Version: 2.6.x

rasa.core.training

extract_story_graph

async extract_story_graph(resource_name: Text, domain: "Domain", use_e2e: bool = False, exclusion_percentage: Optional[int] = None) -> "StoryGraph"

Loads training stories / rules from file or directory.

Arguments:

  • resource_name - Path to file or directory.
  • domain - The model domain.
  • use_e2e - True if Markdown files should be parsed as conversation test files.
  • exclusion_percentage - Percentage of stories which should be dropped. None if all training data should be used.

Returns:

The loaded training data as graph.

load_data

async load_data(resource_name: Union[Text, "TrainingDataImporter"], domain: "Domain", remove_duplicates: bool = True, unique_last_num_states: Optional[int] = None, augmentation_factor: int = 50, tracker_limit: Optional[int] = None, use_story_concatenation: bool = True, debug_plots: bool = False, exclusion_percentage: Optional[int] = None) -> List["TrackerWithCachedStates"]

Load training data from a resource.

Arguments:

  • resource_name - resource to load the data from. either a path or an importer
  • domain - domain used for loading
  • remove_duplicates - should duplicated training examples be removed?
  • unique_last_num_states - number of states in a conversation that make the a tracker unique (this is used to identify duplicates) augmentation_factor: by how much should the story training data be augmented tracker_limit: maximum number of trackers to generate during augmentation use_story_concatenation: should stories be concatenated when doing data augmentation debug_plots: generate debug plots during loading exclusion_percentage: how much data to exclude

Returns:

list of loaded trackers