def testFullFactorial(self):
generator = FullFactorialGenerator()
parameter_values = [[1, 2], ["foo", "bar"]]
generated_points, weights, _ = generator.gen(
n=-1, parameter_values=parameter_values, objective_weights=np.ones(1)
)
expected_points = [[1, "foo"], [1, "bar"], [2, "foo"], [2, "bar"]]
self.assertEqual(generated_points, expected_points)
self.assertEqual(weights, [1 for _ in range(len(expected_points))])
def testFullFactorialFixedFeatures(self):
generator = FullFactorialGenerator(max_cardinality=5, check_cardinality=True)
parameter_values = [[1, 2], ["foo", "bar"]]
generated_points, weights, _ = generator.gen(
n=-1,
parameter_values=parameter_values,
objective_weights=np.ones(1),
fixed_features={1: "foo"},
)
expected_points = [[1, "foo"], [2, "foo"]]
self.assertEqual(generated_points, expected_points)
self.assertEqual(weights, [1 for _ in range(len(expected_points))])
def get_factorial(search_space: SearchSpace) -> DiscreteModelBridge:
"""Instantiates a factorial generator."""
return DiscreteModelBridge(
search_space=search_space,
data=Data(),
model=FullFactorialGenerator(),
transforms=Discrete_X_trans,
)
def get_modelbridge(mock_gen_arms,
mock_observations_from_data,
status_quo_name: Optional[str] = None) -> ModelBridge:
exp = get_experiment()
modelbridge = ModelBridge(
search_space=get_search_space(),
model=FullFactorialGenerator(),
experiment=exp,
data=get_data(),
status_quo_name=status_quo_name,
)
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[get_observation().data],
)
return modelbridge
def testFullFactorialValidation(self):
# Raise error because cardinality exceeds max cardinality
generator = FullFactorialGenerator(max_cardinality=5, check_cardinality=True)
parameter_values = [[1, 2], ["foo", "bar"], [True, False]]
with self.assertRaises(ValueError):
generated_points, weights, _ = generator.gen(
n=-1, parameter_values=parameter_values, objective_weights=np.ones(1)
)
# Raise error because n != -1
generator = FullFactorialGenerator()
parameter_values = [[1, 2], ["foo", "bar"]]
with self.assertRaises(ValueError):
generated_points, weights, _ = generator.gen(
n=5, parameter_values=parameter_values, objective_weights=np.ones(1)
)