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from typing import Dict, List
from lightning.pytorch.callbacks import Callback
from reader.data.relik_reader_sample import RelikReaderSample
from relik.reader.relik_reader_predictor import RelikReaderPredictor
from relik.reader.utils.metrics import compute_metrics
class StrongMatching:
def __call__(self, predicted_samples: List[RelikReaderSample]) -> Dict:
# accumulators
correct_predictions, total_predictions, total_gold = (
0,
0,
0,
)
correct_predictions_strict, total_predictions_strict = (
0,
0,
)
correct_predictions_bound, total_predictions_bound = (
0,
0,
)
correct_span_predictions, total_span_predictions, total_gold_spans = 0, 0, 0
# collect data from samples
for sample in predicted_samples:
if sample.triplets is None:
sample.triplets = []
if sample.entity_candidates:
predicted_annotations_strict = set(
[
(
triplet["subject"]["start"],
triplet["subject"]["end"],
triplet["subject"]["type"],
triplet["relation"]["name"],
triplet["object"]["start"],
triplet["object"]["end"],
triplet["object"]["type"],
)
for triplet in sample.predicted_relations
]
)
gold_annotations_strict = set(
[
(
triplet["subject"]["start"],
triplet["subject"]["end"],
triplet["subject"]["type"],
triplet["relation"]["name"],
triplet["object"]["start"],
triplet["object"]["end"],
triplet["object"]["type"],
)
for triplet in sample.triplets
]
)
predicted_spans_strict = set(sample.predicted_entities)
gold_spans_strict = set(sample.entities)
# strict
correct_span_predictions += len(
predicted_spans_strict.intersection(gold_spans_strict)
)
total_span_predictions += len(predicted_spans_strict)
total_gold_spans += len(gold_spans_strict)
correct_predictions_strict += len(
predicted_annotations_strict.intersection(gold_annotations_strict)
)
total_predictions_strict += len(predicted_annotations_strict)
predicted_annotations = set(
[
(
triplet["subject"]["start"],
triplet["subject"]["end"],
-1,
triplet["relation"]["name"],
triplet["object"]["start"],
triplet["object"]["end"],
-1,
)
for triplet in sample.predicted_relations
]
)
gold_annotations = set(
[
(
triplet["subject"]["start"],
triplet["subject"]["end"],
-1,
triplet["relation"]["name"],
triplet["object"]["start"],
triplet["object"]["end"],
-1,
)
for triplet in sample.triplets
]
)
predicted_spans = set(
[(ss, se) for (ss, se, _) in sample.predicted_entities]
)
gold_spans = set([(ss, se) for (ss, se, _) in sample.entities])
total_gold_spans += len(gold_spans)
correct_predictions_bound += len(predicted_spans.intersection(gold_spans))
total_predictions_bound += len(predicted_spans)
total_predictions += len(predicted_annotations)
total_gold += len(gold_annotations)
# correct relation extraction
correct_predictions += len(
predicted_annotations.intersection(gold_annotations)
)
span_precision, span_recall, span_f1 = compute_metrics(
correct_span_predictions, total_span_predictions, total_gold_spans
)
bound_precision, bound_recall, bound_f1 = compute_metrics(
correct_predictions_bound, total_predictions_bound, total_gold_spans
)
precision, recall, f1 = compute_metrics(
correct_predictions, total_predictions, total_gold
)
if sample.entity_candidates:
precision_strict, recall_strict, f1_strict = compute_metrics(
correct_predictions_strict, total_predictions_strict, total_gold
)
return {
"span-precision": span_precision,
"span-recall": span_recall,
"span-f1": span_f1,
"precision": precision,
"recall": recall,
"f1": f1,
"precision-strict": precision_strict,
"recall-strict": recall_strict,
"f1-strict": f1_strict,
}
else:
return {
"span-precision": bound_precision,
"span-recall": bound_recall,
"span-f1": bound_f1,
"precision": precision,
"recall": recall,
"f1": f1,
}
class REStrongMatchingCallback(Callback):
def __init__(self, dataset_path: str, dataset_conf) -> None:
super().__init__()
self.dataset_path = dataset_path
self.dataset_conf = dataset_conf
self.strong_matching_metric = StrongMatching()
def on_validation_epoch_start(self, trainer, pl_module) -> None:
relik_reader_predictor = RelikReaderPredictor(pl_module.relik_reader_re_model)
predicted_samples = relik_reader_predictor._predict(
self.dataset_path,
None,
self.dataset_conf,
)
predicted_samples = list(predicted_samples)
for k, v in self.strong_matching_metric(predicted_samples).items():
pl_module.log(f"val_{k}", v)
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