mirror of https://github.com/explosion/spaCy.git
143 lines
3.7 KiB
Python
143 lines
3.7 KiB
Python
import itertools
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import pytest
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from spacy.gold import GoldParse
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from spacy.language import Language
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from spacy.tokens import Doc, Span
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from spacy.vocab import Vocab
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from .util import add_vecs_to_vocab, assert_docs_equal
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@pytest.fixture
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def nlp():
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nlp = Language(Vocab())
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textcat = nlp.create_pipe("textcat")
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for label in ("POSITIVE", "NEGATIVE"):
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textcat.add_label(label)
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nlp.add_pipe(textcat)
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nlp.begin_training()
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return nlp
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def test_language_update(nlp):
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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wrongkeyannots = {"LABEL": True}
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doc = Doc(nlp.vocab, words=text.split(" "))
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gold = GoldParse(doc, **annots)
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# Update with doc and gold objects
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nlp.update((doc, gold))
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# Update with text and dict
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nlp.update((text, annots))
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# Update with doc object and dict
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nlp.update((doc, annots))
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# Update with text and gold object
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nlp.update((text, gold))
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# Update with empty doc and gold object
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nlp.update((None, gold))
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# Update badly
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with pytest.raises(ValueError):
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nlp.update((doc, None))
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with pytest.raises(TypeError):
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nlp.update((text, wrongkeyannots))
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def test_language_evaluate(nlp):
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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doc = Doc(nlp.vocab, words=text.split(" "))
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gold = GoldParse(doc, **annots)
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# Evaluate with doc and gold objects
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nlp.evaluate([(doc, gold)])
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# Evaluate with text and dict
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nlp.evaluate([(text, annots)])
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# Evaluate with doc object and dict
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nlp.evaluate([(doc, annots)])
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# Evaluate with text and gold object
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nlp.evaluate([(text, gold)])
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# Evaluate badly
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with pytest.raises(Exception):
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nlp.evaluate([text, gold])
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def test_evaluate_no_pipe(nlp):
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"""Test that docs are processed correctly within Language.pipe if the
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component doesn't expose a .pipe method."""
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def pipe(doc):
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return doc
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text = "hello world"
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annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
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nlp = Language(Vocab())
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nlp.add_pipe(pipe)
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nlp.evaluate([(text, annots)])
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def vector_modification_pipe(doc):
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doc.vector += 1
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return doc
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def userdata_pipe(doc):
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doc.user_data["foo"] = "bar"
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return doc
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def ner_pipe(doc):
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span = Span(doc, 0, 1, label="FIRST")
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doc.ents += (span,)
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return doc
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@pytest.fixture
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def sample_vectors():
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return [
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("spacy", [-0.1, -0.2, -0.3]),
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("world", [-0.2, -0.3, -0.4]),
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("pipe", [0.7, 0.8, 0.9]),
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]
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@pytest.fixture
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def nlp2(nlp, sample_vectors):
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add_vecs_to_vocab(nlp.vocab, sample_vectors)
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nlp.add_pipe(vector_modification_pipe)
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nlp.add_pipe(ner_pipe)
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nlp.add_pipe(userdata_pipe)
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return nlp
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@pytest.fixture
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def texts():
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data = [
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"Hello world.",
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"This is spacy.",
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"You can use multiprocessing with pipe method.",
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"Please try!",
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]
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return data
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe(nlp2, n_process, texts):
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texts = texts * 10
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expecteds = [nlp2(text) for text in texts]
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docs = nlp2.pipe(texts, n_process=n_process, batch_size=2)
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for doc, expected_doc in zip(docs, expecteds):
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assert_docs_equal(doc, expected_doc)
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_stream(nlp2, n_process, texts):
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# check if nlp.pipe can handle infinite length iterator properly.
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stream_texts = itertools.cycle(texts)
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texts0, texts1 = itertools.tee(stream_texts)
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expecteds = (nlp2(text) for text in texts0)
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docs = nlp2.pipe(texts1, n_process=n_process, batch_size=2)
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n_fetch = 20
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for doc, expected_doc in itertools.islice(zip(docs, expecteds), n_fetch):
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assert_docs_equal(doc, expected_doc)
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