        10886 non-null int64\n",
      "holiday       10886 non-null int64\n",
      "workingday    10886 non-null int64\n",
      "weather       10886 non-null int64\n",
      "temp          10886 non-null float64\n",
      "atemp         10886 non-null float64\n",
      "humidity      10886 non-null int64\n",
      "windspeed     10886 non-null float64\n",
      "casual        10886 non-null int64\n",
      "registered    10886 non-null int64\n",
      "count         10886 non-null int64\n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 1020.6+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# конвертируем объект в тип датавремя\n",
    "raw_data.datetime = raw_data.datetime.apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# из исходного признака расчитаем новые признаки: месяц и час\n",
    "raw_data['month'] = raw_data.datetime.apply(lambda x : x.month)\n",
    "raw_data['hour'] = raw_data.datetime.apply(lambda x : x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
       "0 2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "1 2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2 2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "3 2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "4 2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "   humidity  windspeed  casual  registered  count  month  hour  \n",
       "0        81        0.0       3          13     16      1     0  \n",
       "1        80        0.0       8          32     40      1     1  \n",
       "2        80        0.0       5          27     32      1     2  \n",
       "3        75        0.0       3          10     13      1     3  \n",
       "4        75        0.0       0           1      1      1     4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Обучение и отложенный тест"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# разделяем выборку на обучение и тест, \n",
    "#в данном случае выбираются последнике 1000 объектов\n",
    "train_data = raw_data.iloc[:-1000, :]\n",
    "hold_out_test_data = raw_data.iloc[-1000:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10886, 14) (9886, 14) (1000, 14)\n"
     ]
    }
   ],
   "source": [
    "print raw_data.shape, train_data.shape, hold_out_test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train period from 2011-01-01 00:00:00 to 2012-10-16 06:00:00\n",
      "evaluation period from 2012-10-16 07:00:00 to 2012-12-19 23:00:00\n"
     ]
    }
   ],
   "source": [
    "print 'train period from {} to {}'.format(train_data.datetime.min(), train_data.datetime.max())\n",
    "print 'evaluation period from {} to {}'.format(hold_out_test_data.datetime.min(), hold_out_test_data.datetime.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Данные и целевая функция"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#обучение, разделяем целевую метку от параметров модели\n",
    "train_labels = train_data['count'].values\n",
    "train_data = train_data.drop(['datetime', 'count'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#тест\n",
    "test_labels = hold_out_test_data['count'].values\n",
    "test_data = hold_out_test_data.drop(['datetime', 'count'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Целевая функция на обучающей выборке и на отложенном тесте"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd8d710af90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fsNmwi5MkSZIk6aWsmc/GVfVXSXrAhcBUkvGqmmpOLf5us9lh4MyB3TY0YzONz+DagfWJ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8d695e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(figsize = (16, 6))\n",
    "\n",
    "pylab.subplot(1,2,1)\n",
    "pylab.hist(train_labels)\n",
    "pylab.title('train data')\n",
    "\n",
    "pylab.subplot(1,2,2)\n",
    "pylab.hist(test_labels)\n",
    "pylab.title('test data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Числовые признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Произведем сепарацию по численным признакам\n",
    "numeric_columns = ['temp', 'atemp', 'humidity', 'windspeed', 'casual', 'registered', 'month', 'hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data = train_data[numeric_columns]\n",
    "test_data = test_data[numeric_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp   atemp  humidity  windspeed  casual  registered  month  hour\n",
       "0  9.84  14.395        81        0.0       3          13      1     0\n",
       "1  9.02  13.635        80        0.0       8          32      1     1\n",
       "2  9.02  13.635        80        0.0       5          27      1     2\n",
       "3  9.84  14.395        75        0.0       3          10      1     3\n",
       "4  9.84  14.395        75        0.0       0           1      1     4"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9886</th>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>67</td>\n",
       "      <td>6.0032</td>\n",
       "      <td>20</td>\n",
       "      <td>505</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9887</th>\n",
       "      <td>18.04</td>\n",
       "      <td>21.970</td>\n",
       "      <td>62</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>35</td>\n",
       "      <td>800</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9888</th>\n",
       "      <td>19.68</td>\n",
       "      <td>23.485</td>\n",
       "      <td>55</td>\n",
       "      <td>16.9979</td>\n",
       "      <td>32</td>\n",
       "      <td>323</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9889</th>\n",
       "      <td>20.50</td>\n",
       "      <td>24.240</td>\n",
       "      <td>48</td>\n",
       "      <td>19.0012</td>\n",
       "      <td>65</td>\n",
       "      <td>157</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9890</th>\n",
       "      <td>20.50</td>\n",
       "      <td>24.240</td>\n",
       "      <td>45</td>\n",
       "      <td>27.9993</td>\n",
       "      <td>56</td>\n",
       "      <td>172</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp   atemp  humidity  windspeed  casual  registered  month  hour\n",
       "9886  17.22  21.210        67     6.0032      20         505     10     7\n",
       "9887  18.04  21.970        62     0.0000      35         800     10     8\n",
       "9888  19.68  23.485        55    16.9979      32         323     10     9\n",
       "9889  20.50  24.240        48    19.0012      65         157     10    10\n",
       "9890  20.50  24.240        45    27.9993      56         172     10    11"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# выбираем модель на основе градиентного стохастического спуска\n",
    "regressor = linear_model.SGDRegressor(random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34291115138178.953"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# очень высокое значение среднего абсолютных ошибок\n",
    "regressor.fit(train_data, train_labels)\n",
    "metrics.mean_absolute_error(test_labels, regressor.predict(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[525 835 355 222 228 325 328 308 346 446]\n"
     ]
    }
   ],
   "source": [
    "# целевая функция\n",
    "print test_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  8.78217183e+13   1.35599874e+14   5.36003573e+13   1.97358521e+13\n",
      "   2.27343721e+13   3.54305149e+13   3.64421304e+13   2.43694858e+13\n",
      "   3.63676921e+13   5.23707167e+13]\n"
     ]
    }
   ],
   "source": [
    "# предсказания\n",
    "print regressor.predict(test_data)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6.14437479e+10,   4.42635068e+10,   7.00770010e+10,\n",
       "        -7.59442428e+10,  -1.55632061e+11,   1.69398831e+11,\n",
       "        -5.26868159e+10,  -4.95660366e+10])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# коэффициенты регрессии\n",
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Scaling\n",
    "предыдущие ошибки возникли из-за неотмасштабированных признаков, решим эту проблемуЭ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#создаем стандартный scaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(train_data, train_labels)\n",
    "scaled_train_data = scaler.transform(train_data)\n",
    "scaled_test_data = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1179376296343151"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(scaled_train_data, train_labels)\n",
    "metrics.mean_absolute_error(test_labels, regressor.predict(scaled_test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[525 835 355 222 228 325 328 308 346 446]\n"
     ]
    }
   ],
   "source": [
    "print test_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 524.82767322  834.83176277  354.85768824  221.89221002  227.84049612\n",
      "  324.88196372  327.8982227   307.9347954   345.90146969  445.90141764]\n"
     ]
    }
   ],
   "source": [
    "# слишком маленькая ошибка\n",
    "print regressor.predict(scaled_test_data)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подозрительно хорошо?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.41019246e+00  -1.40725539e+00   1.45346928e-02  -4.38984175e-02\n",
      "   5.08589954e+01   1.48004811e+02  -8.23376876e-03   6.37720415e-03]\n"
     ]
    }
   ],
   "source": [
    "# изучим коэффициенты регресии, большинство значений слишком маленькие, 3 признакао выделяются\n",
    "print regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.41, -1.41, 0.01, -0.04, 50.86, 148.0, -0.01, 0.01]\n"
     ]
    }
   ],
   "source": [
    "print map(lambda x : round(x, 2), regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp   atemp  humidity  windspeed  casual  registered  month  hour\n",
       "0  9.84  14.395        81        0.0       3          13      1     0\n",
       "1  9.02  13.635        80        0.0       8          32      1     1\n",
       "2  9.02  13.635        80        0.0       5          27      1     2\n",
       "3  9.84  14.395        75        0.0       3          10      1     3\n",
       "4  9.84  14.395        75        0.0       0           1      1     4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 40, 32, 13,  1,  1,  2,  3,  8, 14])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# в данном случае признаки casual и registered в сумме представляют целевую метку\n",
    "train_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(train_data.registered + train_data.casual == train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_data.drop(['casual', 'registered'], axis = 1, inplace = True)\n",
    "test_data.drop(['casual', 'registered'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaler.fit(train_data, train_labels)\n",
    "scaled_train_data = scaler.transform(train_data)\n",
    "scaled_test_data = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.81123864993025"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(scaled_train_data, train_labels)\n",
    "metrics.mean_absolute_error(test_labels, regressor.predict(scaled_test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31.03, 29.96, -41.93, 6.17, 14.08, 49.6]\n"
     ]
    }
   ],
   "source": [
    "print map(lambda x : round(x, 2), regressor.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# модуль позволяющий строить цепочки преобразований\n",
    "from sklearn.pipeline import Pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#создаем pipeline из двух шагов: scaling и классификация\n",
    "pipeline = Pipeline(steps = [('scaling', scaler), ('regression', regressor)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121.81123864993025"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(train_data, train_labels)\n",
    "metrics.mean_absolute_error(test_labels, pipeline.predict(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подбор параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['regression__random_state',\n",
       " 'regression__n_iter',\n",
       " 'regression__epsilon',\n",
       " 'regression__power_t',\n",
       " 'regression__alpha',\n",
       " 'regression__eta0',\n",
       " 'regression__loss',\n",
       " 'scaling',\n",
       " 'steps',\n",
       " 'scaling__copy',\n",
       " 'regression__fit_intercept',\n",
       " 'regression__warm_start',\n",
       " 'regression__penalty',\n",
       " 'regression__learning_rate',\n",
       " 'regression__average',\n",
       " 'regression__verbose',\n",
       " 'regression__shuffle',\n",
       " 'regression__l1_ratio',\n",
       " 'regression',\n",
       " 'scaling__with_std',\n",
       " 'scaling__with_mean']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parameters_grid = {\n",
    "    'regression__loss' : ['huber', 'epsilon_insensitive', 'squared_loss', ],\n",
    "    'regression__n_iter' : [3, 5, 10, 50], \n",
    "    'regression__penalty' : ['l1', 'l2', 'none'],\n",
    "    'regression__alpha' : [0.0001, 0.01],\n",
    "    'scaling__with_mean' : [0., 0.5],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid_cv = grid_search.GridSearchCV(pipeline, parameters_grid, scoring = 'mean_absolute_error', cv = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min, sys: 1.5 s, total: 1min 1s\n",
      "Wall time: 15.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise',\n",
       "       estimator=Pipeline(steps=[('scaling', StandardScaler(copy=True, with_mean=True, with_std=True)), ('regression', SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\n",
       "       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\n",
       "       loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25,\n",
       "       random_state=0, shuffle=True, verbose=0, warm_start=False))]),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'regression__n_iter': [3, 5, 10, 50], 'regression__loss': ['huber', 'epsilon_insensitive', 'squared_loss'], 'scaling__with_mean': [0.0, 0.5], 'regression__alpha': [0.0001, 0.01], 'regression__penalty': ['l1', 'l2', 'none']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='mean_absolute_error',\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid_cv.fit(train_data, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-108.614496603\n",
      "{'regression__n_iter': 3, 'regression__loss': 'squared_loss', 'scaling__with_mean': 0.0, 'regression__alpha': 0.01, 'regression__penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print grid_cv.best_score_\n",
    "print grid_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка по отложенному тесту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119.98978845935379"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(test_labels, grid_cv.best_estimator_.predict(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232.15899999999999"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_predictions = grid_cv.best_estimator_.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[525 835 355 222 228 325 328 308 346 446]\n"
     ]
    }
   ],
   "source": [
    "print test_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 139.60470681  159.80765341  207.55935972  237.76288054  257.83836668\n",
      "  267.44558034  272.49537469  297.70688522  304.29818873  313.58821156]\n"
     ]
    }
   ],
   "source": [
    "print test_predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-100, 1100)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSYDKZhERObjt9yRJZvZrAE8AOMrMNpjZBQCuB3C2ma0E8PboMZxzLwH4LYCXANwH4KKM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8h8/HrPIiCzCNZ4GOdntmK+rRPelVVAzEFHS5qQyzNasGA0u9HkOgcN4b9BImKFzZpGbecT8Gea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IRkWiddUqcS1u4rXzePSE3KxGS4tG06I6QRDHJo1Pp2Q/Zu72JP2JsbY0Lmk3UmGsrTeiXzML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1EOq9jH9sJSnsjVsoLnWTP/P4WI0623Ym/8Jq2P16DnXxZiUWol9zwwnXXhcur3P4XWd1hU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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2ad0e4f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(figsize=(16, 6))\n",
    "\n",
    "pylab.subplot(1,2,1)\n",
    "pylab.grid(True)\n",
    "pylab.scatter(train_labels, pipeline.predict(train_data), alpha=0.5, color = 'red')\n",
    "pylab.scatter(test_labels, pipeline.predict(test_data), alpha=0.5, color = 'blue')\n",
    "pylab.title('no parameters setting')\n",
    "pylab.xlim(-100,1100)\n",
    "pylab.ylim(-100,1100)\n",
    "\n",
    "pylab.subplot(1,2,2)\n",
    "pylab.grid(True)\n",
    "pylab.scatter(train_labels, grid_cv.best_estimator_.predict(train_data), alpha=0.5, color = 'red')\n",
    "pylab.scatter(test_labels, grid_cv.best_estimator_.predict(test_data), alpha=0.5, color = 'blue')\n",
    "pylab.title('grid search')\n",
    "pylab.xlim(-100,1100)\n",
    "pylab.ylim(-100,1100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}