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 trial_to_sqa(self, trial: BaseTrial) -> SQATrial:
"""Convert Ax Trial to SQLAlchemy.
In addition to creating and storing a new Trial object, we need to
create and store the GeneratorRuns and Runner that it owns.
"""
runner = self.runner_to_sqa(
runner=trial.runner) if trial.runner else None
abandoned_arms = []
generator_runs = []
status_quo_name = None
if isinstance(trial, BatchTrial):
abandoned_arms = [
self.abandoned_arm_to_sqa(abandoned_arm=abandoned_arm)
for abandoned_arm in trial.abandoned_arms_metadata
]
generator_runs = [
self.generator_run_to_sqa(generator_run=struct.generator_run,
weight=struct.weight)
for struct in trial.generator_run_structs
]
if trial.status_quo is not None:
status_quo_generator_run = GeneratorRun(
arms=[trial.status_quo],
weights=[trial._status_quo_weight],
type=GeneratorRunType.STATUS_QUO.name,
)
# this is a hack necessary to get equality tests passing;
# otherwise you can encode same object and get two different results
status_quo_generator_run._time_created = trial.time_created
generator_runs.append(
self.generator_run_to_sqa(
generator_run=status_quo_generator_run))
status_quo_name = trial.status_quo.name
elif isinstance(trial, Trial):
if trial.generator_run:
generator_runs = [
self.generator_run_to_sqa(
generator_run=trial.generator_run)
]
# pyre-fixme: Expected `Base` for 1st...ot `typing.Type[Trial]`.
trial_class: SQATrial = self.config.class_to_sqa_class[Trial]
# pyre-fixme[29]: `SQATrial` is not a function.
return trial_class(
abandoned_reason=trial.abandoned_reason,
deployed_name=trial.deployed_name,
index=trial.index,
is_batch=isinstance(trial, BatchTrial),
num_arms_created=trial._num_arms_created,
run_metadata=trial.run_metadata,
status=trial.status,
status_quo_name=status_quo_name,
time_completed=trial.time_completed,
time_created=trial.time_created,
time_staged=trial.time_staged,
time_run_started=trial.time_run_started,
trial_type=trial.trial_type,
abandoned_arms=abandoned_arms,
generator_runs=generator_runs,
runner=runner,
)
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 trial_to_sqa(self,
trial: BaseTrial,
generator_run_reduced_state: bool = False) -> SQATrial:
"""Convert Ax Trial to SQLAlchemy.
In addition to creating and storing a new Trial object, we need to
create and store the GeneratorRuns and Runner that it owns.
"""
runner = None
if trial.runner:
runner = self.runner_to_sqa(runner=not_none(trial.runner))
abandoned_arms = []
generator_runs = []
status_quo_name = None
optimize_for_power = None
if isinstance(trial, Trial) and trial.generator_run:
gr_sqa = self.generator_run_to_sqa(
generator_run=not_none(trial.generator_run),
reduced_state=generator_run_reduced_state,
)
generator_runs.append(gr_sqa)
elif isinstance(trial, BatchTrial):
for abandoned_arm in trial.abandoned_arms_metadata:
abandoned_arms.append(
self.abandoned_arm_to_sqa(abandoned_arm=abandoned_arm))
for struct in trial.generator_run_structs:
gr_sqa = self.generator_run_to_sqa(
generator_run=struct.generator_run, weight=struct.weight)
generator_runs.append(gr_sqa)
trial_status_quo = trial.status_quo
trial_status_quo_weight_override = trial._status_quo_weight_override
if (trial_status_quo is not None
and trial_status_quo_weight_override is not None):
status_quo_generator_run = GeneratorRun(
arms=[trial_status_quo],
weights=[trial_status_quo_weight_override],
type=GeneratorRunType.STATUS_QUO.name,
)
# This is a hack necessary to get equality tests passing;
# otherwise you can encode same object and get two different results.
status_quo_generator_run._time_created = trial.time_created
gr_sqa = self.generator_run_to_sqa(
generator_run=status_quo_generator_run)
# pyre-ignore Attribute `id` declared in class `SQAGeneratorRun`
# has type `int` but is used as type `Optional[int]`.
gr_sqa.id = trial._status_quo_generator_run_db_id
# pyre-ignore Attribute `id` declared in class `SQAArm`
# has type `int` but is used as type `Optional[int]`.
gr_sqa.arms[0].id = trial._status_quo_arm_db_id
generator_runs.append(gr_sqa)
status_quo_name = trial_status_quo.name
optimize_for_power = getattr(trial, "optimize_for_power", None)
if optimize_for_power is None:
logger.warning(
f"optimize_for_power not present in BatchTrial: {trial.__dict__}"
)
# pyre-ignore[9]: Expected `Base` for 1st...ot `typing.Type[Trial]`.
trial_class: SQATrial = self.config.class_to_sqa_class[Trial]
trial_sqa = trial_class( # pyre-fixme[29]: `SQATrial` is not a function.
id=trial.db_id,
abandoned_reason=trial.abandoned_reason,
deployed_name=trial.deployed_name,
index=trial.index,
is_batch=isinstance(trial, BatchTrial),
num_arms_created=trial._num_arms_created,
optimize_for_power=optimize_for_power,
ttl_seconds=trial.ttl_seconds,
run_metadata=trial.run_metadata,
stop_metadata=trial.stop_metadata,
status=trial.status,
status_quo_name=status_quo_name,
time_completed=trial.time_completed,
time_created=trial.time_created,
time_staged=trial.time_staged,
time_run_started=trial.time_run_started,
trial_type=trial.trial_type,
abandoned_arms=abandoned_arms,
generator_runs=generator_runs,
runner=runner,
generation_step_index=trial._generation_step_index,
properties=trial._properties,
)
return trial_sqa
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
def trial_to_sqa(self, trial: BaseTrial) -> Tuple[SQATrial, T_OBJ_TO_SQA]:
"""Convert Ax Trial 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 Trial object, we need to
create and store the GeneratorRuns and Runner that it owns.
"""
obj_to_sqa = []
runner = None
if trial.runner:
runner = self.runner_to_sqa(runner=not_none(trial.runner))
obj_to_sqa.append((trial.runner, runner))
abandoned_arms = []
generator_runs = []
status_quo_name = None
optimize_for_power = None
if isinstance(trial, Trial) and trial.generator_run:
gr_sqa, _obj_to_sqa = self.generator_run_to_sqa(
generator_run=not_none(trial.generator_run))
generator_runs.append(gr_sqa)
obj_to_sqa.extend(_obj_to_sqa)
elif isinstance(trial, BatchTrial):
for abandoned_arm in trial.abandoned_arms_metadata:
abandoned_arms.append(
self.abandoned_arm_to_sqa(abandoned_arm=abandoned_arm))
for struct in trial.generator_run_structs:
gr_sqa, _obj_to_sqa = self.generator_run_to_sqa(
generator_run=struct.generator_run, weight=struct.weight)
generator_runs.append(gr_sqa)
obj_to_sqa.extend(_obj_to_sqa)
trial_status_quo = trial.status_quo
trial_status_quo_weight_override = trial._status_quo_weight_override
if (trial_status_quo is not None
and trial_status_quo_weight_override is not None):
status_quo_generator_run = GeneratorRun(
arms=[trial_status_quo],
weights=[trial_status_quo_weight_override],
type=GeneratorRunType.STATUS_QUO.name,
)
# This is a hack necessary to get equality tests passing;
# otherwise you can encode same object and get two different results.
status_quo_generator_run._time_created = trial.time_created
gr_sqa, _obj_to_sqa = self.generator_run_to_sqa(
generator_run=status_quo_generator_run)
obj_to_sqa.extend(_obj_to_sqa)
generator_runs.append(gr_sqa)
status_quo_name = trial_status_quo.name
optimize_for_power = getattr(trial, "optimize_for_power", None)
if optimize_for_power is None:
logger.warning(
f"optimize_for_power not present in BatchTrial: {trial.__dict__}"
)
# pyre-ignore[9]: Expected `Base` for 1st...ot `typing.Type[Trial]`.
trial_class: SQATrial = self.config.class_to_sqa_class[Trial]
trial_sqa = trial_class( # pyre-fixme[29]: `SQATrial` is not a function.
abandoned_reason=trial.abandoned_reason,
deployed_name=trial.deployed_name,
index=trial.index,
is_batch=isinstance(trial, BatchTrial),
num_arms_created=trial._num_arms_created,
optimize_for_power=optimize_for_power,
ttl_seconds=trial.ttl_seconds,
run_metadata=trial.run_metadata,
status=trial.status,
status_quo_name=status_quo_name,
time_completed=trial.time_completed,
time_created=trial.time_created,
time_staged=trial.time_staged,
time_run_started=trial.time_run_started,
trial_type=trial.trial_type,
abandoned_arms=abandoned_arms,
generator_runs=generator_runs,
runner=runner,
generation_step_index=trial._generation_step_index,
properties=trial._properties,
)
obj_to_sqa.append((trial, trial_sqa))
return trial_sqa, obj_to_sqa
def trial_to_sqa(self, trial: BaseTrial) -> SQATrial:
"""Convert Ax Trial to SQLAlchemy.
In addition to creating and storing a new Trial object, we need to
create and store the GeneratorRuns and Runner that it owns.
"""
# pyre-fixme[6]: Expected `Runner` for 1st param but got `Optional[Runner]`.
runner = self.runner_to_sqa(
runner=trial.runner) if trial.runner else None
abandoned_arms = []
generator_runs = []
status_quo_name = None
optimize_for_power = None
if isinstance(trial, BatchTrial):
abandoned_arms = [
self.abandoned_arm_to_sqa(abandoned_arm=abandoned_arm)
for abandoned_arm in trial.abandoned_arms_metadata
]
generator_runs = [
self.generator_run_to_sqa(generator_run=struct.generator_run,
weight=struct.weight)
for struct in trial.generator_run_structs
]
# appease pyre
trial_status_quo = trial.status_quo
trial_status_quo_weight_override = trial._status_quo_weight_override
if (trial_status_quo is not None
and trial_status_quo_weight_override is not None):
status_quo_generator_run = GeneratorRun(
arms=[trial_status_quo],
weights=[trial_status_quo_weight_override],
type=GeneratorRunType.STATUS_QUO.name,
)
# this is a hack necessary to get equality tests passing;
# otherwise you can encode same object and get two different results
status_quo_generator_run._time_created = trial.time_created
generator_runs.append(
self.generator_run_to_sqa(
generator_run=status_quo_generator_run))
status_quo_name = trial_status_quo.name
if hasattr(trial, "optimize_for_power"):
optimize_for_power = trial.optimize_for_power
else:
optimize_for_power = None
logger.warning(
f"optimize_for_power not present in BatchTrial: {trial.__dict__}"
)
elif isinstance(trial, Trial):
if trial.generator_run:
generator_runs = [
# pyre-fixme[6]: Expected `GeneratorRun` for 1st param but got
# `Optional[GeneratorRun]`.
self.generator_run_to_sqa(generator_run=trial.generator_run
)
]
# pyre-fixme: Expected `Base` for 1st...ot `typing.Type[Trial]`.
trial_class: SQATrial = self.config.class_to_sqa_class[Trial]
# pyre-fixme[29]: `SQATrial` is not a function.
return trial_class(
abandoned_reason=trial.abandoned_reason,
deployed_name=trial.deployed_name,
index=trial.index,
is_batch=isinstance(trial, BatchTrial),
num_arms_created=trial._num_arms_created,
optimize_for_power=optimize_for_power,
ttl_seconds=trial.ttl_seconds,
run_metadata=trial.run_metadata,
status=trial.status,
status_quo_name=status_quo_name,
time_completed=trial.time_completed,
time_created=trial.time_created,
time_staged=trial.time_staged,
time_run_started=trial.time_run_started,
trial_type=trial.trial_type,
abandoned_arms=abandoned_arms,
generator_runs=generator_runs,
runner=runner,
generation_step_index=trial._generation_step_index,
properties=trial._properties,
)
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,
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),
)
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
