{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7921029,"sourceType":"competition"},{"sourceId":7909467,"sourceType":"datasetVersion","datasetId":4646444},{"sourceId":7933327,"sourceType":"datasetVersion","datasetId":3914692},{"sourceId":8182104,"sourceType":"datasetVersion","datasetId":4844350},{"sourceId":8182674,"sourceType":"datasetVersion","datasetId":4844774},{"sourceId":8184941,"sourceType":"datasetVersion","datasetId":4837528},{"sourceId":8195073,"sourceType":"datasetVersion","datasetId":4853979},{"sourceId":8222452,"sourceType":"datasetVersion","datasetId":4874714},{"sourceId":8222530,"sourceType":"datasetVersion","datasetId":4874763},{"sourceId":8222774,"sourceType":"datasetVersion","datasetId":4874935},{"sourceId":8223092,"sourceType":"datasetVersion","datasetId":4875185},{"sourceId":8226956,"sourceType":"datasetVersion","datasetId":4878228},{"sourceId":8237232,"sourceType":"datasetVersion","datasetId":4885921},{"sourceId":8245854,"sourceType":"datasetVersion","datasetId":4892100},{"sourceId":8251533,"sourceType":"datasetVersion","datasetId":4896142}],"dockerImageVersionId":30698,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 导入包","metadata":{}},{"cell_type":"code","source":"!pip install --no-index --find-links=/kaggle/input/autogluon/v1.0.0  autogluon > /dev/null","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:48:34.430853Z","iopub.execute_input":"2024-05-06T07:48:34.431217Z","iopub.status.idle":"2024-05-06T07:52:04.885586Z","shell.execute_reply.started":"2024-05-06T07:48:34.431190Z","shell.execute_reply":"2024-05-06T07:52:04.884750Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install --no-index --find-links=/kaggle/input/ray-pkgs  ray > /dev/null","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:04.887259Z","iopub.execute_input":"2024-05-06T07:52:04.887849Z","iopub.status.idle":"2024-05-06T07:52:16.816716Z","shell.execute_reply.started":"2024-05-06T07:52:04.887823Z","shell.execute_reply":"2024-05-06T07:52:16.815041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\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\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import StratifiedGroupKFold\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.metrics import roc_auc_score\n\nimport lightgbm as lgb\nfrom catboost import CatBoostClassifier, Pool\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n# warnings.simplefilter(action='ignore', category=PerformanceWarning)\nwarnings.simplefilter(action='ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-06T07:52:16.818009Z","iopub.execute_input":"2024-05-06T07:52:16.818312Z","iopub.status.idle":"2024-05-06T07:52:19.793431Z","shell.execute_reply.started":"2024-05-06T07:52:16.818283Z","shell.execute_reply":"2024-05-06T07:52:19.791953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from autogluon.tabular import TabularDataset, TabularPredictor","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:19.796604Z","iopub.execute_input":"2024-05-06T07:52:19.797282Z","iopub.status.idle":"2024-05-06T07:52:20.313444Z","shell.execute_reply.started":"2024-05-06T07:52:19.797244Z","shell.execute_reply":"2024-05-06T07:52:20.311568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 载入预训练词嵌入向量\nmapping_list = [{'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '2fc785b2': -0.009244342342147624}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '717ddd49': -0.010198146317255427, '6b2ae0fa': -0.008606163076829413, '39a0853f': -0.009460007343705001, 'c8e1a1d0': -0.009559475972849896}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '6b2ae0fa': -0.008606163076829413, '717ddd49': -0.010198146317255427, 'a34a13c8': -0.009400209002023985, 'c8e1a1d0': -0.009559475972849896}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, 'a7fcb6e5': -0.01067335202318039, '3439d993': -0.009634087399850463, 'b6cabe76': -0.008427829736395628, '38c061ee': -0.009184238270014097, 'ecd83604': -0.008519643800658136}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '46b968c3': -0.010325181199429304, '977b2a70': -0.00910213132217604, '1a19667c': -0.009253143047904663, 'e18430ff': -0.009743557093012592, 'ecd83604': -0.008519643800658136}, {'nan': -0.010353292803387149, 'DEDUCTION_6': -0.009541461302736328, 'PENSION_6': -0.007773135793286201, 'SOCIAL_6': -0.007922835314502663}, {'nan': -0.010353292803387149, 'CA': -0.009529872416654447}, {'nan': -0.010353292803387149, 'INSTANT': -0.009242800301308307, 'PERSONALIZED': -0.009389412375753636}, {'CAL': -0.010763557578210717, 'REL': -0.010674549461337316, 'COL': -0.01108257706649359, 'nan': -0.010353292803387149}, {'GBA': -0.010385590854184557, 'DD': -0.009345747091174417, 'SBA': -0.009810261539409263, 'nan': -0.010353292803387149}, {'nan': -0.010353292803387149, 'True': -0.009938715898044848, 'False': -0.01004077808337911}, {'CASH': -0.01036614042072396, 'NDF': -0.010755134375510048, 'POS': -0.01112556775600145, 'nan': -0.010353292803387149}, {'nan': -0.010353292803387149, 'False': -0.01004077808337911}, {'a55475b1': -0.01036039912742126, 'P159_130_59': -0.01015724026137832, 'P21_79_33': -0.01008706574971067, 'P12_6_178': -0.009748995775945938, 'P109_133_183': -0.010111977450340722, 'P53_45_92': -0.010034798644937837, 'P148_110_5': -0.009730161261359171, 'P33_29_177': -0.010372607194672886, 'P201_79_148': -0.010421945485632023, 'P100_96_175': -0.010050285204319684, 'P216_109_183': -0.010147660103863624, 'P143_21_170': -0.010238093309302107, 'P187_118_47': -0.01044126731133114, 'P128_43_169': -0.010447327743541734, 'P177_46_174': -0.010429087066585888, 'P105_126_172': -0.010012213040909046, 'P184_3_97': -0.009539615301112766, 'P121_6_80': -0.009273954019742328, 'P160_128_10': -0.009002370003400983, 'P65_94_74': -0.010210236167343586, 'P110_120_162': -0.010038751877649096, 'P52_56_90': -0.009587214902997706, 'P21_123_87': -0.010218350281149924, 'P181_140_94': -0.009975311817058241, 'P201_108_190': -0.009999745732178175, 'P193_109_73': -0.010342550520363147, 'P137_6_96': -0.00991560991390846, 'P48_73_87': -0.009895332873848398, 'P76_145_175': -0.009880919293361027, 'P3_103_80': -0.009203412726795554, 'P75_42_174': -0.010477584429357497, 'P49_162_170': -0.009951948382422415, 'P156_122_160': -0.01018293551859036, 'P175_82_175': -0.010287337632500263, 'P98_19_172': -0.010302184285819749, 'P156_50_173': -0.010672197839388295, 'P141_125_135': -0.010084859044961073, 'P117_143_46': -0.010221212989411773, 'P137_157_60': -0.010283323596771273, 'P126_161_96': -0.010132839860375498, 'P155_91_176': -0.010018274140444797, 'P38_69_128': -0.01072221538318748, 'P93_39_173': -0.010617517655269637, 'P58_25_91': -0.009767184456132819, 'P187_21_169': -0.01061819133148371}, {'a55475b1': -0.01036039912742126, 'P94_109_143': -0.010348041072575143, 'P24_27_36': -0.010538027966807325, 'P85_114_140': -0.009828406205997453, 'P73_130_169': -0.010026929932413017, 'P30_86_84': -0.01063152122012904, 'P53_10_15': -0.009397322041915382, 'P141_135_146': -0.010252008194489312, 'P180_60_137': -0.010280534223511495, 'P11_56_131': -0.009957410593187888, 'P32_163_96': -0.01001636248899255, 'P46_50_166': -0.010601176891213981, 'P98_38_170': -0.010492528711059398, 'P129_162_80': -0.010083958974576035, 'P120_0_10': -0.008503479785341975, 'P198_89_166': -0.010598842264244013, 'P20_125_107': -0.009909940056028669, 'P203_135_156': -0.010152263635063719, 'P32_86_86': -0.010455522111897153, 'P8_141_180': -0.009558349562787782, 'P91_110_150': -0.00997919951040179, 'P19_25_34': -0.010516422722237925, 'P163_9_145': -0.010114851534126501, 'P5_143_178': -0.009826266094800454, 'P94_154_184': -0.010107092243856641, 'P84_14_61': -0.010327429313548464, 'P99_98_113': -0.010137857158497354, 'P11_156_146': -0.010647510257513204, 'P19_105_83': -0.01030732228114554, 'P205_40_167': -0.010378291766604283, 'P187_10_172': -0.010277019954628486, 'P183_71_60': -0.010050678250233128, 'P195_62_170': -0.010524013223024061, 'P145_77_120': -0.009977142381368745, 'P116_157_162': -0.010127608614766359, 'P145_10_63': -0.010249712710068101, 'P123_22_171': -0.010408512407821036, 'P57_100_127': -0.00929967106588947, 'P185_66_167': -0.010344103054914436, 'P128_12_74': -0.010413461115149403, 'P118_140_56': -0.010103911578236572, 'P23_22_19': -0.010333347871342843, 'P151_143_25': -0.009861598801213253, 'P107_145_100': -0.009432040356188054, 'P65_58_157': -0.010370470332299911, 'P122_66_161': -0.009847096688126555, 'P52_67_90': -0.009701032775107402, 'P69_72_116': -0.010094140128937093, 'P64_121_167': -0.00947982480651414, 'P55_81_55': -0.00987026842801697, 'P198_14_171': -0.010677176676684516, 'P95_76_117': -0.01009134407665139, 'P169_159_178': -0.010410307152720103, 'P118_30_169': -0.010462410893978813, 'P201_100_144': -0.009857400183790332, 'P129_101_181': -0.009945935309739676, 'P16_126_23': -0.010277805445412014, 'P9_82_76': -0.010034311174119162, 'P7_85_64': -0.009817939043424909, 'P60_137_164': -0.00981427203627542, 'P188_66_164': -0.010389747241899746, 'P150_0_30': -0.008994536286839624, 'P68_132_180': -0.009590416760261178, 'P89_110_159': -0.01044229386067741, 'P17_160_152': -0.010590263564002575, 'P175_4_106': -0.009807010073321484, 'P199_114_13': -0.010588210544256071, 'P72_115_176': -0.009645751591127313, 'P28_71_137': -0.010438855287626817, 'P59_114_135': -0.01010412324058052, 'P60_96_75': -0.009412584962243878, 'P191_55_173': -0.010966835502964992, 'P204_22_168': -0.010129746284822735, 'P203_151_99': -0.009745445440254722}, {'a55475b1': -0.01036039912742126, 'P100_96_175': -0.010050285204319684, 'P53_45_92': -0.010034798644937837, 'P159_130_59': -0.01015724026137832, 'P21_79_33': -0.01008706574971067, 'P12_6_178': -0.009748995775945938, 'P148_110_5': -0.009730161261359171, 'P109_133_183': -0.010111977450340722, 'P33_29_177': -0.010372607194672886, 'P128_43_169': -0.010447327743541734, 'P52_56_90': -0.009587214902997706, 'P193_109_73': -0.010342550520363147, 'P201_79_148': -0.010421945485632023, 'P187_118_47': -0.01044126731133114, 'P184_3_97': -0.009539615301112766, 'P177_46_174': -0.010429087066585888, 'P105_126_172': -0.010012213040909046, 'P21_123_87': 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'P58_25_91': -0.009767184456132819}, {'a55475b1': -0.01036039912742126, 'P111_89_135': -0.0103411222911897, 'P75_90_70': -0.009826041613147632, 'P5_163_186': -0.009718715609532561, 'P128_13_121': -0.009974252129192488, 'P108_95_156': -0.010025333633620903, 'P95_36_171': -0.010429524212743976, 'P174_113_42': -0.010283790955175695, 'P135_89_156': -0.010678632890233075, 'P165_57_169': -0.010365713635792417, 'P172_87_177': -0.010304901963612187, 'P136_59_154': -0.010549117576980885, 'P34_45_168': -0.010204939133548882, 'P126_18_172': -0.010260329619844319, 'P185_44_170': -0.010350205091754106, 'P200_75_140': -0.009949162946213619, 'P142_50_170': -0.009997344397561392, 'P30_45_143': -0.010603863090144236, 'P132_130_54': -0.010087964595944262, 'P16_51_146': -0.010610710629255967, 'P123_124_181': -0.009940420201436003, 'P98_55_164': -0.010433793920204456, 'P79_131_167': -0.009882115058654032, 'P137_154_119': -0.010443887771619606, 'P45_86_83': -0.010002018414941935, 'P101_97_152': 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-0.00888597733963555, '7b62420e': -0.009826760374456475, '2fd21cf1': -0.009439484180992016, '168ad9f3': -0.00981696614686219, '46ab00a7': -0.00964194659017418, '5224034a': -0.010031894036994041, '940efad7': -0.009791149119147727, '5994c34a': -0.010349483086921888, '470b7fa6': -0.009588699897297678}, {'nan': -0.010353292803387149, 'ab3c25cf': -0.009157336050535037, 'be4fd70b': -0.009895453034005186, 'daf49a8a': -0.00916562118834463, 'a55475b1': -0.01036039912742126}]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:20.316777Z","iopub.execute_input":"2024-05-06T07:52:20.317569Z","iopub.status.idle":"2024-05-06T07:52:20.622840Z","shell.execute_reply.started":"2024-05-06T07:52:20.317543Z","shell.execute_reply":"2024-05-06T07:52:20.621319Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 装载数据","metadata":{}},{"cell_type":"code","source":"class VotingModel(BaseEstimator, RegressorMixin):\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":{"execution":{"iopub.status.busy":"2024-05-06T07:52:20.624567Z","iopub.execute_input":"2024-05-06T07:52:20.624908Z","iopub.status.idle":"2024-05-06T07:52:20.632832Z","shell.execute_reply.started":"2024-05-06T07:52:20.624884Z","shell.execute_reply":"2024-05-06T07:52:20.630850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 数据包括逾期天数P 出生日期/信用信息D(date) \n# 押金金额A 未偿还金额A(float) \n# 申请原因，合同类型，地区信息（个人情况陈述）M(string like \"96a8fdfe\") \n\n# 信用卡相关信息/利率（float），人员信息，支付信息L(person文件中type类别数据，string)\n# 付款月份T 几个客户价值/关系评估信息T(2018... int)（person中关系，string）\nclass Pipeline:\n    # 数据类型分类\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    # 日期转换成距开卡日转换成日期,出生日期等？\n    @staticmethod\n    def handle_dates(df):\n        for col in df.columns:\n            if col[-1] in (\"D\",):\n                df = df.with_columns(pl.col(col) - 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    # 删除及其稀疏特征和string中复杂度较高的特征\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\n    \n\n# 新增每个特征最大数值列，作为新特征\nclass Aggregator:\n    \n    @staticmethod\n    def num_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"P\", \"A\")]\n\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        expr_var = [pl.var(col).alias(f\"var_{col}\") for col in cols]\n        expr_sum = [pl.sum(col).alias(f\"sum_{col}\") for col in cols]\n        #sum perfect\n        \n        return expr_max + expr_mean + expr_var + expr_sum\n\n    @staticmethod\n    def date_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"D\",)]\n\n        expr_min = [pl.min(col).alias(f\"min_{col}\") for col in cols]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        # expr_var = [pl.var(col).alias(f\"var_{col}\") for col in cols]\n        expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        expr_len = [pl.col(col).count().alias(f\"len_{col}_len\") for col in cols]\n        \n        return expr_min + expr_max + expr_mean + expr_len\n\n    @staticmethod\n    def str_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"M\",)]\n        \n        expr_min = [pl.min(col).alias(f\"min_{col}\") for col in cols]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        expr_last = [pl.last(col).alias(f\"lsat_{col}\") for col in cols]\n        # expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        # expr_var = [pl.var(col).alias(f\"var_{col}\") for col in cols]\n\n        return expr_min + expr_max + expr_last\n    # 数据包括逾期天数P 出生日期/信用信息D(date) \n    # 押金金额A 未偿还金额A(float) \n    # 申请原因，合同类型，地区信息（个人情况陈述）M(string like \"96a8fdfe\") \n\n    # 信用卡相关信息/利率（float），人员信息，支付信息L(person文件中type类别数据，string)\n    # 付款月份T 几个客户价值/关系评估信息T(2018... int)（person中关系，string）\n    @staticmethod\n    def other_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"T\", \"L\")]\n        \n        expr_min = [pl.min(col).alias(f\"min_{col}\") for col in cols]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        #expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        expr_last = [pl.last(col).alias(f\"last_{col}\") for col in cols]\n\n        return expr_min + expr_max + expr_last\n    \n    @staticmethod\n    def count_expr(df):\n        cols = [col for col in df.columns if \"num_group\" in col]\n\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n\n        return expr_max\n    \n    @staticmethod\n    def get_exprs(df):\n        exprs = Aggregator.num_expr(df) + \\\n                Aggregator.date_expr(df) + \\\n                Aggregator.str_expr(df) + \\\n                Aggregator.other_expr(df) + \\\n                Aggregator.count_expr(df)\n        # print(exprs)\n\n        return exprs\n    \n    \n\n# 将每个case_id的数据聚合为一个整体\ndef read_file(path, depth=None):\n    df = pl.read_parquet(path)\n    df = df.pipe(Pipeline.set_table_dtypes)\n    \n    if depth in [1, 2]:\n        df = df.group_by(\"case_id\").agg(Aggregator.get_exprs(df))\n    return df\n\ndef read_files(regex_path, depth=None):\n    chunks = []\n    for path in glob(str(regex_path)):\n        df = pl.read_parquet(path)\n        df = df.pipe(Pipeline.set_table_dtypes)\n        \n        if depth in [1, 2]:\n            df = df.group_by(\"case_id\").agg(Aggregator.get_exprs(df))\n        \n        chunks.append(df)\n        \n    df = pl.concat(chunks, how=\"vertical_relaxed\")\n    df = df.unique(subset=[\"case_id\"])\n    \n    return df\n\n\n\ndef 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_dates)\n    \n    return df_base\n\n\n\ndef reduce_mem_usage(df):\n    \"\"\" iterate through all the columns of a dataframe and modify the data type\n        to reduce memory usage.        \n    \"\"\"\n    start_mem = df.memory_usage().sum() / 1024**2\n    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n    \n    for col in df.columns:\n        col_type = df[col].dtype\n        if str(col_type)==\"category\":\n            continue\n        \n        if col_type != object:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float32)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)\n        else:\n            continue\n    end_mem = df.memory_usage().sum() / 1024**2\n    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n    \n    return df\n\ndef to_pandas(df_data, cat_cols=None):\n    \"\"\"把所有string转换为类别格式\"\"\"\n    df_data = df_data.to_pandas()\n    df_data = reduce_mem_usage(df_data)\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":{"execution":{"iopub.status.busy":"2024-05-06T07:52:20.634033Z","iopub.execute_input":"2024-05-06T07:52:20.634358Z","iopub.status.idle":"2024-05-06T07:52:20.672417Z","shell.execute_reply.started":"2024-05-06T07:52:20.634333Z","shell.execute_reply":"2024-05-06T07:52:20.670502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df_train = pd.read_feather(\"/kaggle/input/useful-data/useful_train.feather\")\n# cat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# cat_cols = ['description_5085714M', 'education_1103M', 'education_88M', 'maritalst_385M', 'maritalst_893M', 'requesttype_4525192L', 'bankacctype_710L', 'cardtype_51L', 'credtype_322L', 'disbursementtype_67L', 'equalitydataagreement_891L', 'inittransactioncode_186L', 'isdebitcard_729L', 'lastapprcommoditycat_1041M', 'lastcancelreason_561M', 'lastrejectcommoditycat_161M', 'lastrejectcommodtypec_5251769M', 'lastrejectreason_759M', 'lastrejectreasonclient_4145040M', 'lastst_736L', 'opencred_647L', 'paytype1st_925L', 'paytype_783L', 'twobodfilling_608L', 'typesuite_864L', 'min_cancelreason_3545846M', 'min_education_1138M', 'min_postype_4733339M', 'min_rejectreason_755M', 'min_rejectreasonclient_4145042M', 'max_cancelreason_3545846M', 'max_education_1138M', 'max_postype_4733339M', 'max_rejectreason_755M', 'max_rejectreasonclient_4145042M', 'lsat_cancelreason_3545846M', 'lsat_education_1138M', 'lsat_postype_4733339M', 'lsat_rejectreason_755M', 'lsat_rejectreasonclient_4145042M', 'min_credacc_status_367L', 'min_credtype_587L', 'min_familystate_726L', 'min_inittransactioncode_279L', 'min_isbidproduct_390L', 'min_isdebitcard_527L', 'min_status_219L', 'max_credacc_status_367L', 'max_credtype_587L', 'max_familystate_726L', 'max_inittransactioncode_279L', 'max_isbidproduct_390L', 'max_isdebitcard_527L', 'max_status_219L', 'last_credacc_status_367L', 'last_credtype_587L', 'last_familystate_726L', 'last_inittransactioncode_279L', 'last_isbidproduct_390L', 'last_status_219L', 'min_classificationofcontr_13M', 'min_contractst_545M', 'min_contractst_964M', 'min_description_351M', 'min_financialinstitution_591M', 'min_purposeofcred_426M', 'min_purposeofcred_874M', 'min_subjectrole_182M', 'min_subjectrole_93M', 'max_classificationofcontr_13M', 'max_classificationofcontr_400M', 'max_contractst_545M', 'max_contractst_964M', 'max_description_351M', 'max_financialinstitution_382M', 'max_financialinstitution_591M', 'max_purposeofcred_426M', 'max_purposeofcred_874M', 'max_subjectrole_182M', 'max_subjectrole_93M', 'lsat_classificationofcontr_13M', 'lsat_classificationofcontr_400M', 'lsat_contractst_545M', 'lsat_contractst_964M', 'lsat_description_351M', 'lsat_financialinstitution_382M', 'lsat_financialinstitution_591M', 'lsat_purposeofcred_426M', 'lsat_purposeofcred_874M', 'lsat_subjectrole_182M', 'lsat_subjectrole_93M', 'min_education_927M', 'min_language1_981M', 'max_education_927M', 'max_empladdr_district_926M', 'max_empladdr_zipcode_114M', 'max_language1_981M', 'lsat_education_927M', 'lsat_empladdr_district_926M', 'lsat_empladdr_zipcode_114M', 'lsat_language1_981M', 'min_contaddr_matchlist_1032L', 'min_contaddr_smempladdr_334L', 'min_empl_employedtotal_800L', 'min_empl_industry_691L', 'min_familystate_447L', 'min_housetype_905L', 'min_incometype_1044T', 'min_relationshiptoclient_415T', 'min_relationshiptoclient_642T', 'min_remitter_829L', 'min_safeguarantyflag_411L', 'min_sex_738L', 'min_type_25L', 'max_contaddr_matchlist_1032L', 'max_contaddr_smempladdr_334L', 'max_empl_employedtotal_800L', 'max_empl_industry_691L', 'max_familystate_447L', 'max_housetype_905L', 'max_incometype_1044T', 'max_relationshiptoclient_415T', 'max_relationshiptoclient_642T', 'max_remitter_829L', 'max_role_1084L', 'max_safeguarantyflag_411L', 'max_sex_738L', 'max_type_25L', 'last_contaddr_matchlist_1032L', 'last_contaddr_smempladdr_334L', 'last_incometype_1044T', 'last_relationshiptoclient_415T', 'last_relationshiptoclient_642T', 'last_remitter_829L', 'last_role_1084L', 'last_safeguarantyflag_411L', 'last_sex_738L', 'last_type_25L', 'min_collater_typofvalofguarant_298M', 'min_collater_typofvalofguarant_407M', 'min_collaterals_typeofguarante_359M', 'min_collaterals_typeofguarante_669M', 'min_subjectroles_name_541M', 'min_subjectroles_name_838M', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_collaterals_typeofguarante_359M', 'max_collaterals_typeofguarante_669M', 'max_subjectroles_name_541M', 'max_subjectroles_name_838M', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'lsat_subjectroles_name_838M']","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:20.675188Z","iopub.execute_input":"2024-05-06T07:52:20.675621Z","iopub.status.idle":"2024-05-06T07:52:20.697064Z","shell.execute_reply.started":"2024-05-06T07:52:20.675597Z","shell.execute_reply":"2024-05-06T07:52:20.694800Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ROOT            = Path(\"/kaggle/input/home-credit-credit-risk-model-stability\")\nTEST_DIR        = ROOT / \"parquet_files\" / \"test\"\n\ndata_store = {\n    \"df_base\": read_file(TEST_DIR / \"test_base.parquet\"),\n    \"depth_0\": [\n        read_file(TEST_DIR / \"test_static_cb_0.parquet\"),\n        read_files(TEST_DIR / \"test_static_0_*.parquet\"),\n    ],\n    \"depth_1\": [\n        read_files(TEST_DIR / \"test_applprev_1_*.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_a_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_b_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_c_1.parquet\", 1),\n        read_files(TEST_DIR / \"test_credit_bureau_a_1_*.parquet\", 1),\n        read_file(TEST_DIR / \"test_credit_bureau_b_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_other_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_person_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_deposit_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_debitcard_1.parquet\", 1),\n    ],\n    \"depth_2\": [\n        read_file(TEST_DIR / \"test_credit_bureau_b_2.parquet\", 2),\n        read_files(TEST_DIR / \"test_credit_bureau_a_2_*.parquet\", 2),\n    ]\n}\n\ndf_test = feature_eng(**data_store)\n\ndel data_store\n\ngc.collect()\n\nprint(\"test data shape:\\t\", df_test.shape)\n\n# print(\"test data shape:\\t\", df_test.shape)\n# X_test = df_test.select([col for col in df_train.columns if col != \"target\"])\n\n# X_test, cat_cols = to_pandas(X_test, cat_cols)\n\n# print(\"test data shape:\\t\", df_test.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:20.698392Z","iopub.execute_input":"2024-05-06T07:52:20.698777Z","iopub.status.idle":"2024-05-06T07:52:21.599897Z","shell.execute_reply.started":"2024-05-06T07:52:20.698748Z","shell.execute_reply":"2024-05-06T07:52:21.597902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ROOT            = Path(\"/kaggle/input/home-credit-credit-risk-model-stability\")\n# TRAIN_DIR       = ROOT / \"parquet_files\" / \"train\"\n# TEST_DIR        = ROOT / \"parquet_files\" / \"test\"\n\n# data_store = {\n#     \"df_base\": read_file(TRAIN_DIR / \"train_base.parquet\"),\n#     \"depth_0\": [\n#         read_file(TRAIN_DIR / \"train_static_cb_0.parquet\"),\n#         read_files(TRAIN_DIR / \"train_static_0_*.parquet\"),\n#     ],\n#     \"depth_1\": [\n#         read_files(TRAIN_DIR / \"train_applprev_1_*.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_tax_registry_a_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_tax_registry_b_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_tax_registry_c_1.parquet\", 1),\n#         read_files(TRAIN_DIR / \"train_credit_bureau_a_1_*.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_credit_bureau_b_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_other_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_person_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_deposit_1.parquet\", 1),\n#         read_file(TRAIN_DIR / \"train_debitcard_1.parquet\", 1),\n#     ],\n#     \"depth_2\": [\n#         read_file(TRAIN_DIR / \"train_credit_bureau_b_2.parquet\", 2),\n#         read_files(TRAIN_DIR / \"train_credit_bureau_a_2_*.parquet\", 2),\n#     ]\n# }\n\n# df_test = feature_eng(**data_store)\n\n# del data_store\n\n# gc.collect()\n\n# # print(\"train data shape:\\t\", df_train.shape)\n\n# df_test = df_test.pipe(Pipeline.filter_cols)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:21.604322Z","iopub.execute_input":"2024-05-06T07:52:21.604693Z","iopub.status.idle":"2024-05-06T07:52:21.611963Z","shell.execute_reply.started":"2024-05-06T07:52:21.604670Z","shell.execute_reply":"2024-05-06T07:52:21.610228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_feather(\"/kaggle/input/fuluseful-data/fuluseful_train.feather\")\ndf_train = df_train.drop(columns=['min_credacc_status_367L', 'max_subjectroles_name_541M', 'len_contractenddate_991D_len', 'commnoinclast6m_3546845L', 'min_subjectroles_name_541M', 'last_safeguarantyflag_411L', 'max_collaterals_typeofguarante_669M', 'numnotactivated_1143L', 'clientscnt_1022L', 'max_actualdpd_943P', 'min_numberofoverdueinstls_725L', 'mean_totaloutstanddebtvalue_668A', 'min_collater_valueofguarantee_1124L', 'lsat_education_927M', 'applicationscnt_464L', 'min_periodicityofpmts_1102L', 'paytype_783L', 'clientscnt_1130L', 'clientscnt_1071L', 'sum_actualdpd_943P', 'numactivecreds_622L', 'len_openingdate_313D_len', 'clientscnt_304L', 'max_rejectreasonclient_4145042M', 'numpmtchanneldd_318L', 'len_birthdate_87D_len', 'numactiverelcontr_750L', 'mastercontrelectronic_519L', 'min_collater_valueofguarantee_876L', 'lsat_purposeofcred_874M', 'len_refreshdate_3813885D_len', 'max_outstandingamount_354A', 'last_persontype_1072L', 'applicationscnt_1086L', 'max_housetype_905L', 'last_type_25L', 'max_contaddr_smempladdr_334L', 'last_credacc_status_367L', 'clientscnt_100L', 'applications30d_658L', 'min_subjectroles_name_838M', 'max_collaterals_typeofguarante_359M', 'max_pmts_month_706T', 'numcontrs3months_479L', 'min_numberofoutstandinstls_520L', 'lsat_classificationofcontr_400M', 'min_numberofoverdueinstls_834L', 'max_numberofoutstandinstls_520L', 'sum_totaloutstanddebtvalue_668A', 'applicationcnt_361L', 'min_pmts_month_158T', 'len_birth_259D_len', 'min_personindex_1023L', 'min_persontype_1072L', 'min_persontype_792L', 'max_persontype_1072L', 'mean_overdueamount_31A', 'max_overdueamount_31A', 'mean_outstandingamount_354A', 'min_contaddr_smempladdr_334L', 'min_isbidproduct_390L', 'min_pmts_month_706T', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_numberofoverdueinstls_834L', 'mastercontrexist_109L', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'min_subjectrole_182M', 'last_contaddr_smempladdr_334L', 'max_pmts_month_158T', 'max_empladdr_zipcode_114M', 'deferredmnthsnum_166L', 'min_contaddr_matchlist_1032L', 'sum_totaldebtoverduevalue_718A', 'lsat_empladdr_zipcode_114M', 'sum_residualamount_488A', 'clientscnt_493L', 'sum_overdueamount_31A', 'sum_outstandingamount_354A', 'lsat_empladdr_district_926M', 'max_contaddr_matchlist_1032L', 'max_empladdr_district_926M', 'lsat_classificationofcontr_13M', 'clientscnt_257L', 'clientscnt_157L', 'max_education_927M', 'lsat_subjectrole_93M', 'lsat_subjectrole_182M', 'lsat_purposeofcred_426M', 'lsat_financialinstitution_591M', 'lsat_description_351M', 'lsat_contractst_964M', 'lsat_contractst_545M', 'lsat_subjectroles_name_838M'])\ncat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\nX_test = df_test.select([col for col in df_train.columns if col != \"target\"])\nX_test, cat_cols = to_pandas(X_test, cat_cols)\nX_test.to_csv(\"Xtest.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:21.613884Z","iopub.execute_input":"2024-05-06T07:52:21.614242Z","iopub.status.idle":"2024-05-06T07:52:22.057302Z","shell.execute_reply.started":"2024-05-06T07:52:21.614214Z","shell.execute_reply":"2024-05-06T07:52:22.056081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%reset -f\n!mkdir -p submission_data\n!mv Xtest.csv submission_data/Xtest.csv  # move file to submission_data dir\nimport glob\nfor f in glob.glob('*'):\n    if not f.startswith('submission_data'):# delete all files if path not starts with submission_data\n        !rm -rf {f}","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:22.058734Z","iopub.execute_input":"2024-05-06T07:52:22.059047Z","iopub.status.idle":"2024-05-06T07:52:23.069602Z","shell.execute_reply.started":"2024-05-06T07:52:22.059021Z","shell.execute_reply":"2024-05-06T07:52:23.068391Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 预测第一部分——autogluon","metadata":{}},{"cell_type":"code","source":"%%time\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\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import StratifiedGroupKFold\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.metrics import roc_auc_score\n\nimport lightgbm as lgb\nfrom catboost import CatBoostClassifier, Pool\n\nimport warnings\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n# warnings.simplefilter(action='ignore', category=PerformanceWarning)\nfrom autogluon.tabular import TabularDataset, TabularPredictor","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:23.071261Z","iopub.execute_input":"2024-05-06T07:52:23.071594Z","iopub.status.idle":"2024-05-06T07:52:23.080013Z","shell.execute_reply.started":"2024-05-06T07:52:23.071566Z","shell.execute_reply":"2024-05-06T07:52:23.078324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:23.082408Z","iopub.execute_input":"2024-05-06T07:52:23.082915Z","iopub.status.idle":"2024-05-06T07:52:23.230703Z","shell.execute_reply.started":"2024-05-06T07:52:23.082891Z","shell.execute_reply":"2024-05-06T07:52:23.229389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class VotingModel(BaseEstimator, RegressorMixin):\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":{"execution":{"iopub.status.busy":"2024-05-06T07:52:23.232280Z","iopub.execute_input":"2024-05-06T07:52:23.232626Z","iopub.status.idle":"2024-05-06T07:52:23.244867Z","shell.execute_reply.started":"2024-05-06T07:52:23.232600Z","shell.execute_reply":"2024-05-06T07:52:23.242978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lgb582 = []\nlgb582.append(lgb.Booster(model_file='/kaggle/input/582model/582lgbm_0.txt'))\nlgb582.append(lgb.Booster(model_file='/kaggle/input/582model/582lgbm_1.txt'))\nlgb582.append(lgb.Booster(model_file='/kaggle/input/582model/582lgbm_2.txt'))\nlgb582.append(lgb.Booster(model_file='/kaggle/input/582model/582lgbm_3.txt'))\nlgb582.append(lgb.Booster(model_file='/kaggle/input/582model/582lgbm_4.txt'))\nlgb582 = VotingModel(lgb582)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:23.246935Z","iopub.execute_input":"2024-05-06T07:52:23.247321Z","iopub.status.idle":"2024-05-06T07:52:23.554004Z","shell.execute_reply.started":"2024-05-06T07:52:23.247282Z","shell.execute_reply":"2024-05-06T07:52:23.552629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# cat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# # 进行sum求总记录数平均值处理\n# # 根据经验对特征进行补充：\n# X_test = df_test.select([col for col in df_train.columns if col != \"target\"])\n\n# X_test, cat_cols = to_pandas(X_test, cat_cols)\ndf_train = pd.read_feather(\"/kaggle/input/fuluseful-data/fuluseful_train.feather\")\ndf_train = df_train.drop(columns=['min_credacc_status_367L', 'max_subjectroles_name_541M', 'len_contractenddate_991D_len', 'commnoinclast6m_3546845L', 'min_subjectroles_name_541M', 'last_safeguarantyflag_411L', 'max_collaterals_typeofguarante_669M', 'numnotactivated_1143L', 'clientscnt_1022L', 'max_actualdpd_943P', 'min_numberofoverdueinstls_725L', 'mean_totaloutstanddebtvalue_668A', 'min_collater_valueofguarantee_1124L', 'lsat_education_927M', 'applicationscnt_464L', 'min_periodicityofpmts_1102L', 'paytype_783L', 'clientscnt_1130L', 'clientscnt_1071L', 'sum_actualdpd_943P', 'numactivecreds_622L', 'len_openingdate_313D_len', 'clientscnt_304L', 'max_rejectreasonclient_4145042M', 'numpmtchanneldd_318L', 'len_birthdate_87D_len', 'numactiverelcontr_750L', 'mastercontrelectronic_519L', 'min_collater_valueofguarantee_876L', 'lsat_purposeofcred_874M', 'len_refreshdate_3813885D_len', 'max_outstandingamount_354A', 'last_persontype_1072L', 'applicationscnt_1086L', 'max_housetype_905L', 'last_type_25L', 'max_contaddr_smempladdr_334L', 'last_credacc_status_367L', 'clientscnt_100L', 'applications30d_658L', 'min_subjectroles_name_838M', 'max_collaterals_typeofguarante_359M', 'max_pmts_month_706T', 'numcontrs3months_479L', 'min_numberofoutstandinstls_520L', 'lsat_classificationofcontr_400M', 'min_numberofoverdueinstls_834L', 'max_numberofoutstandinstls_520L', 'sum_totaloutstanddebtvalue_668A', 'applicationcnt_361L', 'min_pmts_month_158T', 'len_birth_259D_len', 'min_personindex_1023L', 'min_persontype_1072L', 'min_persontype_792L', 'max_persontype_1072L', 'mean_overdueamount_31A', 'max_overdueamount_31A', 'mean_outstandingamount_354A', 'min_contaddr_smempladdr_334L', 'min_isbidproduct_390L', 'min_pmts_month_706T', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_numberofoverdueinstls_834L', 'mastercontrexist_109L', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'min_subjectrole_182M', 'last_contaddr_smempladdr_334L', 'max_pmts_month_158T', 'max_empladdr_zipcode_114M', 'deferredmnthsnum_166L', 'min_contaddr_matchlist_1032L', 'sum_totaldebtoverduevalue_718A', 'lsat_empladdr_zipcode_114M', 'sum_residualamount_488A', 'clientscnt_493L', 'sum_overdueamount_31A', 'sum_outstandingamount_354A', 'lsat_empladdr_district_926M', 'max_contaddr_matchlist_1032L', 'max_empladdr_district_926M', 'lsat_classificationofcontr_13M', 'clientscnt_257L', 'clientscnt_157L', 'max_education_927M', 'lsat_subjectrole_93M', 'lsat_subjectrole_182M', 'lsat_purposeofcred_426M', 'lsat_financialinstitution_591M', 'lsat_description_351M', 'lsat_contractst_964M', 'lsat_contractst_545M', 'lsat_subjectroles_name_838M'])\ncat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n\npredictor = TabularPredictor.load(\"/kaggle/input/auto544-model/ag120532\", require_version_match=False)\n# 此时X_test应该为544 -> 582\n# 进行autogluon预测\nCHUNK_SIZE = 300\nreader = pd.read_csv('/kaggle/working/submission_data/Xtest.csv', chunksize=CHUNK_SIZE, low_memory=False)\ny_pred_valid1 = []\ny_pred_valid2 = []\ny_pred_valid3 = []\ni = 0\nfor X_test in reader:\n    print(i)\n    X_test = X_test.drop(columns=[\"Unnamed: 0\"])\n    X_test[\"len_firstnonzeroinstldate_307D_len\"] = X_test[\"len_firstnonzeroinstldate_307D_len\"].fillna(value=1)\n    X_test[\"len_firstnonzeroinstldate_307D_len\"] = X_test[\"len_firstnonzeroinstldate_307D_len\"].replace(0, 1)\n    for col in X_test.columns:\n        if col[:3] == \"sum\":\n            X_test[\"aver_\" + col] = X_test[col]/X_test[\"len_firstnonzeroinstldate_307D_len\"]\n            X_test[\"aver_\" + col] = X_test[\"aver_\" + col].replace(np.inf, -100)\n    try: \n        y_pred_valid1.append(predictor.predict_proba(X_test).iloc[:,1].values)\n    except Exception as e:\n        print(f\"An error occurred: {e}. Filling batch_y_pred\")\n        # 抛出异常，用lgbm预测值补齐\n        X_test = X_test.drop(columns=[\"case_id\",\"WEEK_NUM\"])\n        X_test[cat_cols] = X_test[cat_cols].astype(\"category\")\n        y_pred_valid1.append(lgb582.predict(X_test))\ny_pred_valid1 = np.concatenate(y_pred_valid1, axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:52:23.556521Z","iopub.execute_input":"2024-05-06T07:52:23.556824Z","iopub.status.idle":"2024-05-06T07:53:13.777838Z","shell.execute_reply.started":"2024-05-06T07:52:23.556801Z","shell.execute_reply":"2024-05-06T07:53:13.774039Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 预测第二部分——词嵌入特征","metadata":{}},{"cell_type":"code","source":"# 载入预训练词嵌入向量\nmapping_list = [{'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '2fc785b2': -0.009244342342147624}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '717ddd49': -0.010198146317255427, '6b2ae0fa': -0.008606163076829413, '39a0853f': -0.009460007343705001, 'c8e1a1d0': -0.009559475972849896}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '6b2ae0fa': -0.008606163076829413, '717ddd49': -0.010198146317255427, 'a34a13c8': -0.009400209002023985, 'c8e1a1d0': -0.009559475972849896}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, 'a7fcb6e5': -0.01067335202318039, '3439d993': -0.009634087399850463, 'b6cabe76': -0.008427829736395628, '38c061ee': -0.009184238270014097, 'ecd83604': -0.008519643800658136}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '46b968c3': -0.010325181199429304, '977b2a70': -0.00910213132217604, '1a19667c': -0.009253143047904663, 'e18430ff': -0.009743557093012592, 'ecd83604': -0.008519643800658136}, {'nan': -0.010353292803387149, 'DEDUCTION_6': -0.009541461302736328, 'PENSION_6': -0.007773135793286201, 'SOCIAL_6': -0.007922835314502663}, {'nan': -0.010353292803387149, 'CA': -0.009529872416654447}, {'nan': -0.010353292803387149, 'INSTANT': 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'REAL_ESTATE': -0.008719522067205313, 'TOURISM': -0.01382685150853528, 'GAMING': -0.010596279932073571, 'CHARITY_RELIGIOUS': -0.008908145123153114, 'INSURANCE': -0.0099911627420776, 'MARKETING': -0.009230503349802651, 'ARMY_POLICE': -0.0075955542509594425, 'RECRUITMENT': -0.008272601561960377}, {'MARRIED': -0.011114102341707621, 'DIVORCED': -0.01116848136424172, 'SINGLE': -0.011252564667567336, 'WIDOWED': -0.0097198666731515, 'LIVING_WITH_PARTNER': -0.009209827139708674, 'nan': -0.010353292803387149}, {'SALARIED_GOVT': -0.007441425726180266, 'EMPLOYED': -0.0107022042408668, 'PRIVATE_SECTOR_EMPLOYEE': -0.010301220575759848, 'RETIRED_PENSIONER': -0.008918678615231102, 'SELFEMPLOYED': -0.00923072733102496, 'HANDICAPPED_2': -0.008299635497261685, 'OTHER': -0.00960748715291023, 'HANDICAPPED_3': -0.008229108603889776, 'HANDICAPPED': -0.010058058036975126}, {'SPOUSE': -0.010601428335576202, 'SIBLING': -0.010290088405630135, 'FRIEND': -0.010256063942354862, 'CHILD': -0.010564697451760216, 'OTHER_RELATIVE': -0.008640507774998696, 'PARENT': -0.009775472980057506, 'COLLEAGUE': -0.010288427194221109, 'NEIGHBOR': -0.010443934585278924, 'OTHER': -0.00960748715291023, 'nan': -0.010353292803387149, 'GRAND_PARENT': -0.009393141418058804}, {'SPOUSE': -0.010601428335576202, 'SIBLING': -0.010290088405630135, 'FRIEND': -0.010256063942354862, 'CHILD': -0.010564697451760216, 'OTHER_RELATIVE': -0.008640507774998696, 'PARENT': -0.009775472980057506, 'COLLEAGUE': -0.010288427194221109, 'NEIGHBOR': -0.010443934585278924, 'OTHER': -0.00960748715291023, 'nan': -0.010353292803387149, 'GRAND_PARENT': -0.009393141418058804}, {'False': -0.01004077808337911, 'nan': -0.010353292803387149}, {'PE': -0.011968071348178455, 'EM': -0.01073034976596432, 'CL': -0.010339277340345157}, {'True': -0.009938715898044848, 'False': -0.01004077808337911, 'nan': -0.010353292803387149}, {'F': -0.010408664621399074, 'M': -0.010727496780437681}, {'PRIMARY_MOBILE': -0.008742051999547584, 'SECONDARY_MOBILE': -0.008805404804737313, 'PHONE': -0.010621909073999328, 'WHATSAPP': -0.010221076569375024, 'HOME_PHONE': -0.008622787311461858}, {'nan': -0.010353292803387149, 'False': -0.01004077808337911}, {'nan': -0.010353292803387149, 'RETIRED_PENSIONER': -0.008918678615231102, 'HANDICAPPED_3': -0.008229108603889776, 'SALARIED_GOVT': -0.007441425726180266, 'HANDICAPPED_2': -0.008299635497261685, 'PRIVATE_SECTOR_EMPLOYEE': -0.010301220575759848, 'EMPLOYED': -0.0107022042408668, 'OTHER': -0.00960748715291023, 'SELFEMPLOYED': -0.00923072733102496}, {'nan': -0.010353292803387149, 'CHILD': -0.010564697451760216, 'OTHER_RELATIVE': -0.008640507774998696, 'SPOUSE': -0.010601428335576202, 'NEIGHBOR': -0.010443934585278924, 'FRIEND': -0.010256063942354862, 'SIBLING': -0.010290088405630135, 'OTHER': -0.00960748715291023, 'COLLEAGUE': -0.010288427194221109, 'GRAND_PARENT': -0.009393141418058804, 'PARENT': -0.009775472980057506}, {'COLLEAGUE': -0.010288427194221109, 'OTHER_RELATIVE': -0.008640507774998696, 'SPOUSE': -0.010601428335576202, 'SIBLING': -0.010290088405630135, 'FRIEND': -0.010256063942354862, 'CHILD': -0.010564697451760216, 'PARENT': -0.009775472980057506, 'NEIGHBOR': -0.010443934585278924, 'OTHER': -0.00960748715291023, 'nan': -0.010353292803387149, 'GRAND_PARENT': -0.009393141418058804}, {'nan': -0.010353292803387149, 'False': -0.01004077808337911}, {'PE': -0.011968071348178455, 'CL': -0.010339277340345157, 'EM': -0.01073034976596432, 'nan': -0.010353292803387149}, {'nan': -0.010353292803387149, 'M': -0.010727496780437681, 'F': -0.010408664621399074}, {'nan': -0.010353292803387149, '8fd95e4b': -0.010252247033804451, '9a0c095e': -0.00929308446439118, '06fb9ba8': -0.008979680874347196, 'a55475b1': -0.01036039912742126, '26cf31be': -0.009758186836810031}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '9a0c095e': -0.00929308446439118, '8fd95e4b': -0.010252247033804451, '06fb9ba8': -0.008979680874347196, '3cbe86ba': -0.00888597733963555, '9276e4bb': -0.010847165233220343}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '3cbe86ba': -0.00888597733963555, '0e63c0f0': -0.008844084763344048, '7b62420e': -0.009826760374456475, '9276e4bb': -0.010847165233220343, '168ad9f3': -0.00981696614686219, '5994c34a': -0.010349483086921888, '5224034a': -0.010031894036994041, '46ab00a7': -0.00964194659017418, '2fd21cf1': -0.009439484180992016, '940efad7': -0.009791149119147727, '470b7fa6': -0.009588699897297678, 'c7a5ad39': -0.009642060571347125}, {'nan': -0.010353292803387149, 'a55475b1': -0.01036039912742126, '0e63c0f0': -0.008844084763344048, '9276e4bb': -0.010847165233220343, '3cbe86ba': -0.00888597733963555, '7b62420e': -0.009826760374456475, '2fd21cf1': -0.009439484180992016, '168ad9f3': -0.00981696614686219, '46ab00a7': -0.00964194659017418, '5224034a': -0.010031894036994041, '940efad7': -0.009791149119147727, '5994c34a': -0.010349483086921888, '470b7fa6': -0.009588699897297678}, {'nan': -0.010353292803387149, 'ab3c25cf': -0.009157336050535037, 'be4fd70b': -0.009895453034005186, 'daf49a8a': -0.00916562118834463, 'a55475b1': -0.01036039912742126}]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:13.786945Z","iopub.execute_input":"2024-05-06T07:53:13.788526Z","iopub.status.idle":"2024-05-06T07:53:14.201084Z","shell.execute_reply.started":"2024-05-06T07:53:13.788397Z","shell.execute_reply":"2024-05-06T07:53:14.199152Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_feather(\"/kaggle/input/fuluseful-data/fuluseful_train.feather\")\ndf_train = df_train.drop(columns=['min_credacc_status_367L', 'max_subjectroles_name_541M', 'len_contractenddate_991D_len', 'commnoinclast6m_3546845L', 'min_subjectroles_name_541M', 'last_safeguarantyflag_411L', 'max_collaterals_typeofguarante_669M', 'numnotactivated_1143L', 'clientscnt_1022L', 'max_actualdpd_943P', 'min_numberofoverdueinstls_725L', 'mean_totaloutstanddebtvalue_668A', 'min_collater_valueofguarantee_1124L', 'lsat_education_927M', 'applicationscnt_464L', 'min_periodicityofpmts_1102L', 'paytype_783L', 'clientscnt_1130L', 'clientscnt_1071L', 'sum_actualdpd_943P', 'numactivecreds_622L', 'len_openingdate_313D_len', 'clientscnt_304L', 'max_rejectreasonclient_4145042M', 'numpmtchanneldd_318L', 'len_birthdate_87D_len', 'numactiverelcontr_750L', 'mastercontrelectronic_519L', 'min_collater_valueofguarantee_876L', 'lsat_purposeofcred_874M', 'len_refreshdate_3813885D_len', 'max_outstandingamount_354A', 'last_persontype_1072L', 'applicationscnt_1086L', 'max_housetype_905L', 'last_type_25L', 'max_contaddr_smempladdr_334L', 'last_credacc_status_367L', 'clientscnt_100L', 'applications30d_658L', 'min_subjectroles_name_838M', 'max_collaterals_typeofguarante_359M', 'max_pmts_month_706T', 'numcontrs3months_479L', 'min_numberofoutstandinstls_520L', 'lsat_classificationofcontr_400M', 'min_numberofoverdueinstls_834L', 'max_numberofoutstandinstls_520L', 'sum_totaloutstanddebtvalue_668A', 'applicationcnt_361L', 'min_pmts_month_158T', 'len_birth_259D_len', 'min_personindex_1023L', 'min_persontype_1072L', 'min_persontype_792L', 'max_persontype_1072L', 'mean_overdueamount_31A', 'max_overdueamount_31A', 'mean_outstandingamount_354A', 'min_contaddr_smempladdr_334L', 'min_isbidproduct_390L', 'min_pmts_month_706T', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_numberofoverdueinstls_834L', 'mastercontrexist_109L', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'min_subjectrole_182M', 'last_contaddr_smempladdr_334L', 'max_pmts_month_158T', 'max_empladdr_zipcode_114M', 'deferredmnthsnum_166L', 'min_contaddr_matchlist_1032L', 'sum_totaldebtoverduevalue_718A', 'lsat_empladdr_zipcode_114M', 'sum_residualamount_488A', 'clientscnt_493L', 'sum_overdueamount_31A', 'sum_outstandingamount_354A', 'lsat_empladdr_district_926M', 'max_contaddr_matchlist_1032L', 'max_empladdr_district_926M', 'lsat_classificationofcontr_13M', 'clientscnt_257L', 'clientscnt_157L', 'max_education_927M', 'lsat_subjectrole_93M', 'lsat_subjectrole_182M', 'lsat_purposeofcred_426M', 'lsat_financialinstitution_591M', 'lsat_description_351M', 'lsat_contractst_964M', 'lsat_contractst_545M', 'lsat_subjectroles_name_838M'])\ncat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# X_test = df_test.select([col for col in df_train.columns if col != \"target\"])\n# X_test, cat_cols = to_pandas(X_test, cat_cols)\n#开始映射词嵌入\nCHUNK_SIZE = 500000\nreader = pd.read_csv('/kaggle/working/submission_data/Xtest.csv', chunksize=CHUNK_SIZE, low_memory=False)\nfor X_test in reader: \n    # print(X_test)\n    X_test = X_test.drop(columns=[\"Unnamed: 0\",\"case_id\",\"WEEK_NUM\"])\n    X_test[cat_cols] = X_test[cat_cols].astype(str)\n\n    for i, mapping in enumerate(mapping_list):\n        X_test[cat_cols[i]+\"_vec\"] = X_test[cat_cols[i]].map(mapping)\n        X_test[cat_cols[i]+\"_vec\"] = X_test[cat_cols[i]+\"_vec\"].fillna(0)\n    print(\"词嵌入完成\")\n    print(X_test.shape)\n    lgb657 = []\n    lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_0.txt'))\n    lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_1.txt'))\n    lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_2.txt'))\n    lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_3.txt'))\n    lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_4.txt'))\n    lgb657 = VotingModel(lgb657)\n\n    cat657 = []\n    cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_0.txt'))\n    cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_1.txt'))\n    cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_2.txt'))\n    cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_3.txt'))\n    cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_4.txt'))               \n    cat657 = VotingModel(cat657)\n    \n    # X_test = X_test.drop(columns=[\"case_id\", \"WEEK_NUM\"])\n    #第二部分catboost预测\n    y_pred_valid2.append(cat657.predict_proba(X_test)[:,1])\n\n    # 第一部分lgbm预测\n    X_test[cat_cols] = X_test[cat_cols].astype(\"category\")\n    y_pred_valid3.append(lgb657.predict(X_test))\ny_pred_valid2 = np.concatenate(y_pred_valid2, axis=0)\ny_pred_valid3 = np.concatenate(y_pred_valid3, axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:14.204111Z","iopub.execute_input":"2024-05-06T07:53:14.207048Z","iopub.status.idle":"2024-05-06T07:53:17.206000Z","shell.execute_reply.started":"2024-05-06T07:53:14.206935Z","shell.execute_reply":"2024-05-06T07:53:17.204217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 最终模型融合","metadata":{}},{"cell_type":"code","source":"# lgbmodel = []\n# lgbmodel.append(lgb.Booster(model_file='/kaggle/input/modelss/lgb_0.txt'))\n# lgbmodel.append(lgb.Booster(model_file='/kaggle/input/modelss/lgb_1.txt'))\n# lgbmodel.append(lgb.Booster(model_file='/kaggle/input/modelss/lgb_2.txt'))\n# lgbmodel.append(lgb.Booster(model_file='/kaggle/input/modelss/lgb_3.txt'))\n# lgbmodel.append(lgb.Booster(model_file='/kaggle/input/modelss/lgb_4.txt'))\n# lgbmodel = VotingModel(lgbmodel)\n\n# lgb544 = []\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_nogrouptrain_0.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_nogrouptrain_1.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_nogrouptrain_2.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_nogrouptrain_3.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_nogrouptrain_4.txt'))\n\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_posthalftrain_0.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_posthalftrain_1.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_posthalftrain_2.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_posthalftrain_3.txt'))\n# lgb544.append(lgb.Booster(model_file='/kaggle/input/544train/lgb544_posthalftrain_4.txt'))\n\n# lgb544 = VotingModel(lgb544)\n\n# catmodel = []\n# catmodel.append(CatBoostClassifier().load_model('/kaggle/input/modelss/cat_0.txt'))\n# catmodel.append(CatBoostClassifier().load_model('/kaggle/input/modelss/cat_1.txt'))\n# catmodel.append(CatBoostClassifier().load_model('/kaggle/input/modelss/cat_2.txt'))\n# catmodel.append(CatBoostClassifier().load_model('/kaggle/input/modelss/cat_3.txt'))\n# catmodel.append(CatBoostClassifier().load_model('/kaggle/input/modelss/cat_4.txt'))               \n# catmodel = VotingModel(catmodel)\n\n\n# lgb657 = []\n# lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_0.txt'))\n# lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_1.txt'))\n# lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_2.txt'))\n# lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_3.txt'))\n# lgb657.append(lgb.Booster(model_file='/kaggle/input/644model/657lgbm_4.txt'))\n# lgb657 = VotingModel(lgb657)\n\n\n# cat657 = []\n# cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_0.txt'))\n# cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_1.txt'))\n# cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_2.txt'))\n# cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_3.txt'))\n# cat657.append(CatBoostClassifier().load_model('/kaggle/input/644model/657cat_4.txt'))               \n# cat657 = VotingModel(cat657)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.209437Z","iopub.execute_input":"2024-05-06T07:53:17.210163Z","iopub.status.idle":"2024-05-06T07:53:17.219290Z","shell.execute_reply.started":"2024-05-06T07:53:17.210047Z","shell.execute_reply":"2024-05-06T07:53:17.217601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # 第一部分lgbm预测\n# y_pred_valid1 = lgbmodel.predict(X_test)\n\n# #第二部分catboost预测\n# X_test[cat_cols] = X_test[cat_cols].astype(str)\n# y_pred_valid2 = catmodel.predict_proba(X_test)[:,1]\n\n# # 第三部分lgbm544\n# df_train = pd.read_feather(\"/kaggle/input/useful-data/useful_train.feather\")\n# df_train = df_train.drop(columns=['min_credacc_status_367L', 'max_subjectroles_name_541M', 'len_contractenddate_991D_len', 'commnoinclast6m_3546845L', 'min_subjectroles_name_541M', 'last_safeguarantyflag_411L', 'max_collaterals_typeofguarante_669M', 'numnotactivated_1143L', 'clientscnt_1022L', 'max_actualdpd_943P', 'min_numberofoverdueinstls_725L', 'mean_totaloutstanddebtvalue_668A', 'min_collater_valueofguarantee_1124L', 'lsat_education_927M', 'applicationscnt_464L', 'min_periodicityofpmts_1102L', 'paytype_783L', 'clientscnt_1130L', 'clientscnt_1071L', 'sum_actualdpd_943P', 'numactivecreds_622L', 'len_openingdate_313D_len', 'clientscnt_304L', 'max_rejectreasonclient_4145042M', 'numpmtchanneldd_318L', 'len_birthdate_87D_len', 'numactiverelcontr_750L', 'mastercontrelectronic_519L', 'min_collater_valueofguarantee_876L', 'lsat_purposeofcred_874M', 'len_refreshdate_3813885D_len', 'max_outstandingamount_354A', 'last_persontype_1072L', 'applicationscnt_1086L', 'max_housetype_905L', 'last_type_25L', 'max_contaddr_smempladdr_334L', 'last_credacc_status_367L', 'clientscnt_100L', 'applications30d_658L', 'min_subjectroles_name_838M', 'max_collaterals_typeofguarante_359M', 'max_pmts_month_706T', 'numcontrs3months_479L', 'min_numberofoutstandinstls_520L', 'lsat_classificationofcontr_400M', 'min_numberofoverdueinstls_834L', 'max_numberofoutstandinstls_520L', 'sum_totaloutstanddebtvalue_668A', 'applicationcnt_361L', 'min_pmts_month_158T', 'len_birth_259D_len', 'min_personindex_1023L', 'min_persontype_1072L', 'min_persontype_792L', 'max_persontype_1072L', 'mean_overdueamount_31A', 'max_overdueamount_31A', 'mean_outstandingamount_354A', 'min_contaddr_smempladdr_334L', 'min_isbidproduct_390L', 'min_pmts_month_706T', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_numberofoverdueinstls_834L', 'mastercontrexist_109L', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'min_subjectrole_182M', 'last_contaddr_smempladdr_334L', 'max_pmts_month_158T', 'max_empladdr_zipcode_114M', 'deferredmnthsnum_166L', 'min_contaddr_matchlist_1032L', 'sum_totaldebtoverduevalue_718A', 'lsat_empladdr_zipcode_114M', 'sum_residualamount_488A', 'clientscnt_493L', 'sum_overdueamount_31A', 'sum_outstandingamount_354A', 'lsat_empladdr_district_926M', 'max_contaddr_matchlist_1032L', 'max_empladdr_district_926M', 'lsat_classificationofcontr_13M', 'clientscnt_257L', 'clientscnt_157L', 'max_education_927M', 'lsat_subjectrole_93M', 'lsat_subjectrole_182M', 'lsat_purposeofcred_426M', 'lsat_financialinstitution_591M', 'lsat_description_351M', 'lsat_contractst_964M', 'lsat_contractst_545M', 'lsat_subjectroles_name_838M'])\n# cat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# X_test = df_test.select([col for col in df_train.columns if col != \"target\"])\n# X_test, cat_cols = to_pandas(X_test, cat_cols)\n# y_pred_valid3 = lgb544.predict(X_test)\n\n# X_test = X_test.drop(columns=[\"case_id\", \"WEEK_NUM\"])\n# #第二部分catboost预测\n# y_pred_valid2 = cat657.predict_proba(X_test)[:,1]\n\n# # 第一部分lgbm预测\n# X_test[cat_cols] = X_test[cat_cols].astype(\"category\")\n# y_pred_valid3 = lgb657.predict(X_test)\n\n\n\n#融合后\nw_lgb = 1\nw_cat = 0.\nw_lgb544 = 0.\ny_pred = w_lgb*y_pred_valid1 + w_cat*y_pred_valid2 + w_lgb544*y_pred_valid3","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.222256Z","iopub.execute_input":"2024-05-06T07:53:17.222776Z","iopub.status.idle":"2024-05-06T07:53:17.245318Z","shell.execute_reply.started":"2024-05-06T07:53:17.222753Z","shell.execute_reply":"2024-05-06T07:53:17.243324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# y_pred_valid1 = pd.DataFrame(y_pred_valid1)\n# y_pred_valid2 = pd.DataFrame(y_pred_valid2)\n# y_pred_valid3 = pd.DataFrame(y_pred_valid3)\n# y_preds = pd.concat([y_pred_valid1,y_pred_valid2,y_pred_valid3], axis=1)\n# y_preds = np.array(y_preds)\n# y_preds = pd.DataFrame(y_preds)\n# print(y_preds)\n# # y_preds.to_feather(\"treasure_y_pred.feather\")\n\n# import joblib\n# from sklearn.ensemble import GradientBoostingClassifier\n# meta_model = joblib.load('/kaggle/input/staking3/final_stacking.pkl') \n# y_pred = meta_model.predict_proba(y_preds)[:, 1]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.247071Z","iopub.execute_input":"2024-05-06T07:53:17.247523Z","iopub.status.idle":"2024-05-06T07:53:17.266402Z","shell.execute_reply.started":"2024-05-06T07:53:17.247464Z","shell.execute_reply":"2024-05-06T07:53:17.264937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 提交\ndf_subm = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")\ndf_subm = df_subm.set_index(\"case_id\")\n\ndf_subm[\"score\"] = y_pred\ndf_subm.to_csv(\"submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.267816Z","iopub.execute_input":"2024-05-06T07:53:17.268114Z","iopub.status.idle":"2024-05-06T07:53:17.303152Z","shell.execute_reply.started":"2024-05-06T07:53:17.268087Z","shell.execute_reply":"2024-05-06T07:53:17.301018Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 训练部分","metadata":{}},{"cell_type":"code","source":"# df_train = pd.read_feather(\"/kaggle/input/df-train/new_train.feather\") #原始版本","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.305506Z","iopub.execute_input":"2024-05-06T07:53:17.305802Z","iopub.status.idle":"2024-05-06T07:53:17.313811Z","shell.execute_reply.started":"2024-05-06T07:53:17.305781Z","shell.execute_reply":"2024-05-06T07:53:17.312122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# df_train = df_train.drop(columns=['min_credacc_status_367L', 'max_subjectroles_name_541M', 'len_contractenddate_991D_len', 'commnoinclast6m_3546845L', 'min_subjectroles_name_541M', 'last_safeguarantyflag_411L', 'max_collaterals_typeofguarante_669M', 'numnotactivated_1143L', 'clientscnt_1022L', 'max_actualdpd_943P', 'min_numberofoverdueinstls_725L', 'mean_totaloutstanddebtvalue_668A', 'min_collater_valueofguarantee_1124L', 'lsat_education_927M', 'applicationscnt_464L', 'min_periodicityofpmts_1102L', 'paytype_783L', 'clientscnt_1130L', 'clientscnt_1071L', 'sum_actualdpd_943P', 'numactivecreds_622L', 'len_openingdate_313D_len', 'clientscnt_304L', 'max_rejectreasonclient_4145042M', 'numpmtchanneldd_318L', 'len_birthdate_87D_len', 'numactiverelcontr_750L', 'mastercontrelectronic_519L', 'min_collater_valueofguarantee_876L', 'lsat_purposeofcred_874M', 'len_refreshdate_3813885D_len', 'max_outstandingamount_354A', 'last_persontype_1072L', 'applicationscnt_1086L', 'max_housetype_905L', 'last_type_25L', 'max_contaddr_smempladdr_334L', 'last_credacc_status_367L', 'clientscnt_100L', 'applications30d_658L', 'min_subjectroles_name_838M', 'max_collaterals_typeofguarante_359M', 'max_pmts_month_706T', 'numcontrs3months_479L', 'min_numberofoutstandinstls_520L', 'lsat_classificationofcontr_400M', 'min_numberofoverdueinstls_834L', 'max_numberofoutstandinstls_520L', 'sum_totaloutstanddebtvalue_668A', 'applicationcnt_361L', 'min_pmts_month_158T', 'len_birth_259D_len', 'min_personindex_1023L', 'min_persontype_1072L', 'min_persontype_792L', 'max_persontype_1072L', 'mean_overdueamount_31A', 'max_overdueamount_31A', 'mean_outstandingamount_354A', 'min_contaddr_smempladdr_334L', 'min_isbidproduct_390L', 'min_pmts_month_706T', 'max_collater_typofvalofguarant_298M', 'max_collater_typofvalofguarant_407M', 'max_numberofoverdueinstls_834L', 'mastercontrexist_109L', 'lsat_collater_typofvalofguarant_298M', 'lsat_collater_typofvalofguarant_407M', 'lsat_collaterals_typeofguarante_359M', 'lsat_collaterals_typeofguarante_669M', 'lsat_subjectroles_name_541M', 'min_subjectrole_182M', 'last_contaddr_smempladdr_334L', 'max_pmts_month_158T', 'max_empladdr_zipcode_114M', 'deferredmnthsnum_166L', 'min_contaddr_matchlist_1032L', 'sum_totaldebtoverduevalue_718A', 'lsat_empladdr_zipcode_114M', 'sum_residualamount_488A', 'clientscnt_493L', 'sum_overdueamount_31A', 'sum_outstandingamount_354A', 'lsat_empladdr_district_926M', 'max_contaddr_matchlist_1032L', 'max_empladdr_district_926M', 'lsat_classificationofcontr_13M', 'clientscnt_257L', 'clientscnt_157L', 'max_education_927M', 'lsat_subjectrole_93M', 'lsat_subjectrole_182M', 'lsat_purposeofcred_426M', 'lsat_financialinstitution_591M', 'lsat_description_351M', 'lsat_contractst_964M', 'lsat_contractst_545M', 'lsat_subjectroles_name_838M'])\n# cat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# print(df_train.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.316241Z","iopub.execute_input":"2024-05-06T07:53:17.316644Z","iopub.status.idle":"2024-05-06T07:53:17.327212Z","shell.execute_reply.started":"2024-05-06T07:53:17.316615Z","shell.execute_reply":"2024-05-06T07:53:17.325248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# X_test = df_train\n\n# X_test[\"len_firstnonzeroinstldate_307D_len\"] = X_test[\"len_firstnonzeroinstldate_307D_len\"].fillna(value=1)\n# X_test[\"len_firstnonzeroinstldate_307D_len\"] = X_test[\"len_firstnonzeroinstldate_307D_len\"].replace(0, 1)\n# for col in X_test.columns:\n#     if col[:3] == \"sum\":\n#         X_test[\"aver_\" + col] = X_test[col]/X_test[\"len_firstnonzeroinstldate_307D_len\"]\n#         X_test[\"aver_\" + col] = X_test[\"aver_\" + col].replace(np.inf, -100)\n# print(X_test.shape)\n# # 此时X_test应该为582\n# # 进行autogluon预测\n# predictor = TabularPredictor.load(\"/kaggle/input/auto544-model/ag120532\", require_version_match=False)\n# y_pred_valid1 = predictor.predict_proba(X_test).iloc[:,1]","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.329255Z","iopub.execute_input":"2024-05-06T07:53:17.329598Z","iopub.status.idle":"2024-05-06T07:53:17.348222Z","shell.execute_reply.started":"2024-05-06T07:53:17.329573Z","shell.execute_reply":"2024-05-06T07:53:17.347188Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print(y_pred_valid1)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.349379Z","iopub.execute_input":"2024-05-06T07:53:17.350724Z","iopub.status.idle":"2024-05-06T07:53:17.370247Z","shell.execute_reply.started":"2024-05-06T07:53:17.350696Z","shell.execute_reply":"2024-05-06T07:53:17.367728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# cat_cols = [col for col in df_train.columns if df_train[col].dtype==\"category\"]\n# X_test = df_train\n# # X_test = df_train.select([col for col in df_train.columns if col != \"target\"])\n# #开始映射词嵌入\n# X_test[cat_cols] = X_test[cat_cols].astype(str)\n# for i, mapping in enumerate(mapping_list):\n#     X_test[cat_cols[i]+\"_vec\"] = X_test[cat_cols[i]].map(mapping)\n# print(X_test.shape)\n\n# y = X_test[\"target\"]\n# X_test = X_test.drop(columns=[\"case_id\", \"WEEK_NUM\",\"target\"])\n# #第二部分catboost预测\n# y_pred_valid2 = cat657.predict_proba(X_test)[:,1]\n\n# # 第一部分lgbm预测\n# X_test[cat_cols] = X_test[cat_cols].astype(\"category\")\n# y_pred_valid3 = lgb657.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.375435Z","iopub.execute_input":"2024-05-06T07:53:17.375955Z","iopub.status.idle":"2024-05-06T07:53:17.387429Z","shell.execute_reply.started":"2024-05-06T07:53:17.375925Z","shell.execute_reply":"2024-05-06T07:53:17.384818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# y_pred_valid1 = pd.DataFrame(y_pred_valid1)\n# y_pred_valid2 = pd.DataFrame(y_pred_valid2)\n# y_pred_valid3 = pd.DataFrame(y_pred_valid3)\n# y_preds = pd.concat([y_pred_valid1,y_pred_valid2,y_pred_valid3], axis=1)\n# y_preds = np.array(y_preds)\n# y_preds = pd.DataFrame(y_preds)\n# print(y_preds)\n# # y_preds.to_feather(\"treasure_y_pred.feather\")\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.392443Z","iopub.execute_input":"2024-05-06T07:53:17.395228Z","iopub.status.idle":"2024-05-06T07:53:17.405802Z","shell.execute_reply.started":"2024-05-06T07:53:17.394940Z","shell.execute_reply":"2024-05-06T07:53:17.403637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# auc_score = roc_auc_score(y, y_pred_valid3)\n# print(\"AUC:\", auc_score)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.409333Z","iopub.execute_input":"2024-05-06T07:53:17.409725Z","iopub.status.idle":"2024-05-06T07:53:17.421426Z","shell.execute_reply.started":"2024-05-06T07:53:17.409701Z","shell.execute_reply":"2024-05-06T07:53:17.419663Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from sklearn.ensemble import GradientBoostingClassifier\n\n# params = {\n#     'n_estimators': 12,\n#     'learning_rate': 0.1,\n#     'max_depth': 3,\n#     'min_samples_split': 3,\n#     'min_samples_leaf': 1\n# }\n\n# meta_model = GradientBoostingClassifier(**params)\n\n# meta_model.fit(y_preds, y)","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.423033Z","iopub.execute_input":"2024-05-06T07:53:17.423377Z","iopub.status.idle":"2024-05-06T07:53:17.437052Z","shell.execute_reply.started":"2024-05-06T07:53:17.423349Z","shell.execute_reply":"2024-05-06T07:53:17.435562Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# print(roc_auc_score(y, meta_model.predict_proba(y_preds)[:, 1]))","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.438583Z","iopub.execute_input":"2024-05-06T07:53:17.439907Z","iopub.status.idle":"2024-05-06T07:53:17.458355Z","shell.execute_reply.started":"2024-05-06T07:53:17.439805Z","shell.execute_reply":"2024-05-06T07:53:17.455643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import joblib\n# joblib.dump(meta_model, 'final_stacking.pkl') ","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.459771Z","iopub.execute_input":"2024-05-06T07:53:17.460056Z","iopub.status.idle":"2024-05-06T07:53:17.471750Z","shell.execute_reply.started":"2024-05-06T07:53:17.460035Z","shell.execute_reply":"2024-05-06T07:53:17.469548Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# meta_model.save_model(\"stacking_final.txt\")","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.473779Z","iopub.execute_input":"2024-05-06T07:53:17.474065Z","iopub.status.idle":"2024-05-06T07:53:17.486590Z","shell.execute_reply.started":"2024-05-06T07:53:17.474043Z","shell.execute_reply":"2024-05-06T07:53:17.484809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # LR kfold过程\n# # 对最终结果取均值\n\n\n# from sklearn.linear_model import LinearRegression\n# from sklearn.metrics import mean_squared_error\n# from sklearn.metrics import roc_auc_score\n# from sklearn.model_selection import train_test_split\n\n# X_train, X_test, y_train, y_test = train_test_split(y_preds, y, test_size=0.2, random_state=42)\n\n# # 创建线性回归模型并进行训练\n# lin_reg = LinearRegression()\n# lin_reg.fit(X_train, y_train)\n\n# # 输出模型参数\n# print(\"Intercept:\", lin_reg.intercept_)\n# print(\"Coefficient:\", lin_reg.coef_)\n\n# # 在测试集上进行预测\n# y_pred = lin_reg.predict(X_test)\n\n# # 计算均方误差\n# mse = mean_squared_error(y_test, y_pred)\n# print(\"Mean Squared Error:\", mse)\n# auc_score = roc_auc_score(y_test, y_pred)\n# print(\"AUC:\", auc_score)\n\n# # 可视化训练集和拟合直线\n# plt.scatter(X_train[0], y_train, color='blue')\n# plt.plot(X_train[0], lin_reg.predict(X_train), color='red')\n# plt.xlabel('X')\n# plt.ylabel('y')\n# plt.title('Linear Regression')\n# plt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-05-06T07:53:17.487921Z","iopub.execute_input":"2024-05-06T07:53:17.488250Z","iopub.status.idle":"2024-05-06T07:53:17.502392Z","shell.execute_reply.started":"2024-05-06T07:53:17.488222Z","shell.execute_reply":"2024-05-06T07:53:17.500427Z"},"trusted":true},"execution_count":null,"outputs":[]}]}