def process(self, message: Message, **kwargs: Any) -> None:
if self._url() is not None:
# mod >
params = kwargs
timezone = self._timezone_from_config_or_request(
self.component_config, params.get("timezone", None))
reference_time = self._reference_time_from_message_or_request(
message, params.get("reference_time", None))
matches = self._duckling_parse(message.text, reference_time,
timezone)
# </ mod
all_extracted = convert_duckling_format_to_rasa(matches)
dimensions = self.component_config["dimensions"]
extracted = DucklingEntityExtractor.filter_irrelevant_entities(
all_extracted, dimensions)
else:
extracted = []
raise_warning(
"Duckling HTTP component in pipeline, but no "
"`url` configuration in the config "
"file nor is `RASA_DUCKLING_HTTP_URL` "
"set as an environment variable. No entities will be extracted!",
docs=DOCS_URL_COMPONENTS + "#ducklinghttpextractor",
)
extracted = self.add_extractor_name(extracted)
message.set(
ENTITIES,
message.get(ENTITIES, []) + extracted,
add_to_output=True,
)
def graph_config_for_recipe(
self,
config: Dict,
cli_parameters: Dict[Text, Any],
training_type: TrainingType = TrainingType.BOTH,
is_finetuning: bool = False,
) -> GraphModelConfiguration:
"""Converts the default config to graphs (see interface for full docstring)."""
mark_as_experimental_feature("graph recipe")
if cli_parameters or is_finetuning:
raise_warning(
"Unlike the Default Recipe, Graph Recipe does not utilize CLI "
"parameters or finetuning and these configurations will be ignored. "
"Add configuration to the recipe itself if you want them to be used.",
docs=DOCS_URL_GRAPH_RECIPE,
)
nlu_target, core_target = self.get_targets(config, training_type)
return GraphModelConfiguration(
train_schema=GraphSchema.from_dict(config.get("train_schema")),
predict_schema=GraphSchema.from_dict(config.get("predict_schema")),
training_type=training_type,
language=config.get("language"),
core_target=core_target,
nlu_target=nlu_target,
)
def test_raise_deprecation():
with pytest.warns(DeprecationWarning) as record:
io_utils.raise_warning("My warning.", DeprecationWarning)
assert len(record) == 1
assert record[0].message.args[0] == "My warning."
assert isinstance(record[0].message, DeprecationWarning)
def check_deterministic_ops() -> None:
"""Warn user if they have set TF_DETERMINISTIC_OPS."""
if os.getenv(TF_DETERMINISTIC_OPS, False):
shared_io_utils.raise_warning(
f"You have set '{TF_DETERMINISTIC_OPS}' to 1. If you are "
f"using one or more GPU(s) and use any of 'SparseFeaturizer', "
f"'TEDPolicy', 'DIETClassifier', 'UnexpecTEDIntentPolicy', or "
f"'ResponseSelector' training and testing will fail as there are no "
f"deterministic GPU implementations of some underlying TF ops.",
category=UserWarning,
)
def train(
self,
training_trackers: List[TrackerWithCachedStates],
domain: Domain,
interpreter: NaturalLanguageInterpreter,
**kwargs: Any,
) -> None:
"""Trains the policy on given training trackers.
Args:
training_trackers: List of training trackers to be used
for training the model.
domain: Domain of the assistant.
interpreter: NLU Interpreter to be used for featurizing the states.
**kwargs: Any other argument.
"""
if not training_trackers:
shared_io_utils.raise_warning(
f"Skipping training of `{self.__class__.__name__}` "
f"as no data was provided. You can exclude this "
f"policy in the configuration "
f"file to avoid this warning.",
category=UserWarning,
)
return
model_data, label_ids = self._prepare_for_training(
training_trackers, domain, interpreter, **kwargs)
if model_data.is_empty():
shared_io_utils.raise_warning(
f"Skipping training of `{self.__class__.__name__}` "
f"as no data was provided. You can exclude this "
f"policy in the configuration "
f"file to avoid this warning.",
category=UserWarning,
)
return
self.run_training(model_data, label_ids)
def test_raise_user_warning():
with pytest.warns(UserWarning) as record:
io_utils.raise_warning("My warning.")
assert len(record) == 1
assert record[0].message.args[0] == "My warning."
def test_raise_future_warning():
with pytest.warns(FutureWarning) as record:
io_utils.raise_warning("My future warning.", FutureWarning)
assert len(record) == 1
assert record[0].message.args[0] == "My future warning."