Version: 3.x
rasa.nlu.featurizers.dense_featurizer.mitie_featurizer
MitieFeaturizer Objects
@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.MESSAGE_FEATURIZER,
is_trainable=False,
model_from="MitieNLP",
)
class MitieFeaturizer(DenseFeaturizer, GraphComponent)
A class that featurizes using Mitie.
required_components
@classmethod
def required_components(cls) -> List[Type]
Components that should be included in the pipeline before this component.
get_default_config
@staticmethod
def get_default_config() -> Dict[Text, Any]
Returns the component's default config.
required_packages
@staticmethod
def required_packages() -> List[Text]
Any extra python dependencies required for this component to run.
__init__
def __init__(config: Dict[Text, Any],
execution_context: ExecutionContext) -> None
Instantiates a new MitieFeaturizer instance.
create
@classmethod
def create(cls, config: Dict[Text, Any], model_storage: ModelStorage,
resource: Resource,
execution_context: ExecutionContext) -> MitieFeaturizer
Creates a new untrained component (see parent class for full docstring).
validate_config
@classmethod
def validate_config(cls, config: Dict[Text, Any]) -> None
Validates that the component is configured properly.
ndim
def ndim(feature_extractor: "mitie.total_word_feature_extractor") -> int
Returns the number of dimensions.
process
def process(messages: List[Message], model: MitieModel) -> List[Message]
Featurizes all given messages in-place.
Returns:
The given list of messages which have been modified in-place.
process_training_data
def process_training_data(training_data: TrainingData,
model: MitieModel) -> TrainingData
Processes the training examples in the given training data in-place.
Arguments:
training_data- Training data.model- A Mitie model.
Returns:
Same training data after processing.
features_for_tokens
def features_for_tokens(
tokens: List[Token],
feature_extractor: "mitie.total_word_feature_extractor"
) -> Tuple[np.ndarray, np.ndarray]
Calculates features.