fastai_DL_nbs/lesson06_rossmann.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "# 多行输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.tabular import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rossmann 建模"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `.label_from_df(cols=dep_var, label_cls=FloatList, log=True)` 如果 label_cls=FloatList 那么其是一个回归问题，如果为整数，那么就是一个分类问题\n",
    "- dropout 扔掉的是激活值而不是权重\n",
    "- 通常，我们只删除隐藏层中的激活。 每当我有一个 minibatch 通过，我随机扔掉一些激活。然后是下一个 minibatch ，我把它们放回去，然后扔掉一些不同的激活值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://github.com/hiromis/notes/raw/master/lesson6/7.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Config.data_path()/'rossmann'\n",
    "train_df = pd.read_pickle(path/'train_clean')\n",
    "test_df = pd.read_pickle(path/'test_clean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfWeek</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>5263</td>\n",
       "      <td>6064</td>\n",
       "      <td>8314</td>\n",
       "      <td>13995</td>\n",
       "      <td>4822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customers</th>\n",
       "      <td>555</td>\n",
       "      <td>625</td>\n",
       "      <td>821</td>\n",
       "      <td>1498</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dayofweek</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dayofyear</th>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_month_end</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_month_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_quarter_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_quarter_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_year_end</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_year_start</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elapsed</th>\n",
       "      <td>1438300800</td>\n",
       "      <td>1438300800</td>\n",
       "      <td>1438300800</td>\n",
       "      <td>1438300800</td>\n",
       "      <td>1438300800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StoreType</th>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assortment</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <td>1270</td>\n",
       "      <td>570</td>\n",
       "      <td>14130</td>\n",
       "      <td>620</td>\n",
       "      <td>29910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <td>2008</td>\n",
       "      <td>2007</td>\n",
       "      <td>2006</td>\n",
       "      <td>2009</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_Sea_Level_PressurehPa</th>\n",
       "      <td>1015</td>\n",
       "      <td>1017</td>\n",
       "      <td>1017</td>\n",
       "      <td>1014</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_VisibilityKm</th>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_VisibilityKm</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min_VisibilitykM</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_Wind_SpeedKm_h</th>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_Wind_SpeedKm_h</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max_Gust_SpeedKm_h</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precipitationmm</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CloudCover</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Events</th>\n",
       "      <td>Fog</td>\n",
       "      <td>Fog</td>\n",
       "      <td>Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindDirDegrees</th>\n",
       "      <td>13</td>\n",
       "      <td>309</td>\n",
       "      <td>354</td>\n",
       "      <td>282</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateName</th>\n",
       "      <td>Hessen</td>\n",
       "      <td>Thueringen</td>\n",
       "      <td>NordrheinWestfalen</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Sachsen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionOpenSince</th>\n",
       "      <td>2008-09-15 00:00:00</td>\n",
       "      <td>2007-11-15 00:00:00</td>\n",
       "      <td>2006-12-15 00:00:00</td>\n",
       "      <td>2009-09-15 00:00:00</td>\n",
       "      <td>2015-04-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionDaysOpen</th>\n",
       "      <td>2510</td>\n",
       "      <td>2815</td>\n",
       "      <td>3150</td>\n",
       "      <td>2145</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CompetitionMonthsOpen</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2Since</th>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>2010-03-29 00:00:00</td>\n",
       "      <td>2011-04-04 00:00:00</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2Days</th>\n",
       "      <td>0</td>\n",
       "      <td>1950</td>\n",
       "      <td>1579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo2Weeks</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AfterSchoolHoliday</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeforeSchoolHoliday</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AfterStateHoliday</th>\n",
       "      <td>57</td>\n",
       "      <td>67</td>\n",
       "      <td>57</td>\n",
       "      <td>67</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeforeStateHoliday</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AfterPromo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BeforePromo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday_bw</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday_bw</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo_bw</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SchoolHoliday_fw</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StateHoliday_fw</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promo_fw</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0                    1  \\\n",
       "index                                        0                    1   \n",
       "Store                                        1                    2   \n",
       "DayOfWeek                                    5                    5   \n",
       "Date                       2015-07-31 00:00:00  2015-07-31 00:00:00   \n",
       "Sales                                     5263                 6064   \n",
       "Customers                                  555                  625   \n",
       "Open                                         1                    1   \n",
       "Promo                                        1                    1   \n",
       "StateHoliday                             False                False   \n",
       "SchoolHoliday                                1                    1   \n",
       "Year                                      2015                 2015   \n",
       "Month                                        7                    7   \n",
       "Week                                        31                   31   \n",
       "Day                                         31                   31   \n",
       "Dayofweek                                    4                    4   \n",
       "Dayofyear                                  212                  212   \n",
       "Is_month_end                              True                 True   \n",
       "Is_month_start                           False                False   \n",
       "Is_quarter_end                           False                False   \n",
       "Is_quarter_start                         False                False   \n",
       "Is_year_end                              False                False   \n",
       "Is_year_start                            False                False   \n",
       "Elapsed                             1438300800           1438300800   \n",
       "StoreType                                    c                    a   \n",
       "Assortment                                   a                    a   \n",
       "CompetitionDistance                       1270                  570   \n",
       "CompetitionOpenSinceMonth                    9                   11   \n",
       "CompetitionOpenSinceYear                  2008                 2007   \n",
       "Promo2                                       0                    1   \n",
       "Promo2SinceWeek                              1                   13   \n",
       "...                                        ...                  ...   \n",
       "Min_Sea_Level_PressurehPa                 1015                 1017   \n",
       "Max_VisibilityKm                            31                   10   \n",
       "Mean_VisibilityKm                           15                   10   \n",
       "Min_VisibilitykM                            10                   10   \n",
       "Max_Wind_SpeedKm_h                          24                   14   \n",
       "Mean_Wind_SpeedKm_h                         11                   11   \n",
       "Max_Gust_SpeedKm_h                         NaN                  NaN   \n",
       "Precipitationmm                              0                    0   \n",
       "CloudCover                                   1                    4   \n",
       "Events                                     Fog                  Fog   \n",
       "WindDirDegrees                              13                  309   \n",
       "StateName                               Hessen           Thueringen   \n",
       "CompetitionOpenSince       2008-09-15 00:00:00  2007-11-15 00:00:00   \n",
       "CompetitionDaysOpen                       2510                 2815   \n",
       "CompetitionMonthsOpen                       24                   24   \n",
       "Promo2Since                1900-01-01 00:00:00  2010-03-29 00:00:00   \n",
       "Promo2Days                                   0                 1950   \n",
       "Promo2Weeks                                  0                   25   \n",
       "AfterSchoolHoliday                           0                    0   \n",
       "BeforeSchoolHoliday                          0                    0   \n",
       "AfterStateHoliday                           57                   67   \n",
       "BeforeStateHoliday                           0                    0   \n",
       "AfterPromo                                   0                    0   \n",
       "BeforePromo                                  0                    0   \n",
       "SchoolHoliday_bw                             5                    5   \n",
       "StateHoliday_bw                              0                    0   \n",
       "Promo_bw                                     5                    5   \n",
       "SchoolHoliday_fw                             7                    1   \n",
       "StateHoliday_fw                              0                    0   \n",
       "Promo_fw                                     5                    1   \n",
       "\n",
       "                                             2                    3  \\\n",
       "index                                        2                    3   \n",
       "Store                                        3                    4   \n",
       "DayOfWeek                                    5                    5   \n",
       "Date                       2015-07-31 00:00:00  2015-07-31 00:00:00   \n",
       "Sales                                     8314                13995   \n",
       "Customers                                  821                 1498   \n",
       "Open                                         1                    1   \n",
       "Promo                                        1                    1   \n",
       "StateHoliday                             False                False   \n",
       "SchoolHoliday                                1                    1   \n",
       "Year                                      2015                 2015   \n",
       "Month                                        7                    7   \n",
       "Week                                        31                   31   \n",
       "Day                                         31                   31   \n",
       "Dayofweek                                    4                    4   \n",
       "Dayofyear                                  212                  212   \n",
       "Is_month_end                              True                 True   \n",
       "Is_month_start                           False                False   \n",
       "Is_quarter_end                           False                False   \n",
       "Is_quarter_start                         False                False   \n",
       "Is_year_end                              False                False   \n",
       "Is_year_start                            False                False   \n",
       "Elapsed                             1438300800           1438300800   \n",
       "StoreType                                    a                    c   \n",
       "Assortment                                   a                    c   \n",
       "CompetitionDistance                      14130                  620   \n",
       "CompetitionOpenSinceMonth                   12                    9   \n",
       "CompetitionOpenSinceYear                  2006                 2009   \n",
       "Promo2                                       1                    0   \n",
       "Promo2SinceWeek                             14                    1   \n",
       "...                                        ...                  ...   \n",
       "Min_Sea_Level_PressurehPa                 1017                 1014   \n",
       "Max_VisibilityKm                            31                   10   \n",
       "Mean_VisibilityKm                           14                   10   \n",
       "Min_VisibilitykM                            10                   10   \n",
       "Max_Wind_SpeedKm_h                          14                   23   \n",
       "Mean_Wind_SpeedKm_h                          5                   16   \n",
       "Max_Gust_SpeedKm_h                         NaN                  NaN   \n",
       "Precipitationmm                              0                    0   \n",
       "CloudCover                                   2                    6   \n",
       "Events                                     Fog                  NaN   \n",
       "WindDirDegrees                             354                  282   \n",
       "StateName                   NordrheinWestfalen               Berlin   \n",
       "CompetitionOpenSince       2006-12-15 00:00:00  2009-09-15 00:00:00   \n",
       "CompetitionDaysOpen                       3150                 2145   \n",
       "CompetitionMonthsOpen                       24                   24   \n",
       "Promo2Since                2011-04-04 00:00:00  1900-01-01 00:00:00   \n",
       "Promo2Days                                1579                    0   \n",
       "Promo2Weeks                                 25                    0   \n",
       "AfterSchoolHoliday                           0                    0   \n",
       "BeforeSchoolHoliday                          0                    0   \n",
       "AfterStateHoliday                           57                   67   \n",
       "BeforeStateHoliday                           0                    0   \n",
       "AfterPromo                                   0                    0   \n",
       "BeforePromo                                  0                    0   \n",
       "SchoolHoliday_bw                             5                    5   \n",
       "StateHoliday_bw                              0                    0   \n",
       "Promo_bw                                     5                    5   \n",
       "SchoolHoliday_fw                             5                    1   \n",
       "StateHoliday_fw                              0                    0   \n",
       "Promo_fw                                     5                    1   \n",
       "\n",
       "                                             4  \n",
       "index                                        4  \n",
       "Store                                        5  \n",
       "DayOfWeek                                    5  \n",
       "Date                       2015-07-31 00:00:00  \n",
       "Sales                                     4822  \n",
       "Customers                                  559  \n",
       "Open                                         1  \n",
       "Promo                                        1  \n",
       "StateHoliday                             False  \n",
       "SchoolHoliday                                1  \n",
       "Year                                      2015  \n",
       "Month                                        7  \n",
       "Week                                        31  \n",
       "Day                                         31  \n",
       "Dayofweek                                    4  \n",
       "Dayofyear                                  212  \n",
       "Is_month_end                              True  \n",
       "Is_month_start                           False  \n",
       "Is_quarter_end                           False  \n",
       "Is_quarter_start                         False  \n",
       "Is_year_end                              False  \n",
       "Is_year_start                            False  \n",
       "Elapsed                             1438300800  \n",
       "StoreType                                    a  \n",
       "Assortment                                   a  \n",
       "CompetitionDistance                      29910  \n",
       "CompetitionOpenSinceMonth                    4  \n",
       "CompetitionOpenSinceYear                  2015  \n",
       "Promo2                                       0  \n",
       "Promo2SinceWeek                              1  \n",
       "...                                        ...  \n",
       "Min_Sea_Level_PressurehPa                 1016  \n",
       "Max_VisibilityKm                            10  \n",
       "Mean_VisibilityKm                           10  \n",
       "Min_VisibilitykM                            10  \n",
       "Max_Wind_SpeedKm_h                          14  \n",
       "Mean_Wind_SpeedKm_h                         11  \n",
       "Max_Gust_SpeedKm_h                         NaN  \n",
       "Precipitationmm                              0  \n",
       "CloudCover                                   4  \n",
       "Events                                     NaN  \n",
       "WindDirDegrees                             290  \n",
       "StateName                              Sachsen  \n",
       "CompetitionOpenSince       2015-04-15 00:00:00  \n",
       "CompetitionDaysOpen                        107  \n",
       "CompetitionMonthsOpen                        3  \n",
       "Promo2Since                1900-01-01 00:00:00  \n",
       "Promo2Days                                   0  \n",
       "Promo2Weeks                                  0  \n",
       "AfterSchoolHoliday                           0  \n",
       "BeforeSchoolHoliday                          0  \n",
       "AfterStateHoliday                           57  \n",
       "BeforeStateHoliday                           0  \n",
       "AfterPromo                                   0  \n",
       "BeforePromo                                  0  \n",
       "SchoolHoliday_bw                             5  \n",
       "StateHoliday_bw                              0  \n",
       "Promo_bw                                     5  \n",
       "SchoolHoliday_fw                             1  \n",
       "StateHoliday_fw                              0  \n",
       "Promo_fw                                     1  \n",
       "\n",
       "[93 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844338, 93)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(41088, 92)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "procs=[FillMissing, Categorify, Normalize]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars = ['Store', 'DayOfWeek', 'Year', 'Month', 'Day', 'StateHoliday', 'CompetitionMonthsOpen',\n",
    "    'Promo2Weeks', 'StoreType', 'Assortment', 'PromoInterval', 'CompetitionOpenSinceYear', 'Promo2SinceYear',\n",
    "    'State', 'Week', 'Events', 'Promo_fw', 'Promo_bw', 'StateHoliday_fw', 'StateHoliday_bw',\n",
    "    'SchoolHoliday_fw', 'SchoolHoliday_bw']\n",
    "\n",
    "cont_vars = ['CompetitionDistance', 'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC',\n",
    "   'Max_Humidity', 'Mean_Humidity', 'Min_Humidity', 'Max_Wind_SpeedKm_h', \n",
    "   'Mean_Wind_SpeedKm_h', 'CloudCover', 'trend', 'trend_DE',\n",
    "   'AfterStateHoliday', 'BeforeStateHoliday', 'Promo', 'SchoolHoliday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_var = 'Sales'\n",
    "df = train_df[cat_vars + cont_vars + [dep_var,'Date']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2015-08-01 00:00:00'), Timestamp('2015-09-17 00:00:00'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Date'].min(), test_df['Date'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41395"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut = train_df['Date'][(train_df['Date'] == train_df['Date'][len(test_df)])].index.max()\n",
    "cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_idx = range(cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5263\n",
       "1     6064\n",
       "2     8314\n",
       "3    13995\n",
       "4     4822\n",
       "Name: Sales, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[dep_var].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (TabularList.from_df(df, path=path, cat_names=cat_vars, cont_names=cont_vars, procs=procs,)\n",
    "                .split_by_idx(valid_idx)\n",
    "                .label_from_df(cols=dep_var, label_cls=FloatList, log=True)\n",
    "                .add_test(TabularList.from_df(test_df, path=path, cat_names=cat_vars, cont_names=cont_vars))\n",
    "                .databunch())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2 id=\"FloatList\" class=\"doc_header\"><code>class</code> <code>FloatList</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/data_block.py#L422\" class=\"source_link\" style=\"float:right\">[source]</a><a class=\"source_link\" data-toggle=\"collapse\" data-target=\"#FloatList-pytest\" style=\"float:right; padding-right:10px\">[test]</a></h2><blockquote><p><code>FloatList</code>(<strong><code>items</code></strong>:<code>Iterator</code>, <strong><code>log</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>classes</code></strong>:<code>Collection</code>=<strong><em><code>None</code></em></strong>, <strong>**<code>kwargs</code></strong>) :: <a href=\"https://docs.fast.ai/data_block.html#ItemList\"><code>ItemList</code></a></p>\n",
       "</blockquote>\n",
       "<div class=\"collapse\" id=\"FloatList-pytest\"><div class=\"card card-body pytest_card\"><a type=\"button\" data-toggle=\"collapse\" data-target=\"#FloatList-pytest\" class=\"close\" aria-label=\"Close\"><span aria-hidden=\"true\">&times;</span></a><p>No tests found for <code>FloatList</code>. To contribute a test please refer to <a href=\"/dev/test.html\">this guide</a> and <a href=\"https://forums.fast.ai/t/improving-expanding-functional-tests/32929\">this discussion</a>.</p></div></div><p><a href=\"https://docs.fast.ai/data_block.html#ItemList\"><code>ItemList</code></a> suitable for storing the floats in items for regression. Will add a <code>log</code> if this flag is <code>True</code>.</p>\n",
       "<p><a href=\"https://docs.fast.ai/data_block.html#FloatList\" target=\"_blank\" rel=\"noreferrer noopener\">Show in docs</a></p>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc(FloatList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_log_y = np.log(np.max(train_df['Sales'])*1.2)\n",
    "y_range = torch.tensor([0, max_log_y], device=defaults.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = tabular_learner(data, layers=[1000,500], ps=[0.001,0.01], emb_drop=0.04, \n",
    "                        y_range=y_range, metrics=exp_rmspe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TabularModel(\n",
       "  (embeds): ModuleList(\n",
       "    (0): Embedding(1116, 81)\n",
       "    (1): Embedding(8, 5)\n",
       "    (2): Embedding(4, 3)\n",
       "    (3): Embedding(13, 7)\n",
       "    (4): Embedding(32, 11)\n",
       "    (5): Embedding(3, 3)\n",
       "    (6): Embedding(26, 10)\n",
       "    (7): Embedding(27, 10)\n",
       "    (8): Embedding(5, 4)\n",
       "    (9): Embedding(4, 3)\n",
       "    (10): Embedding(4, 3)\n",
       "    (11): Embedding(24, 9)\n",
       "    (12): Embedding(9, 5)\n",
       "    (13): Embedding(13, 7)\n",
       "    (14): Embedding(53, 15)\n",
       "    (15): Embedding(22, 9)\n",
       "    (16): Embedding(7, 5)\n",
       "    (17): Embedding(7, 5)\n",
       "    (18): Embedding(4, 3)\n",
       "    (19): Embedding(4, 3)\n",
       "    (20): Embedding(9, 5)\n",
       "    (21): Embedding(9, 5)\n",
       "    (22): Embedding(3, 3)\n",
       "    (23): Embedding(3, 3)\n",
       "  )\n",
       "  (emb_drop): Dropout(p=0.04)\n",
       "  (bn_cont): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (layers): Sequential(\n",
       "    (0): Linear(in_features=233, out_features=1000, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm1d(1000, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.001)\n",
       "    (4): Linear(in_features=1000, out_features=500, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.01)\n",
       "    (8): Linear(in_features=500, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "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": [
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>exp_rmspe</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.028006</td>\n",
       "      <td>0.024756</td>\n",
       "      <td>0.154261</td>\n",
       "      <td>01:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.025776</td>\n",
       "      <td>0.027920</td>\n",
       "      <td>0.148853</td>\n",
       "      <td>01:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.022508</td>\n",
       "      <td>0.032421</td>\n",
       "      <td>0.196211</td>\n",
       "      <td>01:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.017426</td>\n",
       "      <td>0.016468</td>\n",
       "      <td>0.127592</td>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.016923</td>\n",
       "      <td>0.012605</td>\n",
       "      <td>0.112541</td>\n",
       "      <td>01:48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 3e-3, wd=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('s1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "learn.recorder.plot_losses(skip_start=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('s1');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>exp_rmspe</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.012959</td>\n",
       "      <td>0.012961</td>\n",
       "      <td>0.112380</td>\n",
       "      <td>01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.011960</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.108741</td>\n",
       "      <td>01:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.011300</td>\n",
       "      <td>0.012701</td>\n",
       "      <td>0.108706</td>\n",
       "      <td>01:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.010804</td>\n",
       "      <td>0.013819</td>\n",
       "      <td>0.110699</td>\n",
       "      <td>01:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.010680</td>\n",
       "      <td>0.013981</td>\n",
       "      <td>0.110969</td>\n",
       "      <td>02:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('s2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('s2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds=learn.get_preds(DatasetType.Test)\n",
    "test_df[\"Sales\"]=np.exp(test_preds[0].data).numpy().T[0]\n",
    "test_df[[\"Id\",\"Sales\"]]=test_df[[\"Id\",\"Sales\"]].astype(\"int\")\n",
    "test_df[[\"Id\",\"Sales\"]].to_csv(path/\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 提交"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|                                                | 0.00/433k [00:00<?, ?B/s]2019-05-10 17:07:03,263 WARNING Retrying (Retry(total=9, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f042412f550>: Failed to establish a new connection: [Errno 110] Connection timed out',)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:11:26,404 WARNING Retrying (Retry(total=8, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f042412f630>: Failed to establish a new connection: [Errno 110] Connection timed out',)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:15:49,548 WARNING Retrying (Retry(total=7, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.VerifiedHTTPSConnection object at 0x7f042412f748>: Failed to establish a new connection: [Errno 110] Connection timed out',)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:18:04,751 WARNING Retrying (Retry(total=6, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:20:23,907 WARNING Retrying (Retry(total=5, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:22:51,200 WARNING Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:25:32,607 WARNING Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:28:46,443 WARNING Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:32:57,036 WARNING Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "2019-05-10 17:37:06,899 WARNING Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),)': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo\n",
      "\n",
      "HTTPSConnectionPool(host='www.googleapis.com', port=443): Max retries exceeded with url: /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=AEnB2UoQq6rUNqbuHmNpFKLAKeh_cRAZQaB_2dO8ucCkkM6OHZvYt7_-WdGrgSwErpcMfAw5R-kPkjdhvuHZmWER6-yDIXtMZ-vUokqACvuS96gYw4A4OGo (Caused by SSLError(SSLError(\"bad handshake: SysCallError(104, 'ECONNRESET')\",),))\n",
      "Could not submit to competition"
     ]
    }
   ],
   "source": [
    "!kaggle competitions submit rossmann-store-sales -f {path/'submission.csv'} -m \"My submission\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41088, 93)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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