在scikit learn中实现自定义损失函数

from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.metrics import mean_squared_error

class CustomRegressor(BaseEstimator, RegressorMixin):
    def __init__(self, loss_func):
        self.loss_func = loss_func
    
    def fit(self, X, y):
        self.X_ = X
        self.y_ = y
        return self
    
    def predict(self, X):
        return X.dot(self.coef_)
    
    def score(self, X, y):
        y_pred = self.predict(X)
        return -self.loss_func(y, y_pred)

def custom_loss(y_true, y_pred):
    return mean_squared_error(y_true, y_pred)

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

# 生成示例数据
X, y = make_regression(n_samples=100, n_features=1, noise=0.1, random_state=42)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建自定义回归器对象
custom_regressor = CustomRegressor(loss_func=custom_loss)

# 训练模型
custom_regressor.fit(X_train, y_train)

# 预测
y_pred = custom_regressor.predict(X_test)

# 评估模型性能
score = custom_regressor.score(X_test, y_test)
print("Custom Loss Score:", score)