def test_provide(default_model_storage: ModelStorage,
default_execution_context: ExecutionContext):
resource = Resource("some resource")
domain = Domain.load("examples/rules/domain.yml")
trackers = rasa.core.training.load_data("examples/rules/data/rules.yml",
domain)
policy = RulePolicy.create(
RulePolicy.get_default_config(),
default_model_storage,
resource,
default_execution_context,
)
policy.train(trackers, domain)
provider = RuleOnlyDataProvider.load({}, default_model_storage, resource,
default_execution_context)
rule_only_data = provider.provide()
assert rule_only_data
for key in [RULE_ONLY_SLOTS, RULE_ONLY_LOOPS]:
assert rule_only_data[key] == policy.lookup[key]
def test_validate_after_adding_adding_default_parameter(
get_validation_method: Callable[..., ValidationMethodType],
nlu: bool,
core: bool,
):
# create a schema and rely on rasa to fill in defaults later
schema1 = _get_example_schema()
schema1.nodes["nlu-node"] = SchemaNode(needs={},
uses=WhitespaceTokenizer,
constructor_name="",
fn="",
config={})
schema1.nodes["core-node"] = SchemaNode(needs={},
uses=RulePolicy,
constructor_name="",
fn="",
config={})
# training
validate = get_validation_method(finetuning=False,
load=False,
nlu=nlu,
core=core,
graph_schema=schema1)
validate(importer=EmptyDataImporter())
# same schema -- we just explicitly pass default values
schema2 = copy.deepcopy(schema1)
schema2.nodes["nlu-node"] = SchemaNode(
needs={},
uses=WhitespaceTokenizer,
constructor_name="",
fn="",
config=WhitespaceTokenizer.get_default_config(),
)
schema2.nodes["core-node"] = SchemaNode(
needs={},
uses=RulePolicy,
constructor_name="",
fn="",
config=RulePolicy.get_default_config(),
)
# finetuning *does not raise*
loaded_validate = get_validation_method(finetuning=True,
load=True,
nlu=nlu,
core=core,
graph_schema=schema2)
loaded_validate(importer=EmptyDataImporter())
