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https://github.com/explosion/spaCy.git
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b40f44419b
- remove unused code - don't print by default - integrate attrs info into analysis output
115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
from spacy.language import Language
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from spacy.pipe_analysis import get_attr_info, validate_attrs
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from mock import Mock
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import pytest
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def test_component_decorator_assigns():
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@Language.component("c1", assigns=["token.tag", "doc.tensor"])
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def test_component1(doc):
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return doc
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@Language.component(
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"c2", requires=["token.tag", "token.pos"], assigns=["token.lemma", "doc.tensor"]
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)
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def test_component2(doc):
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return doc
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@Language.component(
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"c3", requires=["token.lemma"], assigns=["token._.custom_lemma"]
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)
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def test_component3(doc):
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return doc
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assert Language.has_factory("c1")
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assert Language.has_factory("c2")
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assert Language.has_factory("c3")
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nlp = Language()
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nlp.add_pipe("c1")
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nlp.add_pipe("c2")
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problems = nlp.analyze_pipes()["problems"]
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assert problems["c2"] == ["token.pos"]
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nlp.add_pipe("c3")
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assert get_attr_info(nlp, "doc.tensor")["assigns"] == ["c1", "c2"]
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nlp.add_pipe("c1", name="c4")
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test_component4_meta = nlp.get_pipe_meta("c1")
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assert test_component4_meta.factory == "c1"
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assert nlp.pipe_names == ["c1", "c2", "c3", "c4"]
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assert not Language.has_factory("c4")
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assert nlp.pipe_factories["c1"] == "c1"
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assert nlp.pipe_factories["c4"] == "c1"
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assert get_attr_info(nlp, "doc.tensor")["assigns"] == ["c1", "c2", "c4"]
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assert get_attr_info(nlp, "token.pos")["requires"] == ["c2"]
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assert nlp("hello world")
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def test_component_factories_class_func():
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"""Test that class components can implement a from_nlp classmethod that
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gives them access to the nlp object and config via the factory."""
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class TestComponent5:
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def __call__(self, doc):
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return doc
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mock = Mock()
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mock.return_value = TestComponent5()
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def test_componen5_factory(nlp, foo: str = "bar", name="c5"):
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return mock(nlp, foo=foo)
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Language.factory("c5", func=test_componen5_factory)
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assert Language.has_factory("c5")
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nlp = Language()
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nlp.add_pipe("c5", config={"foo": "bar"})
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assert nlp("hello world")
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mock.assert_called_once_with(nlp, foo="bar")
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def test_analysis_validate_attrs_valid():
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attrs = ["doc.sents", "doc.ents", "token.tag", "token._.xyz", "span._.xyz"]
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assert validate_attrs(attrs)
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for attr in attrs:
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assert validate_attrs([attr])
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with pytest.raises(ValueError):
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validate_attrs(["doc.sents", "doc.xyz"])
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@pytest.mark.parametrize(
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"attr",
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[
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"doc",
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"doc_ents",
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"doc.xyz",
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"token.xyz",
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"token.tag_",
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"token.tag.xyz",
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"token._.xyz.abc",
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"span.label",
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],
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)
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def test_analysis_validate_attrs_invalid(attr):
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with pytest.raises(ValueError):
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validate_attrs([attr])
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def test_analysis_validate_attrs_remove_pipe():
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"""Test that attributes are validated correctly on remove."""
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@Language.component("pipe_analysis_c6", assigns=["token.tag"])
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def c1(doc):
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return doc
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@Language.component("pipe_analysis_c7", requires=["token.pos"])
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def c2(doc):
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return doc
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nlp = Language()
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nlp.add_pipe("pipe_analysis_c6")
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nlp.add_pipe("pipe_analysis_c7")
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problems = nlp.analyze_pipes()["problems"]
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assert problems["pipe_analysis_c7"] == ["token.pos"]
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nlp.remove_pipe("pipe_analysis_c7")
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problems = nlp.analyze_pipes()["problems"]
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assert all(p == [] for p in problems.values())
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