def test_LearnerSCCS_score(self):
lrn = ConvSCCS(n_lags=self.n_lags,
penalized_features=[],
random_state=self.seed)
lrn.fit(self.features, self.labels, self.censoring)
self.assertEqual(lrn.score(),
lrn.score(self.features, self.labels, self.censoring))
def test_LearnerSCCS_fit_KFold_CV(self):
lrn = ConvSCCS(n_lags=self.n_lags,
penalized_features=np.arange(self.n_features),
random_state=self.seed,
C_tv=1e-1,
C_group_l1=1e-1)
lrn.fit(self.features, self.labels, self.censoring)
score = lrn.score()
tv_range = (-5, -1)
groupl1_range = (-5, -1)
lrn.fit_kfold_cv(self.features,
self.labels,
self.censoring,
C_tv_range=tv_range,
C_group_l1_range=groupl1_range,
n_cv_iter=4)
self.assertTrue(lrn.score() <= score)
