Home Credit - Credit Risk Model Stability

本篇是对Kaggle上Home Credit - Credit Risk Model Stability竞赛中的开源代码VotingClassifier Home Credit的解读。原链接在VotingClassifier Home Credit (kaggle.com)。

%%writefile script.py
import sys
from pathlib import Path
import subprocess
import os
import gc
from glob import globimport numpy as np
import pandas as pd
import polars as pl
from datetime import datetime
import seaborn as sns
import matplotlib.pyplot as plt
import joblib
import warnings
warnings.filterwarnings('ignore')ROOT = '/kaggle/input/home-credit-credit-risk-model-stability'from sklearn.model_selection import TimeSeriesSplit, GroupKFold, StratifiedGroupKFold
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.metrics import roc_auc_score
import lightgbm as lgbclass Pipeline:def set_table_dtypes(df):for col in df.columns:if col in ["case_id", "WEEK_NUM", "num_group1", "num_group2"]:df = df.with_columns(pl.col(col).cast(pl.Int64))elif col in ["date_decision"]:df = df.with_columns(pl.col(col).cast(pl.Date))elif col[-1] in ("P", "A"):df = df.with_columns(pl.col(col).cast(pl.Float64))elif col[-1] in ("M",):df = df.with_columns(pl.col(col).cast(pl.String))elif col[-1] in ("D",):df = df.with_columns(pl.col(col).cast(pl.Date))return dfdef handle_dates(df):for col in df.columns:if col[-1] in ("D",):df = df.with_columns(pl.col(col) - pl.col("date_decision"))  #!!?df = df.with_columns(pl.col(col).dt.total_days()) # t - t-1df = df.drop("date_decision", "MONTH")return dfdef filter_cols(df):for col in df.columns:if (col not in ["target", "case_id", "WEEK_NUM"]) & (df[col].dtype == pl.String):freq = df[col].n_unique()if (freq == 1) | (freq > 200):df = df.drop(col)return dfclass Aggregator:#Please add or subtract features yourself, be aware that too many features will take up too much space.def num_expr(df):cols = [col for col in df.columns if col[-1] in ("P", "A")]expr_max = [pl.max(col).alias(f"max_{col}") for col in cols]return expr_maxdef date_expr(df):cols = [col for col in df.columns if col[-1] in ("D")]expr_max = [pl.max(col).alias(f"max_{col}") for col in cols]return  expr_maxdef str_expr(df):cols = [col for col in df.columns if col[-1] in ("M",)]expr_max = [pl.max(col).alias(f"max_{col}") for col in cols]return  expr_maxdef other_expr(df):cols = [col for col in df.columns if col[-1] in ("T", "L")]expr_max = [pl.max(col).alias(f"max_{col}") for col in cols]return  expr_max def count_expr(df):cols = [col for col in df.columns if "num_group" in col]expr_max = [pl.max(col).alias(f"max_{col}") for col in cols] return  expr_maxdef get_exprs(df):exprs = Aggregator.num_expr(df) + \Aggregator.date_expr(df) + \Aggregator.str_expr(df) + \Aggregator.other_expr(df) + \Aggregator.count_expr(df)return exprsdef read_file(path, depth=None):df = pl.read_parquet(path)df = df.pipe(Pipeline.set_table_dtypes)if depth in [1,2]:df = df.group_by("case_id").agg(Aggregator.get_exprs(df)) return dfdef read_files(regex_path, depth=None):chunks = []for path in glob(str(regex_path)):df = pl.read_parquet(path)df = df.pipe(Pipeline.set_table_dtypes)if depth in [1, 2]:df = df.group_by("case_id").agg(Aggregator.get_exprs(df))chunks.append(df)df = pl.concat(chunks, how="vertical_relaxed")df = df.unique(subset=["case_id"])return dfdef feature_eng(df_base, depth_0, depth_1, depth_2):df_base = (df_base.with_columns(month_decision = pl.col("date_decision").dt.month(),weekday_decision = pl.col("date_decision").dt.weekday(),))for i, df in enumerate(depth_0 + depth_1 + depth_2):df_base = df_base.join(df, how="left", on="case_id", suffix=f"_{i}")df_base = df_base.pipe(Pipeline.handle_dates)return df_basedef to_pandas(df_data, cat_cols=None):df_data = df_data.to_pandas()if cat_cols is None:cat_cols = list(df_data.select_dtypes("object").columns)df_data[cat_cols] = df_data[cat_cols].astype("category")return df_data, cat_colsdef reduce_mem_usage(df):""" iterate through all the columns of a dataframe and modify the data typeto reduce memory usage.        """start_mem = df.memory_usage().sum() / 1024**2for col in df.columns:col_type = df[col].dtypeif str(col_type)=="category":continueif col_type != object:c_min = df[col].min()c_max = df[col].max()if str(col_type)[:3] == 'int':if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:df[col] = df[col].astype(np.int8)elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:df[col] = df[col].astype(np.int16)elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:df[col] = df[col].astype(np.int32)elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:df[col] = df[col].astype(np.int64)  else:if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:df[col] = df[col].astype(np.float16)elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:df[col] = df[col].astype(np.float32)else:df[col] = df[col].astype(np.float64)else:continueend_mem = df.memory_usage().sum() / 1024**2    return dfROOT            = Path("/kaggle/input/home-credit-credit-risk-model-stability")TRAIN_DIR       = ROOT / "parquet_files" / "train"
TEST_DIR        = ROOT / "parquet_files" / "test"data_store = {"df_base": read_file(TRAIN_DIR / "train_base.parquet"),"depth_0": [read_file(TRAIN_DIR / "train_static_cb_0.parquet"),read_files(TRAIN_DIR / "train_static_0_*.parquet"),],"depth_1": [read_files(TRAIN_DIR / "train_applprev_1_*.parquet", 1),read_file(TRAIN_DIR / "train_tax_registry_a_1.parquet", 1),read_file(TRAIN_DIR / "train_tax_registry_b_1.parquet", 1),read_file(TRAIN_DIR / "train_tax_registry_c_1.parquet", 1),read_files(TRAIN_DIR / "train_credit_bureau_a_1_*.parquet", 1),read_file(TRAIN_DIR / "train_credit_bureau_b_1.parquet", 1),read_file(TRAIN_DIR / "train_other_1.parquet", 1),read_file(TRAIN_DIR / "train_person_1.parquet", 1),read_file(TRAIN_DIR / "train_deposit_1.parquet", 1),read_file(TRAIN_DIR / "train_debitcard_1.parquet", 1),],"depth_2": [read_file(TRAIN_DIR / "train_credit_bureau_b_2.parquet", 2),]
}df_train = feature_eng(**data_store)
del data_store
gc.collect()
df_train = df_train.pipe(Pipeline.filter_cols)
df_train, cat_cols = to_pandas(df_train)
df_train = reduce_mem_usage(df_train)
nums=df_train.select_dtypes(exclude='category').columns
from itertools import combinations, permutations
nans_df = df_train[nums].isna()
nans_groups={}
for col in nums:cur_group = nans_df[col].sum()try:nans_groups[cur_group].append(col)except:nans_groups[cur_group]=[col]
del nans_df; x=gc.collect()def reduce_group(grps):use = []for g in grps:mx = 0; vx = g[0]for gg in g:n = df_train[gg].nunique()if n>mx:mx = nvx = gguse.append(vx)return usedef group_columns_by_correlation(matrix, threshold=0.8):correlation_matrix = matrix.corr()groups = []remaining_cols = list(matrix.columns)while remaining_cols:col = remaining_cols.pop(0)group = [col]correlated_cols = [col]for c in remaining_cols:if correlation_matrix.loc[col, c] >= threshold:group.append(c)correlated_cols.append(c)groups.append(group)remaining_cols = [c for c in remaining_cols if c not in correlated_cols]return groupsuses=[]
for k,v in nans_groups.items():if len(v)>1:Vs = nans_groups[k]grps= group_columns_by_correlation(df_train[Vs], threshold=0.8)use=reduce_group(grps)uses=uses+useelse:uses=uses+v
df_train=df_train[uses]data_store = {"df_base": read_file(TEST_DIR / "test_base.parquet"),"depth_0": [read_file(TEST_DIR / "test_static_cb_0.parquet"),read_files(TEST_DIR / "test_static_0_*.parquet"),],"depth_1": [read_files(TEST_DIR / "test_applprev_1_*.parquet", 1),read_file(TEST_DIR / "test_tax_registry_a_1.parquet", 1),read_file(TEST_DIR / "test_tax_registry_b_1.parquet", 1),read_file(TEST_DIR / "test_tax_registry_c_1.parquet", 1),read_files(TEST_DIR / "test_credit_bureau_a_1_*.parquet", 1),read_file(TEST_DIR / "test_credit_bureau_b_1.parquet", 1),read_file(TEST_DIR / "test_other_1.parquet", 1),read_file(TEST_DIR / "test_person_1.parquet", 1),read_file(TEST_DIR / "test_deposit_1.parquet", 1),read_file(TEST_DIR / "test_debitcard_1.parquet", 1),],"depth_2": [read_file(TEST_DIR / "test_credit_bureau_b_2.parquet", 2),]
}df_test = feature_eng(**data_store)
del data_store
gc.collect()
df_test = df_test.select([col for col in df_train.columns if col != "target"])
df_test, cat_cols = to_pandas(df_test)
df_test = reduce_mem_usage(df_test)
gc.collect()df_train['target']=0
df_test['target']=1df_train=pd.concat([df_train,df_test])
df_train=reduce_mem_usage(df_train)y = df_train["target"]
df_train= df_train.drop(columns=["target", "case_id", "WEEK_NUM"])joblib.dump((df_train,y,df_test),'data.pkl')