如何告诉SciKit LinearRegression模型预测值不能小于零？

from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
import numpy as np

# 自定义转换函数，将小于零的值设置为零
def non_negative_transform(y):
    return np.maximum(y, 0)

# 创建LinearRegression模型
model = LinearRegression()

# 创建Pipeline，将转换函数应用到模型的输出上
pipeline = Pipeline([
    ('model', model),
    ('transformer', FunctionTransformer(non_negative_transform))
])

# 训练模型
X_train = ...
y_train = ...
pipeline.fit(X_train, y_train)

# 进行预测
X_test = ...
y_pred = pipeline.predict(X_test)

from sklearn.linear_model import LinearRegression
import numpy as np

# 加载数据
X = ...
y = ...

# 将小于零的目标变量值设置为零
y[y < 0] = 0

# 创建LinearRegression模型并训练
model = LinearRegression()
model.fit(X, y)

# 进行预测
X_test = ...
y_pred = model.predict(X_test)