def testUpdate(self, mock_init):
sq_feat = ObservationFeatures({})
sq_data = self.observation_data[0]
ma = DiscreteModelBridge()
ma._training_data = self.observations
model = mock.create_autospec(DiscreteModel, instance=True)
ma._fit(model, self.search_space, self.observation_features,
self.observation_data)
ma._update([sq_feat], [sq_data])
self.assertEqual(ma.parameters, ["x", "y", "z"])
self.assertEqual(sorted(ma.outcomes), ["a", "b"])
self.assertEqual(ma.training_in_design, [True, True, True, False])
def testUpdate(self, mock_init):
ma = DiscreteModelBridge()
ma._training_data = self.observations
model = mock.create_autospec(DiscreteModel, instance=True)
ma._fit(model, self.search_space, self.observation_features,
self.observation_data)
new_feat = ObservationFeatures(parameters={
"x": 0,
"y": "bar",
"z": True
})
new_data = ObservationData(metric_names=["a"],
means=np.array([3.0]),
covariance=np.array([[3.0]]))
ma._update([new_feat], [new_data])
self.assertEqual(ma.parameters, ["x", "y", "z"])
self.assertEqual(sorted(ma.outcomes), ["a", "b"])