spaCy/spacy/tests/pipeline/test_analysis.py

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import spacy.language
from spacy.language import Language, component
from spacy.pipe_analysis import print_summary, validate_attrs
from spacy.pipe_analysis import get_assigns_for_attr, get_requires_for_attr
from spacy.pipe_analysis import count_pipeline_interdependencies
from mock import Mock, ANY
import pytest
def test_component_decorator_function():
@component(name="test")
def test_component(doc):
"""docstring"""
return doc
assert test_component.name == "test"
assert test_component.__doc__ == "docstring"
assert test_component("foo") == "foo"
def test_component_decorator_class():
@component(name="test")
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class TestComponent:
"""docstring1"""
foo = "bar"
def __call__(self, doc):
"""docstring2"""
return doc
def custom(self, x):
"""docstring3"""
return x
assert TestComponent.name == "test"
assert TestComponent.foo == "bar"
assert hasattr(TestComponent, "custom")
test_component = TestComponent()
assert test_component.foo == "bar"
assert test_component("foo") == "foo"
assert hasattr(test_component, "custom")
assert test_component.custom("bar") == "bar"
assert TestComponent.__doc__ == "docstring1"
assert TestComponent.__call__.__doc__ == "docstring2"
assert TestComponent.custom.__doc__ == "docstring3"
assert test_component.__doc__ == "docstring1"
assert test_component.__call__.__doc__ == "docstring2"
assert test_component.custom.__doc__ == "docstring3"
def test_component_decorator_assigns():
spacy.language.ENABLE_PIPELINE_ANALYSIS = True
@component("c1", assigns=["token.tag", "doc.tensor"])
def test_component1(doc):
return doc
@component(
"c2", requires=["token.tag", "token.pos"], assigns=["token.lemma", "doc.tensor"]
)
def test_component2(doc):
return doc
@component("c3", requires=["token.lemma"], assigns=["token._.custom_lemma"])
def test_component3(doc):
return doc
assert "c1" in Language.factories
assert "c2" in Language.factories
assert "c3" in Language.factories
nlp = Language()
nlp.add_pipe(test_component1)
with pytest.warns(UserWarning):
nlp.add_pipe(test_component2)
nlp.add_pipe(test_component3)
assigns_tensor = get_assigns_for_attr(nlp.pipeline, "doc.tensor")
assert [name for name, _ in assigns_tensor] == ["c1", "c2"]
test_component4 = nlp.create_pipe("c1")
assert test_component4.name == "c1"
assert test_component4.factory == "c1"
nlp.add_pipe(test_component4, name="c4")
assert nlp.pipe_names == ["c1", "c2", "c3", "c4"]
assert "c4" not in Language.factories
assert nlp.pipe_factories["c1"] == "c1"
assert nlp.pipe_factories["c4"] == "c1"
assigns_tensor = get_assigns_for_attr(nlp.pipeline, "doc.tensor")
assert [name for name, _ in assigns_tensor] == ["c1", "c2", "c4"]
requires_pos = get_requires_for_attr(nlp.pipeline, "token.pos")
assert [name for name, _ in requires_pos] == ["c2"]
assert print_summary(nlp, no_print=True)
assert nlp("hello world")
def test_component_factories_from_nlp():
"""Test that class components can implement a from_nlp classmethod that
gives them access to the nlp object and config via the factory."""
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class TestComponent5:
def __call__(self, doc):
return doc
mock = Mock()
mock.return_value = TestComponent5()
TestComponent5.from_nlp = classmethod(mock)
TestComponent5 = component("c5")(TestComponent5)
assert "c5" in Language.factories
nlp = Language()
pipe = nlp.create_pipe("c5", config={"foo": "bar"})
nlp.add_pipe(pipe)
assert nlp("hello world")
# The first argument here is the class itself, so we're accepting any here
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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# The model will be initialized to None by the factory
mock.assert_called_once_with(ANY, nlp, None, foo="bar")
def test_analysis_validate_attrs_valid():
attrs = ["doc.sents", "doc.ents", "token.tag", "token._.xyz", "span._.xyz"]
assert validate_attrs(attrs)
for attr in attrs:
assert validate_attrs([attr])
with pytest.raises(ValueError):
validate_attrs(["doc.sents", "doc.xyz"])
@pytest.mark.parametrize(
"attr",
[
"doc",
"doc_ents",
"doc.xyz",
"token.xyz",
"token.tag_",
"token.tag.xyz",
"token._.xyz.abc",
"span.label",
],
)
def test_analysis_validate_attrs_invalid(attr):
with pytest.raises(ValueError):
validate_attrs([attr])
def test_analysis_validate_attrs_remove_pipe():
"""Test that attributes are validated correctly on remove."""
spacy.language.ENABLE_PIPELINE_ANALYSIS = True
@component("c1", assigns=["token.tag"])
def c1(doc):
return doc
@component("c2", requires=["token.pos"])
def c2(doc):
return doc
nlp = Language()
nlp.add_pipe(c1)
with pytest.warns(UserWarning):
nlp.add_pipe(c2)
with pytest.warns(None) as record:
nlp.remove_pipe("c2")
assert not record.list
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def test_pipe_interdependencies():
class Fancifier:
name = "fancifier"
assigns = ("doc._.fancy",)
requires = tuple()
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class FancyNeeder:
name = "needer"
assigns = tuple()
requires = ("doc._.fancy",)
pipeline = [("fancifier", Fancifier()), ("needer", FancyNeeder())]
counts = count_pipeline_interdependencies(pipeline)
assert counts == [1, 0]