stacking模型融合代码
时间: 2023-09-05 09:13:25 浏览: 39
以下是一个简单的 stacking 模型融合的示例代码:
```
# 导入必要的库
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
# 定义基础模型
lr = LinearRegression()
ridge = Ridge(alpha=0.5)
rf = RandomForestRegressor(n_estimators=20, random_state=42)
gbdt = GradientBoostingRegressor(n_estimators=50, random_state=42)
# 定义元模型
meta_model = LinearRegression()
# 定义 stacking 函数
def stacking(model, x_train, y_train, x_test, n_folds):
"""Stacking模型融合"""
# 划分数据集为训练集和测试集
train_num, test_num = x_train.shape[0], x_test.shape[0]
oof_train = np.zeros((train_num,))
oof_test = np.zeros((test_num,))
# 用 KFold 进行交叉验证
kf = KFold(n_splits=n_folds)
for i, (train_index, val_index) in enumerate(kf.split(x_train)):
x_train_kf, y_train_kf = x_train[train_index], y_train[train_index]
x_val_kf, y_val_kf = x_train[val_index], y_train[val_index]
# 训练基础模型并在验证集上预测
model.fit(x_train_kf, y_train_kf)
oof_train[val_index] = model.predict(x_val_kf)
oof_test += model.predict(x_test)
# 对测试集上的预测结果求平均
oof_test /= n_folds
# 计算在训练集上的误差
train_rmse = np.sqrt(mean_squared_error(y_train, oof_train))
# 训练元模型并在测试集上预测
meta_model.fit(oof_train.reshape(-1, 1), y_train)
meta_predict = meta_model.predict(oof_test.reshape(-1, 1))
return meta_predict, train_rmse
# 使用 stacking 进行模型融合
stacked_train_pred, stacked_train_rmse = stacking(lr, x_train, y_train, x_test, n_folds=5)
# 输出在训练集上的误差
print("Stacking RMSE: ", stacked_train_rmse)
```
其中,`x_train` 和 `y_train` 分别为训练集的特征和标签,`x_test` 为测试集特征。`lr`、`ridge`、`rf`、`gbdt` 分别为用于 stacking 的基础模型,`meta_model` 为元模型。在 `stacking` 函数中,我们使用 KFold 进行交叉验证,将基础模型在验证集上的预测结果作为元特征,最终使用元特征训练元模型,并在测试集上进行预测。最后输出在训练集上的误差。