spaCy/spacy/tests/test_new_example.py
Sofie Van Landeghem 8e7557656f
Renaming gold & annotation_setter (#6042)
* version bump to 3.0.0a16

* rename "gold" folder to "training"

* rename 'annotation_setter' to 'set_extra_annotations'

* formatting
2020-09-09 10:31:03 +02:00

247 lines
7.7 KiB
Python

import pytest
from spacy.training.example import Example
from spacy.tokens import Doc
from spacy.vocab import Vocab
def test_Example_init_requires_doc_objects():
vocab = Vocab()
with pytest.raises(TypeError):
Example(None, None)
with pytest.raises(TypeError):
Example(Doc(vocab, words=["hi"]), None)
with pytest.raises(TypeError):
Example(None, Doc(vocab, words=["hi"]))
def test_Example_from_dict_basic():
example = Example.from_dict(
Doc(Vocab(), words=["hello", "world"]), {"words": ["hello", "world"]}
)
assert isinstance(example.x, Doc)
assert isinstance(example.y, Doc)
@pytest.mark.parametrize(
"annots", [{"words": ["ice", "cream"], "weirdannots": ["something", "such"]}]
)
def test_Example_from_dict_invalid(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
with pytest.raises(KeyError):
Example.from_dict(predicted, annots)
@pytest.mark.parametrize(
"pred_words", [["ice", "cream"], ["icecream"], ["i", "ce", "cream"]]
)
@pytest.mark.parametrize("annots", [{"words": ["icecream"], "tags": ["NN"]}])
def test_Example_from_dict_with_tags(pred_words, annots):
vocab = Vocab()
predicted = Doc(vocab, words=pred_words)
example = Example.from_dict(predicted, annots)
for i, token in enumerate(example.reference):
assert token.tag_ == annots["tags"][i]
aligned_tags = example.get_aligned("TAG", as_string=True)
assert aligned_tags == ["NN" for _ in predicted]
@pytest.mark.filterwarnings("ignore::UserWarning")
def test_aligned_tags():
pred_words = ["Apply", "some", "sunscreen", "unless", "you", "can", "not"]
gold_words = ["Apply", "some", "sun", "screen", "unless", "you", "cannot"]
gold_tags = ["VERB", "DET", "NOUN", "NOUN", "SCONJ", "PRON", "VERB"]
annots = {"words": gold_words, "tags": gold_tags}
vocab = Vocab()
predicted = Doc(vocab, words=pred_words)
example1 = Example.from_dict(predicted, annots)
aligned_tags1 = example1.get_aligned("TAG", as_string=True)
assert aligned_tags1 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
# ensure that to_dict works correctly
example2 = Example.from_dict(predicted, example1.to_dict())
aligned_tags2 = example2.get_aligned("TAG", as_string=True)
assert aligned_tags2 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
def test_aligned_tags_multi():
pred_words = ["Applysome", "sunscreen", "unless", "you", "can", "not"]
gold_words = ["Apply", "somesun", "screen", "unless", "you", "cannot"]
gold_tags = ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB"]
annots = {"words": gold_words, "tags": gold_tags}
vocab = Vocab()
predicted = Doc(vocab, words=pred_words)
example = Example.from_dict(predicted, annots)
aligned_tags = example.get_aligned("TAG", as_string=True)
assert aligned_tags == [None, None, "SCONJ", "PRON", "VERB", "VERB"]
@pytest.mark.parametrize(
"annots",
[
{
"words": ["I", "like", "London", "and", "Berlin", "."],
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
"heads": [1, 1, 1, 2, 2, 1],
}
],
)
def test_Example_from_dict_with_parse(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
for i, token in enumerate(example.reference):
assert token.dep_ == annots["deps"][i]
assert token.head.i == annots["heads"][i]
@pytest.mark.parametrize(
"annots",
[
{
"words": ["Sarah", "'s", "sister", "flew"],
"morphs": [
"NounType=prop|Number=sing",
"Poss=yes",
"Number=sing",
"Tense=past|VerbForm=fin",
],
}
],
)
def test_Example_from_dict_with_morphology(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
for i, token in enumerate(example.reference):
assert token.morph_ == annots["morphs"][i]
@pytest.mark.parametrize(
"annots",
[
{
"words": ["This", "is", "one", "sentence", "this", "is", "another"],
"sent_starts": [1, 0, 0, 0, 1, 0, 0],
}
],
)
def test_Example_from_dict_with_sent_start(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
assert len(list(example.reference.sents)) == 2
for i, token in enumerate(example.reference):
assert bool(token.is_sent_start) == bool(annots["sent_starts"][i])
@pytest.mark.parametrize(
"annots",
[
{
"words": ["This", "is", "a", "sentence"],
"cats": {"cat1": 1.0, "cat2": 0.0, "cat3": 0.5},
}
],
)
def test_Example_from_dict_with_cats(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
assert len(list(example.reference.cats)) == 3
assert example.reference.cats["cat1"] == 1.0
assert example.reference.cats["cat2"] == 0.0
assert example.reference.cats["cat3"] == 0.5
@pytest.mark.parametrize(
"annots",
[
{
"words": ["I", "like", "New", "York", "and", "Berlin", "."],
"entities": [(7, 15, "LOC"), (20, 26, "LOC")],
}
],
)
def test_Example_from_dict_with_entities(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
assert len(list(example.reference.ents)) == 2
assert [example.reference[i].ent_iob_ for i in range(7)] == [
"O",
"O",
"B",
"I",
"O",
"B",
"O",
]
assert example.get_aligned("ENT_IOB") == [2, 2, 3, 1, 2, 3, 2]
assert example.reference[2].ent_type_ == "LOC"
assert example.reference[3].ent_type_ == "LOC"
assert example.reference[5].ent_type_ == "LOC"
@pytest.mark.parametrize(
"annots",
[
{
"words": ["I", "like", "New", "York", "and", "Berlin", "."],
"entities": [
(0, 4, "LOC"),
(21, 27, "LOC"),
], # not aligned to token boundaries
}
],
)
def test_Example_from_dict_with_entities_invalid(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
with pytest.warns(UserWarning):
example = Example.from_dict(predicted, annots)
assert len(list(example.reference.ents)) == 0
@pytest.mark.parametrize(
"annots",
[
{
"words": ["I", "like", "New", "York", "and", "Berlin", "."],
"entities": [(7, 15, "LOC"), (20, 26, "LOC")],
"links": {
(7, 15): {"Q60": 1.0, "Q64": 0.0},
(20, 26): {"Q60": 0.0, "Q64": 1.0},
},
}
],
)
def test_Example_from_dict_with_links(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
example = Example.from_dict(predicted, annots)
assert example.reference[0].ent_kb_id_ == ""
assert example.reference[1].ent_kb_id_ == ""
assert example.reference[2].ent_kb_id_ == "Q60"
assert example.reference[3].ent_kb_id_ == "Q60"
assert example.reference[4].ent_kb_id_ == ""
assert example.reference[5].ent_kb_id_ == "Q64"
assert example.reference[6].ent_kb_id_ == ""
@pytest.mark.parametrize(
"annots",
[
{
"words": ["I", "like", "New", "York", "and", "Berlin", "."],
"links": {(7, 14): {"Q7381115": 1.0, "Q2146908": 0.0}},
}
],
)
def test_Example_from_dict_with_links_invalid(annots):
vocab = Vocab()
predicted = Doc(vocab, words=annots["words"])
with pytest.raises(ValueError):
Example.from_dict(predicted, annots)