mirror of
https://github.com/explosion/spaCy.git
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b120fb3511
* Handle errors while multiprocessing Handle errors while multiprocessing without hanging. * Return the traceback for errors raised while processing a batch, which can be handled by the top-level error handler * Allow for shortened batches due to custom error handlers that ignore errors and skip documents * Define custom components at a higher level * Also move up custom error handler * Use simpler component for test * Switch error type * Adjust test * Only call top-level error handler for exceptions * Register custom test components within tests Use global functions (so they can be pickled) but register the components only within the individual tests.
478 lines
16 KiB
Python
478 lines
16 KiB
Python
import itertools
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import logging
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from unittest import mock
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import pytest
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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 spacy.training import Example
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from spacy.lang.en import English
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from spacy.lang.de import German
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from spacy.util import registry, ignore_error, raise_error, logger
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import spacy
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from thinc.api import NumpyOps, get_current_ops
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from .util import add_vecs_to_vocab, assert_docs_equal
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def evil_component(doc):
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if "2" in doc.text:
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raise ValueError("no dice")
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return doc
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def perhaps_set_sentences(doc):
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if not doc.text.startswith("4"):
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doc[-1].is_sent_start = True
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return doc
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def assert_sents_error(doc):
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if not doc.has_annotation("SENT_START"):
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raise ValueError("no sents")
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return doc
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def warn_error(proc_name, proc, docs, e):
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logger = logging.getLogger("spacy")
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logger.warning(f"Trouble with component {proc_name}.")
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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.add_pipe("textcat")
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for label in ("POSITIVE", "NEGATIVE"):
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textcat.add_label(label)
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nlp.initialize()
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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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example = Example.from_dict(doc, annots)
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nlp.update([example])
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# Not allowed to call with just one Example
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with pytest.raises(TypeError):
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nlp.update(example)
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# Update with text and dict: not supported anymore since v.3
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with pytest.raises(TypeError):
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nlp.update((text, annots))
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# Update with doc object and dict
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with pytest.raises(TypeError):
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nlp.update((doc, annots))
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# Create examples badly
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with pytest.raises(ValueError):
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example = Example.from_dict(doc, None)
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with pytest.raises(KeyError):
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example = Example.from_dict(doc, wrongkeyannots)
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def test_language_evaluate(nlp):
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text = "hello world"
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annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
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doc = Doc(nlp.vocab, words=text.split(" "))
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example = Example.from_dict(doc, annots)
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scores = nlp.evaluate([example])
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assert scores["speed"] > 0
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# test with generator
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scores = nlp.evaluate(eg for eg in [example])
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assert scores["speed"] > 0
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# Not allowed to call with just one Example
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with pytest.raises(TypeError):
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nlp.evaluate(example)
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# Evaluate with text and dict: not supported anymore since v.3
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with pytest.raises(TypeError):
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nlp.evaluate([(text, annots)])
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# Evaluate with doc object and dict
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with pytest.raises(TypeError):
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nlp.evaluate([(doc, annots)])
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with pytest.raises(TypeError):
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nlp.evaluate([text, annots])
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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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@Language.component("test_evaluate_no_pipe")
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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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doc = nlp(text)
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nlp.add_pipe("test_evaluate_no_pipe")
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nlp.evaluate([Example.from_dict(doc, 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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Language.component("test_language_vector_modification_pipe", func=vector_modification_pipe)
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Language.component("test_language_userdata_pipe", func=userdata_pipe)
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Language.component("test_language_ner_pipe", func=ner_pipe)
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add_vecs_to_vocab(nlp.vocab, sample_vectors)
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nlp.add_pipe("test_language_vector_modification_pipe")
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nlp.add_pipe("test_language_ner_pipe")
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nlp.add_pipe("test_language_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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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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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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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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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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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_error_handler(n_process):
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"""Test that the error handling of nlp.pipe works well"""
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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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nlp = English()
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nlp.add_pipe("merge_subtokens")
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nlp.initialize()
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texts = ["Curious to see what will happen to this text.", "And this one."]
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# the pipeline fails because there's no parser
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with pytest.raises(ValueError):
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nlp(texts[0])
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with pytest.raises(ValueError):
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list(nlp.pipe(texts, n_process=n_process))
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nlp.set_error_handler(raise_error)
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with pytest.raises(ValueError):
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list(nlp.pipe(texts, n_process=n_process))
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# set explicitely to ignoring
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nlp.set_error_handler(ignore_error)
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docs = list(nlp.pipe(texts, n_process=n_process))
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assert len(docs) == 0
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nlp(texts[0])
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_error_handler_custom(en_vocab, n_process):
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"""Test the error handling of a custom component that has no pipe method"""
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Language.component("my_evil_component", func=evil_component)
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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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nlp = English()
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nlp.add_pipe("my_evil_component")
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texts = ["TEXT 111", "TEXT 222", "TEXT 333", "TEXT 342", "TEXT 666"]
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with pytest.raises(ValueError):
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# the evil custom component throws an error
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list(nlp.pipe(texts))
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nlp.set_error_handler(warn_error)
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logger = logging.getLogger("spacy")
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with mock.patch.object(logger, "warning") as mock_warning:
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# the errors by the evil custom component raise a warning for each
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# bad doc
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docs = list(nlp.pipe(texts, n_process=n_process))
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# HACK/TODO? the warnings in child processes don't seem to be
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# detected by the mock logger
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if n_process == 1:
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mock_warning.assert_called()
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assert mock_warning.call_count == 2
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assert len(docs) + mock_warning.call_count == len(texts)
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assert [doc.text for doc in docs] == ["TEXT 111", "TEXT 333", "TEXT 666"]
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_error_handler_pipe(en_vocab, n_process):
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"""Test the error handling of a component's pipe method"""
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Language.component("my_perhaps_sentences", func=perhaps_set_sentences)
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Language.component("assert_sents_error", func=assert_sents_error)
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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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texts = [f"{str(i)} is enough. Done" for i in range(100)]
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nlp = English()
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nlp.add_pipe("my_perhaps_sentences")
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nlp.add_pipe("assert_sents_error")
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nlp.initialize()
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with pytest.raises(ValueError):
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# assert_sents_error requires sentence boundaries, will throw an error otherwise
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docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
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nlp.set_error_handler(ignore_error)
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docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
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# we lose/ignore the failing 4,40-49 docs
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assert len(docs) == 89
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_error_handler_make_doc_actual(n_process):
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"""Test the error handling for make_doc"""
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# TODO: fix so that the following test is the actual behavior
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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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nlp = English()
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nlp.max_length = 10
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texts = ["12345678901234567890", "12345"] * 10
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with pytest.raises(ValueError):
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list(nlp.pipe(texts, n_process=n_process))
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nlp.default_error_handler = ignore_error
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if n_process == 1:
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with pytest.raises(ValueError):
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list(nlp.pipe(texts, n_process=n_process))
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else:
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docs = list(nlp.pipe(texts, n_process=n_process))
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assert len(docs) == 0
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@pytest.mark.xfail
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@pytest.mark.parametrize("n_process", [1, 2])
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def test_language_pipe_error_handler_make_doc_preferred(n_process):
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"""Test the error handling for make_doc"""
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ops = get_current_ops()
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if isinstance(ops, NumpyOps) or n_process < 2:
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nlp = English()
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nlp.max_length = 10
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texts = ["12345678901234567890", "12345"] * 10
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with pytest.raises(ValueError):
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list(nlp.pipe(texts, n_process=n_process))
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nlp.default_error_handler = ignore_error
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docs = list(nlp.pipe(texts, n_process=n_process))
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assert len(docs) == 0
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def test_language_from_config_before_after_init():
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name = "test_language_from_config_before_after_init"
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ran_before = False
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ran_after = False
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ran_after_pipeline = False
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ran_before_init = False
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ran_after_init = False
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@registry.callbacks(f"{name}_before")
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def make_before_creation():
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def before_creation(lang_cls):
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nonlocal ran_before
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ran_before = True
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assert lang_cls is English
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lang_cls.Defaults.foo = "bar"
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return lang_cls
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return before_creation
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@registry.callbacks(f"{name}_after")
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def make_after_creation():
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def after_creation(nlp):
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nonlocal ran_after
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ran_after = True
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assert isinstance(nlp, English)
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assert nlp.pipe_names == []
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assert nlp.Defaults.foo == "bar"
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nlp.meta["foo"] = "bar"
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return nlp
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return after_creation
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@registry.callbacks(f"{name}_after_pipeline")
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def make_after_pipeline_creation():
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def after_pipeline_creation(nlp):
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nonlocal ran_after_pipeline
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ran_after_pipeline = True
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assert isinstance(nlp, English)
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assert nlp.pipe_names == ["sentencizer"]
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assert nlp.Defaults.foo == "bar"
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assert nlp.meta["foo"] == "bar"
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nlp.meta["bar"] = "baz"
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return nlp
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return after_pipeline_creation
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@registry.callbacks(f"{name}_before_init")
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def make_before_init():
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def before_init(nlp):
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nonlocal ran_before_init
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ran_before_init = True
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nlp.meta["before_init"] = "before"
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return nlp
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return before_init
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@registry.callbacks(f"{name}_after_init")
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def make_after_init():
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def after_init(nlp):
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nonlocal ran_after_init
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ran_after_init = True
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nlp.meta["after_init"] = "after"
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return nlp
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return after_init
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config = {
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"nlp": {
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"pipeline": ["sentencizer"],
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"before_creation": {"@callbacks": f"{name}_before"},
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"after_creation": {"@callbacks": f"{name}_after"},
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"after_pipeline_creation": {"@callbacks": f"{name}_after_pipeline"},
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},
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"components": {"sentencizer": {"factory": "sentencizer"}},
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"initialize": {
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"before_init": {"@callbacks": f"{name}_before_init"},
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"after_init": {"@callbacks": f"{name}_after_init"},
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},
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}
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nlp = English.from_config(config)
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assert nlp.Defaults.foo == "bar"
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assert nlp.meta["foo"] == "bar"
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assert nlp.meta["bar"] == "baz"
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assert "before_init" not in nlp.meta
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assert "after_init" not in nlp.meta
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assert nlp.pipe_names == ["sentencizer"]
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assert nlp("text")
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nlp.initialize()
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assert nlp.meta["before_init"] == "before"
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assert nlp.meta["after_init"] == "after"
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assert all(
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[ran_before, ran_after, ran_after_pipeline, ran_before_init, ran_after_init]
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)
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def test_language_from_config_before_after_init_invalid():
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"""Check that an error is raised if function doesn't return nlp."""
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name = "test_language_from_config_before_after_init_invalid"
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registry.callbacks(f"{name}_before1", func=lambda: lambda nlp: None)
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registry.callbacks(f"{name}_before2", func=lambda: lambda nlp: nlp())
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registry.callbacks(f"{name}_after1", func=lambda: lambda nlp: None)
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registry.callbacks(f"{name}_after1", func=lambda: lambda nlp: English)
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for callback_name in [f"{name}_before1", f"{name}_before2"]:
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config = {"nlp": {"before_creation": {"@callbacks": callback_name}}}
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with pytest.raises(ValueError):
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English.from_config(config)
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for callback_name in [f"{name}_after1", f"{name}_after2"]:
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config = {"nlp": {"after_creation": {"@callbacks": callback_name}}}
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with pytest.raises(ValueError):
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English.from_config(config)
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for callback_name in [f"{name}_after1", f"{name}_after2"]:
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config = {"nlp": {"after_pipeline_creation": {"@callbacks": callback_name}}}
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with pytest.raises(ValueError):
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English.from_config(config)
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def test_language_custom_tokenizer():
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"""Test that a fully custom tokenizer can be plugged in via the registry."""
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name = "test_language_custom_tokenizer"
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class CustomTokenizer:
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"""Dummy "tokenizer" that splits on spaces and adds prefix to each word."""
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def __init__(self, nlp, prefix):
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self.vocab = nlp.vocab
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self.prefix = prefix
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def __call__(self, text):
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words = [f"{self.prefix}{word}" for word in text.split(" ")]
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return Doc(self.vocab, words=words)
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@registry.tokenizers(name)
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def custom_create_tokenizer(prefix: str = "_"):
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def create_tokenizer(nlp):
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return CustomTokenizer(nlp, prefix=prefix)
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return create_tokenizer
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config = {"nlp": {"tokenizer": {"@tokenizers": name}}}
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nlp = English.from_config(config)
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doc = nlp("hello world")
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assert [t.text for t in doc] == ["_hello", "_world"]
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doc = list(nlp.pipe(["hello world"]))[0]
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assert [t.text for t in doc] == ["_hello", "_world"]
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def test_language_from_config_invalid_lang():
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"""Test that calling Language.from_config raises an error and lang defined
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in config needs to match language-specific subclasses."""
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config = {"nlp": {"lang": "en"}}
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with pytest.raises(ValueError):
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Language.from_config(config)
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with pytest.raises(ValueError):
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German.from_config(config)
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def test_spacy_blank():
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nlp = spacy.blank("en")
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assert nlp.config["training"]["dropout"] == 0.1
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config = {"training": {"dropout": 0.2}}
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meta = {"name": "my_custom_model"}
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nlp = spacy.blank("en", config=config, meta=meta)
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assert nlp.config["training"]["dropout"] == 0.2
|
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assert nlp.meta["name"] == "my_custom_model"
|
|
|
|
|
|
@pytest.mark.parametrize("value", [False, None, ["x", "y"], Language, Vocab])
|
|
def test_language_init_invalid_vocab(value):
|
|
err_fragment = "invalid value"
|
|
with pytest.raises(ValueError) as e:
|
|
Language(value)
|
|
assert err_fragment in str(e.value)
|