def test_can_clone_selective_regularization(self):
X = np.random.normal(size=(10, 3))
y = np.random.normal(size=(10, ))
model = SelectiveRegularization(unpenalized_inds=[0],
penalized_model=LassoCV(),
fit_intercept=True)
model.cv = 2
model2 = clone(model, safe=False)
assert model2.cv == 2
def test_can_pass_through_attributes(self):
X = np.random.normal(size=(10, 3))
y = np.random.normal(size=(10, ))
model = SelectiveRegularization(unpenalized_inds=[0],
penalized_model=LassoCV(),
fit_intercept=True)
# _penalized_inds is only set during fitting
with self.assertRaises(AttributeError):
inds = model._penalized_inds
# cv exists on penalized model
old_cv = model.cv
model.cv = 2
model.fit(X, y)
# now we can access _penalized_inds
assert np.array_equal(model._penalized_inds, [1, 2])
# check that we can read the cv attribute back out from the underlying model
assert model.cv == 2