{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.2"},"colab":{"name":"preprocess_numerical.ipynb のコピー","provenance":[{"file_id":"1MozGcwIJyPOhy2fxHoo_0Xvm4NDvO9YF","timestamp":1617611327851}],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"7ScZhDqvHxWm"},"source":["# 数値データに対する前処理コード例\n","- ref.\n","    - preprocess methods\n","        - [機械学習のための特徴量エンジニアリング](https://www.oreilly.co.jp/books/9784873118680/)\n","        - [5.3. Preprocessing data](https://scikit-learn.org/stable/modules/preprocessing.html#normalization)\n","    - data: [YouTuberデータセット公開してみた](https://qiita.com/myaun/items/7e0dd7f3f9d9d2fef497)\n","- 全体の流れ\n","    - データセットの準備。\n","    - 数値データに対する前処理の例\n","        - 手法1：バイナリ化\n","        - 手法2：アドホックな離散化\n","        - 手法3：統計的な離散化\n","        - 手法4：ログスケール化\n","            - デフォルトとログスケールとの比較\n","        - 手法5：標準化\n","        - 手法6：min-maxスケーリング\n","    - 特徴ベクトルに対する前処理の例\n","        - 手法7：正規化\n","        - 手法8：正規分布への写像\n","            - デフォルトとbox-cox写像との比較"]},{"cell_type":"markdown","metadata":{"id":"1n_M-w2iHxWs"},"source":["## 環境構築"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RWL_14XPHxWs","executionInfo":{"status":"ok","timestamp":1620721069698,"user_tz":-540,"elapsed":23777,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"196973c7-3799-4584-9df0-29ab82e71e48"},"source":["!pip install quilt\n","!quilt install haradai1262/YouTuber"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting quilt\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cf/17/5f30e1726fb8a311bb5b976f8deb37580f931d077ceeefb263424955ce4a/quilt-2.9.15-py3-none-any.whl (110kB)\n","\r\u001b[K     |███                             | 10kB 14.7MB/s eta 0:00:01\r\u001b[K     |██████                          | 20kB 18.9MB/s eta 0:00:01\r\u001b[K     |█████████                       | 30kB 22.0MB/s eta 0:00:01\r\u001b[K     |███████████▉                    | 40kB 25.1MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 51kB 27.1MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 61kB 27.9MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 71kB 27.8MB/s eta 0:00:01\r\u001b[K     |███████████████████████▊        | 81kB 29.0MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 92kB 30.1MB/s eta 0:00:01\r\u001b[K     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/usr/local/lib/python3.7/dist-packages (from quilt) (20.9)\n","Requirement already satisfied: appdirs>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from quilt) (1.4.4)\n","Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from quilt) (1.15.0)\n","Requirement already satisfied: xlrd>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from quilt) (1.1.0)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.12.4->quilt) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.12.4->quilt) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.12.4->quilt) (2020.12.5)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.12.4->quilt) (3.0.4)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.0->quilt) (2.8.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.0->quilt) (2018.9)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=16.8->quilt) (2.4.7)\n","Installing collected packages: quilt\n","Successfully installed quilt-2.9.15\n","Downloading package metadata...\n","Downloading 4 fragments (633095200 bytes before compression)...\n","100% 633M/633M [00:18<00:00, 34.4MB/s]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"216bw7OVHxWt"},"source":["## データセットの準備"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"id":"nw8dF89KHxWt","executionInfo":{"status":"ok","timestamp":1620721071555,"user_tz":-540,"elapsed":25617,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"2151f8fa-af58-4492-945e-b3cfd631db47"},"source":["from quilt.data.haradai1262 import YouTuber\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","#df = YouTuber.channels.UUUM()\n","df = YouTuber.channel_videos.UUUM_videos()\n","\n","# check the descriptive statistics of numerical data\n","df.describe()"],"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>likeCount</th>\n","      <th>favoriteCount</th>\n","      <th>dislikeCount</th>\n","      <th>commentCount</th>\n","      <th>TopicIds</th>\n","      <th>idx</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>6.627900e+04</td>\n","      <td>64256.000000</td>\n","      <td>66289.0</td>\n","      <td>64256.000000</td>\n","      <td>65997.000000</td>\n","      <td>0.0</td>\n","      <td>66289.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>4.545539e+05</td>\n","      <td>3233.703390</td>\n","      <td>0.0</td>\n","      <td>296.430014</td>\n","      <td>533.418807</td>\n","      <td>NaN</td>\n","      <td>235.730136</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.328105e+06</td>\n","      <td>9768.090605</td>\n","      <td>0.0</td>\n","      <td>1633.734833</td>\n","      <td>2253.437482</td>\n","      <td>NaN</td>\n","      <td>142.868881</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000e+00</td>\n","      <td>0.000000</td>\n","      <td>0.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3.907100e+04</td>\n","      <td>266.000000</td>\n","      <td>0.0</td>\n","      <td>23.000000</td>\n","      <td>55.000000</td>\n","      <td>NaN</td>\n","      <td>111.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>1.214190e+05</td>\n","      <td>776.000000</td>\n","      <td>0.0</td>\n","      <td>67.000000</td>\n","      <td>150.000000</td>\n","      <td>NaN</td>\n","      <td>229.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.512260e+05</td>\n","      <td>2260.000000</td>\n","      <td>0.0</td>\n","      <td>201.000000</td>\n","      <td>394.000000</td>\n","      <td>NaN</td>\n","      <td>357.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>8.664236e+07</td>\n","      <td>630051.000000</td>\n","      <td>0.0</td>\n","      <td>213677.000000</td>\n","      <td>227598.000000</td>\n","      <td>NaN</td>\n","      <td>501.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          viewCount      likeCount  ...  TopicIds           idx\n","count  6.627900e+04   64256.000000  ...       0.0  66289.000000\n","mean   4.545539e+05    3233.703390  ...       NaN    235.730136\n","std    1.328105e+06    9768.090605  ...       NaN    142.868881\n","min    0.000000e+00       0.000000  ...       NaN      1.000000\n","25%    3.907100e+04     266.000000  ...       NaN    111.000000\n","50%    1.214190e+05     776.000000  ...       NaN    229.000000\n","75%    3.512260e+05    2260.000000  ...       NaN    357.000000\n","max    8.664236e+07  630051.000000  ...       NaN    501.000000\n","\n","[8 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":912},"id":"iIYWGb9ZHxWu","executionInfo":{"status":"ok","timestamp":1620721071556,"user_tz":-540,"elapsed":25602,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"316ae46e-00fd-49c1-c47d-1e1cca6a39e2"},"source":["# the description of data frame\n","df.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>title</th>\n","      <th>description</th>\n","      <th>liveBroadcastContent</th>\n","      <th>tags</th>\n","      <th>publishedAt</th>\n","      <th>thumbnails</th>\n","      <th>viewCount</th>\n","      <th>likeCount</th>\n","      <th>favoriteCount</th>\n","      <th>dislikeCount</th>\n","      <th>commentCount</th>\n","      <th>caption</th>\n","      <th>definition</th>\n","      <th>dimension</th>\n","      <th>duration</th>\n","      <th>projection</th>\n","      <th>TopicIds</th>\n","      <th>relevantTopicIds</th>\n","      <th>idx</th>\n","      <th>cid</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>R7V5d94XkGQ</td>\n","      <td>【大食い】超高級寿司店で３人で食べ放題したらいくらかかるの!?【大トロ1カン2,000円】</td>\n","      <td>提供：ポコロンダンジョンズ\\r\\r\\r\\r\\niOS：https://bit.ly/2sGg...</td>\n","      <td>none</td>\n","      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n","      <td>2018-06-30T04:00:01.000Z</td>\n","      <td>https://i.ytimg.com/vi/R7V5d94XkGQ/default.jpg</td>\n","      <td>2244205.0</td>\n","      <td>27703.0</td>\n","      <td>0</td>\n","      <td>3667.0</td>\n","      <td>8647.0</td>\n","      <td>False</td>\n","      <td>hd</td>\n","      <td>2d</td>\n","      <td>PT21M16S</td>\n","      <td>rectangular</td>\n","      <td>NaN</td>\n","      <td>['/m/02wbm', '/m/019_rr', '/m/019_rr', '/m/02w...</td>\n","      <td>1</td>\n","      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2R9_bkcWNd4</td>\n","      <td>【女王集結】女性YouTuberたちと飲みながら本音トークしてみたら爆笑www</td>\n","      <td>しばなんチャンネルの動画\\r\\r\\r\\r\\nhttps://www.youtube.com/...</td>\n","      <td>none</td>\n","      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n","      <td>2018-06-29T08:00:01.000Z</td>\n","      <td>https://i.ytimg.com/vi/2R9_bkcWNd4/default.jpg</td>\n","      <td>1869268.0</td>\n","      <td>30889.0</td>\n","      <td>0</td>\n","      <td>3483.0</td>\n","      <td>8859.0</td>\n","      <td>False</td>\n","      <td>hd</td>\n","      <td>2d</td>\n","      <td>PT18M38S</td>\n","      <td>rectangular</td>\n","      <td>NaN</td>\n","      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n","      <td>2</td>\n","      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>EU8S-zxS9PI</td>\n","      <td>【悪質】偽物ヒカキン許さねぇ…注意してください！【なりすまし】</td>\n","      <td>◆チャンネル登録はこちら↓\\r\\r\\r\\r\\nhttp://www.youtube.com/...</td>\n","      <td>none</td>\n","      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n","      <td>2018-06-27T08:38:55.000Z</td>\n","      <td>https://i.ytimg.com/vi/EU8S-zxS9PI/default.jpg</td>\n","      <td>1724625.0</td>\n","      <td>33038.0</td>\n","      <td>0</td>\n","      <td>4298.0</td>\n","      <td>11504.0</td>\n","      <td>False</td>\n","      <td>hd</td>\n","      <td>2d</td>\n","      <td>PT6M12S</td>\n","      <td>rectangular</td>\n","      <td>NaN</td>\n","      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n","      <td>3</td>\n","      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5wnfkIfw0jE</td>\n","      <td>ツイッターのヒカキンシンメトリーBotが面白すぎて爆笑www</td>\n","      <td>◆チャンネル登録はこちら↓\\r\\r\\r\\r\\nhttp://www.youtube.com/...</td>\n","      <td>none</td>\n","      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n","      <td>2018-06-25T07:46:07.000Z</td>\n","      <td>https://i.ytimg.com/vi/5wnfkIfw0jE/default.jpg</td>\n","      <td>1109029.0</td>\n","      <td>25986.0</td>\n","      <td>0</td>\n","      <td>5063.0</td>\n","      <td>6852.0</td>\n","      <td>False</td>\n","      <td>hd</td>\n","      <td>2d</td>\n","      <td>PT6M31S</td>\n","      <td>rectangular</td>\n","      <td>NaN</td>\n","      <td>['/m/04rlf', '/m/02jjt', '/m/02jjt']</td>\n","      <td>4</td>\n","      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-6duBsde_XM</td>\n","      <td>【放送事故】酒飲みながら東海オンエア×ヒカキンで質問コーナーやったらヤバかったwww</td>\n","      <td>提供：モンスターストライク\\r\\r\\r\\r\\n▼キャンペーンサイトはこちら\\r\\r\\r\\r\\...</td>\n","      <td>none</td>\n","      <td>['ヒカキン', 'ヒカキンtv', 'hikakintv', 'hikakin', 'ひか...</td>\n","      <td>2018-06-21T08:00:00.000Z</td>\n","      <td>https://i.ytimg.com/vi/-6duBsde_XM/default.jpg</td>\n","      <td>1759797.0</td>\n","      <td>33923.0</td>\n","      <td>0</td>\n","      <td>2150.0</td>\n","      <td>4517.0</td>\n","      <td>False</td>\n","      <td>hd</td>\n","      <td>2d</td>\n","      <td>PT27M7S</td>\n","      <td>rectangular</td>\n","      <td>NaN</td>\n","      <td>['/m/098wr', '/m/019_rr', '/m/02wbm', '/m/019_...</td>\n","      <td>5</td>\n","      <td>UCZf__ehlCEBPop___sldpBUQ</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            id  ...                        cid\n","0  R7V5d94XkGQ  ...  UCZf__ehlCEBPop___sldpBUQ\n","1  2R9_bkcWNd4  ...  UCZf__ehlCEBPop___sldpBUQ\n","2  EU8S-zxS9PI  ...  UCZf__ehlCEBPop___sldpBUQ\n","3  5wnfkIfw0jE  ...  UCZf__ehlCEBPop___sldpBUQ\n","4  -6duBsde_XM  ...  UCZf__ehlCEBPop___sldpBUQ\n","\n","[5 rows x 21 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8pXoHSC0HxWv","executionInfo":{"status":"ok","timestamp":1620721071556,"user_tz":-540,"elapsed":25589,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"2f2fa994-9091-4510-a9b0-d350ba4dc9bf"},"source":["# column 'viewCount''\n","df['viewCount'].sort_values()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["24466    0.0\n","45995    0.0\n","45994    0.0\n","65508    0.0\n","45993    0.0\n","        ... \n","45854    NaN\n","45856    NaN\n","45860    NaN\n","45864    NaN\n","45865    NaN\n","Name: viewCount, Length: 66289, dtype: float64"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ebMtSjgbHxWv","executionInfo":{"status":"ok","timestamp":1620721071557,"user_tz":-540,"elapsed":25577,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"5bc4e6cd-441d-49d9-eaa7-bf1a23ad92ab"},"source":["# drop samples including NaN & 0 on 'viewCount'\n","\n","print('orig_num = ', len(df))\n","print('num of NaN = ', len(df.query('viewCount == \"NaN\"')))\n","df = df[df['viewCount'].notnull()]\n","print('after_num = ', len(df))\n","\n","df = df[df['viewCount'] != 0]\n","print('after_num2 = ', len(df))"],"execution_count":5,"outputs":[{"output_type":"stream","text":["orig_num =  66289\n","num of NaN =  10\n","after_num =  66279\n","after_num2 =  66231\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4C0kWvqkHxWw","executionInfo":{"status":"ok","timestamp":1620721071558,"user_tz":-540,"elapsed":25565,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"6c7ca783-2245-4a1c-ad3c-8ad5255733ed"},"source":["df['viewCount'].describe()"],"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    6.623100e+04\n","mean     4.548834e+05\n","std      1.328530e+06\n","min      2.000000e+00\n","25%      3.919150e+04\n","50%      1.215850e+05\n","75%      3.515195e+05\n","max      8.664236e+07\n","Name: viewCount, dtype: float64"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"B-jBw8yDHxWw","executionInfo":{"status":"ok","timestamp":1620721073053,"user_tz":-540,"elapsed":27047,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"5b2b0e6d-538a-4c10-e987-673b7a31825d"},"source":["# histgram of viewCount\n","\n","%matplotlib inline\n","fontsize = 16\n","\n","fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","df['viewCount'].hist(ax=ax1, bins=100)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('histgram of viewCount', fontsize=fontsize)\n","ax1.set_xlabel('ViewCount', fontsize=fontsize)\n","ax1.set_ylabel('Frequency', fontsize=fontsize)\n","\n","# log\n","df['viewCount'].hist(ax=ax2, bins=100, log=True)\n","ax2.tick_params(labelsize=fontsize)\n","ax2.set_title('histgram of viewCount', fontsize=fontsize)\n","ax2.set_xlabel('ViewCount', fontsize=fontsize)\n","ax2.set_ylabel('Frequency (log)', fontsize=fontsize)"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Frequency (log)')"]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"AKATkO81HxWx"},"source":["## 数値データに対する前処理の例"]},{"cell_type":"markdown","metadata":{"id":"WMW474nmHxWx"},"source":["### 手法1：バイナリ化(binarization)"]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"gUB5uZw0HxWx","executionInfo":{"status":"ok","timestamp":1620721073054,"user_tz":-540,"elapsed":27034,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"18537228-d625-4139-a021-79465ed5bc92"},"source":["# preprocess method 1: binarization by mean value\n","\n","THRESHOLD = df['viewCount'].mean()\n","new_column = df['viewCount'] > THRESHOLD\n","new_column = np.where(new_column == True, 1, 0)\n","temp = pd.DataFrame(df['viewCount'])\n","temp['binary'] = new_column\n","temp.head()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary\n","0  2244205.0       1\n","1  1869268.0       1\n","2  1724625.0       1\n","3  1109029.0       1\n","4  1759797.0       1"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"QrH0ZlFIHxWx"},"source":["### 手法2：アドホックな離散化(ad-hoc discretization)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"p_yNE_bPHxWy","executionInfo":{"status":"ok","timestamp":1620721073055,"user_tz":-540,"elapsed":27021,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"aefe7905-94fe-44bb-a01d-ffd407c27df6"},"source":["# preprocess method 2: discretization 1, ad-hoc division\n","floor = 10000\n","new_column = np.floor_divide(df['viewCount'], floor)\n","temp['discret_floor'] = new_column\n","temp.head()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  discret_floor\n","0  2244205.0       1          224.0\n","1  1869268.0       1          186.0\n","2  1724625.0       1          172.0\n","3  1109029.0       1          110.0\n","4  1759797.0       1          175.0"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":645},"id":"wyhMEa831Aqa","executionInfo":{"status":"ok","timestamp":1620721074474,"user_tz":-540,"elapsed":28426,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"a1cc565e-6acb-41b5-ba8b-cdf870fb21f8"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency (log)', fontsize=fontsize)\n","\n","# discret_floor\n","temp['discret_floor'].hist(ax=ax2, bins=100, log=True)\n","ax2.set_title('discret_floor', fontsize=fontsize)\n","ax2.set_xlabel('discret_floor', fontsize=fontsize)\n","ax2.set_ylabel('Freqency (log)', fontsize=fontsize)"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Freqency (log)')"]},"metadata":{"tags":[]},"execution_count":10},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"2DGEK_AoHxWy"},"source":["### 手法3：統計的な離散化"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_3qhyXhOHxWy","executionInfo":{"status":"ok","timestamp":1620721074475,"user_tz":-540,"elapsed":28413,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"58117ee7-9954-440a-dea5-73c7807c2cc0"},"source":["# preprocess method 3: discretization 2, quantilzation\n","discret_num = 4\n","ranges = np.linspace(0, 1, discret_num)\n","data = df['viewCount'].quantile(ranges)\n","data"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.000000    2.000000e+00\n","0.333333    5.913133e+04\n","0.666667    2.408843e+05\n","1.000000    8.664236e+07\n","Name: viewCount, dtype: float64"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":411},"id":"abTAiICgHxWz","executionInfo":{"status":"ok","timestamp":1620721074476,"user_tz":-540,"elapsed":28400,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"0e1598ef-3a8a-4480-99e0-12f594a44912"},"source":["new_column = pd.qcut(df['viewCount'], discret_num, labels=False)\n","temp['discret_quantile'] = new_column\n","temp"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>66284</th>\n","      <td>131489.0</td>\n","      <td>0</td>\n","      <td>13.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>66285</th>\n","      <td>13271.0</td>\n","      <td>0</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>66286</th>\n","      <td>76266.0</td>\n","      <td>0</td>\n","      <td>7.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>66287</th>\n","      <td>282447.0</td>\n","      <td>0</td>\n","      <td>28.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>66288</th>\n","      <td>6900.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>66231 rows × 4 columns</p>\n","</div>"],"text/plain":["       viewCount  binary  discret_floor  discret_quantile\n","0      2244205.0       1          224.0                 3\n","1      1869268.0       1          186.0                 3\n","2      1724625.0       1          172.0                 3\n","3      1109029.0       1          110.0                 3\n","4      1759797.0       1          175.0                 3\n","...          ...     ...            ...               ...\n","66284   131489.0       0           13.0                 2\n","66285    13271.0       0            1.0                 0\n","66286    76266.0       0            7.0                 1\n","66287   282447.0       0           28.0                 2\n","66288     6900.0       0            0.0                 0\n","\n","[66231 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":645},"id":"qWN9JF00dcNr","executionInfo":{"status":"ok","timestamp":1620721075850,"user_tz":-540,"elapsed":29760,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"e7cf5a61-a970-405a-d45e-636fa6b292db"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency (log)', fontsize=fontsize)\n","\n","# discret_quantile\n","bins = discret_num*2\n","temp['discret_quantile'].hist(ax=ax2, bins=bins, log=True)\n","ax2.set_title('discret_quantile', fontsize=fontsize)\n","ax2.set_xlabel('viewCounts (discret_quantile={})'.format(discret_num), fontsize=fontsize)\n","ax2.set_ylabel('Freqency (log)', fontsize=fontsize)"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Freqency (log)')"]},"metadata":{"tags":[]},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"EseLksGcHxW0"},"source":["### 手法4：ログスケール化(log-scaling)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"vrOpEz46HxW0","executionInfo":{"status":"ok","timestamp":1620721075851,"user_tz":-540,"elapsed":29746,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"d46d68d5-e8aa-4065-9c8e-d7fd9acb2d8e"},"source":["# preprocess method 4: log-scaling\n","new_column = np.log10(df['viewCount'] + 1)\n","temp['log10'] = new_column\n","temp.head()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","      <td>6.351063</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","      <td>6.271672</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","      <td>6.236695</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","      <td>6.044943</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","      <td>6.245463</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  discret_floor  discret_quantile     log10\n","0  2244205.0       1          224.0                 3  6.351063\n","1  1869268.0       1          186.0                 3  6.271672\n","2  1724625.0       1          172.0                 3  6.236695\n","3  1109029.0       1          110.0                 3  6.044943\n","4  1759797.0       1          175.0                 3  6.245463"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"Q4NH3TbwHxW0"},"source":["### デフォルトとログスケールとの比較"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"HoGcYWU8HxW1","executionInfo":{"status":"ok","timestamp":1620721077569,"user_tz":-540,"elapsed":31452,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"f8409701-231a-44bf-9895-6ab872601f93"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency', fontsize=fontsize)\n","\n","# log-scaled\n","temp['log10'].hist(ax=ax2, bins=100)\n","ax2.tick_params(labelsize=fontsize)\n","ax2.set_title('log10', fontsize=fontsize)\n","ax2.set_xlabel('viewCounts', fontsize=fontsize)\n","ax2.set_ylabel('Freqency', fontsize=fontsize)\n"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Freqency')"]},"metadata":{"tags":[]},"execution_count":15},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"34FHK1_5HxW1"},"source":["### 手法5：標準化(standardization)\n","- [5.3.1. Standardization, or mean removal and variance scaling](https://scikit-learn.org/stable/modules/preprocessing.html)\n","- [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"BgF53riYHxW1","executionInfo":{"status":"ok","timestamp":1620721077990,"user_tz":-540,"elapsed":31861,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"bb0b2bb0-59b8-4c3a-fe6c-72d88483bc17"},"source":["from sklearn import preprocessing\n","\n","data = np.array(df['viewCount'].values, dtype='float64')\n","data = data.reshape(len(data), 1)\n","new_column = preprocessing.scale(data)\n","temp['standardization'] = new_column\n","temp.head()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","      <th>standardization</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","      <td>6.351063</td>\n","      <td>1.346854</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","      <td>6.271672</td>\n","      <td>1.064632</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","      <td>6.236695</td>\n","      <td>0.955757</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","      <td>6.044943</td>\n","      <td>0.492387</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","      <td>6.245463</td>\n","      <td>0.982231</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  ...     log10  standardization\n","0  2244205.0       1  ...  6.351063         1.346854\n","1  1869268.0       1  ...  6.271672         1.064632\n","2  1724625.0       1  ...  6.236695         0.955757\n","3  1109029.0       1  ...  6.044943         0.492387\n","4  1759797.0       1  ...  6.245463         0.982231\n","\n","[5 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D4yz6nZwHxW1","executionInfo":{"status":"ok","timestamp":1620721077991,"user_tz":-540,"elapsed":31849,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"7616cd20-da2f-4392-cffb-09a86020faae"},"source":["mean = np.mean(new_column)\n","var = np.var(new_column)\n","print('mean = ', mean, ', var = ', var)\n","\n","temp['standardization'].describe()\n"],"execution_count":17,"outputs":[{"output_type":"stream","text":["mean =  1.2873900181367037e-17 , var =  1.0\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["count    6.623100e+04\n","mean     8.899653e-16\n","std      1.000008e+00\n","min     -3.423971e-01\n","25%     -3.128985e-01\n","50%     -2.508795e-01\n","75%     -7.780379e-02\n","max      6.487481e+01\n","Name: standardization, dtype: float64"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":643},"id":"8g1V_ZYO2zoY","executionInfo":{"status":"ok","timestamp":1620721079490,"user_tz":-540,"elapsed":33334,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"223e1e03-9d4c-4fe8-89cf-e6dcd9ad8e81"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency', fontsize=fontsize)\n","\n","# log-scaled\n","ax2.set_title('standaridization', fontsize=fontsize)\n","ax2.set_xlabel('viewCounts', fontsize=fontsize)\n","ax2.set_ylabel('Freqency (log)', fontsize=fontsize)\n","temp['standardization'].hist(ax=ax2, bins=100, log=True)\n","#plt.hist(temp['standardization'], bins=100, log=True)"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fa9057fc550>"]},"metadata":{"tags":[]},"execution_count":18},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"arbdfRHDHxW2"},"source":["### 手法6：min-maxスケーリング(Min-Max scalering)\n","- [min-max scaler](https://scikit-learn.org/stable/modules/preprocessing.html#scaling-features-to-a-range)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"3SmQVz5gHxW2","executionInfo":{"status":"ok","timestamp":1620721079491,"user_tz":-540,"elapsed":33320,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"fe0971cb-bed4-43b1-ded7-b88597958050"},"source":["\"\"\"\n","min = data.min(axis=0)\n","max = data.max(axis=0)\n","new_column = (data - min) / (max - min)\n","temp['min-max'] = new_column\n","temp.head()\n","\"\"\"\n","\n","new_column = preprocessing.minmax_scale(df['viewCount'])\n","temp['min-max'] = new_column\n","temp.head()"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","      <th>standardization</th>\n","      <th>min-max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","      <td>6.351063</td>\n","      <td>1.346854</td>\n","      <td>0.025902</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","      <td>6.271672</td>\n","      <td>1.064632</td>\n","      <td>0.021575</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","      <td>6.236695</td>\n","      <td>0.955757</td>\n","      <td>0.019905</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","      <td>6.044943</td>\n","      <td>0.492387</td>\n","      <td>0.012800</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","      <td>6.245463</td>\n","      <td>0.982231</td>\n","      <td>0.020311</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  discret_floor  ...     log10  standardization   min-max\n","0  2244205.0       1          224.0  ...  6.351063         1.346854  0.025902\n","1  1869268.0       1          186.0  ...  6.271672         1.064632  0.021575\n","2  1724625.0       1          172.0  ...  6.236695         0.955757  0.019905\n","3  1109029.0       1          110.0  ...  6.044943         0.492387  0.012800\n","4  1759797.0       1          175.0  ...  6.245463         0.982231  0.020311\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":643},"id":"E07e_RlIe75u","executionInfo":{"status":"ok","timestamp":1620721081156,"user_tz":-540,"elapsed":34970,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"17c11cc4-501d-4538-9c74-5f6cf48f0bce"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency (log)', fontsize=fontsize)\n","\n","# min-max\n","ax2.set_title('min-max', fontsize=fontsize)\n","ax2.set_xlabel('viewCounts (min-max)', fontsize=fontsize)\n","ax2.set_ylabel('Freqency (log)', fontsize=fontsize)\n","temp['min-max'].hist(ax=ax2, bins=100, log=True)\n"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fa905211f10>"]},"metadata":{"tags":[]},"execution_count":20},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"XCFVlj6T6rz3"},"source":["## 特徴ベクトルに対する前処理の例"]},{"cell_type":"markdown","metadata":{"id":"NRP5-kd7HxW2"},"source":["### 手法7：正規化(normalization)\n","- 正規化という考え方自体は特徴量（データセットを表と見た時の列）に対しても適用できる。ここでは特徴ベクトル（行）に対して適用した際の値を観察する。\n","- NOTE: the process target is NOT one feature value (one column). **The target of normalization is \"feature vector (one row)\"**.\n","- [5.3.3. Normalization](https://scikit-learn.org/stable/modules/preprocessing.html#normalization)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"tuK79J3SHxW3","executionInfo":{"status":"ok","timestamp":1620721081157,"user_tz":-540,"elapsed":34958,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"3613eecc-b4ca-42cd-d4a0-931bdd67a9be"},"source":["temp.head()"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","      <th>standardization</th>\n","      <th>min-max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","      <td>6.351063</td>\n","      <td>1.346854</td>\n","      <td>0.025902</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","      <td>6.271672</td>\n","      <td>1.064632</td>\n","      <td>0.021575</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","      <td>6.236695</td>\n","      <td>0.955757</td>\n","      <td>0.019905</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","      <td>6.044943</td>\n","      <td>0.492387</td>\n","      <td>0.012800</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","      <td>6.245463</td>\n","      <td>0.982231</td>\n","      <td>0.020311</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  discret_floor  ...     log10  standardization   min-max\n","0  2244205.0       1          224.0  ...  6.351063         1.346854  0.025902\n","1  1869268.0       1          186.0  ...  6.271672         1.064632  0.021575\n","2  1724625.0       1          172.0  ...  6.236695         0.955757  0.019905\n","3  1109029.0       1          110.0  ...  6.044943         0.492387  0.012800\n","4  1759797.0       1          175.0  ...  6.245463         0.982231  0.020311\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"I0QBpNZJHxW3","executionInfo":{"status":"ok","timestamp":1620721081157,"user_tz":-540,"elapsed":34945,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"2580fdec-8a19-427b-9670-3aad0bc71525"},"source":["normalized_l2 = preprocessing.normalize(temp, norm='l2')\n","normalized_l2 = pd.DataFrame(normalized_l2, columns=temp.columns)\n","normalized_l2.head()"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","      <th>standardization</th>\n","      <th>min-max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>4.455921e-07</td>\n","      <td>0.000100</td>\n","      <td>0.000001</td>\n","      <td>0.000003</td>\n","      <td>6.001473e-07</td>\n","      <td>1.154169e-08</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>5.349688e-07</td>\n","      <td>0.000100</td>\n","      <td>0.000002</td>\n","      <td>0.000003</td>\n","      <td>5.695449e-07</td>\n","      <td>1.154169e-08</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>5.798362e-07</td>\n","      <td>0.000100</td>\n","      <td>0.000002</td>\n","      <td>0.000004</td>\n","      <td>5.541823e-07</td>\n","      <td>1.154169e-08</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>9.016897e-07</td>\n","      <td>0.000099</td>\n","      <td>0.000003</td>\n","      <td>0.000005</td>\n","      <td>4.439801e-07</td>\n","      <td>1.154168e-08</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.0</td>\n","      <td>5.682474e-07</td>\n","      <td>0.000099</td>\n","      <td>0.000002</td>\n","      <td>0.000004</td>\n","      <td>5.581503e-07</td>\n","      <td>1.154169e-08</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount        binary  ...  standardization       min-max\n","0        1.0  4.455921e-07  ...     6.001473e-07  1.154169e-08\n","1        1.0  5.349688e-07  ...     5.695449e-07  1.154169e-08\n","2        1.0  5.798362e-07  ...     5.541823e-07  1.154169e-08\n","3        1.0  9.016897e-07  ...     4.439801e-07  1.154168e-08\n","4        1.0  5.682474e-07  ...     5.581503e-07  1.154169e-08\n","\n","[5 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N_S96YIwHxW3","executionInfo":{"status":"ok","timestamp":1620721081158,"user_tz":-540,"elapsed":34933,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"7b07eca0-9e26-4b58-c320-1490ce7798a0"},"source":["sum = 0\n","for item in normalized_l2.values[0]:\n","    sum += item ** 2\n","print('L2 norm = ', sum)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["L2 norm =  0.9999999999999999\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yYCu0qBoHxW3"},"source":["### 手法8：正規分布への写像(Mapping to a Gaussian distribution)\n","- [Mapping to a Gaussian distribution](https://scikit-learn.org/stable/modules/preprocessing.html#mapping-to-a-gaussian-distribution)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201},"id":"mJ6EagaLHxW4","executionInfo":{"status":"ok","timestamp":1620721081158,"user_tz":-540,"elapsed":34920,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"4ef458b2-b44e-4ced-dbe4-413e3c969812"},"source":["pt = preprocessing.PowerTransformer(method='box-cox', standardize=False)\n","orig = df['viewCount'].values.reshape(-1,1)\n","new_column = pt.fit_transform(orig)\n","temp['box-cox'] = new_column\n","temp.head()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>viewCount</th>\n","      <th>binary</th>\n","      <th>discret_floor</th>\n","      <th>discret_quantile</th>\n","      <th>log10</th>\n","      <th>standardization</th>\n","      <th>min-max</th>\n","      <th>box-cox</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2244205.0</td>\n","      <td>1</td>\n","      <td>224.0</td>\n","      <td>3</td>\n","      <td>6.351063</td>\n","      <td>1.346854</td>\n","      <td>0.025902</td>\n","      <td>15.286494</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1869268.0</td>\n","      <td>1</td>\n","      <td>186.0</td>\n","      <td>3</td>\n","      <td>6.271672</td>\n","      <td>1.064632</td>\n","      <td>0.021575</td>\n","      <td>15.086987</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1724625.0</td>\n","      <td>1</td>\n","      <td>172.0</td>\n","      <td>3</td>\n","      <td>6.236695</td>\n","      <td>0.955757</td>\n","      <td>0.019905</td>\n","      <td>14.999160</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1109029.0</td>\n","      <td>1</td>\n","      <td>110.0</td>\n","      <td>3</td>\n","      <td>6.044943</td>\n","      <td>0.492387</td>\n","      <td>0.012800</td>\n","      <td>14.518429</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1759797.0</td>\n","      <td>1</td>\n","      <td>175.0</td>\n","      <td>3</td>\n","      <td>6.245463</td>\n","      <td>0.982231</td>\n","      <td>0.020311</td>\n","      <td>15.021172</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   viewCount  binary  discret_floor  ...  standardization   min-max    box-cox\n","0  2244205.0       1          224.0  ...         1.346854  0.025902  15.286494\n","1  1869268.0       1          186.0  ...         1.064632  0.021575  15.086987\n","2  1724625.0       1          172.0  ...         0.955757  0.019905  14.999160\n","3  1109029.0       1          110.0  ...         0.492387  0.012800  14.518429\n","4  1759797.0       1          175.0  ...         0.982231  0.020311  15.021172\n","\n","[5 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"Vo_BVRwkHxW5"},"source":["### デフォルトとBox-Cox写像との比較"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"7XOJpSFjHxW6","executionInfo":{"status":"ok","timestamp":1620721082647,"user_tz":-540,"elapsed":36397,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"6c834834-c8e0-4b2a-b318-0ceea8186be2"},"source":["fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n","fig.subplots_adjust(hspace=0.4)\n","\n","# default values\n","temp['viewCount'].hist(ax=ax1, bins=100, log=True)\n","ax1.tick_params(labelsize=fontsize)\n","ax1.set_title('default', fontsize=fontsize)\n","ax1.set_xlabel('viewCounts', fontsize=fontsize)\n","ax1.set_ylabel('Freqency (log)', fontsize=fontsize)\n","\n","# box-cox\n","temp['box-cox'].hist(ax=ax2, bins=100)\n","ax2.tick_params(labelsize=fontsize)\n","ax2.set_title('box-cox', fontsize=fontsize)\n","ax2.set_xlabel('viewCounts', fontsize=fontsize)\n","ax2.set_ylabel('Freqency', fontsize=fontsize)"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Freqency')"]},"metadata":{"tags":[]},"execution_count":25},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":377},"id":"rY_NZkJCfUyE","executionInfo":{"status":"ok","timestamp":1620721083328,"user_tz":-540,"elapsed":37064,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}},"outputId":"96d5a529-4764-43a2-81eb-568f85de580e"},"source":["# 似ている分布、log-scaledとの比較\n","\n","fig, ax = plt.subplots(figsize=(10,5))\n","\n","# log-scaled\n","temp['log10'].hist(ax=ax, bins=100)\n","ax.tick_params(labelsize=fontsize)\n","ax.set_title('log10', fontsize=fontsize)\n","ax.set_xlabel('viewCounts (log-scaled)', fontsize=fontsize)\n","ax.set_ylabel('Freqency', fontsize=fontsize)\n"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Freqency')"]},"metadata":{"tags":[]},"execution_count":26},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAn8AAAFZCAYAAADpU0lNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZglZXn///cHZVNEwGVQVAYhMcJvXEcj6hcGXFhFjYILqEAUQ9xFI0iiqBiJChpFFBIjKhhU1CiugDCjUYgiijAaDMqoKMoywzKAwMD9+6Oq5fShu6d7prvPma7367rOdfpUPVV119PL3PNslapCkiRJ3bDeoAOQJEnS7DH5kyRJ6hCTP0mSpA4x+ZMkSeoQkz9JkqQOMfmTJEnqEJM/SZqkJEclmbH1sZK8LMkXkvw6SSU5eYKyT03y/SS3JPlDkuOSbDxTsUmaO0z+JGl4HABsC5wF3DBeoSSPastcBewN/CNwEHDyzIcoaV13z0EHIEn6s92q6k6AJLtPUO4dwBXAvlV1e1v+NuCTSf6lqi6c+VAlrats+ZOkNZRk0yTHJ/l9kluTXJrkDUnSV+5xSb7bdtH+Nslbk7yjvwt5JPFbzTXXB3YHPjeS+LU+B9wGPHsabk3SHGbLnyStgSTrAV8DHge8DbgY2As4DngA8Na23P2BbwO/B15Gk6C9AZi/hpfeFtgIuKR3Y1X9Kckvge3X8LySOsLkT5LWzJ7AU4GDqurkdtuZSe4NHJbkuKq6BngjcC+aLt0rAJJ8C1i2htfdon1fMca+5T37JWlMdvtK0prZCbgT+Ezf9lOADYAd289PAs4fSfwAquoWmlZDSZp1Jn+StGa2AJZX1W192//Qsx/gQTSzcvv9cQ2vO9Lit/l4Ma3heSV1hMmfJK2Z5cAWSTbo275lz36AK4EHjnH8vDW87i+BW4Edejcm2Qh4OPCzNTyvpI4w+ZOkNbOE5m/ovn3b96eZ1HFe+/l8YMckDxkp0C7GvNeaXLRtafwmsF+S3nHbzwc2BL6yJueV1B1O+JCkNfMN4L+BjyV5ALCUZhLIy4H3tJM9oJn9eyjwrSTvoGm1e2P7PmqplyTbc9ds3Y2BrZM8v/28pKqubr8+iiap/FySj9DMHH4fcHpV/Wia71PSHJOqGXtSkSTNKUmOAt5eVWk/bwr8M/A84H40M3g/Cnywev64Jnkc8CFgIXAt8DHg/sBLq2rz/vOPc/ldqmpxT9mdgH8BHgtcD/wn8Naqunnt71TSXGbyJ0mzLMk9gAuBa6rqaYOOR1K32O0rSTMsybuAy4Bf07QQvhx4FE03sSTNKpM/SZp5RfMUkAe3X/8UeE5VfWOgUUnqJLt9JUmSOsSlXiRJkjrE5E+SJKlDHPM3Sfe///1r/vz5gw5jVt10003c+973HnQYQ8P6GM36GM36GM36GM36GM36GG0m6uNHP/rRNVX1gLH2mfxN0vz587ngggsGHcasWrx4MYsWLRp0GEPD+hjN+hjN+hjN+hjN+hjN+hhtJuojya/H22e3ryRJUoeY/EmSJHWIyZ8kSVKHmPxJkiR1iMmfJElSh5j8SZIkdYjJnyRJUoeY/EmSJHWIyZ8kSVKHmPxJkiR1iMmfJElSh/hsX0nSjJl/+Nfutm3ZMXsNIBJJI2z5kyRJ6hCTP0mSpA4x+ZMkSeoQx/xJkmbVZMcBOl5Qmhm2/EmSJHWILX+SpIEbq5VP0syw5U+SJKlDTP4kSZI6xORPkiSpQ0z+JEmSOsTkT5IkqUNM/iRJkjrE5E+SJKlDXOdPkjQtetfqO2zBKg507T5pKNnyJ0mS1CG2/EmS1hk+71daeyZ/kqQp8VFs0rrNbl9JkqQOMfmTJEnqEJM/SZKkDjH5kyRJ6hCTP0mSpA4x+ZMkSeoQkz9JkqQOmdXkL8nzk3whya+T3JLk0iTvSXKfnjLzk9Q4r836zrdRkvclubI933lJdhrjuuslOSLJsiR/SnJRkufNxj1LkiQNk9lu+XsTcAfwVmB34KPAocBZSfpjeQ+wY9/rxr4yHwdeAbwN2Bu4EvhWksf0lXsXcBRwPLAHcD7w+SR7TstdSZIkrSNm+wkfz6qqq3s+L0myHPgksAg4p2ffr6rq/PFOlOTRwIuBg6vqE+22JcBS4J3APu22B9IkncdU1fvbw89Nsh1wDPD16bgxSZqLfJqHNPfMastfX+I34oft+1ZTPN0+wO3AZ3vOvwo4DdgtyYbt5t2ADYBT+o4/BViQZJspXleSJGmdNQzP9t25ff953/b3JPkYcBOwBDiyqi7u2b8DcHlV3dx33FKaZG+79usdgFuBy8YoB7A9cPla3YEkaWDGap1cdsxeA4hEWjcMNPlLshVNF+3ZVXVBu/lW4ETgTOBq4K9oxgh+P8kTq2okSdwCWDHGaZf37B95v66qajXlJEmS5rzcPSeapQsnmwCLgQcDT6yqKyYo+1CalrqvVNUB7bYzgU2r6kl9ZZ8OnAXsVFXfTXISsE9VbdlXbjvg/4CXVtWnx7nuIcAhAPPmzXv8aaedtkb3uq5auXIlm2yyyaDDGBrWx2jWx2hztT4u/t31a3TcvI3hj7dMczBTsGCr+w7u4mOYqz8fa8r6GG0m6mOXXXb5UVUtHGvfQFr+kmwMnAE8HNh5osQPoKp+m+S/gSf0bF4BbD1G8ZGWvOU95TZLkr7Wv/5yY133JOAkgIULF9aiRYsmCnPOWbx4MV2754lYH6NZH6PN1fo4cA0nfBy2YBXHXjy4zqVl+y8a2LXHMld/PtaU9THabNfHrC/ynGR94HRgIbBn3zi+1elN3pYC2yS5V1+Z7YHbuGuM31JgQ2DbMcoB/GwK15ckSVqnzfYiz+sBpwK7As+ZaCmXvuMeBjwV+EHP5jOA9YF9e8rdE3gBcGZV3dpu/ibNrOD9+057AHBJVTnZQ5IkdcZst8l/hCZZezdwU5Le8XpXVNUVSY6lSUrPo5nw8QjgCODO9jgAqurHST4LfLBtTbycZsHobehJ9KrqqiTHAUckuRG4kCZB3JV2LUBJkqSumO3kb4/2/cj21esdNE/hWEqTxB0IbAJcS7P48zuq6tK+Yw6iSQiPBjYDLgJ2r6oL+8odCawEXgdsCVwK7FdVX13rO5IkSVqHzGryV1XzJ1HmP4D/mOT5bgHe2L4mKncHTYJ49GTOK0lat7n2nzS+WZ/wIUmSpMEx+ZMkSeoQkz9JkqQOMfmTJEnqEJM/SZKkDhncs3ckSUNlrBmykuYeW/4kSZI6xORPkiSpQ0z+JEmSOsQxf5LUQY7vk7rLlj9JkqQOMfmTJEnqEJM/SZKkDjH5kyRJ6hCTP0mSpA4x+ZMkSeoQkz9JkqQOMfmTJEnqEBd5lqQ5zgWdJfWy5U+SJKlDTP4kSZI6xG5fSVInjNX9veyYvQYQiTRYtvxJkiR1iMmfJElSh5j8SZIkdYjJnyRJUoeY/EmSJHWIyZ8kSVKHmPxJkiR1iMmfJElSh5j8SZIkdYjJnyRJUoeY/EmSJHXIrD7bN8nzgRcBC4EHAr8Bvgj8c1Xd2FNuc+B9wHOAjYHzgDdU1cV959sIeBdwALAZ8BPgLVX1nb5y6wFvAV4JbAlcCryzqr4wA7cpSQMz1vNrJanXbLf8vQm4A3grsDvwUeBQ4Kw2QSNJgDPa/a8BngesD5yb5CF95/s48ArgbcDewJXAt5I8pq/cu4CjgOOBPYDzgc8n2XOa70+SJGmozWrLH/Csqrq65/OSJMuBTwKLgHOAfYCnALtW1bkASc4DLgf+AXhtu+3RwIuBg6vqE+22JcBS4J3teUjyQJqk85iqen973XOTbAccA3x9xu5WkiRpyMxqy19f4jfih+37Vu37PsDvRxK/9rjraVoDn91z3D7A7cBne8qtAk4DdkuyYbt5N2AD4JS+654CLEiyzZrdjSRJ0rpnGCZ87Ny+/7x93wG4ZIxyS4GHJdmkp9zlVXXzGOU2ALbrKXcrcNkY5QC2X8O4JUmS1jkDTf6SbEXTRXt2VV3Qbt4CWDFG8eXt++aTLLdFz/t1VVWrKSdJkjTnzfaYvz9rW/C+DKwCDhpUHBNJcghwCMC8efNYvHjxYAOaZStXruzcPU/E+hjN+hhtWOrjsAWrBh0CAPM2Hp5YJjJb37Nh+fkYFtbHaLNdHwNJ/pJsTDOG7+HAzlV1Rc/uFdzVutdri579I+9bT1BueU+5zZKkr/Wvv9zdVNVJwEkACxcurEWLFo1XdE5avHgxXbvniVgfo1kfow1LfRw4JEu9HLZgFcdePLD2hUlbtv+iWbnOsPx8DAvrY7TZro9Z7/ZNsj5wOs1af3v2r91HMxZvhzEO3R74TVWt7Cm3TZJ7jVHuNu4a47cU2BDYdoxyAD+b8k1IkiSto2Y1+WvX8jsV2BV4TlWdP0axrwBbJdm557hNgWe1+0acQbP+37495e4JvAA4s6pubTd/k2ZW8P591zkAuKSqLl+rm5IkSVqHzHab/EdokrV3AzcleVLPviva7t+v0DzR45Qkb6bptj0CCPDekcJV9eMknwU+2LYmXk6zYPQ29CR6VXVVkuOAI5LcCFxIkyDuSrsWoCRJUlfMdvK3R/t+ZPvq9Q7gqKq6M8newPuBE4CNaJLBXarqt33HHESTSB5N83i3i4Ddq+rCvnJHAiuB13HX4932q6qvTstdSZIkrSNmNfmrqvmTLLccOLh9TVTuFuCN7WuicnfQJIhHTypQSZKkOWoYFnmWJEnSLDH5kyRJ6hCTP0mSpA4x+ZMkSeqQ4V9+XZI0pvlD8jQPSesWkz9JUmeNlUAvO2avAUQizZ4pdfsmeeZMBSJJkqSZN9Uxf99MclmSNye5/4xEJEmSpBkz1eRvV+CHwLuAK5J8pvcZvJIkSRpuU0r+qmpxVb0IeAjwT8BC4NwkP0/yuiSbz0SQkiRJmh5rtNRLVV1TVe+rqr8EngFcAxxH0xp4cpIF0xmkJEmSpsdarfOXZE/gtcCTgKuATwM7AxcmOXTtw5MkSdJ0mnLyl2TLJEcmuRz4KrAZcADw0Kr6O2A74ETgbdMaqSRJktbalNb5S/IFYG/gT8ApwAlVtbS3TFXdkeQzwN9PW5SSJEmaFlNd5PkvgNcDn66qlROUuxjYZY2jkiRJ0oyYUvJXVY+aZLkbgSVrFJEkSZJmzFSf8LF3klePs+9V7QQQSZIkDampTvj4J+De4+zbuN0vSZKkITXV5O+vgAvH2fcT4JFrF44kSZJm0lQnfKwHbDLOvvsA669dOJKkscw//GuDDkHSHDHV5O8iYH/gS2Ps2x/46VpHJEnSAI2VaC87Zq8BRCLNjKkmf8cCX0jyeeDfgCuArYBDgOcC+05veJIkSZpOU13q5UtJXge8G/ibdnOAlcBrq+qL0xyfJEmSptFUW/6oqg8nORl4MnA/4Brg+6tZ9FmSJElDYMrJH/x5EedvTXMskiRJmmFTTv6SrAc8EXgYsFH//qr61DTEJUmSpBkwpeQvyfbAfwHb0oz161eAyZ8kSdKQmmrL3wntMfsBFwO3TntEkiRJmjFTTf4eBxzorF5JkqR101Qf73YNcNtMBCJJkqSZN9Xk7wPAq5LcYyaCkSRJ0syaarfvA4BHAD9LchawvG9/VdXbpyUySeoon+MraSZNNfn7x56v/2KM/QVMmPwleQjwFmAh8GhgY2CbqlrWV67GOcVjq+onPeXWa8/3SmBL4FLgnVX1hTGu/QrgMGAbYBnwgar62ETxSpIkzSVT6vatqvVW85pMd/B2NLOFVwDfXU3Zk4Ed+16/6CvzLuAo4HhgD+B84PNJ9uwt1CZ+JwJfAHYHPg+ckOTQScQsSZI0J6zREz7W0neqah5AkpcDz5yg7O+q6vzxdiZ5IPAm4Jiqen+7+dwk2wHHAF9vy92T5nnEn66qI3vKPRh4V5J/r6rb1+quJEmS1gFTnfBBGvskeX+STyTZut2+c5tMTaiq7lyTQMexG7ABcErf9lOABUm2aT/vSDNesb/cp2meT/zUaYxJkiRpaE0p+UuyOfB9mqd8vAJ4KU3yRPv58GmNDg5NcmuSm5Ock+T/9e3fgWah6cv6ti9t37fvKQdwyWrKSZIkzWlTbfl7H/BQ4Ck0SV/vI97OBp42TXFB00r398DTgUPa652TZFFPmS2A66qqf3LI8p79ve8rVlNOkiRpTpvqmL9nA2+qqvPGWOvvNzSJ4bSoqpf0fPxuki/TtNwdzSx10yY5hCbxZN68eSxevHg2Ljs0Vq5c2bl7noj1MZr1Mdp01sdhC1ZNy3kGad7Gc+M+Rnz41C/fbduCre476eP9fRnN+hhttutjqsnfJsDvxtm3EaNbAqdVVd2Y5GvA3/ZsXgFsliR9rX8jLXnLe8oBbA5cOUG5/mueBJwEsHDhwlq0aNGa38A6aPHixXTtnidifYxmfYw2nfVx4BxY5++wBas49uJBzCmcPcv2XzTpsv6+jGZ9jDbb9THVbt9LGX927s7AxWsXzqT0JnlLgQ2BbfvKjIzh+1lPObhr7N945SRJkua0qSZ/JwCvT3Ik8LB222ZJDgJeDXxkOoPrlWRTYG/gBz2bvwncDuzfV/wA4JKqurz9fB7Nc4nHKrcc+N60ByxJkjSEptQmX1UnJXk48A7gne3ms4A7gfdW1amTOU+S57dfPr593yPJ1cDVVbUkyZtoHiN3LvB7YGua9fy2pCeBq6qrkhwHHJHkRuBC4AXArsA+PeVuT/JPNIs6/45mcsquwMHAa6rqtqnUgyRJ0rpqygMyqurwJB8FngE8ELgWOKuqfjWF03y+7/MJ7fsSYBFN9/Jz29d9gRtoWuf+tqp+0HfskcBK4HXc9Xi3/arqq31xf6x9ZNxhwJtpJqi8uqpOQJIkqSPWaDRuVf0a+Pc1vWhVTTgxpKrOAM6Y5LnuoJkBfPQkyp5I84g3SZKkTppS8pfkYasvdZeq+s3UwpEkSdJMmmrL3zJGz7Zdnf61ACVJkjRAU03+DqUZY3cD8DngjzTj7PajWQPw3TSPW5MkSdIQmmry90iaGbXP7V1UOck7aZ73+8iqesM0xidJc9r8ObCgs6R1y1TX+XsRcGL/s3Tbzx8DXjxdgUmSJGn6TTX52wR4wDj7Hgjce+3CkSRJ0kyaavK3GPjnJE/o3ZjkiTTj/RZPT1iSJEmaCVNN/l5NM6Hj/CTLkvxPkmU0j0/7U7tfkiRJQ2qqj3e7PMlfAQcCTwIeBFxCk/x9sqpun/YIJUmSNG3W5PFutwP/1r4kSZK0Dlmjx7sleRSwE3A/mtm/f0iyHfDHqrpxOgOUJEnS9Jnq4902BE4B/gYIzdM+zgD+ALwX+AVw+DTHKEmSpGky1Qkf7waeDrwEmEeTAI74BrDbNMUlSZKkGTDVbt8XAf9YVZ9J0v/c3suB+dMSlSRJkmbEVFv+7gf8fIJzbbh24UiSJGkmTbXl73JgR+CcMfY9Ebh0rSOSJGkdMNZzmZcds9cAIpGmZqotf58CDk+yP7B+u62S7AK8AfiP6QxOkiRJ02uqyd97ga8BnwZWtNv+Gzgb+GZVfXgaY5MkSdI0m+oTPu4AXpjkIzQzex8IXEuT+C2Zgfgkac4Yq5tQkmbbpJO/JBsA5wOHV9WZwHdnLCpJkiTNiEl3+1bVbcA2wKqZC0eSJEkzaapj/s4CnjkTgUiSJGnmTXWplw8DpyS5J/BfwJU0j3j7s6r61TTFJkmSpGk21eRvZFLHG2mWdhlL/5M/JEmSNCRWm/wl2RX4QVWtBA6mr6VPkiRJ647JtPydRfNUjx9U1clJ1gMWA39bVf83k8FJkiRpek0m+csYn58K3Gf6w5Ekad013lqOJ+9+71mORBrfVGf7SpIkaR1m8idJktQhk53tu1WSh7df36Nn23X9BV3qRZIkaXhNNvk7fYxt/zVOWZd6kSRJGlKTSf4OmvEoJGmOGW/gvyQN2mqTv6r65GwEIkmSpJk36xM+kjwkyYeTnJfk5iSVZP4Y5TZK8r4kVya5pS2/0xjl1ktyRJJlSf6U5KIkzxvn2q9I8r9Jbk1yaZK/m/47lCRJGl6DmO27HbAfsAL47gTlPg68AngbsDfNc4S/leQxfeXeBRwFHA/sAZwPfD7Jnr2FkrwCOBH4ArA78HnghCSHruX9SJIkrTOm+mzf6fCdqpoHkOTlwDP7CyR5NPBi4OCq+kS7bQmwFHgnsE+77YHAm4Bjqur97eHnJtkOOAb4elvunsC7gU9X1ZE95R4MvCvJv1fV7TNyt5IkSUNk1lv+qurOSRTbB7gd+GzPcauA04DdkmzYbt4N2AA4pe/4U4AFSbZpP+8IPGCMcp8G7kfzxBJJkqQ5b1gXed4BuLyqbu7bvpQm2duup9ytwGVjlAPYvqccwCWrKSdJkjSnDaLbdzK2oBkT2G95z/6R9+uqqiZRjjHO2V9OkqRpd/HvrufAvuV/lh2z14CiUdcNa/I3FJIcAhwCMG/ePBYvXjzYgGbZypUrO3fPE7E+RrM+Ruuvj8MWrBpcMENg3sbWQa+x6qPLvz/+/RhttutjWJO/FcDWY2wfaaFb3lNusyTpa/0bqxzA5jSzhscrN0pVnQScBLBw4cJatGjRZOOfExYvXkzX7nki1sdo1sdo/fXR38rTNYctWMWxFw/rPzGzb6z6WLb/osEEMwT8+zHabNfHsI75Wwpsk+Refdu3B27jrjF+S4ENgW3HKAfws55ycNfYv/HKSZIkzWnDmvydAawP7DuyoV2u5QXAmVV1a7v5mzSzgvfvO/4A4JKqurz9fB5wzTjllgPfm9boJUmShtRA2uSTPL/98vHt+x5JrgaurqolVfXjJJ8FPphkfeBy4FBgG3oSuKq6KslxwBFJbgQupEkQd6VdC7Atd3uSf6JZ1Pl3wNltmYOB11TVbTN5v5IkScNiUAMyPt/3+YT2fQmwqP36IJqFmY8GNgMuAnavqgv7jj0SWAm8DtgSuBTYr6q+2luoqj6WpIDDgDcDvwFeXVUnIElrYf7hX+OwBas6P85P0rphIMlfVWUSZW4B3ti+Jip3B02CePQkznkizSPeJEmSOmlYx/xJkiRpBpj8SZIkdYjJnyRJUoe4AqckSQMwf4wJQj7yTbPBlj9JkqQOMfmTJEnqEJM/SZKkDjH5kyRJ6hCTP0mSpA5xtq8kSUPCGcCaDbb8SZIkdYjJnyRJUoeY/EmSJHWIY/4kaQrGGpMlSesSW/4kSZI6xORPkiSpQ+z2lSRpiLn8i6abLX+SJEkdYvInSZLUISZ/kiRJHWLyJ0mS1CEmf5IkSR1i8idJktQhJn+SJEkd4jp/kjQGH+OmYebaf1obtvxJkiR1iMmfJElSh5j8SZIkdYjJnyRJUoeY/EmSJHWIyZ8kSVKHmPxJkiR1yNAmf0kWJakxXtf1lds8yb8nuSbJTUnOTrJgjPNtlOR9Sa5MckuS85LsNHt3JEmSNHjrwiLPrwV+2PN51cgXSQKcAcwHXgOsAI4Azk3ymKq6oue4jwN7AW8GfgW8CvhWkh2r6iczegeSJM0wF37WZK0Lyd/Pq+r8cfbtAzwF2LWqzgVIch5wOfAPNIkjSR4NvBg4uKo+0W5bAiwF3tmeR5Ikac5bF5K/iewD/H4k8QOoquuTnAE8mzb5a8vdDny2p9yqJKcBhyfZsKpuncW4JQ0RH+UmqUuGdsxfj1OT3JHk2iSfSfKwnn07AJeMccxS4GFJNukpd3lV3TxGuQ2A7aY9akmSpCE0zC1/1wPHAkuAG4DHAm8Fzkvy2Kq6CtgCWDbGscvb982BlW25FROU22L6wpYkSRpeqapBxzBpSR4H/AA4pqr+MckvgAur6oV95V4O/BvwsKr6bZIzgU2r6kl95Z4OnAXsVFXfHeN6hwCHAMybN+/xp5122ozc17BauXIlm2yyyeoLdoT1Mdpcqo+Lf3f9Wp9j3sbwx1umIZg5wvoYbVD1sWCr+87+RSdhLv39mA4zUR+77LLLj6pq4Vj7hrnl726q6sI24XtCu2kFTetevy169o+8bz1BueVj7KOqTgJOAli4cGEtWrRoDaJedy1evJiu3fNErI/R5lJ9HDgNY/4OW7CKYy9ep/6kzijrY7RB1cey/RfN+jUnYy79/ZgOs10f68KYv7GMNFcupRnP12974DdVtbKn3DZJ7jVGuduAy2YkSkmSpCGzTv23LMlC4BHA6e2mrwAHJdm5qpa0ZTYFngV8pufQM4B3APsCn2zL3RN4AXCmM30lSXORa/9pLEOb/CU5lWa9vguB62gmfBwB/A74UFvsK8B5wClJ3sxdizwHeO/Iuarqx0k+C3wwyfrteQ8FtgH2n5UbkjQUXNZFUtcNbfJHs4TLi2ie3HEv4A/AF4G3V9U1AFV1Z5K9gfcDJwAb0SSDu1TVb/vOdxDwbuBoYDPgImD3qrpwFu5FkiRpKAxt8ldV7wHeM4lyy4GD29dE5W4B3ti+JEmSOmldnfAhSZKkNWDyJ0mS1CEmf5IkSR1i8idJktQhQzvhQ5IkTT/X/pPJn6Q5yzX9JOnu7PaVJEnqEJM/SZKkDjH5kyRJ6hCTP0mSpA5xwockSR3nDOBuseVPkiSpQ2z5kzQnuKyLJE2OLX+SJEkdYsufpHWOrXzSzHMc4Nxly58kSVKHmPxJkiR1iMmfJElShzjmT9JQc3yfJE0vW/4kSZI6xORPkiSpQ+z2lSRJk+LyL3ODyZ+koeH4PkmaeXb7SpIkdYjJnyRJUoeY/EmSJHWIY/4kSdIacxLIusfkT9JAOLlDkgbD5E/SjDPRk6Th4Zg/SZKkDjH5kyRJ6hC7fSVJ0rQab6iHE0GGg8mfpGnl+D5J4xn5+3DYglUc2H5tQjj7OtXtm+ShSU5Pcn2SG5J8McnDBh2XJEnSbOlMy1+SewHnALcCLwMKOBo4N8mjquqmQcYnSVIXuU7g7OtM8ge8Ang48IiqugwgyU+B/wNeCRw3wNikdZJdvJK07ulS8rcPcP5I4gdQVZcn+R7wbEz+pAn1J3qHLVhFt/6ESJottgbOrC795d4B+PIY25cC+85yLNJQsOVO0rrChHD6dCn52wJYMcb25cDmsxyLtFomZpI0sbX5O9nlxDFVNegYZkWS24Djqurwvu1HA4dX1QCuZS8AABC7SURBVN0S4SSHAIe0Hx8BXDrjgQ6X+wPXDDqIIWJ9jGZ9jGZ9jGZ9jGZ9jGZ9jDYT9bF1VT1grB1davlbwdgtfOO1CFJVJwEnzWRQwyzJBVW1cNBxDAvrYzTrYzTrYzTrYzTrYzTrY7TZro8urfO3lGbcX7/tgZ/NciySJEkD0aXk7yvAk5I8fGRDkvnAU9p9kiRJc16Xkr9/A5YBX07y7CT70Mz+/S1w4iADG2Kd7fIeh/UxmvUxmvUxmvUxmvUxmvUx2qzWR2cmfAC0j3L7APAMIMC3gddX1bJBxiVJkjRbOpX8SZIkdV2Xun21GkkekuTDSc5LcnOSasdFdlKS5yf5QpJfJ7klyaVJ3pPkPoOObRCS7JbknCR/SHJrkiuSfC7J9oOObRgk+Wb7O3P0oGMZhCSL2vvvf1036NgGKcmeSb6TZGWSG5JckGTXQcc125IsHufno5J8c9DxDUKSpyQ5M8lVSW5McmGSg2fj2l1a6kWrtx2wH/Aj4LvAMwcbzsC9CfgN8FbgCuCxwFHALkmeXFV3DjC2QdiC5mfjBOBq4GHA4cD5SRZU1a8HGdwgJXkR8OhBxzEkXgv8sOfzqkEFMmhJXgkc377eRdPg8hjgXoOMa0D+Hti0b9uONI9W7dykyySPAs4GzgdeAdwMPB/4eJINq+qjM3p9u301Isl6IwlNkpfTTJLZpqtjIpM8oKqu7tv2UuCTwNOq6pzBRDY8kjwC+F/gTVV17KDjGYQkmwM/B94AfAZ4d1X942Cjmn1JFgHnAs+oqrMHHM7Atb0mPweOqKoPDjaa4ZTk48ABwIOqavmg45lNSf6ZpoFhi6pa2bP9PICq2nEmr2+3r/6sgy1ZE+pP/FojLRpbzWYsQ+za9r2zrTvAvwCXVNV/DjoQDZWDgTuBjw06kGGU5F7AvsAZXUv8WhsAtwO39G2/nlnIzUz+pKnZuX3/+UCjGKAk90iyQZK/oFkm6Q9AJxOfJE8FXgq8atCxDJFTk9yR5Nokn2lXWeiip9K0ir8wyS+TrEpyWRJ/VhrPBe5D05PSRSe37x9K8uAkmyV5BfA0mlVJZpRj/qRJSrIV8E7g7Kq6YNDxDND/AI9vv74M2LWqrhpgPAORZAOa5Pf9VdW1536P5XrgWGAJcAPNGNm3AucleWwHf0Ye3L7eR1MPv6Rp6To+yT2r6l8HGdwQeClwFfCNQQcyCFV1STtU4ks04yGhaQn8u6o6baavb/InTUKSTWgWBV8FHDTgcAbtJTQDtx9OM2blrCRP7eDY0H8ANgbePehAhkFV/Rj4cc+mJUm+A/yAZhJI18ZBrkfTsnVgVX2x3XZOOxbwiCQfqo4Ouk/yYODpwL9WVSeHjLQ9J1+gefTs39F0/z4b+FiSP1XVqTN5fZM/aTWSbAycQZPs7FxVVww4pIGqqpEu7/9J8g2aJ+ccTvMHrBParswjgZcDGybZsGf3hkk2A26sqjsGEuCQqKoLk/wCeMKgYxmAa4G/AM7q234msDvwIOD3sx3UkDiAJjnuapcvwD/TtPTtXVW3t9u+neR+wL8m+c+ZHIfvmD9pAknWB04HFgJ7VtXFAw5pqFTVdTRdv9sNOpZZ9nBgI+AUYEXPC5rW0BXAgsGENpS62MK1dDX7uzzB7mXARVV10aADGaAFNHVwe9/2HwD3Ax44kxc3+ZPGkWQ94FRgV+A5VXX+gEMaOknmAX9FM56pS34C7DLGC5qEcBeapLjTkiwEHkHzD1rXfKl9361v++7AFVX1h1mOZyi0PxPb0+1WP2gmyj2mHTvc66+BPwEzOgPabl+NkuT57ZcjA/r3SHI1cHVVLRlQWIPyEZoB2u8GbkrypJ59V3St+zfJl4ALgZ/SDOj/S5q17VbRDPTvjLbFc3H/9iQAv66qu+2b65KcClxO8zNyHc2EjyOA3wEfGmBog/J1mnUPT0xyf+BXNH9Pnkm3xw2/lOZvxoyOaVsHHA98HjgjyQk0Y/72AV4EfKCqbpvJi7vIs0ZJMt4PxJKqWjSbsQxakmXA1uPsfkdVHTV70QxekrfQPAFmW5o1qn5LkwC9p4OTPcbU/v50dZHnI2j+4dqa5gkWf6CZyfn2qrpykLENSpJNgffQPLlhc5qlX46pqs8MNLABaYfR/B44v6qeNeh4Bi3JHsBbgB1ohpH8EjgJOHGmxwub/EmSJHWIY/4kSZI6xORPkiSpQ0z+JEmSOsTkT5IkqUNM/iRJkjrE5E+SJKlDTP4kTVqSSnLULF9zxySfS/L7JLcluTbJWUleluQesxnLGLHNT3JUkodP83k/lOSrPZ8XtXW/aDqvM0zauqwkB07jOUf9vCZ5fZKL26f3SJ3lL4CkqdgR+PfZuliS1wPfA7agWQz16cDBwC+AjwJ7z1Ys45gPvJ3mWb/TIsm2wN8BR03XOfVnJwIPoHm2rNRZPt5N0qTN5vONk+wEHAccX1Wv7dv95STHAfeerXhm0etpHvh+waADmWuq6pYknwLeBHxi0PFIg2LLn9RxSfZtu8ceNca+rye5qOfz3bp9kzw6yVeSrEhyS5LvJfl/Pfuf1x73kJ5tx7bbXt6z7Rntth3aTW+hebj5P4wVd1X9sqp+2nP8E5OcnWRlkpuSfDvJE/tiXZxk8Rj3uSzJyT2fD2xjeVKSU5Pc0HY7fyjJRm2ZRTTPbgU4qy3/567ZJC9O8uM2nhva7sZXjnUvPdfdEDgAWO3jv9J4Q5JL2+7wK5Mc3z5SrLfcA5L8ZxvDiiSfSLLPZLuRk7wuyc/b7+2KJBckeW5fmee23/eRe/1Bkn169r86yXlJlie5Lsn5SfZa3bXbY3duv5c3tt/XbyX5//rK3CPJ0W0d3Nx+n3cY55SnAdsnefJkri/NRSZ/ks4ArqdJOv4syTyah9B/arwDkzwO+D5Nt+wrgOcB1wJnJ3l8W2wJUMCuPYfuSvMg8/5tf6yqpe1Yvl2AM6vqT6u7gTZxXULz/NQDaR4evymwJMmjV3f8BD5N87zNv6HpZn4VcES778L2M8BrabrEdwQuTPJU4JQ2pufQPNv134DNVnO9J7VlvjuJ2N5N0zJ6FvAs4L009/61vjFtXwT2aON+IXA78OFJnJ8k+wPHAv8J7AnsD5xO8/0eKfOa9hpX0XSn7gt8iaZLfMR8muEC+wIvAC4Avppk99Vcfy/g28BKmp/PFwP3Ab6b5KE9RY8C3gqcSlPfZwJfGee0PwFuBCa8tjSnVZUvX746/qJJTK4A1uvZ9npgFfCgnm0FHNXz+dvAz4ENerbdo932Xz3bLgI+0X69BXAnTVLx+54y5wOntV/Pa6/1nknGfzpwHbBZz7ZNaVoOv9izbTGweIzjlwEn93w+sL3+O/rKfRX4Rc/nRW25p/eVexOwfA2+D29p62aDvu0j11nUU4e39sbcbj+gLbdP+/mZ7ef9+sp9pfd8E8RzPHDhBPs3pUmkvri6e+s5Zj2aIUdnAl/u2T6/jenAnm2XAd8e45rXAB9sP29Okxx+bIy6HPXz2rPvuzT/sRj4754vX4N42fInCZrWva0Y3RL3Epp/eK8c64AkGwM7A58H7kxyzyT3BAKcDezUU/wcmpY8aBKZ64APAA9K8sgk9wEez13dqFO1E/DVqrpuZENV3UCT5Oy8hucE+Frf54uBh03iuB8Cmyc5JcneSVbX4jfiwcANVXXbaso9CdiApnWx12k0CfvOPeXuoGmJ63V674ck6418/9rXyCzqHwKPSfLhJE9Pcq++8zwZ2AQ4aaJgkzw+yVeT/LGN73bgGcAjJjjmL4BtgVN7YwNuBs7jrp+vBTRjPz/Xd4rTJgjpapq6ljrJ5E8SwH/TtH69BCDJI4HHMUGXL03r0z2Af6L5x7z39Wqa5Gfkb8y5wNZplkTZBVhSVVcAl7afd6JpDTqnLX8tTbfw1pOMfwtgrCT1DzQtQ2tqed/nW4ENV3dQVS2h6eJ8KE3idXU7HvFu4yr7bNReY3VGul1H3XNVraKpu5H9DwJWVNXtfcf/se/z2xj9/ft2u/1TwKHAXwPfApYn+WKS+e3++7XvV4wXaNs9++02ptfQJIxPAL5Jc7/jeWD7/nHu/vO1d8+1HzTOPfV/7nULsPEE+6U5zdm+kqiqSnIK8Pokh9IkgSu5e4tRr+touig/wjhJYlXd2X75HZoWqF3b18fa7ee0n38N/K6q/q89blU7MeMZSTasqtUlRMuBLcfYviWwoufzn2i6DfttMca2tVJVpwOnJ9mEprXzX4BvJnlIT730u5bVjwuEu5LSLYGlIxvblrH79ey/kiYJX78vAZzXd76TaLq0R9zY3kPRLI9yYpLNabqRjwU+S5MQXtOW3wq4ZJxYdwfuS9P1/OckcYxWxH7Xtu9H0LQk9xtpHR1JgOfRUxfc/R57bcFdsUudY8ufpBGfpunC+xuagf1frKqbxytcVTfRjJ16NM24sAv6Xz1lrwN+TDPhYHvuauE7h6aL8mncvcv3GJpE5r1jXT/JNj0taUuAPdvu45H996GZCLG457BfA3+ZZIOecjvRTCJYEyNJ6bitSFW1sqq+SpNEPYi7WqzG8r/ABumZGT2O82mSnxf2bX8BzX/qF/eUuwfw3L5y+/bF+Pu+792lY9zHiqr6LE336shs2+/T/CfhkAliHUny/px8JvlL4CkTHANNq/AyYIexfrbqrpnePwVuAvbrO76/bnpt055f6iRb/iQBUFW/SPI/NEnXVkzc5TvijTStet9K8nGaVpj703QZ36OqDu8pey7wZuCqqhppoVlMkwzdH/jXvni+k+SNwHFJtgdOBn5D0437NODlNLM/fwq8i6Yr8NtJ/oVmoP9baBKPd/ac9jSaROU/0iztsk17D9dP4l7H8guaMWwHJ1lOkwxe2t7nvPaefw88hGZG8E+q6uoJzved9v2JTNCVWlXLkxwLHJHkJuDrwCOBo2m68L/WljszyfeAk5Lcn2YCxfNpEnZoWm7HleQkmlbA82hm8/4lTavwme35b0xyBPDhJF+gmW17I/AY4E9V9WGaVrtVwKfamB8EvIPmezluA0TbGv0qmjUdN6BJOq+hqdcnA7+pquOq6rokHwCOTHJjG9sTgL8d5542a+/j/RPduzSnDXrGiS9fvobnRbN0SdE387dn/91mT9IkHafRJAe3tsd+Bdizr9we7fGn9W2/qN0+f5yYnkwzqeRKmtaj5TT/wB/A6NnJf02TaKykaQn6NvDEMc73SuD/aMZ9fZ9moskyxp7tu13fsUfR9ob2ne9XNAlO0XTx7kUzRu7Ktk5+SzN27cGT+B78D+3M6J5ti+ibnUszseYNNMnmbe21PgJs2nfsA9rvz400XfWfolmSpYBHryaWl9Ek6CPf28tpJur0X+P5bdy3ADe0X+/ds38/mlbNP9F0zb6QJplf1lNmPn2zfdvtO9J0Sa9oj1/W3s+OPWXuQZP4/qGNYTFNC/NYP6/7t+e536B/33z5GtQrVYUkaTikebbtv9IssTNut/taXuN44CBgi1r9eMo5Jck3gGuq6iWDjkUaFJM/SRoi7aSNi4GPV9Vad022yeR9aVrcNqCZgPEq4H01ult+zkvyGJpWyR2q6rJBxyMNimP+JGmIVDPT+SCacZPT4SaaBbu3pVmm5nKap2G8b5rOvy7ZkqZb2cRPnWbLnyRJUoe41IskSVKHmPxJkiR1iMmfJElSh5j8SZIkdYjJnyRJUoeY/EmSJHXI/w9Sw3bRiISzrwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 720x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"329eSZL8fuCD","executionInfo":{"status":"ok","timestamp":1620721083329,"user_tz":-540,"elapsed":37064,"user":{"displayName":"TOMA Naruaki","photoUrl":"","userId":"11747312442870110137"}}},"source":[""],"execution_count":26,"outputs":[]}]}