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refactor make_span_group
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kadarakos
parent
a281a7c9a1
commit
a07aafc28e
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@ -182,7 +182,7 @@ def make_spancat(
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name=name,
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spans_key=spans_key,
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negative_weight=None,
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allow_overlap=None,
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allow_overlap=True,
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max_positive=max_positive,
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threshold=threshold,
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scorer=scorer,
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@ -303,16 +303,16 @@ class SpanCategorizer(TrainablePipe):
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vocab: Vocab,
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model: Model[Tuple[List[Doc], Ragged], Floats2d],
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suggester: Suggester,
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# XXX Not sure what's the best default name when it can both be spancat
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# TODO Not sure what's the best default name when it can both be spancat
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# and spancat_singlelabel
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name: str = "spancat",
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*,
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add_negative_label: bool = False,
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spans_key: str = "spans",
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negative_weight: Optional[float] = None,
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allow_overlap: Optional[bool] = None,
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negative_weight: Optional[float] = 1.0,
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allow_overlap: Optional[bool] = True,
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max_positive: Optional[int] = None,
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threshold: Optional[float] = None,
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threshold: Optional[float] = 0.5,
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scorer: Optional[Callable] = spancat_score,
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) -> None:
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"""Initialize the multilabel or multiclass span categorizer.
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@ -366,9 +366,7 @@ class SpanCategorizer(TrainablePipe):
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self.name = name
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self.scorer = scorer
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self.add_negative_label = add_negative_label
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if allow_overlap is None:
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self.cfg["allow_overlap"] = True
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elif not allow_overlap and max_positive is not None and max_positive > 1:
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if not allow_overlap and max_positive is not None and max_positive > 1:
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self.cfg["allow_overlap"] = True
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msg.warn(
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"'allow_overlap' can only be False when max_positive=1, "
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@ -446,7 +444,7 @@ class SpanCategorizer(TrainablePipe):
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return len(self.labels)
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@property
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def _negative_label(self) -> Union[int, None]:
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def _negative_label_i(self) -> Union[int, None]:
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"""RETURNS (Union[int, None]): Index of the negative label."""
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if self.add_negative_label:
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return len(self.label_data)
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@ -494,7 +492,6 @@ class SpanCategorizer(TrainablePipe):
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DOCS: https://spacy.io/api/spancategorizer#set_annotations
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"""
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labels = self.labels
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indices, scores = indices_scores
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offset = 0
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for i, doc in enumerate(docs):
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@ -506,7 +503,6 @@ class SpanCategorizer(TrainablePipe):
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doc,
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indices_i,
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scores[offset : offset + indices.lengths[i]],
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labels, # type: ignore[arg-type]
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allow_overlap,
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)
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else:
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@ -514,7 +510,6 @@ class SpanCategorizer(TrainablePipe):
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doc,
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indices_i,
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scores[offset : offset + indices.lengths[i]],
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labels, # type: ignore[arg-type]
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)
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offset += indices.lengths[i]
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@ -604,7 +599,7 @@ class SpanCategorizer(TrainablePipe):
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target = self.model.ops.asarray(target, dtype="f") # type: ignore
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if self.add_negative_label:
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negative_samples = numpy.nonzero(negative_spans)[0]
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target[negative_samples, self._negative_label] = 1.0 # type: ignore
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target[negative_samples, self._negative_label_i] = 1.0 # type: ignore
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# The target will have the values 0 (for untrue predictions) or 1
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# (for true predictions).
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# The scores should be in the range [0, 1].
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@ -672,9 +667,8 @@ class SpanCategorizer(TrainablePipe):
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doc: Doc,
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indices: Ints2d,
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scores: Floats2d,
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labels: List[str],
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) -> SpanGroup:
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# Handle cases when there are zero suggestions
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"""Find the top-k labels for each span (k=max_positive)."""
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spans = SpanGroup(doc, name=self.key)
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if scores.size == 0:
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return spans
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@ -684,9 +678,15 @@ class SpanCategorizer(TrainablePipe):
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max_positive = self.cfg["max_positive"]
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keeps = scores >= threshold
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ranked = (scores * -1).argsort() # type: ignore
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if max_positive is not None:
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assert isinstance(max_positive, int)
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if self.add_negative_label:
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negative_scores = numpy.copy(scores[:, self._negative_label_i])
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scores[:, self._negative_label_i] = -numpy.inf
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ranked = (scores * -1).argsort() # type: ignore
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scores[:, self._negative_label_i] = negative_scores
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else:
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ranked = (scores * -1).argsort() # type: ignore
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span_filter = ranked[:, max_positive:]
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for i, row in enumerate(span_filter):
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keeps[i, row] = False
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@ -697,11 +697,8 @@ class SpanCategorizer(TrainablePipe):
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end = indices[i, 1]
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for j, keep in enumerate(keeps[i]):
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if keep:
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# If the predicted label is the negative label skip it.
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if self.add_negative_label and labels[j] == self._negative_label:
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continue
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else:
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spans.append(Span(doc, start, end, label=labels[j]))
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if j != self._negative_label_i:
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spans.append(Span(doc, start, end, label=self.labels[j]))
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attrs_scores.append(scores[i, j])
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spans.attrs["scores"] = numpy.array(attrs_scores)
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return spans
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@ -711,9 +708,9 @@ class SpanCategorizer(TrainablePipe):
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doc: Doc,
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indices: Ints2d,
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scores: Floats2d,
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labels: List[str],
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allow_overlap: bool = True,
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) -> SpanGroup:
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"""Find the argmax label for each span."""
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# Handle cases when there are zero suggestions
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spans = SpanGroup(doc, name=self.key)
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if scores.size == 0:
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@ -727,7 +724,7 @@ class SpanCategorizer(TrainablePipe):
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keeps = numpy.ones(predicted.shape, dtype=bool)
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# Remove samples where the negative label is the argmax.
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if self.add_negative_label:
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keeps = numpy.logical_and(keeps, predicted != self._negative_label)
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keeps = numpy.logical_and(keeps, predicted != self._negative_label_i)
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# Filter samples according to threshold.
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threshold = self.cfg["threshold"]
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if threshold is not None:
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@ -743,11 +740,12 @@ class SpanCategorizer(TrainablePipe):
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spans = SpanGroup(doc, name=self.key)
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attrs_scores = []
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for i in range(indices.shape[0]):
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if not keeps[i]:
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continue
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label = predicted[i]
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start = indices[i, 0]
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end = indices[i, 1]
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if not keeps[i]:
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continue
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if not allow_overlap:
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if (start, end) in seen:
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@ -755,6 +753,6 @@ class SpanCategorizer(TrainablePipe):
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else:
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seen.add(start, end)
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attrs_scores.append(argmax_scores[i])
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spans.append(Span(doc, start, end, label=labels[label]))
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spans.append(Span(doc, start, end, label=self.labels[label]))
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return spans
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