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Extend score_spans for overlapping & non-labeled spans (#7209)
* extend span scorer with consider_label and allow_overlap * unit test for spans y2x overlap * add score_spans unit test * docs for new fields in scorer.score_spans * rename to include_label * spell out if-else for clarity * rename to 'labeled' Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
This commit is contained in:
Sofie Van Landeghem
GitHub
parent
c362006cb9
commit
204c2f116b
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@ -311,6 +311,8 @@ class Scorer:
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*,
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getter: Callable[[Doc, str], Iterable[Span]] = getattr,
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has_annotation: Optional[Callable[[Doc], bool]] = None,
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labeled: bool = True,
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allow_overlap: bool = False,
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**cfg,
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) -> Dict[str, Any]:
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"""Returns PRF scores for labeled spans.
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@ -323,6 +325,11 @@ class Scorer:
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has_annotation (Optional[Callable[[Doc], bool]]) should return whether a `Doc`
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has annotation for this `attr`. Docs without annotation are skipped for
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scoring purposes.
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labeled (bool): Whether or not to include label information in
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the evaluation. If set to 'False', two spans will be considered
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equal if their start and end match, irrespective of their label.
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allow_overlap (bool): Whether or not to allow overlapping spans.
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If set to 'False', the alignment will automatically resolve conflicts.
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RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
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the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
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@ -351,33 +358,42 @@ class Scorer:
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gold_spans = set()
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pred_spans = set()
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for span in getter(gold_doc, attr):
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gold_span = (span.label_, span.start, span.end - 1)
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if labeled:
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gold_span = (span.label_, span.start, span.end - 1)
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else:
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gold_span = (span.start, span.end - 1)
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gold_spans.add(gold_span)
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gold_per_type[span.label_].add((span.label_, span.start, span.end - 1))
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gold_per_type[span.label_].add(gold_span)
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pred_per_type = {label: set() for label in labels}
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for span in example.get_aligned_spans_x2y(getter(pred_doc, attr)):
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pred_spans.add((span.label_, span.start, span.end - 1))
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pred_per_type[span.label_].add((span.label_, span.start, span.end - 1))
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for span in example.get_aligned_spans_x2y(getter(pred_doc, attr), allow_overlap):
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if labeled:
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pred_span = (span.label_, span.start, span.end - 1)
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else:
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pred_span = (span.start, span.end - 1)
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pred_spans.add(pred_span)
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pred_per_type[span.label_].add(pred_span)
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# Scores per label
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for k, v in score_per_type.items():
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if k in pred_per_type:
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v.score_set(pred_per_type[k], gold_per_type[k])
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if labeled:
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for k, v in score_per_type.items():
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if k in pred_per_type:
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v.score_set(pred_per_type[k], gold_per_type[k])
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# Score for all labels
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score.score_set(pred_spans, gold_spans)
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if len(score) > 0:
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return {
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f"{attr}_p": score.precision,
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f"{attr}_r": score.recall,
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f"{attr}_f": score.fscore,
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f"{attr}_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
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}
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else:
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return {
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# Assemble final result
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final_scores = {
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f"{attr}_p": None,
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f"{attr}_r": None,
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f"{attr}_f": None,
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f"{attr}_per_type": None,
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}
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if labeled:
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final_scores[f"{attr}_per_type"] = None
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if len(score) > 0:
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final_scores[f"{attr}_p"] = score.precision
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final_scores[f"{attr}_r"] = score.recall
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final_scores[f"{attr}_f"] = score.fscore
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if labeled:
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final_scores[f"{attr}_per_type"] = {k: v.to_dict() for k, v in score_per_type.items()}
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return final_scores
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@staticmethod
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def score_cats(
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@ -6,7 +6,7 @@ from spacy.training.iob_utils import offsets_to_biluo_tags
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from spacy.scorer import Scorer, ROCAUCScore, PRFScore
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from spacy.scorer import _roc_auc_score, _roc_curve
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from spacy.lang.en import English
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from spacy.tokens import Doc
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from spacy.tokens import Doc, Span
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test_las_apple = [
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@ -405,6 +405,51 @@ def test_roc_auc_score():
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_ = score.score # noqa: F841
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def test_score_spans():
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nlp = English()
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text = "This is just a random sentence."
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key = "my_spans"
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gold = nlp.make_doc(text)
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pred = nlp.make_doc(text)
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spans = []
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spans.append(gold.char_span(0, 4, label="PERSON"))
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spans.append(gold.char_span(0, 7, label="ORG"))
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spans.append(gold.char_span(8, 12, label="ORG"))
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gold.spans[key] = spans
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def span_getter(doc, span_key):
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return doc.spans[span_key]
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# Predict exactly the same, but overlapping spans will be discarded
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pred.spans[key] = spans
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eg = Example(pred, gold)
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter)
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assert scores[f"{key}_p"] == 1.0
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assert scores[f"{key}_r"] < 1.0
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# Allow overlapping, now both precision and recall should be 100%
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pred.spans[key] = spans
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eg = Example(pred, gold)
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
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assert scores[f"{key}_p"] == 1.0
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assert scores[f"{key}_r"] == 1.0
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# Change the predicted labels
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new_spans = [Span(pred, span.start, span.end, label="WRONG") for span in spans]
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pred.spans[key] = new_spans
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eg = Example(pred, gold)
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
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assert scores[f"{key}_p"] == 0.0
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assert scores[f"{key}_r"] == 0.0
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assert f"{key}_per_type" in scores
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# Discard labels from the evaluation
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True, labeled=False)
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assert scores[f"{key}_p"] == 1.0
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assert scores[f"{key}_r"] == 1.0
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assert f"{key}_per_type" not in scores
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def test_prf_score():
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cand = {"hi", "ho"}
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gold1 = {"yo", "hi"}
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@ -422,4 +467,4 @@ def test_prf_score():
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assert (c.precision, c.recall, c.fscore) == approx((0.25, 0.5, 0.33333333))
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a += b
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assert (a.precision, a.recall, a.fscore) == approx((c.precision, c.recall, c.fscore))
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assert (a.precision, a.recall, a.fscore) == approx((c.precision, c.recall, c.fscore))
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@ -426,6 +426,29 @@ def test_aligned_spans_x2y(en_vocab, en_tokenizer):
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assert [(ent.start, ent.end) for ent in ents_x2y] == [(0, 2), (4, 6)]
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def test_aligned_spans_y2x_overlap(en_vocab, en_tokenizer):
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text = "I flew to San Francisco Valley"
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nlp = English()
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doc = nlp(text)
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# the reference doc has overlapping spans
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gold_doc = nlp.make_doc(text)
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spans = []
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prefix = "I flew to "
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spans.append(gold_doc.char_span(len(prefix), len(prefix + "San Francisco"), label="CITY"))
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spans.append(gold_doc.char_span(len(prefix), len(prefix + "San Francisco Valley"), label="VALLEY"))
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spans_key = "overlap_ents"
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gold_doc.spans[spans_key] = spans
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example = Example(doc, gold_doc)
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spans_gold = example.reference.spans[spans_key]
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assert [(ent.start, ent.end) for ent in spans_gold] == [(3, 5), (3, 6)]
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# Ensure that 'get_aligned_spans_y2x' has the aligned entities correct
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spans_y2x_no_overlap = example.get_aligned_spans_y2x(spans_gold, allow_overlap=False)
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assert [(ent.start, ent.end) for ent in spans_y2x_no_overlap] == [(3, 5)]
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spans_y2x_overlap = example.get_aligned_spans_y2x(spans_gold, allow_overlap=True)
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assert [(ent.start, ent.end) for ent in spans_y2x_overlap] == [(3, 5), (3, 6)]
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def test_gold_ner_missing_tags(en_tokenizer):
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doc = en_tokenizer("I flew to Silicon Valley via London.")
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biluo_tags = [None, "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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@ -213,18 +213,19 @@ cdef class Example:
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else:
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return [None] * len(self.x)
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def get_aligned_spans_x2y(self, x_spans):
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return self._get_aligned_spans(self.y, x_spans, self.alignment.x2y)
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def get_aligned_spans_x2y(self, x_spans, allow_overlap=False):
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return self._get_aligned_spans(self.y, x_spans, self.alignment.x2y, allow_overlap)
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def get_aligned_spans_y2x(self, y_spans):
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return self._get_aligned_spans(self.x, y_spans, self.alignment.y2x)
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def get_aligned_spans_y2x(self, y_spans, allow_overlap=False):
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return self._get_aligned_spans(self.x, y_spans, self.alignment.y2x, allow_overlap)
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def _get_aligned_spans(self, doc, spans, align):
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def _get_aligned_spans(self, doc, spans, align, allow_overlap):
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seen = set()
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output = []
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for span in spans:
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indices = align[span.start : span.end].data.ravel()
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indices = [idx for idx in indices if idx not in seen]
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if not allow_overlap:
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indices = [idx for idx in indices if idx not in seen]
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if len(indices) >= 1:
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aligned_span = Span(doc, indices[0], indices[-1] + 1, label=span.label)
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target_text = span.text.lower().strip().replace(" ", "")
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@ -237,7 +238,7 @@ cdef class Example:
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def get_aligned_ner(self):
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if not self.y.has_annotation("ENT_IOB"):
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return [None] * len(self.x) # should this be 'missing' instead of 'None' ?
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x_ents = self.get_aligned_spans_y2x(self.y.ents)
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x_ents = self.get_aligned_spans_y2x(self.y.ents, allow_overlap=False)
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# Default to 'None' for missing values
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x_tags = offsets_to_biluo_tags(
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self.x,
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@ -33,8 +33,8 @@ both documents.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `predicted` | The document containing (partial) predictions. Cannot be `None`. ~~Doc~~ |
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| `reference` | The document containing gold-standard annotations. Cannot be `None`. ~~Doc~~ |
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| `predicted` | The document containing (partial) predictions. Cannot be `None`. ~~Doc~~ |
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| `reference` | The document containing gold-standard annotations. Cannot be `None`. ~~Doc~~ |
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| _keyword-only_ | |
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| `alignment` | An object holding the alignment between the tokens of the `predicted` and `reference` documents. ~~Optional[Alignment]~~ |
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@ -56,11 +56,11 @@ see the [training format documentation](/api/data-formats#dict-input).
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> example = Example.from_dict(predicted, {"words": token_ref, "tags": tags_ref})
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------- |
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| `predicted` | The document containing (partial) predictions. Cannot be `None`. ~~Doc~~ |
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| `example_dict` | `Dict[str, obj]` | The gold-standard annotations as a dictionary. Cannot be `None`. ~~Dict[str, Any]~~ |
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| **RETURNS** | The newly constructed object. ~~Example~~ |
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------- |
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| `predicted` | The document containing (partial) predictions. Cannot be `None`. ~~Doc~~ |
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| `example_dict` | The gold-standard annotations as a dictionary. Cannot be `None`. ~~Dict[str, Any]~~ |
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| **RETURNS** | The newly constructed object. ~~Example~~ |
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## Example.text {#text tag="property"}
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@ -211,10 +211,11 @@ align to the tokenization in [`Example.predicted`](/api/example#predicted).
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> assert [(ent.start, ent.end) for ent in ents_y2x] == [(0, 1)]
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------------------------- |
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| `y_spans` | `Span` objects aligned to the tokenization of `reference`. ~~Iterable[Span]~~ |
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| **RETURNS** | `Span` objects aligned to the tokenization of `predicted`. ~~List[Span]~~ |
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| Name | Description |
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| --------------- | -------------------------------------------------------------------------------------------- |
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| `y_spans` | `Span` objects aligned to the tokenization of `reference`. ~~Iterable[Span]~~ |
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| `allow_overlap` | Whether the resulting `Span` objects may overlap or not. Set to `False` by default. ~~bool~~ |
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| **RETURNS** | `Span` objects aligned to the tokenization of `predicted`. ~~List[Span]~~ |
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## Example.get_aligned_spans_x2y {#get_aligned_spans_x2y tag="method"}
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@ -238,10 +239,11 @@ against the original gold-standard annotation.
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> assert [(ent.start, ent.end) for ent in ents_x2y] == [(0, 2)]
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------------------------- |
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| `x_spans` | `Span` objects aligned to the tokenization of `predicted`. ~~Iterable[Span]~~ |
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| **RETURNS** | `Span` objects aligned to the tokenization of `reference`. ~~List[Span]~~ |
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| Name | Description |
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| --------------- | -------------------------------------------------------------------------------------------- |
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| `x_spans` | `Span` objects aligned to the tokenization of `predicted`. ~~Iterable[Span]~~ |
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| `allow_overlap` | Whether the resulting `Span` objects may overlap or not. Set to `False` by default. ~~bool~~ |
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| **RETURNS** | `Span` objects aligned to the tokenization of `reference`. ~~List[Span]~~ |
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## Example.to_dict {#to_dict tag="method"}
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@ -137,14 +137,16 @@ Returns PRF scores for labeled or unlabeled spans.
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> print(scores["ents_f"])
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> ```
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. ~~Callable[[Doc, str], Iterable[Span]]~~ |
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| `has_annotation` | Defaults to `None`. If provided, `has_annotation(doc)` should return whether a `Doc` has annotation for this `attr`. Docs without annotation are skipped for scoring purposes. ~~Optional[Callable[[Doc], bool]]~~ |
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| **RETURNS** | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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| Name | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | The `Example` objects holding both the predictions and the correct gold-standard annotations. ~~Iterable[Example]~~ |
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| `attr` | The attribute to score. ~~str~~ |
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| _keyword-only_ | |
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| `getter` | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`. ~~Callable[[Doc, str], Iterable[Span]]~~ |
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| `has_annotation` | Defaults to `None`. If provided, `has_annotation(doc)` should return whether a `Doc` has annotation for this `attr`. Docs without annotation are skipped for scoring purposes. ~~str~~ |
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| `labeled` | Defaults to `True`. If set to `False`, two spans will be considered equal if their start and end match, irrespective of their label. ~~bool~~ |
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| `allow_overlap` | Defaults to `False`. Whether or not to allow overlapping spans. If set to `False`, the alignment will automatically resolve conflicts. ~~bool~~ |
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| **RETURNS** | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## Scorer.score_deps {#score_deps tag="staticmethod" new="3"}
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