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f5c551a43a
* Error for ill-formed input to iob_to_biluo() Check for empty label in iob_to_biluo(), which can result from ill-formed input. * Check for empty NER label in debug-data
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags
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from spacy.gold import spans_from_biluo_tags, GoldParse, iob_to_biluo
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from spacy.gold import GoldCorpus, docs_to_json
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from spacy.lang.en import English
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from spacy.tokens import Doc
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from .util import make_tempdir
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import pytest
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import srsly
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def test_gold_biluo_U(en_vocab):
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words = ["I", "flew", "to", "London", "."]
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spaces = [True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to London"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "U-LOC", "O"]
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def test_gold_biluo_BL(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "."]
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spaces = [True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "B-LOC", "L-LOC", "O"]
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def test_gold_biluo_BIL(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
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spaces = [True, True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
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def test_gold_biluo_overlap(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
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spaces = [True, True, True, True, True, False, True]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [
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(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
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(len("I flew to "), len("I flew to San Francisco"), "LOC"),
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]
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with pytest.raises(ValueError):
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biluo_tags_from_offsets(doc, entities)
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def test_gold_biluo_misalign(en_vocab):
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words = ["I", "flew", "to", "San", "Francisco", "Valley."]
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spaces = [True, True, True, True, True, False]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "-", "-", "-"]
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def test_roundtrip_offsets_biluo_conversion(en_tokenizer):
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text = "I flew to Silicon Valley via London."
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biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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offsets = [(10, 24, "LOC"), (29, 35, "GPE")]
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doc = en_tokenizer(text)
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biluo_tags_converted = biluo_tags_from_offsets(doc, offsets)
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assert biluo_tags_converted == biluo_tags
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offsets_converted = offsets_from_biluo_tags(doc, biluo_tags)
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assert offsets_converted == offsets
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def test_biluo_spans(en_tokenizer):
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doc = en_tokenizer("I flew to Silicon Valley via London.")
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biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
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spans = spans_from_biluo_tags(doc, biluo_tags)
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assert len(spans) == 2
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assert spans[0].text == "Silicon Valley"
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assert spans[0].label_ == "LOC"
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assert spans[1].text == "London"
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assert spans[1].label_ == "GPE"
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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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gold = GoldParse(doc, entities=biluo_tags) # noqa: F841
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def test_iob_to_biluo():
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good_iob = ["O", "O", "B-LOC", "I-LOC", "O", "B-PERSON"]
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good_biluo = ["O", "O", "B-LOC", "L-LOC", "O", "U-PERSON"]
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bad_iob = ["O", "O", "\"", "B-LOC", "I-LOC"]
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converted_biluo = iob_to_biluo(good_iob)
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assert good_biluo == converted_biluo
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with pytest.raises(ValueError):
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iob_to_biluo(bad_iob)
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def test_roundtrip_docs_to_json():
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text = "I flew to Silicon Valley via London."
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cats = {"TRAVEL": 1.0, "BAKING": 0.0}
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nlp = English()
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doc = nlp(text)
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doc.cats = cats
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doc[0].is_sent_start = True
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for i in range(1, len(doc)):
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doc[i].is_sent_start = False
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with make_tempdir() as tmpdir:
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json_file = tmpdir / "roundtrip.json"
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srsly.write_json(json_file, [docs_to_json(doc)])
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goldcorpus = GoldCorpus(str(json_file), str(json_file))
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reloaded_doc, goldparse = next(goldcorpus.train_docs(nlp))
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assert len(doc) == goldcorpus.count_train()
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assert text == reloaded_doc.text
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assert "TRAVEL" in goldparse.cats
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assert "BAKING" in goldparse.cats
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assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
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assert cats["BAKING"] == goldparse.cats["BAKING"]
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