def testFit(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)
self.assertEqual(ma.parameters, ["x", "y", "z"])
self.assertEqual(sorted(ma.outcomes), ["a", "b"])
Xs = {
"a": [[0, "foo", True], [1, "foo", True], [1, "bar", True]],
"b": [[0, "foo", True], [1, "foo", True]],
}
Ys = {"a": [[1.0], [2.0], [3.0]], "b": [[-1.0], [-2.0]]}
Yvars = {"a": [[1.0], [2.0], [3.0]], "b": [[6.0], [7.0]]}
parameter_values = [[0.0, 1.0], ["foo", "bar"], [True]]
model_fit_args = model.fit.mock_calls[0][2]
for i, x in enumerate(model_fit_args["Xs"]):
self.assertEqual(x, Xs[ma.outcomes[i]])
for i, y in enumerate(model_fit_args["Ys"]):
self.assertEqual(y, Ys[ma.outcomes[i]])
for i, v in enumerate(model_fit_args["Yvars"]):
self.assertEqual(v, Yvars[ma.outcomes[i]])
self.assertEqual(model_fit_args["parameter_values"], parameter_values)
sq_feat = ObservationFeatures({})
sq_data = self.observation_data[0]
with self.assertRaises(ValueError):
ma._fit(
model,
self.search_space,
self.observation_features + [sq_feat],
self.observation_data + [sq_data],
)
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"])