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NER align tests (#5656) 

* one_to_man works better. misalignment doesn't yet.

* fix tests

* restore example

* xfail alignment tests
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Sofie Van Landeghem 2020-06-29 13:59:17 +02:00 committed by GitHub
parent 2d9604d39c
commit fc3cb1fa9e
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2 changed files with 57 additions and 32 deletions
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79
spacy/tests/test_gold.py
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@ -230,15 +230,14 @@ def test_json2docs_no_ner(en_vocab):
Doc(
doc.vocab,
words=[w.text for w in doc],
spaces=[bool(w.whitespace_) for w in doc]
spaces=[bool(w.whitespace_) for w in doc],
),
doc
doc,
)
ner_tags = eg.get_aligned_ner()
assert ner_tags == [None, None, None, None, None]
def test_split_sentences(en_vocab):
words = ["I", "flew", "to", "San Francisco Valley", "had", "loads of fun"]
doc = Doc(en_vocab, words=words)
@ -283,8 +282,8 @@ def test_split_sentences(en_vocab):
assert split_examples[1].text == "had loads of fun "
def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
# one-to-many
@pytest.mark.xfail(reason="Alignment should be fixed after example refactor")
def test_gold_biluo_one_to_many(en_vocab, en_tokenizer):
words = ["I", "flew to", "San Francisco Valley", "."]
spaces = [True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
@ -292,9 +291,28 @@ def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
gold_words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "O", "U-LOC", "O"]
entities = [
(len("I "), len("I flew to"), "ORG"),
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
]
gold_words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "U-ORG", "U-LOC", "O"]
entities = [
(len("I "), len("I flew"), "ORG"),
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
]
gold_words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", None, "U-LOC", "O"]
# many-to-one
def test_gold_biluo_many_to_one(en_vocab, en_tokenizer):
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
@ -304,31 +322,38 @@ def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
# misaligned
entities = [
(len("I "), len("I flew to"), "ORG"),
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
]
gold_words = ["I", "flew to", "San Francisco Valley", "."]
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "B-ORG", "L-ORG", "B-LOC", "I-LOC", "L-LOC", "O"]
@pytest.mark.xfail(reason="Alignment should be fixed after example refactor")
def test_gold_biluo_misaligned(en_vocab, en_tokenizer):
words = ["I flew", "to", "San Francisco", "Valley", "."]
spaces = [True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
offset_start = len("I flew to ")
offset_end = len("I flew to San Francisco Valley")
entities = [(offset_start, offset_end, "LOC")]
links = {(offset_start, offset_end): {"Q816843": 1.0}}
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
gold_words = ["I", "flew to", "San", "Francisco Valley", "."]
example = Example.from_dict(
doc, {"words": gold_words, "entities": entities, "links": links}
)
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == [None, "O", "B-LOC", "L-LOC", "O"]
#assert example.get_aligned("ENT_KB_ID", as_string=True) == [
# "",
# "",
# "Q816843",
# "Q816843",
# "",
#]
#assert example.to_dict()["doc_annotation"]["links"][(offset_start, offset_end)] == {
# "Q816843": 1.0
#}
assert ner_tags == ["O", "O", "B-LOC", "L-LOC", "O"]
entities = [
(len("I "), len("I flew to"), "ORG"),
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
]
gold_words = ["I", "flew to", "San", "Francisco Valley", "."]
example = Example.from_dict(doc, {"words": gold_words, "entities": entities})
ner_tags = example.get_aligned_ner()
assert ner_tags == [None, None, "B-LOC", "L-LOC", "O"]
def test_gold_biluo_additional_whitespace(en_vocab, en_tokenizer):
# additional whitespace tokens in GoldParse words
words, spaces = get_words_and_spaces(
["I", "flew", "to", "San Francisco", "Valley", "."],
@ -344,7 +369,8 @@ def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "O", "O", "O", "B-LOC", "L-LOC", "O"]
# from issue #4791
def test_gold_biluo_4791(en_vocab, en_tokenizer):
doc = en_tokenizer("I'll return the ₹54 amount")
gold_words = ["I", "'ll", "return", "the", "₹", "54", "amount"]
gold_spaces = [False, True, True, True, False, True, False]
@ -593,7 +619,6 @@ def test_tuple_format_implicit_invalid():
_train(train_data)
def _train(train_data):
nlp = English()
ner = nlp.create_pipe("ner")