def testdiscreteModelCrossValidate(self):
discrete_model = DiscreteModel()
with self.assertRaises(NotImplementedError):
discrete_model.cross_validate(Xs_train=[[[0]]],
Ys_train=[[1]],
Yvars_train=[[1]],
X_test=[[1]])
def testDiscreteModelFit(self):
discrete_model = DiscreteModel()
discrete_model.fit(
Xs=[[[0]]],
Ys=[[0]],
Yvars=[[1]],
parameter_values=[[0, 1]],
outcome_names=[],
)
def testdiscreteModelGen(self):
discrete_model = DiscreteModel()
with self.assertRaises(NotImplementedError):
discrete_model.gen(
n=1, parameter_values=[[0, 1]], objective_weights=np.array([1])
)
def testdiscreteModelPredict(self):
discrete_model = DiscreteModel()
with self.assertRaises(NotImplementedError):
discrete_model.predict([[0]])
def test_discrete_model_feature_importances(self):
discrete_model = DiscreteModel()
with self.assertRaises(NotImplementedError):
discrete_model.feature_importances()
def test_discrete_model_get_state(self):
discrete_model = DiscreteModel()
self.assertEqual(discrete_model._get_state(), {})
