rasa.shared.nlu.training_data.util

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transform_entity_synonyms

def transform_entity_synonyms(
    synonyms: List[Dict[Text, Any]],
    known_synonyms: Optional[Dict[Text, Any]] = None
) -> Dict[Text, Any]:

Transforms the entity synonyms into a text->value dictionary.

get_file_format_extension

def get_file_format_extension(resource_name: Text) -> Text:

Get the file extension based on training data format. It supports both a folder and a file, and tries to guess the format as follows:

• if the resource is a file and has a known format, return this format's extension
• if the resource is a folder and all the resources have the same known format, return its extension
• otherwise, default to DEFAULT_FILE_FORMAT (yml).

Arguments:

• resource_name - The name of the resource, can be a file or a folder.

Returns:

The resource file format.

remove_untrainable_entities_from

def remove_untrainable_entities_from(example: Dict[Text, Any]) -> None:

Remove untrainable entities from serialized training example example.

Entities with an untrainable extractor will be removed. Untrainable extractors are defined in rasa.nlu.constants.PRETRAINED_EXTRACTORS.

Arguments:

• example - Serialized training example to inspect.

intent_response_key_to_template_key

def intent_response_key_to_template_key(intent_response_key: Text) -> Text:

Resolve the response template key for a given intent response key.

Arguments:

• intent_response_key - retrieval intent with the response key suffix attached.
• Returns - The corresponding response template.

template_key_to_intent_response_key

def template_key_to_intent_response_key(template_key: Text) -> Text:

Resolve the intent response key for the given response template.

Arguments:

• template_key - Name of the response template.
• Returns - The corresponding intent response key.

has_string_escape_chars

def has_string_escape_chars(s: Text) -> bool:

Checks whether there are any escape characters in the string.

encode_string

def encode_string(s: Text) -> Text:

Return an encoded python string.

decode_string

def decode_string(s: Text) -> Text:

Return a decoded python string.

build_entity

def build_entity(start: int,
                  end: int,
                  value: Text,
                  entity_type: Text,
                  role: Optional[Text] = None,
                  group: Optional[Text] = None,
                  **kwargs: Any) -> Dict[Text, Any]:

Builds a standard entity dictionary.

Adds additional keyword parameters.

Arguments:

• start - start position of entity
• end - end position of entity
• value - text value of the entity
• entity_type - name of the entity type
• role - role of the entity
• group - group of the entity
• **kwargs - additional parameters

Returns:

an entity dictionary

sparse_matrix_to_string

def sparse_matrix_to_string(m: scipy.sparse.spmatrix) -> Text:

Turns a sparse matrix into a string.

Will return a line "(i,j) v" for each value in the matrix.

Taken from official scipy source to operate on full sparse matrix to not have to change the maxprint property in-place. https://github.com/scipy/scipy/blob/v1.7.0/scipy/sparse/base.py#L258