def _create_loop_unhappy_lookup_from_states(
self,
trackers_as_states: List[List[State]],
trackers_as_actions: List[List[Text]],
) -> Dict[Text, Text]:
"""Creates lookup dictionary from the tracker represented as states.
Args:
trackers_as_states: representation of the trackers as a list of states
trackers_as_actions: representation of the trackers as a list of actions
Returns:
lookup dictionary
"""
lookup = {}
for states, actions in zip(trackers_as_states, trackers_as_actions):
action = actions[0]
active_loop = get_active_loop_name(states[-1])
# even if there are two identical feature keys
# their loop will be the same
if not active_loop:
continue
states = self._states_for_unhappy_loop_predictions(states)
feature_key = self._create_feature_key(states)
if not feature_key:
continue
# Since rule snippets and stories inside the loop contain
# only unhappy paths, notify the loop that
# it was predicted after an answer to a different question and
# therefore it should not validate user input
if (
# loop is predicted after action_listen in unhappy path,
# therefore no validation is needed
is_prev_action_listen_in_state(states[-1])
and action == active_loop
):
lookup[feature_key] = DO_NOT_VALIDATE_LOOP
elif (
# some action other than action_listen and active_loop
# is predicted in unhappy path,
# therefore active_loop shouldn't be predicted by the rule
not is_prev_action_listen_in_state(states[-1])
and action not in {ACTION_LISTEN_NAME, active_loop}
):
lookup[feature_key] = DO_NOT_PREDICT_LOOP_ACTION
return lookup
def encode_state(
self, state: State, interpreter: NaturalLanguageInterpreter
) -> Dict[Text, List["Features"]]:
"""Encode the given state with the help of the given interpreter.
Args:
state: The state to encode
interpreter: The interpreter used to encode the state
Returns:
A dictionary of state_type to list of features.
"""
state_features = {}
for state_type, sub_state in state.items():
if state_type == PREVIOUS_ACTION:
state_features.update(
self._extract_state_features(sub_state, interpreter, sparse=True)
)
# featurize user only if it is "real" user input,
# i.e. input from a turn after action_listen
if state_type == USER and is_prev_action_listen_in_state(state):
state_features.update(
self._extract_state_features(sub_state, interpreter, sparse=True)
)
if sub_state.get(ENTITIES):
state_features[ENTITIES] = self._create_features(
sub_state, ENTITIES, sparse=True
)
if state_type in {SLOTS, ACTIVE_LOOP}:
state_features[state_type] = self._create_features(
sub_state, state_type, sparse=True
)
return state_features
def encode_state(
self,
state: State,
precomputations: Optional[MessageContainerForCoreFeaturization],
) -> Dict[Text, List[Features]]:
"""Encode the given state.
Args:
state: The state to encode
precomputations: Contains precomputed features and attributes.
Returns:
A dictionary of state_type to list of features.
"""
state_features = {}
for state_type, sub_state in state.items():
if state_type == PREVIOUS_ACTION:
state_features.update(
self._extract_state_features(
sub_state, precomputations=precomputations, sparse=True,
)
)
# featurize user only if it is "real" user input,
# i.e. input from a turn after action_listen
if state_type == USER and is_prev_action_listen_in_state(state):
state_features.update(
self._extract_state_features(
sub_state, precomputations=precomputations, sparse=True,
)
)
if sub_state.get(ENTITIES):
state_features[ENTITIES] = self._create_features(
sub_state, ENTITIES, sparse=True
)
if state_type in {SLOTS, ACTIVE_LOOP}:
state_features[state_type] = self._create_features(
sub_state, state_type, sparse=True
)
return state_features
def _choose_last_user_input(self, trackers_as_states: List[List[State]],
use_text_for_last_user_input: bool) -> None:
for states in trackers_as_states:
last_state = states[-1]
# only update the state of the real user utterance
if not is_prev_action_listen_in_state(last_state):
continue
if use_text_for_last_user_input:
# remove intent features to only use text
if last_state.get(USER, {}).get(INTENT):
del last_state[USER][INTENT]
# don't add entities if text is used for featurization
if last_state.get(USER, {}).get(ENTITIES):
del last_state[USER][ENTITIES]
else:
# remove text features to only use intent
if last_state.get(USER, {}).get(TEXT):
del last_state[USER][TEXT]
# make sure that all dialogue steps are either intent or text based
self._remove_user_text_if_intent(trackers_as_states)
