def testFitAndUpdate(self, mock_init):
sq_feat = ObservationFeatures({})
sq_data = self.observation_data[2]
sq_obs = Observation(
features=ObservationFeatures({}),
data=self.observation_data[2],
arm_name="status_quo",
)
ma = NumpyModelBridge()
ma._training_data = self.observations + [sq_obs]
model = mock.create_autospec(NumpyModel, instance=True)
# No out of design points allowed in direct calls to fit.
with self.assertRaises(ValueError):
ma._fit(
model,
self.search_space,
self.observation_features + [sq_feat],
self.observation_data + [sq_data],
)
ma._fit(model, self.search_space, self.observation_features,
self.observation_data)
self.assertEqual(ma.parameters, ["x", "z", "y"])
self.assertEqual(sorted(ma.outcomes), ["a", "b"])
Xs = {
"a": np.array([[0.2, 3.0, 1.2], [0.4, 3.0, 1.4], [0.6, 3.0, 1.6]]),
"b": np.array([[0.2, 3.0, 1.2], [0.4, 3.0, 1.4]]),
}
Ys = {
"a": np.array([[1.0], [2.0], [3.0]]),
"b": np.array([[-1.0], [-2.0]])
}
Yvars = {
"a": np.array([[1.0], [2.0], [3.0]]),
"b": np.array([[6.0], [7.0]])
}
# put fidelity parameter to the last column
bounds = [(0.0, 1.0), (0.0, 5.0), (1.0, 2.0)]
model_fit_args = model.fit.mock_calls[0][2]
for i, x in enumerate(model_fit_args["Xs"]):
self.assertTrue(np.array_equal(x, Xs[ma.outcomes[i]]))
for i, y in enumerate(model_fit_args["Ys"]):
self.assertTrue(np.array_equal(y, Ys[ma.outcomes[i]]))
for i, v in enumerate(model_fit_args["Yvars"]):
self.assertTrue(np.array_equal(v, Yvars[ma.outcomes[i]]))
self.assertEqual(model_fit_args["bounds"], bounds)
self.assertEqual(model_fit_args["feature_names"], ["x", "z", "y"])
# And update
ma._update(
observation_features=self.observation_features,
observation_data=self.observation_data,
)
# Calling _update requires passing ALL data.
model_update_args = model.update.mock_calls[0][2]
for i, x in enumerate(model_update_args["Xs"]):
self.assertTrue(np.array_equal(x, Xs[ma.outcomes[i]]))
for i, y in enumerate(model_update_args["Ys"]):
self.assertTrue(np.array_equal(y, Ys[ma.outcomes[i]]))
for i, v in enumerate(model_update_args["Yvars"]):
self.assertTrue(np.array_equal(v, Yvars[ma.outcomes[i]]))
def testFitAndUpdate(self, mock_init):
sq_feat = ObservationFeatures({})
sq_data = self.observation_data[2]
sq_obs = Observation(
features=ObservationFeatures({}),
data=self.observation_data[2],
arm_name="status_quo",
)
ma = NumpyModelBridge()
ma._training_data = self.observations + [sq_obs]
model = mock.create_autospec(NumpyModel, instance=True)
ma._fit(
model,
self.search_space,
self.observation_features + [sq_feat],
self.observation_data + [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])
Xs = {
"a": np.array([[0.2, 1.2, 3.0], [0.4, 1.4, 3.0], [0.6, 1.6, 3]]),
"b": np.array([[0.2, 1.2, 3.0], [0.4, 1.4, 3.0]]),
}
Ys = {
"a": np.array([[1.0], [2.0], [3.0]]),
"b": np.array([[-1.0], [-2.0]])
}
Yvars = {
"a": np.array([[1.0], [2.0], [3.0]]),
"b": np.array([[6.0], [7.0]])
}
bounds = [(0.0, 1.0), (1.0, 2.0), (0.0, 5.0)]
model_fit_args = model.fit.mock_calls[0][2]
for i, x in enumerate(model_fit_args["Xs"]):
self.assertTrue(np.array_equal(x, Xs[ma.outcomes[i]]))
for i, y in enumerate(model_fit_args["Ys"]):
self.assertTrue(np.array_equal(y, Ys[ma.outcomes[i]]))
for i, v in enumerate(model_fit_args["Yvars"]):
self.assertTrue(np.array_equal(v, Yvars[ma.outcomes[i]]))
self.assertEqual(model_fit_args["bounds"], bounds)
self.assertEqual(model_fit_args["feature_names"], ["x", "y", "z"])
# And update
ma.training_in_design.extend([True, True, True, True])
ma._update(
observation_features=self.observation_features + [sq_feat],
observation_data=self.observation_data + [sq_data],
)
self.assertEqual(ma.training_in_design, [True, True, True, False] * 2)
model_update_args = model.update.mock_calls[0][2]
for i, x in enumerate(model_update_args["Xs"]):
self.assertTrue(np.array_equal(x, Xs[ma.outcomes[i]]))
for i, y in enumerate(model_update_args["Ys"]):
self.assertTrue(np.array_equal(y, Ys[ma.outcomes[i]]))
for i, v in enumerate(model_update_args["Yvars"]):
self.assertTrue(np.array_equal(v, Yvars[ma.outcomes[i]]))
