{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## What's this\n- このNotebookは本コンペの概要と、提供データの各カラムの説明をまとめていきます\n- またMLBについて詳しくないので調べた内容などを記載します\n","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-07-10T10:02:09.314804Z","iopub.execute_input":"2021-07-10T10:02:09.315245Z","iopub.status.idle":"2021-07-10T10:02:09.327108Z","shell.execute_reply.started":"2021-07-10T10:02:09.315195Z","shell.execute_reply":"2021-07-10T10:02:09.325915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# MLB Player Digital Engagement Forecasting\n- 以下翻訳\n- このコンテストでは、ファンがMLBプレーヤーのデジタルコンテンツを将来の日付範囲で毎日どのように利用するかを予測します。\n- プレーヤーのパフォーマンスデータ、ソーシャルメディアデータ、市場規模などのチーム要因にアクセスできます。\n- 成功したモデルは、どのシグナルがエンゲージメントと最も強く相関し、影響を与えるかについての新しい洞察を提供します。\n- MLBオールスターラウンドをシーズンを通して予測できるかどうか、またはチームの25人のプレーヤーのそれぞれが脚光を浴びているときを想像してみてください。\n- これらの洞察は、アメリカの娯楽のファンダムを深く掘り下げるときに可能になります。\n- この種の最初の方法の一部として、プレーヤーレベルでのデジタルエンゲージメントをこのきめ細かい日常的な方法で理解しようとします。\n- 同時に、Google Cloudのデータ分析、Vertex AI、MLOpsツールを使用して、MLBがイノベーションをより簡単に構築できるように支援します。 \n- MLBファンとプレーヤーのエンゲージメントの未来を形作る上で役割を果たすことができます。\n  - メモ:MLBオールスターに出場する選手はファン投票や監督推薦によって選ばれる\n\n- 今回はコードコンペティションのため Kaggle Notebook の形で提出する必要があります","metadata":{}},{"cell_type":"markdown","source":"# Evaluation\n- 4つのターゲット変数のMCMAEになります\n- ターゲット変数の詳細は不明です\n  - メモ:Google主催なので検索などのアクセス量がターゲットかな\n  \n## MCMAE(mean column-wise mean absolute error)\n- 各列のRMSEの平均\n$$ \\frac {1}{m} \\sum_{j=1}^{m} \\sqrt{\\frac {1}{n} \\sum_{i=1}^{n}} (y_{ij} - \\hat{y}_{ij})^2 $$","metadata":{}},{"cell_type":"markdown","source":"# Data\n- teams.csv\n  - チームのマスタデータ\n  - メジャーリーグにはア・リーグ,ナ・リーグ各15チーム系30チームがあります\n- seasons.csv\n  - シーズンの情報\n  - 2020年はCOVID-19のためオールスターゲームが行われませんでした\n  - また通常シーズン162試合に対して60試合のみの開催となりました\n- players.csv\n  - 選手のマスタデータ\n- awards.csv\n  - 受賞データ\n  - オールスターゲームへの選出も含まれるのでシーズン,アワード,プレイヤーで一意になる\n- train.csv\n  - こちらにtargetの値が含まれています.構造が特殊なので後述\n- example_train.csv\n  - trainのサンプル\n- examle_sample_submition.csv\n  - submitionファイルのサンプル\n","metadata":{}},{"cell_type":"markdown","source":"## teams.csv\n- idで一意\n- メジャーリーグにはア・リーグ,ナ・リーグ各15チーム系30チームがあります\n- locationName Chicagoにはシカゴ・ブルズ(ナ)とホワイトソックス(ア)があるため29になります","metadata":{}},{"cell_type":"code","source":"teams = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/teams.csv')\nteams","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.328756Z","iopub.execute_input":"2021-07-10T10:02:09.329115Z","iopub.status.idle":"2021-07-10T10:02:09.373025Z","shell.execute_reply.started":"2021-07-10T10:02:09.329085Z","shell.execute_reply":"2021-07-10T10:02:09.37205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"teams.describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.374805Z","iopub.execute_input":"2021-07-10T10:02:09.375085Z","iopub.status.idle":"2021-07-10T10:02:09.640638Z","shell.execute_reply.started":"2021-07-10T10:02:09.37506Z","shell.execute_reply":"2021-07-10T10:02:09.639736Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## seasons.csv\n- 2020年はCOVID-19のためオールスターゲームが行われませんでした\n- また通常シーズン162試合に対して60試合のみの開催となりました","metadata":{"execution":{"iopub.status.busy":"2021-07-10T07:25:43.90667Z","iopub.execute_input":"2021-07-10T07:25:43.907067Z","iopub.status.idle":"2021-07-10T07:25:43.913792Z","shell.execute_reply.started":"2021-07-10T07:25:43.907031Z","shell.execute_reply":"2021-07-10T07:25:43.91211Z"}}},{"cell_type":"code","source":"seasons = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/seasons.csv')\nseasons","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.642171Z","iopub.execute_input":"2021-07-10T10:02:09.64248Z","iopub.status.idle":"2021-07-10T10:02:09.669939Z","shell.execute_reply.started":"2021-07-10T10:02:09.64245Z","shell.execute_reply":"2021-07-10T10:02:09.669031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## players.csv\n- 選手のマスタデータ\n- DOB\n  - 誕生日\n","metadata":{}},{"cell_type":"code","source":"players = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/players.csv')\nplayers","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.670993Z","iopub.execute_input":"2021-07-10T10:02:09.671262Z","iopub.status.idle":"2021-07-10T10:02:09.710515Z","shell.execute_reply.started":"2021-07-10T10:02:09.671219Z","shell.execute_reply":"2021-07-10T10:02:09.70972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players.describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.711612Z","iopub.execute_input":"2021-07-10T10:02:09.711878Z","iopub.status.idle":"2021-07-10T10:02:09.768669Z","shell.execute_reply.started":"2021-07-10T10:02:09.711853Z","shell.execute_reply":"2021-07-10T10:02:09.767747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- ポジションの集計","metadata":{}},{"cell_type":"code","source":"players[['primaryPositionCode','primaryPositionName']].groupby('primaryPositionName').count()","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.770653Z","iopub.execute_input":"2021-07-10T10:02:09.770915Z","iopub.status.idle":"2021-07-10T10:02:09.783101Z","shell.execute_reply.started":"2021-07-10T10:02:09.77089Z","shell.execute_reply":"2021-07-10T10:02:09.782199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- playerForTestSetAndFuturePredsの集計","metadata":{}},{"cell_type":"code","source":"players[['playerForTestSetAndFuturePreds','playerId']].groupby('playerForTestSetAndFuturePreds').count()","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.784735Z","iopub.execute_input":"2021-07-10T10:02:09.785167Z","iopub.status.idle":"2021-07-10T10:02:09.798445Z","shell.execute_reply.started":"2021-07-10T10:02:09.785125Z","shell.execute_reply":"2021-07-10T10:02:09.7975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## awards.csv","metadata":{}},{"cell_type":"code","source":"awards = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/awards.csv')\nawards","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.799551Z","iopub.execute_input":"2021-07-10T10:02:09.80003Z","iopub.status.idle":"2021-07-10T10:02:09.840288Z","shell.execute_reply.started":"2021-07-10T10:02:09.800002Z","shell.execute_reply":"2021-07-10T10:02:09.839556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"awards.describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.841395Z","iopub.execute_input":"2021-07-10T10:02:09.841665Z","iopub.status.idle":"2021-07-10T10:02:09.894832Z","shell.execute_reply.started":"2021-07-10T10:02:09.84164Z","shell.execute_reply":"2021-07-10T10:02:09.893972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- awardは全部で455種類.\n- awardによっては片方のリーグにしかないものもある","metadata":{}},{"cell_type":"code","source":"awards[['awardName','playerId']].groupby('awardName').count().sort_values(['playerId'])","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.895938Z","iopub.execute_input":"2021-07-10T10:02:09.896194Z","iopub.status.idle":"2021-07-10T10:02:09.912168Z","shell.execute_reply.started":"2021-07-10T10:02:09.89617Z","shell.execute_reply":"2021-07-10T10:02:09.911205Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- 受賞の多いプレイヤー","metadata":{}},{"cell_type":"code","source":"awards[['awardName','playerId','playerName']].groupby(['playerId','playerName']).count().sort_values(['awardName'])","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.91329Z","iopub.execute_input":"2021-07-10T10:02:09.913555Z","iopub.status.idle":"2021-07-10T10:02:09.941308Z","shell.execute_reply.started":"2021-07-10T10:02:09.91353Z","shell.execute_reply":"2021-07-10T10:02:09.940578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# train.csv\n- 日付をキーに各追加情報がjsonで格納されている","metadata":{}},{"cell_type":"code","source":"\nfrom pandas.io.json import json_normalize\nimport json \ntrain = pd.read_csv('../input/mlb-player-digital-engagement-forecasting/train.csv',nrows=100)\ntrain","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:09.94219Z","iopub.execute_input":"2021-07-10T10:02:09.942577Z","iopub.status.idle":"2021-07-10T10:02:12.054479Z","shell.execute_reply.started":"2021-07-10T10:02:09.94255Z","shell.execute_reply":"2021-07-10T10:02:12.053745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 各jsonを展開","metadata":{}},{"cell_type":"code","source":"def unpack_json(json_str):\n    return pd.DataFrame() if pd.isna(json_str) else pd.read_json(json_str)\n\n\ndef unpack_data(data, dfs=None, n_jobs=-1):\n    if dfs is not None:\n        data = data.loc[:, dfs]\n    unnested_dfs = {}\n    for name, column in data.iteritems():\n        daily_dfs = Parallel(n_jobs=n_jobs)(\n            delayed(unpack_json)(item) for date, item in column.iteritems())\n        df = pd.concat(daily_dfs)\n        unnested_dfs[name] = df\n    return unnested_dfs","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:12.055451Z","iopub.execute_input":"2021-07-10T10:02:12.055806Z","iopub.status.idle":"2021-07-10T10:02:12.061994Z","shell.execute_reply.started":"2021-07-10T10:02:12.055779Z","shell.execute_reply":"2021-07-10T10:02:12.061109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfs = [\n    'nextDayPlayerEngagement',  # targets\n    'playerBoxScores',  # features\n    'games',\n    'rosters',\n    'teamBoxScores',\n    'transactions',\n    'standings',\n    'awards',\n    'events',\n    'playerTwitterFollowers',\n    'teamTwitterFollowers',\n]\n\n# Read training data\ntraining = train\n\n# Convert training data date field to datetime type\ntraining['date'] = pd.to_datetime(training['date'], format=\"%Y%m%d\")\ntraining = training.set_index('date').to_period('D')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:21:55.323498Z","iopub.execute_input":"2021-07-10T10:21:55.3239Z","iopub.status.idle":"2021-07-10T10:21:55.334318Z","shell.execute_reply.started":"2021-07-10T10:21:55.323868Z","shell.execute_reply":"2021-07-10T10:21:55.333293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs = unpack_data(training, dfs=dfs)","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:21:57.591507Z","iopub.execute_input":"2021-07-10T10:21:57.59192Z","iopub.status.idle":"2021-07-10T10:22:05.596795Z","shell.execute_reply.started":"2021-07-10T10:21:57.591887Z","shell.execute_reply":"2021-07-10T10:22:05.595943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## nextDayPlayerEngagement\n- 日付,プレイヤーidごとにtarget値","metadata":{}},{"cell_type":"code","source":"training_dfs['nextDayPlayerEngagement']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:14.435491Z","iopub.execute_input":"2021-07-10T10:02:14.435792Z","iopub.status.idle":"2021-07-10T10:02:14.455Z","shell.execute_reply.started":"2021-07-10T10:02:14.435749Z","shell.execute_reply":"2021-07-10T10:02:14.454118Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## playerBoxScores\n- プレイヤーの成績\n- 一番影響度が高そう\n","metadata":{}},{"cell_type":"code","source":"training_dfs['playerBoxScores']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:02:14.456205Z","iopub.execute_input":"2021-07-10T10:02:14.456527Z","iopub.status.idle":"2021-07-10T10:02:14.5054Z","shell.execute_reply.started":"2021-07-10T10:02:14.456499Z","shell.execute_reply":"2021-07-10T10:02:14.504421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['playerBoxScores'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:09:10.541174Z","iopub.execute_input":"2021-07-10T10:09:10.54162Z","iopub.status.idle":"2021-07-10T10:09:10.761885Z","shell.execute_reply.started":"2021-07-10T10:09:10.541582Z","shell.execute_reply":"2021-07-10T10:09:10.76086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## games","metadata":{}},{"cell_type":"code","source":"training_dfs['games']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:23:07.09127Z","iopub.execute_input":"2021-07-10T10:23:07.091676Z","iopub.status.idle":"2021-07-10T10:23:07.148311Z","shell.execute_reply.started":"2021-07-10T10:23:07.091639Z","shell.execute_reply":"2021-07-10T10:23:07.147405Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['games'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:23:17.800244Z","iopub.execute_input":"2021-07-10T10:23:17.800641Z","iopub.status.idle":"2021-07-10T10:23:17.910038Z","shell.execute_reply.started":"2021-07-10T10:23:17.800602Z","shell.execute_reply":"2021-07-10T10:23:17.909029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## rosters","metadata":{}},{"cell_type":"code","source":"training_dfs['rosters']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:25:34.503558Z","iopub.execute_input":"2021-07-10T10:25:34.503959Z","iopub.status.idle":"2021-07-10T10:25:34.524781Z","shell.execute_reply.started":"2021-07-10T10:25:34.50393Z","shell.execute_reply":"2021-07-10T10:25:34.52378Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['rosters'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:25:41.328834Z","iopub.execute_input":"2021-07-10T10:25:41.329406Z","iopub.status.idle":"2021-07-10T10:25:41.488708Z","shell.execute_reply.started":"2021-07-10T10:25:41.329352Z","shell.execute_reply":"2021-07-10T10:25:41.48774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## teamBoxScores","metadata":{}},{"cell_type":"code","source":"training_dfs['teamBoxScores']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:27:23.161653Z","iopub.execute_input":"2021-07-10T10:27:23.162063Z","iopub.status.idle":"2021-07-10T10:27:23.194005Z","shell.execute_reply.started":"2021-07-10T10:27:23.162031Z","shell.execute_reply":"2021-07-10T10:27:23.19325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['teamBoxScores'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:27:30.89747Z","iopub.execute_input":"2021-07-10T10:27:30.897829Z","iopub.status.idle":"2021-07-10T10:27:31.047903Z","shell.execute_reply.started":"2021-07-10T10:27:30.897796Z","shell.execute_reply":"2021-07-10T10:27:31.0469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## transactions\n","metadata":{}},{"cell_type":"code","source":"training_dfs['transactions']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:27:54.022907Z","iopub.execute_input":"2021-07-10T10:27:54.023317Z","iopub.status.idle":"2021-07-10T10:27:54.056854Z","shell.execute_reply.started":"2021-07-10T10:27:54.023281Z","shell.execute_reply":"2021-07-10T10:27:54.055903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['transactions'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:28:03.650339Z","iopub.execute_input":"2021-07-10T10:28:03.650687Z","iopub.status.idle":"2021-07-10T10:28:03.717014Z","shell.execute_reply.started":"2021-07-10T10:28:03.650659Z","shell.execute_reply":"2021-07-10T10:28:03.716047Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## standings","metadata":{}},{"cell_type":"code","source":"training_dfs['standings']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:29:21.153986Z","iopub.execute_input":"2021-07-10T10:29:21.154405Z","iopub.status.idle":"2021-07-10T10:29:21.197124Z","shell.execute_reply.started":"2021-07-10T10:29:21.154362Z","shell.execute_reply":"2021-07-10T10:29:21.196016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['standings'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:30:01.581029Z","iopub.execute_input":"2021-07-10T10:30:01.581482Z","iopub.status.idle":"2021-07-10T10:30:01.715094Z","shell.execute_reply.started":"2021-07-10T10:30:01.581447Z","shell.execute_reply":"2021-07-10T10:30:01.713989Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## awards","metadata":{}},{"cell_type":"code","source":"training_dfs['awards']","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:30:36.972435Z","iopub.execute_input":"2021-07-10T10:30:36.972817Z","iopub.status.idle":"2021-07-10T10:30:36.998691Z","shell.execute_reply.started":"2021-07-10T10:30:36.972781Z","shell.execute_reply":"2021-07-10T10:30:36.997509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dfs['awards'].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2021-07-10T10:30:30.387062Z","iopub.execute_input":"2021-07-10T10:30:30.387486Z","iopub.status.idle":"2021-07-10T10:30:30.421011Z","shell.execute_reply.started":"2021-07-10T10:30:30.387449Z","shell.execute_reply":"2021-07-10T10:30:30.419845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 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