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Ignore misaligned in Morphologizer.get_loss (#6363)

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Fix bug where `Morphologizer.get_loss` treated misaligned annotation as
`EMPTY_MORPH` rather than ignoring it. Remove unneeded default `EMPTY_MORPH`
mappings.
This commit is contained in:
Adriane Boyd 2020-11-10 13:15:09 +01:00 committed by GitHub
parent a0c899a0ff
commit a7e7d6c6c9
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2 changed files with 28 additions and 11 deletions
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11
spacy/pipeline/morphologizer.pyx
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@ -92,9 +92,6 @@ class Morphologizer(Tagger):
# 2) labels_pos stores a mapping from morph+POS->POS
cfg = {"labels_morph": labels_morph or {}, "labels_pos": labels_pos or {}}
self.cfg = dict(sorted(cfg.items()))
# add mappings for empty morph
self.cfg["labels_morph"][Morphology.EMPTY_MORPH] = Morphology.EMPTY_MORPH
self.cfg["labels_pos"][Morphology.EMPTY_MORPH] = POS_IDS[""]
@property
def labels(self):
@ -201,8 +198,8 @@ class Morphologizer(Tagger):
doc_tag_ids = doc_tag_ids.get()
for j, tag_id in enumerate(doc_tag_ids):
morph = self.labels[tag_id]
doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels_morph"][morph])
doc.c[j].pos = self.cfg["labels_pos"][morph]
doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels_morph"].get(morph, 0))
doc.c[j].pos = self.cfg["labels_pos"].get(morph, 0)
def get_loss(self, examples, scores):
"""Find the loss and gradient of loss for the batch of documents and
@ -228,8 +225,8 @@ class Morphologizer(Tagger):
# doesn't, so if either is None, treat both as None here so that
# truths doesn't end up with an unknown morph+POS combination
if pos is None or morph is None:
pos = None
morph = None
label = None
else:
label_dict = Morphology.feats_to_dict(morph)
if pos:
label_dict[self.POS_FEAT] = pos

20
spacy/tests/pipeline/test_morphologizer.py
View File

@ -116,3 +116,23 @@ def test_overfitting_IO():
no_batch_deps = [doc.to_array([MORPH]) for doc in [nlp(text) for text in texts]]
assert_equal(batch_deps_1, batch_deps_2)
assert_equal(batch_deps_1, no_batch_deps)
# Test without POS
nlp.remove_pipe("morphologizer")
nlp.add_pipe("morphologizer")
for example in train_examples:
for token in example.reference:
token.pos_ = ""
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["morphologizer"] < 0.00001
# Test the trained model
test_text = "I like blue ham"
doc = nlp(test_text)
gold_morphs = ["Feat=N", "Feat=V", "", ""]
gold_pos_tags = ["", "", "", ""]
assert [str(t.morph) for t in doc] == gold_morphs
assert [t.pos_ for t in doc] == gold_pos_tags