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This is unreleased documentation for Rasa Open Source Documentation Master/Unreleased version.
For the latest released documentation, see the latest version (2.2.x).

Version: Master/Unreleased

rasa.utils.train_utils

normalize

normalize(values: np.ndarray, ranking_length: Optional[int] = 0) -> np.ndarray

Normalizes an array of positive numbers over the top ranking_length values.

Other values will be set to 0.

update_similarity_type

update_similarity_type(config: Dict[Text, Any]) -> Dict[Text, Any]

If SIMILARITY_TYPE is set to 'auto', update the SIMILARITY_TYPE depending on the LOSS_TYPE.

Arguments:

  • config - model configuration
  • Returns - updated model configuration

align_token_features

align_token_features(list_of_tokens: List[List["Token"]], in_token_features: np.ndarray, shape: Optional[Tuple] = None) -> np.ndarray

Align token features to match tokens.

ConveRTTokenizer, LanguageModelTokenizers might split up tokens into sub-tokens. We need to take the mean of the sub-token vectors and take that as token vector.

Arguments:

  • list_of_tokens - tokens for examples
  • in_token_features - token features from ConveRT
  • shape - shape of feature matrix

Returns:

Token features.

update_evaluation_parameters

update_evaluation_parameters(config: Dict[Text, Any]) -> Dict[Text, Any]

If EVAL_NUM_EPOCHS is set to -1, evaluate at the end of the training.

Arguments:

  • config - model configuration
  • Returns - updated model configuration

load_tf_hub_model

load_tf_hub_model(model_url: Text) -> Any

Load model from cache if possible, otherwise from TFHub

check_deprecated_options

check_deprecated_options(config: Dict[Text, Any]) -> Dict[Text, Any]

Update the config according to changed config params.

If old model configuration parameters are present in the provided config, replace them with the new parameters and log a warning.

Arguments:

  • config - model configuration
  • Returns - updated model configuration

check_core_deprecated_options

check_core_deprecated_options(config: Dict[Text, Any]) -> Dict[Text, Any]

Update the core config according to changed config params.

If old model configuration parameters are present in the provided config, replace them with the new parameters and log a warning.

Arguments:

  • config - model configuration
  • Returns - updated model configuration

entity_label_to_tags

entity_label_to_tags(model_predictions: Dict[Text, Any], entity_tag_specs: List["EntityTagSpec"], bilou_flag: bool = False, prediction_index: int = 0) -> Tuple[Dict[Text, List[Text]], Dict[Text, List[float]]]

Convert the output predictions for entities to the actual entity tags.

Arguments:

  • model_predictions - the output predictions using the entity tag indices
  • entity_tag_specs - the entity tag specifications
  • bilou_flag - if 'True', the BILOU tagging schema was used
  • prediction_index - the index in the batch of predictions to use for entity extraction

Returns:

A map of entity tag type, e.g. entity, role, group, to actual entity tags and confidences.

override_defaults

override_defaults(defaults: Optional[Dict[Text, Any]], custom: Optional[Dict[Text, Any]]) -> Dict[Text, Any]

Override default config with the given config.

We cannot use dict.update method because configs contain nested dicts.

Arguments:

  • defaults - default config
  • custom - user config containing new parameters

Returns:

updated config

init_split_entities

init_split_entities(split_entities_config, default_split_entity) -> Dict[Text, bool]

Initialise the behaviour for splitting entities by comma (or not).

Returns:

Defines desired behaviour for splitting specific entity types and default behaviour for splitting any entity types for which no behaviour is defined.