def generator_run_from_json(
object_json: Dict[str, Any],
decoder_registry: Dict[str, Type],
class_decoder_registry: Dict[str, Callable[[Dict[str, Any]], Any]],
) -> GeneratorRun:
"""Load Ax GeneratorRun from JSON."""
time_created_json = object_json.pop("time_created")
type_json = object_json.pop("generator_run_type")
index_json = object_json.pop("index")
generator_run = GeneratorRun(
**{
k: object_from_json(
v,
decoder_registry=decoder_registry,
class_decoder_registry=class_decoder_registry,
)
for k, v in object_json.items()
})
generator_run._time_created = object_from_json(
time_created_json,
decoder_registry=decoder_registry,
class_decoder_registry=class_decoder_registry,
)
generator_run._generator_run_type = object_from_json(
type_json,
decoder_registry=decoder_registry,
class_decoder_registry=class_decoder_registry,
)
generator_run._index = object_from_json(
index_json,
decoder_registry=decoder_registry,
class_decoder_registry=class_decoder_registry,
)
return generator_run
def generator_run_from_sqa(
self, generator_run_sqa: SQAGeneratorRun) -> GeneratorRun:
"""Convert SQLAlchemy GeneratorRun to Ax GeneratorRun."""
arms = []
weights = []
opt_config = None
search_space = None
for arm_sqa in generator_run_sqa.arms:
arms.append(self.arm_from_sqa(arm_sqa=arm_sqa))
weights.append(arm_sqa.weight)
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=generator_run_sqa.metrics)
if len(tracking_metrics) > 0:
raise SQADecodeError( # pragma: no cover
"GeneratorRun should not have tracking metrics.")
search_space = self.search_space_from_sqa(
parameters_sqa=generator_run_sqa.parameters,
parameter_constraints_sqa=generator_run_sqa.parameter_constraints,
)
best_arm_predictions = None
model_predictions = None
if (generator_run_sqa.best_arm_parameters is not None
and generator_run_sqa.best_arm_predictions is not None):
best_arm = Arm(
name=generator_run_sqa.best_arm_name,
parameters=generator_run_sqa.best_arm_parameters,
)
best_arm_predictions = (
best_arm,
tuple(generator_run_sqa.best_arm_predictions),
)
model_predictions = (tuple(generator_run_sqa.model_predictions)
if generator_run_sqa.model_predictions is not None
else None)
generator_run = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=opt_config,
search_space=search_space,
fit_time=generator_run_sqa.fit_time,
gen_time=generator_run_sqa.gen_time,
best_arm_predictions=best_arm_predictions,
model_predictions=model_predictions,
)
generator_run._time_created = generator_run_sqa.time_created
generator_run._generator_run_type = self.get_enum_name(
value=generator_run_sqa.generator_run_type,
enum=self.config.generator_run_type_enum,
)
generator_run._index = generator_run_sqa.index
return generator_run
def generator_run_from_json(object_json: Dict[str, Any]) -> GeneratorRun:
"""Load Ax GeneratorRun from JSON."""
time_created_json = object_json.pop("time_created")
type_json = object_json.pop("generator_run_type")
index_json = object_json.pop("index")
generator_run = GeneratorRun(
**{k: object_from_json(v)
for k, v in object_json.items()})
generator_run._time_created = object_from_json(time_created_json)
generator_run._generator_run_type = object_from_json(type_json)
generator_run._index = object_from_json(index_json)
return generator_run
def generator_run_from_sqa(self,
generator_run_sqa: SQAGeneratorRun,
reduced_state: bool = False) -> GeneratorRun:
"""Convert SQLAlchemy GeneratorRun to Ax GeneratorRun.
Args:
generator_run_sqa: `SQAGeneratorRun` to decode.
reduced_state: Whether to load generator runs with a slightly reduced state
(without model state, search space, and optimization config).
"""
arms = []
weights = []
opt_config = None
search_space = None
for arm_sqa in generator_run_sqa.arms:
arms.append(self.arm_from_sqa(arm_sqa=arm_sqa))
weights.append(arm_sqa.weight)
if not reduced_state:
(
opt_config,
tracking_metrics,
) = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=generator_run_sqa.metrics)
if len(tracking_metrics) > 0:
raise SQADecodeError( # pragma: no cover
"GeneratorRun should not have tracking metrics.")
search_space = self.search_space_from_sqa(
parameters_sqa=generator_run_sqa.parameters,
parameter_constraints_sqa=generator_run_sqa.
parameter_constraints,
)
best_arm_predictions = None
model_predictions = None
if (generator_run_sqa.best_arm_parameters is not None
and generator_run_sqa.best_arm_predictions is not None):
best_arm = Arm(
name=generator_run_sqa.best_arm_name,
parameters=not_none(generator_run_sqa.best_arm_parameters),
)
best_arm_predictions = (
best_arm,
tuple(not_none(generator_run_sqa.best_arm_predictions)),
)
model_predictions = (
tuple(not_none(generator_run_sqa.model_predictions))
if generator_run_sqa.model_predictions is not None else None)
generator_run = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=opt_config,
search_space=search_space,
fit_time=generator_run_sqa.fit_time,
gen_time=generator_run_sqa.gen_time,
best_arm_predictions=best_arm_predictions, # pyre-ignore[6]
model_predictions=model_predictions,
model_key=generator_run_sqa.model_key,
model_kwargs=None if reduced_state else object_from_json(
generator_run_sqa.model_kwargs),
bridge_kwargs=None if reduced_state else object_from_json(
generator_run_sqa.bridge_kwargs),
gen_metadata=None if reduced_state else object_from_json(
generator_run_sqa.gen_metadata),
model_state_after_gen=None if reduced_state else object_from_json(
generator_run_sqa.model_state_after_gen),
generation_step_index=generator_run_sqa.generation_step_index,
candidate_metadata_by_arm_signature=object_from_json(
generator_run_sqa.candidate_metadata_by_arm_signature),
)
generator_run._time_created = generator_run_sqa.time_created
generator_run._generator_run_type = self.get_enum_name(
value=generator_run_sqa.generator_run_type,
enum=self.config.generator_run_type_enum,
)
generator_run._index = generator_run_sqa.index
generator_run.db_id = generator_run_sqa.id
return generator_run
def generator_run_from_sqa(
self, generator_run_sqa: SQAGeneratorRun
) -> GeneratorRun:
"""Convert SQLAlchemy GeneratorRun to Ax GeneratorRun."""
arms = []
weights = []
opt_config = None
search_space = None
for arm_sqa in generator_run_sqa.arms:
arms.append(self.arm_from_sqa(arm_sqa=arm_sqa))
weights.append(arm_sqa.weight)
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=generator_run_sqa.metrics
)
if len(tracking_metrics) > 0:
raise SQADecodeError( # pragma: no cover
"GeneratorRun should not have tracking metrics."
)
search_space = self.search_space_from_sqa(
parameters_sqa=generator_run_sqa.parameters,
parameter_constraints_sqa=generator_run_sqa.parameter_constraints,
)
best_arm_predictions = None
model_predictions = None
if (
generator_run_sqa.best_arm_parameters is not None
and generator_run_sqa.best_arm_predictions is not None
):
best_arm = Arm(
name=generator_run_sqa.best_arm_name,
# pyre-fixme[6]: Expected `Dict[str, Optional[Union[bool, float,
# int, str]]]` for 2nd param but got `Optional[Dict[str,
# Optional[Union[bool, float, int, str]]]]`.
parameters=generator_run_sqa.best_arm_parameters,
)
best_arm_predictions = (
best_arm,
# pyre-fixme[6]: Expected `Iterable[_T_co]` for 1st param but got
# `Optional[Tuple[Dict[str, float], Optional[Dict[str, Dict[str,
# float]]]]]`.
tuple(generator_run_sqa.best_arm_predictions),
)
model_predictions = (
# pyre-fixme[6]: Expected `Iterable[_T_co]` for 1st param but got
# `Optional[Tuple[Dict[str, List[float]], Dict[str, Dict[str,
# List[float]]]]]`.
tuple(generator_run_sqa.model_predictions)
if generator_run_sqa.model_predictions is not None
else None
)
generator_run = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=opt_config,
search_space=search_space,
fit_time=generator_run_sqa.fit_time,
gen_time=generator_run_sqa.gen_time,
# pyre-fixme[6]: Expected `Optional[Tuple[Arm, Optional[Tuple[Dict[str,
# float], Optional[Dict[str, Dict[str, float]]]]]]]` for 7th param but got
# `Optional[Tuple[Arm, Tuple[Any, ...]]]`.
best_arm_predictions=best_arm_predictions,
model_predictions=model_predictions,
model_key=generator_run_sqa.model_key,
model_kwargs=object_from_json(generator_run_sqa.model_kwargs),
bridge_kwargs=object_from_json(generator_run_sqa.bridge_kwargs),
gen_metadata=object_from_json(generator_run_sqa.gen_metadata),
model_state_after_gen=object_from_json(
generator_run_sqa.model_state_after_gen
),
generation_step_index=generator_run_sqa.generation_step_index,
candidate_metadata_by_arm_signature=object_from_json(
generator_run_sqa.candidate_metadata_by_arm_signature
),
)
generator_run._time_created = generator_run_sqa.time_created
generator_run._generator_run_type = self.get_enum_name(
value=generator_run_sqa.generator_run_type,
enum=self.config.generator_run_type_enum,
)
generator_run._index = generator_run_sqa.index
return generator_run
def generator_run_from_sqa(
self,
generator_run_sqa: SQAGeneratorRun,
reduced_state: bool,
immutable_search_space_and_opt_config: bool,
) -> GeneratorRun:
"""Convert SQLAlchemy GeneratorRun to Ax GeneratorRun.
Args:
generator_run_sqa: `SQAGeneratorRun` to decode.
reduced_state: Whether to load generator runs with a slightly reduced state
(without model state, search space, and optimization config).
immutable_search_space_and_opt_config: Whether to load generator runs
without search space and optimization config. Unlike `reduced_state`,
we do still load model state.
"""
arms = []
weights = []
opt_config = None
search_space = None
for arm_sqa in generator_run_sqa.arms:
arms.append(self.arm_from_sqa(arm_sqa=arm_sqa))
weights.append(arm_sqa.weight)
if not reduced_state and not immutable_search_space_and_opt_config:
(
opt_config,
tracking_metrics,
) = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=generator_run_sqa.metrics)
if len(tracking_metrics) > 0:
raise SQADecodeError( # pragma: no cover
"GeneratorRun should not have tracking metrics.")
search_space = self.search_space_from_sqa(
parameters_sqa=generator_run_sqa.parameters,
parameter_constraints_sqa=generator_run_sqa.
parameter_constraints,
)
best_arm_predictions = None
model_predictions = None
if (generator_run_sqa.best_arm_parameters is not None
and generator_run_sqa.best_arm_predictions is not None):
best_arm = Arm(
name=generator_run_sqa.best_arm_name,
parameters=not_none(generator_run_sqa.best_arm_parameters),
)
best_arm_predictions = (
best_arm,
tuple(not_none(generator_run_sqa.best_arm_predictions)),
)
model_predictions = (
tuple(not_none(generator_run_sqa.model_predictions))
if generator_run_sqa.model_predictions is not None else None)
generator_run = GeneratorRun(
arms=arms,
weights=weights,
optimization_config=opt_config,
search_space=search_space,
fit_time=generator_run_sqa.fit_time,
gen_time=generator_run_sqa.gen_time,
best_arm_predictions=best_arm_predictions, # pyre-ignore[6]
# pyre-fixme[6]: Expected `Optional[Tuple[typing.Dict[str, List[float]],
# typing.Dict[str, typing.Dict[str, List[float]]]]]` for 8th param but got
# `Optional[typing.Tuple[Union[typing.Dict[str, List[float]],
# typing.Dict[str, typing.Dict[str, List[float]]]], ...]]`.
model_predictions=model_predictions,
model_key=generator_run_sqa.model_key,
model_kwargs=None if reduced_state else object_from_json(
generator_run_sqa.model_kwargs,
decoder_registry=self.config.json_decoder_registry,
class_decoder_registry=self.config.json_class_decoder_registry,
),
bridge_kwargs=None if reduced_state else object_from_json(
generator_run_sqa.bridge_kwargs,
decoder_registry=self.config.json_decoder_registry,
class_decoder_registry=self.config.json_class_decoder_registry,
),
gen_metadata=None if reduced_state else object_from_json(
generator_run_sqa.gen_metadata,
decoder_registry=self.config.json_decoder_registry,
class_decoder_registry=self.config.json_class_decoder_registry,
),
model_state_after_gen=None if reduced_state else object_from_json(
generator_run_sqa.model_state_after_gen,
decoder_registry=self.config.json_decoder_registry,
class_decoder_registry=self.config.json_class_decoder_registry,
),
generation_step_index=generator_run_sqa.generation_step_index,
candidate_metadata_by_arm_signature=object_from_json(
generator_run_sqa.candidate_metadata_by_arm_signature,
decoder_registry=self.config.json_decoder_registry,
class_decoder_registry=self.config.json_class_decoder_registry,
),
)
generator_run._time_created = generator_run_sqa.time_created
generator_run._generator_run_type = self.get_enum_name(
value=generator_run_sqa.generator_run_type,
enum=self.config.generator_run_type_enum,
)
generator_run._index = generator_run_sqa.index
generator_run.db_id = generator_run_sqa.id
return generator_run
