def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def __init__(self,
alphas=[0.1, 1.0, 10.0],
fit_intercept=True,
normalize=False,
scoring=None,
cv=None,
gcv_mode=None,
store_cv_values=False):
self._hyperparams = {
'alphas': alphas,
'fit_intercept': fit_intercept,
'normalize': normalize,
'scoring': scoring,
'cv': cv,
'gcv_mode': gcv_mode,
'store_cv_values': store_cv_values
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self,
alpha=1.0,
fit_intercept=True,
normalize=False,
copy_X=True,
max_iter=None,
tol=0.001,
solver='auto',
random_state=None):
self._hyperparams = {
'alpha': alpha,
'fit_intercept': fit_intercept,
'normalize': normalize,
'copy_X': copy_X,
'max_iter': max_iter,
'tol': tol,
'solver': solver,
'random_state': random_state
}
self._wrapped_model = SKLModel(**self._hyperparams)