如何在GridSearchCV中使用感知器对数据进行预处理？

from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Perceptron
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 创建一个管道，包括预处理步骤和模型
pipeline = Pipeline([
    ('scaler', StandardScaler()),  # 特征缩放
    ('perceptron', Perceptron())  # 感知器模型
])

# 定义参数网格
parameters = {
    'perceptron__penalty': ['l2', 'l1', 'elasticnet'],
    'perceptron__alpha': [0.0001, 0.001, 0.01],
    'perceptron__max_iter': [1000, 2000]
}

# 创建 GridSearchCV 对象
grid_search = GridSearchCV(pipeline, parameters, cv=5)

# 训练模型
grid_search.fit(X, y)

# 输出最佳参数
print("Best parameters set found on development set:")
print(grid_search.best_params_)