import sklearn
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import (
    mean_absolute_error,
    mean_squared_error,
    r2_score,
    root_mean_squared_error,
)
from sklearn.model_selection import train_test_split


X, y = load_diabetes(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42,
)

model = Ridge(alpha=1.0)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

mae = mean_absolute_error(y_test, predictions)
mse = mean_squared_error(y_test, predictions)
rmse = root_mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f"scikit-learn {sklearn.__version__}")
print(f"test rows: {len(y_test)}")
print(f"mean absolute error: {mae:.2f}")
print(f"mean squared error: {mse:.2f}")
print(f"root mean squared error: {rmse:.2f}")
print(f"r2 score: {r2:.3f}")
print(f"first actual/predicted: {y_test[0]:.1f} / {predictions[0]:.1f}")