AIQuest_demand_RandomForest_2.7004563.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"AIQuest_demand_RandomForest_2.7004563.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMN3Dy0sQv1aNU99dn9u2cd"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QbTvN4p4nw3p","executionInfo":{"status":"ok","timestamp":1633416411096,"user_tz":-540,"elapsed":22018,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"299b9681-df02-466c-9257-ea9d23ea6663"},"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: click in /usr/local/lib/python3.7/dist-packages (from signate) (7.1.2)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from signate) (2.8.2)\n","Collecting urllib3>=1.26.4\n","  Downloading urllib3-1.26.7-py2.py3-none-any.whl (138 kB)\n","\u001b[K     |████████████████████████████████| 138 kB 6.9 MB/s \n","\u001b[?25hRequirement 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","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from signate) (0.8.9)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from signate) (2021.5.30)\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=fec94c3a7a6218956bcb2d72779e2ce407390321b9cd6eb2becf8333b12edc74\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"]},{"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":1633416418584,"user_tz":-540,"elapsed":7502,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"f8107de1-31f8-4bc4-f5c5-302367a72d24"},"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                                   -                              5178\n","             24  【練習問題】お弁当の需要予測                                           -                              6352\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          1065\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           246\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           525\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           338\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          1328\n","            122  【練習問題】自動車の評価                                               -           Knowledge           302\n","            123  【練習問題】オゾンレベルの分類                                         -           Knowledge            38\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            30\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            46\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           140\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                                 -                               449\n","            266  【練習問題】民泊サービスの宿泊価格予測                                 -                               461\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  -                   269\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                                     -                               139\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                      395\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":1633416418585,"user_tz":-540,"elapsed":16,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"68ef5bcf-13cd-48c8-ab85-3a3970a7e24d"},"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":1633416436857,"user_tz":-540,"elapsed":18278,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"8f6a35dc-ab17-43a2-8c89-93b6a3ac6b6e"},"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":"ok","timestamp":1633416928156,"user_tz":-540,"elapsed":491320,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"b89e2950-c1c0-4d4a-9da4-a6fe8d488543"},"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\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","model = rf(n_estimators=50,\n","           random_state=1234)   \n","    \n","model.fit(x_train, y_train)\n","y_pred = model.predict(x_valid)\n","rmse = np.sqrt(mean_squared_error(y_valid, y_pred))\n","print(rmse)\n","\n","#特徴量の重要度\n","feature = model.feature_importances_\n","label = X_train.columns[0:] #特徴量の名前\n","indices = np.argsort(feature)[::1] #特徴量の重要度順(降順)\n","\n","# プロット\n","x = range(len(feature))\n","y = feature[indices]\n","y_label = label[indices]\n","plt.barh(x, y, align = 'center')\n","plt.yticks(x, y_label)\n","plt.xlabel(\"importance_num\")\n","plt.ylabel(\"label\")\n","plt.show()\n","\n","\"\"\"\n","予測精度：\n","1.9038107395758823\n","\"\"\"\n","\n","'''\n","テストデータの予測\n","'''\n","\n","# テストデータにおける予測\n","pred = model.predict(X_test)\n","\n","'''\n","提出\n","'''\n","\n","# 提出用サンプルの読み込み\n","sub = pd.read_csv(SAMPLESUB_PATH, header=None)\n","\n","# カラム1の値を置き換え\n","sub[1] = pred\n","\n","# CSVファイルの出力\n","sub.to_csv(SUB_PATH, header=None, index=False)\n","\n","\"\"\"\n","スコア：\n","2.7758802\n","\"\"\""],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","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'], 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'\\nスコア：\\n2.7758802\\n'"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"Mtz-ULRX5ESD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1633416928157,"user_tz":-540,"elapsed":14,"user":{"displayName":"中田光隆","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"06690068059315868411"}},"outputId":"dd30ea62-9bc4-47a9-9842-89a4ab2df2c1"},"source":["print(X_train.columns)"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Index(['month_bining', 'id', 'store_id', 'sales_before_12', 'sales_before_11',\n","       'sales_before_10', 'sales_before_9', 'sales_before_8', 'sales_before_7',\n","       'sales_before_6', 'sales_before_5', 'sales_before_4', 'sales_before_3',\n","       'sales_before_2', 'category_id', 'category', 'year', 'month',\n","       'sales_mean', 'store_earnings'],\n","      dtype='object')\n"]}]}]}