async def _get_e2e_entity_evaluation_result(
processor: "MessageProcessor",
tracker: DialogueStateTracker,
prediction: PolicyPrediction,
) -> Optional[EntityEvaluationResult]:
previous_event = tracker.events[-1]
if isinstance(previous_event, SlotSet):
# UserUttered events with entities can be followed by SlotSet events
# if slots are defined in the domain
previous_event = tracker.get_last_event_for(
(UserUttered, ActionExecuted))
if isinstance(previous_event, UserUttered):
entities_predicted_by_policies = [
entity for prediction_event in prediction.events
if isinstance(prediction_event, EntitiesAdded)
for entity in prediction_event.entities
]
entity_targets = previous_event.entities
if entity_targets or entities_predicted_by_policies:
text = previous_event.text
if text:
parsed_message = await processor.parse_message(
UserMessage(text=text))
if parsed_message:
tokens = [
Token(text[start:end], start, end)
for start, end in parsed_message.get(
TOKENS_NAMES[TEXT], [])
]
return EntityEvaluationResult(
entity_targets, entities_predicted_by_policies, tokens,
text)
return None
def _get_e2e_entity_evaluation_result(
processor: "MessageProcessor",
tracker: DialogueStateTracker,
prediction: PolicyPrediction,
) -> Optional[EntityEvaluationResult]:
previous_event = tracker.events[-1]
if isinstance(previous_event, UserUttered):
entities_predicted_by_policies = [
entity for prediction_event in prediction.events
if isinstance(prediction_event, EntitiesAdded)
for entity in prediction_event.entities
]
entity_targets = previous_event.entities
if entity_targets or entities_predicted_by_policies:
text = previous_event.text
parsed_message = processor.interpreter.featurize_message(
Message(data={TEXT: text}))
tokens = parsed_message.get(TOKENS_NAMES[TEXT])
return EntityEvaluationResult(entity_targets,
entities_predicted_by_policies,
tokens, text)
"start": 42,
"end": 56,
"value": "Alexanderplatz",
"entity": "location",
"extractor": "EntityExtractorA",
},
{
"start": 42,
"end": 64,
"value": "Alexanderplatz tonight",
"entity": "movie",
"extractor": "EntityExtractorB",
},
]
EN_entity_result = EntityEvaluationResult(EN_targets, EN_predicted, EN_tokens)
EN_entity_result_no_tokens = EntityEvaluationResult(EN_targets, EN_predicted,
[])
def test_token_entity_intersection():
# included
intsec = determine_intersection(CH_correct_segmentation[1],
CH_correct_entity)
assert intsec == len(CH_correct_segmentation[1].text)
# completely outside
intsec = determine_intersection(CH_correct_segmentation[2],
CH_correct_entity)
assert intsec == 0
"start": 42,
"end": 56,
"value": "Alexanderplatz",
"entity": "location",
"extractor": "EntityExtractorA",
},
{
"start": 42,
"end": 64,
"value": "Alexanderplatz tonight",
"entity": "movie",
"extractor": "EntityExtractorB",
},
]
EN_entity_result = EntityEvaluationResult(
EN_targets, EN_predicted, EN_tokens, " ".join([t.text for t in EN_tokens]))
EN_entity_result_no_tokens = EntityEvaluationResult(EN_targets, EN_predicted,
[], "")
def test_token_entity_intersection():
# included
intsec = determine_intersection(CH_correct_segmentation[1],
CH_correct_entity)
assert intsec == len(CH_correct_segmentation[1].text)
# completely outside
intsec = determine_intersection(CH_correct_segmentation[2],
CH_correct_entity)
assert intsec == 0
