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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 (2.8.x).

Version: Master/Unreleased

rasa.core.featurizers._tracker_featurizers

InvalidStory Objects

class InvalidStory(RasaException)

Exception that can be raised if story cannot be featurized.

TrackerFeaturizer Objects

class TrackerFeaturizer()

Base class for actual tracker featurizers.

__init__

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

Initializes the tracker featurizer.

Arguments:

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

training_states_actions_and_entities

def training_states_actions_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, actions, 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, actions, and entity data.

training_states_and_actions

def training_states_and_actions(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 actions.

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 actions.

training_states_and_labels

def 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
def 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

def prepare_for_featurization(domain: Domain, interpreter: NaturalLanguageInterpreter, 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.
  • interpreter - NLU Interpreter for featurizing states.
  • bilou_tagging - Whether to consider bilou tagging.

featurize_trackers

def featurize_trackers(trackers: List[DialogueStateTracker], domain: Domain, interpreter: NaturalLanguageInterpreter, 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
  • interpreter - the interpreter
  • 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

def 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

def create_state_features(trackers: List[DialogueStateTracker], domain: Domain, interpreter: NaturalLanguageInterpreter, 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
  • interpreter - The interpreter
  • 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

def 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
def 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

def 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

def 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__

def __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
def 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

def 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

def 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

def 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

def 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.