lightgbm-ann-weight-with-love.ipynb

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   "source": [
    "Credit to @columbia2131 - I started with his notebook and then added an external data set with descriptive statistics of the targets for each player."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "freelance-lambda",
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   "source": [
    "Combitiona of Ken Miller\n",
    "\n",
    "https://www.kaggle.com/mlconsult/1-3816-lb-lbgm-descriptive-stats-param-tune\n",
    "\n",
    "and \n",
    "\n",
    "\n",
    "https://www.kaggle.com/ulrich07/mlb-ann-with-lags-tf-keras\n",
    "\n",
    "\n",
    "if you find this useful, I appreciate your upvote :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-jacob",
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   },
   "source": [
    "## About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dirty-stopping",
   "metadata": {
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   "source": [
    "Train.csv is stored as a csv file with each column as follows.  \n",
    "\n",
    "train.csvを以下のようにして各カラムをcsvファイルとして保管しています。\n",
    "\n",
    "Train.csv ถูกเก็บเป็นไฟล์ csv ด้วยโค้ดข้างล่าง"
   ]
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  {
   "cell_type": "code",
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   "id": "colored-substance",
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   "source": [
    "%%capture\n",
    "\"\"\"\n",
    "!pip install pandarallel \n",
    "\n",
    "import gc\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "\n",
    "from pandarallel import pandarallel\n",
    "pandarallel.initialize()\n",
    "\n",
    "BASE_DIR = Path('../input/mlb-player-digital-engagement-forecasting')\n",
    "train = pd.read_csv(BASE_DIR / 'train.csv')\n",
    "\n",
    "null = np.nan\n",
    "true = True\n",
    "false = False\n",
    "\n",
    "for col in train.columns:\n",
    "\n",
    "    if col == 'date': continue\n",
    "\n",
    "    _index = train[col].notnull()\n",
    "    train.loc[_index, col] = train.loc[_index, col].parallel_apply(lambda x: eval(x))\n",
    "\n",
    "    outputs = []\n",
    "    for index, date, record in train.loc[_index, ['date', col]].itertuples():\n",
    "        _df = pd.DataFrame(record)\n",
    "        _df['index'] = index\n",
    "        _df['date'] = date\n",
    "        outputs.append(_df)\n",
    "\n",
    "    outputs = pd.concat(outputs).reset_index(drop=True)\n",
    "\n",
    "    outputs.to_csv(f'{col}_train.csv', index=False)\n",
    "    outputs.to_pickle(f'{col}_train.pkl')\n",
    "\n",
    "    del outputs\n",
    "    del train[col]\n",
    "    gc.collect()\n",
    "\"\"\""
   ]
  },
  {
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   "id": "individual-villa",
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    "execution": {
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    "tags": []
   },
   "source": [
    "## Training"
   ]
  },
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   "id": "medium-force",
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   "outputs": [
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from datetime import timedelta\n",
    "from functools import reduce\n",
    "from tqdm import tqdm\n",
    "import lightgbm as lgbm\n",
    "import mlb"
   ]
  },
  {
   "cell_type": "code",
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   "id": "legislative-values",
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   "outputs": [],
   "source": [
    "BASE_DIR = Path('../input/mlb-player-digital-engagement-forecasting')\n",
    "TRAIN_DIR = Path('../input/mlb-pdef-train-dataset')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "middle-violation",
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    "tags": []
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   "outputs": [],
   "source": [
    "players = pd.read_csv(BASE_DIR / 'players.csv')\n",
    "\n",
    "rosters = pd.read_pickle(TRAIN_DIR / 'rosters_train.pkl')\n",
    "targets = pd.read_pickle(TRAIN_DIR / 'nextDayPlayerEngagement_train.pkl')\n",
    "scores = pd.read_pickle(TRAIN_DIR / 'playerBoxScores_train.pkl')\n",
    "scores = scores.groupby(['playerId', 'date']).sum().reset_index()"
   ]
  },
  {
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   "execution_count": 5,
   "id": "possible-commission",
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   "source": [
    "targets_cols = ['playerId', 'target1', 'target2', 'target3', 'target4', 'date']\n",
    "players_cols = ['playerId', 'primaryPositionName']\n",
    "rosters_cols = ['playerId', 'teamId', 'status', 'date']\n",
    "scores_cols = ['playerId', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n",
    "       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n",
    "       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n",
    "       'atBats', 'caughtStealing', 'stolenBases', 'groundIntoDoublePlay',\n",
    "       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n",
    "       'leftOnBase', 'sacBunts', 'sacFlies', 'catchersInterference',\n",
    "       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n",
    "       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n",
    "       'lossesPitching', 'flyOutsPitching', 'airOutsPitching',\n",
    "       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n",
    "       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n",
    "       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n",
    "       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n",
    "       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n",
    "       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n",
    "       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n",
    "       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n",
    "       'inheritedRunnersScored', 'catchersInterferencePitching',\n",
    "       'sacBuntsPitching', 'sacFliesPitching', 'saves', 'holds', 'blownSaves',\n",
    "       'assists', 'putOuts', 'errors', 'chances', 'date']\n",
    "\n",
    "feature_cols = ['label_playerId', 'label_primaryPositionName', 'label_teamId',\n",
    "       'label_status', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n",
    "       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n",
    "       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n",
    "       'atBats', 'caughtStealing', 'stolenBases', 'groundIntoDoublePlay',\n",
    "       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n",
    "       'leftOnBase', 'sacBunts', 'sacFlies', 'catchersInterference',\n",
    "       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n",
    "       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n",
    "       'lossesPitching', 'flyOutsPitching', 'airOutsPitching',\n",
    "       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n",
    "       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n",
    "       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n",
    "       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n",
    "       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n",
    "       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n",
    "       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n",
    "       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n",
    "       'inheritedRunnersScored', 'catchersInterferencePitching',\n",
    "       'sacBuntsPitching', 'sacFliesPitching', 'saves', 'holds', 'blownSaves',\n",
    "       'assists', 'putOuts', 'errors', 'chances','target1_mean',\n",
    " 'target1_median',\n",
    " 'target1_std',\n",
    " 'target1_min',\n",
    " 'target1_max',\n",
    " 'target1_prob',\n",
    " 'target2_mean',\n",
    " 'target2_median',\n",
    " 'target2_std',\n",
    " 'target2_min',\n",
    " 'target2_max',\n",
    " 'target2_prob',\n",
    " 'target3_mean',\n",
    " 'target3_median',\n",
    " 'target3_std',\n",
    " 'target3_min',\n",
    " 'target3_max',\n",
    " 'target3_prob',\n",
    " 'target4_mean',\n",
    " 'target4_median',\n",
    " 'target4_std',\n",
    " 'target4_min',\n",
    " 'target4_max',\n",
    " 'target4_prob']"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "['playerId',\n",
       " 'target1_mean',\n",
       " 'target1_median',\n",
       " 'target1_std',\n",
       " 'target1_min',\n",
       " 'target1_max',\n",
       " 'target1_prob',\n",
       " 'target2_mean',\n",
       " 'target2_median',\n",
       " 'target2_std',\n",
       " 'target2_min',\n",
       " 'target2_max',\n",
       " 'target2_prob',\n",
       " 'target3_mean',\n",
       " 'target3_median',\n",
       " 'target3_std',\n",
       " 'target3_min',\n",
       " 'target3_max',\n",
       " 'target3_prob',\n",
       " 'target4_mean',\n",
       " 'target4_median',\n",
       " 'target4_std',\n",
       " 'target4_min',\n",
       " 'target4_max',\n",
       " 'target4_prob']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "player_target_stats = pd.read_csv(\"../input/player-target-stats/player_target_stats.csv\")\n",
    "data_names=player_target_stats.columns.values.tolist()\n",
    "data_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bronze-spirituality",
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   "outputs": [],
   "source": [
    "# creat dataset\n",
    "train = targets[targets_cols].merge(players[players_cols], on=['playerId'], how='left')\n",
    "train = train.merge(rosters[rosters_cols], on=['playerId', 'date'], how='left')\n",
    "train = train.merge(scores[scores_cols], on=['playerId', 'date'], how='left')\n",
    "train = train.merge(player_target_stats, how='inner', left_on=[\"playerId\"],right_on=[\"playerId\"])\n",
    "\n",
    "\n",
    "# label encoding\n",
    "player2num = {c: i for i, c in enumerate(train['playerId'].unique())}\n",
    "position2num = {c: i for i, c in enumerate(train['primaryPositionName'].unique())}\n",
    "teamid2num = {c: i for i, c in enumerate(train['teamId'].unique())}\n",
    "status2num = {c: i for i, c in enumerate(train['status'].unique())}\n",
    "train['label_playerId'] = train['playerId'].map(player2num)\n",
    "train['label_primaryPositionName'] = train['primaryPositionName'].map(position2num)\n",
    "train['label_teamId'] = train['teamId'].map(teamid2num)\n",
    "train['label_status'] = train['status'].map(status2num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "developing-poison",
   "metadata": {
    "_cell_guid": "ffc2b454-9e48-44f8-b4fa-d2d87ea5e89d",
    "_uuid": "ddc79e19-ca6c-40d2-bb71-cf5df58549ce",
    "collapsed": false,
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     "status": "completed"
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   "outputs": [],
   "source": [
    "train_X = train[feature_cols]\n",
    "train_y = train[['target1', 'target2', 'target3', 'target4']]\n",
    "\n",
    "_index = (train['date'] < 20210401)\n",
    "x_train = train_X.loc[_index].reset_index(drop=True)\n",
    "y_train = train_y.loc[_index].reset_index(drop=True)\n",
    "x_valid = train_X.loc[~_index].reset_index(drop=True)\n",
    "y_valid = train_y.loc[~_index].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "photographic-aggregate",
   "metadata": {
    "_cell_guid": "50c7c4dd-9076-4e0a-98bc-bb2e56f0ecb4",
    "_uuid": "c616b4b9-961c-4fcc-9643-391e8225a43c",
    "collapsed": false,
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l1: 0.711989\n",
      "[200]\tvalid_0's l1: 0.708027\n",
      "[300]\tvalid_0's l1: 0.706246\n",
      "[400]\tvalid_0's l1: 0.706096\n",
      "[500]\tvalid_0's l1: 0.706112\n",
      "Early stopping, best iteration is:\n",
      "[408]\tvalid_0's l1: 0.706095\n",
      "mae: 0.7060952435706853\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[58]\tvalid_0's l1: 1.43981\n",
      "mae: 1.4398068857203308\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l1: 0.552142\n",
      "[200]\tvalid_0's l1: 0.552652\n",
      "Early stopping, best iteration is:\n",
      "[103]\tvalid_0's l1: 0.552138\n",
      "mae: 0.552138150703566\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l1: 1.1339\n",
      "[200]\tvalid_0's l1: 1.12942\n",
      "Early stopping, best iteration is:\n",
      "[143]\tvalid_0's l1: 1.12908\n",
      "mae: 1.1290846201773048\n",
      "score: 0.9567812250429717\n"
     ]
    }
   ],
   "source": [
    "def fit_lgbm(x_train, y_train, x_valid, y_valid, params: dict=None, verbose=100):\n",
    "    oof_pred = np.zeros(len(y_valid), dtype=np.float32)\n",
    "    model = lgbm.LGBMRegressor(**params)\n",
    "    model.fit(x_train, y_train, \n",
    "        eval_set=[(x_valid, y_valid)],  \n",
    "        early_stopping_rounds=verbose, \n",
    "        verbose=verbose)\n",
    "    oof_pred = model.predict(x_valid)\n",
    "    score = mean_absolute_error(oof_pred, y_valid)\n",
    "    print('mae:', score)\n",
    "    return oof_pred, model, score\n",
    "\n",
    "\n",
    "# training lightgbm\n",
    "\n",
    "params1 = {\n",
    " 'objective':'mae',\n",
    " 'reg_alpha': 0.1,\n",
    " 'reg_lambda': 0.1, \n",
    " 'n_estimators': 10000,\n",
    " 'learning_rate': 0.1,\n",
    " 'random_state': 42,\n",
    " \"num_leaves\": 100\n",
    "}\n",
    "\n",
    "\n",
    "params2 = {\n",
    " 'objective':'mae',\n",
    " 'reg_alpha': 0.1,\n",
    " 'reg_lambda': 0.1, \n",
    " 'n_estimators': 80,\n",
    " 'learning_rate': 0.1,\n",
    " 'random_state': 42,\n",
    " \"num_leaves\": 22\n",
    "}\n",
    "\n",
    "params4 = {\n",
    " 'objective':'mae',\n",
    " 'reg_alpha': 0.1,\n",
    " 'reg_lambda': 0.1, \n",
    " 'n_estimators': 10000,\n",
    " 'learning_rate': 0.1,\n",
    " 'random_state': 42,\n",
    " \"num_leaves\": 100\n",
    "}\n",
    "\n",
    "\n",
    "params = {\n",
    " 'objective':'mae',\n",
    " 'reg_alpha': 0.1,\n",
    " 'reg_lambda': 0.1, \n",
    " 'n_estimators': 10000,\n",
    " 'learning_rate': 0.1,\n",
    " 'random_state': 42,\n",
    " \"num_leaves\": 100\n",
    "}\n",
    "\n",
    "\n",
    "oof1, model1, score1 = fit_lgbm(\n",
    "    x_train, y_train['target1'],\n",
    "    x_valid, y_valid['target1'],\n",
    "    params1\n",
    ")\n",
    "\n",
    "oof2, model2, score2 = fit_lgbm(\n",
    "    x_train, y_train['target2'],\n",
    "    x_valid, y_valid['target2'],\n",
    "    params2\n",
    ")\n",
    "\n",
    "oof3, model3, score3 = fit_lgbm(\n",
    "    x_train, y_train['target3'],\n",
    "    x_valid, y_valid['target3'],\n",
    "    params\n",
    ")\n",
    "\n",
    "oof4, model4, score4 = fit_lgbm(\n",
    "    x_train, y_train['target4'],\n",
    "    x_valid, y_valid['target4'],\n",
    "    params4\n",
    ")\n",
    "\n",
    "score = (score1+score2+score3+score4) / 4\n",
    "print(f'score: {score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alternative-dealer",
   "metadata": {
    "_cell_guid": "25cf48a6-84c3-4d5d-a312-1a47c050722e",
    "_uuid": "372a2718-6f2d-4128-97cb-fefd653e1f40",
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     "start_time": "2021-07-09T02:14:43.454132",
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    "tags": []
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "respective-median",
   "metadata": {
    "_cell_guid": "08cf9a6e-1f2c-4acd-b714-703d641d996a",
    "_uuid": "7828a113-8dc7-4691-b2a6-b232ca0525dc",
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    "execution": {
     "iopub.execute_input": "2021-07-09T02:14:43.539521Z",
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   "outputs": [],
   "source": [
    "players_cols = ['playerId', 'primaryPositionName']\n",
    "rosters_cols = ['playerId', 'teamId', 'status']\n",
    "scores_cols = ['playerId', 'battingOrder', 'gamesPlayedBatting', 'flyOuts',\n",
    "       'groundOuts', 'runsScored', 'doubles', 'triples', 'homeRuns',\n",
    "       'strikeOuts', 'baseOnBalls', 'intentionalWalks', 'hits', 'hitByPitch',\n",
    "       'atBats', 'caughtStealing', 'stolenBases', 'groundIntoDoublePlay',\n",
    "       'groundIntoTriplePlay', 'plateAppearances', 'totalBases', 'rbi',\n",
    "       'leftOnBase', 'sacBunts', 'sacFlies', 'catchersInterference',\n",
    "       'pickoffs', 'gamesPlayedPitching', 'gamesStartedPitching',\n",
    "       'completeGamesPitching', 'shutoutsPitching', 'winsPitching',\n",
    "       'lossesPitching', 'flyOutsPitching', 'airOutsPitching',\n",
    "       'groundOutsPitching', 'runsPitching', 'doublesPitching',\n",
    "       'triplesPitching', 'homeRunsPitching', 'strikeOutsPitching',\n",
    "       'baseOnBallsPitching', 'intentionalWalksPitching', 'hitsPitching',\n",
    "       'hitByPitchPitching', 'atBatsPitching', 'caughtStealingPitching',\n",
    "       'stolenBasesPitching', 'inningsPitched', 'saveOpportunities',\n",
    "       'earnedRuns', 'battersFaced', 'outsPitching', 'pitchesThrown', 'balls',\n",
    "       'strikes', 'hitBatsmen', 'balks', 'wildPitches', 'pickoffsPitching',\n",
    "       'rbiPitching', 'gamesFinishedPitching', 'inheritedRunners',\n",
    "       'inheritedRunnersScored', 'catchersInterferencePitching',\n",
    "       'sacBuntsPitching', 'sacFliesPitching', 'saves', 'holds', 'blownSaves',\n",
    "       'assists', 'putOuts', 'errors', 'chances']\n",
    "\n",
    "null = np.nan\n",
    "true = True\n",
    "false = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "headed-bumper",
   "metadata": {
    "_cell_guid": "e45d8e46-64b0-456a-b4f3-7ada7385e365",
    "_uuid": "b308ded0-7d7e-4024-8a41-b1d0997cbeb3",
    "collapsed": false,
    "execution": {
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    "papermill": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2506176, 6)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:55<00:00,  2.79s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2506176, 87)\n",
      "(2464956, 87)\n",
      "Model: \"ANN\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs (InputLayer)          [(None, 84)]              0         \n",
      "_________________________________________________________________\n",
      "d1 (Dense)                   (None, 50)                4250      \n",
      "_________________________________________________________________\n",
      "d2 (Dense)                   (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "preds (Dense)                (None, 4)                 204       \n",
      "=================================================================\n",
      "Total params: 7,004\n",
      "Trainable params: 7,004\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "FOLD: 0\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 4s 57ms/step - loss: 1.4686 - val_loss: 1.0874\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.08741, saving model to w0.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.9427 - val_loss: 0.9771\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.08741 to 0.97706, saving model to w0.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8577 - val_loss: 0.9328\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.97706 to 0.93283, saving model to w0.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8249 - val_loss: 0.9041\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.93283 to 0.90408, saving model to w0.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8014 - val_loss: 0.8856\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.90408 to 0.88564, saving model to w0.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7826 - val_loss: 0.8735\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.88564 to 0.87352, saving model to w0.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.7720 - val_loss: 0.8642\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.87352 to 0.86422, saving model to w0.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 3s 43ms/step - loss: 0.7647 - val_loss: 0.8572\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.86422 to 0.85723, saving model to w0.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7580 - val_loss: 0.8529\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.85723 to 0.85293, saving model to w0.h5\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7530 - val_loss: 0.8486\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.85293 to 0.84862, saving model to w0.h5\n",
      "9/9 [==============================] - 0s 11ms/step\n",
      "FOLD: 1\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 3s 45ms/step - loss: 1.2665 - val_loss: 0.7600\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.75996, saving model to w1.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.9316 - val_loss: 0.7054\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.75996 to 0.70542, saving model to w1.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8675 - val_loss: 0.6763\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.70542 to 0.67630, saving model to w1.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.8384 - val_loss: 0.6576\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.67630 to 0.65759, saving model to w1.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.8164 - val_loss: 0.6464\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.65759 to 0.64636, saving model to w1.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8036 - val_loss: 0.6416\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.64636 to 0.64158, saving model to w1.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7957 - val_loss: 0.6364\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.64158 to 0.63637, saving model to w1.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7916 - val_loss: 0.6350\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.63637 to 0.63502, saving model to w1.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.7866 - val_loss: 0.6324\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.63502 to 0.63241, saving model to w1.h5\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 3s 43ms/step - loss: 0.7844 - val_loss: 0.6316\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.63241 to 0.63160, saving model to w1.h5\n",
      "9/9 [==============================] - 0s 11ms/step\n",
      "FOLD: 2\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 3s 45ms/step - loss: 1.9006 - val_loss: 1.1918\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.19183, saving model to w2.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.9546 - val_loss: 1.0625\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.19183 to 1.06248, saving model to w2.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8617 - val_loss: 1.0114\n",
      "\n",
      "Epoch 00003: val_loss improved from 1.06248 to 1.01144, saving model to w2.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8225 - val_loss: 0.9760\n",
      "\n",
      "Epoch 00004: val_loss improved from 1.01144 to 0.97603, saving model to w2.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7952 - val_loss: 0.9521\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.97603 to 0.95206, saving model to w2.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7769 - val_loss: 0.9375\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.95206 to 0.93753, saving model to w2.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 42ms/step - loss: 0.7657 - val_loss: 0.9279\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.93753 to 0.92786, saving model to w2.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7577 - val_loss: 0.9230\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.92786 to 0.92305, saving model to w2.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7528 - val_loss: 0.9143\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.92305 to 0.91428, saving model to w2.h5\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7490 - val_loss: 0.9086\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.91428 to 0.90857, saving model to w2.h5\n",
      "9/9 [==============================] - 0s 12ms/step\n",
      "FOLD: 3\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 3s 46ms/step - loss: 1.2268 - val_loss: 0.9117\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.91174, saving model to w3.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.9061 - val_loss: 0.8332\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.91174 to 0.83318, saving model to w3.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.8422 - val_loss: 0.7987\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.83318 to 0.79874, saving model to w3.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.8125 - val_loss: 0.7782\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.79874 to 0.77822, saving model to w3.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7933 - val_loss: 0.7653\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.77822 to 0.76532, saving model to w3.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7806 - val_loss: 0.7561\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.76532 to 0.75612, saving model to w3.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7712 - val_loss: 0.7505\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.75612 to 0.75052, saving model to w3.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 2s 42ms/step - loss: 0.7667 - val_loss: 0.7491\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.75052 to 0.74906, saving model to w3.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.7666 - val_loss: 0.7452\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.74906 to 0.74522, saving model to w3.h5\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7617 - val_loss: 0.7438\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.74522 to 0.74378, saving model to w3.h5\n",
      "9/9 [==============================] - 0s 12ms/step\n",
      "FOLD: 4\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 3s 46ms/step - loss: 1.4706 - val_loss: 1.0413\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 1.04132, saving model to w4.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 3s 46ms/step - loss: 0.9451 - val_loss: 0.9461\n",
      "\n",
      "Epoch 00002: val_loss improved from 1.04132 to 0.94609, saving model to w4.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.8789 - val_loss: 0.8992\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.94609 to 0.89918, saving model to w4.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8450 - val_loss: 0.8708\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.89918 to 0.87082, saving model to w4.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8194 - val_loss: 0.8536\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.87082 to 0.85360, saving model to w4.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8060 - val_loss: 0.8391\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.85360 to 0.83913, saving model to w4.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7877 - val_loss: 0.8259\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.83913 to 0.82592, saving model to w4.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7784 - val_loss: 0.8171\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.82592 to 0.81710, saving model to w4.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 42ms/step - loss: 0.7709 - val_loss: 0.8201\n",
      "\n",
      "Epoch 00009: val_loss did not improve from 0.81710\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7643 - val_loss: 0.8068\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.81710 to 0.80685, saving model to w4.h5\n",
      "9/9 [==============================] - 0s 11ms/step\n",
      "FOLD: 5\n",
      "Epoch 1/10\n",
      "59/59 [==============================] - 3s 45ms/step - loss: 1.5902 - val_loss: 0.8836\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.88360, saving model to w5.h5\n",
      "Epoch 2/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.9618 - val_loss: 0.7952\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.88360 to 0.79522, saving model to w5.h5\n",
      "Epoch 3/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8796 - val_loss: 0.7551\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.79522 to 0.75512, saving model to w5.h5\n",
      "Epoch 4/10\n",
      "59/59 [==============================] - 3s 46ms/step - loss: 0.8435 - val_loss: 0.7318\n",
      "\n",
      "Epoch 00004: val_loss improved from 0.75512 to 0.73183, saving model to w5.h5\n",
      "Epoch 5/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8200 - val_loss: 0.7154\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.73183 to 0.71542, saving model to w5.h5\n",
      "Epoch 6/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.8081 - val_loss: 0.7019\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.71542 to 0.70190, saving model to w5.h5\n",
      "Epoch 7/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7945 - val_loss: 0.6938\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.70190 to 0.69378, saving model to w5.h5\n",
      "Epoch 8/10\n",
      "59/59 [==============================] - 2s 40ms/step - loss: 0.7860 - val_loss: 0.6904\n",
      "\n",
      "Epoch 00008: val_loss improved from 0.69378 to 0.69037, saving model to w5.h5\n",
      "Epoch 9/10\n",
      "59/59 [==============================] - 2s 41ms/step - loss: 0.7835 - val_loss: 0.6870\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.69037 to 0.68696, saving model to w5.h5\n",
      "Epoch 10/10\n",
      "59/59 [==============================] - 2s 39ms/step - loss: 0.7802 - val_loss: 0.6864\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.68696 to 0.68636, saving model to w5.h5\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "mae: 0.7709652458154707\n",
      "mse: 3.9190866798226565\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import timedelta\n",
    "from tqdm import tqdm\n",
    "import gc\n",
    "from functools import reduce\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "ROOT_DIR = \"../input/mlb-player-digital-engagement-forecasting\"\n",
    "\n",
    "#=======================#\n",
    "def 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",
    "#============================#\n",
    "def reducer(left, right):\n",
    "    return left.merge(right, on=\"playerId\")\n",
    "#========================\n",
    "\n",
    "TGTCOLS = [\"target1\",\"target2\",\"target3\",\"target4\"]\n",
    "def 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",
    "#=================================\n",
    "def 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",
    "#===============\n",
    "\n",
    "tr = pd.read_csv(\"../input/mlb-data/target.csv\")\n",
    "print(tr.shape)\n",
    "gc.collect()\n",
    "\n",
    "tr[\"EvalDate\"] = pd.to_datetime(tr[\"EvalDate\"])\n",
    "tr[\"EvalDate\"] = tr[\"EvalDate\"] + timedelta(days=-1)\n",
    "tr[\"EvalYear\"] = tr[\"EvalDate\"].dt.year\n",
    "\n",
    "MED_DF = tr.groupby([\"playerId\",\"EvalYear\"])[TGTCOLS].median().reset_index()\n",
    "MEDCOLS = [\"tgt1_med\",\"tgt2_med\", \"tgt3_med\", \"tgt4_med\"]\n",
    "MED_DF.columns = [\"playerId\",\"EvalYear\"] + MEDCOLS\n",
    "\n",
    "LAGS = list(range(1,21))\n",
    "FECOLS = [f\"{col}_{lag}\" for lag in reversed(LAGS) for col in TGTCOLS]\n",
    "\n",
    "for lag in tqdm(LAGS):\n",
    "    tr = train_lag(tr, lag=lag)\n",
    "    gc.collect()\n",
    "#===========\n",
    "tr = tr.sort_values(by=[\"playerId\", \"EvalDate\"])\n",
    "print(tr.shape)\n",
    "tr = tr.dropna()\n",
    "print(tr.shape)\n",
    "tr = tr.merge(MED_DF, on=[\"playerId\",\"EvalYear\"])\n",
    "gc.collect()\n",
    "\n",
    "X = tr[FECOLS+MEDCOLS].values\n",
    "y = tr[TGTCOLS].values\n",
    "cl = tr[\"playerId\"].values\n",
    "\n",
    "NFOLDS = 6\n",
    "skf = StratifiedKFold(n_splits=NFOLDS)\n",
    "folds = skf.split(X, cl)\n",
    "folds = list(folds)\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.layers as L\n",
    "import tensorflow.keras.models as M\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "tf.random.set_seed(777)\n",
    "\n",
    "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\n",
    "\n",
    "net = make_model(X.shape[1])\n",
    "print(net.summary())\n",
    "\n",
    "oof = np.zeros(y.shape)\n",
    "nets = []\n",
    "for 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=10, batch_size=35_000, 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",
    "#\n",
    "\n",
    "mae = mean_absolute_error(y, oof)\n",
    "mse = mean_squared_error(y, oof, squared=False)\n",
    "print(\"mae:\", mae)\n",
    "print(\"mse:\", mse)\n",
    "\n",
    "# Historical information to use in prediction time\n",
    "bound_dt = pd.to_datetime(\"2021-01-01\")\n",
    "LAST = tr.loc[tr.EvalDate>bound_dt].copy()\n",
    "\n",
    "LAST_MED_DF = MED_DF.loc[MED_DF.EvalYear==2021].copy()\n",
    "LAST_MED_DF.drop(\"EvalYear\", axis=1, inplace=True)\n",
    "del tr\n",
    "\n",
    "#\"\"\"\n",
    "import mlb\n",
    "FE = []; SUB = [];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "baking-mortality",
   "metadata": {
    "_cell_guid": "c3148c3f-68b2-46f6-b8d6-3284e68b507a",
    "_uuid": "bc76106c-5572-4d75-ae68-28381f7406b7",
    "collapsed": false,
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This version of the API is not optimized and should not be used to estimate the runtime of your code on the hidden test set.\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "env = mlb.make_env() # initialize the environment\n",
    "iter_test = env.iter_test() # iterator which loops over each date in test set\n",
    "\n",
    "for (test_df, sample_prediction_df) in iter_test: # make predictions here\n",
    "    \n",
    "    sub = copy.deepcopy(sample_prediction_df.reset_index())\n",
    "    sample_prediction_df = copy.deepcopy(sample_prediction_df.reset_index(drop=True))\n",
    "    \n",
    "    # LGBM summit\n",
    "    # creat dataset\n",
    "    sample_prediction_df['playerId'] = sample_prediction_df['date_playerId']\\\n",
    "                                        .map(lambda x: int(x.split('_')[1]))\n",
    "    # Dealing with missing values\n",
    "    if test_df['rosters'].iloc[0] == test_df['rosters'].iloc[0]:\n",
    "        test_rosters = pd.DataFrame(eval(test_df['rosters'].iloc[0]))\n",
    "    else:\n",
    "        test_rosters = pd.DataFrame({'playerId': sample_prediction_df['playerId']})\n",
    "        for col in rosters.columns:\n",
    "            if col == 'playerId': continue\n",
    "            test_rosters[col] = np.nan\n",
    "            \n",
    "    if test_df['playerBoxScores'].iloc[0] == test_df['playerBoxScores'].iloc[0]:\n",
    "        test_scores = pd.DataFrame(eval(test_df['playerBoxScores'].iloc[0]))\n",
    "    else:\n",
    "        test_scores = pd.DataFrame({'playerId': sample_prediction_df['playerId']})\n",
    "        for col in scores.columns:\n",
    "            if col == 'playerId': continue\n",
    "            test_scores[col] = np.nan\n",
    "    test_scores = test_scores.groupby('playerId').sum().reset_index()\n",
    "    test = sample_prediction_df[['playerId']].copy()\n",
    "    test = test.merge(players[players_cols], on='playerId', how='left')\n",
    "    test = test.merge(test_rosters[rosters_cols], on='playerId', how='left')\n",
    "    test = test.merge(test_scores[scores_cols], on='playerId', how='left')\n",
    "    test = test.merge(player_target_stats, how='inner', left_on=[\"playerId\"],right_on=[\"playerId\"])\n",
    "    \n",
    "\n",
    "    test['label_playerId'] = test['playerId'].map(player2num)\n",
    "    test['label_primaryPositionName'] = test['primaryPositionName'].map(position2num)\n",
    "    test['label_teamId'] = test['teamId'].map(teamid2num)\n",
    "    test['label_status'] = test['status'].map(status2num)\n",
    "    \n",
    "    test_X = test[feature_cols]\n",
    "    \n",
    "    # predict\n",
    "    pred1 = model1.predict(test_X)\n",
    "    pred2 = model2.predict(test_X)\n",
    "    pred3 = model3.predict(test_X)\n",
    "    pred4 = model4.predict(test_X)\n",
    "    \n",
    "    # merge submission\n",
    "    sample_prediction_df['target1'] = np.clip(pred1, 0, 100)\n",
    "    sample_prediction_df['target2'] = np.clip(pred2, 0, 100)\n",
    "    sample_prediction_df['target3'] = np.clip(pred3, 0, 100)\n",
    "    sample_prediction_df['target4'] = np.clip(pred4, 0, 100)\n",
    "    sample_prediction_df = sample_prediction_df.fillna(0.)\n",
    "    del sample_prediction_df['playerId']\n",
    "    # TF summit\n",
    "    # Features computation at Evaluation Date\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",
    "    # Blending\n",
    "    blend = pd.concat(\n",
    "        [sub[['date_playerId']],\n",
    "        (0.1*sub.drop('date_playerId', axis=1) + 0.9*sample_prediction_df.drop('date_playerId', axis=1))],\n",
    "        axis=1\n",
    "    )\n",
    "    env.predict(blend)\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "strategic-badge",
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    {
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       "      <th>1</th>\n",
       "      <td>20210501_605218</td>\n",
       "      <td>0.004394</td>\n",
       "      <td>0.397932</td>\n",
       "      <td>2.023864e-03</td>\n",
       "      <td>0.841400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210501_621563</td>\n",
       "      <td>0.164514</td>\n",
       "      <td>2.891772</td>\n",
       "      <td>3.119705e-02</td>\n",
       "      <td>0.731918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210501_670084</td>\n",
       "      <td>0.017729</td>\n",
       "      <td>1.020336</td>\n",
       "      <td>5.109109e-03</td>\n",
       "      <td>0.258483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210501_670970</td>\n",
       "      <td>0.006736</td>\n",
       "      <td>0.178890</td>\n",
       "      <td>3.121584e-03</td>\n",
       "      <td>0.103058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>20210501_596049</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.024692</td>\n",
       "      <td>1.615166e-08</td>\n",
       "      <td>0.040952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>20210501_642851</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.048513</td>\n",
       "      <td>1.615166e-08</td>\n",
       "      <td>0.082510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>20210501_596071</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.145307</td>\n",
       "      <td>1.880186e-08</td>\n",
       "      <td>0.058525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>20210501_664901</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.374723</td>\n",
       "      <td>6.911587e-03</td>\n",
       "      <td>0.190230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>20210501_605525</td>\n",
       "      <td>0.002357</td>\n",
       "      <td>0.676325</td>\n",
       "      <td>1.250110e-07</td>\n",
       "      <td>0.116019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1187 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date_playerId   target1   target2       target3   target4\n",
       "0     20210501_488726  0.947934  5.378781  6.273849e-02  1.969434\n",
       "1     20210501_605218  0.004394  0.397932  2.023864e-03  0.841400\n",
       "2     20210501_621563  0.164514  2.891772  3.119705e-02  0.731918\n",
       "3     20210501_670084  0.017729  1.020336  5.109109e-03  0.258483\n",
       "4     20210501_670970  0.006736  0.178890  3.121584e-03  0.103058\n",
       "...               ...       ...       ...           ...       ...\n",
       "1182  20210501_596049  0.000090  0.024692  1.615166e-08  0.040952\n",
       "1183  20210501_642851  0.000091  0.048513  1.615166e-08  0.082510\n",
       "1184  20210501_596071  0.000402  0.145307  1.880186e-08  0.058525\n",
       "1185  20210501_664901  0.000545  0.374723  6.911587e-03  0.190230\n",
       "1186  20210501_605525  0.002357  0.676325  1.250110e-07  0.116019\n",
       "\n",
       "[1187 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    [sub[['date_playerId']],\n",
    "    (sub.drop('date_playerId', axis=1) + sample_prediction_df.drop('date_playerId', axis=1)) / 2],\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "coordinated-pension",
   "metadata": {
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     "iopub.execute_input": "2021-07-09T02:19:07.236540Z",
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     "iopub.status.idle": "2021-07-09T02:19:07.246027Z",
     "shell.execute_reply": "2021-07-09T02:19:07.245473Z",
     "shell.execute_reply.started": "2021-07-09T01:46:34.482368Z"
    },
    "jupyter": {
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    },
    "papermill": {
     "duration": 0.640101,
     "end_time": "2021-07-09T02:19:07.246173",
     "exception": false,
     "start_time": "2021-07-09T02:19:06.606072",
     "status": "completed"
    },
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_playerId</th>\n",
       "      <th>target1</th>\n",
       "      <th>target2</th>\n",
       "      <th>target3</th>\n",
       "      <th>target4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>20210501_488726</td>\n",
       "      <td>1.795630e+00</td>\n",
       "      <td>7.208038</td>\n",
       "      <td>1.070018e-01</td>\n",
       "      <td>2.328425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210501_605218</td>\n",
       "      <td>3.892296e-03</td>\n",
       "      <td>0.584776</td>\n",
       "      <td>4.047728e-03</td>\n",
       "      <td>0.779458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210501_621563</td>\n",
       "      <td>2.477554e-01</td>\n",
       "      <td>4.011201</td>\n",
       "      <td>1.429004e-02</td>\n",
       "      <td>0.979123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210501_670084</td>\n",
       "      <td>2.117302e-03</td>\n",
       "      <td>1.157500</td>\n",
       "      <td>1.116430e-03</td>\n",
       "      <td>0.101867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210501_670970</td>\n",
       "      <td>1.111622e-03</td>\n",
       "      <td>0.123450</td>\n",
       "      <td>5.534922e-03</td>\n",
       "      <td>0.084547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>20210501_596049</td>\n",
       "      <td>2.303960e-22</td>\n",
       "      <td>0.044535</td>\n",
       "      <td>3.230333e-08</td>\n",
       "      <td>0.043576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>20210501_642851</td>\n",
       "      <td>3.696029e-06</td>\n",
       "      <td>0.080746</td>\n",
       "      <td>3.230333e-08</td>\n",
       "      <td>0.088603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>20210501_596071</td>\n",
       "      <td>2.811818e-04</td>\n",
       "      <td>0.238227</td>\n",
       "      <td>3.760372e-08</td>\n",
       "      <td>0.063559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>20210501_664901</td>\n",
       "      <td>1.090635e-03</td>\n",
       "      <td>0.345884</td>\n",
       "      <td>1.243232e-03</td>\n",
       "      <td>0.145848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>20210501_605525</td>\n",
       "      <td>7.507696e-04</td>\n",
       "      <td>0.827500</td>\n",
       "      <td>2.500219e-07</td>\n",
       "      <td>0.115777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1187 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date_playerId       target1   target2       target3   target4\n",
       "0     20210501_488726  1.795630e+00  7.208038  1.070018e-01  2.328425\n",
       "1     20210501_605218  3.892296e-03  0.584776  4.047728e-03  0.779458\n",
       "2     20210501_621563  2.477554e-01  4.011201  1.429004e-02  0.979123\n",
       "3     20210501_670084  2.117302e-03  1.157500  1.116430e-03  0.101867\n",
       "4     20210501_670970  1.111622e-03  0.123450  5.534922e-03  0.084547\n",
       "...               ...           ...       ...           ...       ...\n",
       "1182  20210501_596049  2.303960e-22  0.044535  3.230333e-08  0.043576\n",
       "1183  20210501_642851  3.696029e-06  0.080746  3.230333e-08  0.088603\n",
       "1184  20210501_596071  2.811818e-04  0.238227  3.760372e-08  0.063559\n",
       "1185  20210501_664901  1.090635e-03  0.345884  1.243232e-03  0.145848\n",
       "1186  20210501_605525  7.507696e-04  0.827500  2.500219e-07  0.115777\n",
       "\n",
       "[1187 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_prediction_df"
   ]
  }
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