{"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":"### 3rd Place Solution\nThis is the third place solution code. Most of the logic is executed by base-64 decoding the source code maintained on my GitHub repository.\nFor the rest of the code, have a look at my GitHub repository.\n\n- solution: https://www.kaggle.com/c/mlb-player-digital-engagement-forecasting/discussion/256620\n- GitHub: https://github.com/nyanp/mlb-player-digital-engagement","metadata":{}},{"cell_type":"code","source":"import gc\nimport os\nimport json\nimport sys\nimport yaml\nimport time\nimport traceback\nfrom contextlib import contextmanager\n\nimport lightgbm as lgb\nimport pandas as pd\nimport numpy as np\n\n\nPREDICT_GBDT = True\nPREDICT_NN = True\nUSE_UPDATED_DATA = True\n\n@contextmanager\ndef timer(name):\n    s = time.time()\n    yield\n    elapsed = time.time() - s\n    print(f\"[{name}] {elapsed:.3f}s\")\n\n\ndef in_colab():\n    return 'google.colab' in sys.modules\n\ndef in_kaggle():\n    return 'kaggle_web_client' in sys.modules","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# copied from github repository\n# https://github.com/nyanp/mlb-player-digital-engagement\n\nif in_kaggle():\n    import gzip\n    import base64\n    import os\n    from pathlib import Path\n    from typing import Dict\n    # this is base64 encoded source code\n    file_data: Dict = {'src/benchmark.py': 'H4sIACV0BWEC/21S24rbMBB9N/gfxL7ILonDXp4WDE1327JQKGT9FoKQ7ZGjVpaENGkbQv69ki+b9CKDdayjOTNzxmkie2scksbYYzp/GP8G/fGCUfaQJsKZPlzXCL9QyZq8CQwnPde8A5cm8QnBheW4L7i1oNuMFsWK5pEZRLxrCgEcDw5mlXdXlEfzf8Jy58FdMWny/u/0LYihXpdp3kP+mCYkLE/K4bSIrywfD48SVDtCUNx6aP+8RZbEj7R1UmMmbranKHrekdMU8Fjci7O/GVqr1pvPHyv29PXLa9DZUuSuA7ylCzLBuwu8v8AHukuT53W1Zs8vmxAYvZLaHnDVq3ppFT+CW7ayk8jVEnQX2uxB41IEkxruUeqOpsl6U718Wj9VVyLaINTGfF9xh1LwBj0dpxMtqkE3+2y2B34ERabCplhvWlDRr+3IxaW6uvhgjMdg68AzIRWUZpryNyN1dl3BggjaA/JRjI2NnuS5qKWmeU5C7UQSqcn2dkHudmOiXawtgp8S99MMqTK8pXOZwySHn6Mkr3Ev0PGQenZvQQ4e2MG2HKEtK3eAxdTaUEf5b5v56IgUhLE4WsZIGdxjrA+6jNEp8eRWmvwG2zJ5VDcDAAA=', 'src/config.py': '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', 'src/constants.py': 'H4sIACV0BWEC/4VYXW+jVhB9j+T/cN8cS3bFp4GoWQlj7LjCBgFeZ1tVFrHJBskGC7DatNr/Xu7lI8xcZ+u3nBkOc+bOzB2SnC9ZXpL0er68k6gg6WVwN7hzN/vANgN3Qx7JH/KYKGOijsl0TLQx0cfE+BM47SXqNrgj1e8+vfxyjMq4TM7xVLkfSoKoTQRlIkjD0ZjcMoriRBCHo9H4UwJ9IsgTybhNoE9EYSLpPyUwGIFwm8CgBLLwMwJJmAjaRJJvElTG/41AqjRKzOcWgcgioDkY3LHMOubMdoIqqf/WhMPoryg/Dh/avxm2Xjkz11+aQYUL455hETjrgMFiH17vWljqw52z3EfDFlX6qN2iKiC2nxt4CojN1lvrw5bpN7Deh82F41PQgL6dEqhwG259m+FAoul4s/YBIHL+kRIgc9XBQOcusJ7MtUdxFfLXzkDnpgGBSs/qmHUUobujqAEpalQCKjdumykJqPRWrlMfuwREbr55rT8QOTPXjumHFAcqQ/u5ei1VKako6y0OhK5X8wp/ojjQutqELa6jOmx5DJ6f+ssC789wEeWyxSX+vZRfllGFtbjC8zBc5fPA+Kc8D8M1VNgtrvP5YfxAbzUjG39F4HmovyLy/gyHnepuW1zmccYP9K6dmeeY31hlKSrOdNPeUHEDaogmsC13M6cWHVnMxcoPaGkpBhwqDb8qwEqkP4aLqEJXdRepEsSDhgYV9JP5/FwbFGioGr3GVYj7bg1PcTgNjQaHXxu9Dt2d7rWofZdLOv2AVtPzGpapiKu/wWH37jpcxlXS4Ap8acBAFQ7o1ncKp04doIbGSOsMR9TvHYmB7o8G16DQOhBNxL3TOONB3OIynty221iAzqXVPaHibm5woNX7CBNOqQ7W0ZBtmkcz8PHVzaYLvD/DRTR8Gx5d4nHmL+PsN/4K/16Gq+h+bOLXpzwP44ddW20H7NB1IHdXjahvFDVQIz+5C3r7Aq1P/tz2Z9TdEPF1taaohK82huKDXYU1jsq3w9EV69coumN9v1o+3A21aMhS+6Nrtudv1IYfjcOwKKPyWljZMUYblYk3KX+Nl6j5VMAL1FwU8PY01/Dm5Dl4awq2eGGa+V/xsrRY23hRqvcecHxz22IrEtJZvl/iWyqDJdYZLEwsNLCwTtcLsU5ri3UGyw0n1MZCQx/rnNvcQuhuQ05nHSbsx+2aWwOro+c2QMvZcdufH2641c+3Q7b2oVxesiIpkyzdRGecTy8pD29xjhPoXsvXJD4dmQWGErUPgGgWSV6UZBYVMZfU+JClx9YEd5e3JO8scP1+q77rijK74Fx7SXp4I09JWbIYQNLncZF8T6uvk+OHg8E/7F/TtFYsfJKnsKo9lKdV+pEPuN91CZQ+TSDIVBeacjutIENdrFMU6vfqKG+E6XPNwc0AHKmDA7RxbAsc1Q4fl3kjwjaZN5pYxFFKOEoZR6ngKFUc5RRHqeEodVxMBq4gNgxBybi36uQlycs3K7umZf6OpG0Dbg7Ps3OSJocoJX58ub6ckgNW+zVO43+u8Snimu36EmHh3jXOy4z4ySHDKVjHf9co/CKK0ugY4WT8Fl2ilPu8zU7Z+SWJcF68iuJM0fY2Gtz9oJ/6lhnaS9dfVWvh3nLBR//lFL3H+R5cV91QG17y5Bzl7x6skZ69frrMo7SIDtSl2PduBFFrAhjcHeNXkhT7LN0XcVRk6f2xfID/pRiRyRfykmWnh5r8NctJ7Uujy8tx+1dcjakkJb3/DD185CB5JRU1+fIInh2RqHqIGn597NGMeg/SXx6X1zwlYX6Na0MDLKJTUSH/AYXoxjvHEgAA', 'src/event_level_model.py': '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', 'src/feature.py': 'H4sIACV0BWEC/8UZ227bNvQ9QP6B8B4sra6WdE26CPDD0K1FgbYvu2CAYAiMRMWqdXFJKk1q+N93DkXaFCXZTjtgRovE5LnfD5OX65pLUovzs4zXJZGP67y6I3l7/JoWBb0t2Iy8z4U8Pzs/0xdrWqVUEPi3TjVmexQ0Mi8M+pKKZdyex/XtJ5ZIw+VzWhog/B0pqwvBkyBjVDaciSC7uLiIb6nIEwP74yDYJYAV9O4w0AsAEpU4DPQzAKU0Lx5jIak8AvwSgNcFfWQ8pndHuF8BrGS0PA55rUSQTOYlOwz5CiDZPaukOE71F4AtmaSHoW7QRiypqzSuecr4ELSQNd8J9gd+Qed9ePfx3Ye/PsTvf31L5uTlFZ6dn6UsI3dMxhmEFONrnlfSSzM/PD8j8FkymgJwNkk3BavwYjtpb/KM6BMyn5MXVxfXl6+uNZaFOZGc5pXGWcIBxpsHkn5llWDS64cfkvR9jYC397RomBJiswz/2U6i8HrRXnMGRqnUBXDbxps9PHxJsyCvUvYQXSzIcwKxpT72xfPLzs3EsUhrc5BOLj0IcRanOQ+JkHxGLGuF7UFFS6YvIc5jzj43OWcluj4kAGYsKiQrQZmq5iUt8q9sxwYJeIqMVn5dC5nlD6j5NN64RLdTdEEuQDwOOJJxIJZqAoQVgpHptGOmWgSoSvCpzqudPh1VZmhKFHC70cy3QTbx92YRrAAPGZEFuCqE4hL8RiV9g1IAAa1NAbUoVBUpApssZuRLLpexpBwsO/+TN8zYA5SwrghEApm2v0/BSBJM16W5jzD7GIyEPzz7zCfPSGRoLVAFZf9kyUoK8LaL20NPGz6pi6aEMjQnkQ60rOYkOyQK3Cd43xKKsoV1iR8qBINcVDAQfpoDmGuqKZJNsvVUAm+yra80z+oGjAEIcskIlBuaoYmne8JGzmfzPV+jpvZ5mkUaarH3YlHT1KiuA8H1Y1f6nrltzx4AVXVIJQXYchoEP5nAmQ5hORGuskYVKhMqVqQa/1lFSyviGws44RGNZNzKV95bud7dWTLNnhIJmGFOcKn60TGJm3S9kuHvCQL7gK7XrEo98BBn2nVLxj0k7PuOxwEImkNCsY5CgNGHXMwvrRQu6Yo93fnak6qXPCE4wBIf64r9hzGiQhfSxODd1nUBiFhRBuWm9yyW9YnQKbtt7uZjt3YFe0OhwJ4QxCIED89fXg1aAAa3W5qsUL4KrBSidKPc7Z5v/NdiKFm+LUeg+J5WXDej2RPasT6aTduWMralcSYWIeHZMvYaRHZzcxM/vUtEHcRFj6Vz71YSzDesBRuLetv2n5buleqUbqS0llOXrjrBij0K0522lmHcdBhWGwMsoUXSFR0/q5Dctw6bwS8u1xwmAWCLjNDGZn5gUFKk8DpWiVYLq2ZtR+RQkiYUetV3iLIacrhRsSfDgYizzGIfnyS77QQUx4pQixtcTqyImoxJ7QwLKSsGwaIOscUT+OyFP8ZqD9nj1k0GlKkuRMcYR2YrPdaqSpRNUKOmgC0qJJve7GZU1pEPW4ffxVajUqvREPpejQ4Ft6MPVsEBr5gSYQ0CZhjIZ4TXX9SYBkuyqbAYsBzOkbNjcjPwtYbrOAOMAceAFk3UhostGf4z29fKCGqjBVCV9UH7VUQKsf3yLp0sFoEo8oTFTQXrvofEdxbYTZAS14vXdSXZg/R0sWopmJ8BrsQzJdtQW+pOCIG1mPe5H8R3y+Gs1x/n7kFPnV0BBeSmSsbSzVSTsC8P+LgpsBchvgfm8fswuwbXAusAC4eVUwVrL0c4bgLYk2Fn+xuX198hdDgGuQ4V1RM2qy2seyRt1mBXMGdKaMJrIXbBORmQFUNbYTs1U2fviDgOVKRILMyO1VG75wPNVY1SI+TbihBnZL+3KAbD0D8QgElWetvRGCNymzVsWIEDxrf3s1Y/3IU7LOEKJjABFoDwtJjAwobUzbvIMepG+T79MhcllclyRtjDGtZslkJpM+BbGOUT2dAixE1xckR/g3WixsPmGpcwQBm6O+odqxhXUamJHRPxlOT5Vjmf7ikNETRrrFQ6xPv1xbzC6YVMf/f7jQJaAES3vVUZYDG821mIHbaQSt0dpisRzGS40KU5F17nfWd8APUxvrAa1qv2IWakho5Wz1GXWTpEw65aBPDdrK/dAbIFGNtMhicYbOTPoCW7i/Eg5QOa9aaF3Wigo6996TSuS9W2Mr5oG78NbWyW8IqKes/VL9S39QNMS+CvmNNq5QHAjLgbm3nXaV/6zWNlm3v7Tebkvf6k5f4bN/zvWPOHV+n+g1B3f+x8BWfbU6jqXP73vCP9b9ufs/Yde4GCyDu4qWULN3f1TL1/isy2hBaYUY9tqRABEascClU6daoFRLjMq4bZJUvyR4fB0JvTjGjrgECzbphYPNhDwtZyRF6csLnVfPCPYpYOrqzmb1mcJgzHyCGiu8sgwwcPGYMAnu8QWkNVOD/7F408woGWGwAA', 'src/model_bank.py': 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'src/nn.py': 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', 'src/parallel.py': 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'src/train.py': 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2WG9XvByaRkpJh6pxQ731/mzwXVtaVqn1yOrmAI6VaF0CPd1oGSzhJRclffl9vlXWd43AoBj+7vZusl6PGjzOJnfXuxPKziqOqk80qDqOfCQV2yGY+mDBqtErrO9SiNLhldHgxe+L4P8pWN2IWijBsd9wTW46DNyGQ1NIpBot5MMUgA6UFka4NQGk7v1CPU2BWHd44bvYg9IPS/WiQ+WbQRTHe6pYb+E4ZtRIWvpGnDZQP/t1kijj57aHfhc7E9vvs201w5qhYlz0MxGy91ORoyTfg8p9SLyQ1GTYMZ9GfIeF0cBTT3Z54LfSVsR8AuFrICZO+OupfJVfKY0nnRMppNVbv33rf32Nh7iAPwAA', 'src/util.py': 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'src/.ipynb_checkpoints/nn-checkpoint.py': 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', 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'src/features/f500_team_agg.py': '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', 'src/features/f600_datetime.py': 'H4sIACV0BWEC/02OwQrCMAyG74W+Q27bBMtOHnYQQd9CREqbStG1pY24Ir677ZjDkEu+5P/zm+hHoBysu4Edg48EJ6uIM86WMUinZYLSQVdsqiJFJQxKekZMP92mbjk7LLw9N1pmb16I9+bScabRgNn1/XXFraJpgKN3hBN1sN3PvwfOoFTE4uLg/ecylAiCfDEgJDti1Qst7SNXJGfeifX+U+N8AWoYNrvhAAAA', 'src/features/f700_events_agg.py': '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', 'src/features/f800_meta.py': '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', 'src/features/f900_second_order.py': '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', 'src/features/helper.py': 'H4sIACV0BWEC/31TPW+DMBDdkfgPtwWkJG2kqAMSnTq3Q7pVVXTEdmUVjGVfpPDv6wsmIYHGC+h9+O4dh3JtA9RZbX5AN7Z1BB+WdGuwTpM0iZA5NrYD9GAso4pN3h3WnpzERqjB+qkbuZNOS787Mywm1xVpAuFEUdUS1dLIwy/TTBg0jUQD5YhbR/Cq0DMCPeLxNOXxNKog9GwNhnvVnGTMY12be77HLlU8iUmJgKWJPB2kpeI+sLH/BI3EJGDE54INd80ECtQYvzTaW84NDh9DSBWDZpTHfvk4SUdn2BHYLDy07zV572Wf0ErtBXY+w4KVAklS2ImX7RKqYrIeSzDlapPD6vWydF+qbpG+h41REMaYVTmUJTxPm3lvjezBGj3tyfPs1/17qOUJG5uZ/HpZVGl/tj66MAIZwmqw5fDEofhmIWvCl222WcLibTGeAHZ7nsL8AG6QB7mv/8x9K9WjJlh8s2eTZH+/bqrW7wMAAA==', 'src/features/__init__.py': 'H4sIACV0BWEC/22N2wnDMAxF/wvdQQOYLNFBjOIqrcGyjKRA0ulrmge09O9y7mtSYTBNw0Tos5INIxpB5ibqsMMAD/J4JALcpDot/oVjyfZDLD2JMQBx8zX+2wpQRRlLftHZqsidZ4sNtUsn7e59qyUpM9fP1fXyBo+ljmG7AAAA', 'src/features/.ipynb_checkpoints/base-checkpoint.py': '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'}\n    for path, encoded in file_data.items():\n        print(path)\n        path = Path(path)\n        path.parent.mkdir(exist_ok=True)\n        path.write_bytes(gzip.decompress(base64.b64decode(encoded)))\n    # output current commit hash\n    print('33ef1ed85ea58b0718e936e7234539eaa758c5d7')\nelse:\n    sys.path.append('../')\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load config\n\nfrom src.feature import *\nfrom src.parser import *\nfrom src.train import *\nfrom src.model_bank import *\nfrom src.nn import *\nfrom src.store import Store\n\nif in_kaggle():\n    print('in kaggle')\n    MODEL_DATA_PATH = '../input/mlb-dataset/'\n    FEATURE_DATA_PATH = '../input/mlb-feature-data/'\n    TRAIN_DATA_PATH = '../input/mlb-player-digital-engagement-forecasting'\nelse:\n    MODEL_DATA_PATH = './artifacts/'\n    FEATURE_DATA_PATH = '../input/mlb-player-digital-engagement-forecasting/'\n    TRAIN_DATA_PATH = '../input/mlb-player-digital-engagement-forecasting'\n\nwith open(os.path.join(MODEL_DATA_PATH, 'gbdt_new2', 'config.yaml'), 'r') as f:\n    config = yaml.load(f)\n\nwith open(os.path.join(MODEL_DATA_PATH, 'nn_new', 'nn_meta.json')) as f:\n    nn_meta = json.load(f)\n\nfeature_set = []\nnum_seeds = 1\n\nif PREDICT_GBDT:\n    feature_set += list(config['feature_set']['value'])\n\n    for subset_features in ['features_per_target', 'features_per_lag']:\n        if subset_features in config:\n            per_target_features = config[subset_features]['value']\n            for fs in config[subset_features]['value'].values():\n                feature_set.extend(fs)\n\n    if 'extra_df_on' in config and config['extra_df_on']['value']:\n        feature_set.extend(['f803', 'f804', 'f805'])\n\n    if 'num_seeds' in config and config['num_seeds']['value']:\n        num_seeds = config['num_seeds']['value']\n\nif PREDICT_NN:\n    nn_feature_set = [\n        'f022', 'f001', 'f002', 'f024', 'f023',\n        'f005', 'f021', 'f014', 'f037',\n        'f100', 'f102', 'f103', 'f105', 'f110', 'f111', 'f120', 'f121', 'f131',\n        'f300',\n        'f400', 'f401', 'f402', 'f403', 'f404', 'f408', 'f410',\n        'f020', 'f303', 'f058'\n    ]\n    feature_set.extend(nn_feature_set)\n\nfeature_set = list(sorted(list(set(feature_set))))\nfeature_func = [get_feature(f) for f in feature_set]\nlags = config['lag_requirements']['value']\nevent_level_models = [\n    lgb.Booster(model_file=os.path.join(MODEL_DATA_PATH, f'meta_model_target{i}.bin')) for i in [1, 2, 3, 4]\n]\n\nprint(f\"feature set: {feature_set}\")\n\nprint(f\"nn metadata: {nn_meta}\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with timer('load'):\n    store = Store.train(TRAIN_DATA_PATH,\n                        FEATURE_DATA_PATH,\n                        use_updated=USE_UPDATED_DATA,\n                        event_model=event_level_models,\n                        debug=not in_kaggle())\n\nmodel_bank = ModelBank(MODEL_DATA_PATH, lags, store.last_timestamp, nn_meta if PREDICT_NN else None, num_seeds=num_seeds)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if in_kaggle():\n    import mlb\nelse:\n    from src.dummy_mlb import MLBEmulator\n    mlb = MLBEmulator(multiple_days_per_iter=True, eval_start_day=20210701, eval_end_day=20210705)\n\n\nenv = mlb.make_env()\niter_test = env.iter_test()\n\ndfs = []\nbase_dfs = []\n\nfor n, (test_df, sample_prediction_df) in enumerate(iter_test):\n    try:\n        gc.collect()\n        for i, row in test_df.iterrows():\n            store.append(row)\n\n        current_lag = model_bank.gbdt_lags.get_current_lag(store.last_timestamp)\n        pred_base_df = make_df_base_from_test(sample_prediction_df)\n        pred_df = make_feature(pred_base_df, store, feature_set,\n                               load_from_store=False,\n                               save_to_store=False,\n                               lag_requirements=current_lag)\n\n        pred_gbdt = None\n        pred_mlp = None\n        pred_cnn = None\n\n        if PREDICT_GBDT:\n            try:\n                pred_gbdt = np.zeros((len(pred_df), 4))\n                models = model_bank.get_gbdt_models(store.last_timestamp, verbose=n < 3)\n\n                for i in range(4):\n                    features = model_bank.get_current_features(i, store.last_timestamp)\n                    X = pred_df[features].values.astype(np.float32)\n                    pred_gbdt[:, i] += models[i].predict(X)\n            except Exception:\n                print(f\"Error in predict GBDT!!! {traceback.format_exc()}\")\n                pred_gbdt = None\n\n        if PREDICT_NN:\n            try:\n                model = model_bank.get_mlp_model(store.last_timestamp)\n                pred_mlp = model.predict(pred_df)\n                model = model_bank.get_cnn_model(store.last_timestamp)\n                pred_cnn = model.predict(pred_df)\n            except Exception:\n                print(f\"Error in predict NN!!! {traceback.format_exc()}\")\n\n        predictions = ensemble(current_lag, pred_gbdt, pred_mlp, pred_cnn)\n\n        if n < 3:\n            base_dfs.append(pred_base_df.copy())\n            dfs.append(pred_df.copy())\n\n        for i in range(4):\n            sample_prediction_df[f'target{i+1}'] = np.clip(predictions[:, i], 0, 100)\n    except:\n        print('ERROR!!!')\n        print(traceback.format_exc())\n        pass\n\n    env.predict(sample_prediction_df)\n\npd.concat(dfs).to_csv('prediction_feature_df.csv', index=False)\npd.concat(base_dfs).to_csv('prediction_base_df.csv', index=False)\n\nsample_prediction_df.head()","metadata":{"collapsed":false,"pycharm":{"name":"#%%\n"},"jupyter":{"outputs_hidden":false}},"execution_count":null,"outputs":[]}]}