rasa/core/agent.py
async def load_data(self,
                        resource_name: Text,
                        remove_duplicates: bool = True,
                        unique_last_num_states: Optional[int] = None,
                        augmentation_factor: int = 20,
                        tracker_limit: Optional[int] = None,
                        use_story_concatenation: bool = True,
                        debug_plots: bool = False,
                        exclusion_percentage: int = None
                        ) -> List[DialogueStateTracker]:
        """Load training data from a resource."""

        max_history = self._max_history()

        if unique_last_num_states is None:
            # for speed up of data generation
            # automatically detect unique_last_num_states
            # if it was not set and
            # if all featurizers are MaxHistoryTrackerFeaturizer
            if self._are_all_featurizers_using_a_max_history():
                unique_last_num_states = max_history
        elif unique_last_num_states < max_history:
            # possibility of data loss
            logger.warning("unique_last_num_states={} but "
                           "maximum max_history={}."
                           "Possibility of data loss. "
                           "It is recommended to set "
                           "unique_last_num_states to "
                           "at least maximum max_history."
                           "".format(unique_last_num_states, max_history))

        return await training.load_data(
            resource_name, self.domain,
            remove_duplicates, unique_last_num_states,
            augmentation_factor,
            tracker_limit, use_story_concatenation,
            debug_plots,
            exclusion_percentage=exclusion_percentage)
Load training data from a resource.
Load training data from a resource.
Load training data from a resource .
async def load_data(self,
                        resource_name: Text,
                        remove_duplicates: bool = True,
                        unique_last_num_states: Optional[int] = None,
                        augmentation_factor: int = 20,
                        tracker_limit: Optional[int] = None,
                        use_story_concatenation: bool = True,
                        debug_plots: bool = False,
                        exclusion_percentage: int = None
                        ) -> List[DialogueStateTracker]:
        max_history = self._max_history()
        if unique_last_num_states is None:
            if self._are_all_featurizers_using_a_max_history():
                unique_last_num_states = max_history
        elif unique_last_num_states < max_history:
            logger.warning("unique_last_num_states={} but "
                           "maximum max_history={}."
                           "Possibility of data loss. "
                           "It is recommended to set "
                           "unique_last_num_states to "
                           "at least maximum max_history."
                           "".format(unique_last_num_states, max_history))
        return await training.load_data(
            resource_name, self.domain,
            remove_duplicates, unique_last_num_states,
            augmentation_factor,
            tracker_limit, use_story_concatenation,
            debug_plots,
            exclusion_percentage=exclusion_percentage)
async def load_data ( self , resource_name : Text , remove_duplicates : bool = True , unique_last_num_states : Optional [ int ] = None , augmentation_factor : int = 20 , tracker_limit : Optional [ int ] = None , use_story_concatenation : bool = True , debug_plots : bool = False , exclusion_percentage : int = None ) -> List [ DialogueStateTracker ] : max_history = self . _max_history ( ) if unique_last_num_states is None : if self . _are_all_featurizers_using_a_max_history ( ) : unique_last_num_states = max_history elif unique_last_num_states < max_history : logger . warning ( "unique_last_num_states={} but " "maximum max_history={}." "Possibility of data loss. " "It is recommended to set " "unique_last_num_states to " "at least maximum max_history." "" . format ( unique_last_num_states , max_history ) ) return await training . load_data ( resource_name , self . domain , remove_duplicates , unique_last_num_states , augmentation_factor , tracker_limit , use_story_concatenation , debug_plots , exclusion_percentage = exclusion_percentage )
Load training data from a resource.
Load training data from a resource .
"""Load training data from a resource.""" # for speed up of data generation # automatically detect unique_last_num_states # if it was not set and # if all featurizers are MaxHistoryTrackerFeaturizer # possibility of data loss
self
resource_name
Text
remove_duplicates
bool
unique_last_num_states
Optional[int]
augmentation_factor
int
tracker_limit
Optional[int]
use_story_concatenation
bool
debug_plots
bool
exclusion_percentage
int