from sklearn.linear_model import ElasticNet
from sklearn.linear_model import SGDRegressor

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np

# データの分割
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# ElasticNet の初期化
# model = ElasticNet(alpha=1.0, l1_ratio=0.0, random_state=42)
model = SGDRegressor(
    penalty='elasticnet',
    alpha=1e-5,
    l1_ratio=0.0,
    max_iter=10000,
    tol=1e-3,
    random_state=42,
)

# 学習
model.fit(X_train, y_train)

# 予測
y_pred = model.predict(X_test)

# 評価指標
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(np.expm1(y_test), np.expm1(y_pred))
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error (MSE): {mse:.4f}")
print(f"R^2 Score: {r2:.4f}")
print(f"Mean Absolute Error (MAE): {mae:.4f}")