def feature_config(self):
return rlt.ModelFeatureConfig(
id_mapping_config={
"page": rlt.IdMapping(ids=list(range(100, 100 + self.embedding_size)))
},
sequence_features_type=SequenceFeatures,
)
def _test_discrete_dqn_net_builder(
self,
chooser: DiscreteDQNNetBuilder__Union,
state_feature_config: Optional[rlt.ModelFeatureConfig] = None,
serving_module_class=DiscreteDqnPredictorWrapper,
) -> None:
builder = chooser.value
state_dim = 3
state_feature_config = state_feature_config or rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=f"f{i}", feature_id=i)
for i in range(state_dim)
])
state_dim = len(state_feature_config.float_feature_infos)
state_norm_params = {
fi.feature_id: NormalizationParameters(feature_type=CONTINUOUS,
mean=0.0,
stddev=1.0)
for fi in state_feature_config.float_feature_infos
}
action_names = ["L", "R"]
q_network = builder.build_q_network(state_feature_config,
state_norm_params,
len(action_names))
x = q_network.input_prototype()
y = q_network(x).q_values
self.assertEqual(y.shape, (1, 2))
serving_module = builder.build_serving_module(q_network,
state_norm_params,
action_names,
state_feature_config)
self.assertIsInstance(serving_module, serving_module_class)
def feature_config(self):
return rlt.ModelFeatureConfig(
id_mapping_config={
"page": rlt.IdMapping(ids=list(range(100, 100 + self.embedding_size)))
},
id_list_feature_configs=[
rlt.IdFeatureConfig(
name="page_id", feature_id=2002, id_mapping_name="page"
)
],
)
def test_discrete_wrapper_with_id_list(self):
state_normalization_parameters = {i: _cont_norm() for i in range(1, 5)}
state_preprocessor = Preprocessor(state_normalization_parameters,
False)
action_dim = 2
state_feature_config = rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=str(i), feature_id=i)
for i in range(1, 5)
],
id_list_feature_configs=[
rlt.IdListFeatureConfig(name="A",
feature_id=10,
id_mapping_name="A_mapping")
],
id_mapping_config={"A_mapping": rlt.IdMapping(ids=[0, 1, 2])},
)
dqn = FullyConnectedDQNWithEmbedding(
state_dim=len(state_normalization_parameters),
action_dim=action_dim,
sizes=[16],
activations=["relu"],
model_feature_config=state_feature_config,
embedding_dim=8,
)
dqn_with_preprocessor = DiscreteDqnWithPreprocessorWithIdList(
dqn, state_preprocessor, state_feature_config)
action_names = ["L", "R"]
wrapper = DiscreteDqnPredictorWrapperWithIdList(
dqn_with_preprocessor, action_names, state_feature_config)
input_prototype = dqn_with_preprocessor.input_prototype()
output_action_names, q_values = wrapper(*input_prototype)
self.assertEqual(action_names, output_action_names)
self.assertEqual(q_values.shape, (1, 2))
feature_id_to_name = {
config.feature_id: config.name
for config in state_feature_config.id_list_feature_configs
}
state_id_list_features = {
feature_id_to_name[k]: v
for k, v in input_prototype[1].items()
}
expected_output = dqn(
rlt.PreprocessedState(state=rlt.PreprocessedFeatureVector(
float_features=state_preprocessor(*input_prototype[0]),
id_list_features=state_id_list_features,
))).q_values
self.assertTrue((expected_output == q_values).all())
def test_fully_connected_with_id_list(self):
# Intentionally used this long path to make sure we included it in __init__.py
chooser = DiscreteDQNNetBuilderChooser(
FullyConnectedWithEmbedding=discrete_dqn.fully_connected_with_embedding.FullyConnectedWithEmbedding.config_type()()
)
state_feature_config = rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=str(i), feature_id=i) for i in range(1, 5)
],
id_list_feature_configs=[
rlt.IdListFeatureConfig(
name="A", feature_id=10, id_mapping_name="A_mapping"
)
],
id_mapping_config={"A_mapping": rlt.IdMapping(ids=[0, 1, 2])},
)
self._test_discrete_dqn_net_builder(
chooser,
state_feature_config=state_feature_config,
serving_module_class=DiscreteDqnPredictorWrapperWithIdList,
)
class DiscreteDQNBase(ModelManager):
target_action_distribution: Optional[List[float]] = None
state_feature_config: Optional[rlt.ModelFeatureConfig] = field(
default_factory=lambda: rlt.ModelFeatureConfig(float_feature_infos=[]))
preprocessing_options: Optional[PreprocessingOptions] = None
reader_options: Optional[ReaderOptions] = None
def __post_init__(self):
super().__init__()
self._metrics_to_score = None
self._q_network: Optional[ModelBase] = None
@classmethod
def normalization_key(cls) -> str:
return DiscreteNormalizationParameterKeys.STATE
@property
def metrics_to_score(self) -> List[str]:
assert self.reward_options is not None
if self._metrics_to_score is None:
self._metrics_to_score = get_metrics_to_score(
self._reward_options.metric_reward_values)
return self._metrics_to_score
@property
def should_generate_eval_dataset(self) -> bool:
return self.eval_parameters.calc_cpe_in_training
def _set_normalization_parameters(
self, normalization_data_map: Dict[str, NormalizationData]):
"""
Set normalization parameters on current instance
"""
state_norm_data = normalization_data_map.get(self.normalization_key(),
None)
assert state_norm_data is not None
assert state_norm_data.dense_normalization_parameters is not None
self.state_normalization_parameters = (
state_norm_data.dense_normalization_parameters)
def run_feature_identification(
self, input_table_spec: TableSpec) -> Dict[str, NormalizationData]:
preprocessing_options = self.preprocessing_options or PreprocessingOptions(
)
logger.info("Overriding whitelist_features")
state_features = [
ffi.feature_id
for ffi in self.state_feature_config.float_feature_infos
]
preprocessing_options = preprocessing_options._replace(
whitelist_features=state_features)
state_normalization_parameters = identify_normalization_parameters(
input_table_spec, "state_features", preprocessing_options)
return {
DiscreteNormalizationParameterKeys.STATE:
NormalizationData(
dense_normalization_parameters=state_normalization_parameters)
}
def query_data(
self,
input_table_spec: TableSpec,
sample_range: Optional[Tuple[float, float]],
reward_options: RewardOptions,
eval_dataset: bool,
) -> Dataset:
# sort is set to False because EvaluationPageHandler sort the data anyway
return query_data(
input_table_spec,
self.action_names,
self.rl_parameters.use_seq_num_diff_as_time_diff,
sample_range=sample_range,
metric_reward_values=reward_options.metric_reward_values,
custom_reward_expression=reward_options.custom_reward_expression,
additional_reward_expression=reward_options.
additional_reward_expression,
multi_steps=self.multi_steps,
gamma=self.rl_parameters.gamma,
sort=False,
)
@property
def multi_steps(self) -> Optional[int]:
return self.rl_parameters.multi_steps
def build_batch_preprocessor(self) -> BatchPreprocessor:
raise NotImplementedError
def train(self, train_dataset: Dataset, eval_dataset: Optional[Dataset],
num_epochs: int) -> RLTrainingOutput:
"""
Train the model
Returns partially filled RLTrainningOutput. 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,
)
return RLTrainingOutput(
training_report=reporter.generate_training_report())