def test_status_quo_for_non_monolithic_data(self, mock_gen):
mock_gen.return_value = (
[
ObservationFeatures(parameters={
"x1": float(i),
"x2": float(i)
},
trial_index=np.int64(1)) for i in range(5)
],
[1] * 5,
None,
{},
)
exp = get_branin_experiment_with_multi_objective(with_status_quo=True)
sobol = Models.SOBOL(search_space=exp.search_space)
exp.new_batch_trial(sobol.gen(5)).set_status_quo_and_optimize_power(
status_quo=exp.status_quo).run()
# create data where metrics vary in start and end times
data = get_non_monolithic_branin_moo_data()
with warnings.catch_warnings(record=True) as ws:
bridge = ModelBridge(
experiment=exp,
data=data,
model=Model(),
search_space=exp.search_space,
)
# just testing it doesn't error
bridge.gen(5)
self.assertTrue(any("start_time" in str(w.message) for w in ws))
self.assertTrue(any("end_time" in str(w.message) for w in ws))
self.assertEqual(bridge.status_quo.arm_name, "status_quo")
def test_update(self, _mock_update, _mock_gen):
exp = get_experiment_for_value()
exp.optimization_config = get_optimization_config_no_constraints()
ss = get_search_space_for_range_values()
exp.search_space = ss
modelbridge = ModelBridge(
search_space=ss, model=Model(), transforms=[Log], experiment=exp
)
exp.new_trial(generator_run=modelbridge.gen(1))
modelbridge._set_training_data(
observations_from_data(
data=Data(
pd.DataFrame(
[
{
"arm_name": "0_0",
"metric_name": "m1",
"mean": 3.0,
"sem": 1.0,
}
]
)
),
experiment=exp,
),
ss,
)
exp.new_trial(generator_run=modelbridge.gen(1))
modelbridge.update(
new_data=Data(
pd.DataFrame(
[{"arm_name": "1_0", "metric_name": "m1", "mean": 5.0, "sem": 0.0}]
)
),
experiment=exp,
)
exp.new_trial(generator_run=modelbridge.gen(1))
# Trying to update with unrecognised metric should error.
with self.assertRaisesRegex(ValueError, "Unrecognised metric"):
modelbridge.update(
new_data=Data(
pd.DataFrame(
[
{
"arm_name": "1_0",
"metric_name": "m2",
"mean": 5.0,
"sem": 0.0,
}
]
)
),
experiment=exp,
)
def test_ood_gen(self, _):
# Test fit_out_of_design by returning OOD candidats
exp = get_experiment_for_value()
ss = SearchSpace([RangeParameter("x", ParameterType.FLOAT, 0.0, 1.0)])
modelbridge = ModelBridge(
search_space=ss,
model=Model(),
transforms=[],
experiment=exp,
data=0,
fit_out_of_design=True,
)
obs = ObservationFeatures(parameters={"x": 3.0})
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([obs], [2], None, {}),
)
gr = modelbridge.gen(n=1)
self.assertEqual(gr.arms[0].parameters, obs.parameters)
# Test clamping arms by setting fit_out_of_design=False
modelbridge = ModelBridge(
search_space=ss,
model=Model(),
transforms=[],
experiment=exp,
data=0,
fit_out_of_design=False,
)
obs = ObservationFeatures(parameters={"x": 3.0})
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([obs], [2], None, {}),
)
gr = modelbridge.gen(n=1)
self.assertEqual(gr.arms[0].parameters, {"x": 1.0})
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(
search_space=ss, model=Model(), transforms=[], experiment=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={},
)
def testModelBridge(self, mock_fit, mock_gen_arms,
mock_observations_from_data):
# Test that on init transforms are stored and applied in the correct order
transforms = [transform_1, transform_2]
exp = get_experiment_for_value()
ss = get_search_space_for_value()
modelbridge = ModelBridge(
search_space=ss,
model=Model(),
transforms=transforms,
experiment=exp,
data=0,
)
self.assertFalse(
modelbridge._experiment_has_immutable_search_space_and_opt_config)
self.assertEqual(list(modelbridge.transforms.keys()),
["Cast", "transform_1", "transform_2"])
fit_args = mock_fit.mock_calls[0][2]
self.assertTrue(
fit_args["search_space"] == get_search_space_for_value(8.0))
self.assertTrue(fit_args["observation_features"] == [])
self.assertTrue(fit_args["observation_data"] == [])
self.assertTrue(mock_observations_from_data.called)
# Test prediction on out of design features.
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
side_effect=ValueError("Out of Design"),
)
# This point is in design, and thus failures in predict are legitimate.
with mock.patch.object(ModelBridge,
"model_space",
return_value=get_search_space_for_range_values):
with self.assertRaises(ValueError):
modelbridge.predict([get_observation2().features])
# This point is out of design, and not in training data.
with self.assertRaises(ValueError):
modelbridge.predict([get_observation_status_quo0().features])
# Now it's in the training data.
with mock.patch.object(
ModelBridge,
"get_training_data",
return_value=[get_observation_status_quo0()],
):
# Return raw training value.
self.assertEqual(
modelbridge.predict([get_observation_status_quo0().features]),
unwrap_observation_data([get_observation_status_quo0().data]),
)
# Test that transforms are applied correctly on predict
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[get_observation2trans().data],
)
modelbridge.predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Check that _single_predict is equivalent here.
modelbridge._single_predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Test transforms applied on gen
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([get_observation1trans().features], [2], None, {}),
)
oc = OptimizationConfig(objective=Objective(metric=Metric(
name="test_metric")))
modelbridge._set_kwargs_to_save(model_key="TestModel",
model_kwargs={},
bridge_kwargs={})
gr = modelbridge.gen(
n=1,
search_space=get_search_space_for_value(),
optimization_config=oc,
pending_observations={"a": [get_observation2().features]},
fixed_features=ObservationFeatures({"x": 5}),
)
self.assertEqual(gr._model_key, "TestModel")
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc,
pending_observations={"a": [get_observation2trans().features]},
fixed_features=ObservationFeatures({"x": 36}),
model_gen_options=None,
)
mock_gen_arms.assert_called_with(
arms_by_signature={},
observation_features=[get_observation1().features])
# Gen with no pending observations and no fixed features
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=None)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=None,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Gen with multi-objective optimization config.
oc2 = OptimizationConfig(objective=ScalarizedObjective(
metrics=[Metric(name="test_metric"),
Metric(name="test_metric_2")]))
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=oc2)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc2,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Test transforms applied on cross_validate
modelbridge._cross_validate = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._cross_validate",
autospec=True,
return_value=[get_observation1trans().data],
)
cv_training_data = [get_observation2()]
cv_test_points = [get_observation1().features]
cv_predictions = modelbridge.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points)
modelbridge._cross_validate.assert_called_with(
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
obs_feats=[get_observation2trans().features],
obs_data=[get_observation2trans().data],
cv_test_points=[get_observation1().features
], # untransformed after
)
self.assertTrue(cv_predictions == [get_observation1().data])
# Test stored training data
obs = modelbridge.get_training_data()
self.assertTrue(obs == [get_observation1(), get_observation2()])
self.assertEqual(modelbridge.metric_names, {"a", "b"})
self.assertIsNone(modelbridge.status_quo)
self.assertTrue(
modelbridge.model_space == get_search_space_for_value())
self.assertEqual(modelbridge.training_in_design, [False, False])
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
# Test feature_importances
with self.assertRaises(NotImplementedError):
modelbridge.feature_importances("a")
# Test transform observation features
with mock.patch(
"ax.modelbridge.base.ModelBridge._transform_observation_features",
autospec=True,
) as mock_tr:
modelbridge.transform_observation_features(
[get_observation2().features])
mock_tr.assert_called_with(modelbridge,
[get_observation2trans().features])