#library imports
from sklearn.linear_model import Ridge
import numpy as np 
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

#Get Dataset and set X and y data.
(X_data, y_data) = load_dataset()

#splitting
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, random_state = 0)

#creating a model with any alpha value for regularization.
ridge = Ridge(alpha=20.0)

#using model (fit method).
ridge.fit(X_train, y_train)

#results and attributes
print('ridge regression linear model intercept: {}'
     .format(ridge.intercept_))
print('ridge regression linear model coeff:\n{}'
     .format(ridge.coef_))
print('R-squared score (training): {:.3f}'
     .format(ridge.score(X_train, y_train)))
print('R-squared score (test): {:.3f}'
     .format(ridge.score(X_test, y_test)))
print('Number of non-zero features: {}'
     .format(np.sum(ridge.coef_ != 0)))