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Version: Main/Unreleased

rasa.core.featurizers.tracker_featurizers

InvalidStory Objects

class InvalidStory(RasaException)

Exception that can be raised if story cannot be featurized.

__init__

| __init__(message: Text) -> None

Creates an InvalidStory exception.

Arguments:

  • message - a custom exception message.

TrackerFeaturizer Objects

class TrackerFeaturizer()

Base class for actual tracker featurizers.

__init__

| __init__(state_featurizer: Optional[SingleStateFeaturizer] = None) -> None

Initializes the tracker featurizer.

Arguments:

  • state_featurizer - The state featurizer used to encode tracker states.

training_states_and_labels

| training_states_and_labels(trackers: List[DialogueStateTracker], domain: Domain, omit_unset_slots: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[List[List[State]], List[List[Text]]]

Transforms trackers to states and labels.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • omit_unset_slots - If True do not include the initial values of slots.
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training states.

Returns:

Trackers as states and labels.

training_states_labels_and_entities

| @abstractmethod
| training_states_labels_and_entities(trackers: List[DialogueStateTracker], domain: Domain, omit_unset_slots: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[List[List[State]], List[List[Text]], List[List[Dict[Text, Any]]]]

Transforms trackers to states, labels, and entity data.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • omit_unset_slots - If True do not include the initial values of slots.
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training states.

Returns:

Trackers as states, labels, and entity data.

prepare_for_featurization

| prepare_for_featurization(domain: Domain, bilou_tagging: bool = False) -> None

Ensures that the featurizer is ready to be called during training.

State featurizer needs to build its vocabulary from the domain for it to be ready to be used during training.

Arguments:

  • domain - Domain of the assistant.
  • bilou_tagging - Whether to consider bilou tagging.

featurize_trackers

| featurize_trackers(trackers: List[DialogueStateTracker], domain: Domain, precomputations: Optional[MessageContainerForCoreFeaturization], bilou_tagging: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[
| List[List[Dict[Text, List[Features]]]],
| np.ndarray,
| List[List[Dict[Text, List[Features]]]],
| ]

Featurizes the training trackers.

Arguments:

  • trackers - list of training trackers
  • domain - the domain
  • precomputations - Contains precomputed features and attributes.
  • bilou_tagging - indicates whether BILOU tagging should be used or not
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training state features.

Returns:

  • a dictionary of state types (INTENT, TEXT, ACTION_NAME, ACTION_TEXT, ENTITIES, SLOTS, ACTIVE_LOOP) to a list of features for all dialogue turns in all training trackers
  • the label ids (e.g. action ids) for every dialogue turn in all training trackers
  • A dictionary of entity type (ENTITY_TAGS) to a list of features containing entity tag ids for text user inputs otherwise empty dict for all dialogue turns in all training trackers

prediction_states

| prediction_states(trackers: List[DialogueStateTracker], domain: Domain, use_text_for_last_user_input: bool = False, ignore_rule_only_turns: bool = False, rule_only_data: Optional[Dict[Text, Any]] = None, ignore_action_unlikely_intent: bool = False) -> List[List[State]]

Transforms trackers to states for prediction.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • use_text_for_last_user_input - Indicates whether to use text or intent label for featurizing last user input.
  • ignore_rule_only_turns - If True ignore dialogue turns that are present only in rules.
  • rule_only_data - Slots and loops, which only occur in rules but not in stories.
  • ignore_action_unlikely_intent - Whether to remove states containing action_unlikely_intent from prediction states.

Returns:

Trackers as states for prediction.

create_state_features

| create_state_features(trackers: List[DialogueStateTracker], domain: Domain, precomputations: Optional[MessageContainerForCoreFeaturization], use_text_for_last_user_input: bool = False, ignore_rule_only_turns: bool = False, rule_only_data: Optional[Dict[Text, Any]] = None, ignore_action_unlikely_intent: bool = False) -> List[List[Dict[Text, List[Features]]]]

Creates state features for prediction.

Arguments:

  • trackers - A list of state trackers
  • domain - The domain
  • precomputations - Contains precomputed features and attributes.
  • use_text_for_last_user_input - Indicates whether to use text or intent label for featurizing last user input.
  • ignore_rule_only_turns - If True ignore dialogue turns that are present only in rules.
  • rule_only_data - Slots and loops, which only occur in rules but not in stories.
  • ignore_action_unlikely_intent - Whether to remove any states containing action_unlikely_intent from state features.

Returns:

Dictionaries of state type (INTENT, TEXT, ACTION_NAME, ACTION_TEXT, ENTITIES, SLOTS, ACTIVE_LOOP) to a list of features for all dialogue turns in all trackers.

persist

| persist(path: Union[Text, Path]) -> None

Persists the tracker featurizer to the given path.

Arguments:

  • path - The path to persist the tracker featurizer to.

load

| @staticmethod
| load(path: Union[Text, Path]) -> Optional[TrackerFeaturizer]

Loads the featurizer from file.

Arguments:

  • path - The path to load the tracker featurizer from.

Returns:

The loaded tracker featurizer.

FullDialogueTrackerFeaturizer Objects

class FullDialogueTrackerFeaturizer(TrackerFeaturizer)

Creates full dialogue training data for time distributed architectures.

Creates training data that uses each time output for prediction.

training_states_labels_and_entities

| training_states_labels_and_entities(trackers: List[DialogueStateTracker], domain: Domain, omit_unset_slots: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[
| List[List[State]], List[List[Optional[Text]]], List[List[Dict[Text, Any]]]
| ]

Transforms trackers to states, action labels, and entity data.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • omit_unset_slots - If True do not include the initial values of slots.
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training states.

Returns:

Trackers as states, action labels, and entity data.

prediction_states

| prediction_states(trackers: List[DialogueStateTracker], domain: Domain, use_text_for_last_user_input: bool = False, ignore_rule_only_turns: bool = False, rule_only_data: Optional[Dict[Text, Any]] = None, ignore_action_unlikely_intent: bool = False) -> List[List[State]]

Transforms trackers to states for prediction.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • use_text_for_last_user_input - Indicates whether to use text or intent label for featurizing last user input.
  • ignore_rule_only_turns - If True ignore dialogue turns that are present only in rules.
  • rule_only_data - Slots and loops, which only occur in rules but not in stories.
  • ignore_action_unlikely_intent - Whether to remove any states containing action_unlikely_intent from prediction states.

Returns:

Trackers as states for prediction.

MaxHistoryTrackerFeaturizer Objects

class MaxHistoryTrackerFeaturizer(TrackerFeaturizer)

Truncates the tracker history into max_history long sequences.

Creates training data from trackers where actions are the output prediction labels. Tracker state sequences which represent policy input are truncated to not excede max_history states.

__init__

| __init__(state_featurizer: Optional[SingleStateFeaturizer] = None, max_history: Optional[int] = None, remove_duplicates: bool = True) -> None

Initializes the tracker featurizer.

Arguments:

  • state_featurizer - The state featurizer used to encode the states.
  • max_history - The maximum length of an extracted state sequence.
  • remove_duplicates - Keep only unique training state sequence/label pairs.

slice_state_history

| @staticmethod
| slice_state_history(states: List[State], slice_length: Optional[int]) -> List[State]

Slices states from the trackers history.

Arguments:

  • states - The states
  • slice_length - The slice length

Returns:

The sliced states.

training_states_labels_and_entities

| training_states_labels_and_entities(trackers: List[DialogueStateTracker], domain: Domain, omit_unset_slots: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[List[List[State]], List[List[Text]], List[List[Dict[Text, Any]]]]

Transforms trackers to states, action labels, and entity data.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • omit_unset_slots - If True do not include the initial values of slots.
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training states.

Returns:

Trackers as states, labels, and entity data.

prediction_states

| prediction_states(trackers: List[DialogueStateTracker], domain: Domain, use_text_for_last_user_input: bool = False, ignore_rule_only_turns: bool = False, rule_only_data: Optional[Dict[Text, Any]] = None, ignore_action_unlikely_intent: bool = False) -> List[List[State]]

Transforms trackers to states for prediction.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • use_text_for_last_user_input - Indicates whether to use text or intent label for featurizing last user input.
  • ignore_rule_only_turns - If True ignore dialogue turns that are present only in rules.
  • rule_only_data - Slots and loops, which only occur in rules but not in stories.
  • ignore_action_unlikely_intent - Whether to remove any states containing action_unlikely_intent from prediction states.

Returns:

Trackers as states for prediction.

IntentMaxHistoryTrackerFeaturizer Objects

class IntentMaxHistoryTrackerFeaturizer(MaxHistoryTrackerFeaturizer)

Truncates the tracker history into max_history long sequences.

Creates training data from trackers where intents are the output prediction labels. Tracker state sequences which represent policy input are truncated to not excede max_history states.

training_states_labels_and_entities

| training_states_labels_and_entities(trackers: List[DialogueStateTracker], domain: Domain, omit_unset_slots: bool = False, ignore_action_unlikely_intent: bool = False) -> Tuple[List[List[State]], List[List[Text]], List[List[Dict[Text, Any]]]]

Transforms trackers to states, intent labels, and entity data.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • omit_unset_slots - If True do not include the initial values of slots.
  • ignore_action_unlikely_intent - Whether to remove action_unlikely_intent from training states.

Returns:

Trackers as states, labels, and entity data.

prediction_states

| prediction_states(trackers: List[DialogueStateTracker], domain: Domain, use_text_for_last_user_input: bool = False, ignore_rule_only_turns: bool = False, rule_only_data: Optional[Dict[Text, Any]] = None, ignore_action_unlikely_intent: bool = False) -> List[List[State]]

Transforms trackers to states for prediction.

Arguments:

  • trackers - The trackers to transform.
  • domain - The domain.
  • use_text_for_last_user_input - Indicates whether to use text or intent label for featurizing last user input.
  • ignore_rule_only_turns - If True ignore dialogue turns that are present only in rules.
  • rule_only_data - Slots and loops, which only occur in rules but not in stories.
  • ignore_action_unlikely_intent - Whether to remove any states containing action_unlikely_intent from prediction states.

Returns:

Trackers as states for prediction.