def test_encode_decode_numpy(self):
arr = np.array([[1, 2, 3], [4, 5, 6]])
self.assertTrue(np.array_equal(arr, object_from_json(object_to_json(arr))))
def generator_run_to_sqa(
self, generator_run: GeneratorRun, weight: Optional[float] = None
) -> SQAGeneratorRun:
"""Convert Ax GeneratorRun to SQLAlchemy and compile a list of (object,
sqa_counterpart) tuples to set `db_id` on user-facing classes after
the conversion is complete and the SQL session is flushed (SQLAlchemy
classes receive their `id` attributes during `session.flush()`).
In addition to creating and storing a new GeneratorRun object, we need to
create and store copies of the Arms, Metrics, Parameters, and
ParameterConstraints owned by this GeneratorRun.
"""
arms = []
for arm, arm_weight in generator_run.arm_weights.items():
arms.append(self.arm_to_sqa(arm=arm, weight=arm_weight))
metrics = self.optimization_config_to_sqa(generator_run.optimization_config)
parameters, parameter_constraints = self.search_space_to_sqa(
generator_run.search_space
)
best_arm_name = None
best_arm_parameters = None
best_arm_predictions = None
if generator_run.best_arm_predictions is not None:
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
best_arm = generator_run.best_arm_predictions[0]
best_arm_predictions = list(generator_run.best_arm_predictions[1])
best_arm_name = best_arm._name
best_arm_parameters = best_arm.parameters
model_predictions = (
# pyre-fixme[6]: Expected `Iterable[Variable[_T]]` for 1st param but got
# `Optional[typing.Tuple[Dict[str, List[float]], Dict[str, Dict[str,
# List[float]]]]]`.
list(generator_run.model_predictions)
if generator_run.model_predictions is not None
else None
)
generator_run_type = self.get_enum_value(
value=generator_run.generator_run_type,
enum=self.config.generator_run_type_enum,
)
# pyre-fixme: Expected `Base` for 1st...ing.Type[GeneratorRun]`.
generator_run_class: SQAGeneratorRun = self.config.class_to_sqa_class[
GeneratorRun
]
gr_sqa = generator_run_class( # pyre-ignore[29]: `SQAGeneratorRun` not a func.
id=generator_run.db_id,
arms=arms,
metrics=metrics,
parameters=parameters,
parameter_constraints=parameter_constraints,
time_created=generator_run.time_created,
generator_run_type=generator_run_type,
weight=weight,
index=generator_run.index,
fit_time=generator_run.fit_time,
gen_time=generator_run.gen_time,
best_arm_name=best_arm_name,
best_arm_parameters=best_arm_parameters,
best_arm_predictions=best_arm_predictions,
model_predictions=model_predictions,
model_key=generator_run._model_key,
model_kwargs=object_to_json(generator_run._model_kwargs),
bridge_kwargs=object_to_json(generator_run._bridge_kwargs),
gen_metadata=object_to_json(generator_run._gen_metadata),
model_state_after_gen=object_to_json(generator_run._model_state_after_gen),
generation_step_index=generator_run._generation_step_index,
candidate_metadata_by_arm_signature=object_to_json(
generator_run._candidate_metadata_by_arm_signature
),
)
return gr_sqa
def generator_run_to_sqa(
self,
generator_run: GeneratorRun,
weight: Optional[float] = None) -> SQAGeneratorRun:
"""Convert Ax GeneratorRun to SQLAlchemy.
In addition to creating and storing a new GeneratorRun object, we need to
create and store copies of the Arms, Metrics, Parameters, and
ParameterConstraints owned by this GeneratorRun.
"""
arms = [
self.arm_to_sqa(arm=arm, weight=weight)
for (arm, weight) in generator_run.arm_weights.items()
]
metrics = self.optimization_config_to_sqa(
generator_run.optimization_config)
parameters, parameter_constraints = self.search_space_to_sqa(
generator_run.search_space)
best_arm_name = None
best_arm_parameters = None
best_arm_predictions = None
if generator_run.best_arm_predictions is not None:
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
best_arm = generator_run.best_arm_predictions[0]
best_arm_predictions = list(generator_run.best_arm_predictions[1])
best_arm_name = best_arm._name
best_arm_parameters = best_arm.parameters
model_predictions = (
# pyre-fixme[6]: Expected `Iterable[Variable[_T]]` for 1st param but got
# `Optional[typing.Tuple[Dict[str, List[float]], Dict[str, Dict[str,
# List[float]]]]]`.
list(generator_run.model_predictions)
if generator_run.model_predictions is not None else None)
generator_run_type = self.get_enum_value(
value=generator_run.generator_run_type,
enum=self.config.generator_run_type_enum,
)
# pyre-fixme: Expected `Base` for 1st...ing.Type[GeneratorRun]`.
generator_run_class: SQAGeneratorRun = self.config.class_to_sqa_class[
GeneratorRun]
# pyre-fixme[29]: `SQAGeneratorRun` is not a function.
return generator_run_class(
arms=arms,
metrics=metrics,
parameters=parameters,
parameter_constraints=parameter_constraints,
time_created=generator_run.time_created,
generator_run_type=generator_run_type,
weight=weight,
index=generator_run.index,
fit_time=generator_run.fit_time,
gen_time=generator_run.gen_time,
best_arm_name=best_arm_name,
best_arm_parameters=best_arm_parameters,
best_arm_predictions=best_arm_predictions,
model_predictions=model_predictions,
model_key=generator_run._model_key,
model_kwargs=object_to_json(generator_run._model_kwargs),
bridge_kwargs=object_to_json(generator_run._bridge_kwargs),
gen_metadata=object_to_json(generator_run._gen_metadata),
model_state_after_gen=object_to_json(
generator_run._model_state_after_gen),
generation_step_index=generator_run._generation_step_index,
candidate_metadata_by_arm_signature=object_to_json(
generator_run._candidate_metadata_by_arm_signature),
)
def generator_run_to_sqa(
self,
generator_run: GeneratorRun,
weight: Optional[float] = None,
reduced_state: bool = False,
) -> SQAGeneratorRun:
"""Convert Ax GeneratorRun to SQLAlchemy.
In addition to creating and storing a new GeneratorRun object, we need to
create and store copies of the Arms, Metrics, Parameters, and
ParameterConstraints owned by this GeneratorRun.
"""
arms = []
for arm, arm_weight in generator_run.arm_weights.items():
arms.append(self.arm_to_sqa(arm=arm, weight=arm_weight))
metrics = self.optimization_config_to_sqa(
generator_run.optimization_config)
parameters, parameter_constraints = self.search_space_to_sqa(
generator_run.search_space)
best_arm_name = None
best_arm_parameters = None
best_arm_predictions = None
if generator_run.best_arm_predictions is not None:
best_arm = generator_run.best_arm_predictions[0]
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
best_arm_predictions = list(generator_run.best_arm_predictions[1])
best_arm_name = best_arm._name
best_arm_parameters = best_arm.parameters
model_predictions = (list(generator_run.model_predictions)
if generator_run.model_predictions is not None
else None)
generator_run_type = self.get_enum_value(
value=generator_run.generator_run_type,
enum=self.config.generator_run_type_enum,
)
# pyre-fixme: Expected `Base` for 1st...ing.Type[GeneratorRun]`.
generator_run_class: SQAGeneratorRun = self.config.class_to_sqa_class[
GeneratorRun]
gr_sqa = generator_run_class( # pyre-ignore[29]: `SQAGeneratorRun` not a func.
id=generator_run.db_id,
arms=arms,
metrics=metrics,
parameters=parameters,
parameter_constraints=parameter_constraints,
time_created=generator_run.time_created,
generator_run_type=generator_run_type,
weight=weight,
index=generator_run.index,
fit_time=generator_run.fit_time,
gen_time=generator_run.gen_time,
best_arm_name=best_arm_name,
best_arm_parameters=best_arm_parameters,
best_arm_predictions=best_arm_predictions,
model_predictions=model_predictions,
model_key=generator_run._model_key,
model_kwargs=object_to_json(generator_run._model_kwargs)
if not reduced_state else None,
bridge_kwargs=object_to_json(generator_run._bridge_kwargs)
if not reduced_state else None,
gen_metadata=object_to_json(generator_run._gen_metadata)
if not reduced_state else None,
model_state_after_gen=object_to_json(
generator_run._model_state_after_gen)
if not reduced_state else None,
generation_step_index=generator_run._generation_step_index,
candidate_metadata_by_arm_signature=object_to_json(
generator_run._candidate_metadata_by_arm_signature),
)
return gr_sqa
