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Few more Example unit tests (#5720)

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* small fixes in Example, UX

* add gold tests for aligned_spans and get_aligned_parse

* sentencizer unnecessary
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Sofie Van Landeghem 2020-07-07 18:46:00 +02:00 committed by GitHub
parent 433dc3c9c9
commit a39a110c4e
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4 changed files with 82 additions and 28 deletions
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5
spacy/errors.py
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@ -477,15 +477,14 @@ class Errors(object):
E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
# TODO: fix numbering after merging develop into master
E969 = ("Expected string values for field '{field}', but received {types} instead. ")
E970 = ("Can not execute command '{str_command}'. Do you have '{tool}' installed?")
E971 = ("Found incompatible lengths in Doc.from_array: {array_length} for the "
"array and {doc_length} for the Doc itself.")
E972 = ("Example.__init__ got None for '{arg}'. Requires Doc.")
E973 = ("Unexpected type for NER data")
E974 = ("Unknown {obj} attribute: {key}")
E975 = ("The method 'Example.from_dict' expects a Doc as first argument, "
"but got {type}")
E976 = ("The method 'Example.from_dict' expects a dict as second argument, "
E976 = ("The method 'Example.from_dict' expects a {type} as {n} argument, "
"but received None.")
E977 = ("Can not compare a MorphAnalysis with a string object. "
"This is likely a bug in spaCy, so feel free to open an issue.")

15
spacy/gold/example.pyx
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@ -28,7 +28,6 @@ cpdef Doc annotations2doc(vocab, tok_annot, doc_annot):
cdef class Example:
def __init__(self, Doc predicted, Doc reference, *, alignment=None):
""" Doc can either be text, or an actual Doc """
if predicted is None:
raise TypeError(Errors.E972.format(arg="predicted"))
if reference is None:
@ -59,17 +58,15 @@ cdef class Example:
@classmethod
def from_dict(cls, Doc predicted, dict example_dict):
if predicted is None:
raise ValueError(Errors.E976.format(n="first", type="Doc"))
if example_dict is None:
raise ValueError(Errors.E976)
if not isinstance(predicted, Doc):
raise TypeError(Errors.E975.format(type=type(predicted)))
raise ValueError(Errors.E976.format(n="second", type="dict"))
example_dict = _fix_legacy_dict_data(example_dict)
tok_dict, doc_dict = _parse_example_dict_data(example_dict)
if "ORTH" not in tok_dict:
tok_dict["ORTH"] = [tok.text for tok in predicted]
tok_dict["SPACY"] = [tok.whitespace_ for tok in predicted]
if not _has_field(tok_dict, "SPACY"):
spaces = _guess_spaces(predicted.text, tok_dict["ORTH"])
return Example(
predicted,
annotations2doc(predicted.vocab, tok_dict, doc_dict)
@ -257,7 +254,11 @@ def _annot2array(vocab, tok_annot, doc_annot):
values.append([vocab.morphology.add(v) for v in value])
else:
attrs.append(key)
values.append([vocab.strings.add(v) for v in value])
try:
values.append([vocab.strings.add(v) for v in value])
except TypeError:
types= set([type(v) for v in value])
raise TypeError(Errors.E969.format(field=key, types=types))
array = numpy.asarray(values, dtype="uint64")
return attrs, array.T

2
spacy/tests/parser/test_nonproj.py
View File

@ -45,7 +45,7 @@ def test_parser_ancestors(tree, cyclic_tree, partial_tree, multirooted_tree):
def test_parser_contains_cycle(tree, cyclic_tree, partial_tree, multirooted_tree):
assert contains_cycle(tree) is None
assert contains_cycle(cyclic_tree) == set([3, 4, 5])
assert contains_cycle(cyclic_tree) == {3, 4, 5}
assert contains_cycle(partial_tree) is None
assert contains_cycle(multirooted_tree) is None

88
spacy/tests/test_gold.py
View File

@ -5,6 +5,7 @@ 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
@ -272,72 +273,72 @@ def test_split_sentences(en_vocab):
def test_gold_biluo_one_to_many(en_vocab, en_tokenizer):
words = ["Mr. and ", "Mrs. Smith", "flew to", "San Francisco Valley", "."]
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 "
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", "."]
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("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", "."]
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("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", "."]
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", "."]
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 "
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", "."]
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("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", "."]
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", "."]
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 "
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", "."]
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("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", "."]
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"]
@ -407,6 +408,49 @@ def test_biluo_spans(en_tokenizer):
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"]
@ -414,6 +458,16 @@ def test_gold_ner_missing_tags(en_tokenizer):
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"]