def test_merge_token_labels_to_slot(self):
data = get_test_sample()
for i in data:
self.assertEqual(
test_utils.merge_token_labels_to_slot(i["token_ranges"], i["labels"]),
i["output"],
)
self.assertEqual(
test_utils.merge_token_labels_to_slot(
i["token_ranges"],
[test_utils.strip_bio_prefix(l) for l in i["labels"]],
use_bio_label=False,
),
i["output"],
)
def gen_extra_context(self):
self.all_context["slots_prediction"] = [
merge_token_labels_to_slot(
token_range,
self.process_pred(word_pred[0:seq_len]),
self.use_bio_labels,
) for word_pred, seq_len, token_range in zip(
self.all_word_preds,
self.all_context[DatasetFieldName.SEQ_LENS],
self.all_context[DatasetFieldName.TOKEN_RANGE],
)
]
self.all_context[DOC_LABEL_NAMES] = self.doc_label_names
def calculate_metric(self):
return compute_prf1_metrics([
NodesPredictionPair(
get_slots(
merge_token_labels_to_slot(
token_range,
self.process_pred(pred[0:seq_len]),
self.use_bio_labels,
)),
get_slots(slots_label),
) for pred, seq_len, token_range, slots_label in zip(
self.all_preds,
self.all_context[DatasetFieldName.SEQ_LENS],
self.all_context[DatasetFieldName.TOKEN_RANGE],
self.all_context[DatasetFieldName.RAW_WORD_LABEL],
)
])[1]
