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spaCy/spacy/tests/test_gold.py
Ines Montani 24f72c669c Merge branch 'develop' into master-tmp
2020-05-21 18:39:06 +02:00

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from spacy.errors import AlignmentError
from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags
from spacy.gold import spans_from_biluo_tags, GoldParse, iob_to_biluo, align
from spacy.gold import GoldCorpus, docs_to_json, Example, DocAnnotation
from spacy.lang.en import English
from spacy.syntax.nonproj import is_nonproj_tree
from spacy.tokens import Doc
from spacy.util import get_words_and_spaces, compounding, minibatch
import pytest
import srsly
from .util import make_tempdir
@pytest.fixture
def doc():
text = "Sarah's sister flew to Silicon Valley via London."
tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
pos = [
"PROPN",
"PART",
"NOUN",
"VERB",
"ADP",
"PROPN",
"PROPN",
"ADP",
"PROPN",
"PUNCT",
]
morphs = [
"NounType=prop|Number=sing",
"Poss=yes",
"Number=sing",
"Tense=past|VerbForm=fin",
"",
"NounType=prop|Number=sing",
"NounType=prop|Number=sing",
"",
"NounType=prop|Number=sing",
"PunctType=peri",
]
# head of '.' is intentionally nonprojective for testing
heads = [2, 0, 3, 3, 3, 6, 4, 3, 7, 5]
deps = [
"poss",
"case",
"nsubj",
"ROOT",
"prep",
"compound",
"pobj",
"prep",
"pobj",
"punct",
]
lemmas = [
"Sarah",
"'s",
"sister",
"fly",
"to",
"Silicon",
"Valley",
"via",
"London",
".",
]
biluo_tags = ["U-PERSON", "O", "O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
nlp = English()
doc = nlp(text)
for i in range(len(tags)):
doc[i].tag_ = tags[i]
doc[i].pos_ = pos[i]
doc[i].morph_ = morphs[i]
doc[i].lemma_ = lemmas[i]
doc[i].dep_ = deps[i]
doc[i].head = doc[heads[i]]
doc.ents = spans_from_biluo_tags(doc, biluo_tags)
doc.cats = cats
doc.is_tagged = True
doc.is_parsed = True
return doc
@pytest.fixture()
def merged_dict():
return {
"ids": [1, 2, 3, 4, 5, 6, 7],
"words": ["Hi", "there", "everyone", "It", "is", "just", "me"],
"tags": ["INTJ", "ADV", "PRON", "PRON", "AUX", "ADV", "PRON"],
"sent_starts": [1, 0, 0, 1, 0, 0, 0, 0],
}
def test_gold_biluo_U(en_vocab):
words = ["I", "flew", "to", "London", "."]
spaces = [True, True, True, False, True]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to London"), "LOC")]
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "O", "U-LOC", "O"]
def test_gold_biluo_BL(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "."]
spaces = [True, True, True, True, False, True]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco"), "LOC")]
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "O", "B-LOC", "L-LOC", "O"]
def test_gold_biluo_BIL(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, True]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
def test_gold_biluo_overlap(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, True]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [
(len("I flew to "), len("I flew to San Francisco Valley"), "LOC"),
(len("I flew to "), len("I flew to San Francisco"), "LOC"),
]
with pytest.raises(ValueError):
biluo_tags_from_offsets(doc, entities)
def test_gold_biluo_misalign(en_vocab):
words = ["I", "flew", "to", "San", "Francisco", "Valley."]
spaces = [True, True, True, True, True, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
with pytest.warns(UserWarning):
tags = biluo_tags_from_offsets(doc, entities)
assert tags == ["O", "O", "O", "-", "-", "-"]
def test_gold_biluo_different_tokenization(en_vocab, en_tokenizer):
# one-to-many
words = ["I", "flew to", "San Francisco Valley", "."]
spaces = [True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
gp = GoldParse(
doc,
words=["I", "flew", "to", "San", "Francisco", "Valley", "."],
entities=entities,
)
assert gp.ner == ["O", "O", "U-LOC", "O"]
# many-to-one
words = ["I", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
gp = GoldParse(
doc, words=["I", "flew to", "San Francisco Valley", "."], entities=entities
)
assert gp.ner == ["O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
# misaligned
words = ["I flew", "to", "San Francisco", "Valley", "."]
spaces = [True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
gp = GoldParse(
doc, words=["I", "flew to", "San", "Francisco Valley", "."], entities=entities,
)
assert gp.ner == ["O", "O", "B-LOC", "L-LOC", "O"]
# additional whitespace tokens in GoldParse words
words, spaces = get_words_and_spaces(
["I", "flew", "to", "San Francisco", "Valley", "."],
"I flew to San Francisco Valley.",
)
doc = Doc(en_vocab, words=words, spaces=spaces)
entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
gp = GoldParse(
doc,
words=["I", "flew", " ", "to", "San Francisco Valley", "."],
entities=entities,
)
assert gp.ner == ["O", "O", "O", "O", "B-LOC", "L-LOC", "O"]
# from issue #4791
data = (
"I'll return the ₹54 amount",
{
"words": ["I", "'ll", "return", "the", "₹", "54", "amount"],
"entities": [(16, 19, "MONEY")],
},
)
gp = GoldParse(en_tokenizer(data[0]), **data[1])
assert gp.ner == ["O", "O", "O", "O", "U-MONEY", "O"]
data = (
"I'll return the $54 amount",
{
"words": ["I", "'ll", "return", "the", "$", "54", "amount"],
"entities": [(16, 19, "MONEY")],
},
)
gp = GoldParse(en_tokenizer(data[0]), **data[1])
assert gp.ner == ["O", "O", "O", "O", "B-MONEY", "L-MONEY", "O"]
def test_roundtrip_offsets_biluo_conversion(en_tokenizer):
text = "I flew to Silicon Valley via London."
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
offsets = [(10, 24, "LOC"), (29, 35, "GPE")]
doc = en_tokenizer(text)
biluo_tags_converted = biluo_tags_from_offsets(doc, offsets)
assert biluo_tags_converted == biluo_tags
offsets_converted = offsets_from_biluo_tags(doc, biluo_tags)
assert offsets_converted == offsets
def test_biluo_spans(en_tokenizer):
doc = en_tokenizer("I flew to Silicon Valley via London.")
biluo_tags = ["O", "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
spans = spans_from_biluo_tags(doc, biluo_tags)
assert len(spans) == 2
assert spans[0].text == "Silicon Valley"
assert spans[0].label_ == "LOC"
assert spans[1].text == "London"
assert spans[1].label_ == "GPE"
def test_gold_ner_missing_tags(en_tokenizer):
doc = en_tokenizer("I flew to Silicon Valley via London.")
biluo_tags = [None, "O", "O", "B-LOC", "L-LOC", "O", "U-GPE", "O"]
gold = GoldParse(doc, entities=biluo_tags) # noqa: F841
def test_iob_to_biluo():
good_iob = ["O", "O", "B-LOC", "I-LOC", "O", "B-PERSON"]
good_biluo = ["O", "O", "B-LOC", "L-LOC", "O", "U-PERSON"]
bad_iob = ["O", "O", '"', "B-LOC", "I-LOC"]
converted_biluo = iob_to_biluo(good_iob)
assert good_biluo == converted_biluo
with pytest.raises(ValueError):
iob_to_biluo(bad_iob)
def test_roundtrip_docs_to_json(doc):
nlp = English()
text = doc.text
tags = [t.tag_ for t in doc]
pos = [t.pos_ for t in doc]
morphs = [t.morph_ for t in doc]
lemmas = [t.lemma_ for t in doc]
deps = [t.dep_ for t in doc]
heads = [t.head.i for t in doc]
biluo_tags = iob_to_biluo(
[t.ent_iob_ + "-" + t.ent_type_ if t.ent_type_ else "O" for t in doc]
)
cats = doc.cats
# roundtrip to JSON
with make_tempdir() as tmpdir:
json_file = tmpdir / "roundtrip.json"
srsly.write_json(json_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(train=str(json_file), dev=str(json_file))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
assert text == reloaded_example.text
assert tags == goldparse.tags
assert pos == goldparse.pos
assert morphs == goldparse.morphs
assert lemmas == goldparse.lemmas
assert deps == goldparse.labels
assert heads == goldparse.heads
assert biluo_tags == goldparse.ner
assert "TRAVEL" in goldparse.cats
assert "BAKING" in goldparse.cats
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
assert cats["BAKING"] == goldparse.cats["BAKING"]
# roundtrip to JSONL train dicts
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "roundtrip.jsonl"
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
assert text == reloaded_example.text
assert tags == goldparse.tags
assert pos == goldparse.pos
assert morphs == goldparse.morphs
assert lemmas == goldparse.lemmas
assert deps == goldparse.labels
assert heads == goldparse.heads
assert biluo_tags == goldparse.ner
assert "TRAVEL" in goldparse.cats
assert "BAKING" in goldparse.cats
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
assert cats["BAKING"] == goldparse.cats["BAKING"]
# roundtrip to JSONL tuples
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "roundtrip.jsonl"
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
# load and rewrite as JSONL tuples
srsly.write_jsonl(jsonl_file, goldcorpus.train_examples)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
reloaded_example = next(goldcorpus.dev_dataset(nlp))
goldparse = reloaded_example.gold
assert len(doc) == goldcorpus.count_train()
assert text == reloaded_example.text
assert tags == goldparse.tags
assert deps == goldparse.labels
assert heads == goldparse.heads
assert lemmas == goldparse.lemmas
assert biluo_tags == goldparse.ner
assert "TRAVEL" in goldparse.cats
assert "BAKING" in goldparse.cats
assert cats["TRAVEL"] == goldparse.cats["TRAVEL"]
assert cats["BAKING"] == goldparse.cats["BAKING"]
def test_projective_train_vs_nonprojective_dev(doc):
nlp = English()
deps = [t.dep_ for t in doc]
heads = [t.head.i for t in doc]
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
train_reloaded_example = next(goldcorpus.train_dataset(nlp))
train_goldparse = train_reloaded_example.gold
dev_reloaded_example = next(goldcorpus.dev_dataset(nlp))
dev_goldparse = dev_reloaded_example.gold
assert is_nonproj_tree([t.head.i for t in doc]) is True
assert is_nonproj_tree(train_goldparse.heads) is False
assert heads[:-1] == train_goldparse.heads[:-1]
assert heads[-1] != train_goldparse.heads[-1]
assert deps[:-1] == train_goldparse.labels[:-1]
assert deps[-1] != train_goldparse.labels[-1]
assert heads == dev_goldparse.heads
assert deps == dev_goldparse.labels
def test_ignore_misaligned(doc):
nlp = English()
text = doc.text
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
data = [docs_to_json(doc)]
data[0]["paragraphs"][0]["raw"] = text.replace("Sarah", "Jane")
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, data)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
with pytest.raises(AlignmentError):
train_reloaded_example = next(goldcorpus.train_dataset(nlp))
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
data = [docs_to_json(doc)]
data[0]["paragraphs"][0]["raw"] = text.replace("Sarah", "Jane")
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, data)
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
# doesn't raise an AlignmentError, but there is nothing to iterate over
# because the only example can't be aligned
train_reloaded_example = list(goldcorpus.train_dataset(nlp, ignore_misaligned=True))
assert len(train_reloaded_example) == 0
def test_make_orth_variants(doc):
nlp = English()
with make_tempdir() as tmpdir:
jsonl_file = tmpdir / "test.jsonl"
# write to JSONL train dicts
srsly.write_jsonl(jsonl_file, [docs_to_json(doc)])
goldcorpus = GoldCorpus(str(jsonl_file), str(jsonl_file))
# due to randomness, test only that this runs with no errors for now
train_reloaded_example = next(goldcorpus.train_dataset(nlp, orth_variant_level=0.2))
train_goldparse = train_reloaded_example.gold # noqa: F841
@pytest.mark.parametrize(
"tokens_a,tokens_b,expected",
[
(["a", "b", "c"], ["ab", "c"], (3, [-1, -1, 1], [-1, 2], {0: 0, 1: 0}, {})),
(
["a", "b", '"', "c"],
['ab"', "c"],
(4, [-1, -1, -1, 1], [-1, 3], {0: 0, 1: 0, 2: 0}, {}),
),
(["a", "bc"], ["ab", "c"], (4, [-1, -1], [-1, -1], {0: 0}, {1: 1})),
(
["ab", "c", "d"],
["a", "b", "cd"],
(6, [-1, -1, -1], [-1, -1, -1], {1: 2, 2: 2}, {0: 0, 1: 0}),
),
(
["a", "b", "cd"],
["a", "b", "c", "d"],
(3, [0, 1, -1], [0, 1, -1, -1], {}, {2: 2, 3: 2}),
),
([" ", "a"], ["a"], (1, [-1, 0], [1], {}, {})),
],
)
def test_align(tokens_a, tokens_b, expected):
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_a, tokens_b)
assert (cost, list(a2b), list(b2a), a2b_multi, b2a_multi) == expected
# check symmetry
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_b, tokens_a)
assert (cost, list(b2a), list(a2b), b2a_multi, a2b_multi) == expected
def test_goldparse_startswith_space(en_tokenizer):
text = " a"
doc = en_tokenizer(text)
g = GoldParse(doc, words=["a"], entities=["U-DATE"], deps=["ROOT"], heads=[0])
assert g.words == [" ", "a"]
assert g.ner == [None, "U-DATE"]
assert g.labels == [None, "ROOT"]
def test_gold_constructor():
"""Test that the GoldParse constructor works fine"""
nlp = English()
doc = nlp("This is a sentence")
gold = GoldParse(doc, cats={"cat1": 1.0, "cat2": 0.0})
assert gold.cats["cat1"]
assert not gold.cats["cat2"]
assert gold.words == ["This", "is", "a", "sentence"]
def test_gold_orig_annot():
nlp = English()
doc = nlp("This is a sentence")
gold = GoldParse(doc, cats={"cat1": 1.0, "cat2": 0.0})
assert gold.orig.words == ["This", "is", "a", "sentence"]
assert gold.cats["cat1"]
doc_annotation = DocAnnotation(cats={"cat1": 0.0, "cat2": 1.0})
gold2 = GoldParse.from_annotation(doc, doc_annotation, gold.orig)
assert gold2.orig.words == ["This", "is", "a", "sentence"]
assert not gold2.cats["cat1"]
def test_tuple_format_implicit():
"""Test tuple format with implicit GoldParse creation"""
train_data = [
("Uber blew through $1 million a week", {"entities": [(0, 4, "ORG")]}),
(
"Spotify steps up Asia expansion",
{"entities": [(0, 8, "ORG"), (17, 21, "LOC")]},
),
("Google rebrands its business apps", {"entities": [(0, 6, "ORG")]}),
]
_train(train_data)
def test_tuple_format_implicit_invalid():
"""Test that an error is thrown for an implicit invalid GoldParse field"""
train_data = [
("Uber blew through $1 million a week", {"frumble": [(0, 4, "ORG")]}),
(
"Spotify steps up Asia expansion",
{"entities": [(0, 8, "ORG"), (17, 21, "LOC")]},
),
("Google rebrands its business apps", {"entities": [(0, 6, "ORG")]}),
]
with pytest.raises(TypeError):
_train(train_data)
def _train(train_data):
nlp = English()
ner = nlp.create_pipe("ner")
ner.add_label("ORG")
ner.add_label("LOC")
nlp.add_pipe(ner)
optimizer = nlp.begin_training()
for i in range(5):
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
nlp.update(batch, sgd=optimizer, losses=losses)
def test_split_sents(merged_dict):
nlp = English()
example = Example()
example.set_token_annotation(**merged_dict)
assert len(example.get_gold_parses(merge=False, vocab=nlp.vocab)) == 2
assert len(example.get_gold_parses(merge=True, vocab=nlp.vocab)) == 1
split_examples = example.split_sents()
assert len(split_examples) == 2
token_annotation_1 = split_examples[0].token_annotation
assert token_annotation_1.ids == [1, 2, 3]
assert token_annotation_1.words == ["Hi", "there", "everyone"]
assert token_annotation_1.tags == ["INTJ", "ADV", "PRON"]
assert token_annotation_1.sent_starts == [1, 0, 0]
token_annotation_2 = split_examples[1].token_annotation
assert token_annotation_2.ids == [4, 5, 6, 7]
assert token_annotation_2.words == ["It", "is", "just", "me"]
assert token_annotation_2.tags == ["PRON", "AUX", "ADV", "PRON"]
assert token_annotation_2.sent_starts == [1, 0, 0, 0]
def test_tuples_to_example(merged_dict):
ex = Example()
ex.set_token_annotation(**merged_dict)
cats = {"TRAVEL": 1.0, "BAKING": 0.0}
ex.set_doc_annotation(cats=cats)
ex_dict = ex.to_dict()
assert ex_dict["token_annotation"]["ids"] == merged_dict["ids"]
assert ex_dict["token_annotation"]["words"] == merged_dict["words"]
assert ex_dict["token_annotation"]["tags"] == merged_dict["tags"]
assert ex_dict["token_annotation"]["sent_starts"] == merged_dict["sent_starts"]
assert ex_dict["doc_annotation"]["cats"] == cats
def test_empty_example_goldparse():
nlp = English()
doc = nlp("")
example = Example(doc=doc)
assert len(example.get_gold_parses()) == 1
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