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_transform_callback_int(self, *_):
exp = get_branin_experiment(with_batch=True)
data = get_branin_data(trial_indices=exp.trials)
parameters = [
RangeParameter(name="x1",
parameter_type=ParameterType.INT,
lower=1,
upper=10),
RangeParameter(name="x2",
parameter_type=ParameterType.INT,
lower=5,
upper=15),
]
gpei = TorchModelBridge(
experiment=exp,
data=data,
search_space=SearchSpace(parameters=parameters),
model=BotorchModel(),
transforms=[IntToFloat],
torch_dtype=torch.double,
fit_out_of_design=True,
)
transformed = gpei._transform_callback([5.4, 7.6])
self.assertTrue(np.allclose(transformed, [5, 8]))
np_mb = ArrayModelBridge(
experiment=exp,
data=exp.fetch_data(),
search_space=SearchSpace(parameters=parameters),
model=NumpyModel(),
transforms=[IntToFloat],
)
transformed = np_mb._transform_callback(np.array([5.4, 7.6]))
self.assertTrue(np.allclose(transformed, [5, 8]))
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()), ["t1", "t2"])
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}})
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_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_candidate_metadata_propagation(self, mock_model_fit,
mock_model_update, mock_model_gen):
exp = get_branin_experiment(with_status_quo=True, with_batch=True)
# Check that the metadata is correctly re-added to observation
# features during `fit`.
preexisting_batch_gr = exp.trials[0]._generator_run_structs[
0].generator_run
preexisting_batch_gr._candidate_metadata_by_arm_signature = {
preexisting_batch_gr.arms[0].signature: {
"preexisting_batch_cand_metadata": "some_value"
}
}
modelbridge = ArrayModelBridge(
search_space=exp.search_space,
experiment=exp,
model=NumpyModel(),
data=get_branin_data(),
)
self.assertTrue(
np.array_equal(
mock_model_fit.call_args[1].get("Xs"),
np.array([[list(exp.trials[0].arms[0].parameters.values())]]),
))
self.assertEqual(
mock_model_fit.call_args[1].get("candidate_metadata"),
[[{
"preexisting_batch_cand_metadata": "some_value"
}]],
)
# Check that `gen` correctly propagates the metadata to the GR.
gr = modelbridge.gen(n=1)
self.assertEqual(
gr.candidate_metadata_by_arm_signature,
{
gr.arms[0].signature: {
"some_key": "some_value_0"
},
gr.arms[1].signature: {
"some_key": "some_value_1"
},
},
)
# Check that the metadata is correctly re-added to observation
# features during `update`.
batch = exp.new_batch_trial(generator_run=gr)
modelbridge.update(
experiment=exp,
new_data=get_branin_data(trial_indices=[batch.index]))
self.assertTrue(
np.array_equal(
mock_model_update.call_args[1].get("Xs"),
np.array(
[[list(exp.trials[0].arms[0].parameters.values()), [1,
2]]]),
))
self.assertEqual(
mock_model_update.call_args[1].get("candidate_metadata"),
[[
{
"preexisting_batch_cand_metadata": "some_value"
},
# new data contained data just for arm '1_0', not for '1_1',
# so we don't expect to see '{"some_key": "some_value_1"}'
# in candidate metadata.
{
"some_key": "some_value_0"
},
]],
)
# Check that `None` candidate metadata is handled correctly.
mock_model_gen.return_value = (
np.array([[2, 4], [3, 5]]),
np.array([1, 2]),
None,
{},
)
gr = modelbridge.gen(n=1)
self.assertIsNone(gr.candidate_metadata_by_arm_signature)
# Check that the metadata is correctly re-added to observation
# features during `update`.
batch = exp.new_batch_trial(generator_run=gr)
modelbridge.update(
experiment=exp,
new_data=get_branin_data(trial_indices=[batch.index]))
self.assertTrue(
np.array_equal(
mock_model_update.call_args[1].get("Xs"),
np.array([[
list(exp.trials[0].arms[0].parameters.values()), [1, 2],
[2, 4]
]]),
))
self.assertEqual(
mock_model_update.call_args[1].get("candidate_metadata"),
[[
{
"preexisting_batch_cand_metadata": "some_value"
},
{
"some_key": "some_value_0"
},
{},
]],
)
# Check that no candidate metadata is handled correctly.
exp = get_branin_experiment(with_status_quo=True)
modelbridge = ArrayModelBridge(search_space=exp.search_space,
experiment=exp,
model=NumpyModel())
# Hack in outcome names to bypass validation (since we instantiated model
# without data).
modelbridge.outcomes = modelbridge._metric_names = next(
iter(exp.metrics))
gr = modelbridge.gen(n=1)
self.assertIsNone(
mock_model_fit.call_args[1].get("candidate_metadata"))
self.assertIsNone(gr.candidate_metadata_by_arm_signature)
batch = exp.new_batch_trial(generator_run=gr)
modelbridge.update(
experiment=exp,
new_data=get_branin_data(trial_indices=[batch.index]))
