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"}}},{"cell_type":"markdown","source":"### CREDITS\nhttps://www.kaggle.com/ulrich07/mlb-ann-with-lags-tf-keras\nthanks. I copied your code.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-06-16T15:46:24.94492Z","iopub.execute_input":"2021-06-16T15:46:24.945502Z","iopub.status.idle":"2021-06-16T15:46:25.761799Z","shell.execute_reply.started":"2021-06-16T15:46:24.945463Z","shell.execute_reply":"2021-06-16T15:46:25.760806Z"}}},{"cell_type":"markdown","source":"ミッション：このコンテストでは、将来の日付範囲でファンがMLBプレーヤーのデジタルコンテンツに毎日どのように関与するかを予測します。 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。 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。","metadata":{}},{"cell_type":"markdown","source":"研究しているのでしばらくお待ちください。インターネットを検索すると、半年で金メダルとりました、みたいな記事がいろいろでてくるのですが。。。<br>\n私にはまったく手ごたえがありません。<br>\n\nコンペの一番優しいものを選択してそれに集中しようとしています。<br>\nしかし、全部ムズカシイ。どうしようもないので、全部手をだして絞っていこうとおもっています。<br>\n\nMLBのコンペはこれは２つ目のコードなのですが、手法が違うようです。<br>\nこのコードは、しばらくおいておこうかな<br>\n愚痴書いて、今日は終わりということで。\n","metadata":{},"attachments":{"843d7710-2699-4f33-aa8f-bafb627de395.png":{"image/png":"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"}}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom datetime import timedelta\nfrom tqdm import tqdm\nimport gc\nfrom functools import reduce\nfrom sklearn.model_selection import StratifiedKFold","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:35.397646Z","iopub.execute_input":"2021-06-21T13:45:35.398175Z","iopub.status.idle":"2021-06-21T13:45:36.406034Z","shell.execute_reply.started":"2021-06-21T13:45:35.398097Z","shell.execute_reply":"2021-06-21T13:45:36.40508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def make_df(df, col, bool_in=False):\n    tp = df.loc[ ~df[col].isnull() ,[col]].copy()\n    df.drop(col, axis=1, inplace=True)\n    \n    tp[col] = tp[col].str.replace(\"null\",'\"\"')\n    if bool_in:\n        tp[col] = tp[col].str.replace(\"false\",'\"False\"')\n        tp[col] = tp[col].str.replace(\"true\",'\"True\"')\n    tp[col] = tp[col].apply(lambda x: eval(x) )\n    a = tp[col].sum()\n    gc.collect()\n    return pd.DataFrame(a)","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:36.407209Z","iopub.execute_input":"2021-06-21T13:45:36.40746Z","iopub.status.idle":"2021-06-21T13:45:36.413848Z","shell.execute_reply.started":"2021-06-21T13:45:36.407435Z","shell.execute_reply":"2021-06-21T13:45:36.412751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ROOT_DIR = \"../input/mlb-player-digital-engagement-forecasting\"","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:36.415478Z","iopub.execute_input":"2021-06-21T13:45:36.415716Z","iopub.status.idle":"2021-06-21T13:45:36.426517Z","shell.execute_reply.started":"2021-06-21T13:45:36.415693Z","shell.execute_reply":"2021-06-21T13:45:36.425622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## UTILITY FUNCTIONS","metadata":{}},{"cell_type":"code","source":"#=======================#\ndef flatten(df, col):\n    du = (df.pivot(index=\"playerId\", columns=\"EvalDate\", \n               values=col).add_prefix(f\"{col}_\").\n      rename_axis(None, axis=1).reset_index())\n    return du\n#============================#\ndef reducer(left, right):\n    return left.merge(right, on=\"playerId\")\n#========================","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:36.429663Z","iopub.execute_input":"2021-06-21T13:45:36.429962Z","iopub.status.idle":"2021-06-21T13:45:36.436963Z","shell.execute_reply.started":"2021-06-21T13:45:36.429933Z","shell.execute_reply":"2021-06-21T13:45:36.43599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"TGTCOLS = [\"target1\",\"target2\",\"target3\",\"target4\"]\ndef train_lag(df, lag=1):\n    dp = df[[\"playerId\",\"EvalDate\"]+TGTCOLS].copy()\n    dp[\"EvalDate\"]  =dp[\"EvalDate\"] + timedelta(days=lag) \n    df = df.merge(dp, on=[\"playerId\", \"EvalDate\"], suffixes=[\"\",f\"_{lag}\"], how=\"left\")\n    return df\n#=================================\ndef test_lag(sub):\n    sub[\"playerId\"] = sub[\"date_playerId\"].apply(lambda s: int(  s.split(\"_\")[1]  ) )\n    assert sub.date.nunique() == 1\n    dte = sub[\"date\"].unique()[0]\n    \n    eval_dt = pd.to_datetime(dte, format=\"%Y%m%d\")\n    dtes = [eval_dt + timedelta(days=-k) for k in LAGS]\n    mp_dtes = {eval_dt + timedelta(days=-k):k for k in LAGS}\n    \n    sl = LAST.loc[LAST.EvalDate.between(dtes[-1], dtes[0]), [\"EvalDate\",\"playerId\"]+TGTCOLS].copy()\n    sl[\"EvalDate\"] = sl[\"EvalDate\"].map(mp_dtes)\n    du = [flatten(sl, col) for col in TGTCOLS]\n    du = reduce(reducer, du)\n    return du, eval_dt\n    #\n#===============","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:36.438009Z","iopub.execute_input":"2021-06-21T13:45:36.438392Z","iopub.status.idle":"2021-06-21T13:45:36.450323Z","shell.execute_reply.started":"2021-06-21T13:45:36.438355Z","shell.execute_reply":"2021-06-21T13:45:36.449392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n#tr = pd.read_csv(f\"{ROOT_DIR}/train.csv\")\ntr = pd.read_csv(\"../input/mlb-data/target.csv\")\nprint(tr.shape)\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:36.451633Z","iopub.execute_input":"2021-06-21T13:45:36.451933Z","iopub.status.idle":"2021-06-21T13:45:40.339673Z","shell.execute_reply.started":"2021-06-21T13:45:36.451903Z","shell.execute_reply":"2021-06-21T13:45:40.338857Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tr[\"EvalDate\"] = pd.to_datetime(tr[\"EvalDate\"])\ntr[\"EvalDate\"] = tr[\"EvalDate\"] + timedelta(days=-1)\ntr[\"EvalYear\"] = tr[\"EvalDate\"].dt.year","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:40.340915Z","iopub.execute_input":"2021-06-21T13:45:40.341255Z","iopub.status.idle":"2021-06-21T13:45:40.909098Z","shell.execute_reply.started":"2021-06-21T13:45:40.341217Z","shell.execute_reply":"2021-06-21T13:45:40.908154Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MED_DF = tr.groupby([\"playerId\",\"EvalYear\"])[TGTCOLS].median().reset_index()\nMEDCOLS = [\"tgt1_med\",\"tgt2_med\", \"tgt3_med\", \"tgt4_med\"]\nMED_DF.columns = [\"playerId\",\"EvalYear\"] + MEDCOLS","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:40.911472Z","iopub.execute_input":"2021-06-21T13:45:40.911749Z","iopub.status.idle":"2021-06-21T13:45:41.408547Z","shell.execute_reply.started":"2021-06-21T13:45:40.911721Z","shell.execute_reply":"2021-06-21T13:45:41.407596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"MED_DF.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:41.410017Z","iopub.execute_input":"2021-06-21T13:45:41.410266Z","iopub.status.idle":"2021-06-21T13:45:41.427788Z","shell.execute_reply.started":"2021-06-21T13:45:41.410241Z","shell.execute_reply":"2021-06-21T13:45:41.426859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LAGS = list(range(1,31))\nFECOLS = [f\"{col}_{lag}\" for lag in reversed(LAGS) for col in TGTCOLS]","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:41.430517Z","iopub.execute_input":"2021-06-21T13:45:41.430769Z","iopub.status.idle":"2021-06-21T13:45:41.43548Z","shell.execute_reply.started":"2021-06-21T13:45:41.430744Z","shell.execute_reply":"2021-06-21T13:45:41.434726Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nfor lag in tqdm(LAGS):\n    tr = train_lag(tr, lag=lag)\n    gc.collect()\n#===========\ntr = tr.sort_values(by=[\"playerId\", \"EvalDate\"])\nprint(tr.shape)\ntr = tr.dropna()\nprint(tr.shape)\ntr = tr.merge(MED_DF, on=[\"playerId\",\"EvalYear\"])\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:45:41.436446Z","iopub.execute_input":"2021-06-21T13:45:41.436738Z","iopub.status.idle":"2021-06-21T13:47:33.685659Z","shell.execute_reply.started":"2021-06-21T13:45:41.436709Z","shell.execute_reply":"2021-06-21T13:47:33.68505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tr.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:33.686746Z","iopub.execute_input":"2021-06-21T13:47:33.687007Z","iopub.status.idle":"2021-06-21T13:47:33.710756Z","shell.execute_reply.started":"2021-06-21T13:47:33.686982Z","shell.execute_reply":"2021-06-21T13:47:33.709968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = tr[FECOLS+MEDCOLS].values\ny = tr[TGTCOLS].values\ncl = tr[\"playerId\"].values","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:33.711702Z","iopub.execute_input":"2021-06-21T13:47:33.711967Z","iopub.status.idle":"2021-06-21T13:47:36.151498Z","shell.execute_reply.started":"2021-06-21T13:47:33.711942Z","shell.execute_reply":"2021-06-21T13:47:36.150677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"NFOLDS = 5\nskf = StratifiedKFold(n_splits=NFOLDS)\nfolds = skf.split(X, cl)\nfolds = list(folds)","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:36.152481Z","iopub.execute_input":"2021-06-21T13:47:36.15272Z","iopub.status.idle":"2021-06-21T13:47:40.245338Z","shell.execute_reply.started":"2021-06-21T13:47:36.152696Z","shell.execute_reply":"2021-06-21T13:47:40.244583Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:40.246733Z","iopub.execute_input":"2021-06-21T13:47:40.247379Z","iopub.status.idle":"2021-06-21T13:47:40.253337Z","shell.execute_reply.started":"2021-06-21T13:47:40.247336Z","shell.execute_reply":"2021-06-21T13:47:40.252392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Neural Net Training\n\n![image.png](attachment:270a4bbf-5fe5-4964-8ff9-b156cc07502a.png)","metadata":{},"attachments":{"270a4bbf-5fe5-4964-8ff9-b156cc07502a.png":{"image/png":"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"}}},{"cell_type":"code","source":"import tensorflow as tf\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.models as M\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\nfrom tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:40.254789Z","iopub.execute_input":"2021-06-21T13:47:40.255091Z","iopub.status.idle":"2021-06-21T13:47:45.631352Z","shell.execute_reply.started":"2021-06-21T13:47:40.255065Z","shell.execute_reply":"2021-06-21T13:47:45.630468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:ad0080b7-8364-4c8b-b1d3-353427648498.png)","metadata":{},"attachments":{"ad0080b7-8364-4c8b-b1d3-353427648498.png":{"image/png":"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"}}},{"cell_type":"code","source":"def make_model(n_in):\n    inp = L.Input(name=\"inputs\", shape=(n_in,))\n    x = L.Dense(50, activation=\"relu\", name=\"d1\")(inp)\n    x = L.Dense(50, activation=\"relu\", name=\"d2\")(x)\n    preds = L.Dense(4, activation=\"linear\", name=\"preds\")(x)\n    \n    model = M.Model(inp, preds, name=\"ANN\")\n    model.compile(loss=\"mean_absolute_error\", optimizer=\"adam\")\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:45.63263Z","iopub.execute_input":"2021-06-21T13:47:45.632895Z","iopub.status.idle":"2021-06-21T13:47:45.637841Z","shell.execute_reply.started":"2021-06-21T13:47:45.63287Z","shell.execute_reply":"2021-06-21T13:47:45.637192Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"I think this model is simple. but its effective.","metadata":{}},{"cell_type":"code","source":"net = make_model(X.shape[1])\nprint(net.summary())","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:45.638696Z","iopub.execute_input":"2021-06-21T13:47:45.639196Z","iopub.status.idle":"2021-06-21T13:47:45.743071Z","shell.execute_reply.started":"2021-06-21T13:47:45.639158Z","shell.execute_reply":"2021-06-21T13:47:45.742468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"oof = np.zeros(y.shape)\nnets = []\nEPOCHS  = 15\nfor idx in range(NFOLDS):\n    print(\"FOLD:\", idx)\n    tr_idx, val_idx = folds[idx]\n    ckpt = ModelCheckpoint(f\"w{idx}.h5\", monitor='val_loss', verbose=1, save_best_only=True,mode='min')\n    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=3, min_lr=0.0005)\n    es = EarlyStopping(monitor='val_loss', patience=6)\n    reg = make_model(X.shape[1])\n    reg.fit(X[tr_idx], y[tr_idx], epochs=EPOCHS, batch_size=30_000, \n            validation_data=(X[val_idx], y[val_idx]),\n            verbose=1, callbacks=[ckpt, reduce_lr, es])\n    reg.load_weights(f\"w{idx}.h5\")\n    oof[val_idx] = reg.predict(X[val_idx], batch_size=50_000, verbose=1)\n    nets.append(reg)\n    gc.collect()\n    #\n#","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:47:45.744124Z","iopub.execute_input":"2021-06-21T13:47:45.744379Z","iopub.status.idle":"2021-06-21T13:48:48.006619Z","shell.execute_reply.started":"2021-06-21T13:47:45.744352Z","shell.execute_reply":"2021-06-21T13:48:48.005039Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mae = mean_absolute_error(y, oof)\nmse = mean_squared_error(y, oof, squared=False)\nprint(\"mae:\", mae)\nprint(\"mse:\", mse)","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.007496Z","iopub.status.idle":"2021-06-21T13:48:48.008044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Historical information to use in prediction time\nbound_dt = pd.to_datetime(\"2021-01-01\")\nLAST = tr.loc[tr.EvalDate>bound_dt].copy()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.008886Z","iopub.status.idle":"2021-06-21T13:48:48.009397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LAST_MED_DF = MED_DF.loc[MED_DF.EvalYear==2021].copy()\nLAST_MED_DF.drop(\"EvalYear\", axis=1, inplace=True)\ndel tr","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.010221Z","iopub.status.idle":"2021-06-21T13:48:48.010733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LAST.shape, LAST_MED_DF.shape, MED_DF.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.011526Z","iopub.status.idle":"2021-06-21T13:48:48.012039Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#nets[0].summary()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.012858Z","iopub.status.idle":"2021-06-21T13:48:48.013374Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#\"\"\"\nimport mlb\nFE = []; SUB = [];\nenv = mlb.make_env() # initialize the environment\niter_test = env.iter_test() # iterator which loops over each date in test set\n\nfor (test_df, sub) in iter_test:\n    # Features computation at Evaluation Date\n    sub = sub.reset_index()\n    sub_fe, eval_dt = test_lag(sub)\n    sub_fe = sub_fe.merge(LAST_MED_DF, on=\"playerId\", how=\"left\")\n    sub_fe = sub_fe.fillna(0.)\n    \n    _preds = 0.\n    for reg in nets:\n        _preds += reg.predict(sub_fe[FECOLS + MEDCOLS]) / NFOLDS\n    sub_fe[TGTCOLS] = np.clip(_preds, 0, 100)\n    sub.drop([\"date\"]+TGTCOLS, axis=1, inplace=True)\n    sub = sub.merge(sub_fe[[\"playerId\"]+TGTCOLS], on=\"playerId\", how=\"left\")\n    sub.drop(\"playerId\", axis=1, inplace=True)\n    sub = sub.fillna(0.)\n    # Submit\n    env.predict(sub)\n    # Update Available information\n    sub_fe[\"EvalDate\"] = eval_dt\n    #sub_fe.drop(MEDCOLS, axis=1, inplace=True)\n    LAST = LAST.append(sub_fe)\n    LAST = LAST.drop_duplicates(subset=[\"EvalDate\",\"playerId\"], keep=\"last\")\n#\"\"\"","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.014208Z","iopub.status.idle":"2021-06-21T13:48:48.014739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub.head()","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.015551Z","iopub.status.idle":"2021-06-21T13:48:48.016067Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LAST.shape, sub_fe.shape","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.016886Z","iopub.status.idle":"2021-06-21T13:48:48.017391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#df_tr[\"dte\"] = pd.to_datetime(df_tr[\"date\"], format='%Y%m%d')","metadata":{"execution":{"iopub.status.busy":"2021-06-21T13:48:48.018215Z","iopub.status.idle":"2021-06-21T13:48:48.018717Z"},"trusted":true},"execution_count":null,"outputs":[]}]}