def train(self, train_dataset: Dataset, eval_dataset: Optional[Dataset],
num_epochs: int) -> RLTrainingOutput:
reporter = ActorCriticReporter()
# pyre-fixme[16]: `RLTrainer` has no attribute `add_observer`.
self.trainer.add_observer(reporter)
evaluator = Evaluator(
action_names=None,
# pyre-fixme[16]: `ActorCriticBase` has no attribute `rl_parameters`.
gamma=self.rl_parameters.gamma,
model=self.trainer,
metrics_to_score=self.metrics_to_score,
)
# pyre-fixme[16]: `Evaluator` has no attribute `add_observer`.
evaluator.add_observer(reporter)
batch_preprocessor = self.build_batch_preprocessor()
train_and_evaluate_generic(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
trainer=self.trainer,
num_epochs=num_epochs,
use_gpu=self.use_gpu,
batch_preprocessor=batch_preprocessor,
reporter=reporter,
evaluator=evaluator,
reader_options=self.reader_options,
)
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report())
return RLTrainingOutput(training_report=training_report)
def train(
self, train_dataset: Dataset, eval_dataset: Optional[Dataset], num_epochs: int
) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainingOutput. The field that should not be filled
are:
- output_path
- warmstart_output_path
- vis_metrics
- validation_output
"""
logger.info("Creating reporter")
reporter = DiscreteDQNReporter(
self.trainer_param.actions,
target_action_distribution=self.target_action_distribution,
)
logger.info("Adding reporter to trainer")
self.trainer.add_observer(reporter)
training_page_handler = TrainingPageHandler(self.trainer)
training_page_handler.add_observer(reporter)
evaluator = Evaluator(
self.action_names,
self.rl_parameters.gamma,
self.trainer,
metrics_to_score=self.metrics_to_score,
)
logger.info("Adding reporter to evaluator")
evaluator.add_observer(reporter)
evaluation_page_handler = EvaluationPageHandler(
self.trainer, evaluator, reporter
)
batch_preprocessor = self.build_batch_preprocessor()
train_and_evaluate_generic(
train_dataset,
eval_dataset,
self.trainer,
num_epochs,
self.use_gpu,
batch_preprocessor,
training_page_handler,
evaluation_page_handler,
reader_options=self.reader_options,
)
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report()
)
return RLTrainingOutput(training_report=training_report)
def train(
self,
train_dataset: Dataset,
eval_dataset: Optional[Dataset],
num_epochs: int,
reader_options: ReaderOptions,
) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainingOutput.
The field that should not be filled are:
- output_path
"""
reporter = DiscreteDQNReporter(
self.trainer_param.actions,
target_action_distribution=self.target_action_distribution,
)
# pyre-fixme[16]: `RLTrainer` has no attribute `add_observer`.
self.trainer.add_observer(reporter)
evaluator = Evaluator(
self.action_names,
self.rl_parameters.gamma,
self.trainer,
metrics_to_score=self.metrics_to_score,
)
# pyre-fixme[16]: `Evaluator` has no attribute `add_observer`.
evaluator.add_observer(reporter)
batch_preprocessor = self.build_batch_preprocessor()
train_and_evaluate_generic(
train_dataset,
eval_dataset,
# pyre-fixme[6]: Expected `RLTrainer` for 3rd param but got `Trainer`.
# pyre-fixme[6]: Expected `RLTrainer` for 3rd param but got `Trainer`.
self.trainer,
num_epochs,
self.use_gpu,
batch_preprocessor,
reporter,
evaluator,
reader_options=self.reader_options,
)
# pyre-fixme[16]: `RLTrainingReport` has no attribute `make_union_instance`.
training_report = RLTrainingReport.make_union_instance(
reporter.generate_training_report()
)
return RLTrainingOutput(training_report=training_report)