Version: 2.x
rasa.shared.nlu.training_data.features
Features Objects
class Features()
Stores the features produced by any featurizer.
__init__
| __init__(features: Union[np.ndarray, scipy.sparse.spmatrix], feature_type: Text, attribute: Text, origin: Union[Text, List[Text]]) -> None
Initializes the Features object.
Arguments:
features- The features.feature_type- Type of the feature, e.g. FEATURE_TYPE_SENTENCE.attribute- Message attribute, e.g. INTENT or TEXT.origin- Name of the component that created the features.
is_sparse
| is_sparse() -> bool
Checks if features are sparse or not.
Returns:
True, if features are sparse, false otherwise.
is_dense
| is_dense() -> bool
Checks if features are dense or not.
Returns:
True, if features are dense, false otherwise.
combine_with_features
| combine_with_features(additional_features: Optional["Features"]) -> None
Combine the incoming features with this instance's features.
Arguments:
additional_features- additional features to add
Returns:
Combined features.
__key__
| __key__() -> Tuple[
| Text, Text, Union[np.ndarray, scipy.sparse.spmatrix], Union[Text, List[Text]]
| ]
Returns a 4-tuple of defining properties.
Returns:
Tuple of type, attribute, features, and origin properties.
__eq__
| __eq__(other: Any) -> bool
Tests if the self Feature equals to the other.
Arguments:
other- The other object.
Returns:
True when the other object is a Feature and has the same
type, attribute, and feature tensors.