def test_override_defaults_supervised_embeddings_pipeline():
builder = ComponentBuilder()
_config = RasaNLUModelConfig(
{
"language": "en",
"pipeline": [
{"name": "SpacyNLP"},
{"name": "SpacyTokenizer"},
{"name": "SpacyFeaturizer", "pooling": "max"},
{
"name": "DIETClassifier",
"epochs": 10,
"hidden_layers_sizes": {"text": [256, 128]},
},
],
}
)
idx_featurizer = _config.component_names.index("SpacyFeaturizer")
idx_classifier = _config.component_names.index("DIETClassifier")
component1 = builder.create_component(
_config.for_component(idx_featurizer), _config
)
assert component1.component_config["pooling"] == "max"
component2 = builder.create_component(
_config.for_component(idx_classifier), _config
)
assert component2.component_config["epochs"] == 10
assert (
component2.defaults["hidden_layers_sizes"].keys()
== component2.component_config["hidden_layers_sizes"].keys()
)
def test_create_component_exception_messages(
component_builder: ComponentBuilder,
blank_config: RasaNLUModelConfig,
test_input: Text,
expected_output: Text,
error: Exception,
):
with pytest.raises(error):
component_config = {"name": test_input}
component_builder.create_component(component_config, blank_config)
def test_override_defaults_supervised_embeddings_pipeline():
cfg = config.load("data/test/config_embedding_test.yml")
builder = ComponentBuilder()
component1_cfg = cfg.for_component(0)
component1 = builder.create_component(component1_cfg, cfg)
assert component1.max_ngram == 3
component2_cfg = cfg.for_component(1)
component2 = builder.create_component(component2_cfg, cfg)
assert component2.epochs == 10
def validate_rasa_config(config: Dict):
"""
validates bot config.yml content for invalid entries
:param config: configuration
:return: None
"""
rasa_config = RasaNLUModelConfig(config)
component_builder = ComponentBuilder()
for i in range(len(rasa_config.pipeline)):
component_cfg = rasa_config.for_component(i)
component_builder.create_component(component_cfg, rasa_config)
configuration.load(config)
def test_builder_create_by_module_path(component_builder: ComponentBuilder,
blank_config: RasaNLUModelConfig):
from rasa.nlu.featurizers.sparse_featurizer.regex_featurizer import RegexFeaturizer
path = "rasa.nlu.featurizers.sparse_featurizer.regex_featurizer.RegexFeaturizer"
component_config = {"name": path}
component = component_builder.create_component(component_config,
blank_config)
assert type(component) == RegexFeaturizer
def _build_pipeline(
cfg: RasaNLUModelConfig, component_builder: ComponentBuilder
) -> List[Component]:
"""Transform the passed names of the pipeline components into classes"""
pipeline = []
# Transform the passed names of the pipeline components into classes
for i in range(len(cfg.pipeline)):
component_cfg = cfg.for_component(i)
component = component_builder.create_component(component_cfg, cfg)
pipeline.append(component)
return pipeline
def _build_pipeline(
self, cfg: RasaNLUModelConfig, component_builder: ComponentBuilder
) -> List[Component]:
"""Transform the passed names of the pipeline components into classes."""
pipeline = []
# Transform the passed names of the pipeline components into classes
for index, pipeline_component in enumerate(cfg.pipeline):
component_cfg = cfg.for_component(index)
component = component_builder.create_component(component_cfg, cfg)
components.validate_component_keys(component, pipeline_component)
pipeline.append(component)
if not self.skip_validation:
components.validate_pipeline(pipeline)
return pipeline
def spacy_nlp(component_builder: ComponentBuilder,
blank_config: RasaNLUModelConfig):
spacy_nlp_config = {"name": "SpacyNLP", "model": "en_core_web_md"}
return component_builder.create_component(spacy_nlp_config,
blank_config).nlp
def mitie_feature_extractor(component_builder: ComponentBuilder, blank_config):
mitie_nlp_config = {"name": "MitieNLP"}
return component_builder.create_component(mitie_nlp_config,
blank_config).extractor