def testGetProperties(self):
properties = get_object_properties(Metric(name="foo"))
self.assertEqual(properties, {"name": "foo"})
properties = get_object_properties(
Metric(name="foo", lower_is_better=True))
self.assertEqual(properties, {"name": "foo", "lower_is_better": True})
def testGetProperties(self):
# Extract default value.
properties = get_object_properties(Metric(name="foo"))
self.assertEqual(properties, {"name": "foo", "lower_is_better": None})
# Extract passed value.
properties = get_object_properties(Metric(name="foo", lower_is_better=True))
self.assertEqual(properties, {"name": "foo", "lower_is_better": True})
def benchmark_problem_to_dict(
benchmark_problem: BenchmarkProblem) -> Dict[str, Any]:
"""Converts an Ax benchmark problem to a serializable dictionary."""
if isinstance(benchmark_problem, SimpleBenchmarkProblem):
if benchmark_problem.uses_synthetic_function:
function_name = benchmark_problem.f.name # pyre-ignore[16]
f = None
else:
function_name = benchmark_problem.f.__name__ # pyre-ignore[16]
f = pickle.dumps(benchmark_problem.f, 0).decode()
return {
"__type": benchmark_problem.__class__.__name__,
"uses_synthetic_function":
benchmark_problem.uses_synthetic_function,
"function_name": function_name,
# If the benchamrk problem uses a custom callable, pickle it.
"f": f,
"name": benchmark_problem.name,
"domain": benchmark_problem.domain,
"minimize": benchmark_problem.minimize,
"noise_sd": benchmark_problem.noise_sd,
"evaluate_suggested": benchmark_problem.evaluate_suggested,
"optimal_value": benchmark_problem.optimal_value,
}
elif isinstance(benchmark_problem, BenchmarkProblem):
properties = get_object_properties(object=benchmark_problem)
properties["__type"] = benchmark_problem.__class__.__name__
return properties
else: # pragma: no cover
raise ValueError(
f"Expected benchmark problem, got: {benchmark_problem}.")
def runner_to_sqa(self, runner: Runner) -> SQARunner:
"""Convert Ax Runner to SQLAlchemy."""
runner_type = RUNNER_REGISTRY.get(type(runner))
if runner_type is None:
raise SQAEncodeError(
"Cannot encode runner to SQLAlchemy because runner's "
"subclass is invalid.") # pragma: no cover
properties = get_object_properties(object=runner)
# pyre-fixme: Expected `Base` for 1st...t `typing.Type[Runner]`.
runner_class: SQARunner = self.config.class_to_sqa_class[Runner]
# pyre-fixme[29]: `SQARunner` is not a function.
return runner_class(runner_type=runner_type, properties=properties)
def get_metric_type_and_properties(
self, metric: Metric) -> Tuple[int, Dict[str, Any]]:
"""Given an Ax Metric, convert its type into a member of MetricType enum,
and construct a dictionary to be stored in the database `properties`
json blob.
"""
metric_type = METRIC_REGISTRY.get(type(metric))
if metric_type is None:
raise SQAEncodeError(
"Cannot encode metric to SQLAlchemy because metric's "
"subclass is invalid.") # pragma: no cover
# name and lower_is_better are stored directly on the metric,
# so we don't need to include these in the properties blob
properties = get_object_properties(
object=metric, exclude_fields=["name", "lower_is_better"])
return metric_type, properties
def runner_to_dict(runner: SyntheticRunner) -> Dict[str, Any]:
"""Convert Ax synthetic runner to a dictionary."""
properties = get_object_properties(object=runner)
properties["__type"] = runner.__class__.__name__
return properties
def metric_to_dict(metric: Metric) -> Dict[str, Any]:
"""Convert Ax metric to a dictionary."""
properties = get_object_properties(object=metric)
properties["__type"] = metric.__class__.__name__
return properties
def observation_features_to_dict(obs_features: ObservationFeatures) -> Dict[str, Any]:
"""Converts Ax observation features to a dictionary"""
properties = get_object_properties(object=obs_features)
properties['_type'] = obs_features.__class__.__name__
return properties