如何从scikit-learn决策树中提取决策规则？

from sklearn.tree import DecisionTreeClassifier

def extract_decision_rules(tree, feature_names):
    rules = []

    def recurse(node, rule):
        if tree.feature[node] != -2:  # -2表示叶节点
            name = feature_names[tree.feature[node]]
            threshold = tree.threshold[node]
            # 对于每个分支，递归地提取规则
            recurse(tree.children_left[node], rule + f" and {name} <= {threshold}")
            recurse(tree.children_right[node], rule + f" and {name} > {threshold}")
        else:
            # 叶节点，添加规则到列表
            target = tree.value[node].argmax() if tree.value[node].ndim > 1 else tree.value[node]
            rules.append((rule, target))

    recurse(0, "")
    return rules

# 示例使用
X = [[0, 0], [1, 1]]
y = [0, 1]
feature_names = ['feature_1', 'feature_2']
clf = DecisionTreeClassifier()
clf.fit(X, y)
rules = extract_decision_rules(clf.tree_, feature_names)
for rule in rules:
    print(f"Rule: {rule[0]} -> Class: {rule[1]}")