AIQuest_demand_RandomForest_GridSearch.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"AIQuest_demand_RandomForest_GridSearch.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyNQKFCK3JAPOPj+toX8SjFX"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"TPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QbTvN4p4nw3p","executionInfo":{"status":"ok","timestamp":1633533546686,"user_tz":-540,"elapsed":32670,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"86a261ad-5a40-4160-ef95-12c91863d076"},"source":["!pip install signate\n","\n","from googleapiclient.discovery import build\n","import io, os\n","from googleapiclient.http import MediaIoBaseDownload\n","from google.colab import auth\n","\n","\n","auth.authenticate_user()\n","\n","drive_service = build('drive', 'v3')\n","results = drive_service.files().list(\n","        q=\"name = 'signate.json'\", fields=\"files(id)\").execute()\n","signate_api_key = results.get('files', [])\n","\n","filename = \"/root/.signate/signate.json\"\n","os.makedirs(os.path.dirname(filename), exist_ok=True)\n","\n","request = drive_service.files().get_media(fileId=signate_api_key[0]['id'])\n","fh = io.FileIO(filename, 'wb')\n","downloader = MediaIoBaseDownload(fh, request)\n","done = False\n","while done is False:\n","    status, done = downloader.next_chunk()\n","    print(\"Download %d%%.\" % int(status.progress() * 100))\n","os.chmod(filename, 600)"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting signate\n","  Downloading signate-0.9.8-py3-none-any.whl (37 kB)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from signate) (1.15.0)\n","Collecting wget\n","  Downloading wget-3.2.zip (10 kB)\n","Collecting urllib3>=1.26.4\n","  Downloading urllib3-1.26.7-py2.py3-none-any.whl (138 kB)\n","\u001b[K     |████████████████████████████████| 138 kB 9.3 MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from signate) (2.8.2)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from signate) (2021.5.30)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from signate) (0.8.9)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from signate) (7.1.2)\n","Building wheels for collected packages: wget\n","  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9672 sha256=b399b644efe331856d82f0cd4648668b25135d87a870589f7235d214e859d86d\n","  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n","Successfully built wget\n","Installing collected packages: wget, urllib3, signate\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","requests 2.23.0 requires urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you have urllib3 1.26.7 which is incompatible.\n","datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n","Successfully installed signate-0.9.8 urllib3-1.26.7 wget-3.2\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (3.0.4) doesn't match a supported version!\n","  RequestsDependencyWarning)\n","WARNING:googleapiclient.discovery_cache:file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 33, in <module>\n","    from oauth2client.contrib.locked_file import LockedFile\n","ModuleNotFoundError: No module named 'oauth2client.contrib.locked_file'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 37, in <module>\n","    from oauth2client.locked_file import LockedFile\n","ModuleNotFoundError: No module named 'oauth2client.locked_file'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/googleapiclient/discovery_cache/__init__.py\", line 44, in autodetect\n","    from . import file_cache\n","  File \"/usr/local/lib/python3.7/dist-packages/googleapiclient/discovery_cache/file_cache.py\", line 41, in <module>\n","    \"file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth\"\n","ImportError: file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth\n","WARNING:google.auth._default:No project ID could be determined. Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable\n"]},{"output_type":"stream","name":"stdout","text":["Download 100%.\n"]}]},{"cell_type":"code","metadata":{"id":"0KR418EDn8O5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633533564021,"user_tz":-540,"elapsed":17354,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"e91fd7ea-60e1-4c26-b4d8-121706c72c62"},"source":["! signate list\n","! signate files --competition-id=492\n","! signate download --competition-id=492"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["  competitionId  title                                                                  closing     prize        submitters\n","---------------  ---------------------------------------------------------------------  ----------  ---------  ------------\n","              1  【練習問題】銀行の顧客ターゲティング                                   -                              5181\n","             24  【練習問題】お弁当の需要予測                                           -                              6356\n","             27  【練習問題】Jリーグの観客動員数予測                                    -                              1433\n","            100  【練習問題】手書き文字認識                                             -           Knowledge           175\n","            102  【練習問題】タイタニックの生存予測                                     -           Knowledge          1066\n","            103  【練習問題】音楽ラベリング                                             -           Knowledge            61\n","            104  【練習問題】スパムメール分類                                           -           Knowledge           131\n","            105  【練習問題】毒キノコの分類                                             -           Knowledge           247\n","            106  【練習問題】アワビの年齢予測                                           -           Knowledge           354\n","            107  【練習問題】国勢調査からの収入予測                                     -           Knowledge           526\n","            108  【練習問題】画像ラベリング（20種類）                                   -           Knowledge           221\n","            112  【練習問題】ワインの品種の予測                                         -           Knowledge           296\n","            113  【練習問題】山火事の消失面積予測                                       -           Knowledge           187\n","            114  【練習問題】レンタル自転車の利用者数予測                               -           Knowledge           371\n","            115  【練習問題】アヤメの分類                                               -           Knowledge           339\n","            116  【練習問題】活動センサーログからの動作予測                             -           Knowledge            44\n","            118  【練習問題】テニスの試合結果の予測                                     -           Knowledge           110\n","            121  【練習問題】自動車の走行距離予測                                       -           Knowledge          1330\n","            122  【練習問題】自動車の評価                                               -           Knowledge           304\n","            123  【練習問題】オゾンレベルの分類                                         -           Knowledge            39\n","            124  【練習問題】ボットの判別                                               -           Knowledge           288\n","            125  【練習問題】ガラスの分類                                               -           Knowledge           132\n","            126  【練習問題】林型の分類                                                 -           Knowledge            32\n","            127  【練習問題】ゲーム選手のリーグ分類                                     -           Knowledge            39\n","            128  【練習問題】ステンレス板の欠陥分類                                     -           Knowledge            47\n","            129  【練習問題】都市サイクルの燃料消費量予測                               -           Knowledge            76\n","            130  【練習問題】天秤のバランス分類                                         -           Knowledge            58\n","            132  【練習問題】ネット広告のクリック予測                                   -           Knowledge           141\n","            133  【練習問題】画像ラベリング（10種類）                                   -                               330\n","            135  【練習問題】ネット画像の分類                                           -                                71\n","            262  【SOTA】国立国会図書館の画像データレイアウト認識                       2100-12-31  -                    55\n","            263  【SOTA】産業技術総合研究所 衛星画像分析コンテスト                      2100-12-31  -                    32\n","            264  【SOTA】マイナビ × SIGNATE Student Cup 2019: 賃貸物件の家賃予測        2100-12-31  -                   321\n","            265  【練習問題】健診データによる肝疾患判定                                 -                               450\n","            266  【練習問題】民泊サービスの宿泊価格予測                                 -                               463\n","            267  【SOTA】海洋研究開発機構 熱帯低気圧（台風等）検出アルゴリズム作成      2100-12-31  -                    13\n","            268  【SOTA】オプト レコメンドエンジン作成                                  2100-12-31                       27\n","            269  【SOTA】アップル 引越し需要予測                                        2100-12-31  -                   270\n","            270  【SOTA】Weather Challenge：雲画像予測                                  2100-12-31  -                     5\n","            271  【SOTA】JR西日本 走行中の北陸新幹線車両台車部の着雪量予測              2100-12-31  -                    14\n","            288  【SOTA】Sansan 名刺の項目予測                                          2100-12-31  -                    22\n","            294  【練習問題】債務不履行リスクの低減                                     -                               140\n","            358  【練習問題】機械稼働音の異常検知                                       -                                52\n","            404  【練習問題】モノクロ顔画像の感情分類                                   -                                42\n","            406  【練習問題】鋳造製品の欠陥検出                                         -                               130\n","            409  【練習問題】株価の推移予測                                             -                               138\n","            489  【AIQuest2021】PBL_01 需要予測・在庫最適化（小売業）ビジネス導入課題①  2022-02-05                      521\n","            490  【AIQuest2021】PBL_01 需要予測・在庫最適化（小売業）ビジネス導入課題②  2022-02-05                      516\n","            491  【AIQuest2021】PBL_01 需要予測・在庫最適化（小売業）ビジネス導入課題③  2022-02-05                      514\n","            492  【AIQuest2021】PBL_01 需要予測・在庫最適化（小売業）AI課題             2021-10-07                      447\n","  fileId  name                                           title                                                   size  updated_at\n","--------  ---------------------------------------------  --------------------------------------------------  --------  -------------------\n","    2201  PBL01_h_exercise4_question.pdf                 PBL01_需要予測・在庫最適化_h.演習④設問                734746  2021-08-20 09:33:52\n","    2202  PBL01_i_exercise4_supplementary-documents.pdf  PBL01_需要予測・在庫最適化_i.演習④ガイドコンテンツ   1410304  2021-08-19 14:50:11\n","    2114  sales_history.csv                              販売実績データ                                      33293020  2021-08-19 14:50:11\n","    2115  item_categories.csv                            商品カテゴリーデータ                                  113142  2021-08-19 14:50:07\n","    2116  category_names.csv                             カテゴリ名称データ                                       879  2021-08-19 14:50:07\n","    2117  test.csv                                       評価用データ                                           31978  2021-08-19 14:50:07\n","    2118  sample_submission.csv                          応募用サンプルファイル                                 26430  2021-08-19 14:50:07\n","    2410  PBL01_sample_code.ipynb                        サンプルコード（9/18追加）                            107000  2021-09-18 11:24:06\n","    2411  requirements.txt                               サンプルコードについて（9/18追加）                        82  2021-09-18 11:24:30\n","requirements.txt\n","\n","category_names.csv\n","\n","sample_submission.csv\n","\n","test.csv\n","\n","PBL01_sample_code.ipynb\n","\n","item_categories.csv\n","\n","PBL01_h_exercise4_question.pdf\n","\n","PBL01_i_exercise4_supplementary-documents.pdf\n","\n","sales_history.csv\n","\n","\u001b[32m\n","Download completed.\u001b[0m\n"]}]},{"cell_type":"code","metadata":{"id":"8nCDxjlIoQJt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633533564023,"user_tz":-540,"elapsed":49,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"4146497f-7a76-4909-d575-3896010419a9"},"source":["# ディレクトリ内のデータの確認\n","%ls"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["adc.json                                       requirements.txt\n","category_names.csv                             sales_history.csv\n","item_categories.csv                            \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","PBL01_h_exercise4_question.pdf                 sample_submission.csv\n","PBL01_i_exercise4_supplementary-documents.pdf  test.csv\n","PBL01_sample_code.ipynb\n"]}]},{"cell_type":"code","metadata":{"id":"Xtt3uuaQmXId","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633533583554,"user_tz":-540,"elapsed":19561,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"01b89563-b799-4f2d-f678-c5f69bf0d445"},"source":["# Googleドライブのマウント\n","from google.colab import drive\n","drive.mount('/content/drive/')"],"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive/\n"]}]},{"cell_type":"markdown","metadata":{"id":"RnctI5aCzH37"},"source":["#baseline"]},{"cell_type":"code","metadata":{"id":"t1t8jDKrp4ah","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1633536022435,"user_tz":-540,"elapsed":141915,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"a3c3a286-2837-49b7-f690-270af1bf8690"},"source":["'''\n","初期設定\n","'''\n","\n","DATA_PATH = '/content/drive/My Drive/AIQuest2021/PBL_01_需要予測/Phase2/demand/processed_data/processed_train_test_df.csv'\n","SAMPLESUB_PATH = 'sample_submission.csv'\n","SUB_PATH = '/content/drive/My Drive/AIQuest2021/PBL_01_需要予測/Phase2/demand/submit/demand_RandomForest.csv'\n","\n","\n","# ライブラリのインポート\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.preprocessing import LabelEncoder\n","\n","# データの読み込み\n","df = pd.read_csv(DATA_PATH)\n","print(df.isnull().sum())\n","print(df.dtypes)\n","\n","# object型の変数の取得\n","categories = df.columns[df.dtypes == 'object']\n","print(categories)\n","\n","# label Encoding\n","for cat in categories:\n","    le = LabelEncoder() \n","    print(cat)\n","    \n","    df[cat].fillna('missing', inplace=True)\n","    le = le.fit(df[cat])\n","    df[cat] = le.transform(df[cat])\n","    # LabelEncoderは数値に変換するだけであるため、最後にastype('category')としておく\n","    df[cat] = df[cat].astype('category') \n","\n","# trainとtestに分割\n","train = df[~df['sales'].isnull()]\n","test = df[df['sales'].isnull()]\n","\n","# 説明変数と目的変数を指定\n","X_train = train.drop(['sales', 'price', 'category_detail'], axis=1)\n","Y_train = train['sales']\n","X_test = test.drop(['sales', 'price', 'category_detail'], axis=1)\n","\n","'''\n","モデルの構築と評価\n","'''\n","\n","# ライブラリのインポート\n","from sklearn.ensemble import RandomForestRegressor as rf\n","from sklearn.model_selection import train_test_split, GridSearchCV\n","from sklearn.metrics import mean_squared_error\n","\n","x_train, x_valid, y_train, y_valid = train_test_split(X_train, Y_train,\n","                                                      test_size=0.2, random_state=0,\n","                                                      shuffle=False)\n","\n","# パラメーター\n","params = {\n","    'n_estimators': [20, 30],\n","    'max_features': [i for i in range(1, x_train.shape[1])],\n","    'max_features': ['auto', 'sqrt', 'log2'],\n","    'max_depth': [5, 10, 20, 30, 50],\n","    'n_jobs': [-1]\n","}\n","\n","cv = GridSearchCV(rf(), params, cv = 3, n_jobs = -1, verbose=True)\n","cv.fit(x_train, y_train)\n","print(cv.best_estimator_)"],"execution_count":9,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["month_bining          0\n","id                    0\n","store_id              0\n","sales              3060\n","sales_before_12       0\n","sales_before_11       0\n","sales_before_10       0\n","sales_before_9        0\n","sales_before_8        0\n","sales_before_7        0\n","sales_before_6        0\n","sales_before_5        0\n","sales_before_4        0\n","sales_before_3        0\n","sales_before_2        0\n","category_id           0\n","category              0\n","category_detail       0\n","year                  0\n","month                 0\n","price                 0\n","sales_mean            0\n","store_earnings        0\n","dtype: int64\n","month_bining       float64\n","id                 float64\n","store_id           float64\n","sales              float64\n","sales_before_12    float64\n","sales_before_11    float64\n","sales_before_10    float64\n","sales_before_9     float64\n","sales_before_8     float64\n","sales_before_7     float64\n","sales_before_6     float64\n","sales_before_5     float64\n","sales_before_4     float64\n","sales_before_3     float64\n","sales_before_2     float64\n","category_id          int64\n","category            object\n","category_detail     object\n","year                 int64\n","month                int64\n","price              float64\n","sales_mean         float64\n","store_earnings     float64\n","dtype: object\n","Index(['category', 'category_detail'], dtype='object')\n","category\n","category_detail\n","Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n","[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed: 28.1min\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-9-6d5294716a03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m 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711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1149\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1151\u001b[0;31m         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689\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1052\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1054\u001b[0;31m                 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