{"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"}],"dockerImageVersionId":30665,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Reference \n(LGB + Cat ensemble) +Stacking https://www.kaggle.com/code/harrychan123/lgb-cat-ensemble-stacking","metadata":{}},{"cell_type":"markdown","source":"# Let's get to work!","metadata":{}},{"cell_type":"markdown","source":"## Imports ","metadata":{}},{"cell_type":"code","source":"import sys\nfrom pathlib import Path\nimport subprocess\nimport os\nimport gc\nfrom glob import glob\n\nimport numpy as np\nimport pandas as pd\nimport polars as pl\nfrom datetime import datetime\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nROOT = '/kaggle/input/home-credit-credit-risk-model-stability'\n\nfrom sklearn.model_selection import TimeSeriesSplit, GroupKFold, StratifiedGroupKFold\nfrom sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.metrics import roc_auc_score\nimport lightgbm as lgb\n\nfrom imblearn.over_sampling import SMOTE\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.impute import KNNImputer\nfrom sklearn.preprocessing import OrdinalEncoder","metadata":{"_uuid":"6d51fb38-3f61-48b9-a8be-f4928616260c","_cell_guid":"3ed5829e-7666-462e-abcc-a5492ae4fcec","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-25T06:44:27.732426Z","iopub.execute_input":"2024-04-25T06:44:27.732963Z","iopub.status.idle":"2024-04-25T06:44:34.57044Z","shell.execute_reply.started":"2024-04-25T06:44:27.732921Z","shell.execute_reply":"2024-04-25T06:44:34.568977Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"class Pipeline:\n\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.Int64))\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,strict=False))\n return df\n\n def handle_dates(df):\n for col in df.columns: \n if col.endswith(\"D\"):\n # Calculate the difference in days between each date column and date_decision\n df = df.with_columns(\n (pl.col(\"date_decision\") - pl.col(col)).dt.total_days().alias(col)\n )\n df = df.with_columns(pl.col(col).fill_null(np.nan)) \n # Drop date_decision column\n df = df.drop(\"date_decision\")\n# print(df.dtypes) # for Debugging\n return df\n\n def filter_cols(df,base_df = None,test=False):\n #for test data\n for col in df.columns:\n if col not in [\"target\", \"case_id\", \"WEEK_NUM\"]:\n isnull = df[col].is_null().mean()\n if isnull > 0.7:\n df = df.drop(col)\n columns_to_drop = []\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 if (freq == 1) or (freq > 100):\n columns_to_drop.append(col)\n\n df = df.drop(columns_to_drop)\n return df\n\n\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 expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n return expr_max\n\n @staticmethod\n def date_expr(df):\n cols = [col for col in df.columns if col[-1] in (\"D\",)]\n expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n return expr_max\n\n @staticmethod\n def str_expr(df):\n cols = [col for col in df.columns if col[-1] in (\"M\",)]\n expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n return expr_max\n\n @staticmethod\n def other_expr(df):\n cols = [col for col in df.columns if col[-1] in (\"T\", \"L\")]\n expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n return expr_max\n\n @staticmethod\n def count_expr(df):\n cols = [col for col in df.columns if \"num_group\" in col]\n expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\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 return exprs\n\ndef read_file(path, depth=None):\n df = pl.read_parquet(path)\n df = df.pipe(Pipeline.set_table_dtypes)\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 if depth in [1, 2]:\n df = df.group_by(\"case_id\").agg(Aggregator.get_exprs(df))\n chunks.append(df)\n df = pl.concat(chunks, how=\"vertical_relaxed\")\n df = df.unique(subset=[\"case_id\"])\n return df\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 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 df_base = df_base.pipe(Pipeline.set_table_dtypes)\n df_base = df_base.pipe(Pipeline.handle_dates)\n return df_base\n\ndef to_pandas(df_data, cat_cols=None):\n df_data = df_data.to_pandas()\n if cat_cols is None:\n cat_cols = list(df_data.select_dtypes(\"object\").columns)\n df_data[cat_cols] = df_data[cat_cols].astype(\"category\")\n return df_data, cat_cols\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 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 try:\n if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n df[col] = df[col].astype(np.float16)\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 except:\n continue\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","metadata":{"_uuid":"d341d4b9-d40b-4520-b1d2-e00e21e1e7a0","_cell_guid":"f918ffff-f322-4e08-9468-2f841260c4d4","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-25T06:06:36.577608Z","iopub.execute_input":"2024-04-25T06:06:36.578155Z","iopub.status.idle":"2024-04-25T06:06:36.633861Z","shell.execute_reply.started":"2024-04-25T06:06:36.578116Z","shell.execute_reply":"2024-04-25T06:06:36.632774Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"## Processing functions","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"markdown","source":"Little Testing","metadata":{}},{"cell_type":"code","source":"data = {\n 'category1': ['type1', 'type2', 'type3', 'type1', 'type2', 'unknown'],\n 'category2': ['A', 'B', 'C', 'unknown', 'B', 'A'],\n 'target': [1, 0, 1, 0, 1, 0]\n}\n\na = pl.DataFrame(data)\na = a.pipe(Pipeline.filter_cols)\na","metadata":{"_uuid":"98a12b88-27a8-43f8-91c1-afcd0742b581","_cell_guid":"2ece85e3-0c27-4d52-a97b-216236e3ff58","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-23T16:52:32.26661Z","iopub.execute_input":"2024-04-23T16:52:32.26708Z","iopub.status.idle":"2024-04-23T16:52:32.29697Z","shell.execute_reply.started":"2024-04-23T16:52:32.267045Z","shell.execute_reply":"2024-04-23T16:52:32.295564Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"shape: (6, 3)\n┌───────────┬───────────┬────────┐\n│ category1 ┆ category2 ┆ target │\n│ --- ┆ --- ┆ --- │\n│ str ┆ str ┆ i64 │\n╞═══════════╪═══════════╪════════╡\n│ type1 ┆ A ┆ 1 │\n│ type2 ┆ B ┆ 0 │\n│ type3 ┆ C ┆ 1 │\n│ type1 ┆ unknown ┆ 0 │\n│ type2 ┆ B ┆ 1 │\n│ unknown ┆ A ┆ 0 │\n└───────────┴───────────┴────────┘","text/html":"
\nshape: (6, 3)
category1category2target
strstri64
"type1""A"1
"type2""B"0
"type3""C"1
"type1""unknown"0
"type2""B"1
"unknown""A"0
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Train data","metadata":{}},{"cell_type":"code","source":"ROOT = Path(\"/kaggle/input/home-credit-credit-risk-model-stability\")\n\nTRAIN_DIR = ROOT / \"parquet_files\" / \"train\"\nTEST_DIR = ROOT / \"parquet_files\" / \"test\"","metadata":{"_uuid":"dffa0116-9b3c-4aa5-bfda-c6de1d75fcf1","_cell_guid":"dd049f8a-eff3-4308-a5ed-6bf1630dc9d1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-25T06:44:21.338771Z","iopub.execute_input":"2024-04-25T06:44:21.339919Z","iopub.status.idle":"2024-04-25T06:44:21.374236Z","shell.execute_reply.started":"2024-04-25T06:44:21.339871Z","shell.execute_reply":"2024-04-25T06:44:21.372031Z"},"trusted":true},"execution_count":2,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ROOT \u001b[38;5;241m=\u001b[39m \u001b[43mPath\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/kaggle/input/home-credit-credit-risk-model-stability\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m TRAIN_DIR \u001b[38;5;241m=\u001b[39m ROOT \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparquet_files\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4\u001b[0m TEST_DIR \u001b[38;5;241m=\u001b[39m ROOT \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparquet_files\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m\"\u001b[39m\n","\u001b[0;31mNameError\u001b[0m: name 'Path' is not defined"],"ename":"NameError","evalue":"name 'Path' is not defined","output_type":"error"}]},{"cell_type":"code","source":"%%time\ndata_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 read_file(TRAIN_DIR / \"train_applprev_2.parquet\", 2),\n read_file(TRAIN_DIR / \"train_person_2.parquet\", 2)\n ]\n}","metadata":{"_uuid":"329b6c89-aef4-4e8b-b4ce-eca792d4c22a","_cell_guid":"dbeaee46-7f54-4be9-9249-05a9c5b98ff7","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-25T06:06:47.878767Z","iopub.execute_input":"2024-04-25T06:06:47.879239Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Processing data","metadata":{}},{"cell_type":"code","source":"\ndf_train = feature_eng(**data_store) # import train data \nprint(\"train data shape:\\t\", df_train.shape)\n# gc.collect()\n# spamming gc.collect praying for memory to not full\ngc.collect()\ndf_train = df_train.pipe(Pipeline.filter_cols) # fillter column\ngc.collect()\ndf_train, cat_cols = to_pandas(df_train) # tranform to pandas dataframe, easier to work with\ngc.collect()\ndf_train = reduce_mem_usage(df_train) # as the name said\ngc.collect()\nprint(\"train data shape:\\t\", df_train.shape)\n#nums=df_train.select_dtypes(exclude='category').columns\n# IDK what is going on for now\n\n''''from itertools import combinations, permutations\n#df_train=df_train[nums]\nnans_df = df_train[nums].isna()\nnans_groups={}\nfor col in nums:\n cur_group = nans_df[col].sum()\n try:\n nans_groups[cur_group].append(col)\n except:\n nans_groups[cur_group]=[col]\ndel nans_df; x=gc.collect()\n\ndef reduce_group(grps):\n use = []\n for g in grps:\n mx = 0; vx = g[0]\n for gg in g:\n n = df_train[gg].nunique()\n if n>mx:\n mx = n\n vx = gg\n #print(str(gg)+'-'+str(n),', ',end='')\n use.append(vx)\n #print()\n print('Use these',use)\n return use\n\ndef group_columns_by_correlation(matrix, threshold=0.8):\n correlation_matrix = matrix.corr()\n groups = []\n remaining_cols = list(matrix.columns)\n while remaining_cols:\n col = remaining_cols.pop(0)\n group = [col]\n correlated_cols = [col]\n for c in remaining_cols:\n if correlation_matrix.loc[col, c] >= threshold:\n group.append(c)\n correlated_cols.append(c)\n groups.append(group)\n remaining_cols = [c for c in remaining_cols if c not in correlated_cols]\n return groups\n\nuses=[]\nfor k,v in nans_groups.items():\n if len(v)>1:\n Vs = nans_groups[k]\n #cross_features=list(combinations(Vs, 2))\n #make_corr(Vs)\n grps= group_columns_by_correlation(df_train[Vs], threshold=0.8)\n use=reduce_group(grps)\n uses=uses+use\n #make_corr(use)\n else:\n uses=uses+v\n print('####### NAN count =',k)\nprint(uses)\nprint(len(uses))\nuses=uses+list(df_train.select_dtypes(include='category').columns)\nprint(len(uses))\ndf_train=df_train[uses]'''","metadata":{"_uuid":"d6d05875-9880-41f6-9c96-6d0a6d4fb3dc","_cell_guid":"72ab5d13-1bd8-4422-a0c8-aeddb6f24d37","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")\ndevice='gpu'\n#n_samples=200000\nn_est=6000\nDRY_RUN = True if sample.shape[0] == 10 else False \nif DRY_RUN:\n device='cpu'\n df_train = df_train.iloc[:60000]\n #n_samples=10000\n n_est=600\nprint(device)","metadata":{"_uuid":"c4f8f390-c43c-4cdd-9c8b-4eb0f5c7bc3b","_cell_guid":"3101eed8-0d7b-4abb-98ab-d94f4707a011","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-23T15:44:32.806297Z","iopub.execute_input":"2024-04-23T15:44:32.806755Z","iopub.status.idle":"2024-04-23T15:44:32.82481Z","shell.execute_reply.started":"2024-04-23T15:44:32.80672Z","shell.execute_reply":"2024-04-23T15:44:32.823268Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"cpu\n","output_type":"stream"}]},{"cell_type":"code","source":"df_train","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get the columns to drop\ncolumns_to_drop = [col for col in df_train.columns if col.startswith('max_num_group')]\n\n# Drop the columns\ndf_train_pandas = df_train.drop(columns_to_drop, axis=1)\n\ndf_train_pandas","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\ndef fill_nan_values(df):\n # Iterate through each column in the DataFrame\n for column in df.columns:\n # Get the data type of the column\n column_dtype = df[column].dtype\n print(f\"Column '{column}' has data type: {column_dtype}\")\n \n if pd.api.types.is_numeric_dtype(column_dtype):\n # If the column is of numeric data type\n # Check if the column contains infinite values\n if np.isinf(df[column]).any():\n print(f\"Column '{column}' contains infinite values.\")\n # Replace infinite values with NaN\n df[column].replace([np.inf, -np.inf], np.nan, inplace=True)\n # Fill NaN values with a specific value (e.g., mean)\n mean_value = df[column].mean()\n print(f\"Filling NaN in column '{column}' with mean value: {mean_value}\")\n df[column].fillna(mean_value, inplace=True)\n else:\n # Fill NaN values with mean if they exist\n if pd.isna(df[column].mean()):\n print(f\"Mean value for column '{column}' is NaN. Filling NaN with 0.\")\n df[column].fillna(0, inplace=True)\n else:\n mean_value = df[column].mean()\n print(f\"Filling NaN in column '{column}' with mean value: {mean_value}\")\n df[column].fillna(mean_value, inplace=True)\n \n elif pd.api.types.is_categorical_dtype(column_dtype) or pd.api.types.is_object_dtype(column_dtype) or pd.api.types.is_bool_dtype(column_dtype):\n # If the column is of categorical, object (string), or boolean data type, fill NaN with the mode\n mode_value = df[column].mode()\n if not mode_value.empty:\n print(f\"Filling NaN in column '{column}' with mode value: {mode_value.iloc[0]}\")\n df[column].fillna(mode_value.iloc[0], inplace=True)\n \n return df\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-04-25T05:49:40.471734Z","iopub.execute_input":"2024-04-25T05:49:40.472447Z","iopub.status.idle":"2024-04-25T05:49:40.48509Z","shell.execute_reply.started":"2024-04-25T05:49:40.472412Z","shell.execute_reply":"2024-04-25T05:49:40.484014Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"df_train = fill_nan_values(df_train_pandas)","metadata":{"execution":{"iopub.status.busy":"2024-04-25T05:49:43.859131Z","iopub.execute_input":"2024-04-25T05:49:43.859509Z","iopub.status.idle":"2024-04-25T05:49:51.738734Z","shell.execute_reply.started":"2024-04-25T05:49:43.859481Z","shell.execute_reply":"2024-04-25T05:49:51.737438Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Column 'case_id' has data type: int32\nFilling NaN in column 'case_id' with mean value: 1286076.571738679\nColumn 'WEEK_NUM' has data type: int8\nFilling NaN in column 'WEEK_NUM' with mean value: 40.76903617638254\nColumn 'target' has data type: int8\nFilling NaN in column 'target' with mean value: 0.03143727577671242\nColumn 'month_decision' has data type: int8\nFilling NaN in column 'month_decision' with mean value: 6.425583578258144\nColumn 'weekday_decision' has data type: int8\nFilling NaN in column 'weekday_decision' with mean value: 3.9840141118612604\nColumn 'birthdate_574D' has data type: datetime64[ms]\nColumn 'dateofbirth_337D' has data type: datetime64[ms]\nColumn 'days120_123L' has data type: float16\nMean value for column 'days120_123L' is NaN. Filling NaN with 0.\nColumn 'days180_256L' has data type: float16\nMean value for column 'days180_256L' is NaN. Filling NaN with 0.\nColumn 'days30_165L' has data type: float16\nMean value for column 'days30_165L' is NaN. Filling NaN with 0.\nColumn 'days360_512L' has data type: float16\nMean value for column 'days360_512L' is NaN. Filling NaN with 0.\nColumn 'days90_310L' has data type: float16\nMean value for column 'days90_310L' is NaN. Filling NaN with 0.\nColumn 'description_5085714M' has data type: category\nFilling NaN in column 'description_5085714M' with mode value: a55475b1\nColumn 'education_1103M' has data type: category\nFilling NaN in column 'education_1103M' with mode value: a55475b1\nColumn 'education_88M' has data type: category\nFilling NaN in column 'education_88M' with mode value: a55475b1\nColumn 'firstquarter_103L' has data type: float16\nMean value for column 'firstquarter_103L' is NaN. Filling NaN with 0.\nColumn 'fourthquarter_440L' has data type: float16\nMean value for column 'fourthquarter_440L' is NaN. Filling NaN with 0.\nColumn 'maritalst_385M' has data type: category\nFilling NaN in column 'maritalst_385M' with mode value: a55475b1\nColumn 'maritalst_893M' has data type: category\nFilling NaN in column 'maritalst_893M' with mode value: a55475b1\nColumn 'numberofqueries_373L' has data type: float16\nMean value for column 'numberofqueries_373L' is NaN. Filling NaN with 0.\nColumn 'pmtscount_423L' has data type: float16\nMean value for column 'pmtscount_423L' is NaN. Filling NaN with 0.\nColumn 'pmtssum_45A' has data type: float32\nFilling NaN in column 'pmtssum_45A' with mean value: 13199.9365234375\nColumn 'requesttype_4525192L' has data type: category\nFilling NaN in column 'requesttype_4525192L' with mode value: DEDUCTION_6\nColumn 'responsedate_1012D' has data type: datetime64[ms]\nColumn 'responsedate_4527233D' has data type: datetime64[ms]\nColumn 'secondquarter_766L' has data type: float16\nMean value for column 'secondquarter_766L' is NaN. Filling NaN with 0.\nColumn 'thirdquarter_1082L' has data type: float16\nMean value for column 'thirdquarter_1082L' is NaN. Filling NaN with 0.\nColumn 'actualdpdtolerance_344P' has data type: float16\nMean value for column 'actualdpdtolerance_344P' is NaN. Filling NaN with 0.\nColumn 'amtinstpaidbefduel24m_4187115A' has data type: float32\nFilling NaN in column 'amtinstpaidbefduel24m_4187115A' with mean value: 55958.3203125\nColumn 'annuity_780A' has data type: float32\nFilling NaN in column 'annuity_780A' with mean value: 4039.20654296875\nColumn 'annuitynextmonth_57A' has data type: float32\nFilling NaN in column 'annuitynextmonth_57A' with mean value: 1435.7747802734375\nColumn 'applicationcnt_361L' has data type: float16\nFilling NaN in column 'applicationcnt_361L' with mean value: 0.0\nColumn 'applications30d_658L' has data type: float16\nMean value for column 'applications30d_658L' is NaN. Filling NaN with 0.\nColumn 'applicationscnt_1086L' has data type: float16\nMean value for column 'applicationscnt_1086L' is NaN. Filling NaN with 0.\nColumn 'applicationscnt_464L' has data type: float16\nMean value for column 'applicationscnt_464L' is NaN. Filling NaN with 0.\nColumn 'applicationscnt_629L' has data type: float16\nMean value for column 'applicationscnt_629L' is NaN. Filling NaN with 0.\nColumn 'applicationscnt_867L' has data type: float16\nMean value for column 'applicationscnt_867L' is NaN. Filling NaN with 0.\nColumn 'avgdbddpdlast24m_3658932P' has data type: float16\nMean value for column 'avgdbddpdlast24m_3658932P' is NaN. Filling NaN with 0.\nColumn 'avgdbddpdlast3m_4187120P' has data type: float16\nMean value for column 'avgdbddpdlast3m_4187120P' is NaN. Filling NaN with 0.\nColumn 'avgdbdtollast24m_4525197P' has data type: float16\nMean value for column 'avgdbdtollast24m_4525197P' is NaN. Filling NaN with 0.\nColumn 'avgdpdtolclosure24_3658938P' has data type: float16\nMean value for column 'avgdpdtolclosure24_3658938P' is NaN. Filling NaN with 0.\nColumn 'avginstallast24m_3658937A' has data type: float32\nFilling NaN in column 'avginstallast24m_3658937A' with mean value: 5401.5869140625\nColumn 'avgmaxdpdlast9m_3716943P' has data type: float16\nMean value for column 'avgmaxdpdlast9m_3716943P' is NaN. Filling NaN with 0.\nColumn 'avgoutstandbalancel6m_4187114A' has data type: float32\nFilling NaN in column 'avgoutstandbalancel6m_4187114A' with mean value: 45984.82421875\nColumn 'avgpmtlast12m_4525200A' has data type: float32\nFilling NaN in column 'avgpmtlast12m_4525200A' with mean value: 6403.5732421875\nColumn 'bankacctype_710L' has data type: category\nFilling NaN in column 'bankacctype_710L' with mode value: CA\nColumn 'clientscnt12m_3712952L' has data type: float16\nFilling NaN in column 'clientscnt12m_3712952L' with mean value: 0.0\nColumn 'clientscnt3m_3712950L' has data type: float16\nFilling NaN in column 'clientscnt3m_3712950L' with mean value: 0.0\nColumn 'clientscnt6m_3712949L' has data type: float16\nFilling NaN in column 'clientscnt6m_3712949L' with mean value: 0.0\nColumn 'clientscnt_100L' has data type: float16\nMean value for column 'clientscnt_100L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_1022L' has data type: float16\nMean value for column 'clientscnt_1022L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_1071L' has data type: float16\nFilling NaN in column 'clientscnt_1071L' with mean value: 0.0\nColumn 'clientscnt_1130L' has data type: float16\nFilling NaN in column 'clientscnt_1130L' with mean value: 0.0\nColumn 'clientscnt_157L' has data type: float16\nMean value for column 'clientscnt_157L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_257L' has data type: float16\nFilling NaN in column 'clientscnt_257L' with mean value: 0.0\nColumn 'clientscnt_304L' has data type: float16\nMean value for column 'clientscnt_304L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_360L' has data type: float16\nFilling NaN in column 'clientscnt_360L' with mean value: 0.0\nColumn 'clientscnt_493L' has data type: float16\nFilling NaN in column 'clientscnt_493L' with mean value: 0.0\nColumn 'clientscnt_533L' has data type: float16\nMean value for column 'clientscnt_533L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_887L' has data type: float16\nMean value for column 'clientscnt_887L' is NaN. Filling NaN with 0.\nColumn 'clientscnt_946L' has data type: float16\nFilling NaN in column 'clientscnt_946L' with mean value: 0.0\nColumn 'cntincpaycont9m_3716944L' has data type: float16\nMean value for column 'cntincpaycont9m_3716944L' is NaN. Filling NaN with 0.\nColumn 'cntpmts24_3658933L' has data type: float16\nMean value for column 'cntpmts24_3658933L' is NaN. Filling NaN with 0.\nColumn 'commnoinclast6m_3546845L' has data type: float16\nFilling NaN in column 'commnoinclast6m_3546845L' with mean value: 0.0\nColumn 'credamount_770A' has data type: float32\nFilling NaN in column 'credamount_770A' with mean value: 49870.1640625\nColumn 'credtype_322L' has data type: category\nFilling NaN in column 'credtype_322L' with mode value: COL\nColumn 'currdebt_22A' has data type: float32\nFilling NaN in column 'currdebt_22A' with mean value: 19682.416015625\nColumn 'currdebtcredtyperange_828A' has data type: float32\nFilling NaN in column 'currdebtcredtyperange_828A' with mean value: 10996.361328125\nColumn 'datefirstoffer_1144D' has data type: datetime64[ms]\nColumn 'datelastunpaid_3546854D' has data type: datetime64[ms]\nColumn 'daysoverduetolerancedd_3976961L' has data type: float16\nMean value for column 'daysoverduetolerancedd_3976961L' is NaN. Filling NaN with 0.\nColumn 'deferredmnthsnum_166L' has data type: float16\nFilling NaN in column 'deferredmnthsnum_166L' with mean value: 0.0\nColumn 'disbursedcredamount_1113A' has data type: float32\nFilling NaN in column 'disbursedcredamount_1113A' with mean value: 46074.75390625\nColumn 'disbursementtype_67L' has data type: category\nFilling NaN in column 'disbursementtype_67L' with mode value: SBA\nColumn 'downpmt_116A' has data type: float32\nFilling NaN in column 'downpmt_116A' with mean value: 552.3809204101562\nColumn 'dtlastpmtallstes_4499206D' has data type: datetime64[ms]\nColumn 'eir_270L' has data type: float16\nMean value for column 'eir_270L' is NaN. Filling NaN with 0.\nColumn 'firstclxcampaign_1125D' has data type: datetime64[ms]\nColumn 'firstdatedue_489D' has data type: datetime64[ms]\nColumn 'homephncnt_628L' has data type: float16\nMean value for column 'homephncnt_628L' is NaN. Filling NaN with 0.\nColumn 'inittransactioncode_186L' has data type: category\nFilling NaN in column 'inittransactioncode_186L' with mode value: POS\nColumn 'interestrate_311L' has data type: float16\nMean value for column 'interestrate_311L' is NaN. Filling NaN with 0.\nColumn 'isbidproduct_1095L' has data type: float16\nMean value for column 'isbidproduct_1095L' is NaN. Filling NaN with 0.\nColumn 'lastactivateddate_801D' has data type: datetime64[ms]\nColumn 'lastapplicationdate_877D' has data type: datetime64[ms]\nColumn 'lastapprcommoditycat_1041M' has data type: category\nFilling NaN in column 'lastapprcommoditycat_1041M' with mode value: a55475b1\nColumn 'lastapprcredamount_781A' has data type: float32\nFilling NaN in column 'lastapprcredamount_781A' with mean value: 36890.34765625\nColumn 'lastapprdate_640D' has data type: datetime64[ms]\nColumn 'lastcancelreason_561M' has data type: category\nFilling NaN in column 'lastcancelreason_561M' with mode value: a55475b1\nColumn 'lastdelinqdate_224D' has data type: datetime64[ms]\nColumn 'lastrejectcommoditycat_161M' has data type: category\nFilling NaN in column 'lastrejectcommoditycat_161M' with mode value: a55475b1\nColumn 'lastrejectcredamount_222A' has data type: float32\nFilling NaN in column 'lastrejectcredamount_222A' with mean value: 51049.05859375\nColumn 'lastrejectdate_50D' has data type: datetime64[ms]\nColumn 'lastrejectreason_759M' has data type: category\nFilling NaN in column 'lastrejectreason_759M' with mode value: a55475b1\nColumn 'lastrejectreasonclient_4145040M' has data type: category\nFilling NaN in column 'lastrejectreasonclient_4145040M' with mode value: a55475b1\nColumn 'lastst_736L' has data type: category\nFilling NaN in column 'lastst_736L' with mode value: D\nColumn 'maininc_215A' has data type: float32\nFilling NaN in column 'maininc_215A' with mean value: 49485.65234375\nColumn 'mastercontrelectronic_519L' has data type: float16\nFilling NaN in column 'mastercontrelectronic_519L' with mean value: 0.0\nColumn 'mastercontrexist_109L' has data type: float16\nFilling NaN in column 'mastercontrexist_109L' with mean value: 0.0\nColumn 'maxannuity_159A' has data type: float32\nFilling NaN in column 'maxannuity_159A' with mean value: 34606.56640625\nColumn 'maxdbddpdlast1m_3658939P' has data type: float16\nMean value for column 'maxdbddpdlast1m_3658939P' is NaN. Filling NaN with 0.\nColumn 'maxdbddpdtollast12m_3658940P' has data type: float16\nMean value for column 'maxdbddpdtollast12m_3658940P' is NaN. Filling NaN with 0.\nColumn 'maxdbddpdtollast6m_4187119P' has data type: float16\nMean value for column 'maxdbddpdtollast6m_4187119P' is NaN. Filling NaN with 0.\nColumn 'maxdebt4_972A' has data type: float32\nFilling NaN in column 'maxdebt4_972A' with mean value: 48397.60546875\nColumn 'maxdpdfrom6mto36m_3546853P' has data type: float16\nMean value for column 'maxdpdfrom6mto36m_3546853P' is NaN. Filling NaN with 0.\nColumn 'maxdpdinstldate_3546855D' has data type: datetime64[ms]\nColumn 'maxdpdinstlnum_3546846P' has data type: float16\nMean value for column 'maxdpdinstlnum_3546846P' is NaN. Filling NaN with 0.\nColumn 'maxdpdlast12m_727P' has data type: float16\nMean value for column 'maxdpdlast12m_727P' is NaN. Filling NaN with 0.\nColumn 'maxdpdlast24m_143P' has data type: float16\nMean value for column 'maxdpdlast24m_143P' is NaN. Filling NaN with 0.\nColumn 'maxdpdlast3m_392P' has data type: float16\nMean value for column 'maxdpdlast3m_392P' is NaN. Filling NaN with 0.\nColumn 'maxdpdlast6m_474P' has data type: float16\nMean value for column 'maxdpdlast6m_474P' is NaN. Filling NaN with 0.\nColumn 'maxdpdlast9m_1059P' has data type: float16\nMean value for column 'maxdpdlast9m_1059P' is NaN. Filling NaN with 0.\nColumn 'maxdpdtolerance_374P' has data type: float16\nMean value for column 'maxdpdtolerance_374P' is NaN. Filling NaN with 0.\nColumn 'maxinstallast24m_3658928A' has data type: float32\nFilling NaN in column 'maxinstallast24m_3658928A' with mean value: 15394.1064453125\nColumn 'maxlnamtstart6m_4525199A' has data type: float32\nFilling NaN in column 'maxlnamtstart6m_4525199A' with mean value: 44908.55078125\nColumn 'maxoutstandbalancel12m_4187113A' has data type: float32\nFilling NaN in column 'maxoutstandbalancel12m_4187113A' with mean value: 71363.984375\nColumn 'maxpmtlast3m_4525190A' has data type: float32\nFilling NaN in column 'maxpmtlast3m_4525190A' with mean value: 9970.8193359375\nColumn 'mindbddpdlast24m_3658935P' has data type: float16\nMean value for column 'mindbddpdlast24m_3658935P' is NaN. Filling NaN with 0.\nColumn 'mindbdtollast24m_4525191P' has data type: float16\nMean value for column 'mindbdtollast24m_4525191P' is NaN. Filling NaN with 0.\nColumn 'mobilephncnt_593L' has data type: float16\nMean value for column 'mobilephncnt_593L' is NaN. Filling NaN with 0.\nColumn 'monthsannuity_845L' has data type: float16\nMean value for column 'monthsannuity_845L' is NaN. Filling NaN with 0.\nColumn 'numactivecreds_622L' has data type: float16\nMean value for column 'numactivecreds_622L' is NaN. Filling NaN with 0.\nColumn 'numactivecredschannel_414L' has data type: float16\nMean value for column 'numactivecredschannel_414L' is NaN. Filling NaN with 0.\nColumn 'numactiverelcontr_750L' has data type: float16\nMean value for column 'numactiverelcontr_750L' is NaN. Filling NaN with 0.\nColumn 'numcontrs3months_479L' has data type: float16\nMean value for column 'numcontrs3months_479L' is NaN. Filling NaN with 0.\nColumn 'numincomingpmts_3546848L' has data type: float16\nMean value for column 'numincomingpmts_3546848L' is NaN. Filling NaN with 0.\nColumn 'numinstlallpaidearly3d_817L' has data type: float16\nMean value for column 'numinstlallpaidearly3d_817L' is NaN. Filling NaN with 0.\nColumn 'numinstls_657L' has data type: float16\nMean value for column 'numinstls_657L' is NaN. Filling NaN with 0.\nColumn 'numinstlsallpaid_934L' has data type: float16\nMean value for column 'numinstlsallpaid_934L' is NaN. Filling NaN with 0.\nColumn 'numinstlswithdpd10_728L' has data type: float16\nMean value for column 'numinstlswithdpd10_728L' is NaN. Filling NaN with 0.\nColumn 'numinstlswithdpd5_4187116L' has data type: float16\nMean value for column 'numinstlswithdpd5_4187116L' is NaN. Filling NaN with 0.\nColumn 'numinstlswithoutdpd_562L' has data type: float16\nMean value for column 'numinstlswithoutdpd_562L' is NaN. Filling NaN with 0.\nColumn 'numinstmatpaidtearly2d_4499204L' has data type: float16\nMean value for column 'numinstmatpaidtearly2d_4499204L' is NaN. Filling NaN with 0.\nColumn 'numinstpaid_4499208L' has data type: float16\nMean value for column 'numinstpaid_4499208L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly3d_3546850L' has data type: float16\nMean value for column 'numinstpaidearly3d_3546850L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly3dest_4493216L' has data type: float16\nMean value for column 'numinstpaidearly3dest_4493216L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly5d_1087L' has data type: float16\nMean value for column 'numinstpaidearly5d_1087L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly5dest_4493211L' has data type: float16\nMean value for column 'numinstpaidearly5dest_4493211L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly5dobd_4499205L' has data type: float16\nMean value for column 'numinstpaidearly5dobd_4499205L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearly_338L' has data type: float16\nMean value for column 'numinstpaidearly_338L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidearlyest_4493214L' has data type: float16\nMean value for column 'numinstpaidearlyest_4493214L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidlastcontr_4325080L' has data type: float16\nMean value for column 'numinstpaidlastcontr_4325080L' is NaN. Filling NaN with 0.\nColumn 'numinstpaidlate1d_3546852L' has data type: float16\nMean value for column 'numinstpaidlate1d_3546852L' is NaN. Filling NaN with 0.\nColumn 'numinstregularpaid_973L' has data type: float16\nMean value for column 'numinstregularpaid_973L' is NaN. Filling NaN with 0.\nColumn 'numinstregularpaidest_4493210L' has data type: float16\nMean value for column 'numinstregularpaidest_4493210L' is NaN. Filling NaN with 0.\nColumn 'numinsttopaygr_769L' has data type: float16\nMean value for column 'numinsttopaygr_769L' is NaN. Filling NaN with 0.\nColumn 'numinsttopaygrest_4493213L' has data type: float16\nMean value for column 'numinsttopaygrest_4493213L' is NaN. Filling NaN with 0.\nColumn 'numinstunpaidmax_3546851L' has data type: float16\nMean value for column 'numinstunpaidmax_3546851L' is NaN. Filling NaN with 0.\nColumn 'numinstunpaidmaxest_4493212L' has data type: float16\nMean value for column 'numinstunpaidmaxest_4493212L' is NaN. Filling NaN with 0.\nColumn 'numnotactivated_1143L' has data type: float16\nFilling NaN in column 'numnotactivated_1143L' with mean value: 0.0\nColumn 'numpmtchanneldd_318L' has data type: float16\nFilling NaN in column 'numpmtchanneldd_318L' with mean value: 0.0\nColumn 'numrejects9m_859L' has data type: float16\nMean value for column 'numrejects9m_859L' is NaN. Filling NaN with 0.\nColumn 'opencred_647L' has data type: category\nFilling NaN in column 'opencred_647L' with mode value: False\nColumn 'paytype1st_925L' has data type: category\nFilling NaN in column 'paytype1st_925L' with mode value: OTHER\nColumn 'paytype_783L' has data type: category\nFilling NaN in column 'paytype_783L' with mode value: OTHER\nColumn 'pctinstlsallpaidearl3d_427L' has data type: float16\nMean value for column 'pctinstlsallpaidearl3d_427L' is NaN. Filling NaN with 0.\nColumn 'pctinstlsallpaidlat10d_839L' has data type: float16\nMean value for column 'pctinstlsallpaidlat10d_839L' is NaN. Filling NaN with 0.\nColumn 'pctinstlsallpaidlate1d_3546856L' has data type: float16\nMean value for column 'pctinstlsallpaidlate1d_3546856L' is NaN. Filling NaN with 0.\nColumn 'pctinstlsallpaidlate4d_3546849L' has data type: float16\nMean value for column 'pctinstlsallpaidlate4d_3546849L' is NaN. Filling NaN with 0.\nColumn 'pctinstlsallpaidlate6d_3546844L' has data type: float16\nMean value for column 'pctinstlsallpaidlate6d_3546844L' is NaN. Filling NaN with 0.\nColumn 'pmtnum_254L' has data type: float16\nMean value for column 'pmtnum_254L' is NaN. Filling NaN with 0.\nColumn 'posfpd10lastmonth_333P' has data type: float16\nFilling NaN in column 'posfpd10lastmonth_333P' with mean value: 0.0\nColumn 'posfpd30lastmonth_3976960P' has data type: float16\nFilling NaN in column 'posfpd30lastmonth_3976960P' with mean value: 0.0\nColumn 'posfstqpd30lastmonth_3976962P' has data type: float16\nFilling NaN in column 'posfstqpd30lastmonth_3976962P' with mean value: 0.0\nColumn 'price_1097A' has data type: float32\nFilling NaN in column 'price_1097A' with mean value: 34464.9765625\nColumn 'sellerplacecnt_915L' has data type: float16\nMean value for column 'sellerplacecnt_915L' is NaN. Filling NaN with 0.\nColumn 'sellerplacescnt_216L' has data type: float16\nMean value for column 'sellerplacescnt_216L' is NaN. Filling NaN with 0.\nColumn 'sumoutstandtotal_3546847A' has data type: float32\nFilling NaN in column 'sumoutstandtotal_3546847A' with mean value: 27681.2578125\nColumn 'sumoutstandtotalest_4493215A' has data type: float32\nFilling NaN in column 'sumoutstandtotalest_4493215A' with mean value: 28309.73828125\nColumn 'totaldebt_9A' has data type: float32\nFilling NaN in column 'totaldebt_9A' with mean value: 19683.1171875\nColumn 'totalsettled_863A' has data type: float32\nFilling NaN in column 'totalsettled_863A' with mean value: 92238.1640625\nColumn 'totinstallast1m_4525188A' has data type: float32\nFilling NaN in column 'totinstallast1m_4525188A' with mean value: 10411.375\nColumn 'twobodfilling_608L' has data type: category\nFilling NaN in column 'twobodfilling_608L' with mode value: FO\nColumn 'typesuite_864L' has data type: category\nFilling NaN in column 'typesuite_864L' with mode value: AL\nColumn 'max_actualdpd_943P' has data type: float16\nMean value for column 'max_actualdpd_943P' is NaN. Filling NaN with 0.\nColumn 'max_annuity_853A' has data type: float32\nFilling NaN in column 'max_annuity_853A' with mean value: 5674.02685546875\nColumn 'max_credacc_credlmt_575A' has data type: float32\nFilling NaN in column 'max_credacc_credlmt_575A' with mean value: 10458.873046875\nColumn 'max_credamount_590A' has data type: float32\nFilling NaN in column 'max_credamount_590A' with mean value: 71643.421875\nColumn 'max_currdebt_94A' has data type: float32\nFilling NaN in column 'max_currdebt_94A' with mean value: 18619.470703125\nColumn 'max_downpmt_134A' has data type: float32\nFilling NaN in column 'max_downpmt_134A' with mean value: 1699.2587890625\nColumn 'max_mainoccupationinc_437A' has data type: float32\nFilling NaN in column 'max_mainoccupationinc_437A' with mean value: 64670.9140625\nColumn 'max_maxdpdtolerance_577P' has data type: float16\nMean value for column 'max_maxdpdtolerance_577P' is NaN. Filling NaN with 0.\nColumn 'max_outstandingdebt_522A' has data type: float32\nFilling NaN in column 'max_outstandingdebt_522A' with mean value: 25082.240234375\nColumn 'first_approvaldate_319D' has data type: datetime64[ms]\nColumn 'first_creationdate_885D' has data type: datetime64[ms]\nColumn 'first_dateactivated_425D' has data type: datetime64[ms]\nColumn 'first_dtlastpmt_581D' has data type: datetime64[ms]\nColumn 'first_dtlastpmtallstes_3545839D' has data type: datetime64[ms]\nColumn 'first_employedfrom_700D' has data type: datetime64[ms]\nColumn 'first_firstnonzeroinstldate_307D' has data type: datetime64[ms]\nColumn 'first_cancelreason_3545846M' has data type: category\nFilling NaN in column 'first_cancelreason_3545846M' with mode value: a55475b1\nColumn 'first_education_1138M' has data type: category\nFilling NaN in column 'first_education_1138M' with mode value: P97_36_170\nColumn 'first_postype_4733339M' has data type: category\nFilling NaN in column 'first_postype_4733339M' with mode value: a55475b1\nColumn 'first_rejectreason_755M' has data type: category\nFilling NaN in column 'first_rejectreason_755M' with mode value: a55475b1\nColumn 'first_rejectreasonclient_4145042M' has data type: category\nFilling NaN in column 'first_rejectreasonclient_4145042M' with mode value: a55475b1\nColumn 'first_childnum_21L' has data type: float16\nMean value for column 'first_childnum_21L' is NaN. Filling NaN with 0.\nColumn 'first_credtype_587L' has data type: category\nFilling NaN in column 'first_credtype_587L' with mode value: COL\nColumn 'first_familystate_726L' has data type: category\nFilling NaN in column 'first_familystate_726L' with mode value: MARRIED\nColumn 'first_inittransactioncode_279L' has data type: category\nFilling NaN in column 'first_inittransactioncode_279L' with mode value: POS\nColumn 'first_isbidproduct_390L' has data type: category\nFilling NaN in column 'first_isbidproduct_390L' with mode value: False\nColumn 'first_isdebitcard_527L' has data type: category\nFilling NaN in column 'first_isdebitcard_527L' with mode value: False\nColumn 'first_pmtnum_8L' has data type: float16\nMean value for column 'first_pmtnum_8L' is NaN. Filling NaN with 0.\nColumn 'first_status_219L' has data type: category\nFilling NaN in column 'first_status_219L' with mode value: K\nColumn 'first_tenor_203L' has data type: float16\nMean value for column 'first_tenor_203L' is NaN. Filling NaN with 0.\nColumn 'max_amount_4527230A' has data type: float32\nFilling NaN in column 'max_amount_4527230A' with mean value: 4156.4140625\nColumn 'first_recorddate_4527225D' has data type: datetime64[ms]\nColumn 'max_pmtamount_36A' has data type: float32\nFilling NaN in column 'max_pmtamount_36A' with mean value: 3881.558349609375\nColumn 'first_processingdate_168D' has data type: datetime64[ms]\nColumn 'max_credlmt_230A' has data type: float32\nFilling NaN in column 'max_credlmt_230A' with mean value: 50563.3828125\nColumn 'max_credlmt_935A' has data type: float32\nFilling NaN in column 'max_credlmt_935A' with mean value: 128196.375\nColumn 'max_debtoutstand_525A' has data type: float32\nFilling NaN in column 'max_debtoutstand_525A' with mean value: 202230.09375\nColumn 'max_debtoverdue_47A' has data type: float32\nFilling NaN in column 'max_debtoverdue_47A' with mean value: 2587.660400390625\nColumn 'max_dpdmax_139P' has data type: float16\nMean value for column 'max_dpdmax_139P' is NaN. Filling NaN with 0.\nColumn 'max_dpdmax_757P' has data type: float32\nFilling NaN in column 'max_dpdmax_757P' with mean value: 222.62863159179688\nColumn 'max_instlamount_768A' has data type: float32\nFilling NaN in column 'max_instlamount_768A' with mean value: 4311.19580078125\nColumn 'max_instlamount_852A' has data type: float32\nFilling NaN in column 'max_instlamount_852A' with mean value: 946.8782348632812\nColumn 'max_monthlyinstlamount_332A' has data type: float32\nFilling NaN in column 'max_monthlyinstlamount_332A' with mean value: 8316.9931640625\nColumn 'max_monthlyinstlamount_674A' has data type: float32\nFilling NaN in column 'max_monthlyinstlamount_674A' with mean value: 26131.9296875\nColumn 'max_outstandingamount_354A' has data type: float32\nFilling NaN in column 'max_outstandingamount_354A' with mean value: 24.534435272216797\nColumn 'max_outstandingamount_362A' has data type: float32\nFilling NaN in column 'max_outstandingamount_362A' with mean value: 224485.296875\nColumn 'max_overdueamount_31A' has data type: float32\nFilling NaN in column 'max_overdueamount_31A' with mean value: 109.58946228027344\nColumn 'max_overdueamount_659A' has data type: float32\nFilling NaN in column 'max_overdueamount_659A' with mean value: 2536.054443359375\nColumn 'max_overdueamountmax2_14A' has data type: float32\nFilling NaN in column 'max_overdueamountmax2_14A' with mean value: 6976.5322265625\nColumn 'max_overdueamountmax2_398A' has data type: float32\nFilling NaN in column 'max_overdueamountmax2_398A' with mean value: 28298.107421875\nColumn 'max_overdueamountmax_155A' has data type: float32\nFilling NaN in column 'max_overdueamountmax_155A' with mean value: 5567.93994140625\nColumn 'max_overdueamountmax_35A' has data type: float32\nFilling NaN in column 'max_overdueamountmax_35A' with mean value: 22240.892578125\nColumn 'max_residualamount_488A' has data type: float32\nFilling NaN in column 'max_residualamount_488A' with mean value: 2.2333462238311768\nColumn 'max_residualamount_856A' has data type: float32\nFilling NaN in column 'max_residualamount_856A' with mean value: 48643.2734375\nColumn 'max_totalamount_6A' has data type: float32\nFilling NaN in column 'max_totalamount_6A' with mean value: 196683.109375\nColumn 'max_totalamount_996A' has data type: float32\nFilling NaN in column 'max_totalamount_996A' with mean value: 285793.3125\nColumn 'max_totaldebtoverduevalue_178A' has data type: float32\nFilling NaN in column 'max_totaldebtoverduevalue_178A' with mean value: 2831.3076171875\nColumn 'max_totaldebtoverduevalue_718A' has data type: float32\nFilling NaN in column 'max_totaldebtoverduevalue_718A' with mean value: 111.08036804199219\nColumn 'max_totaloutstanddebtvalue_39A' has data type: float32\nFilling NaN in column 'max_totaloutstanddebtvalue_39A' with mean value: 216288.421875\nColumn 'max_totaloutstanddebtvalue_668A' has data type: float32\nFilling NaN in column 'max_totaloutstanddebtvalue_668A' with mean value: 44.038761138916016\nColumn 'first_dateofcredend_289D' has data type: datetime64[ms]\nColumn 'first_dateofcredend_353D' has data type: datetime64[ms]\nColumn 'first_dateofcredstart_181D' has data type: datetime64[ms]\nColumn 'first_dateofcredstart_739D' has data type: datetime64[ms]\nColumn 'first_dateofrealrepmt_138D' has data type: datetime64[ms]\nColumn 'first_lastupdate_1112D' has data type: datetime64[ms]\nColumn 'first_lastupdate_388D' has data type: datetime64[ms]\nColumn 'first_numberofoverdueinstlmaxdat_148D' has data type: datetime64[ms]\nColumn 'first_overdueamountmax2date_1002D' has data type: datetime64[ms]\nColumn 'first_refreshdate_3813885D' has data type: datetime64[ms]\nColumn 'first_classificationofcontr_13M' has data type: category\nFilling NaN in column 'first_classificationofcontr_13M' with mode value: ea6782cc\nColumn 'first_contractst_545M' has data type: category\nFilling NaN in column 'first_contractst_545M' with mode value: 7241344e\nColumn 'first_description_351M' has data type: category\nFilling NaN in column 'first_description_351M' with mode value: a55475b1\nColumn 'first_purposeofcred_426M' has data type: category\nFilling NaN in column 'first_purposeofcred_426M' with mode value: 60c73645\nColumn 'first_purposeofcred_874M' has data type: category\nFilling NaN in column 'first_purposeofcred_874M' with mode value: 60c73645\nColumn 'first_subjectrole_182M' has data type: category\nFilling NaN in column 'first_subjectrole_182M' with mode value: a55475b1\nColumn 'first_subjectrole_93M' has data type: category\nFilling NaN in column 'first_subjectrole_93M' with mode value: a55475b1\nColumn 'first_dpdmaxdatemonth_442T' has data type: float16\nMean value for column 'first_dpdmaxdatemonth_442T' is NaN. Filling NaN with 0.\nColumn 'first_dpdmaxdatemonth_89T' has data type: float16\nMean value for column 'first_dpdmaxdatemonth_89T' is NaN. Filling NaN with 0.\nColumn 'first_dpdmaxdateyear_596T' has data type: float16\nMean value for column 'first_dpdmaxdateyear_596T' is NaN. Filling NaN with 0.\nColumn 'first_dpdmaxdateyear_896T' has data type: float16\nMean value for column 'first_dpdmaxdateyear_896T' is NaN. Filling NaN with 0.\nColumn 'first_nominalrate_281L' has data type: float16\nMean value for column 'first_nominalrate_281L' is NaN. Filling NaN with 0.\nColumn 'first_nominalrate_498L' has data type: float16\nMean value for column 'first_nominalrate_498L' is NaN. Filling NaN with 0.\nColumn 'first_numberofcontrsvalue_258L' has data type: float16\nMean value for column 'first_numberofcontrsvalue_258L' is NaN. Filling NaN with 0.\nColumn 'first_numberofcontrsvalue_358L' has data type: float16\nMean value for column 'first_numberofcontrsvalue_358L' is NaN. Filling NaN with 0.\nColumn 'first_numberofinstls_229L' has data type: float16\nMean value for column 'first_numberofinstls_229L' is NaN. Filling NaN with 0.\nColumn 'first_numberofinstls_320L' has data type: float16\nMean value for column 'first_numberofinstls_320L' is NaN. Filling NaN with 0.\nColumn 'first_numberofoutstandinstls_520L' has data type: float16\nMean value for column 'first_numberofoutstandinstls_520L' is NaN. Filling NaN with 0.\nColumn 'first_numberofoutstandinstls_59L' has data type: float16\nMean value for column 'first_numberofoutstandinstls_59L' is NaN. Filling NaN with 0.\nColumn 'first_numberofoverdueinstlmax_1039L' has data type: float16\nMean value for column 'first_numberofoverdueinstlmax_1039L' is NaN. Filling NaN with 0.\nColumn 'first_numberofoverdueinstlmax_1151L' has data type: float32\nFilling NaN in column 'first_numberofoverdueinstlmax_1151L' with mean value: 95.2259292602539\nColumn 'first_numberofoverdueinstls_725L' has data type: float16\nMean value for column 'first_numberofoverdueinstls_725L' is NaN. Filling NaN with 0.\nColumn 'first_numberofoverdueinstls_834L' has data type: float16\nMean value for column 'first_numberofoverdueinstls_834L' is NaN. Filling NaN with 0.\nColumn 'first_overdueamountmaxdatemonth_284T' has data type: float16\nMean value for column 'first_overdueamountmaxdatemonth_284T' is NaN. Filling NaN with 0.\nColumn 'first_overdueamountmaxdatemonth_365T' has data type: float16\nMean value for column 'first_overdueamountmaxdatemonth_365T' is NaN. Filling NaN with 0.\nColumn 'first_overdueamountmaxdateyear_2T' has data type: float16\nMean value for column 'first_overdueamountmaxdateyear_2T' is NaN. Filling NaN with 0.\nColumn 'first_overdueamountmaxdateyear_994T' has data type: float16\nMean value for column 'first_overdueamountmaxdateyear_994T' is NaN. Filling NaN with 0.\nColumn 'first_periodicityofpmts_1102L' has data type: float16\nMean value for column 'first_periodicityofpmts_1102L' is NaN. Filling NaN with 0.\nColumn 'first_periodicityofpmts_837L' has data type: float16\nMean value for column 'first_periodicityofpmts_837L' is NaN. Filling NaN with 0.\nColumn 'max_mainoccupationinc_384A' has data type: float32\nFilling NaN in column 'max_mainoccupationinc_384A' with mean value: 57707.47265625\nColumn 'first_birth_259D' has data type: datetime64[ms]\nColumn 'first_empl_employedfrom_271D' has data type: datetime64[ms]\nColumn 'first_education_927M' has data type: category\nFilling NaN in column 'first_education_927M' with mode value: a55475b1\nColumn 'first_language1_981M' has data type: category\nFilling NaN in column 'first_language1_981M' with mode value: P10_39_147\nColumn 'first_contaddr_matchlist_1032L' has data type: category\nFilling NaN in column 'first_contaddr_matchlist_1032L' with mode value: False\nColumn 'first_contaddr_smempladdr_334L' has data type: category\nFilling NaN in column 'first_contaddr_smempladdr_334L' with mode value: False\nColumn 'first_empl_employedtotal_800L' has data type: category\nFilling NaN in column 'first_empl_employedtotal_800L' with mode value: MORE_FIVE\nColumn 'first_empl_industry_691L' has data type: category\nFilling NaN in column 'first_empl_industry_691L' with mode value: OTHER\nColumn 'first_familystate_447L' has data type: category\nFilling NaN in column 'first_familystate_447L' with mode value: MARRIED\nColumn 'first_incometype_1044T' has data type: category\nFilling NaN in column 'first_incometype_1044T' with mode value: PRIVATE_SECTOR_EMPLOYEE\nColumn 'first_personindex_1023L' has data type: float16\nFilling NaN in column 'first_personindex_1023L' with mean value: 0.0\nColumn 'first_persontype_1072L' has data type: float16\nMean value for column 'first_persontype_1072L' is NaN. Filling NaN with 0.\nColumn 'first_persontype_792L' has data type: float16\nMean value for column 'first_persontype_792L' is NaN. Filling NaN with 0.\nColumn 'first_role_1084L' has data type: category\nFilling NaN in column 'first_role_1084L' with mode value: CL\nColumn 'first_safeguarantyflag_411L' has data type: category\nFilling NaN in column 'first_safeguarantyflag_411L' with mode value: True\nColumn 'first_sex_738L' has data type: category\nFilling NaN in column 'first_sex_738L' with mode value: F\nColumn 'first_type_25L' has data type: category\nFilling NaN in column 'first_type_25L' with mode value: PRIMARY_MOBILE\nColumn 'max_pmts_dpd_1073P' has data type: float16\nMean value for column 'max_pmts_dpd_1073P' is NaN. Filling NaN with 0.\nColumn 'max_pmts_dpd_303P' has data type: float32\nFilling NaN in column 'max_pmts_dpd_303P' with mean value: 222.63592529296875\nColumn 'max_pmts_overdue_1140A' has data type: float32\nFilling NaN in column 'max_pmts_overdue_1140A' with mean value: 5567.93994140625\nColumn 'max_pmts_overdue_1152A' has data type: float32\nFilling NaN in column 'max_pmts_overdue_1152A' with mean value: 22240.892578125\nColumn 'first_collater_typofvalofguarant_298M' has data type: category\nFilling NaN in column 'first_collater_typofvalofguarant_298M' with mode value: 9a0c095e\nColumn 'first_collater_typofvalofguarant_407M' has data type: category\nFilling NaN in column 'first_collater_typofvalofguarant_407M' with mode value: 9a0c095e\nColumn 'first_collaterals_typeofguarante_359M' has data type: category\nFilling NaN in column 'first_collaterals_typeofguarante_359M' with mode value: c7a5ad39\nColumn 'first_collaterals_typeofguarante_669M' has data type: category\nFilling NaN in column 'first_collaterals_typeofguarante_669M' with mode value: c7a5ad39\nColumn 'first_subjectroles_name_541M' has data type: category\nFilling NaN in column 'first_subjectroles_name_541M' with mode value: ab3c25cf\nColumn 'first_subjectroles_name_838M' has data type: category\nFilling NaN in column 'first_subjectroles_name_838M' with mode value: ab3c25cf\nColumn 'first_collater_valueofguarantee_1124L' has data type: float32\nFilling NaN in column 'first_collater_valueofguarantee_1124L' with mean value: 89009.4140625\nColumn 'first_collater_valueofguarantee_876L' has data type: float32\nFilling NaN in column 'first_collater_valueofguarantee_876L' with mean value: 432489.625\nColumn 'first_pmts_month_158T' has data type: float16\nMean value for column 'first_pmts_month_158T' is NaN. Filling NaN with 0.\nColumn 'first_pmts_month_706T' has data type: float16\nMean value for column 'first_pmts_month_706T' is NaN. Filling NaN with 0.\nColumn 'first_pmts_year_1139T' has data type: float16\nMean value for column 'first_pmts_year_1139T' is NaN. Filling NaN with 0.\nColumn 'first_pmts_year_507T' has data type: float16\nMean value for column 'first_pmts_year_507T' is NaN. Filling NaN with 0.\nColumn 'first_cacccardblochreas_147M' has data type: category\nFilling NaN in column 'first_cacccardblochreas_147M' with mode value: a55475b1\nColumn 'first_conts_type_509L' has data type: category\nFilling NaN in column 'first_conts_type_509L' with mode value: PRIMARY_MOBILE\nColumn 'first_conts_role_79M' has data type: category\nFilling NaN in column 'first_conts_role_79M' with mode value: a55475b1\nColumn 'first_empls_economicalst_849M' has data type: category\nFilling NaN in column 'first_empls_economicalst_849M' with mode value: a55475b1\nColumn 'first_empls_employer_name_740M' has data type: category\nFilling NaN in column 'first_empls_employer_name_740M' with mode value: a55475b1\n","output_type":"stream"}]},{"cell_type":"code","source":"df_train","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:09:05.288973Z","iopub.execute_input":"2024-04-23T16:09:05.289453Z","iopub.status.idle":"2024-04-23T16:09:05.615633Z","shell.execute_reply.started":"2024-04-23T16:09:05.289421Z","shell.execute_reply":"2024-04-23T16:09:05.613875Z"},"trusted":true},"execution_count":56,"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":" case_id MONTH WEEK_NUM target month_decision weekday_decision \\\n0 0 201901 0 0 1 4 \n1 1 201901 0 0 1 4 \n2 2 201901 0 0 1 5 \n3 3 201901 0 0 1 4 \n4 4 201901 0 1 1 5 \n... ... ... ... ... ... ... \n1526654 2703450 202010 91 0 10 1 \n1526655 2703451 202010 91 0 10 1 \n1526656 2703452 202010 91 0 10 1 \n1526657 2703453 202010 91 0 10 1 \n1526658 2703454 202010 91 0 10 1 \n\n assignmentdate_238D assignmentdate_4527235D \\\n0 0.0 0.0 \n1 0.0 0.0 \n2 0.0 0.0 \n3 0.0 0.0 \n4 0.0 0.0 \n... ... ... \n1526654 0.0 0.0 \n1526655 0.0 0.0 \n1526656 0.0 0.0 \n1526657 0.0 0.0 \n1526658 0.0 0.0 \n\n assignmentdate_4955616D birthdate_574D ... first_pmts_month_158T \\\n0 0.0 0.0 ... 0.0 \n1 0.0 0.0 ... 0.0 \n2 0.0 0.0 ... 0.0 \n3 0.0 0.0 ... 0.0 \n4 0.0 0.0 ... 0.0 \n... ... ... ... ... \n1526654 998.0 0.0 ... 2.0 \n1526655 5592.0 0.0 ... 2.0 \n1526656 0.0 0.0 ... 2.0 \n1526657 4616.0 0.0 ... 2.0 \n1526658 7348.0 0.0 ... 2.0 \n\n first_pmts_month_706T first_pmts_year_1139T first_pmts_year_507T \\\n0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 0.0 0.0 \n... ... ... ... \n1526654 2.0 2018.0 2006.0 \n1526655 2.0 2019.0 2015.0 \n1526656 2.0 2018.0 2012.0 \n1526657 2.0 2019.0 2008.0 \n1526658 2.0 2020.0 2005.0 \n\n first_cacccardblochreas_147M first_conts_type_509L \\\n0 a55475b1 PRIMARY_MOBILE \n1 a55475b1 PRIMARY_MOBILE \n2 a55475b1 PRIMARY_MOBILE \n3 a55475b1 PHONE \n4 a55475b1 PRIMARY_MOBILE \n... ... ... \n1526654 a55475b1 PRIMARY_MOBILE \n1526655 a55475b1 PRIMARY_MOBILE \n1526656 a55475b1 PRIMARY_MOBILE \n1526657 a55475b1 PRIMARY_MOBILE \n1526658 a55475b1 PRIMARY_MOBILE \n\n first_empls_employedfrom_796D first_conts_role_79M \\\n0 2996.0 a55475b1 \n1 2996.0 a55475b1 \n2 2996.0 a55475b1 \n3 2996.0 a55475b1 \n4 2996.0 a55475b1 \n... ... ... \n1526654 2996.0 a55475b1 \n1526655 2996.0 a55475b1 \n1526656 2996.0 a55475b1 \n1526657 2996.0 a55475b1 \n1526658 2996.0 a55475b1 \n\n first_empls_economicalst_849M first_empls_employer_name_740M \n0 a55475b1 a55475b1 \n1 a55475b1 a55475b1 \n2 a55475b1 a55475b1 \n3 a55475b1 a55475b1 \n4 a55475b1 a55475b1 \n... ... ... \n1526654 a55475b1 a55475b1 \n1526655 a55475b1 a55475b1 \n1526656 a55475b1 a55475b1 \n1526657 a55475b1 a55475b1 \n1526658 a55475b1 a55475b1 \n\n[1526659 rows x 325 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
case_idMONTHWEEK_NUMtargetmonth_decisionweekday_decisionassignmentdate_238Dassignmentdate_4527235Dassignmentdate_4955616Dbirthdate_574D...first_pmts_month_158Tfirst_pmts_month_706Tfirst_pmts_year_1139Tfirst_pmts_year_507Tfirst_cacccardblochreas_147Mfirst_conts_type_509Lfirst_empls_employedfrom_796Dfirst_conts_role_79Mfirst_empls_economicalst_849Mfirst_empls_employer_name_740M
0020190100140.00.00.00.0...0.00.00.00.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
1120190100140.00.00.00.0...0.00.00.00.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
2220190100150.00.00.00.0...0.00.00.00.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
3320190100140.00.00.00.0...0.00.00.00.0a55475b1PHONE2996.0a55475b1a55475b1a55475b1
4420190101150.00.00.00.0...0.00.00.00.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
..................................................................
152665427034502020109101010.00.0998.00.0...2.02.02018.02006.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
152665527034512020109101010.00.05592.00.0...2.02.02019.02015.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
152665627034522020109101010.00.00.00.0...2.02.02018.02012.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
152665727034532020109101010.00.04616.00.0...2.02.02019.02008.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
152665827034542020109101010.00.07348.00.0...2.02.02020.02005.0a55475b1PRIMARY_MOBILE2996.0a55475b1a55475b1a55475b1
\n

1526659 rows × 325 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"\ndf_train[cat_cols] = df_train[cat_cols].astype(str)\nimport polars as pl\nfrom sklearn.preprocessing import OrdinalEncoder\n\n\n# Fit Ordinal Encoder on Training Data\nencoder = OrdinalEncoder(handle_unknown=\"use_encoded_value\", unknown_value=np.nan)\nencoder.fit(df_train[cat_cols])\n\n# Transform Training Data\ndf_train[cat_cols] = encoder.transform(df_train[cat_cols])\ndf_train[cat_cols] = df_train[cat_cols].fillna(-1)\ndf_train[cat_cols] = df_train[cat_cols].astype(int)","metadata":{"execution":{"iopub.status.busy":"2024-04-25T05:50:16.240737Z","iopub.execute_input":"2024-04-25T05:50:16.241173Z","iopub.status.idle":"2024-04-25T05:51:38.699463Z","shell.execute_reply.started":"2024-04-25T05:50:16.241139Z","shell.execute_reply":"2024-04-25T05:51:38.698091Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"## Feature Selection","metadata":{"_uuid":"f8086977-546a-4fa9-86dc-8ea3c4dc394b","_cell_guid":"3152a026-8de2-4579-b700-ca9eb55dc1d7","trusted":true}},{"cell_type":"code","source":"df_train","metadata":{"execution":{"iopub.status.busy":"2024-04-25T05:51:45.032092Z","iopub.execute_input":"2024-04-25T05:51:45.032607Z","iopub.status.idle":"2024-04-25T05:51:45.654457Z","shell.execute_reply.started":"2024-04-25T05:51:45.032561Z","shell.execute_reply":"2024-04-25T05:51:45.652634Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":" case_id WEEK_NUM target month_decision weekday_decision \\\n0 0 0 0 1 4 \n1 1 0 0 1 4 \n2 2 0 0 1 5 \n3 3 0 0 1 4 \n4 4 0 1 1 5 \n... ... ... ... ... ... \n1526654 2703450 91 0 10 1 \n1526655 2703451 91 0 10 1 \n1526656 2703452 91 0 10 1 \n1526657 2703453 91 0 10 1 \n1526658 2703454 91 0 10 1 \n\n birthdate_574D dateofbirth_337D days120_123L days180_256L \\\n0 NaT NaT 0.0 0.0 \n1 NaT NaT 0.0 0.0 \n2 NaT NaT 0.0 0.0 \n3 NaT NaT 0.0 0.0 \n4 NaT NaT 0.0 0.0 \n... ... ... ... ... \n1526654 NaT 1960-01-01 0.0 0.0 \n1526655 NaT 1950-11-01 0.0 0.0 \n1526656 NaT 1977-08-01 2.0 2.0 \n1526657 NaT 1950-02-01 2.0 2.0 \n1526658 NaT 1948-04-01 1.0 1.0 \n\n days30_165L ... first_collater_valueofguarantee_876L \\\n0 0.0 ... 432489.625 \n1 0.0 ... 432489.625 \n2 0.0 ... 432489.625 \n3 0.0 ... 432489.625 \n4 0.0 ... 432489.625 \n... ... ... ... \n1526654 0.0 ... 0.000 \n1526655 0.0 ... 0.000 \n1526656 0.0 ... 0.000 \n1526657 1.0 ... 0.000 \n1526658 0.0 ... 176000.000 \n\n first_pmts_month_158T first_pmts_month_706T first_pmts_year_1139T \\\n0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 0.0 0.0 \n... ... ... ... \n1526654 2.0 2.0 2018.0 \n1526655 2.0 2.0 2019.0 \n1526656 2.0 2.0 2018.0 \n1526657 2.0 2.0 2019.0 \n1526658 2.0 2.0 2020.0 \n\n first_pmts_year_507T first_cacccardblochreas_147M \\\n0 0.0 8 \n1 0.0 8 \n2 0.0 8 \n3 0.0 8 \n4 0.0 8 \n... ... ... \n1526654 2006.0 8 \n1526655 2015.0 8 \n1526656 2012.0 8 \n1526657 2008.0 8 \n1526658 2005.0 8 \n\n first_conts_type_509L first_conts_role_79M \\\n0 5 3 \n1 5 3 \n2 5 3 \n3 3 3 \n4 5 3 \n... ... ... \n1526654 5 3 \n1526655 5 3 \n1526656 5 3 \n1526657 5 3 \n1526658 5 3 \n\n first_empls_economicalst_849M first_empls_employer_name_740M \n0 8 1 \n1 8 1 \n2 8 1 \n3 8 1 \n4 8 1 \n... ... ... \n1526654 8 1 \n1526655 8 1 \n1526656 8 1 \n1526657 8 1 \n1526658 8 1 \n\n[1526659 rows x 312 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
case_idWEEK_NUMtargetmonth_decisionweekday_decisionbirthdate_574Ddateofbirth_337Ddays120_123Ldays180_256Ldays30_165L...first_collater_valueofguarantee_876Lfirst_pmts_month_158Tfirst_pmts_month_706Tfirst_pmts_year_1139Tfirst_pmts_year_507Tfirst_cacccardblochreas_147Mfirst_conts_type_509Lfirst_conts_role_79Mfirst_empls_economicalst_849Mfirst_empls_employer_name_740M
000014NaTNaT0.00.00.0...432489.6250.00.00.00.085381
110014NaTNaT0.00.00.0...432489.6250.00.00.00.085381
220015NaTNaT0.00.00.0...432489.6250.00.00.00.085381
330014NaTNaT0.00.00.0...432489.6250.00.00.00.083381
440115NaTNaT0.00.00.0...432489.6250.00.00.00.085381
..................................................................
15266542703450910101NaT1960-01-010.00.00.0...0.0002.02.02018.02006.085381
15266552703451910101NaT1950-11-010.00.00.0...0.0002.02.02019.02015.085381
15266562703452910101NaT1977-08-012.02.00.0...0.0002.02.02018.02012.085381
15266572703453910101NaT1950-02-012.02.01.0...0.0002.02.02019.02008.085381
15266582703454910101NaT1948-04-011.01.00.0...176000.0002.02.02020.02005.085381
\n

1526659 rows × 312 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import Lasso\nfrom sklearn.metrics import roc_auc_score\n\n# Assuming df_train is your DataFrame containing the data\n# Preprocess data (handle missing values, encode categorical variables, etc.)\n# For demonstration purposes, assuming preprocessing has been done already\n\n# Split data into features (X) and target variable (y)\nX = df_train.drop(columns=[\"target\"])\ny = df_train[\"target\"]\n\n# Split data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Initialize and train Lasso regression model\nlasso_model = Lasso(alpha=0.1) # You can adjust the alpha parameter for regularization\nlasso_model.fit(X_train, y_train)\n\n# Check if the model has been trained successfully\nif hasattr(lasso_model, 'coef_'):\n # Select features based on coefficients\n selected_features = X.columns[lasso_model.coef_ != 0]\n\n # Calculate AUC score\n y_pred_prob = lasso_model.predict(X_test)\n auc_score = roc_auc_score(y_test, y_pred_prob)\n print(\"AUC Score:\", auc_score)\n\n # Get coefficients and corresponding features\n selected_features_sorted = selected_features[np.argsort(lasso_model.coef_[lasso_model.coef_ != 0])]\n coefficients_sorted = sorted(lasso_model.coef_[lasso_model.coef_ != 0])\n\n # Plot feature importances for selected features\n plt.figure(figsize=(10, 100))\n plt.barh(selected_features_sorted, coefficients_sorted)\n plt.title('Feature Importance by Lasso Coefficients (Selected Features)')\n plt.xlabel('Coefficient Value')\n plt.ylabel('Features')\n plt.tight_layout() # Adjust layout to prevent clipping of labels\n plt.show()\nelse:\n print(\"Model training was unsuccessful. Please check your data and preprocessing steps.\")\n","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:29:55.416838Z","iopub.execute_input":"2024-04-23T16:29:55.418487Z","iopub.status.idle":"2024-04-23T16:37:43.88526Z","shell.execute_reply.started":"2024-04-23T16:29:55.418443Z","shell.execute_reply":"2024-04-23T16:37:43.883491Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"AUC Score: 0.7325734301482991\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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= [col for col in df_train.columns if col not in selected_features]\n\n# Drop columns that are not in the selected features list\ndf_train = df_train.drop(columns=columns_to_drop)\n\ndf_train\n","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:49:36.70766Z","iopub.execute_input":"2024-04-23T16:49:36.708544Z","iopub.status.idle":"2024-04-23T16:49:38.103655Z","shell.execute_reply.started":"2024-04-23T16:49:36.708488Z","shell.execute_reply":"2024-04-23T16:49:38.1019Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":" case_id assignmentdate_238D assignmentdate_4955616D \\\n0 0 0.0 0.0 \n1 1 0.0 0.0 \n2 2 0.0 0.0 \n3 3 0.0 0.0 \n4 4 0.0 0.0 \n... ... ... ... \n1526654 2703450 0.0 998.0 \n1526655 2703451 0.0 5592.0 \n1526656 2703452 0.0 0.0 \n1526657 2703453 0.0 4616.0 \n1526658 2703454 0.0 7348.0 \n\n birthdate_574D dateofbirth_337D pmtssum_45A \\\n0 0.0 0.0 13199.936523 \n1 0.0 0.0 13199.936523 \n2 0.0 0.0 13199.936523 \n3 0.0 0.0 13199.936523 \n4 0.0 0.0 13199.936523 \n... ... ... ... \n1526654 0.0 22192.0 13199.936523 \n1526655 0.0 25536.0 13199.936523 \n1526656 0.0 15768.0 13199.936523 \n1526657 0.0 25808.0 13199.936523 \n1526658 0.0 26480.0 13199.936523 \n\n amtinstpaidbefduel24m_4187115A annuity_780A annuitynextmonth_57A \\\n0 55958.320312 1917.599976 0.000000 \n1 55958.320312 3134.000000 0.000000 \n2 55958.320312 4937.000000 0.000000 \n3 55958.320312 4643.600098 0.000000 \n4 55958.320312 3390.199951 0.000000 \n... ... ... ... \n1526654 176561.359375 3675.400146 0.000000 \n1526655 301276.468750 7088.600098 6191.600098 \n1526656 14232.400391 7788.800293 0.000000 \n1526657 197371.578125 1195.400024 2827.199951 \n1526658 82949.601562 4533.800293 2986.800049 \n\n avgdbddpdlast24m_3658932P ... first_empl_employedfrom_271D \\\n0 0.0 ... 475.0 \n1 0.0 ... 3718.0 \n2 0.0 ... 3244.0 \n3 0.0 ... 233.0 \n4 0.0 ... 1481.0 \n... ... ... ... \n1526654 -23.0 ... 0.0 \n1526655 -18.0 ... 0.0 \n1526656 -12.0 ... 0.0 \n1526657 -33.0 ... 0.0 \n1526658 -6.0 ... 0.0 \n\n first_openingdate_313D first_openingdate_857D max_pmts_dpd_1073P \\\n0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 0.0 0.0 \n... ... ... ... \n1526654 0.0 0.0 0.0 \n1526655 0.0 0.0 0.0 \n1526656 0.0 0.0 16.0 \n1526657 2240.0 2240.0 0.0 \n1526658 0.0 0.0 0.0 \n\n max_pmts_overdue_1140A max_pmts_overdue_1152A \\\n0 5567.939941 22240.892578 \n1 5567.939941 22240.892578 \n2 5567.939941 22240.892578 \n3 5567.939941 22240.892578 \n4 5567.939941 22240.892578 \n... ... ... \n1526654 0.000000 4316.439941 \n1526655 0.000000 0.000000 \n1526656 4884.298340 0.000000 \n1526657 0.000000 2693.199951 \n1526658 0.000000 4875.020020 \n\n first_collater_valueofguarantee_1124L \\\n0 89009.414062 \n1 89009.414062 \n2 89009.414062 \n3 89009.414062 \n4 89009.414062 \n... ... \n1526654 0.000000 \n1526655 0.000000 \n1526656 0.000000 \n1526657 0.000000 \n1526658 0.000000 \n\n first_collater_valueofguarantee_876L first_pmts_year_1139T \\\n0 432489.625 0.0 \n1 432489.625 0.0 \n2 432489.625 0.0 \n3 432489.625 0.0 \n4 432489.625 0.0 \n... ... ... \n1526654 0.000 2018.0 \n1526655 0.000 2019.0 \n1526656 0.000 2018.0 \n1526657 0.000 2019.0 \n1526658 176000.000 2020.0 \n\n first_pmts_year_507T \n0 0.0 \n1 0.0 \n2 0.0 \n3 0.0 \n4 0.0 \n... ... \n1526654 2006.0 \n1526655 2015.0 \n1526656 2012.0 \n1526657 2008.0 \n1526658 2005.0 \n\n[1526659 rows x 108 columns]","text/html":"
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case_idassignmentdate_238Dassignmentdate_4955616Dbirthdate_574Ddateofbirth_337Dpmtssum_45Aamtinstpaidbefduel24m_4187115Aannuity_780Aannuitynextmonth_57Aavgdbddpdlast24m_3658932P...first_empl_employedfrom_271Dfirst_openingdate_313Dfirst_openingdate_857Dmax_pmts_dpd_1073Pmax_pmts_overdue_1140Amax_pmts_overdue_1152Afirst_collater_valueofguarantee_1124Lfirst_collater_valueofguarantee_876Lfirst_pmts_year_1139Tfirst_pmts_year_507T
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152665427034500.0998.00.022192.013199.936523176561.3593753675.4001460.000000-23.0...0.00.00.00.00.0000004316.4399410.0000000.0002018.02006.0
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152665727034530.04616.00.025808.013199.936523197371.5781251195.4000242827.199951-33.0...0.02240.02240.00.00.0000002693.1999510.0000000.0002019.02008.0
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1526659 rows × 108 columns

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"},"metadata":{}}]},{"cell_type":"code","source":"y = df_train[\"target\"]\nweeks = df_train[\"WEEK_NUM\"]\ndf_train= df_train.drop(columns=[\"target\", \"case_id\", \"WEEK_NUM\"])\ncv = StratifiedGroupKFold(n_splits=5, shuffle=False)","metadata":{"_uuid":"cb387be5-14c2-42be-bb37-002eafca6bb4","_cell_guid":"59206b02-5b58-4d76-a7af-7df80da2b4dc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T14:48:29.76739Z","iopub.execute_input":"2024-04-22T14:48:29.767638Z","iopub.status.idle":"2024-04-22T14:48:29.876234Z","shell.execute_reply.started":"2024-04-22T14:48:29.767617Z","shell.execute_reply":"2024-04-22T14:48:29.875494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Applying OrdinalEncoder to handle catagory columns","metadata":{}},{"cell_type":"markdown","source":"### Setting up parameters","metadata":{}},{"cell_type":"code","source":"params = {\n \"boosting_type\": \"gbdt\",\n \"objective\": \"binary\",\n \"metric\": \"auc\",\n \"max_depth\": 10, \n \"learning_rate\": 0.05,\n \"n_estimators\": 2000, \n \"colsample_bytree\": 0.8,\n \"colsample_bynode\": 0.8,\n \"verbose\": -1,\n \"random_state\": 42,\n \"reg_alpha\": 0.1,\n \"reg_lambda\": 10,\n \"extra_trees\":True,\n 'num_leaves':64,\n \"device\": device, \n \"verbose\": -1,\n}\n\nparams2 = {\n \"booster\": \"gbtree\",\n \"objective\": \"binary:logistic\",\n \"eval_metric\": \"auc\",\n \"max_depth\": 10,\n \"learning_rate\": 0.05,\n \"n_estimators\": 1000,\n \"colsample_bytree\": 0.8,\n \"colsample_bynode\": 0.8,\n \"alpha\": 0.1, \n \"lambda\": 10, \n \"tree_method\": 'gpu_hist' if device == 'gpu' else 'auto',\n \"random_state\": 42,\n \"verbosity\": 0,\n \"enable_categorical\":True,\n}\n\n# rf_params = {\n# \"n_estimators\": 100, # Number of trees in the forest\n# \"criterion\": \"gini\", # Criteria for splitting: either \"gini\" or \"entropy\"\n# \"max_depth\": None, # Maximum depth of the tree. None means unlimited depth.\n# \"min_samples_split\": 2, # Minimum number of samples required to split an internal node\n# \"min_samples_leaf\": 1, # Minimum number of samples required to be at a leaf node\n# \"max_features\": \"auto\", # Number of features to consider when looking for the best split\n# \"bootstrap\": True, # Whether bootstrap samples are used when building trees\n# \"random_state\": 42, # Seed for random number generator\n# \"n_jobs\": -1, # Number of jobs to run in parallel (-1 means using all processors)\n# \"verbose\": 0, # Controls the verbosity when fitting and predicting\n# }","metadata":{"_uuid":"6904e6de-80d6-47cf-baf3-78dce2c7c672","_cell_guid":"33472c64-261e-40c7-9275-b266fec0005e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T14:52:24.876268Z","iopub.execute_input":"2024-04-22T14:52:24.877142Z","iopub.status.idle":"2024-04-22T14:52:24.884328Z","shell.execute_reply.started":"2024-04-22T14:52:24.87711Z","shell.execute_reply":"2024-04-22T14:52:24.883393Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.to_csv('df_train.csv')","metadata":{"_uuid":"5c56ee3f-5e38-4cfa-bb60-a9a6b674c7b2","_cell_guid":"359aa0f4-9070-4fb3-9e67-4ffd79d72940","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T14:48:29.889505Z","iopub.status.idle":"2024-04-22T14:48:29.889845Z","shell.execute_reply.started":"2024-04-22T14:48:29.889686Z","shell.execute_reply":"2024-04-22T14:48:29.8897Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train[cat_cols] = df_train[cat_cols].fillna(0.0)\ndf_train[cat_cols].to_csv('catcol.csv')","metadata":{"execution":{"iopub.status.busy":"2024-04-22T14:52:28.748569Z","iopub.execute_input":"2024-04-22T14:52:28.749433Z","iopub.status.idle":"2024-04-22T14:52:30.22559Z","shell.execute_reply.started":"2024-04-22T14:52:28.749382Z","shell.execute_reply":"2024-04-22T14:52:30.224628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%time\n\n\nfrom catboost import CatBoostClassifier, Pool\nimport xgboost as xgb\n# from sklearn.ensemble import RandomForestClassifier\n# from sklearn.datasets import make_classification\nfitted_models_cat = []\nfitted_models_lgb = []\nfitted_models_xgb = []\n# fitted_models_rfc = []\n\ncv_scores_cat = []\ncv_scores_lgb = []\ncv_scores_xgb = []\n# cv_scores_rfc = []\n\nfor idx_train, idx_valid in cv.split(df_train, y, groups=weeks):#\n X_train, y_train = df_train.iloc[idx_train], y.iloc[idx_train]# \n X_valid, y_valid = df_train.iloc[idx_valid], y.iloc[idx_valid]\n train_pool = Pool(X_train, y_train,cat_features=cat_cols)\n \n val_pool = Pool(X_valid, y_valid,cat_features=cat_cols)\n clf = CatBoostClassifier(\n eval_metric='AUC',\n task_type='GPU',\n learning_rate=0.03,\n iterations=n_est)\n random_seed=3107\n clf.fit(train_pool, eval_set=val_pool,verbose=300)\n fitted_models_cat.append(clf)\n y_pred_valid = clf.predict_proba(X_valid)[:,1]\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_cat.append(auc_score)\n \n \n X_train[cat_cols] = X_train[cat_cols].astype(\"category\")\n X_valid[cat_cols] = X_valid[cat_cols].astype(\"category\")\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(200), lgb.early_stopping(100)] )\n \n fitted_models_lgb.append(model)\n y_pred_valid = model.predict_proba(X_valid)[:,1]\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_lgb.append(auc_score)\n \n \n model2 = xgb.XGBClassifier(**params2)\n model2.fit(\n X_train, y_train,\n eval_set=[(X_valid, y_valid)],\n early_stopping_rounds=100, verbose=False)\n \n fitted_models_xgb.append(model2)\n y_pred_valid = model2.predict_proba(X_valid)[:, 1]\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_xgb.append(auc_score)\n \n# model3 = RandomForestClassifier()\n\n# model3.fit(\n# X_train, y_train)\n# fitted_models_rfc.append(model3)\n# y_pred_valid = model3.predict_proba(X_valid)[:, 1]\n# auc_score = roc_auc_score(y_valid, y_pred_valid)\n# cv_scores_rfc.append(auc_score)\n \n del clf, model, model2# ,model3\n gc.collect()\n \n \nprint('cat')\nprint(\"CV AUC scores: \", cv_scores_cat)\nprint(\"Maximum CV AUC score: \", max(cv_scores_cat))\n\n\nprint('lgb')\nprint(\"CV AUC scores: \", cv_scores_lgb)\nprint(\"Maximum CV AUC score: \", max(cv_scores_lgb))\n\nprint('xgb')\nprint(\"CV AUC scores: \", cv_scores_xgb)\nprint(\"Maximum CV AUC score: \", max(cv_scores_xgb))\n\n# print('rfc')\n# print(\"CV AUC scores: \", cv_scores_xgb)\n# print(\"Maximum CV AUC score: \", max(cv_scores_rfc))","metadata":{"_uuid":"13c97864-af48-48f5-b86d-823b812e715b","_cell_guid":"8fb6d171-ef22-4758-a922-1d245dd868e3","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T14:52:34.145245Z","iopub.execute_input":"2024-04-22T14:52:34.145631Z","iopub.status.idle":"2024-04-22T15:02:11.398329Z","shell.execute_reply.started":"2024-04-22T14:52:34.145605Z","shell.execute_reply":"2024-04-22T15:02:11.397294Z"},"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 \n y_preds = [estimator.predict_proba(X) for estimator in self.estimators[:5]]\n \n X[cat_cols] = X[cat_cols].astype(\"category\")\n y_preds += [estimator.predict_proba(X) for estimator in self.estimators[5:10]]\n y_preds+=y_preds #tang trong so\n y_preds += [estimator.predict_proba(X) for estimator in self.estimators[10:]]\n print(len(y_preds))\n return np.mean(y_preds, axis=0)\n\nmodel = VotingModel(fitted_models_cat+fitted_models_lgb+fitted_models_xgb)","metadata":{"_uuid":"d925f610-48b8-4881-a9bc-31175330236b","_cell_guid":"3d29ac20-ddc5-4fde-8d3f-397e084a7128","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:04:48.445316Z","iopub.execute_input":"2024-04-22T15:04:48.445998Z","iopub.status.idle":"2024-04-22T15:04:48.454806Z","shell.execute_reply.started":"2024-04-22T15:04:48.445966Z","shell.execute_reply":"2024-04-22T15:04:48.453906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_predict\nfrom catboost import CatBoostClassifier, Pool\nfrom lightgbm import LGBMClassifier\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import roc_auc_score\nimport pandas as pd\n\nmodels = [\n ('CatBoost', CatBoostClassifier(eval_metric='AUC', task_type='GPU', learning_rate=0.03, iterations=n_est, random_seed=3107)),\n ('LightGBM', LGBMClassifier(**params)),\n ('XGBoost', XGBClassifier(**params2))\n]\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nparams = {\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\nmeta_model = GradientBoostingClassifier(**params)\n\n\n\n\nfitted_models_cb = []\nfitted_models_lgb = []\nfitted_models_xgb = []\nfitted_models_rfc = []\ncv_scores_cb = []\ncv_scores_lgb = []\ncv_scores_xgb = []\ncv_scores_rfc = []\n\nmeta_features = pd.DataFrame(index=df_train.index, columns=['CatBoost', 'LightGBM', 'XGBoost'])\n\nfor name, model in models:\n for idx_train, idx_valid in cv.split(df_train, y, groups=weeks):\n X_train, y_train = df_train.iloc[idx_train], y.iloc[idx_train]\n X_valid, y_valid = df_train.iloc[idx_valid], y.iloc[idx_valid]\n\n if name == 'CatBoost':\n X_train[cat_cols] = X_train[cat_cols].astype(str)\n X_valid[cat_cols] = X_valid[cat_cols].astype(str)\n train_pool = Pool(X_train, y_train, cat_features=cat_cols)\n val_pool = Pool(X_valid, y_valid, cat_features=cat_cols)\n model.fit(train_pool, eval_set=val_pool, verbose=False)\n y_pred_valid = model.predict_proba(val_pool)[:, 1]\n fitted_models_cb.append(model)\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_cb.append(auc_score)\n elif name == 'LightGBM':\n X_train[cat_cols] = X_train[cat_cols].astype('category')\n X_valid[cat_cols] = X_valid[cat_cols].astype('category')\n model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], callbacks=[lgb.log_evaluation(200), lgb.early_stopping(100)])\n fitted_models_lgb.append(model)\n y_pred_valid = model.predict_proba(X_valid)[:, 1]\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_lgb.append(auc_score)\n else: # XGBoost\n X_train[cat_cols] = X_train[cat_cols].astype('category')\n X_valid[cat_cols] = X_valid[cat_cols].astype('category')\n model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], early_stopping_rounds=100, verbose=False)\n fitted_models_xgb.append(model)\n y_pred_valid = model.predict_proba(X_valid)[:, 1]\n auc_score = roc_auc_score(y_valid, y_pred_valid)\n cv_scores_xgb.append(auc_score)\n# else : # Random forest\n# X_train[cat_cols] = X_train[cat_cols].astype('category')\n# X_valid[cat_cols] = X_valid[cat_cols].astype('category')\n# model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], early_stopping_rounds=100, verbose=False)\n# fitted_models_rfc.append(model)\n# y_pred_valid = model.predict_proba(X_valid)[:, 1]\n# auc_score = roc_auc_score(y_valid, y_pred_valid)\n# cv_scores_rfc.append(auc_score)\n\n meta_features.loc[X_valid.index, name] = y_pred_valid","metadata":{"_uuid":"ceaf47bc-beea-4afc-95d7-926597207afa","_cell_guid":"4ffb439d-fec1-4fa8-8e78-78e5410b9820","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:04:51.135047Z","iopub.execute_input":"2024-04-22T15:04:51.135419Z","iopub.status.idle":"2024-04-22T15:14:28.128305Z","shell.execute_reply.started":"2024-04-22T15:04:51.135388Z","shell.execute_reply":"2024-04-22T15:14:28.127413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"meta_model.fit(meta_features, y)","metadata":{"_uuid":"28653c29-b897-49f9-be65-2316b290a375","_cell_guid":"18c3e571-f69c-425f-b38f-175f8eeed92f","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:17:10.475846Z","iopub.execute_input":"2024-04-22T15:17:10.476593Z","iopub.status.idle":"2024-04-22T15:17:11.433824Z","shell.execute_reply.started":"2024-04-22T15:17:10.476561Z","shell.execute_reply":"2024-04-22T15:17:11.432891Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submision","metadata":{"_uuid":"6b20c95b-3c8e-4e4d-ac39-83c906ddb800","_cell_guid":"51de17d9-9a23-4b29-82a2-a371f4de5c29","trusted":true}},{"cell_type":"code","source":"data_store_test = {\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 read_file(TEST_DIR / \"test_applprev_2.parquet\", 2),\n read_file(TEST_DIR / \"test_person_2.parquet\", 2)\n ]\n}","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:58:14.670517Z","iopub.execute_input":"2024-04-23T16:58:14.671032Z","iopub.status.idle":"2024-04-23T16:58:15.007264Z","shell.execute_reply.started":"2024-04-23T16:58:14.670998Z","shell.execute_reply":"2024-04-23T16:58:15.005732Z"},"trusted":true},"execution_count":96,"outputs":[]},{"cell_type":"code","source":"df_test = feature_eng(**data_store_test)\nprint(\"test data shape:\\t\", df_test.shape)\ndel data_store_test\ngc.collect()\ndf_test = df_test.select([col for col in df_train.columns if col != \"target\"])\nprint(\"train data shape:\\t\", df_train.shape)\nprint(\"test data shape:\\t\", df_test.shape)\n\ndf_test, cat_cols = to_pandas(df_test, cat_cols)\ndf_test = reduce_mem_usage(df_test)\ndf_test[cat_cols] = df_test[cat_cols].astype(str)\n\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-04-25T05:35:10.556327Z","iopub.execute_input":"2024-04-25T05:35:10.556754Z","iopub.status.idle":"2024-04-25T05:35:10.592712Z","shell.execute_reply.started":"2024-04-25T05:35:10.556721Z","shell.execute_reply":"2024-04-25T05:35:10.591111Z"},"trusted":true},"execution_count":4,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df_test \u001b[38;5;241m=\u001b[39m \u001b[43mfeature_eng\u001b[49m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdata_store_test)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest data shape:\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, df_test\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m data_store_test\n","\u001b[0;31mNameError\u001b[0m: name 'feature_eng' is not defined"],"ename":"NameError","evalue":"name 'feature_eng' is not defined","output_type":"error"}]},{"cell_type":"code","source":"# Get the columns to drop\ncolumns_to_drop = [col for col in df_test.columns if col.startswith('max_num_group')]\n\n# Drop the columns\ndf_test_pandas = df_test.drop(columns_to_drop, axis=1)\n\ndf_test_pandas","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:57:32.554273Z","iopub.execute_input":"2024-04-23T16:57:32.555194Z","iopub.status.idle":"2024-04-23T16:57:32.631346Z","shell.execute_reply.started":"2024-04-23T16:57:32.555152Z","shell.execute_reply":"2024-04-23T16:57:32.629666Z"},"trusted":true},"execution_count":93,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/polars/utils/deprecation.py:96\u001b[0m, in \u001b[0;36mdeprecate_parameter_as_positional..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 96\u001b[0m param_args \u001b[38;5;241m=\u001b[39m \u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mold_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n","\u001b[0;31mKeyError\u001b[0m: 'columns'","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[93], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m columns_to_drop \u001b[38;5;241m=\u001b[39m [col \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m df_test\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;28;01mif\u001b[39;00m col\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax_num_group\u001b[39m\u001b[38;5;124m'\u001b[39m)]\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Drop the columns\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m df_test_pandas \u001b[38;5;241m=\u001b[39m \u001b[43mdf_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns_to_drop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m df_test_pandas\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/polars/utils/deprecation.py:98\u001b[0m, in \u001b[0;36mdeprecate_parameter_as_positional..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 96\u001b[0m param_args \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(old_name)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m issue_deprecation_warning(\n\u001b[1;32m 101\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnamed `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mold_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` param is deprecated; use positional `*args` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 102\u001b[0m version\u001b[38;5;241m=\u001b[39mversion,\n\u001b[1;32m 103\u001b[0m )\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(param_args, Sequence) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(param_args, \u001b[38;5;28mstr\u001b[39m):\n","\u001b[0;31mTypeError\u001b[0m: DataFrame.drop() got an unexpected keyword argument 'axis'"],"ename":"TypeError","evalue":"DataFrame.drop() got an unexpected keyword argument 'axis'","output_type":"error"}]},{"cell_type":"code","source":"# Transform Test Data using the same encoder instance to ensure consistency\ndf_test[cat_cols] = encoder.transform(df_test[cat_cols])\ndf_train[cat_cols] = df_train[cat_cols].fillna(-1)\ndf_train[cat_cols] = df_train[cat_cols].astype(int)","metadata":{"execution":{"iopub.status.busy":"2024-04-23T16:57:41.157655Z","iopub.execute_input":"2024-04-23T16:57:41.158156Z","iopub.status.idle":"2024-04-23T16:57:41.228429Z","shell.execute_reply.started":"2024-04-23T16:57:41.158121Z","shell.execute_reply":"2024-04-23T16:57:41.226288Z"},"trusted":true},"execution_count":94,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mColumnNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_672/3166757498.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Transform Test Data using the same encoder instance to ensure consistency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.10/site-packages/polars/dataframe/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_str_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_str\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1708\u001b[0m \u001b[0;31m# select multiple columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1709\u001b[0m \u001b[0;31m# df[[\"foo\", \"bar\"]]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_pydf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1711\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_int_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1712\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# fall through to next if isinstance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mColumnNotFoundError\u001b[0m: description_5085714M"],"ename":"ColumnNotFoundError","evalue":"description_5085714M","output_type":"error"}]},{"cell_type":"code","source":"test_meta_features = pd.DataFrame(index=df_test.index, columns=['CatBoost', 'LightGBM', 'XGBoost'])","metadata":{"_uuid":"aae72955-bc1d-4996-ad1a-d460e704b62a","_cell_guid":"d640ca44-5285-4c49-b88c-4134dd299c73","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:20:33.456577Z","iopub.execute_input":"2024-04-22T15:20:33.456941Z","iopub.status.idle":"2024-04-22T15:20:33.518879Z","shell.execute_reply.started":"2024-04-22T15:20:33.456912Z","shell.execute_reply":"2024-04-22T15:20:33.51776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# CatBoost\n\n\nfor model in fitted_models_cat:\n df_test[cat_cols] = df_test[cat_cols].astype(str)\n y_pred_test = model.predict_proba(df_test)[:, 1]\n test_meta_features['CatBoost'] = test_meta_features['CatBoost'].add(y_pred_test, fill_value=0)\n\ntest_meta_features['CatBoost'] /= len(fitted_models_cat)\n\n# LightGBM\nfor model in fitted_models_lgb:\n df_test[cat_cols] = df_test[cat_cols].astype(\"category\")\n y_pred_test = model.predict_proba(df_test)[:, 1]\n test_meta_features['LightGBM'] = test_meta_features['LightGBM'].add(y_pred_test, fill_value=0)\n\ntest_meta_features['LightGBM'] /= len(fitted_models_lgb)\n\n# XGBoost\nfor model in fitted_models_xgb:\n df_test[cat_cols] = df_test[cat_cols].astype(\"category\")\n y_pred_test = model.predict_proba(df_test)[:, 1]\n test_meta_features['XGBoost'] = test_meta_features['XGBoost'].add(y_pred_test, fill_value=0)\n\ntest_meta_features['XGBoost'] /= len(fitted_models_xgb)\n\n# for model in fitted_models_rfc:\n# df_test[cat_cols] = df_test[cat_cols].astype(\"category\")\n# y_pred_test = model.predict_proba(df_test)[:, 1]\n# test_meta_features['RandomForest'] = test_meta_features['RandomForest'].add(y_pred_test, fill_value=0)\n\n# test_meta_features['RandomForest'] /= len(fitted_models_rfc)","metadata":{"_uuid":"57127fd1-13eb-4950-8863-b1974613a393","_cell_guid":"0f84a19a-81ee-4104-b4e8-b539c73e8004","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:20:35.658938Z","iopub.execute_input":"2024-04-22T15:20:35.659856Z","iopub.status.idle":"2024-04-22T15:41:25.767891Z","shell.execute_reply.started":"2024-04-22T15:20:35.659814Z","shell.execute_reply":"2024-04-22T15:41:25.767057Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_meta_features","metadata":{"_uuid":"97b55495-8403-4ab3-bf62-c8c6ec88662c","_cell_guid":"4e06ef71-ae0c-4205-b254-0125739f3214","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:43:56.61515Z","iopub.execute_input":"2024-04-22T15:43:56.615965Z","iopub.status.idle":"2024-04-22T15:43:56.637025Z","shell.execute_reply.started":"2024-04-22T15:43:56.615926Z","shell.execute_reply":"2024-04-22T15:43:56.636005Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = pd.Series(meta_model.predict_proba(test_meta_features)[:, 1], index=df_test.index)","metadata":{"_uuid":"45576e94-f142-4e4e-bc4b-f781b3068dd5","_cell_guid":"e310ffbc-655f-474a-b4ac-e658d5ec144e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-22T15:44:01.573965Z","iopub.execute_input":"2024-04-22T15:44:01.574574Z","iopub.status.idle":"2024-04-22T15:44:02.228929Z","shell.execute_reply.started":"2024-04-22T15:44:01.574542Z","shell.execute_reply":"2024-04-22T15:44:02.227774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subm = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv')\ntest_base = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/test/test_base.csv')\ndf_subm[\"case_id\"] = test_base[\"case_id\"]\ndf_subm[\"score\"] = y_pred\ndf_subm.to_csv(\"submission.csv\")\ndf_subm","metadata":{"_uuid":"7afe1241-8249-4fe7-b0ad-c67ab52160c6","_cell_guid":"c67452cd-2b3d-4c70-8589-9688f350a5e8","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-04-23T05:17:52.365626Z","iopub.execute_input":"2024-04-23T05:17:52.366134Z","iopub.status.idle":"2024-04-23T05:17:52.386378Z","shell.execute_reply.started":"2024-04-23T05:17:52.3661Z","shell.execute_reply":"2024-04-23T05:17:52.385408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_uuid":"119c016d-604e-48c8-a260-30d5ce31d46d","_cell_guid":"3a004570-1272-473e-9211-39e2c3eaef3c","collapsed":false,"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]}]}