rasa/core/policies/policy.py
def featurize_for_training(
        self,
        training_trackers: List[DialogueStateTracker],
        domain: Domain,
        **kwargs: Any
    ) -> DialogueTrainingData:
        """Transform training trackers into a vector representation.
        The trackers, consisting of multiple turns, will be transformed
        into a float vector which can be used by a ML model."""

        training_data = self.featurizer.featurize_trackers(training_trackers,
                                                           domain)

        max_training_samples = kwargs.get('max_training_samples')
        if max_training_samples is not None:
            logger.debug("Limit training data to {} training samples."
                         "".format(max_training_samples))
            training_data.limit_training_data_to(max_training_samples)

        return training_data
Transform training trackers into a vector representation.
        The trackers, consisting of multiple turns, will be transformed
        into a float vector which can be used by a ML model.
Transform training trackers into a vector representation.
The trackers, consisting of multiple turns, will be transformed
into a float vector which can be used by a ML model.
Transform training trackers into a vector representation . The trackers consisting of multiple turns will be transformed into a float vector which can be used by a ML model .
def featurize_for_training(
        self,
        training_trackers: List[DialogueStateTracker],
        domain: Domain,
        **kwargs: Any
    ) -> DialogueTrainingData:
        training_data = self.featurizer.featurize_trackers(training_trackers,
                                                           domain)
        max_training_samples = kwargs.get('max_training_samples')
        if max_training_samples is not None:
            logger.debug("Limit training data to {} training samples."
                         "".format(max_training_samples))
            training_data.limit_training_data_to(max_training_samples)
        return training_data
def featurize_for_training ( self , training_trackers : List [ DialogueStateTracker ] , domain : Domain , ** kwargs : Any ) -> DialogueTrainingData : training_data = self . featurizer . featurize_trackers ( training_trackers , domain ) max_training_samples = kwargs . get ( 'max_training_samples' ) if max_training_samples is not None : logger . debug ( "Limit training data to {} training samples." "" . format ( max_training_samples ) ) training_data . limit_training_data_to ( max_training_samples ) return training_data
Transform training trackers into a vector representation.
Transform training trackers into a vector representation .
"""Transform training trackers into a vector representation.
        The trackers, consisting of multiple turns, will be transformed
        into a float vector which can be used by a ML model."""
self
training_trackers
List[DialogueStateTracker]
domain
Domain
kwargs
Any