def test_best_point(
self,
_mock_gen,
_mock_best_point,
_mock_fit,
_mock_predict,
_mock_gen_arms,
_mock_unwrap,
_mock_obs_from_data,
):
exp = Experiment(get_search_space_for_range_value(), "test")
modelbridge = ArrayModelBridge(get_search_space_for_range_value(),
NumpyModel(), [t1, t2], exp, 0)
self.assertEqual(list(modelbridge.transforms.keys()),
["Cast", "t1", "t2"])
# _fit is mocked, which typically sets this.
modelbridge.outcomes = ["a"]
run = modelbridge.gen(
n=1,
optimization_config=OptimizationConfig(
objective=Objective(metric=Metric("a"), minimize=False),
outcome_constraints=[],
),
)
arm, predictions = run.best_arm_predictions
self.assertEqual(arm.parameters, {})
self.assertEqual(predictions[0], {"m": 1.0})
self.assertEqual(predictions[1], {"m": {"m": 2.0}})
# test check that optimization config is required
with self.assertRaises(ValueError):
run = modelbridge.gen(n=1, optimization_config=None)
def test_best_point(
self,
_mock_gen,
_mock_best_point,
_mock_fit,
_mock_predict,
_mock_gen_arms,
_mock_unwrap,
_mock_obs_from_data,
):
exp = Experiment(search_space=get_search_space_for_range_value(),
name="test")
modelbridge = ArrayModelBridge(
search_space=get_search_space_for_range_value(),
model=NumpyModel(),
transforms=[t1, t2],
experiment=exp,
data=Data(),
)
self.assertEqual(list(modelbridge.transforms.keys()),
["Cast", "t1", "t2"])
# _fit is mocked, which typically sets this.
modelbridge.outcomes = ["a"]
run = modelbridge.gen(
n=1,
optimization_config=OptimizationConfig(
objective=Objective(metric=Metric("a"), minimize=False),
outcome_constraints=[],
),
)
arm, predictions = run.best_arm_predictions
self.assertEqual(arm.parameters, {})
self.assertEqual(predictions[0], {"m": 1.0})
self.assertEqual(predictions[1], {"m": {"m": 2.0}})
# test check that optimization config is required
with self.assertRaises(ValueError):
run = modelbridge.gen(n=1, optimization_config=None)
# test optimization config validation - raise error when
# ScalarizedOutcomeConstraint contains a metric that is not in the outcomes
with self.assertRaises(ValueError):
run = modelbridge.gen(
n=1,
optimization_config=OptimizationConfig(
objective=Objective(metric=Metric("a"), minimize=False),
outcome_constraints=[
ScalarizedOutcomeConstraint(
metrics=[Metric("wrong_metric_name")],
weights=[1.0],
op=ComparisonOp.LEQ,
bound=0,
)
],
),
)
def test_importances(
self,
_mock_feature_importances,
_mock_fit,
_mock_predict,
_mock_gen_arms,
_mock_unwrap,
_mock_obs_from_data,
):
exp = Experiment(get_search_space_for_range_value(), "test")
modelbridge = ArrayModelBridge(get_search_space_for_range_value(),
NumpyModel(), [t1, t2], exp, 0)
modelbridge.outcomes = ["a", "b"]
self.assertEqual(modelbridge.feature_importances("a"), {"x": [1.0]})
self.assertEqual(modelbridge.feature_importances("b"), {"x": [2.0]})
def test_gen_on_experiment_with_imm_ss_and_opt_conf(self, _, __):
exp = get_experiment_for_value()
exp._properties[Keys.IMMUTABLE_SEARCH_SPACE_AND_OPT_CONF] = True
exp.optimization_config = get_optimization_config_no_constraints()
ss = get_search_space_for_range_value()
modelbridge = ModelBridge(search_space=ss,
model=Model(),
transforms=[],
experiment=exp)
self.assertTrue(
modelbridge._experiment_has_immutable_search_space_and_opt_config)
gr = modelbridge.gen(1)
self.assertIsNone(gr.optimization_config)
self.assertIsNone(gr.search_space)
def testGenWithDefaults(self, _, mock_gen):
exp = get_experiment_for_value()
exp.optimization_config = get_optimization_config_no_constraints()
ss = get_search_space_for_range_value()
modelbridge = ModelBridge(ss, None, [], exp)
modelbridge.gen(1)
mock_gen.assert_called_with(
modelbridge,
n=1,
search_space=ss,
fixed_features=ObservationFeatures(parameters={}),
model_gen_options=None,
optimization_config=OptimizationConfig(
objective=Objective(metric=Metric("test_metric"),
minimize=False),
outcome_constraints=[],
),
pending_observations={},
)
