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spaCy/spacy/tests/test_gold.py

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import numpy
from spacy.gold import biluo_tags_from_offsets, offsets_from_biluo_tags
from spacy.gold import spans_from_biluo_tags, iob_to_biluo
from spacy.gold import Corpus, docs_to_json
from spacy.gold.example import Example
from spacy.gold.converters import json2docs
from spacy.lang.en import English
from spacy.pipeline import EntityRuler
from spacy.tokens import Doc, DocBin
from spacy.util import get_words_and_spaces, minibatch
from thinc.api import compounding
import pytest
import srsly
from .util import make_tempdir
from ..gold.augment import make_orth_variants_example
@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"],
"spaces": [True, True, True, True, True, True, False],
"tags": ["INTJ", "ADV", "PRON", "PRON", "AUX", "ADV", "PRON"],
"sent_starts": [1, 0, 0, 1, 0, 0, 0],
}
@pytest.fixture
def vocab():
nlp = English()
return nlp.vocab
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_example_constructor(en_vocab):
words = ["I", "like", "stuff"]
tags = ["NOUN", "VERB", "NOUN"]
tag_ids = [en_vocab.strings.add(tag) for tag in tags]
predicted = Doc(en_vocab, words=words)
reference = Doc(en_vocab, words=words)
reference = reference.from_array("TAG", numpy.array(tag_ids, dtype="uint64"))
example = Example(predicted, reference)
tags = example.get_aligned("TAG", as_string=True)
assert tags == ["NOUN", "VERB", "NOUN"]
def test_example_from_dict_tags(en_vocab):
words = ["I", "like", "stuff"]
tags = ["NOUN", "VERB", "NOUN"]
predicted = Doc(en_vocab, words=words)
example = Example.from_dict(predicted, {"TAGS": tags})
tags = example.get_aligned("TAG", as_string=True)
assert tags == ["NOUN", "VERB", "NOUN"]
def test_example_from_dict_no_ner(en_vocab):
words = ["a", "b", "c", "d"]
spaces = [True, True, False, True]
predicted = Doc(en_vocab, words=words, spaces=spaces)
example = Example.from_dict(predicted, {"words": words})
ner_tags = example.get_aligned_ner()
assert ner_tags == [None, None, None, None]
def test_example_from_dict_some_ner(en_vocab):
words = ["a", "b", "c", "d"]
spaces = [True, True, False, True]
predicted = Doc(en_vocab, words=words, spaces=spaces)
example = Example.from_dict(
predicted, {"words": words, "entities": ["U-LOC", None, None, None]}
)
ner_tags = example.get_aligned_ner()
assert ner_tags == ["U-LOC", None, None, None]
def test_json2docs_no_ner(en_vocab):
data = [
{
"id": 1,
"paragraphs": [
{
"sentences": [
{
"tokens": [
{"dep": "nn", "head": 1, "tag": "NNP", "orth": "Ms."},
{
"dep": "nsubj",
"head": 1,
"tag": "NNP",
"orth": "Haag",
},
{
"dep": "ROOT",
"head": 0,
"tag": "VBZ",
"orth": "plays",
},
{
"dep": "dobj",
"head": -1,
"tag": "NNP",
"orth": "Elianti",
},
{"dep": "punct", "head": -2, "tag": ".", "orth": "."},
]
}
]
}
],
}
]
docs = json2docs(data)
assert len(docs) == 1
for doc in docs:
assert not doc.is_nered
for token in doc:
assert token.ent_iob == 0
eg = Example(
Doc(
doc.vocab,
words=[w.text for w in doc],
spaces=[bool(w.whitespace_) for w in 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)
gold_words = [
"I",
"flew",
"to",
"San",
"Francisco",
"Valley",
"had",
"loads",
"of",
"fun",
]
sent_starts = [True, False, False, False, False, False, True, False, False, False]
example = Example.from_dict(doc, {"words": gold_words, "sent_starts": sent_starts})
assert example.text == "I flew to San Francisco Valley had loads of fun "
split_examples = example.split_sents()
assert len(split_examples) == 2
assert split_examples[0].text == "I flew to San Francisco Valley "
assert split_examples[1].text == "had loads of fun "
words = ["I", "flew", "to", "San", "Francisco", "Valley", "had", "loads", "of fun"]
doc = Doc(en_vocab, words=words)
gold_words = [
"I",
"flew",
"to",
"San Francisco",
"Valley",
"had",
"loads of",
"fun",
]
sent_starts = [True, False, False, False, False, True, False, False]
example = Example.from_dict(doc, {"words": gold_words, "sent_starts": sent_starts})
assert example.text == "I flew to San Francisco Valley had loads of fun "
split_examples = example.split_sents()
assert len(split_examples) == 2
assert split_examples[0].text == "I flew to San Francisco Valley "
assert split_examples[1].text == "had loads of fun "
def test_gold_biluo_one_to_many(en_vocab, en_tokenizer):
words = ["Mr and ", "Mrs Smith", "flew to", "San Francisco Valley", "."]
spaces = [True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
prefix = "Mr and Mrs Smith flew to "
entities = [(len(prefix), len(prefix + "San Francisco Valley"), "LOC")]
gold_words = ["Mr and Mrs Smith", "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", "O", "U-LOC", "O"]
entities = [
(len("Mr and "), len("Mr and Mrs Smith"), "PERSON"), # "Mrs Smith" is a PERSON
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
gold_words = ["Mr and", "Mrs", "Smith", "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-PERSON", "O", "U-LOC", "O"]
entities = [
(len("Mr and "), len("Mr and Mrs"), "PERSON"), # "Mrs" is a Person
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
gold_words = ["Mr and", "Mrs", "Smith", "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, "O", "U-LOC", "O"]
def test_gold_biluo_many_to_one(en_vocab, en_tokenizer):
words = ["Mr and", "Mrs", "Smith", "flew", "to", "San", "Francisco", "Valley", "."]
spaces = [True, True, True, True, True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
prefix = "Mr and Mrs Smith flew to "
entities = [(len(prefix), len(prefix + "San Francisco Valley"), "LOC")]
gold_words = ["Mr and Mrs Smith", "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", "O", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
entities = [
(len("Mr and "), len("Mr and Mrs Smith"), "PERSON"), # "Mrs Smith" is a PERSON
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
gold_words = ["Mr and", "Mrs Smith", "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-PERSON", "L-PERSON", "O", "O", "B-LOC", "I-LOC", "L-LOC", "O"]
def test_gold_biluo_misaligned(en_vocab, en_tokenizer):
words = ["Mr and Mrs", "Smith", "flew", "to", "San Francisco", "Valley", "."]
spaces = [True, True, True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
prefix = "Mr and Mrs Smith flew to "
entities = [(len(prefix), len(prefix + "San Francisco Valley"), "LOC")]
gold_words = ["Mr", "and Mrs Smith", "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", "O", "O", "B-LOC", "L-LOC", "O"]
entities = [
(len("Mr and "), len("Mr and Mrs Smith"), "PERSON"), # "Mrs Smith" is a PERSON
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
gold_words = ["Mr and", "Mrs Smith", "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, "O", "O", "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", "."],
"I flew to San Francisco Valley.",
)
doc = Doc(en_vocab, words=words, spaces=spaces)
prefix = "I flew to "
entities = [(len(prefix), len(prefix + "San Francisco Valley"), "LOC")]
gold_words = ["I", "flew", " ", "to", "San Francisco Valley", "."]
gold_spaces = [True, True, False, True, False, False]
example = Example.from_dict(
doc, {"words": gold_words, "spaces": gold_spaces, "entities": entities}
)
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "O", "O", "O", "B-LOC", "L-LOC", "O"]
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]
entities = [(16, 19, "MONEY")]
example = Example.from_dict(
doc, {"words": gold_words, "spaces": gold_spaces, "entities": entities}
)
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "O", "O", "O", "U-MONEY", "O"]
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]
entities = [(16, 19, "MONEY")]
example = Example.from_dict(
doc, {"words": gold_words, "spaces": gold_spaces, "entities": entities}
)
ner_tags = example.get_aligned_ner()
assert ner_tags == ["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)
offsets_converted = [ent for ent in offsets if ent[2]]
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)
spans = [span for span in spans if span.label_]
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_aligned_spans_y2x(en_vocab, en_tokenizer):
words = ["Mr and Mrs Smith", "flew", "to", "San Francisco Valley", "."]
spaces = [True, True, True, False, False]
doc = Doc(en_vocab, words=words, spaces=spaces)
prefix = "Mr and Mrs Smith flew to "
entities = [
(0, len("Mr and Mrs Smith"), "PERSON"),
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
tokens_ref = ["Mr", "and", "Mrs", "Smith", "flew", "to", "San", "Francisco", "Valley", "."]
example = Example.from_dict(doc, {"words": tokens_ref, "entities": entities})
ents_ref = example.reference.ents
assert [(ent.start, ent.end) for ent in ents_ref] == [(0, 4), (6, 9)]
ents_y2x = example.get_aligned_spans_y2x(ents_ref)
assert [(ent.start, ent.end) for ent in ents_y2x] == [(0, 1), (3, 4)]
def test_aligned_spans_x2y(en_vocab, en_tokenizer):
text = "Mr and Mrs Smith flew to San Francisco Valley"
nlp = English()
ruler = EntityRuler(nlp)
patterns = [{"label": "PERSON", "pattern": "Mr and Mrs Smith"},
{"label": "LOC", "pattern": "San Francisco Valley"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
doc = nlp(text)
assert [(ent.start, ent.end) for ent in doc.ents] == [(0, 4), (6, 9)]
prefix = "Mr and Mrs Smith flew to "
entities = [
(0, len("Mr and Mrs Smith"), "PERSON"),
(len(prefix), len(prefix + "San Francisco Valley"), "LOC"),
]
tokens_ref = ["Mr and Mrs", "Smith", "flew", "to", "San Francisco", "Valley"]
example = Example.from_dict(doc, {"words": tokens_ref, "entities": entities})
assert [(ent.start, ent.end) for ent in example.reference.ents] == [(0, 2), (4, 6)]
# Ensure that 'get_aligned_spans_x2y' has the aligned entities correct
ents_pred = example.predicted.ents
assert [(ent.start, ent.end) for ent in ents_pred] == [(0, 4), (6, 9)]
ents_x2y = example.get_aligned_spans_x2y(ents_pred)
assert [(ent.start, ent.end) for ent in ents_x2y] == [(0, 2), (4, 6)]
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"]
example = Example.from_dict(doc, {"entities": biluo_tags})
assert example.get_aligned("ENT_IOB") == [0, 2, 2, 3, 1, 2, 3, 2]
def test_projectivize(en_tokenizer):
doc = en_tokenizer("He pretty quickly walks away")
heads = [3, 2, 3, 0, 2]
example = Example.from_dict(doc, {"heads": heads})
proj_heads, proj_labels = example.get_aligned_parse(projectivize=True)
nonproj_heads, nonproj_labels = example.get_aligned_parse(projectivize=False)
assert proj_heads == [3, 2, 3, 0, 3]
assert nonproj_heads == [3, 2, 3, 0, 2]
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_docbin(doc):
nlp = English()
text = doc.text
idx = [t.idx for t in doc]
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]
cats = doc.cats
ents = [(e.start_char, e.end_char, e.label_) for e in doc.ents]
# roundtrip to DocBin
with make_tempdir() as tmpdir:
# use a separate vocab to test that all labels are added
reloaded_nlp = English()
json_file = tmpdir / "roundtrip.json"
srsly.write_json(json_file, [docs_to_json(doc)])
goldcorpus = Corpus(str(json_file), str(json_file))
output_file = tmpdir / "roundtrip.spacy"
data = DocBin(docs=[doc]).to_bytes()
with output_file.open("wb") as file_:
file_.write(data)
goldcorpus = Corpus(train_loc=str(output_file), dev_loc=str(output_file))
reloaded_example = next(goldcorpus.dev_dataset(nlp=reloaded_nlp))
assert len(doc) == goldcorpus.count_train(reloaded_nlp)
assert text == reloaded_example.reference.text
assert idx == [t.idx for t in reloaded_example.reference]
assert tags == [t.tag_ for t in reloaded_example.reference]
assert pos == [t.pos_ for t in reloaded_example.reference]
assert morphs == [t.morph_ for t in reloaded_example.reference]
assert lemmas == [t.lemma_ for t in reloaded_example.reference]
assert deps == [t.dep_ for t in reloaded_example.reference]
assert heads == [t.head.i for t in reloaded_example.reference]
assert ents == [
(e.start_char, e.end_char, e.label_) for e in reloaded_example.reference.ents
]
assert "TRAVEL" in reloaded_example.reference.cats
assert "BAKING" in reloaded_example.reference.cats
assert cats["TRAVEL"] == reloaded_example.reference.cats["TRAVEL"]
assert cats["BAKING"] == reloaded_example.reference.cats["BAKING"]
def test_make_orth_variants(doc):
nlp = English()
with make_tempdir() as tmpdir:
output_file = tmpdir / "roundtrip.spacy"
data = DocBin(docs=[doc]).to_bytes()
with output_file.open("wb") as file_:
file_.write(data)
goldcorpus = Corpus(train_loc=str(output_file), dev_loc=str(output_file))
# due to randomness, test only that this runs with no errors for now
train_example = next(goldcorpus.train_dataset(nlp))
make_orth_variants_example(nlp, train_example, orth_variant_level=0.2)
@pytest.mark.skip("Outdated")
@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): # noqa
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_a, tokens_b) # noqa
assert (cost, list(a2b), list(b2a), a2b_multi, b2a_multi) == expected # noqa
# check symmetry
cost, a2b, b2a, a2b_multi, b2a_multi = align(tokens_b, tokens_a) # noqa
assert (cost, list(b2a), list(a2b), b2a_multi, a2b_multi) == expected # noqa
def test_goldparse_startswith_space(en_tokenizer):
text = " a"
doc = en_tokenizer(text)
gold_words = ["a"]
entities = ["U-DATE"]
deps = ["ROOT"]
heads = [0]
example = Example.from_dict(
doc, {"words": gold_words, "entities": entities, "deps": deps, "heads": heads}
)
ner_tags = example.get_aligned_ner()
assert ner_tags == ["O", "U-DATE"]
assert example.get_aligned("DEP", as_string=True) == [None, "ROOT"]
def test_gold_constructor():
"""Test that the Example constructor works fine"""
nlp = English()
doc = nlp("This is a sentence")
example = Example.from_dict(doc, {"cats": {"cat1": 1.0, "cat2": 0.0}})
assert example.get_aligned("ORTH", as_string=True) == [
"This",
"is",
"a",
"sentence",
]
assert example.reference.cats["cat1"]
assert not example.reference.cats["cat2"]
def test_tuple_format_implicit():
"""Test tuple format"""
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_tuples(train_data)
def test_tuple_format_implicit_invalid():
"""Test that an error is thrown for an implicit invalid 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(KeyError):
_train_tuples(train_data)
def _train_tuples(train_data):
nlp = English()
ner = nlp.create_pipe("ner")
ner.add_label("ORG")
ner.add_label("LOC")
nlp.add_pipe(ner)
train_examples = []
for t in train_data:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.begin_training()
for i in range(5):
losses = {}
batches = minibatch(train_examples, 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.from_dict(
Doc(nlp.vocab, words=merged_dict["words"], spaces=merged_dict["spaces"]),
merged_dict,
)
assert example.text == "Hi there everyone It is just me"
split_examples = example.split_sents()
assert len(split_examples) == 2
assert split_examples[0].text == "Hi there everyone "
assert split_examples[1].text == "It is just me"
token_annotation_1 = split_examples[0].to_dict()["token_annotation"]
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].to_dict()["token_annotation"]
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]
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