按阈值0.3/0.4/0.5/0.6计算通过率和坏账率

def solve():
    from pyodide.http import open_url
    from io import StringIO
    loans_featured_csv = open_url(____).read()
    import pandas as pd, numpy as np
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from io import StringIO
    df = pd.read_csv(StringIO(____))
    num_cols=[c for c in df.select_dtypes(____).columns if c not in ['____','____'] and not c.endswith(____)]
    X=df[num_cols].fillna(____); y=df['____']
    X_tr,X_te,y_tr,y_te=train_test_split(____)
    sc=StandardScaler(); Xs_tr=sc.fit_transform(____); Xs_te=sc.transform(____)
    lr=LogisticRegression(____); lr.fit(____)
    prob=lr.predict_proba(____)[:,____]
    print(____)
    for thr in [____,____,____,____]:
        pass_mask=prob<thr
        through_rate=pass_mask.mean()
        bad_rate=y_te.values[pass_mask].mean() if pass_mask.sum()>____ else ____
        print(____)