{"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":50160,"databundleVersionId":7921029,"sourceType":"competition"}],"dockerImageVersionId":30665,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"papermill":{"default_parameters":{},"duration":1221.035913,"end_time":"2024-03-11T20:40:31.474791","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-03-11T20:20:10.438878","version":"2.5.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# competitive data analysis homework\n\nБыл модифицирован этот ноутбук: https://www.kaggle.com/code/greysky/home-credit-baseline\n\nСкор вырос: 0.563->0.565\n\n- Был протестирован CatBoost, метрики поднять не удалось, код: https://www.kaggle.com/code/diordan/cb-try-home-credit\n\n- Были перебраны гиперпараметры под gini stability метрику. Чтобы не отъедать квоту на kaggle, гп перебирались локально, код: https://pastebin.com/y5VRkiuA. Посылка: https://www.kaggle.com/code/diordan/home-credit/notebook\n\n- Были добавлены альтерантивные аггрегации. Локально отобрал признаки с лучшими аггрегациями, результат в этом ноутбуке.","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-03-11T20:20:13.177936Z","iopub.status.busy":"2024-03-11T20:20:13.177674Z","iopub.status.idle":"2024-03-11T20:20:19.340830Z","shell.execute_reply":"2024-03-11T20:20:19.340062Z"},"papermill":{"duration":6.175783,"end_time":"2024-03-11T20:20:19.343026","exception":false,"start_time":"2024-03-11T20:20:13.167243","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Note: I'm looking for a job in Europe, if you like my work don't hesitate to reach =)\n\nimport os\nimport gc\nfrom glob import glob\nfrom pathlib import Path\nfrom datetime import datetime\n\nimport numpy as np\nimport pandas as pd\nimport polars as pl\n\nfrom datetime import datetime\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import StratifiedGroupKFold\nfrom sklearn.base import BaseEstimator, ClassifierMixin\n\nimport lightgbm as lgb\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:38:48.661064Z","iopub.execute_input":"2024-03-17T14:38:48.661728Z","iopub.status.idle":"2024-03-17T14:38:53.959986Z","shell.execute_reply.started":"2024-03-17T14:38:48.661686Z","shell.execute_reply":"2024-03-17T14:38:53.958991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# features = pd.read_csv(\"features.csv\").iloc[:300, 1:]","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:38:53.961911Z","iopub.execute_input":"2024-03-17T14:38:53.962641Z","iopub.status.idle":"2024-03-17T14:38:53.966729Z","shell.execute_reply.started":"2024-03-17T14:38:53.962606Z","shell.execute_reply":"2024-03-17T14:38:53.965469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"features = ['price_1097A',\n 'annuity_780A',\n 'max_numberofoverdueinstlmaxdat_148D',\n 'pmtnum_254L',\n 'lastrejectdate_50D',\n 'max_amount_4527230A',\n 'mean_amount_4527230A',\n 'dateofbirth_337D',\n 'validfrom_1069D',\n 'credamount_770A',\n 'disbursedcredamount_1113A',\n 'mobilephncnt_593L',\n 'median_dateofcredstart_739D',\n 'min_dateofcredstart_739D',\n 'max_overdueamountmax2date_1002D',\n 'max_dateofcredstart_739D',\n 'datelastinstal40dpd_247D',\n 'min_amount_4527230A',\n 'pmtaverage_4527227A',\n 'mean_dateofcredstart_739D',\n 'std_annuity_853A',\n 'max_employedfrom_700D',\n 'lastcancelreason_561M',\n 'std_recorddate_4527225D',\n 'std_num_group2_13',\n 'std_amount_4527230A',\n 'maxdpdinstldate_3546855D',\n 'median_amount_4527230A',\n 'max_dateofcredend_353D',\n 'std_dpdmaxdatemonth_442T',\n 'lastdelinqdate_224D',\n 'min_totalamount_6A',\n 'max_totalamount_6A',\n 'max_dateofrealrepmt_138D',\n 'min_refreshdate_3813885D',\n 'std_mainoccupationinc_437A',\n 'mean_mainoccupationinc_437A',\n 'std_numberofoverdueinstlmaxdat_148D',\n 'std_overdueamountmaxdatemonth_284T',\n 'median_mainoccupationinc_437A',\n 'max_dtlastpmtallstes_3545839D',\n 'std_overdueamountmax2date_1002D',\n 'lastrejectcredamount_222A',\n 'std_lastupdate_1112D',\n 'maxdbddpdlast1m_3658939P',\n 'inittransactionamount_650A',\n 'min_dateofcredend_289D',\n 'mean_totalamount_6A',\n 'max_overdueamountmax2date_1142D',\n 'max_sex_738L',\n 'std_monthlyinstlamount_674A',\n 'maxannuity_159A',\n 'max_dtlastpmt_581D',\n 'mean_dpdmaxdatemonth_442T',\n 'datelastunpaid_3546854D',\n 'min_mainoccupationinc_437A',\n 'cntpmts24_3658933L',\n 'median_residualamount_856A',\n 'max_dateofcredstart_181D',\n 'pctinstlsallpaidearl3d_427L',\n 'min_amount_4917619A',\n 'median_totalamount_6A',\n 'max_firstnonzeroinstldate_307D',\n 'std_employedfrom_700D',\n 'avgdbddpdlast3m_4187120P',\n 'std_monthlyinstlamount_332A',\n 'median_refreshdate_3813885D',\n 'std_totalamount_6A',\n 'maxinstallast24m_3658928A',\n 'max_mainoccupationinc_437A',\n 'median_credamount_590A',\n 'mean_refreshdate_3813885D',\n 'max_birth_259D',\n 'max_lastupdate_388D',\n 'mean_monthlyinstlamount_674A',\n 'mean_annuity_853A',\n 'median_numberofoverdueinstlmaxdat_148D',\n 'max_dateofcredend_289D',\n 'firstclxcampaign_1125D',\n 'std_dateofcredend_289D',\n 'std_refreshdate_3813885D',\n 'max_numberofoverdueinstlmaxdat_641D',\n 'min_dateofcredstart_181D',\n 'min_residualamount_856A',\n 'min_credamount_590A',\n 'interestrate_311L',\n 'median_annuity_853A',\n 'min_overdueamountmax2date_1002D',\n 'totinstallast1m_4525188A',\n 'std_credamount_590A',\n 'std_dtlastpmt_581D',\n 'eir_270L',\n 'std_numberofinstls_229L',\n 'mean_overdueamountmaxdatemonth_284T',\n 'median_overdueamountmax2date_1002D',\n 'max_annuity_853A',\n 'dtlastpmtallstes_4499206D',\n 'min_totaloutstanddebtvalue_39A',\n 'std_dateofcredstart_181D',\n 'days360_512L',\n 'max_residualamount_856A',\n 'min_outstandingamount_362A',\n 'std_pmtnum_8L',\n 'maininc_215A',\n 'mean_nominalrate_498L',\n 'mean_dateofcredend_289D',\n 'max_monthlyinstlamount_332A',\n 'std_firstnonzeroinstldate_307D',\n 'median_monthlyinstlamount_674A',\n 'std_dateofcredstart_739D',\n 'min_annualeffectiverate_199L',\n 'min_birth_259D',\n 'lastapprcommoditycat_1041M',\n 'min_employedfrom_700D',\n 'median_employedfrom_700D',\n 'applicationscnt_867L',\n 'days180_256L',\n 'datefirstoffer_1144D',\n 'mean_residualamount_856A',\n 'cntincpaycont9m_3716944L',\n 'mindbddpdlast24m_3658935P',\n 'mean_numberofoverdueinstlmaxdat_148D',\n 'maxlnamtstart6m_4525199A',\n 'mean_pmtnum_8L',\n 'max_amount_4917619A',\n 'mean_birth_259D',\n 'mean_pmts_dpd_1073P',\n 'max_incometype_1044T',\n 'numberofqueries_373L',\n 'median_dateofcredend_289D',\n 'mean_tenor_203L',\n 'min_numberofoverdueinstlmaxdat_148D',\n 'median_dateofcredstart_181D',\n 'std_pmts_year_507T',\n 'avglnamtstart24m_4525187A',\n 'median_lastupdate_388D',\n 'mean_credamount_590A',\n 'std_pmts_month_706T',\n 'mean_numberofinstls_229L',\n 'min_numberofinstls_229L',\n 'max_monthlyinstlamount_674A',\n 'max_instlamount_768A',\n 'mean_overdueamountmax2date_1002D',\n 'std_dpdmaxdateyear_896T',\n 'std_tenor_203L',\n 'maxdbddpdtollast12m_3658940P',\n 'mean_monthlyinstlamount_332A',\n 'median_birth_259D',\n 'isbidproduct_1095L',\n 'mean_employedfrom_700D',\n 'maxdbddpdtollast6m_4187119P',\n 'min_annuity_853A',\n 'min_dtlastpmt_581D',\n 'median_num_group1_3',\n 'min_nominalrate_498L',\n 'std_dpdmax_757P',\n 'min_incometype_1044T',\n 'avgpmtlast12m_4525200A',\n 'median_numberofoverdueinstlmax_1039L',\n 'mean_maxdpdtolerance_577P',\n 'max_num_group1_3',\n 'mean_num_group2_13',\n 'maxpmtlast3m_4525190A',\n 'std_dateofcredend_353D',\n 'median_nominalrate_498L',\n 'mean_num_group1_3',\n 'max_numberofoverdueinstlmax_1039L',\n 'std_creationdate_885D',\n 'mindbdtollast24m_4525191P',\n 'max_lastupdate_1112D',\n 'median_dateofcredend_353D',\n 'pctinstlsallpaidlate1d_3546856L',\n 'avginstallast24m_3658937A',\n 'std_dateofrealrepmt_138D',\n 'std_pmts_dpd_1073P',\n 'std_annualeffectiverate_199L',\n 'median_monthlyinstlamount_332A',\n 'std_nominalrate_498L',\n 'min_totalamount_996A',\n 'min_numberofinstls_320L',\n 'min_numberofoutstandinstls_59L',\n 'std_outstandingdebt_522A',\n 'std_overdueamountmaxdatemonth_365T',\n 'std_numberofinstls_320L',\n 'std_overdueamountmax_35A',\n 'avgdbddpdlast24m_3658932P',\n 'numincomingpmts_3546848L',\n 'mean_outstandingdebt_522A',\n 'min_numberofoverdueinstlmaxdat_641D',\n 'pctinstlsallpaidlat10d_839L',\n 'median_dateofrealrepmt_138D',\n 'mean_prolongationcount_1120L',\n 'max_credamount_590A',\n 'median_firstnonzeroinstldate_307D',\n 'mean_pmts_dpd_303P',\n 'std_num_group1_3',\n 'amtinstpaidbefduel24m_4187115A',\n 'median_lastupdate_1112D',\n 'numinstlswithdpd10_728L',\n 'std_numberofoverdueinstlmaxdat_641D',\n 'std_overdueamountmaxdateyear_994T',\n 'median_overdueamountmax2date_1142D',\n 'mean_pmts_overdue_1152A',\n 'mean_dateofcredstart_181D',\n 'std_residualamount_856A',\n 'avgoutstandbalancel6m_4187114A',\n 'monthsannuity_845L',\n 'std_lastupdate_388D',\n 'std_overdueamountmax2_398A',\n 'std_dpdmaxdatemonth_89T',\n 'median_dpdmax_757P',\n 'lastrejectcommoditycat_161M',\n 'mean_pmts_overdue_1140A',\n 'std_dateactivated_425D',\n 'median_creationdate_885D',\n 'max_credlmt_935A',\n 'weekday_decision',\n 'max_credlmt_230A',\n 'min_monthlyinstlamount_332A',\n 'max_numberofoutstandinstls_59L',\n 'min_lastupdate_1112D',\n 'std_maxdpdtolerance_577P',\n 'mean_firstnonzeroinstldate_307D',\n 'maxdpdinstlnum_3546846P',\n 'max_totalamount_996A',\n 'std_overdueamountmax2date_1142D',\n 'median_dtlastpmtallstes_3545839D',\n 'std_outstandingamount_362A',\n 'median_numberofinstls_229L',\n 'std_totalamount_996A',\n 'mean_numberofoverdueinstlmax_1151L',\n 'min_lastupdate_388D',\n 'max_num_group1_4',\n 'mean_amount_4917619A',\n 'std_numberofoverdueinstlmax_1151L',\n 'avgdbdtollast24m_4525197P',\n 'days30_165L',\n 'median_pmts_overdue_1152A',\n 'min_deductiondate_4917603D',\n 'numinstlswithoutdpd_562L',\n 'median_deductiondate_4917603D',\n 'median_numberofoverdueinstlmaxdat_641D',\n 'std_dtlastpmtallstes_3545839D',\n 'mean_numberofoutstandinstls_59L',\n 'min_overdueamountmax2date_1142D',\n 'min_relationshiptoclient_642T',\n 'totalsettled_863A',\n 'min_familystate_726L',\n 'min_monthlyinstlamount_674A',\n 'median_overdueamountmaxdatemonth_284T',\n 'min_relationshiptoclient_415T',\n 'min_annualeffectiverate_63L',\n 'maxdebt4_972A',\n 'std_approvaldate_319D',\n 'median_amount_4917619A',\n 'days120_123L',\n 'max_empl_employedfrom_271D',\n 'min_dateofcredend_353D',\n 'median_overdueamountmax_35A',\n 'mean_numberofoverdueinstlmax_1039L',\n#  'lastapplicationdate_877D',\n#  'mean_overdueamountmax2_398A',\n#  'min_firstnonzeroinstldate_307D',\n#  'max_numberofinstls_229L',\n#  'median_dpdmaxdatemonth_442T',\n#  'std_deductiondate_4917603D',\n#  'std_pmts_year_1139T',\n#  'min_creationdate_885D',\n#  'max_annualeffectiverate_63L',\n#  'mean_dtlastpmtallstes_3545839D',\n#  'mean_dpdmaxdatemonth_89T',\n#  'median_numberofoutstandinstls_59L',\n#  'mean_overdueamountmaxdatemonth_365T',\n#  'lastapprcredamount_781A',\n#  'mean_dpdmax_757P',\n#  'median_nominalrate_281L',\n#  'min_dtlastpmtallstes_3545839D',\n#  'maxoutstandbalancel12m_4187113A',\n#  'maxdpdlast24m_143P',\n#  'median_dtlastpmt_581D',\n#  'std_numberofoverdueinstlmax_1039L',\n#  'min_empl_employedfrom_271D',\n#  'mean_overdueamountmax2date_1142D',\n#  'std_credlmt_230A',\n#  'min_credlmt_935A',\n#  'std_pmts_overdue_1140A',\n#  'median_numberofoverdueinstlmax_1151L',\n#  'mean_deductiondate_4917603D',\n#  'std_childnum_21L',\n#  'mean_currdebt_94A',\n#  'std_nominalrate_281L',\n#  'mean_empl_employedfrom_271D',\n#  'avgmaxdpdlast9m_3716943P',\n#  'median_overdueamountmax2_398A',\n#  'numinstmatpaidtearly2d_4499204L',\n#  'std_pmts_overdue_1152A',\n#  'mean_dpdmaxdateyear_596T',\n#  'std_amount_4917619A',\n#  'mean_totalamount_996A',\n#  'mean_num_group1_13'\n]","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:38:53.968022Z","iopub.execute_input":"2024-03-17T14:38:53.968354Z","iopub.status.idle":"2024-03-17T14:38:53.996220Z","shell.execute_reply.started":"2024-03-17T14:38:53.968326Z","shell.execute_reply":"2024-03-17T14:38:53.995331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Pre-Fitted Voting Model","metadata":{"papermill":{"duration":0.008777,"end_time":"2024-03-11T20:20:19.361579","exception":false,"start_time":"2024-03-11T20:20:19.352802","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class VotingModel(BaseEstimator, ClassifierMixin):\n    def __init__(self, estimators):\n        super().__init__()\n        self.estimators = estimators\n        \n    def fit(self, X, y=None):\n        return self\n    \n    def predict(self, X):\n        y_preds = [estimator.predict(X) for estimator in self.estimators]\n        return np.mean(y_preds, axis=0)\n    \n    def predict_proba(self, X):\n        y_preds = [estimator.predict_proba(X) for estimator in self.estimators]\n        return np.mean(y_preds, axis=0)","metadata":{"papermill":{"duration":0.018278,"end_time":"2024-03-11T20:20:19.389382","exception":false,"start_time":"2024-03-11T20:20:19.371104","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:53.997311Z","iopub.execute_input":"2024-03-17T14:38:53.997593Z","iopub.status.idle":"2024-03-17T14:38:54.011734Z","shell.execute_reply.started":"2024-03-17T14:38:53.997570Z","shell.execute_reply":"2024-03-17T14:38:54.010912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Pipeline","metadata":{"papermill":{"duration":0.008615,"end_time":"2024-03-11T20:20:19.406942","exception":false,"start_time":"2024-03-11T20:20:19.398327","status":"completed"},"tags":[]}},{"cell_type":"code","source":"some_date = datetime(2024, 3, 16)\n\nclass Pipeline:\n    @staticmethod\n    def set_table_dtypes(df):\n        for col in df.columns:\n            if col in [\"case_id\", \"WEEK_NUM\", \"num_group1\", \"num_group2\"]:\n                df = df.with_columns(pl.col(col).cast(pl.Int32))\n            elif col in [\"date_decision\"]:\n                df = df.with_columns(pl.col(col).cast(pl.Date))\n            elif col[-1] in (\"P\", \"A\"):\n                df = df.with_columns(pl.col(col).cast(pl.Float64))\n            elif col[-1] in (\"M\",):\n                df = df.with_columns(pl.col(col).cast(pl.String))\n            elif col[-1] in (\"D\",):\n                df = df.with_columns(pl.col(col).cast(pl.Date))            \n\n        return df\n    \n    @staticmethod\n    def handle_dates1(df):\n        for col in df.columns:\n            if col[-1] in (\"D\",):\n                df = df.with_columns((pl.col(col) - some_date).alias(col))\n                \n        df = df.drop(\"date_decision\", \"MONTH\")\n\n        return df\n    \n    @staticmethod\n    def handle_dates2(df):\n        for col in df.columns:\n            if col[-1] in (\"D\",) or col[-2:] in (\"D#\",):\n                df = df.with_columns(pl.col(col) + (some_date - pl.col(\"date_decision\")))\n                df = df.with_columns(pl.col(col).dt.total_days())\n                df = df.with_columns(pl.col(col).cast(pl.Float32))\n                \n        df = df.drop(\"date_decision\", \"MONTH\")\n\n        return df\n    \n    @staticmethod\n    def filter_cols(df):\n        for col in df.columns:\n            if col not in [\"target\", \"case_id\", \"WEEK_NUM\"]:\n                isnull = df[col].is_null().mean()\n\n                if isnull > 0.95:\n                    df = df.drop(col)\n\n        for col in df.columns:\n            if (col not in [\"target\", \"case_id\", \"WEEK_NUM\"]) & (df[col].dtype == pl.String):\n                freq = df[col].n_unique()\n\n                if (freq == 1) | (freq > 200):\n                    df = df.drop(col)\n\n        return df","metadata":{"papermill":{"duration":0.023479,"end_time":"2024-03-11T20:20:19.439407","exception":false,"start_time":"2024-03-11T20:20:19.415928","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.014218Z","iopub.execute_input":"2024-03-17T14:38:54.014543Z","iopub.status.idle":"2024-03-17T14:38:54.029563Z","shell.execute_reply.started":"2024-03-17T14:38:54.014518Z","shell.execute_reply":"2024-03-17T14:38:54.028386Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Automatic Aggregation","metadata":{"papermill":{"duration":0.008664,"end_time":"2024-03-11T20:20:19.457100","exception":false,"start_time":"2024-03-11T20:20:19.448436","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class Aggregator:\n    @staticmethod\n    def num_expr(df, agg, name):\n        cols = [col for col in df.columns if col[-1] in (\"P\", \"A\")]\n        aliases = [(f\"{name}_{col}\", col) for col in cols]\n        aliases = list(filter(lambda x: x[0] in features, aliases))\n        expr = [agg(col).alias(col_name) for col_name, col in aliases]\n\n        return expr\n\n    @staticmethod\n    def date_expr(df, agg, name):\n        cols = [col for col in df.columns if col[-1] in (\"D\",)]\n        aliases = [(f\"{name}_{col}\", col) for col in cols]\n        aliases = list(filter(lambda x: x[0] in features, aliases))\n        expr = [agg(col).alias(col_name) for col_name, col in aliases]\n\n        return expr\n\n    @staticmethod\n    def str_expr(df, agg, name):\n        cols = [col for col in df.columns if col[-1] in (\"M\",)]\n        aliases = [(f\"{name}_{col}\", col) for col in cols]\n        aliases = list(filter(lambda x: x[0] in features, aliases))\n        expr = [agg(col).alias(col_name) for col_name, col in aliases]\n\n        return expr\n\n    @staticmethod\n    def other_expr(df, agg, name):\n        cols = [col for col in df.columns if col[-1] in (\"T\", \"L\")]\n        aliases = [(f\"{name}_{col}\", col) for col in cols]\n        aliases = list(filter(lambda x: x[0] in features, aliases))\n        expr = [agg(col).alias(col_name) for col_name, col in aliases]\n\n        return expr\n    \n    @staticmethod\n    def count_expr(df, agg, name):\n        cols = [col for col in df.columns if \"num_group\" in col]\n        aliases = [(f\"{name}_{col}\", col) for col in cols]\n        aliases = list(filter(lambda x: x[0] in features, aliases))\n        expr = [agg(col).alias(col_name) for col_name, col in aliases]\n\n        return expr\n\n    @staticmethod\n    def get_exprs(df, agg, name):\n        exprs = Aggregator.num_expr(df, agg, name) + \\\n                Aggregator.date_expr(df, agg, name) + \\\n                Aggregator.other_expr(df, agg, name) + \\\n                Aggregator.count_expr(df, agg, name)\n        #                 Aggregator.str_expr(df, agg) + \\\n\n        return exprs","metadata":{"papermill":{"duration":0.021569,"end_time":"2024-03-11T20:20:19.488325","exception":false,"start_time":"2024-03-11T20:20:19.466756","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.030695Z","iopub.execute_input":"2024-03-17T14:38:54.031045Z","iopub.status.idle":"2024-03-17T14:38:54.045827Z","shell.execute_reply.started":"2024-03-17T14:38:54.030998Z","shell.execute_reply":"2024-03-17T14:38:54.045085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### File I/O","metadata":{"papermill":{"duration":0.009719,"end_time":"2024-03-11T20:20:19.506836","exception":false,"start_time":"2024-03-11T20:20:19.497117","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def read_file(path, is_train, depth=None):\n    df = pl.read_parquet(path)\n    df = df.pipe(Pipeline.set_table_dtypes)\n    if depth is not None:\n        df = df.pipe(Pipeline.handle_dates1)\n    \n    if depth in [1, 2]:\n        df = df.group_by(\"case_id\").agg(\n            Aggregator.get_exprs(df, pl.max, \"max\") + \\\n            Aggregator.get_exprs(df, pl.min, \"min\") + \\\n            Aggregator.get_exprs(df, pl.mean, \"mean\") + \\\n            Aggregator.get_exprs(df, pl.median, \"median\") + \\\n            Aggregator.get_exprs(df, pl.std, \"std\")\n        )\n    if is_train:\n        df = df.pipe(Pipeline.filter_cols)\n    return df\n\ndef read_files(regex_path, is_train, depth=None):\n    chunks = []\n    for path in glob(str(regex_path)):\n        df = read_file(path, False, depth)\n#         print(df.shape)\n        chunks.append(df)\n    \n    df = pl.concat(chunks, how=\"vertical_relaxed\")\n    df = df.unique(subset=[\"case_id\"])\n    if is_train:\n        df = df.pipe(Pipeline.filter_cols)\n\n    return df","metadata":{"papermill":{"duration":0.018108,"end_time":"2024-03-11T20:20:19.533703","exception":false,"start_time":"2024-03-11T20:20:19.515595","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.047006Z","iopub.execute_input":"2024-03-17T14:38:54.047312Z","iopub.status.idle":"2024-03-17T14:38:54.060992Z","shell.execute_reply.started":"2024-03-17T14:38:54.047288Z","shell.execute_reply":"2024-03-17T14:38:54.060068Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Feature Engineering","metadata":{"papermill":{"duration":0.00854,"end_time":"2024-03-11T20:20:19.550995","exception":false,"start_time":"2024-03-11T20:20:19.542455","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def feature_eng(df_base, depth_0, depth_1, depth_2):\n    df_base = (\n        df_base\n        .with_columns(\n            month_decision = pl.col(\"date_decision\").dt.month(),\n            weekday_decision = pl.col(\"date_decision\").dt.weekday(),\n        )\n    )\n\n    for i, df in enumerate(depth_0 + depth_1 + depth_2):\n        df_base = df_base.join(df, how=\"left\", on=\"case_id\", suffix=f\"_{i}\")\n\n    df_base = df_base.pipe(Pipeline.handle_dates2)\n    \n    return df_base","metadata":{"papermill":{"duration":0.01688,"end_time":"2024-03-11T20:20:19.576796","exception":false,"start_time":"2024-03-11T20:20:19.559916","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.062300Z","iopub.execute_input":"2024-03-17T14:38:54.062737Z","iopub.status.idle":"2024-03-17T14:38:54.071741Z","shell.execute_reply.started":"2024-03-17T14:38:54.062707Z","shell.execute_reply":"2024-03-17T14:38:54.070900Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def to_pandas(df_data, cat_cols=None):\n    df_data = df_data.to_pandas()\n    \n    if cat_cols is None:\n        cat_cols = list(df_data.select_dtypes(\"object\").columns)\n    \n    df_data[cat_cols] = df_data[cat_cols].astype(\"category\")\n    \n    return df_data, cat_cols","metadata":{"papermill":{"duration":0.015752,"end_time":"2024-03-11T20:20:19.601370","exception":false,"start_time":"2024-03-11T20:20:19.585618","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.072831Z","iopub.execute_input":"2024-03-17T14:38:54.073130Z","iopub.status.idle":"2024-03-17T14:38:54.082298Z","shell.execute_reply.started":"2024-03-17T14:38:54.073108Z","shell.execute_reply":"2024-03-17T14:38:54.081360Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Configuration","metadata":{"papermill":{"duration":0.00861,"end_time":"2024-03-11T20:20:19.619092","exception":false,"start_time":"2024-03-11T20:20:19.610482","status":"completed"},"tags":[]}},{"cell_type":"code","source":"ROOT            = Path(\"/kaggle/input/home-credit-credit-risk-model-stability\")\n# ROOT = Path(\".\")\nTRAIN_DIR       = ROOT / \"parquet_files\" / \"train\"\nTEST_DIR        = ROOT / \"parquet_files\" / \"test\"","metadata":{"papermill":{"duration":0.015255,"end_time":"2024-03-11T20:20:19.643247","exception":false,"start_time":"2024-03-11T20:20:19.627992","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.083534Z","iopub.execute_input":"2024-03-17T14:38:54.083792Z","iopub.status.idle":"2024-03-17T14:38:54.092153Z","shell.execute_reply.started":"2024-03-17T14:38:54.083770Z","shell.execute_reply":"2024-03-17T14:38:54.091182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Train Files Read & Feature Engineering","metadata":{"papermill":{"duration":0.008715,"end_time":"2024-03-11T20:20:19.660605","exception":false,"start_time":"2024-03-11T20:20:19.651890","status":"completed"},"tags":[]}},{"cell_type":"code","source":"data_store = {\n    \"df_base\": read_file(TRAIN_DIR / \"train_base.parquet\", True),\n    \"depth_0\": [\n        read_file(TRAIN_DIR / \"train_static_cb_0.parquet\", True, 0),\n        read_files(TRAIN_DIR / \"train_static_0_*.parquet\", True, 0),\n    ],\n    \"depth_1\": [\n        read_files(TRAIN_DIR / \"train_applprev_1_*.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_a_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_b_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_c_1.parquet\", True, 1),\n        read_files(TRAIN_DIR / \"train_credit_bureau_a_1_*.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_credit_bureau_b_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_other_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_person_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_deposit_1.parquet\", True, 1),\n        read_file(TRAIN_DIR / \"train_debitcard_1.parquet\", True, 1),\n    ],\n    \"depth_2\": [\n        read_file(TRAIN_DIR / \"train_credit_bureau_b_2.parquet\", True, 2),\n        read_files(TRAIN_DIR / \"train_credit_bureau_a_2_*.parquet\", True, 2),\n    ]\n}","metadata":{"papermill":{"duration":126.876005,"end_time":"2024-03-11T20:22:26.545249","exception":false,"start_time":"2024-03-11T20:20:19.669244","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:38:54.093375Z","iopub.execute_input":"2024-03-17T14:38:54.093753Z","iopub.status.idle":"2024-03-17T14:41:19.978225Z","shell.execute_reply.started":"2024-03-17T14:38:54.093723Z","shell.execute_reply":"2024-03-17T14:41:19.977420Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = feature_eng(**data_store)\n\nprint(\"train data shape:\\t\", df_train.shape)","metadata":{"papermill":{"duration":11.999532,"end_time":"2024-03-11T20:22:38.554478","exception":false,"start_time":"2024-03-11T20:22:26.554946","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:19.979385Z","iopub.execute_input":"2024-03-17T14:41:19.979693Z","iopub.status.idle":"2024-03-17T14:41:31.641795Z","shell.execute_reply.started":"2024-03-17T14:41:19.979666Z","shell.execute_reply":"2024-03-17T14:41:31.640763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Test Files Read & Feature Engineering","metadata":{"papermill":{"duration":0.009104,"end_time":"2024-03-11T20:22:38.572989","exception":false,"start_time":"2024-03-11T20:22:38.563885","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# data_store = {\n#     \"df_base\": read_file(TEST_DIR / \"test_base.parquet\", False,),\n#     \"depth_0\": [\n#         read_file(TEST_DIR / \"test_static_cb_0.parquet\", False, 0),\n#         read_files(TEST_DIR / \"test_static_0_*.parquet\", False, 0),\n#     ],\n#     \"depth_1\": [\n#         read_files(TEST_DIR / \"test_applprev_1_*.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_tax_registry_a_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_tax_registry_b_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_tax_registry_c_1.parquet\", False, 1),\n#         read_files(TEST_DIR / \"test_credit_bureau_a_1_*.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_credit_bureau_b_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_other_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_person_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_deposit_1.parquet\", False, 1),\n#         read_file(TEST_DIR / \"test_debitcard_1.parquet\", False, 1),\n#     ],\n#     \"depth_2\": [\n#         read_file(TEST_DIR / \"test_credit_bureau_b_2.parquet\", False, 2),\n#         read_files(TEST_DIR / \"test_credit_bureau_a_2_*.parquet\", False, 2),\n#     ]\n# }","metadata":{"papermill":{"duration":0.589237,"end_time":"2024-03-11T20:22:39.171145","exception":false,"start_time":"2024-03-11T20:22:38.581908","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:31.642931Z","iopub.execute_input":"2024-03-17T14:41:31.643219Z","iopub.status.idle":"2024-03-17T14:41:31.648117Z","shell.execute_reply.started":"2024-03-17T14:41:31.643194Z","shell.execute_reply":"2024-03-17T14:41:31.647176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df_test = feature_eng(**data_store)\n\n# print(\"test data shape:\\t\", df_test.shape)","metadata":{"papermill":{"duration":0.051709,"end_time":"2024-03-11T20:22:39.232155","exception":false,"start_time":"2024-03-11T20:22:39.180446","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:31.652223Z","iopub.execute_input":"2024-03-17T14:41:31.652518Z","iopub.status.idle":"2024-03-17T14:41:31.665827Z","shell.execute_reply.started":"2024-03-17T14:41:31.652495Z","shell.execute_reply":"2024-03-17T14:41:31.665071Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Feature Elimination","metadata":{"papermill":{"duration":0.008776,"end_time":"2024-03-11T20:22:39.250093","exception":false,"start_time":"2024-03-11T20:22:39.241317","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df_train = df_train.pipe(Pipeline.filter_cols)\n# df_test = df_test.select([col for col in df_train.columns if col != \"target\"])\n\nprint(\"train data shape:\\t\", df_train.shape)\n# print(\"test data shape:\\t\", df_test.shape)","metadata":{"papermill":{"duration":2.758307,"end_time":"2024-03-11T20:22:42.017667","exception":false,"start_time":"2024-03-11T20:22:39.259360","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:31.666849Z","iopub.execute_input":"2024-03-17T14:41:31.667178Z","iopub.status.idle":"2024-03-17T14:41:33.279780Z","shell.execute_reply.started":"2024-03-17T14:41:31.667149Z","shell.execute_reply":"2024-03-17T14:41:33.278931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Pandas Conversion","metadata":{"papermill":{"duration":0.009039,"end_time":"2024-03-11T20:22:42.036115","exception":false,"start_time":"2024-03-11T20:22:42.027076","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df_train, cat_cols = to_pandas(df_train)\n# df_test, cat_cols = to_pandas(df_test, cat_cols)","metadata":{"papermill":{"duration":18.703961,"end_time":"2024-03-11T20:23:00.749238","exception":false,"start_time":"2024-03-11T20:22:42.045277","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:33.281025Z","iopub.execute_input":"2024-03-17T14:41:33.281403Z","iopub.status.idle":"2024-03-17T14:41:42.925007Z","shell.execute_reply.started":"2024-03-17T14:41:33.281353Z","shell.execute_reply":"2024-03-17T14:41:42.924179Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Garbage Collection","metadata":{"papermill":{"duration":0.00925,"end_time":"2024-03-11T20:23:00.767926","exception":false,"start_time":"2024-03-11T20:23:00.758676","status":"completed"},"tags":[]}},{"cell_type":"code","source":"del data_store\n\ngc.collect()","metadata":{"papermill":{"duration":0.137016,"end_time":"2024-03-11T20:23:00.913914","exception":false,"start_time":"2024-03-11T20:23:00.776898","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:42.926099Z","iopub.execute_input":"2024-03-17T14:41:42.926356Z","iopub.status.idle":"2024-03-17T14:41:43.358831Z","shell.execute_reply.started":"2024-03-17T14:41:42.926333Z","shell.execute_reply":"2024-03-17T14:41:43.357886Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### EDA","metadata":{"papermill":{"duration":0.009084,"end_time":"2024-03-11T20:23:00.932380","exception":false,"start_time":"2024-03-11T20:23:00.923296","status":"completed"},"tags":[]}},{"cell_type":"code","source":"print(\"Train is duplicated:\\t\", df_train[\"case_id\"].duplicated().any())\nprint(\"Train Week Range:\\t\", (df_train[\"WEEK_NUM\"].min(), df_train[\"WEEK_NUM\"].max()))\n\nprint()\n\n# print(\"Test is duplicated:\\t\", df_test[\"case_id\"].duplicated().any())\n# print(\"Test Week Range:\\t\", (df_test[\"WEEK_NUM\"].min(), df_test[\"WEEK_NUM\"].max()))","metadata":{"papermill":{"duration":0.038139,"end_time":"2024-03-11T20:23:00.979829","exception":false,"start_time":"2024-03-11T20:23:00.941690","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:43.359975Z","iopub.execute_input":"2024-03-17T14:41:43.360306Z","iopub.status.idle":"2024-03-17T14:41:43.393764Z","shell.execute_reply.started":"2024-03-17T14:41:43.360281Z","shell.execute_reply":"2024-03-17T14:41:43.392845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sns.lineplot(\n#     data=df_train,\n#     x=\"WEEK_NUM\",\n#     y=\"target\",\n# )\n# plt.show()","metadata":{"papermill":{"duration":16.554713,"end_time":"2024-03-11T20:23:17.543990","exception":false,"start_time":"2024-03-11T20:23:00.989277","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:43.394985Z","iopub.execute_input":"2024-03-17T14:41:43.395312Z","iopub.status.idle":"2024-03-17T14:41:43.399476Z","shell.execute_reply.started":"2024-03-17T14:41:43.395286Z","shell.execute_reply":"2024-03-17T14:41:43.398439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Training","metadata":{"papermill":{"duration":0.01291,"end_time":"2024-03-11T20:23:17.567408","exception":false,"start_time":"2024-03-11T20:23:17.554498","status":"completed"},"tags":[]}},{"cell_type":"code","source":"X = df_train.drop(columns=[\"target\", \"case_id\", \"WEEK_NUM\"])\ny = df_train[\"target\"]\nweeks = df_train[\"WEEK_NUM\"]\n\ncv = StratifiedGroupKFold(n_splits=20, shuffle=False)\n\nfrom sklearn.metrics import roc_auc_score\nWEEK_NUM = None\ndef gini_stability(base, w_fallingrate=88.0, w_resstd=-0.5):\n    gini_in_time = base.loc[:, [\"WEEK_NUM\", \"target\", \"score\"]]\\\n        .sort_values(\"WEEK_NUM\")\\\n        .groupby(\"WEEK_NUM\")[[\"target\", \"score\"]]\\\n        .apply(lambda x: 2*roc_auc_score(x[\"target\"], x[\"score\"])-1).tolist()\n    \n    x = np.arange(len(gini_in_time))\n    y = gini_in_time\n    a, b = np.polyfit(x, y, 1)\n    y_hat = a*x + b\n    residuals = y - y_hat\n    res_std = np.std(residuals)\n    avg_gini = np.mean(gini_in_time)\n    return avg_gini + w_fallingrate * min(0, a) + w_resstd * res_std\n\ndef gini_stability_metric(target, pred):\n    base = pd.DataFrame()\n    base[\"WEEK_NUM\"] = np.asarray(WEEK_NUM)\n    base[\"target\"] = np.asarray(target)\n    base[\"score\"] = np.asarray(pred)\n    \n    stab = gini_stability(base)\n    del base\n    gc.collect()\n    \n    return \"gini_stability\", stab, True\n\nparams = {\n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"max_depth\": 8,\n    \"learning_rate\": 0.05,\n    \"n_estimators\": 1000,\n    \"colsample_bytree\": 0.8,\n    \"colsample_bynode\": 0.8,\n    \"class_weight\": \"balanced\",\n    \"verbose\": -1,\n    \"random_state\": 42,\n    \"device\": \"gpu\",\n}\n\nfitted_models = []\n\nfor idx_train, idx_valid in cv.split(X, y, groups=weeks):\n    X_train, y_train = X.iloc[idx_train], y.iloc[idx_train]\n    X_valid, y_valid = X.iloc[idx_valid], y.iloc[idx_valid]\n\n    # WEEK_NUM = df_train.loc[idx_valid, \"WEEK_NUM\"]\n    WEEK_NUM = weeks.iloc[idx_valid]\n\n    model = lgb.LGBMClassifier(**params)\n    model.fit(\n        X_train, y_train,\n        eval_set=[(X_valid, y_valid)],\n        callbacks=[lgb.log_evaluation(100), lgb.early_stopping(100)],\n        eval_metric=gini_stability_metric,\n    )\n\n    fitted_models.append(model)\n\nmodel = VotingModel(fitted_models)","metadata":{"papermill":{"duration":1032.311042,"end_time":"2024-03-11T20:40:29.890585","exception":false,"start_time":"2024-03-11T20:23:17.579543","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:41:43.400917Z","iopub.execute_input":"2024-03-17T14:41:43.401401Z","iopub.status.idle":"2024-03-17T14:56:31.547856Z","shell.execute_reply.started":"2024-03-17T14:41:43.401377Z","shell.execute_reply":"2024-03-17T14:56:31.546840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del X\ncolumns = df_train.columns.copy()\ndel df_train\n\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:56:31.548992Z","iopub.execute_input":"2024-03-17T14:56:31.549305Z","iopub.status.idle":"2024-03-17T14:56:31.684807Z","shell.execute_reply.started":"2024-03-17T14:56:31.549280Z","shell.execute_reply":"2024-03-17T14:56:31.683903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_store = {\n    \"df_base\": read_file(TEST_DIR / \"test_base.parquet\", False,),\n    \"depth_0\": [\n        read_file(TEST_DIR / \"test_static_cb_0.parquet\", False, 0),\n        read_files(TEST_DIR / \"test_static_0_*.parquet\", False, 0),\n    ],\n    \"depth_1\": [\n        read_files(TEST_DIR / \"test_applprev_1_*.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_tax_registry_a_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_tax_registry_b_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_tax_registry_c_1.parquet\", False, 1),\n        read_files(TEST_DIR / \"test_credit_bureau_a_1_*.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_credit_bureau_b_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_other_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_person_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_deposit_1.parquet\", False, 1),\n        read_file(TEST_DIR / \"test_debitcard_1.parquet\", False, 1),\n    ],\n    \"depth_2\": [\n        read_file(TEST_DIR / \"test_credit_bureau_b_2.parquet\", False, 2),\n        read_files(TEST_DIR / \"test_credit_bureau_a_2_*.parquet\", False, 2),\n    ]\n}","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:56:31.686248Z","iopub.execute_input":"2024-03-17T14:56:31.686625Z","iopub.status.idle":"2024-03-17T14:56:32.022190Z","shell.execute_reply.started":"2024-03-17T14:56:31.686594Z","shell.execute_reply":"2024-03-17T14:56:32.021354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test = feature_eng(**data_store)\n\nprint(\"test data shape:\\t\", df_test.shape)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:56:32.023316Z","iopub.execute_input":"2024-03-17T14:56:32.023603Z","iopub.status.idle":"2024-03-17T14:56:32.076526Z","shell.execute_reply.started":"2024-03-17T14:56:32.023577Z","shell.execute_reply":"2024-03-17T14:56:32.075575Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test = df_test.select([col for col in columns if col != \"target\"])","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:56:32.077762Z","iopub.execute_input":"2024-03-17T14:56:32.078418Z","iopub.status.idle":"2024-03-17T14:56:32.084809Z","shell.execute_reply.started":"2024-03-17T14:56:32.078384Z","shell.execute_reply":"2024-03-17T14:56:32.083904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test, cat_cols = to_pandas(df_test, cat_cols)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T14:56:32.085970Z","iopub.execute_input":"2024-03-17T14:56:32.086283Z","iopub.status.idle":"2024-03-17T14:56:32.123677Z","shell.execute_reply.started":"2024-03-17T14:56:32.086258Z","shell.execute_reply":"2024-03-17T14:56:32.122930Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Prediction","metadata":{"papermill":{"duration":0.015335,"end_time":"2024-03-11T20:40:29.921663","exception":false,"start_time":"2024-03-11T20:40:29.906328","status":"completed"},"tags":[]}},{"cell_type":"code","source":"X_test = df_test.drop(columns=[\"WEEK_NUM\"])\nX_test = X_test.set_index(\"case_id\")\n\ny_pred = pd.Series(model.predict_proba(X_test)[:, 1], index=X_test.index)","metadata":{"papermill":{"duration":0.29704,"end_time":"2024-03-11T20:40:30.234187","exception":false,"start_time":"2024-03-11T20:40:29.937147","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:56:32.124703Z","iopub.execute_input":"2024-03-17T14:56:32.124973Z","iopub.status.idle":"2024-03-17T14:56:32.188385Z","shell.execute_reply.started":"2024-03-17T14:56:32.124950Z","shell.execute_reply":"2024-03-17T14:56:32.187515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Submission","metadata":{"papermill":{"duration":0.015506,"end_time":"2024-03-11T20:40:30.266313","exception":false,"start_time":"2024-03-11T20:40:30.250807","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df_subm = pd.read_csv(ROOT / \"sample_submission.csv\")\ndf_subm = df_subm.set_index(\"case_id\")\n\ndf_subm[\"score\"] = y_pred","metadata":{"papermill":{"duration":0.035461,"end_time":"2024-03-11T20:40:30.317145","exception":false,"start_time":"2024-03-11T20:40:30.281684","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:56:32.189574Z","iopub.execute_input":"2024-03-17T14:56:32.189871Z","iopub.status.idle":"2024-03-17T14:56:32.200714Z","shell.execute_reply.started":"2024-03-17T14:56:32.189844Z","shell.execute_reply":"2024-03-17T14:56:32.199861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Check null: \", df_subm[\"score\"].isnull().any())\n\ndf_subm.head()","metadata":{"papermill":{"duration":0.031645,"end_time":"2024-03-11T20:40:30.364269","exception":false,"start_time":"2024-03-11T20:40:30.332624","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:56:32.201857Z","iopub.execute_input":"2024-03-17T14:56:32.202196Z","iopub.status.idle":"2024-03-17T14:56:32.215898Z","shell.execute_reply.started":"2024-03-17T14:56:32.202163Z","shell.execute_reply":"2024-03-17T14:56:32.214868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subm.to_csv(\"submission.csv\")","metadata":{"papermill":{"duration":0.024175,"end_time":"2024-03-11T20:40:30.404950","exception":false,"start_time":"2024-03-11T20:40:30.380775","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-03-17T14:56:32.217380Z","iopub.execute_input":"2024-03-17T14:56:32.217964Z","iopub.status.idle":"2024-03-17T14:56:32.224680Z","shell.execute_reply.started":"2024-03-17T14:56:32.217932Z","shell.execute_reply":"2024-03-17T14:56:32.223301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.015608,"end_time":"2024-03-11T20:40:30.436155","exception":false,"start_time":"2024-03-11T20:40:30.420547","status":"completed"},"tags":[]},"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":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}