cht-video-game-sales-xgboost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 透過資料預測產品是否能成功\n",
    "\n",
    "_**使用 XGBoost模型來預測銷售量是否超過\"熱門\"的設定範圍**_\n",
    "\n",
    "---\n",
    "\n",
    "---\n",
    "\n",
    "## 內容說明\n",
    "\n",
    "1. [背景](#背景)\n",
    "1. [設定](#設定)\n",
    "1. [資料](#資料)\n",
    "1. [訓練模型](#訓練模型)\n",
    "1. [部署模型](#部署模型)\n",
    "1. [評估模型](#評估模型)\n",
    "1. [額外評估](#額外評估)\n",
    "\n",
    "\n",
    "## 背景\n",
    "\n",
    "　　我們一般會透過使用者、評論家、社群媒體等等的評論來判斷一個產品最後是否會成功。特別是在遊戲產業，評論跟評分對於遊戲的成功有著巨大的影響。\n",
    "然而，不是所有被評為不好的遊戲最後會失敗，也不是備受好評的遊戲會熱門(這邊熱門指的是銷售量)。透過機器學習來預測遊戲是否熱門，可以找到除了評論之外的相關資料屬性。\n",
    "\n",
    "　　這個Case，我們會使用一個電玩遊戲銷售量及評分的資料集。這個[資料集](http://www.metacritic.com/browse/games/release-date/available)包含的屬性有使用者評論(user reviews)、評論家評論(critic reviews)、銷售量(sales)、ESRB評分(ESRB ratings)等等。使用者及評論家評論被統計成0到10分或0到100分。雖然這樣很方便，但是在相對小的資料集會造成重要的影響。\n",
    "  \n",
    "　　處理在小資料集在機器學習中是常見的問題，小資料集加上資料類別屬性分佈不均的問題會導致分析難度增加。在這種狀況，使用整體學習器(ensemble learner)是一個適合的選擇，這個notebook會使用XGBoost整體學習器(ensemble learner)來預測遊戲是否熱門。\n",
    "\n",
    "\n",
    "\n",
    "## 設定\n",
    "\n",
    "Let's start by specifying:\n",
    "\n",
    "開始設定細節\n",
    "\n",
    "- 設定你想要放資料跟訓練模型的S3 bucket跟位置(prefix)，記得要跟所使用的Notebook Instance同個區域(Region)\n",
    "\n",
    "- 設定IAM role arn給training跟host的instance存取資料，一般可以直接使用`get_execution_role()`來取得之前設定的權限，如果需要額外設定也可以修改這個參數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "isConfigCell": true
   },
   "outputs": [],
   "source": [
    "bucket = 'your-s3-bucket-name'\n",
    "prefix = 'sagemaker/videogames_xgboost'\n",
    " \n",
    "import sagemaker\n",
    "\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下來載入所需要的Python套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                \n",
    "import pandas as pd                               \n",
    "import matplotlib.pyplot as plt   \n",
    "from IPython.display import Image                 \n",
    "from IPython.display import display               \n",
    "from sklearn.datasets import dump_svmlight_file   \n",
    "from time import gmtime, strftime                 \n",
    "import sys                                        \n",
    "import math                                       \n",
    "import json\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 資料\n",
    "\n",
    "將公開的資料集下載到你的notebook的instance內"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "      <td>NaN</td>\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>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tetris</td>\n",
       "      <td>GB</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Puzzle</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>23.20</td>\n",
       "      <td>2.26</td>\n",
       "      <td>4.22</td>\n",
       "      <td>0.58</td>\n",
       "      <td>30.26</td>\n",
       "      <td>NaN</td>\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>6</th>\n",
       "      <td>New Super Mario Bros.</td>\n",
       "      <td>DS</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.28</td>\n",
       "      <td>9.14</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.88</td>\n",
       "      <td>29.80</td>\n",
       "      <td>89.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>431.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wii Play</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>13.96</td>\n",
       "      <td>9.18</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2.84</td>\n",
       "      <td>28.92</td>\n",
       "      <td>58.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>129.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Super Mario Bros. Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>14.44</td>\n",
       "      <td>6.94</td>\n",
       "      <td>4.70</td>\n",
       "      <td>2.24</td>\n",
       "      <td>28.32</td>\n",
       "      <td>87.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>594.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Duck Hunt</td>\n",
       "      <td>NES</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>26.93</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>28.31</td>\n",
       "      <td>NaN</td>\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>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>16709</th>\n",
       "      <td>15 Days</td>\n",
       "      <td>PC</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>DTP Entertainment</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>DTP Entertainment</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16710</th>\n",
       "      <td>Men in Black II: Alien Escape</td>\n",
       "      <td>GC</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Shooter</td>\n",
       "      <td>Infogrames</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tbd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atari</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16711</th>\n",
       "      <td>Aiyoku no Eustia</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Misc</td>\n",
       "      <td>dramatic create</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16712</th>\n",
       "      <td>Woody Woodpecker in Crazy Castle 5</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Kemco</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16713</th>\n",
       "      <td>SCORE International Baja 1000: The Official Game</td>\n",
       "      <td>PS2</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Activision</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16714</th>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16715</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Codemasters</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16716</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Idea Factory</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16717</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Wanadoo</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\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>16718</th>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Name Platform  \\\n",
       "0                                            Wii Sports      Wii   \n",
       "1                                     Super Mario Bros.      NES   \n",
       "2                                        Mario Kart Wii      Wii   \n",
       "3                                     Wii Sports Resort      Wii   \n",
       "4                              Pokemon Red/Pokemon Blue       GB   \n",
       "5                                                Tetris       GB   \n",
       "6                                 New Super Mario Bros.       DS   \n",
       "7                                              Wii Play      Wii   \n",
       "8                             New Super Mario Bros. Wii      Wii   \n",
       "9                                             Duck Hunt      NES   \n",
       "...                                                 ...      ...   \n",
       "16709                                           15 Days       PC   \n",
       "16710                     Men in Black II: Alien Escape       GC   \n",
       "16711                                  Aiyoku no Eustia      PSV   \n",
       "16712                Woody Woodpecker in Crazy Castle 5      GBA   \n",
       "16713  SCORE International Baja 1000: The Official Game      PS2   \n",
       "16714                     Samurai Warriors: Sanada Maru      PS3   \n",
       "16715                                  LMA Manager 2007     X360   \n",
       "16716                           Haitaka no Psychedelica      PSV   \n",
       "16717                                  Spirits & Spells      GBA   \n",
       "16718                               Winning Post 8 2016      PSV   \n",
       "\n",
       "       Year_of_Release         Genre          Publisher  NA_Sales  EU_Sales  \\\n",
       "0               2006.0        Sports           Nintendo     41.36     28.96   \n",
       "1               1985.0      Platform           Nintendo     29.08      3.58   \n",
       "2               2008.0        Racing           Nintendo     15.68     12.76   \n",
       "3               2009.0        Sports           Nintendo     15.61     10.93   \n",
       "4               1996.0  Role-Playing           Nintendo     11.27      8.89   \n",
       "5               1989.0        Puzzle           Nintendo     23.20      2.26   \n",
       "6               2006.0      Platform           Nintendo     11.28      9.14   \n",
       "7               2006.0          Misc           Nintendo     13.96      9.18   \n",
       "8               2009.0      Platform           Nintendo     14.44      6.94   \n",
       "9               1984.0       Shooter           Nintendo     26.93      0.63   \n",
       "...                ...           ...                ...       ...       ...   \n",
       "16709           2009.0     Adventure  DTP Entertainment      0.00      0.01   \n",
       "16710           2003.0       Shooter         Infogrames      0.01      0.00   \n",
       "16711           2014.0          Misc    dramatic create      0.00      0.00   \n",
       "16712           2002.0      Platform              Kemco      0.01      0.00   \n",
       "16713           2008.0        Racing         Activision      0.00      0.00   \n",
       "16714           2016.0        Action         Tecmo Koei      0.00      0.00   \n",
       "16715           2006.0        Sports        Codemasters      0.00      0.01   \n",
       "16716           2016.0     Adventure       Idea Factory      0.00      0.00   \n",
       "16717           2003.0      Platform            Wanadoo      0.01      0.00   \n",
       "16718           2016.0    Simulation         Tecmo Koei      0.00      0.00   \n",
       "\n",
       "       JP_Sales  Other_Sales  Global_Sales  Critic_Score  Critic_Count  \\\n",
       "0          3.77         8.45         82.53          76.0          51.0   \n",
       "1          6.81         0.77         40.24           NaN           NaN   \n",
       "2          3.79         3.29         35.52          82.0          73.0   \n",
       "3          3.28         2.95         32.77          80.0          73.0   \n",
       "4         10.22         1.00         31.37           NaN           NaN   \n",
       "5          4.22         0.58         30.26           NaN           NaN   \n",
       "6          6.50         2.88         29.80          89.0          65.0   \n",
       "7          2.93         2.84         28.92          58.0          41.0   \n",
       "8          4.70         2.24         28.32          87.0          80.0   \n",
       "9          0.28         0.47         28.31           NaN           NaN   \n",
       "...         ...          ...           ...           ...           ...   \n",
       "16709      0.00         0.00          0.01          63.0           6.0   \n",
       "16710      0.00         0.00          0.01           NaN           NaN   \n",
       "16711      0.01         0.00          0.01           NaN           NaN   \n",
       "16712      0.00         0.00          0.01           NaN           NaN   \n",
       "16713      0.00         0.00          0.01           NaN           NaN   \n",
       "16714      0.01         0.00          0.01           NaN           NaN   \n",
       "16715      0.00         0.00          0.01           NaN           NaN   \n",
       "16716      0.01         0.00          0.01           NaN           NaN   \n",
       "16717      0.00         0.00          0.01           NaN           NaN   \n",
       "16718      0.01         0.00          0.01           NaN           NaN   \n",
       "\n",
       "      User_Score  User_Count          Developer Rating  \n",
       "0              8       322.0           Nintendo      E  \n",
       "1            NaN         NaN                NaN    NaN  \n",
       "2            8.3       709.0           Nintendo      E  \n",
       "3              8       192.0           Nintendo      E  \n",
       "4            NaN         NaN                NaN    NaN  \n",
       "5            NaN         NaN                NaN    NaN  \n",
       "6            8.5       431.0           Nintendo      E  \n",
       "7            6.6       129.0           Nintendo      E  \n",
       "8            8.4       594.0           Nintendo      E  \n",
       "9            NaN         NaN                NaN    NaN  \n",
       "...          ...         ...                ...    ...  \n",
       "16709        5.8         8.0  DTP Entertainment    NaN  \n",
       "16710        tbd         NaN              Atari      T  \n",
       "16711        NaN         NaN                NaN    NaN  \n",
       "16712        NaN         NaN                NaN    NaN  \n",
       "16713        NaN         NaN                NaN    NaN  \n",
       "16714        NaN         NaN                NaN    NaN  \n",
       "16715        NaN         NaN                NaN    NaN  \n",
       "16716        NaN         NaN                NaN    NaN  \n",
       "16717        NaN         NaN                NaN    NaN  \n",
       "16718        NaN         NaN                NaN    NaN  \n",
       "\n",
       "[16719 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_filename = 'Video_Games_Sales_as_at_22_Dec_2016.csv'\n",
    "data_bucket = 'sagemaker-workshop-pdx'\n",
    "\n",
    "s3 = boto3.resource('s3')\n",
    "s3.Bucket(data_bucket).download_file(raw_data_filename, 'raw_data.csv')\n",
    "\n",
    "data = pd.read_csv('./raw_data.csv')\n",
    "pd.set_option('display.max_rows', 20) \n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "在分析之前，需要先定義要預測的目標。遊戲開發預算可能需要數千萬美金，所以對開發商來講，回收成本跟創造利潤是很重要的考量。\n",
    "因此，先定義\"熱門遊戲\"為在全球超過1百萬銷售量的遊戲(Global_Sales這邊的單位為百萬)，所以大於1單位就是熱門。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['y'] = (data['Global_Sales'] > 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "當預測的目標被定義後，來觀察一下熱門跟非熱門的資料分佈狀況"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E       3991\n",
       "T       2961\n",
       "M       1563\n",
       "E10+    1420\n",
       "EC         8\n",
       "K-A        3\n",
       "RP         3\n",
       "AO         1\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa05933ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['not a hit', 'hit'], data['y'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不出所料，在我們設定的指標下，只有一小部份的遊戲被認為是熱門。\n",
    "接下來我們選擇我們想要的資料屬性來預測遊戲是否熱門。\n",
    "我們可以透過資料視覺化來比較評分(review scores)跟全球銷售量(global sales)，確認我們假設評分對銷售量有影響。\n",
    "這邊取對數(log)當單位來讓呈現更清楚。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa05379630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa04e2f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = data.filter(['User_Score','Critic_Score', 'Global_Sales', 'Genre', 'Rating'], axis=1)\n",
    "viz['Genre'] = viz['Genre'].astype('category')\n",
    "viz['User_Score'] = pd.Series(viz['User_Score'].apply(pd.to_numeric, errors='coerce'))\n",
    "viz['User_Score'] = viz['User_Score'].mask(np.isnan(viz[\"User_Score\"]), viz['Critic_Score'] / 10.0)\n",
    "viz.plot(kind='scatter', logx=True, logy=True, x='Critic_Score', y='Global_Sales')\n",
    "viz.plot(kind='scatter', logx=True, logy=True, x='User_Score', y='Global_Sales')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析遊戲類別(Genre)與銷售量跟評分的關係"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/matplotlib/scale.py:111: RuntimeWarning: invalid value encountered in less_equal\n",
      "  out[a <= 0] = -1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9f2321550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff9f230f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups1 = viz.groupby('Rating')\n",
    "groups2 = viz.groupby('Genre')\n",
    "\n",
    "# Plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "ax.set_yscale('log') # set log scale\n",
    "ax.set_xscale('log')\n",
    "for name, groups1 in groups1:\n",
    "    ax.plot(groups1.User_Score, groups1.Global_Sales, marker='o', linestyle='', ms=12, label=name)\n",
    "ax.legend()\n",
    "\n",
    "plt.ylabel('Global_Sales')\n",
    "plt.xlabel('User_Score')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "ax1.set_yscale('log') # set log scale\n",
    "ax1.set_xscale('log')\n",
    "for name, groups2 in groups2:\n",
    "    ax1.plot(groups2.User_Score, groups2.Global_Sales, marker='o', linestyle='', ms=12, label=name)\n",
    "ax1.legend()\n",
    "\n",
    "plt.ylabel('Global_Sales')\n",
    "plt.xlabel('User_Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們關於評分於銷售量的假設看起來是合理的。我們也注意到其他屬性的一些關係。\n",
    "例如ESRB的評級有蠻大的影響力，因為通常被分為\"E\"級的遊戲，受眾會多於被分為\"M\"級這類限制年齡的遊戲，\n",
    "但是還需要考量到遊戲類型(genre)這個屬性，例如說射擊類或動作類，所以被分為\"M\"級的遊戲也可能是熱門遊戲。\n",
    "因此希望透過模型來協助分析這些關係。\n",
    "\n",
    "接下來，可以把覺得與銷售量相關的欄位過濾出來，去除掉像是遊戲名稱這種對銷售量沒有關係的欄位資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['Name', 'Year_of_Release', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales', 'Critic_Count', 'User_Count', 'Developer'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "將需要的欄位過濾出來後，就可以來計算一下有多少缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Platform           0\n",
       "Genre              2\n",
       "Publisher         54\n",
       "Critic_Score    8582\n",
       "User_Score      6704\n",
       "Rating          6769\n",
       "y                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "將有缺失值的資料過濾掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User_Score 處理掉非數值的資料轉換成數值資料\n",
    "如果沒辦法轉換用Critic_Score替代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['User_Score'] = data['User_Score'].apply(pd.to_numeric, errors='coerce')\n",
    "data['User_Score'] = data['User_Score'].mask(np.isnan(data[\"User_Score\"]), data['Critic_Score'] / 10.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用one-hot encoding轉換類別資料成dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['y'] = data['y'].apply(lambda y: 'yes' if y == True else 'no')\n",
    "model_data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "避免模型過度擬合(overfitting)，我們隨機將資料分成3個份，70%用於訓練。20%用來評估模型的準確度，最後10%用來測試最後的結果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, validation_data, test_data = np.split(model_data.sample(frac=1, random_state=1729), [int(0.7 * len(model_data)), int(0.9 * len(model_data))])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost所使用的資料格式為libSVM，轉換資料並放到S3作為SageMaker訓練使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump_svmlight_file(X=train_data.drop(['y_no', 'y_yes'], axis=1), y=train_data['y_yes'], f='train.libsvm')\n",
    "dump_svmlight_file(X=validation_data.drop(['y_no', 'y_yes'], axis=1), y=validation_data['y_yes'], f='validation.libsvm')\n",
    "dump_svmlight_file(X=test_data.drop(['y_no', 'y_yes'], axis=1), y=test_data['y_yes'], f='test.libsvm')\n",
    "\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(prefix + '/train/train.libsvm').upload_file('train.libsvm')\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(prefix + '/validation/validation.libsvm').upload_file('validation.libsvm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 訓練模型\n",
    "\n",
    "資料已經準備完畢，接下來是使用XGBoost model來訓練。XGBoost有很多超參數可以調整來優化模型。以下列出比較會用到的參數。\n",
    "\n",
    "- `max_depth`: 樹的深度，太小會不足以反應資料的特性，太多會導致過度擬合\n",
    "\n",
    "- `eta`: 學習的步驟大小，用來避免過度擬合\n",
    "- `eval_metric`: 用來評估模型的指標\n",
    "- `scale_pos_weight`: 如果遇到不平衡的分佈可以用這個參數來作調整\n",
    "\n",
    "直接開始訓練吧! 訓練過程約6-10分鐘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_name = 'videogames-xgboost-' + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "print(\"Training job\", job_name)\n",
    "\n",
    "containers = {\n",
    "                'us-west-2': '433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest',\n",
    "                'us-east-1': '811284229777.dkr.ecr.us-east-1.amazonaws.com/xgboost:latest',\n",
    "                'us-east-2': '825641698319.dkr.ecr.us-east-2.amazonaws.com/xgboost:latest',\n",
    "                'eu-west-1': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost:latest'\n",
    "             }\n",
    "\n",
    "create_training_params = \\\n",
    "{\n",
    "    \"RoleArn\": role,\n",
    "    \"TrainingJobName\": job_name,\n",
    "    \"AlgorithmSpecification\": {\n",
    "        \"TrainingImage\": containers[boto3.Session().region_name],\n",
    "        \"TrainingInputMode\": \"File\"\n",
    "    },\n",
    "    \"ResourceConfig\": {\n",
    "        \"InstanceCount\": 1,\n",
    "        \"InstanceType\": \"ml.m4.xlarge\",\n",
    "        \"VolumeSizeInGB\": 10\n",
    "    },\n",
    "    \"InputDataConfig\": [\n",
    "        {\n",
    "            \"ChannelName\": \"train\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": \"s3://{}/{}/train\".format(bucket, prefix),\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\"\n",
    "                }\n",
    "            },\n",
    "            \"ContentType\": \"libsvm\",\n",
    "            \"CompressionType\": \"None\"\n",
    "        },\n",
    "        {\n",
    "            \"ChannelName\": \"validation\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": \"s3://{}/{}/validation\".format(bucket, prefix),\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\"\n",
    "                }\n",
    "            },\n",
    "            \"ContentType\": \"libsvm\",\n",
    "            \"CompressionType\": \"None\"\n",
    "        }\n",
    "    ],\n",
    "    \"OutputDataConfig\": {\n",
    "        \"S3OutputPath\": \"s3://{}/{}/xgboost-video-games/output\".format(bucket, prefix)\n",
    "    },\n",
    "    \"HyperParameters\": {\n",
    "        \"max_depth\":\"3\",\n",
    "        \"eta\":\"0.1\",\n",
    "        \"eval_metric\":\"auc\",\n",
    "        \"scale_pos_weight\":\"2.0\",\n",
    "        \"subsample\":\"0.5\",\n",
    "        \"objective\":\"binary:logistic\",\n",
    "        \"num_round\":\"100\"\n",
    "    },\n",
    "    \"StoppingCondition\": {\n",
    "        \"MaxRuntimeInSeconds\": 60 * 60\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "sm = boto3.client('sagemaker')\n",
    "sm.create_training_job(**create_training_params)\n",
    "\n",
    "status = sm.describe_training_job(TrainingJobName=job_name)['TrainingJobStatus']\n",
    "print(status)\n",
    "\n",
    "try:\n",
    "    sm.get_waiter('training_job_completed_or_stopped').wait(TrainingJobName=job_name)\n",
    "finally:\n",
    "    status = sm.describe_training_job(TrainingJobName=job_name)['TrainingJobStatus']\n",
    "    print(\"Training job ended with status: \" + status)\n",
    "    if status == 'Failed':\n",
    "        message = sm.describe_training_job(TrainingJobName=job_name)['FailureReason']\n",
    "        print('Training failed with the following error: {}'.format(message))\n",
    "        raise Exception('Training job failed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 部署模型\n",
    "\n",
    "現在將訓練好的模型部署到serverless的結點作服務\n",
    "\n",
    "1. 指定container\n",
    "1. 指定使用model \n",
    "1. 建立服務model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_model_response = sm.create_model(\n",
    "    ModelName=job_name,\n",
    "    ExecutionRoleArn=role,\n",
    "    PrimaryContainer={\n",
    "        'Image': containers[boto3.Session().region_name],\n",
    "        'ModelDataUrl': sm.describe_training_job(TrainingJobName=job_name)['ModelArtifacts']['S3ModelArtifacts']})\n",
    "\n",
    "print(create_model_response['ModelArn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "建立服務\n",
    "1. 決定EC2 instance的機型\n",
    "1. 決定EC2的數量\n",
    "1. 決定model名稱\n",
    "\n",
    "設定好後建立endpoint\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgboost_endpoint_config = 'videogames-xgboost-endpoint-config-' + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "print(xgboost_endpoint_config)\n",
    "create_endpoint_config_response = sm.create_endpoint_config(\n",
    "    EndpointConfigName=xgboost_endpoint_config,\n",
    "    ProductionVariants=[{\n",
    "        'InstanceType': 'ml.t2.medium',\n",
    "        'InitialInstanceCount': 1,\n",
    "        'ModelName': job_name,\n",
    "        'VariantName': 'AllTraffic'}])\n",
    "\n",
    "print(\"Endpoint Config Arn: \" + create_endpoint_config_response['EndpointConfigArn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "xgboost_endpoint = 'EXAMPLE-videogames-xgb-endpoint-' + strftime(\"%Y%m%d%H%M\", gmtime())\n",
    "print(xgboost_endpoint)\n",
    "create_endpoint_response = sm.create_endpoint(\n",
    "    EndpointName=xgboost_endpoint,\n",
    "    EndpointConfigName=xgboost_endpoint_config)\n",
    "print(create_endpoint_response['EndpointArn'])\n",
    "\n",
    "resp = sm.describe_endpoint(EndpointName=xgboost_endpoint)\n",
    "status = resp['EndpointStatus']\n",
    "print(\"Status: \" + status)\n",
    "\n",
    "try:\n",
    "    sm.get_waiter('endpoint_in_service').wait(EndpointName=xgboost_endpoint)\n",
    "finally:\n",
    "    resp = sm.describe_endpoint(EndpointName=xgboost_endpoint)\n",
    "    status = resp['EndpointStatus']\n",
    "    print(\"Arn: \" + resp['EndpointArn'])\n",
    "    print(\"Status: \" + status)\n",
    "\n",
    "    if status != 'InService':\n",
    "        message = sm.describe_endpoint(EndpointName=xgboost_endpoint)['FailureReason']\n",
    "        print('Endpoint creation failed with the following error: {}'.format(message))\n",
    "        raise Exception('Endpoint creation did not succeed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 評估模型\n",
    "\n",
    "開始使用測試資料來測試模型是否準確且可以適用各種狀況\n",
    "\n",
    "透過分類來預測熱門為1，不熱門為0後建立分析的矩陣"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = boto3.client('runtime.sagemaker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_predict(data, endpoint_name, content_type):\n",
    "    payload = '\\n'.join(data)\n",
    "    response = runtime.invoke_endpoint(EndpointName=endpoint_name, \n",
    "                                   ContentType=content_type, \n",
    "                                   Body=payload)\n",
    "    result = response['Body'].read()\n",
    "    result = result.decode(\"utf-8\")\n",
    "    result = result.split(',')\n",
    "    preds = [float((num)) for num in result]\n",
    "    preds = [round(num) for num in preds]\n",
    "    return preds\n",
    "\n",
    "def batch_predict(data, batch_size, endpoint_name, content_type):\n",
    "    items = len(data)\n",
    "    arrs = []\n",
    "    \n",
    "    for offset in range(0, items, batch_size):\n",
    "        if offset+batch_size < items:\n",
    "            results = do_predict(data[offset:(offset+batch_size)], endpoint_name, content_type)\n",
    "            arrs.extend(results)\n",
    "        else:\n",
    "            arrs.extend(do_predict(data[offset:items], endpoint_name, content_type))\n",
    "        sys.stdout.write('.')\n",
    "    return(arrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".........\n",
      "error rate=0.146949\n",
      "CPU times: user 20 ms, sys: 4 ms, total: 24 ms\n",
      "Wall time: 406 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import json\n",
    "\n",
    "with open('test.libsvm', 'r') as f:\n",
    "    payload = f.read().strip()\n",
    "\n",
    "labels = [int(line.split(' ')[0]) for line in payload.split('\\n')]\n",
    "test_data = [line for line in payload.split('\\n')]\n",
    "preds = batch_predict(test_data, 100, xgboost_endpoint, 'text/x-libsvm')\n",
    "\n",
    "print ('\\nerror rate=%f' % ( sum(1 for i in range(len(preds)) if preds[i]!=labels[i]) /float(len(preds))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Actual</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>599</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Actual       0   1\n",
       "Predicted         \n",
       "0          599  72\n",
       "1           46  86"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=np.array(labels), columns=np.array(preds), rownames=['Predicted'], colnames=['Actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "欄 0 1 代表預測狀況\n",
    "\n",
    "列 0 1 代表實際狀況\n",
    "\n",
    "從矩陣可以分析\n",
    "\n",
    "622 + 49 + 59 + 73 = 803\n",
    "\n",
    "判斷正確的狀況為 622 + 73 / 803 = 0.8655\n",
    "判斷錯誤的狀況為 49 + 59 / 803 = 0.1345\n",
    "\n",
    "從矩陣可以分析看到整體正確率為86.55%\n",
    "從矩陣可以分析看到整體誤判率為13.45%\n",
    "\n",
    "\n",
    "可以透過增加`scale_pos_weight` 這個超參數來增加真陽性(true positives)的比例，但是代價是會減少準確率，\n",
    "這之間的權衡就取決於商業上的考量，比較偽陽性(false positives)跟偽陰性(false negative)的成本。\n",
    "\n",
    "例如:\n",
    "比起把實際上熱門遊戲誤判別成不熱門，把實際上不熱門遊戲判別成熱門遊戲，會導致決策的風險更高，\n",
    "因此保守的判別實際熱門遊戲為不熱門，可能是比較好的選擇。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 額外評估\n",
    "\n",
    "可以透過其他方式來增加準確率,\n",
    "例如調整超參數跟作特徵工程。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm.delete_endpoint(EndpointName=xgboost_endpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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  "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
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