def get_MTGP(
experiment: MultiTypeExperiment,
data: Data,
search_space: Optional[SearchSpace] = None,
) -> TorchModelBridge:
"""Instantiates a Multi-task GP model that generates points with EI."""
trial_index_to_type = {
t.index: t.trial_type
for t in experiment.trials.values()
}
return TorchModelBridge(
experiment=experiment,
search_space=search_space or experiment.search_space,
data=data,
model=BotorchModel(),
transforms=MTGP_trans,
transform_configs={
"TrialAsTask": {
"trial_level_map": {
"trial_type": trial_index_to_type
}
},
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
},
torch_dtype=torch.double,
torch_device=DEFAULT_TORCH_DEVICE,
)
def setUp(self):
self.experiment = get_multi_type_experiment(add_trials=True)
self.data = self.experiment.fetch_data()
self.observations = observations_from_data(self.experiment, self.data)
self.observation_data = [o.data for o in self.observations]
self.observation_features = [o.features for o in self.observations]
self.tconfig = tconfig_from_mt_experiment(self.experiment)
def testMultipleMetrics(self):
# Create copy of online metric for offline
online_metric = copy(self.experiment.metrics["m1"])
online_metric._name = "m3"
self.experiment.add_tracking_metric(online_metric, "type2", "m4")
tconfig = tconfig_from_mt_experiment(self.experiment)
ConvertMetricNames(
None, self.observation_features, self.observation_data, config=tconfig
)
def get_MTGP(
experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None,
trial_index: Optional[int] = None,
) -> TorchModelBridge:
"""Instantiates a Multi-task Gaussian Process (MTGP) model that generates
points with EI.
If the input experiment is a MultiTypeExperiment then a
Multi-type Multi-task GP model will be instantiated.
Otherwise, the model will be a Single-type Multi-task GP.
"""
if isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
}
else:
# Set transforms for a Single-type MTGP model.
transforms = ST_MTGP_trans
transform_configs = None
# Choose the status quo features for the experiment from the selected trial.
# If trial_index is None, we will look for a status quo from the last
# experiment trial to use as a status quo for the experiment.
if trial_index is None:
trial_index = len(experiment.trials) - 1
elif trial_index >= len(experiment.trials):
raise ValueError("trial_index is bigger than the number of experiment trials")
# pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
status_quo = experiment.trials[trial_index].status_quo
if status_quo is None:
status_quo_features = None
else:
status_quo_features = ObservationFeatures(
parameters=status_quo.parameters, trial_index=trial_index
)
return TorchModelBridge(
experiment=experiment,
search_space=search_space or experiment.search_space,
data=data,
model=BotorchModel(),
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch.double,
torch_device=DEFAULT_TORCH_DEVICE,
status_quo_features=status_quo_features,
)
def get_MTGP(
experiment: Experiment,
data: Data,
is_multi_type: bool = True,
search_space: Optional[SearchSpace] = None,
) -> TorchModelBridge:
"""Instantiates a Multi-task GP model that generates points with EI.
Args:
is_multi_type: If is_multi_type is True then experiment should be a
MultiTypeExperiment and a Multi-type Multi-task GP model will be
instantiated.
Otherwise, the model will be a Single-type Multi-task GP.
"""
if is_multi_type and isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type
for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"TrialAsTask": {
"trial_level_map": {
"trial_type": trial_index_to_type
}
},
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
}
elif is_multi_type:
raise ValueError(
"If is_multi_type is True, the input experiment type should be "
"MultiTypeExperiment.")
else:
transforms = ST_MTGP_trans
transform_configs = None
return TorchModelBridge(
experiment=experiment,
search_space=search_space or experiment.search_space,
data=data,
model=BotorchModel(),
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch.double,
torch_device=DEFAULT_TORCH_DEVICE,
)
def get_MTGP(experiment: Experiment,
data: Data,
search_space: Optional[SearchSpace] = None) -> TorchModelBridge:
"""Instantiates a Multi-task Gaussian Process (MTGP) model that generates
points with EI.
If the input experiment is a MultiTypeExperiment then a
Multi-type Multi-task GP model will be instantiated.
Otherwise, the model will be a Single-type Multi-task GP.
"""
if isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type
for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"TrialAsTask": {
"trial_level_map": {
"trial_type": trial_index_to_type
}
},
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
}
else:
# Set transforms for a Single-type MTGP model.
transforms = ST_MTGP_trans
transform_configs = None
return TorchModelBridge(
experiment=experiment,
search_space=search_space or experiment.search_space,
data=data,
model=BotorchModel(),
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch.double,
torch_device=DEFAULT_TORCH_DEVICE,
)
def get_MTGP_PAREGO(
experiment: Experiment,
data: Data,
trial_index: Optional[int] = None,
objective_thresholds: Optional[TRefPoint] = None,
search_space: Optional[SearchSpace] = None,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
) -> MultiObjectiveTorchModelBridge:
"""Instantiates a multi-objective, multi-task model that uses qParEGO.
qParEGO optimizes random augmented chebyshev scalarizations of the multiple
objectives. This allows it to explore non-convex pareto frontiers.
"""
# pyre-ignore: [16] `Optional` has no attribute `objective`.
if not isinstance(experiment.optimization_config.objective, MultiObjective):
raise ValueError("Multi-objective optimization requires multiple objectives.")
elif data.df.empty: # pragma: no cover
raise ValueError("MultiObjectiveOptimization requires non-empty data.")
if isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
"TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
}
else:
# Set transforms for a Single-type MTGP model.
transforms = ST_MTGP_trans
transform_configs = None
# Choose the status quo features for the experiment from the selected trial.
# If trial_index is None, we will look for a status quo from the last
# experiment trial to use as a status quo for the experiment.
if trial_index is None:
trial_index = len(experiment.trials) - 1
elif trial_index >= len(experiment.trials):
raise ValueError("trial_index is bigger than the number of experiment trials")
# pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
status_quo = experiment.trials[trial_index].status_quo
if status_quo is None:
status_quo_features = None
else:
status_quo_features = ObservationFeatures(
parameters=status_quo.parameters,
# pyre-fixme[6]: Expected `Optional[numpy.int64]` for 2nd param but got
# `int`.
trial_index=trial_index,
)
return checked_cast(
MultiObjectiveTorchModelBridge,
Models.MOO(
experiment=experiment,
data=data,
objective_thresholds=objective_thresholds,
search_space=search_space or experiment.search_space,
torch_dtype=dtype,
torch_device=device,
acqf_constructor=get_NEI,
status_quo_features=status_quo_features,
transforms=transforms,
transform_configs=transform_configs,
default_model_gen_options={
"acquisition_function_kwargs": {
"chebyshev_scalarization": True,
"sequential": True,
}
},
),
)
def get_MTGP_NEHVI(
experiment: Experiment,
data: Data,
objective_thresholds: Optional[List[ObjectiveThreshold]] = None,
search_space: Optional[SearchSpace] = None,
dtype: torch.dtype = torch.double,
device: torch.device = DEFAULT_TORCH_DEVICE,
trial_index: Optional[int] = None,
) -> TorchModelBridge:
"""Instantiates a Multi-task Gaussian Process (MTGP) model that generates
points with qNEHVI.
If the input experiment is a MultiTypeExperiment then a
Multi-type Multi-task GP model will be instantiated.
Otherwise, the model will be a Single-type Multi-task GP.
"""
# pyre-ignore: [16] `Optional` has no attribute `objective`.
if not isinstance(experiment.optimization_config.objective, MultiObjective):
raise ValueError("Multi-objective optimization requires multiple objectives.")
elif data.df.empty: # pragma: no cover
raise ValueError("MultiObjectiveOptimization requires non-empty data.")
if isinstance(experiment, MultiTypeExperiment):
trial_index_to_type = {
t.index: t.trial_type for t in experiment.trials.values()
}
transforms = MT_MTGP_trans
transform_configs = {
"ConvertMetricNames": tconfig_from_mt_experiment(experiment),
"TrialAsTask": {"trial_level_map": {"trial_type": trial_index_to_type}},
}
else:
# Set transforms for a Single-type MTGP model.
transforms = ST_MTGP_trans
transform_configs = None
# Choose the status quo features for the experiment from the selected trial.
# If trial_index is None, we will look for a status quo from the last
# experiment trial to use as a status quo for the experiment.
if trial_index is None:
trial_index = len(experiment.trials) - 1
elif trial_index >= len(experiment.trials):
raise ValueError("trial_index is bigger than the number of experiment trials")
# pyre-fixme[16]: `ax.core.base_trial.BaseTrial` has no attribute `status_quo`.
status_quo = experiment.trials[trial_index].status_quo
if status_quo is None:
status_quo_features = None
else:
status_quo_features = ObservationFeatures(
parameters=status_quo.parameters,
# pyre-fixme[6]: Expected `Optional[numpy.int64]` for 2nd param but got
# `int`.
trial_index=trial_index,
)
return checked_cast(
MultiObjectiveTorchModelBridge,
Models.MOO(
experiment=experiment,
data=data,
objective_thresholds=objective_thresholds,
search_space=search_space or experiment.search_space,
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=dtype,
torch_device=device,
status_quo_features=status_quo_features,
default_model_gen_options={
"optimizer_kwargs": {
# having a batch limit is very important for avoiding
# memory issues in the initialization
"batch_limit": DEFAULT_EHVI_BATCH_LIMIT,
"sequential": True,
},
},
),
)
