def testGen(self, mock_init, mock_gen): # Test with constraints modelbridge = RandomModelBridge() modelbridge.parameters = ["x", "y", "z"] modelbridge.transforms = OrderedDict() modelbridge.model = RandomModel() observation_features, weights, best_obsf, _ = modelbridge._gen( n=3, search_space=self.search_space, pending_observations={}, fixed_features=ObservationFeatures({"z": 3.0}), optimization_config=None, model_gen_options=self.model_gen_options, ) gen_args = mock_gen.mock_calls[0][2] self.assertEqual(gen_args["n"], 3) self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0), (0.0, 5.0)]) self.assertTrue( np.array_equal( gen_args["linear_constraints"][0], np.array([[1.0, -1, 0.0], [-1.0, 0.0, -1.0]]), )) self.assertTrue( np.array_equal(gen_args["linear_constraints"][1], np.array([[0.0], [-3.5]]))) self.assertEqual(gen_args["fixed_features"], {2: 3.0}) self.assertEqual(gen_args["model_gen_options"], {"option": "yes"}) self.assertEqual(observation_features[0].parameters, { "x": 1.0, "y": 2.0, "z": 3.0 }) self.assertEqual(observation_features[1].parameters, { "x": 3.0, "y": 4.0, "z": 3.0 }) self.assertTrue(np.array_equal(weights, np.array([1.0, 2.0]))) # Test with no constraints, no fixed feature, no pending observations search_space = SearchSpace(self.parameters[:2]) modelbridge.parameters = ["x", "y"] modelbridge._gen( n=3, search_space=search_space, pending_observations={}, fixed_features=ObservationFeatures({}), optimization_config=None, model_gen_options=self.model_gen_options, ) gen_args = mock_gen.mock_calls[1][2] self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0)]) self.assertIsNone(gen_args["linear_constraints"]) self.assertIsNone(gen_args["fixed_features"])
def testCrossValidate(self, mock_init): modelbridge = RandomModelBridge() modelbridge.transforms = OrderedDict() modelbridge.parameters = ["x", "y", "z"] with self.assertRaises(NotImplementedError): modelbridge._cross_validate([], [], [])
def testPredict(self, mock_init): modelbridge = RandomModelBridge() modelbridge.transforms = OrderedDict() modelbridge.parameters = ["x", "y", "z"] with self.assertRaises(NotImplementedError): modelbridge._predict([])