def create_policy(self, serving: bool) -> Policy:
if serving:
return create_predictor_policy_from_model(
self.build_serving_module(),
max_num_actions=self.num_candidates,
slate_size=self.slate_size,
)
else:
scorer = slate_q_scorer(num_candidates=self.num_candidates,
q_network=self._q_network)
sampler = TopKSampler(k=self.slate_size)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(
self,
trainer_module: ReAgentLightningModule,
serving: bool = False,
normalization_data_map: Optional[Dict[str, NormalizationData]] = None,
):
if serving:
assert normalization_data_map
return create_predictor_policy_from_model(
self.build_serving_module(trainer_module, normalization_data_map),
# pyre-fixme[16]: `SlateQBase` has no attribute `num_candidates`.
max_num_actions=self.num_candidates,
# pyre-fixme[16]: `SlateQBase` has no attribute `slate_size`.
slate_size=self.slate_size,
)
else:
scorer = slate_q_scorer(
num_candidates=self.num_candidates,
# pyre-fixme[6]: Expected `ModelBase` for 2nd param but got
# `Union[torch.Tensor, torch.nn.Module]`.
q_network=trainer_module.q_network,
)
sampler = TopKSampler(k=self.slate_size)
return Policy(scorer=scorer, sampler=sampler)