对比随机森林与LR在同一测试集上的AUC和KS

def solve():
    from pyodide.http import open_url
    from io import StringIO
    loans_featured_csv = open_url(____).read()
    import pandas as pd
    from sklearn.linear_model import LogisticRegression
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import roc_auc_score
    from sklearn.preprocessing import StandardScaler
    from scipy.stats import ks_2samp
    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(____)
    rf=RandomForestClassifier(____); rf.fit(____)
    for name,prob in [('____',lr.predict_proba(____)[:,____]),('____',rf.predict_proba(____)[:,____])]:
        auc=roc_auc_score(____)
        ks,_=ks_2samp(____)
        print(____)