mirror of
https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
473 lines
16 KiB
Python
473 lines
16 KiB
Python
import pytest
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from spacy.tokens import Doc
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from spacy.training.example import Example
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from spacy.util import to_ternary_int
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from spacy.vocab import Vocab
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def test_Example_init_requires_doc_objects():
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vocab = Vocab()
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with pytest.raises(TypeError):
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Example(None, None)
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with pytest.raises(TypeError):
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Example(Doc(vocab, words=["hi"]), None)
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with pytest.raises(TypeError):
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Example(None, Doc(vocab, words=["hi"]))
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def test_Example_from_dict_basic():
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example = Example.from_dict(
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Doc(Vocab(), words=["hello", "world"]), {"words": ["hello", "world"]}
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)
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assert isinstance(example.x, Doc)
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assert isinstance(example.y, Doc)
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@pytest.mark.parametrize(
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"annots", [{"words": ["ice", "cream"], "weirdannots": ["something", "such"]}]
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)
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def test_Example_from_dict_invalid(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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with pytest.raises(KeyError):
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Example.from_dict(predicted, annots)
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@pytest.mark.parametrize(
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"pred_words", [["ice", "cream"], ["icecream"], ["i", "ce", "cream"]]
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)
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@pytest.mark.parametrize("annots", [{"words": ["icecream"], "tags": ["NN"]}])
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def test_Example_from_dict_with_tags(pred_words, annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=pred_words)
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example = Example.from_dict(predicted, annots)
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for i, token in enumerate(example.reference):
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assert token.tag_ == annots["tags"][i]
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aligned_tags = example.get_aligned("TAG", as_string=True)
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assert aligned_tags == ["NN" for _ in predicted]
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@pytest.mark.filterwarnings("ignore::UserWarning")
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def test_aligned_tags():
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pred_words = ["Apply", "some", "sunscreen", "unless", "you", "can", "not"]
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gold_words = ["Apply", "some", "sun", "screen", "unless", "you", "cannot"]
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gold_tags = ["VERB", "DET", "NOUN", "NOUN", "SCONJ", "PRON", "VERB"]
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annots = {"words": gold_words, "tags": gold_tags}
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vocab = Vocab()
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predicted = Doc(vocab, words=pred_words)
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example1 = Example.from_dict(predicted, annots)
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aligned_tags1 = example1.get_aligned("TAG", as_string=True)
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assert aligned_tags1 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
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# ensure that to_dict works correctly
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example2 = Example.from_dict(predicted, example1.to_dict())
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aligned_tags2 = example2.get_aligned("TAG", as_string=True)
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assert aligned_tags2 == ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB", "VERB"]
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def test_aligned_tags_multi():
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pred_words = ["Applysome", "sunscreen", "unless", "you", "can", "not"]
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gold_words = ["Apply", "somesun", "screen", "unless", "you", "cannot"]
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gold_tags = ["VERB", "DET", "NOUN", "SCONJ", "PRON", "VERB"]
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annots = {"words": gold_words, "tags": gold_tags}
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vocab = Vocab()
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predicted = Doc(vocab, words=pred_words)
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example = Example.from_dict(predicted, annots)
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aligned_tags = example.get_aligned("TAG", as_string=True)
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assert aligned_tags == [None, None, "SCONJ", "PRON", "VERB", "VERB"]
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "London", "and", "Berlin", "."],
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"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
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"heads": [1, 1, 1, 2, 2, 1],
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}
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],
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)
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def test_Example_from_dict_with_parse(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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for i, token in enumerate(example.reference):
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assert token.dep_ == annots["deps"][i]
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assert token.head.i == annots["heads"][i]
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["Sarah", "'s", "sister", "flew"],
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"morphs": [
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"NounType=prop|Number=sing",
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"Poss=yes",
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"Number=sing",
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"Tense=past|VerbForm=fin",
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],
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}
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],
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)
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def test_Example_from_dict_with_morphology(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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for i, token in enumerate(example.reference):
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assert str(token.morph) == annots["morphs"][i]
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["This", "is", "one", "sentence", "this", "is", "another"],
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"sent_starts": [1, False, 0, None, True, -1, -5.7],
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}
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],
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)
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def test_Example_from_dict_with_sent_start(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.sents)) == 2
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for i, token in enumerate(example.reference):
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if to_ternary_int(annots["sent_starts"][i]) == 1:
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assert token.is_sent_start is True
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elif to_ternary_int(annots["sent_starts"][i]) == 0:
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assert token.is_sent_start is None
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else:
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assert token.is_sent_start is False
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["This", "is", "a", "sentence"],
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"cats": {"cat1": 1.0, "cat2": 0.0, "cat3": 0.5},
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}
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],
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)
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def test_Example_from_dict_with_cats(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.cats)) == 3
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assert example.reference.cats["cat1"] == 1.0
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assert example.reference.cats["cat2"] == 0.0
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assert example.reference.cats["cat3"] == 0.5
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"entities": [(7, 15, "LOC"), (20, 26, "LOC")],
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}
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],
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)
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def test_Example_from_dict_with_entities(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.ents)) == 2
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# fmt: off
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assert [example.reference[i].ent_iob_ for i in range(7)] == ["O", "O", "B", "I", "O", "B", "O"]
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assert example.get_aligned("ENT_IOB") == [2, 2, 3, 1, 2, 3, 2]
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# fmt: on
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assert example.reference[2].ent_type_ == "LOC"
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assert example.reference[3].ent_type_ == "LOC"
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assert example.reference[5].ent_type_ == "LOC"
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def test_Example_from_dict_with_empty_entities():
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annots = {
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"entities": [],
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}
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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# entities as empty list sets everything to O
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assert example.reference.has_annotation("ENT_IOB")
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assert len(list(example.reference.ents)) == 0
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assert all(token.ent_iob_ == "O" for token in example.reference)
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# various unset/missing entities leaves entities unset
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annots["entities"] = None
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example = Example.from_dict(predicted, annots)
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assert not example.reference.has_annotation("ENT_IOB")
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annots.pop("entities", None)
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example = Example.from_dict(predicted, annots)
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assert not example.reference.has_annotation("ENT_IOB")
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"entities": [
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(0, 4, "LOC"),
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(21, 27, "LOC"),
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], # not aligned to token boundaries
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}
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],
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)
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def test_Example_from_dict_with_entities_invalid(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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with pytest.warns(UserWarning):
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.ents)) == 0
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"entities": [
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(7, 15, "LOC"),
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(11, 15, "LOC"),
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(20, 26, "LOC"),
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], # overlapping
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}
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],
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)
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def test_Example_from_dict_with_entities_overlapping(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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with pytest.raises(ValueError):
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Example.from_dict(predicted, annots)
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": {
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"cities": [(7, 15, "LOC"), (20, 26, "LOC")],
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"people": [(0, 1, "PERSON")],
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},
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}
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],
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)
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def test_Example_from_dict_with_spans(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.ents)) == 0
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assert len(list(example.reference.spans["cities"])) == 2
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assert len(list(example.reference.spans["people"])) == 1
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for span in example.reference.spans["cities"]:
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assert span.label_ == "LOC"
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for span in example.reference.spans["people"]:
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assert span.label_ == "PERSON"
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": {
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"cities": [(7, 15, "LOC"), (11, 15, "LOC"), (20, 26, "LOC")],
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"people": [(0, 1, "PERSON")],
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},
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}
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],
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)
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def test_Example_from_dict_with_spans_overlapping(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert len(list(example.reference.ents)) == 0
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assert len(list(example.reference.spans["cities"])) == 3
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assert len(list(example.reference.spans["people"])) == 1
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for span in example.reference.spans["cities"]:
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assert span.label_ == "LOC"
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for span in example.reference.spans["people"]:
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assert span.label_ == "PERSON"
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": [(0, 1, "PERSON")],
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},
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": {"cities": (7, 15, "LOC")},
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},
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": {"cities": [7, 11]},
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},
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"spans": {"cities": [[7]]},
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},
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],
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)
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def test_Example_from_dict_with_spans_invalid(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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with pytest.raises(ValueError):
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Example.from_dict(predicted, annots)
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"entities": [(7, 15, "LOC"), (20, 26, "LOC")],
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"links": {
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(7, 15): {"Q60": 1.0, "Q64": 0.0},
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(20, 26): {"Q60": 0.0, "Q64": 1.0},
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},
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}
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],
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)
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def test_Example_from_dict_with_links(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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example = Example.from_dict(predicted, annots)
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assert example.reference[0].ent_kb_id_ == ""
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assert example.reference[1].ent_kb_id_ == ""
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assert example.reference[2].ent_kb_id_ == "Q60"
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assert example.reference[3].ent_kb_id_ == "Q60"
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assert example.reference[4].ent_kb_id_ == ""
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assert example.reference[5].ent_kb_id_ == "Q64"
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assert example.reference[6].ent_kb_id_ == ""
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@pytest.mark.parametrize(
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"annots",
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[
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{
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"words": ["I", "like", "New", "York", "and", "Berlin", "."],
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"links": {(7, 14): {"Q7381115": 1.0, "Q2146908": 0.0}},
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}
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],
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)
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def test_Example_from_dict_with_links_invalid(annots):
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vocab = Vocab()
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predicted = Doc(vocab, words=annots["words"])
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with pytest.raises(ValueError):
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Example.from_dict(predicted, annots)
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def test_Example_from_dict_sentences():
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vocab = Vocab()
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predicted = Doc(vocab, words=["One", "sentence", ".", "one", "more"])
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annots = {"sent_starts": [1, 0, 0, 1, 0]}
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ex = Example.from_dict(predicted, annots)
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assert len(list(ex.reference.sents)) == 2
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# this currently throws an error - bug or feature?
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# predicted = Doc(vocab, words=["One", "sentence", "not", "one", "more"])
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# annots = {"sent_starts": [1, 0, 0, 0, 0]}
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# ex = Example.from_dict(predicted, annots)
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# assert len(list(ex.reference.sents)) == 1
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predicted = Doc(vocab, words=["One", "sentence", "not", "one", "more"])
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annots = {"sent_starts": [1, -1, 0, 0, 0]}
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ex = Example.from_dict(predicted, annots)
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assert len(list(ex.reference.sents)) == 1
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def test_Example_missing_deps():
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vocab = Vocab()
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words = ["I", "like", "London", "and", "Berlin", "."]
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deps = ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"]
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heads = [1, 1, 1, 2, 2, 1]
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annots_head_only = {"words": words, "heads": heads}
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annots_head_dep = {"words": words, "heads": heads, "deps": deps}
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predicted = Doc(vocab, words=words)
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# when not providing deps, the head information is considered to be missing
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# in this case, the token's heads refer to themselves
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example_1 = Example.from_dict(predicted, annots_head_only)
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assert [t.head.i for t in example_1.reference] == [0, 1, 2, 3, 4, 5]
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# when providing deps, the head information is actually used
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example_2 = Example.from_dict(predicted, annots_head_dep)
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assert [t.head.i for t in example_2.reference] == heads
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def test_Example_missing_heads():
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vocab = Vocab()
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words = ["I", "like", "London", "and", "Berlin", "."]
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deps = ["nsubj", "ROOT", "dobj", None, "conj", "punct"]
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heads = [1, 1, 1, None, 2, 1]
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annots = {"words": words, "heads": heads, "deps": deps}
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predicted = Doc(vocab, words=words)
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example = Example.from_dict(predicted, annots)
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parsed_heads = [t.head.i for t in example.reference]
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assert parsed_heads[0] == heads[0]
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assert parsed_heads[1] == heads[1]
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assert parsed_heads[2] == heads[2]
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assert parsed_heads[4] == heads[4]
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assert parsed_heads[5] == heads[5]
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expected = [True, True, True, False, True, True]
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assert [t.has_head() for t in example.reference] == expected
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# Ensure that the missing head doesn't create an artificial new sentence start
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expected = [True, False, False, False, False, False]
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assert example.get_aligned_sent_starts() == expected
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def test_Example_aligned_whitespace(en_vocab):
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words = ["a", " ", "b"]
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tags = ["A", "SPACE", "B"]
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predicted = Doc(en_vocab, words=words)
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reference = Doc(en_vocab, words=words, tags=tags)
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example = Example(predicted, reference)
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assert example.get_aligned("TAG", as_string=True) == tags
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@pytest.mark.issue("11260")
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def test_issue11260():
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annots = {
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"words": ["I", "like", "New", "York", "."],
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"spans": {
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"cities": [(7, 15, "LOC", "")],
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"people": [(0, 1, "PERSON", "")],
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},
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}
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vocab = Vocab()
|
|
predicted = Doc(vocab, words=annots["words"])
|
|
example = Example.from_dict(predicted, annots)
|
|
assert len(example.reference.spans["cities"]) == 1
|
|
assert len(example.reference.spans["people"]) == 1
|
|
|
|
output_dict = example.to_dict()
|
|
assert "spans" in output_dict["doc_annotation"]
|
|
assert output_dict["doc_annotation"]["spans"]["cities"] == annots["spans"]["cities"]
|
|
assert output_dict["doc_annotation"]["spans"]["people"] == annots["spans"]["people"]
|
|
|
|
output_example = Example.from_dict(predicted, output_dict)
|
|
|
|
assert len(output_example.reference.spans["cities"]) == len(
|
|
example.reference.spans["cities"]
|
|
)
|
|
assert len(output_example.reference.spans["people"]) == len(
|
|
example.reference.spans["people"]
|
|
)
|
|
for span in example.reference.spans["cities"]:
|
|
assert span.label_ == "LOC"
|
|
assert span.text == "New York"
|
|
assert span.start_char == 7
|
|
for span in example.reference.spans["people"]:
|
|
assert span.label_ == "PERSON"
|
|
assert span.text == "I"
|
|
assert span.start_char == 0
|