mirror of
https://github.com/explosion/spaCy.git
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7d50804644
* Migrate regressions 1-1000 * Move serialize test to correct file * Remove tests that won't work in v3 * Migrate regressions 1000-1500 Removed regression test 1250 because v3 doesn't support the old LEX scheme anymore. * Add missing imports in serializer tests * Migrate tests 1500-2000 * Migrate regressions from 2000-2500 * Migrate regressions from 2501-3000 * Migrate regressions from 3000-3501 * Migrate regressions from 3501-4000 * Migrate regressions from 4001-4500 * Migrate regressions from 4501-5000 * Migrate regressions from 5001-5501 * Migrate regressions from 5501 to 7000 * Migrate regressions from 7001 to 8000 * Migrate remaining regression tests * Fixing missing imports * Update docs with new system [ci skip] * Update CONTRIBUTING.md - Fix formatting - Update wording * Remove lemmatizer tests in el lang * Move a few tests into the general tokenizer * Separate Doc and DocBin tests
605 lines
20 KiB
Python
605 lines
20 KiB
Python
import gc
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import numpy
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import pytest
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from thinc.api import get_current_ops
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from spacy.lang.en import English
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from spacy.lang.en.syntax_iterators import noun_chunks
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from spacy.language import Language
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from spacy.pipeline import TrainablePipe
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from spacy.tokens import Doc
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from spacy.training import Example
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from spacy.util import SimpleFrozenList, get_arg_names
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from spacy.vocab import Vocab
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@pytest.fixture
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def nlp():
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return Language()
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@Language.component("new_pipe")
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def new_pipe(doc):
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return doc
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@Language.component("other_pipe")
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def other_pipe(doc):
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return doc
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@pytest.mark.issue(1506)
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def test_issue1506():
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def string_generator():
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for _ in range(10001):
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yield "It's sentence produced by that bug."
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for _ in range(10001):
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yield "I erase some hbdsaj lemmas."
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for _ in range(10001):
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yield "I erase lemmas."
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for _ in range(10001):
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yield "It's sentence produced by that bug."
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for _ in range(10001):
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yield "It's sentence produced by that bug."
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nlp = English()
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for i, d in enumerate(nlp.pipe(string_generator())):
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# We should run cleanup more than one time to actually cleanup data.
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# In first run — clean up only mark strings as «not hitted».
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if i == 10000 or i == 20000 or i == 30000:
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gc.collect()
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for t in d:
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str(t.lemma_)
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@pytest.mark.issue(1654)
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def test_issue1654():
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nlp = Language(Vocab())
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assert not nlp.pipeline
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@Language.component("component")
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def component(doc):
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return doc
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nlp.add_pipe("component", name="1")
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nlp.add_pipe("component", name="2", after="1")
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nlp.add_pipe("component", name="3", after="2")
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assert nlp.pipe_names == ["1", "2", "3"]
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nlp2 = Language(Vocab())
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assert not nlp2.pipeline
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nlp2.add_pipe("component", name="3")
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nlp2.add_pipe("component", name="2", before="3")
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nlp2.add_pipe("component", name="1", before="2")
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assert nlp2.pipe_names == ["1", "2", "3"]
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@pytest.mark.issue(3880)
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def test_issue3880():
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"""Test that `nlp.pipe()` works when an empty string ends the batch.
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Fixed in v7.0.5 of Thinc.
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"""
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texts = ["hello", "world", "", ""]
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nlp = English()
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nlp.add_pipe("parser").add_label("dep")
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nlp.add_pipe("ner").add_label("PERSON")
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nlp.add_pipe("tagger").add_label("NN")
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nlp.initialize()
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for doc in nlp.pipe(texts):
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pass
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@pytest.mark.issue(5082)
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def test_issue5082():
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# Ensure the 'merge_entities' pipeline does something sensible for the vectors of the merged tokens
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nlp = English()
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vocab = nlp.vocab
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array1 = numpy.asarray([0.1, 0.5, 0.8], dtype=numpy.float32)
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array2 = numpy.asarray([-0.2, -0.6, -0.9], dtype=numpy.float32)
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array3 = numpy.asarray([0.3, -0.1, 0.7], dtype=numpy.float32)
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array4 = numpy.asarray([0.5, 0, 0.3], dtype=numpy.float32)
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array34 = numpy.asarray([0.4, -0.05, 0.5], dtype=numpy.float32)
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vocab.set_vector("I", array1)
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vocab.set_vector("like", array2)
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vocab.set_vector("David", array3)
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vocab.set_vector("Bowie", array4)
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text = "I like David Bowie"
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "david"}, {"LOWER": "bowie"}]}
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]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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parsed_vectors_1 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_1) == 4
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ops = get_current_ops()
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[0]), array1)
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[1]), array2)
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[2]), array3)
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_1[3]), array4)
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nlp.add_pipe("merge_entities")
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parsed_vectors_2 = [t.vector for t in nlp(text)]
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assert len(parsed_vectors_2) == 3
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[0]), array1)
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[1]), array2)
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numpy.testing.assert_array_equal(ops.to_numpy(parsed_vectors_2[2]), array34)
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@pytest.mark.issue(5458)
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def test_issue5458():
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# Test that the noun chuncker does not generate overlapping spans
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# fmt: off
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words = ["In", "an", "era", "where", "markets", "have", "brought", "prosperity", "and", "empowerment", "."]
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vocab = Vocab(strings=words)
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deps = ["ROOT", "det", "pobj", "advmod", "nsubj", "aux", "relcl", "dobj", "cc", "conj", "punct"]
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pos = ["ADP", "DET", "NOUN", "ADV", "NOUN", "AUX", "VERB", "NOUN", "CCONJ", "NOUN", "PUNCT"]
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heads = [0, 2, 0, 9, 6, 6, 2, 6, 7, 7, 0]
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# fmt: on
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en_doc = Doc(vocab, words=words, pos=pos, heads=heads, deps=deps)
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en_doc.noun_chunks_iterator = noun_chunks
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# if there are overlapping spans, this will fail with an E102 error "Can't merge non-disjoint spans"
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nlp = English()
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merge_nps = nlp.create_pipe("merge_noun_chunks")
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merge_nps(en_doc)
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def test_multiple_predictions():
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class DummyPipe(TrainablePipe):
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def __init__(self):
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self.model = "dummy_model"
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def predict(self, docs):
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return ([1, 2, 3], [4, 5, 6])
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def set_annotations(self, docs, scores):
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return docs
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nlp = Language()
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doc = nlp.make_doc("foo")
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dummy_pipe = DummyPipe()
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dummy_pipe(doc)
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def test_add_pipe_no_name(nlp):
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nlp.add_pipe("new_pipe")
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assert "new_pipe" in nlp.pipe_names
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def test_add_pipe_duplicate_name(nlp):
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nlp.add_pipe("new_pipe", name="duplicate_name")
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with pytest.raises(ValueError):
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nlp.add_pipe("new_pipe", name="duplicate_name")
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@pytest.mark.parametrize("name", ["parser"])
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def test_add_pipe_first(nlp, name):
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nlp.add_pipe("new_pipe", name=name, first=True)
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assert nlp.pipeline[0][0] == name
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@pytest.mark.parametrize("name1,name2", [("parser", "lambda_pipe")])
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def test_add_pipe_last(nlp, name1, name2):
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Language.component("new_pipe2", func=lambda doc: doc)
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nlp.add_pipe("new_pipe2", name=name2)
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nlp.add_pipe("new_pipe", name=name1, last=True)
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assert nlp.pipeline[0][0] != name1
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assert nlp.pipeline[-1][0] == name1
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def test_cant_add_pipe_first_and_last(nlp):
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with pytest.raises(ValueError):
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nlp.add_pipe("new_pipe", first=True, last=True)
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@pytest.mark.parametrize("name", ["test_get_pipe"])
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def test_get_pipe(nlp, name):
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with pytest.raises(KeyError):
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nlp.get_pipe(name)
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.get_pipe(name) == new_pipe
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@pytest.mark.parametrize(
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"name,replacement,invalid_replacement",
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[("test_replace_pipe", "other_pipe", lambda doc: doc)],
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)
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def test_replace_pipe(nlp, name, replacement, invalid_replacement):
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, new_pipe)
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nlp.add_pipe("new_pipe", name=name)
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, invalid_replacement)
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nlp.replace_pipe(name, replacement)
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assert nlp.get_pipe(name) == nlp.create_pipe(replacement)
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def test_replace_last_pipe(nlp):
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nlp.add_pipe("sentencizer")
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nlp.add_pipe("ner")
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assert nlp.pipe_names == ["sentencizer", "ner"]
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nlp.replace_pipe("ner", "ner")
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assert nlp.pipe_names == ["sentencizer", "ner"]
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def test_replace_pipe_config(nlp):
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nlp.add_pipe("entity_linker")
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nlp.add_pipe("sentencizer")
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assert nlp.get_pipe("entity_linker").incl_prior is True
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nlp.replace_pipe("entity_linker", "entity_linker", config={"incl_prior": False})
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assert nlp.get_pipe("entity_linker").incl_prior is False
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@pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")])
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def test_rename_pipe(nlp, old_name, new_name):
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with pytest.raises(ValueError):
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nlp.rename_pipe(old_name, new_name)
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nlp.add_pipe("new_pipe", name=old_name)
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nlp.rename_pipe(old_name, new_name)
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assert nlp.pipeline[0][0] == new_name
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@pytest.mark.parametrize("name", ["my_component"])
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def test_remove_pipe(nlp, name):
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with pytest.raises(ValueError):
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nlp.remove_pipe(name)
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nlp.add_pipe("new_pipe", name=name)
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assert len(nlp.pipeline) == 1
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removed_name, removed_component = nlp.remove_pipe(name)
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assert not len(nlp.pipeline)
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assert removed_name == name
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assert removed_component == new_pipe
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_method(nlp, name):
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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disabled = nlp.select_pipes(disable=name)
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assert not nlp.has_pipe(name)
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disabled.restore()
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@pytest.mark.parametrize("name", ["my_component"])
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def test_enable_pipes_method(nlp, name):
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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disabled = nlp.select_pipes(enable=[])
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assert not nlp.has_pipe(name)
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disabled.restore()
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_context(nlp, name):
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"""Test that an enabled component stays enabled after running the context manager."""
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with nlp.select_pipes(disable=name):
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assert not nlp.has_pipe(name)
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assert nlp.has_pipe(name)
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_context_restore(nlp, name):
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"""Test that a disabled component stays disabled after running the context manager."""
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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nlp.disable_pipe(name)
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assert not nlp.has_pipe(name)
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with nlp.select_pipes(disable=name):
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assert not nlp.has_pipe(name)
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assert not nlp.has_pipe(name)
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def test_select_pipes_list_arg(nlp):
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for name in ["c1", "c2", "c3"]:
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with nlp.select_pipes(disable=["c1", "c2"]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(enable="c3"):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(enable=["c1", "c2"], disable="c3"):
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assert nlp.has_pipe("c1")
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assert nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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with nlp.select_pipes(enable=[]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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with nlp.select_pipes(enable=["c1", "c2", "c3"], disable=[]):
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assert nlp.has_pipe("c1")
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assert nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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with nlp.select_pipes(disable=["c1", "c2", "c3"], enable=[]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert not nlp.has_pipe("c3")
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def test_select_pipes_errors(nlp):
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for name in ["c1", "c2", "c3"]:
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nlp.add_pipe("new_pipe", name=name)
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assert nlp.has_pipe(name)
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with pytest.raises(ValueError):
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nlp.select_pipes()
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=["c1", "c2"], disable=["c1"])
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=["c1", "c2"], disable=[])
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with pytest.raises(ValueError):
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nlp.select_pipes(enable=[], disable=["c3"])
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disabled = nlp.select_pipes(disable=["c2"])
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nlp.remove_pipe("c2")
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with pytest.raises(ValueError):
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disabled.restore()
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@pytest.mark.parametrize("n_pipes", [100])
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def test_add_lots_of_pipes(nlp, n_pipes):
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Language.component("n_pipes", func=lambda doc: doc)
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for i in range(n_pipes):
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nlp.add_pipe("n_pipes", name=f"pipe_{i}")
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assert len(nlp.pipe_names) == n_pipes
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@pytest.mark.parametrize("component", [lambda doc: doc, {"hello": "world"}])
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def test_raise_for_invalid_components(nlp, component):
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with pytest.raises(ValueError):
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nlp.add_pipe(component)
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@pytest.mark.parametrize("component", ["ner", "tagger", "parser", "textcat"])
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def test_pipe_base_class_add_label(nlp, component):
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label = "TEST"
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pipe = nlp.create_pipe(component)
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pipe.add_label(label)
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if component == "tagger":
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# Tagger always has the default coarse-grained label scheme
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assert label in pipe.labels
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else:
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assert pipe.labels == (label,)
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def test_pipe_labels(nlp):
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input_labels = {
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"ner": ["PERSON", "ORG", "GPE"],
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"textcat": ["POSITIVE", "NEGATIVE"],
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}
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for name, labels in input_labels.items():
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nlp.add_pipe(name)
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pipe = nlp.get_pipe(name)
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for label in labels:
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pipe.add_label(label)
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assert len(pipe.labels) == len(labels)
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assert len(nlp.pipe_labels) == len(input_labels)
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for name, labels in nlp.pipe_labels.items():
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assert sorted(input_labels[name]) == sorted(labels)
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def test_add_pipe_before_after():
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"""Test that before/after works with strings and ints."""
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nlp = Language()
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nlp.add_pipe("ner")
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with pytest.raises(ValueError):
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nlp.add_pipe("textcat", before="parser")
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nlp.add_pipe("textcat", before="ner")
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assert nlp.pipe_names == ["textcat", "ner"]
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with pytest.raises(ValueError):
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nlp.add_pipe("parser", before=3)
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with pytest.raises(ValueError):
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nlp.add_pipe("parser", after=3)
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nlp.add_pipe("parser", after=0)
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assert nlp.pipe_names == ["textcat", "parser", "ner"]
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nlp.add_pipe("tagger", before=2)
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assert nlp.pipe_names == ["textcat", "parser", "tagger", "ner"]
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", after=1, first=True)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", before="ner", after=2)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", before=True)
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with pytest.raises(ValueError):
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nlp.add_pipe("entity_ruler", first=False)
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def test_disable_enable_pipes():
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name = "test_disable_enable_pipes"
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results = {}
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def make_component(name):
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results[name] = ""
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def component(doc):
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nonlocal results
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results[name] = doc.text
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return doc
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return component
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c1 = Language.component(f"{name}1", func=make_component(f"{name}1"))
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c2 = Language.component(f"{name}2", func=make_component(f"{name}2"))
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nlp = Language()
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nlp.add_pipe(f"{name}1")
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nlp.add_pipe(f"{name}2")
|
|
assert results[f"{name}1"] == ""
|
|
assert results[f"{name}2"] == ""
|
|
assert nlp.pipeline == [(f"{name}1", c1), (f"{name}2", c2)]
|
|
assert nlp.pipe_names == [f"{name}1", f"{name}2"]
|
|
nlp.disable_pipe(f"{name}1")
|
|
assert nlp.disabled == [f"{name}1"]
|
|
assert nlp.component_names == [f"{name}1", f"{name}2"]
|
|
assert nlp.pipe_names == [f"{name}2"]
|
|
assert nlp.config["nlp"]["disabled"] == [f"{name}1"]
|
|
nlp("hello")
|
|
assert results[f"{name}1"] == "" # didn't run
|
|
assert results[f"{name}2"] == "hello" # ran
|
|
nlp.enable_pipe(f"{name}1")
|
|
assert nlp.disabled == []
|
|
assert nlp.pipe_names == [f"{name}1", f"{name}2"]
|
|
assert nlp.config["nlp"]["disabled"] == []
|
|
nlp("world")
|
|
assert results[f"{name}1"] == "world"
|
|
assert results[f"{name}2"] == "world"
|
|
nlp.disable_pipe(f"{name}2")
|
|
nlp.remove_pipe(f"{name}2")
|
|
assert nlp.components == [(f"{name}1", c1)]
|
|
assert nlp.pipeline == [(f"{name}1", c1)]
|
|
assert nlp.component_names == [f"{name}1"]
|
|
assert nlp.pipe_names == [f"{name}1"]
|
|
assert nlp.disabled == []
|
|
assert nlp.config["nlp"]["disabled"] == []
|
|
nlp.rename_pipe(f"{name}1", name)
|
|
assert nlp.components == [(name, c1)]
|
|
assert nlp.component_names == [name]
|
|
nlp("!")
|
|
assert results[f"{name}1"] == "!"
|
|
assert results[f"{name}2"] == "world"
|
|
with pytest.raises(ValueError):
|
|
nlp.disable_pipe(f"{name}2")
|
|
nlp.disable_pipe(name)
|
|
assert nlp.component_names == [name]
|
|
assert nlp.pipe_names == []
|
|
assert nlp.config["nlp"]["disabled"] == [name]
|
|
nlp("?")
|
|
assert results[f"{name}1"] == "!"
|
|
|
|
|
|
def test_pipe_methods_frozen():
|
|
"""Test that spaCy raises custom error messages if "frozen" properties are
|
|
accessed. We still want to use a list here to not break backwards
|
|
compatibility, but users should see an error if they're trying to append
|
|
to nlp.pipeline etc."""
|
|
nlp = Language()
|
|
ner = nlp.add_pipe("ner")
|
|
assert nlp.pipe_names == ["ner"]
|
|
for prop in [
|
|
nlp.pipeline,
|
|
nlp.pipe_names,
|
|
nlp.components,
|
|
nlp.component_names,
|
|
nlp.disabled,
|
|
nlp.factory_names,
|
|
]:
|
|
assert isinstance(prop, list)
|
|
assert isinstance(prop, SimpleFrozenList)
|
|
with pytest.raises(NotImplementedError):
|
|
nlp.pipeline.append(("ner2", ner))
|
|
with pytest.raises(NotImplementedError):
|
|
nlp.pipe_names.pop()
|
|
with pytest.raises(NotImplementedError):
|
|
nlp.components.sort()
|
|
with pytest.raises(NotImplementedError):
|
|
nlp.component_names.clear()
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"pipe", ["tagger", "parser", "ner", "textcat", "morphologizer"]
|
|
)
|
|
def test_pipe_label_data_exports_labels(pipe):
|
|
nlp = Language()
|
|
pipe = nlp.add_pipe(pipe)
|
|
# Make sure pipe has pipe labels
|
|
assert getattr(pipe, "label_data", None) is not None
|
|
# Make sure pipe can be initialized with labels
|
|
initialize = getattr(pipe, "initialize", None)
|
|
assert initialize is not None
|
|
assert "labels" in get_arg_names(initialize)
|
|
|
|
|
|
@pytest.mark.parametrize("pipe", ["senter", "entity_linker"])
|
|
def test_pipe_label_data_no_labels(pipe):
|
|
nlp = Language()
|
|
pipe = nlp.add_pipe(pipe)
|
|
assert getattr(pipe, "label_data", None) is None
|
|
initialize = getattr(pipe, "initialize", None)
|
|
if initialize is not None:
|
|
assert "labels" not in get_arg_names(initialize)
|
|
|
|
|
|
def test_warning_pipe_begin_training():
|
|
with pytest.warns(UserWarning, match="begin_training"):
|
|
|
|
class IncompatPipe(TrainablePipe):
|
|
def __init__(self):
|
|
...
|
|
|
|
def begin_training(*args, **kwargs):
|
|
...
|
|
|
|
|
|
def test_pipe_methods_initialize():
|
|
"""Test that the [initialize] config reflects the components correctly."""
|
|
nlp = Language()
|
|
nlp.add_pipe("tagger")
|
|
assert "tagger" not in nlp.config["initialize"]["components"]
|
|
nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]}
|
|
assert nlp.config["initialize"]["components"]["tagger"] == {"labels": ["hello"]}
|
|
nlp.remove_pipe("tagger")
|
|
assert "tagger" not in nlp.config["initialize"]["components"]
|
|
nlp.add_pipe("tagger")
|
|
assert "tagger" not in nlp.config["initialize"]["components"]
|
|
nlp.config["initialize"]["components"]["tagger"] = {"labels": ["hello"]}
|
|
nlp.rename_pipe("tagger", "my_tagger")
|
|
assert "tagger" not in nlp.config["initialize"]["components"]
|
|
assert nlp.config["initialize"]["components"]["my_tagger"] == {"labels": ["hello"]}
|
|
nlp.config["initialize"]["components"]["test"] = {"foo": "bar"}
|
|
nlp.add_pipe("ner", name="test")
|
|
assert "test" in nlp.config["initialize"]["components"]
|
|
nlp.remove_pipe("test")
|
|
assert "test" not in nlp.config["initialize"]["components"]
|
|
|
|
|
|
def test_update_with_annotates():
|
|
name = "test_with_annotates"
|
|
results = {}
|
|
|
|
def make_component(name):
|
|
results[name] = ""
|
|
|
|
def component(doc):
|
|
nonlocal results
|
|
results[name] += doc.text
|
|
return doc
|
|
|
|
return component
|
|
|
|
Language.component(f"{name}1", func=make_component(f"{name}1"))
|
|
Language.component(f"{name}2", func=make_component(f"{name}2"))
|
|
|
|
components = set([f"{name}1", f"{name}2"])
|
|
|
|
nlp = English()
|
|
texts = ["a", "bb", "ccc"]
|
|
examples = []
|
|
for text in texts:
|
|
examples.append(Example(nlp.make_doc(text), nlp.make_doc(text)))
|
|
|
|
for components_to_annotate in [
|
|
[],
|
|
[f"{name}1"],
|
|
[f"{name}1", f"{name}2"],
|
|
[f"{name}2", f"{name}1"],
|
|
]:
|
|
for key in results:
|
|
results[key] = ""
|
|
nlp = English(vocab=nlp.vocab)
|
|
nlp.add_pipe(f"{name}1")
|
|
nlp.add_pipe(f"{name}2")
|
|
nlp.update(examples, annotates=components_to_annotate)
|
|
for component in components_to_annotate:
|
|
assert results[component] == "".join(eg.predicted.text for eg in examples)
|
|
for component in components - set(components_to_annotate):
|
|
assert results[component] == ""
|