评分卡分段统计各段违约率和通过率

def solve():
    from pyodide.http import open_url
    from io import StringIO
    loans_clean_csv = open_url(____).read()
    loans_featured_csv = open_url(____).read()
    scorecard_data_csv = open_url(____).read()
    woe_table_csv = open_url(____).read()
    import pandas as pd, numpy as np, math
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from io import StringIO
    df = pd.read_csv(StringIO(____))
    woe_cols=[c for c in df.columns if c.endswith(____)]
    X=df[woe_cols].fillna(____); y=df['____']
    X_tr,X_te,y_tr,y_te=train_test_split(____)
    lr=LogisticRegression(____); lr.fit(____)
    prob=lr.predict_proba(____)[:,____].clip(____)
    B=____/math.log(____); A=____+B*math.log(____/____)
    score=A-B*np.log(prob/(____-prob))
    seg=pd.cut(____)
    result=pd.DataFrame(____).groupby(____).agg(count=(____),default_rate=(____)).reset_index()
    result['____']=result['____'].round(____)
    print(result.to_string(____))