how to optimize on integer-valued parameters? about ax

[sdaulton]

Hi @overshiki, you can specify that a RangeParameter is an integer using ParameterType.INT.. E.g. RangeParameter(name="x", parameter_type=ParameterType.INT, lower=1, upper=10). By default, Ax will apply an IntToFloat transform on generated candidates that basically does (1). You can see all the default transforms here. If you are generating batches of new candidates and you are using sequentially (greedy) selecting each point in the batch (the default in Ax), where each candidate in the batch is sequentially selected and subsequent candidates are conditioned upon the already selected points, then each new candidate is rounded before the successive conditioning to improve the optimization performance. The docs mention that you can use integer RangeParameters here, but I will add a better description as to the behavior.

[danielrjiang]

Also, you can see how the transformation / untransformation happens before and after a point is generated here, similar to how you describe in (1):

Ax/ax/modelbridge/base.py

Lines 491 to 522 in 951aa7b

	# Transform
	for t in self.transforms.values():
	search_space = t.transform_search_space(search_space)
	if optimization_config is not None:
	optimization_config = t.transform_optimization_config(
	optimization_config=optimization_config,
	modelbridge=self,
	fixed_features=fixed_features,
	)
	for metric, po in pending_observations.items():
	pending_observations[metric] = t.transform_observation_features(po)
	fixed_features = t.transform_observation_features([fixed_features])[0]
	# Apply terminal transform and gen
	observation_features, weights, best_obsf = self._gen(
	n=n,
	search_space=search_space,
	optimization_config=optimization_config,
	pending_observations=pending_observations,
	fixed_features=fixed_features,
	model_gen_options=model_gen_options,
	)
	# Apply reverse transforms
	# pyre-fixme[6]: Expected `Sequence[_T]` for 1st param but got `ValuesView[Tr...
	for t in reversed(self.transforms.values()): # noqa T484
	observation_features = t.untransform_observation_features(
	observation_features
	)
	if best_obsf is not None:
	best_obsf = t.untransform_observation_features([best_obsf])[0]