def test_evaluation_store_serialise(entity_predictions, entity_targets):
from rasa.shared.nlu.training_data.formats.readerwriter import TrainingDataWriter
store = EvaluationStore(entity_predictions=entity_predictions,
entity_targets=entity_targets)
targets, predictions = store.serialise()
assert len(targets) == len(predictions)
i_pred = 0
i_target = 0
for i, prediction in enumerate(predictions):
target = targets[i]
if prediction != "None" and target != "None":
predicted = entity_predictions[i_pred]
assert prediction == TrainingDataWriter.generate_entity(
predicted.get("text"), predicted)
assert predicted.get("start") == entity_targets[i_target].get(
"start")
assert predicted.get("end") == entity_targets[i_target].get("end")
if prediction != "None":
i_pred += 1
if target != "None":
i_target += 1
def _generate_entity_training_data(entity: Dict[Text, Any]) -> Text:
return TrainingDataWriter.generate_entity(entity.get("text"), entity)