输出决策树规则并统计高风险叶节点

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.tree import DecisionTreeClassifier,export_text
    from sklearn.model_selection import train_test_split
    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(____)
    dt=DecisionTreeClassifier(____)
    dt.fit(____)
    print(export_text(dt,feature_names=list(____)))
    leaf_mask=dt.tree_.children_left==-____
    leaf_values=dt.tree_.value[leaf_mask]
    leaf_dr=leaf_values[:,____,____]/(____)
    print(____)