在python中保存多个不同的多项式回归对象

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from joblib import dump, load

# 创建数据
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([1, 4, 9, 16, 25])

# 创建多项式特征
poly_features = PolynomialFeatures(degree=2)

# 创建多项式回归模型
poly_model = LinearRegression()
X_poly = poly_features.fit_transform(X)
poly_model.fit(X_poly, y)

# 创建另一个多项式回归模型
poly_model_2 = LinearRegression()
X_poly_2 = poly_features.fit_transform(X)
poly_model_2.fit(X_poly_2, y)

# 保存多个多项式回归对象
models = {
    'poly_model': poly_model,
    'poly_model_2': poly_model_2
}
dump(models, 'poly_models.joblib')

# 加载多个多项式回归对象
loaded_models = load('poly_models.joblib')
poly_model_loaded = loaded_models['poly_model']
poly_model_2_loaded = loaded_models['poly_model_2']

# 使用加载的模型进行预测
X_test = np.array([6]).reshape(-1, 1)
X_test_poly = poly_features.transform(X_test)
print("Prediction using poly_model:", poly_model_loaded.predict(X_test_poly))
print("Prediction using poly_model_2:", poly_model_2_loaded.predict(X_test_poly))