def testCrossValidate(self, mock_init):
ma = DiscreteModelBridge()
model = mock.MagicMock(DiscreteModel, autospec=True, instance=True)
model.cross_validate.return_value = (
np.array([[1.0, -1], [2.0, -2]]),
np.stack((np.array([[1.0, 4.0],
[4.0, 6]]), np.array([[2.0, 5.0], [5.0, 7]]))),
)
ma.model = model
ma.parameters = ["x", "y", "z"]
ma.outcomes = ["a", "b"]
observation_data = ma._cross_validate(self.observation_features,
self.observation_data,
self.observation_features)
Xs = [
[[0, "foo", True], [1, "foo", True], [1, "bar", True]],
[[0, "foo", True], [1, "foo", True]],
]
Ys = [[[1.0], [2.0], [3.0]], [[-1.0], [-2.0]]]
Yvars = [[[1.0], [2.0], [3.0]], [[6.0], [7.0]]]
Xtest = [[0, "foo", True], [1, "foo", True], [1, "bar", True]]
# Transform to arrays:
model_cv_args = model.cross_validate.mock_calls[0][2]
for i, x in enumerate(model_cv_args["Xs_train"]):
self.assertEqual(x, Xs[i])
for i, y in enumerate(model_cv_args["Ys_train"]):
self.assertEqual(y, Ys[i])
for i, v in enumerate(model_cv_args["Yvars_train"]):
self.assertEqual(v, Yvars[i])
self.assertEqual(model_cv_args["X_test"], Xtest)
# Transform from arrays:
for i, od in enumerate(observation_data):
self.assertEqual(od, self.observation_data[i])
def testPredict(self, mock_init):
ma = DiscreteModelBridge()
model = mock.MagicMock(DiscreteModel, autospec=True, instance=True)
model.predict.return_value = (
np.array([[1.0, -1], [2.0, -2]]),
np.stack((np.array([[1.0, 4.0],
[4.0, 6]]), np.array([[2.0, 5.0], [5.0, 7]]))),
)
ma.model = model
ma.parameters = ["x", "y", "z"]
ma.outcomes = ["a", "b"]
observation_data = ma._predict(self.observation_features)
X = [[0, "foo", True], [1, "foo", True], [1, "bar", True]]
self.assertTrue(model.predict.mock_calls[0][2]["X"], X)
for i, od in enumerate(observation_data):
self.assertEqual(od, self.observation_data[i])
def testGen(self, mock_init):
# Test with constraints
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=True),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, False)
],
)
ma = DiscreteModelBridge()
model = mock.MagicMock(DiscreteModel, autospec=True, instance=True)
model.gen.return_value = ([[0.0, 2.0, 3.0], [1.0, 1.0,
3.0]], [1.0, 2.0])
ma.model = model
ma.parameters = ["x", "y", "z"]
ma.outcomes = ["a", "b"]
observation_features, weights, best_observation = ma._gen(
n=3,
search_space=self.search_space,
optimization_config=optimization_config,
pending_observations=self.pending_observations,
fixed_features=ObservationFeatures({}),
model_gen_options=self.model_gen_options,
)
gen_args = model.gen.mock_calls[0][2]
self.assertEqual(gen_args["n"], 3)
self.assertEqual(gen_args["parameter_values"],
[[0.0, 1.0], ["foo", "bar"], [True]])
self.assertTrue(
np.array_equal(gen_args["objective_weights"], np.array([-1.0,
0.0])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][0],
np.array([[0.0, -1.0]])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][1],
np.array([[-2]])))
self.assertEqual(gen_args["pending_observations"][0], [])
self.assertEqual(gen_args["pending_observations"][1],
[[0, "foo", True]])
self.assertEqual(gen_args["model_gen_options"], {"option": "yes"})
self.assertEqual(observation_features[0].parameters, {
"x": 0.0,
"y": 2.0,
"z": 3.0
})
self.assertEqual(observation_features[1].parameters, {
"x": 1.0,
"y": 1.0,
"z": 3.0
})
self.assertEqual(weights, [1.0, 2.0])
# Test with no constraints, no fixed feature, no pending observations
search_space = SearchSpace(self.parameters[:2])
optimization_config.outcome_constraints = []
ma.parameters = ["x", "y"]
ma._gen(
n=3,
search_space=search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options={},
)
gen_args = model.gen.mock_calls[1][2]
self.assertEqual(gen_args["parameter_values"],
[[0.0, 1.0], ["foo", "bar"]])
self.assertIsNone(gen_args["outcome_constraints"])
self.assertIsNone(gen_args["pending_observations"])
# Test validation
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=False),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, True)
],
)
with self.assertRaises(ValueError):
ma._gen(
n=3,
search_space=search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options={},
)