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 freeze_current_state(state: State) -> FrozenState:
frozen_state = frozenset({
key: frozenset(values.items())
if isinstance(values, Dict) else frozenset(values)
for key, values in state.items()
}.items())
return frozen_state
def _does_rule_match_state(rule_state: State,
conversation_state: State) -> bool:
for state_type, rule_sub_state in rule_state.items():
conversation_sub_state = conversation_state.get(state_type, {})
for key, value_from_rules in rule_sub_state.items():
if isinstance(value_from_rules, list):
# json dumps and loads tuples as lists,
# so we need to convert them back
value_from_rules = tuple(value_from_rules)
value_from_conversation = conversation_sub_state.get(key)
if (
# value should be set, therefore
# check whether it is the same as in the state
value_from_rules and
value_from_rules != SHOULD_NOT_BE_SET and
value_from_conversation != value_from_rules
) or (
# value shouldn't be set, therefore
# it should be None or non existent in the state
value_from_rules == SHOULD_NOT_BE_SET
and value_from_conversation
# during training `SHOULD_NOT_BE_SET` is provided. Hence, we also
# have to check for the value of the slot state
and value_from_conversation != SHOULD_NOT_BE_SET):
return False
return True
def freeze_current_state(state: State) -> FrozenState:
"""Convert State dict into a hashable format FrozenState.
Args:
state: The state which should be converted
Return:
hashable form of the state of type `FrozenState`
"""
return frozenset({
key: frozenset(values.items())
if isinstance(values, Dict) else frozenset(values)
for key, values in state.items()
}.items())
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 _does_rule_match_state(rule_state: State,
conversation_state: State) -> bool:
for state_type, rule_sub_state in rule_state.items():
conversation_sub_state = conversation_state.get(state_type, {})
for key, value in rule_sub_state.items():
if isinstance(value, list):
# json dumps and loads tuples as lists,
# so we need to convert them back
value = tuple(value)
if (
# value should be set, therefore
# check whether it is the same as in the state
value and value != SHOULD_NOT_BE_SET and
conversation_sub_state.get(key) != value
) or (
# value shouldn't be set, therefore
# it should be None or non existent in the state
value == SHOULD_NOT_BE_SET
and conversation_sub_state.get(key)):
return False
return True