def run_feature_identification(
self, input_table_spec: TableSpec) -> Dict[str, NormalizationData]:
# Run state feature identification
state_normalization_parameters = identify_normalization_parameters(
input_table_spec,
InputColumn.STATE_FEATURES,
self.get_state_preprocessing_options(),
)
# Run action feature identification
action_normalization_parameters = identify_normalization_parameters(
input_table_spec,
InputColumn.ACTION,
self.get_action_preprocessing_options(),
)
return {
NormalizationKey.STATE:
NormalizationData(
dense_normalization_parameters=state_normalization_parameters),
NormalizationKey.ACTION:
NormalizationData(
dense_normalization_parameters=action_normalization_parameters
),
}
def run_feature_identification(
self, input_table_spec: TableSpec) -> Dict[str, NormalizationData]:
# Run state feature identification
state_preprocessing_options = (
# pyre-fixme[16]: `ActorCriticBase` has no attribute
# `_state_preprocessing_options`.
self._state_preprocessing_options or PreprocessingOptions())
state_features = [
ffi.feature_id
for ffi in self.state_feature_config.float_feature_infos
]
logger.info(f"state whitelist_features: {state_features}")
state_preprocessing_options = state_preprocessing_options._replace(
whitelist_features=state_features)
state_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES,
state_preprocessing_options)
# Run action feature identification
action_preprocessing_options = (
# pyre-fixme[16]: `ActorCriticBase` has no attribute
# `_action_preprocessing_options`.
self._action_preprocessing_options or PreprocessingOptions())
action_features = [
ffi.feature_id
for ffi in self.action_feature_config.float_feature_infos
]
logger.info(f"action whitelist_features: {action_features}")
# pyre-fixme[16]: `ActorCriticBase` has no attribute `actor_net_builder`.
actor_net_builder = self.actor_net_builder.value
action_feature_override = actor_net_builder.default_action_preprocessing
logger.info(
f"Default action_feature_override is {action_feature_override}")
if self.action_feature_override is not None:
action_feature_override = self.action_feature_override
assert action_preprocessing_options.feature_overrides is None
action_preprocessing_options = action_preprocessing_options._replace(
whitelist_features=action_features,
feature_overrides={
fid: action_feature_override
for fid in action_features
},
)
action_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.ACTION, action_preprocessing_options)
return {
NormalizationKey.STATE:
NormalizationData(
dense_normalization_parameters=state_normalization_parameters),
NormalizationKey.ACTION:
NormalizationData(
dense_normalization_parameters=action_normalization_parameters
),
}
def run_feature_identification(
self, input_table_spec: TableSpec
) -> Dict[str, NormalizationData]:
state_preprocessing_options = (
self.model_manager.state_preprocessing_options or PreprocessingOptions()
)
state_features = [
ffi.feature_id
for ffi in self.model_manager.state_feature_config.float_feature_infos
]
logger.info(f"state allowedlist_features: {state_features}")
state_preprocessing_options = replace(
state_preprocessing_options, allowedlist_features=state_features
)
state_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES, state_preprocessing_options
)
if self.model_manager.discrete_action_names:
return {
NormalizationKey.STATE: NormalizationData(
dense_normalization_parameters=state_normalization_parameters
)
}
# Run action feature identification
action_preprocessing_options = (
self.model_manager.action_preprocessing_options or PreprocessingOptions()
)
action_features = [
ffi.feature_id
for ffi in self.model_manager.action_feature_config.float_feature_infos
]
logger.info(f"action allowedlist_features: {action_features}")
action_preprocessing_options = replace(
action_preprocessing_options, allowedlist_features=action_features
)
action_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.ACTION, action_preprocessing_options
)
return {
NormalizationKey.STATE: NormalizationData(
dense_normalization_parameters=state_normalization_parameters
),
NormalizationKey.ACTION: NormalizationData(
dense_normalization_parameters=action_normalization_parameters
),
}
def run_feature_identification(
self, input_table_spec: TableSpec
) -> Dict[str, NormalizationData]:
# Run state feature identification
state_preprocessing_options = (
self._state_preprocessing_options or PreprocessingOptions()
)
state_features = [
ffi.feature_id for ffi in self.state_feature_config.float_feature_infos
]
logger.info(f"state whitelist_features: {state_features}")
state_preprocessing_options = state_preprocessing_options._replace(
whitelist_features=state_features
)
state_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES, state_preprocessing_options
)
# Run action feature identification
action_preprocessing_options = (
self._action_preprocessing_options or PreprocessingOptions()
)
action_features = [
ffi.feature_id for ffi in self.action_feature_config.float_feature_infos
]
logger.info(f"action whitelist_features: {action_features}")
action_preprocessing_options = action_preprocessing_options._replace(
whitelist_features=action_features
)
action_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.ACTION, action_preprocessing_options
)
return {
NormalizationKey.STATE: NormalizationData(
dense_normalization_parameters=state_normalization_parameters
),
NormalizationKey.ACTION: NormalizationData(
dense_normalization_parameters=action_normalization_parameters
),
}
def run_feature_identification(
self, input_table_spec: TableSpec
) -> Dict[str, NormalizationData]:
state_preprocessing_options = (
self._state_preprocessing_options or PreprocessingOptions()
)
state_features = [
ffi.feature_id for ffi in self.state_feature_config.float_feature_infos
]
logger.info(f"state allowedlist_features: {state_features}")
state_preprocessing_options = state_preprocessing_options._replace(
allowedlist_features=state_features
)
state_normalization_parameters = identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES, state_preprocessing_options
)
item_preprocessing_options = (
self._item_preprocessing_options or PreprocessingOptions()
)
item_features = [
ffi.feature_id for ffi in self.item_feature_config.float_feature_infos
]
logger.info(f"item allowedlist_features: {item_features}")
item_preprocessing_options = item_preprocessing_options._replace(
allowedlist_features=item_features,
sequence_feature_id=self.slate_feature_id,
)
item_normalization_parameters = identify_normalization_parameters(
input_table_spec,
InputColumn.STATE_SEQUENCE_FEATURES,
item_preprocessing_options,
)
return {
NormalizationKey.STATE: NormalizationData(
dense_normalization_parameters=state_normalization_parameters
),
NormalizationKey.ITEM: NormalizationData(
dense_normalization_parameters=item_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)
return {
NormalizationKey.STATE:
NormalizationData(dense_normalization_parameters=
identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES,
preprocessing_options))
}
def run_feature_identification(
self, input_table_spec: TableSpec
) -> Dict[str, NormalizationData]:
preprocessing_options = (
self.model_manager.preprocessing_options or PreprocessingOptions()
)
state_features = [
ffi.feature_id
for ffi in self.model_manager.state_feature_config.float_feature_infos
]
logger.info(f"Overriding allowedlist_features: {state_features}")
preprocessing_options = replace(
preprocessing_options, allowedlist_features=state_features
)
return {
NormalizationKey.STATE: NormalizationData(
dense_normalization_parameters=identify_normalization_parameters(
input_table_spec, InputColumn.STATE_FEATURES, preprocessing_options
)
)
}
def test_preprocessing(self):
distributions = {}
distributions["0"] = {"mean": 0, "stddev": 1}
distributions["1"] = {"mean": 4, "stddev": 3}
def get_random_feature():
return {
k: np.random.normal(loc=info["mean"], scale=info["stddev"])
for k, info in distributions.items()
}
data = [(i, get_random_feature()) for i in range(NUM_ROWS)]
df = self.sc.parallelize(data).toDF(["i", COL_NAME])
df.show()
df.createOrReplaceTempView(TABLE_NAME)
num_samples = NUM_ROWS // 2
preprocessing_options = PreprocessingOptions(num_samples=num_samples)
table_spec = TableSpec(table_name=TABLE_NAME)
normalization_params = identify_normalization_parameters(
table_spec,
COL_NAME,
preprocessing_options,
seed=self.test_class_seed)
logger.info(normalization_params)
for k, info in distributions.items():
logger.info(f"Expect {k} to be normal with "
f"mean {info['mean']}, stddev {info['stddev']}.")
assert normalization_params[k].feature_type == CONTINUOUS
assert (
abs(normalization_params[k].mean - info["mean"]) < 0.05
), f"{normalization_params[k].mean} not close to {info['mean']}"
assert abs(
normalization_params[k].stddev - info["stddev"] < 0.2
), f"{normalization_params[k].stddev} not close to {info['stddev']}"
logger.info("identify_normalization_parameters seems fine.")
