Version: 3.x
rasa.nlu.featurizers.sparse_featurizer.regex_featurizer
RegexFeaturizer Objects
@DefaultV1Recipe.register(
DefaultV1Recipe.ComponentType.MESSAGE_FEATURIZER, is_trainable=True
)
class RegexFeaturizer(SparseFeaturizer, GraphComponent)
Adds message features based on regex expressions.
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.
__init__
def __init__(config: Dict[Text, Any],
model_storage: ModelStorage,
resource: Resource,
execution_context: ExecutionContext,
known_patterns: Optional[List[Dict[Text, Text]]] = None) -> None
Constructs new features for regexes and lookup table using regex expressions.
Arguments:
config
- Configuration for the component.model_storage
- Storage which graph components can use to persist and load themselves.resource
- Resource locator for this component which can be used to persist and load itself from themodel_storage
.execution_context
- Information about the current graph run.known_patterns
- Regex Patterns the component should pre-load itself with.
create
@classmethod
def create(cls, config: Dict[Text, Any], model_storage: ModelStorage,
resource: Resource,
execution_context: ExecutionContext) -> RegexFeaturizer
Creates a new untrained component (see parent class for full docstring).
train
def train(training_data: TrainingData) -> Resource
Trains the component with all patterns extracted from training data.
process_training_data
def process_training_data(training_data: TrainingData) -> TrainingData
Processes the training examples (see parent class for full docstring).
process
def process(messages: List[Message]) -> List[Message]
Featurizes all given messages in-place.
Returns:
the given list of messages which have been modified in-place
load
@classmethod
def load(cls, config: Dict[Text, Any], model_storage: ModelStorage,
resource: Resource, execution_context: ExecutionContext,
**kwargs: Any) -> RegexFeaturizer
Loads trained 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.