def testBestArm(self):
generator_run = GeneratorRun(
arms=self.arms,
weights=self.weights,
optimization_config=get_optimization_config(),
search_space=get_search_space(),
best_arm_predictions=(self.arms[0], ({"a": 1.0}, {"a": {"a": 2.0}})),
)
self.assertEqual(
generator_run.best_arm_predictions,
(self.arms[0], ({"a": 1.0}, {"a": {"a": 2.0}})),
)
def testTrackingMetricsMerge(self):
# Tracking and optimization metrics should get merged
# m1 is on optimization_config while m3 is not
exp = Experiment(
name="test2",
search_space=get_search_space(),
optimization_config=get_optimization_config(),
tracking_metrics=[Metric(name="m1"),
Metric(name="m3")],
)
self.assertEqual(
len(exp.optimization_config.metrics) + 1, len(exp.metrics))
def get_generation_strategy(
with_experiment: bool = False,
with_callable_model_kwarg: bool = True) -> GenerationStrategy:
gs = choose_generation_strategy(search_space=get_search_space(),
should_deduplicate=True)
if with_experiment:
gs._experiment = get_experiment()
fake_func = get_experiment
if with_callable_model_kwarg:
# pyre-ignore[16]: testing hack to test serialization of callable kwargs
# in generation steps.
gs._steps[0].model_kwargs["model_constructor"] = fake_func
return gs
def testEmptyMetrics(self):
empty_experiment = Experiment(name="test_experiment",
search_space=get_search_space())
self.assertEqual(empty_experiment.num_trials, 0)
with self.assertRaises(ValueError):
empty_experiment.fetch_data()
batch = empty_experiment.new_batch_trial()
self.assertEqual(empty_experiment.num_trials, 1)
with self.assertRaises(ValueError):
batch.fetch_data()
empty_experiment.add_tracking_metric(Metric(name="some_metric"))
empty_experiment.attach_data(get_data())
self.assertFalse(empty_experiment.fetch_data().df.empty)
def testModelPredictions(self):
self.assertEqual(self.unweighted_run.model_predictions, get_model_predictions())
self.assertEqual(
self.unweighted_run.model_predictions_by_arm,
get_model_predictions_per_arm(),
)
run_no_model_predictions = GeneratorRun(
arms=self.arms,
weights=self.weights,
optimization_config=get_optimization_config(),
search_space=get_search_space(),
)
self.assertIsNone(run_no_model_predictions.model_predictions)
self.assertIsNone(run_no_model_predictions.model_predictions_by_arm)
def testEmptyMetrics(self):
empty_experiment = Experiment(name="test_experiment",
search_space=get_search_space())
self.assertEqual(empty_experiment.num_trials, 0)
with self.assertRaises(ValueError):
empty_experiment.fetch_data()
batch = empty_experiment.new_batch_trial()
batch.mark_running(no_runner_required=True)
self.assertEqual(empty_experiment.num_trials, 1)
with self.assertRaises(ValueError):
batch.fetch_data()
empty_experiment.add_tracking_metric(Metric(name="ax_test_metric"))
self.assertTrue(empty_experiment.fetch_data().df.empty)
empty_experiment.attach_data(get_data())
batch.mark_completed()
self.assertFalse(empty_experiment.fetch_data().df.empty)
def testExperimentParameterUpdates(self):
experiment = get_experiment_with_batch_trial()
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAParameter).count(),
len(experiment.search_space.parameters),
)
# update a parameter
# (should perform update in place)
search_space = get_search_space()
parameter = get_choice_parameter()
parameter.add_values(["foobar"])
search_space.update_parameter(parameter)
experiment.search_space = search_space
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAParameter).count(),
len(experiment.search_space.parameters),
)
# add a parameter
parameter = RangeParameter(name="x1",
parameter_type=ParameterType.FLOAT,
lower=-5,
upper=10)
search_space.add_parameter(parameter)
experiment.search_space = search_space
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAParameter).count(),
len(experiment.search_space.parameters),
)
# remove a parameter
# (old one should be deleted)
del search_space._parameters["x1"]
experiment.search_space = search_space
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAParameter).count(),
len(experiment.search_space.parameters),
)
loaded_experiment = load_experiment(experiment.name)
self.assertEqual(experiment, loaded_experiment)
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 test_relativize_transform_requires_a_modelbridge_to_have_status_quo_data(self):
sobol = Models.SOBOL(search_space=get_search_space())
self.assertIsNone(sobol.status_quo)
with self.assertRaisesRegex(ValueError, "status quo data"):
Relativize(
search_space=None,
observation_features=[],
observation_data=[],
modelbridge=sobol,
).transform_observation_data(
observation_data=[
ObservationData(
metric_names=["foo"],
means=np.array([2]),
covariance=np.array([[0.1]]),
)
],
observation_features=[ObservationFeatures(parameters={"x": 1})],
)
def setUp(self):
self.model_predictions = get_model_predictions()
self.optimization_config = get_optimization_config()
self.search_space = get_search_space()
self.arms = get_arms()
self.weights = [2, 1, 1]
self.unweighted_run = GeneratorRun(
arms=self.arms,
optimization_config=self.optimization_config,
search_space=self.search_space,
model_predictions=self.model_predictions,
fit_time=4.0,
gen_time=10.0,
)
self.weighted_run = GeneratorRun(
arms=self.arms,
weights=self.weights,
optimization_config=self.optimization_config,
search_space=self.search_space,
model_predictions=self.model_predictions,
)
def testBasicProperties(self):
self.assertEqual(self.experiment.status_quo, get_status_quo())
self.assertEqual(self.experiment.search_space, get_search_space())
self.assertEqual(self.experiment.optimization_config,
get_optimization_config())
self.assertEqual(self.experiment.is_test, True)
def get_generation_strategy(
with_experiment: bool = False) -> GenerationStrategy:
gs = choose_generation_strategy(search_space=get_search_space())
if with_experiment:
gs._experiment = get_experiment()
return gs
def get_generation_strategy() -> GenerationStrategy:
return choose_generation_strategy(search_space=get_search_space())
