给个stacking模型融合的代码

好的，下面是一个简单的 stacking 模型融合的代码示例：

import numpy as np
from sklearn.model_selection import KFold
from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin, clone

class StackingRegressor(BaseEstimator, RegressorMixin, TransformerMixin):
    def __init__(self, base_models, meta_model, n_folds=5):
        self.base_models = base_models
        self.meta_model = meta_model
        self.n_folds = n_folds

    def fit(self, X, y):
        self.base_models_ = [list() for x in self.base_models]
        self.meta_model_ = clone(self.meta_model)

        kfold = KFold(n_splits=self.n_folds, shuffle=True)

        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))

        for i, model in enumerate(self.base_models):
            for train_index, holdout_index in kfold.split(X, y):
                instance = clone(model)
                self.base_models_[i].append(instance)

                instance.fit(X[train_index], y[train_index])
                y_pred = instance.predict(X[holdout_index])
                out_of_fold_predictions[holdout_index, i] = y_pred

        self.meta_model_.fit(out_of_fold_predictions, y)
        return self

    def predict(self, X):
        meta_features = np.column_stack([
            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)
            for base_models in self.base_models_
        ])
        return self.meta_model_.predict(meta_features)

使用方法：

from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

# 加载数据集
boston = load_boston()
X, y = boston.data, boston.target

# 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=0)

# 定义基本模型
base_models = [RandomForestRegressor(n_estimators=10, random_state=0),
               LinearRegression()]

# 定义元模型
meta_model = LinearRegression()

# 定义 stacking 模型
stacking_model = StackingRegressor(base_models, meta_model)

# 训练 stacking 模型
stacking_model.fit(X_train, y_train)

# 在测试集上预测
y_pred = stacking_model.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print("MSE: %.4f" % mse)

注意：代码中的这个 `StackingRegressor` 类只是一个简单的示例，如果需要进行更复杂的模型融合，需要根据具体情况进行修改和优化。