def test_not_predict_fallback_intent(message: Message, component_config: Dict):
old_message_state = copy.deepcopy(message)
classifier = FallbackClassifier(component_config=component_config)
classifier.process(message)
assert message == old_message_state
def test_predict_fallback_intent():
threshold = 0.5
message = Message(
"some message",
data={
INTENT: {
"name": "greet",
INTENT_CONFIDENCE_KEY: 0.234891876578331
},
INTENT_RANKING_KEY: [
{
"name": "greet",
INTENT_CONFIDENCE_KEY: 0.234891876578331
},
{
"name": "stop",
INTENT_CONFIDENCE_KEY: threshold - 0.0001
},
{
"name": "affirm",
INTENT_CONFIDENCE_KEY: 0
},
{
"name": "inform",
INTENT_CONFIDENCE_KEY: -100
},
{
"name": "deny",
INTENT_CONFIDENCE_KEY: 0.0879683718085289
},
],
},
)
old_message_state = copy.deepcopy(message)
classifier = FallbackClassifier(
component_config={THRESHOLD_KEY: threshold})
classifier.process(message)
expected_intent = {
"name": DEFAULT_NLU_FALLBACK_INTENT_NAME,
INTENT_CONFIDENCE_KEY: 1.0,
}
assert message.data[INTENT] == expected_intent
old_intent_ranking = old_message_state.data[INTENT_RANKING_KEY]
current_intent_ranking = message.data[INTENT_RANKING_KEY]
assert len(current_intent_ranking) == len(old_intent_ranking) + 1
assert all(item in current_intent_ranking for item in old_intent_ranking)
assert current_intent_ranking[0] == expected_intent
def create_fallback_classifier(
component_config: Dict[Text, Any],
default_model_storage: ModelStorage,
default_execution_context: ExecutionContext,
):
classifier = FallbackClassifier.create(
{
**FallbackClassifier.get_default_config(),
**component_config
},
default_model_storage,
Resource("fallback"),
default_execution_context,
)
return classifier
def test_predict_fallback_intent(message: Message, component_config: Dict):
old_message_state = copy.deepcopy(message)
classifier = FallbackClassifier(component_config=component_config)
classifier.process(message)
expected_intent = {
INTENT_NAME_KEY: DEFAULT_NLU_FALLBACK_INTENT_NAME,
PREDICTED_CONFIDENCE_KEY: 1.0,
}
assert message.data[INTENT] == expected_intent
old_intent_ranking = old_message_state.data[INTENT_RANKING_KEY]
current_intent_ranking = message.data[INTENT_RANKING_KEY]
assert len(current_intent_ranking) == len(old_intent_ranking) + 1
assert all(item in current_intent_ranking for item in old_intent_ranking)
assert current_intent_ranking[0] == expected_intent
def test_not_predict_fallback_intent():
threshold = 0.5
message = Message(
"some message",
data={
INTENT: {
"name": "greet",
INTENT_CONFIDENCE_KEY: threshold
},
INTENT_RANKING_KEY: [
{
"name": "greet",
INTENT_CONFIDENCE_KEY: 0.234891876578331
},
{
"name": "stop",
INTENT_CONFIDENCE_KEY: 0.1
},
{
"name": "affirm",
INTENT_CONFIDENCE_KEY: 0
},
{
"name": "inform",
INTENT_CONFIDENCE_KEY: -100
},
{
"name": "deny",
INTENT_CONFIDENCE_KEY: 0.0879683718085289
},
],
},
)
old_message_state = copy.deepcopy(message)
classifier = FallbackClassifier(
component_config={THRESHOLD_KEY: threshold})
classifier.process(message)
assert message == old_message_state
def __init__(self, prediction_to_return: Dict[Text, Any]) -> None:
# add intent classifier to make sure intents are evaluated
super().__init__([FallbackClassifier()], None)
self.prediction = prediction_to_return
def test_defaults():
classifier = FallbackClassifier({})
assert classifier.component_config[THRESHOLD_KEY] == DEFAULT_NLU_FALLBACK_THRESHOLD
assert classifier.component_config[AMBIGUITY_THRESHOLD_KEY] == 0.1
def test_default_threshold():
classifier = FallbackClassifier({})
assert classifier.component_config[
THRESHOLD_KEY] == DEFAULT_NLU_FALLBACK_THRESHOLD