def testDecodeGenerationStrategy(self):
generation_strategy = get_generation_strategy()
experiment = get_branin_experiment()
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertGreater(len(new_generation_strategy._steps), 0)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Model has not yet been initialized on this GS since it hasn't generated
# anything yet.
self.assertIsNone(new_generation_strategy.model)
# Check that we can encode and decode the generation strategy after
# it has generated some generator runs.
generation_strategy = new_generation_strategy
gr = generation_strategy.gen(experiment)
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Since this GS has now generated one generator run, model should have
# been initialized and restored when decoding from JSON.
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
# Check that we can encode and decode the generation strategy after
# it has generated some trials and been updated with some data.
generation_strategy = new_generation_strategy
experiment.new_trial(gr) # Add previously generated GR as trial.
# Make generation strategy aware of the trial's data via `gen`.
generation_strategy.gen(experiment, data=get_branin_data())
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
def from_json_snapshot(serialized):
"""Recreate a `CoreAxClient` from a JSON snapshot."""
ax_client = CoreAxClient(
experiment=object_from_json(serialized.pop("experiment")),
generation_strategy=generation_strategy_from_json(
generation_strategy_json=serialized.pop(
"generation_strategy")))
return ax_client
def testDecodeGenerationStrategy(self):
generation_strategy = get_generation_strategy()
experiment = get_branin_experiment()
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertGreater(len(new_generation_strategy._steps), 0)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Model has not yet been initialized on this GS since it hasn't generated
# anything yet.
self.assertIsNone(new_generation_strategy.model)
# Check that we can encode and decode the generation strategy after
# it has generated some trials.
generation_strategy = new_generation_strategy
experiment.new_trial(generator_run=generation_strategy.gen(experiment))
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
# `_seen_trial_indices_by_status` attribute of a GS is not saved in DB,
# so it will be None in the restored version of the GS.
# Hackily removing it from the original GS to check equality.
generation_strategy._seen_trial_indices_by_status = None
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Since this GS has now generated one generator run, model should have
# been initialized and restored when decoding from JSON.
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
# Check that we can encode and decode the generation strategy after
# it has generated some trials and been updated with some data.
generation_strategy = new_generation_strategy
experiment.new_trial(
generation_strategy.gen(experiment, data=get_branin_data()))
gs_json = object_to_json(generation_strategy)
new_generation_strategy = generation_strategy_from_json(gs_json)
# `_seen_trial_indices_by_status` attribute of a GS is not saved in DB,
# so it will be None in the restored version of the GS.
# Hackily removing it from the original GS to check equality.
generation_strategy._seen_trial_indices_by_status = None
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
def from_json_snapshot(serialized: Dict[str, Any], **kwargs) -> "AxClient":
"""Recreate an `AxClient` from a JSON snapshot."""
experiment = object_from_json(serialized.pop("experiment"))
serialized_generation_strategy = serialized.pop("generation_strategy")
ax_client = AxClient(
generation_strategy=generation_strategy_from_json(
generation_strategy_json=serialized_generation_strategy)
if serialized_generation_strategy is not None else None,
enforce_sequential_optimization=serialized.pop(
"_enforce_sequential_optimization"),
**kwargs,
)
ax_client._experiment = experiment
return ax_client
def from_json_snapshot(serialized: Dict[str, Any]) -> "AxClient":
"""Recreate an `AxClient` from a JSON snapshot."""
experiment = object_from_json(serialized.pop("experiment"))
ax_client = AxClient(
generation_strategy=generation_strategy_from_json(
generation_strategy_json=serialized.pop(
"generation_strategy")),
enforce_sequential_optimization=serialized.pop(
"_enforce_sequential_optimization"),
)
ax_client._experiment = experiment
ax_client._updated_trials = object_from_json(
serialized.pop("_updated_trials"))
return ax_client
def testDecodeGenerationStrategy(self):
generation_strategy = get_generation_strategy()
experiment = get_branin_experiment()
gs_json = object_to_json(
generation_strategy,
encoder_registry=DEPRECATED_ENCODER_REGISTRY,
class_encoder_registry=DEPRECATED_CLASS_ENCODER_REGISTRY,
)
new_generation_strategy = generation_strategy_from_json(
gs_json,
decoder_registry=DEPRECATED_DECODER_REGISTRY,
class_decoder_registry=DEPRECATED_CLASS_DECODER_REGISTRY,
)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertGreater(len(new_generation_strategy._steps), 0)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Model has not yet been initialized on this GS since it hasn't generated
# anything yet.
self.assertIsNone(new_generation_strategy.model)
# Check that we can encode and decode the generation strategy after
# it has generated some generator runs. Since we now need to `gen`,
# we remove the fake callable kwarg we added, since model does not
# expect it.
generation_strategy = get_generation_strategy(with_callable_model_kwarg=False)
gr = generation_strategy.gen(experiment)
gs_json = object_to_json(
generation_strategy,
encoder_registry=DEPRECATED_ENCODER_REGISTRY,
class_encoder_registry=DEPRECATED_CLASS_ENCODER_REGISTRY,
)
new_generation_strategy = generation_strategy_from_json(
gs_json,
decoder_registry=DEPRECATED_DECODER_REGISTRY,
class_decoder_registry=DEPRECATED_CLASS_DECODER_REGISTRY,
)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
# Since this GS has now generated one generator run, model should have
# been initialized and restored when decoding from JSON.
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
# Check that we can encode and decode the generation strategy after
# it has generated some trials and been updated with some data.
generation_strategy = new_generation_strategy
experiment.new_trial(gr) # Add previously generated GR as trial.
# Make generation strategy aware of the trial's data via `gen`.
generation_strategy.gen(experiment, data=get_branin_data())
gs_json = object_to_json(
generation_strategy,
encoder_registry=DEPRECATED_ENCODER_REGISTRY,
class_encoder_registry=DEPRECATED_CLASS_ENCODER_REGISTRY,
)
new_generation_strategy = generation_strategy_from_json(
gs_json,
decoder_registry=DEPRECATED_DECODER_REGISTRY,
class_decoder_registry=DEPRECATED_CLASS_DECODER_REGISTRY,
)
self.assertEqual(generation_strategy, new_generation_strategy)
self.assertIsInstance(new_generation_strategy._steps[0].model, Models)
self.assertIsInstance(new_generation_strategy.model, ModelBridge)
def initialize_from_json_snapshot(self, serialized):
other = CoreAxClient(
experiment=object_from_json(serialized["experiment"]),
generation_strategy=generation_strategy_from_json(
generation_strategy_json=serialized["generation_strategy"]))
self.__dict__.update(other.__dict__)
