我想要将AUC wrt绘制到决策树的深度，但min_samples_split值在变化

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score

# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 设置参数范围
depths = range(1, 21)
min_samples_splits = [2, 5, 10, 20]
auc_values = {ms: [] for ms in min_samples_splits}

# 训练模型并计算AUC
for depth in depths:
    for ms in min_samples_splits:
        clf = DecisionTreeClassifier(max_depth=depth, min_samples_split=ms, random_state=42)
        clf.fit(X_train, y_train)
        y_pred_proba = clf.predict_proba(X_test)[:, 1]
        auc = roc_auc_score(y_test, y_pred_proba)
        auc_values[ms].append(auc)

# 绘制图形
plt.figure(figsize=(12, 6))
for ms in min_samples_splits:
    plt.plot(depths, auc_values[ms], label=f'min_samples_split={ms}')
plt.xlabel('Tree Depth')
plt.ylabel('AUC')
plt.title('AUC vs Tree Depth for Different min_samples_split Values')
plt.legend()
plt.grid(True)
plt.show()