mirror of
https://github.com/explosion/spaCy.git
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da7ad97519
* Update `TextCatBOW` to use the fixed `SparseLinear` layer A while ago, we fixed the `SparseLinear` layer to use all available parameters: https://github.com/explosion/thinc/pull/754 This change updates `TextCatBOW` to `v3` which uses the new `SparseLinear_v2` layer. This results in a sizeable improvement on a text categorization task that was tested. While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent` option to make it possible to change the hidden size. Ideally, we'd just have an option called `length`. But the way that `TextCatBOW` uses hashes results in a non-uniform distribution of parameters when the length is not a power of two. * Replace TexCatBOW `length_exponent` parameter by `length` We now round up the length to the next power of two if it isn't a power of two. * Remove some tests for TextCatBOW.v2 * Fix missing import
603 lines
20 KiB
Python
603 lines
20 KiB
Python
import pytest
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try:
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from pydantic.v1 import StrictInt, StrictStr
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except ImportError:
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from pydantic import StrictInt, StrictStr # type: ignore
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from thinc.api import ConfigValidationError, Linear, Model
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import spacy
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from spacy.lang.de import German
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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from spacy.tokens import Doc
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from spacy.util import SimpleFrozenDict, combine_score_weights, registry
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from ..util import make_tempdir
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@pytest.mark.issue(5137)
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def test_issue5137():
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factory_name = "test_issue5137"
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pipe_name = "my_component"
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@Language.factory(factory_name)
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class MyComponent:
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def __init__(self, nlp, name=pipe_name, categories="all_categories"):
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self.nlp = nlp
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self.categories = categories
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self.name = name
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def __call__(self, doc):
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pass
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def to_disk(self, path, **kwargs):
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pass
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def from_disk(self, path, **cfg):
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pass
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nlp = English()
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my_component = nlp.add_pipe(factory_name, name=pipe_name)
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assert my_component.categories == "all_categories"
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with make_tempdir() as tmpdir:
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nlp.to_disk(tmpdir)
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overrides = {"components": {pipe_name: {"categories": "my_categories"}}}
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nlp2 = spacy.load(tmpdir, config=overrides)
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assert nlp2.get_pipe(pipe_name).categories == "my_categories"
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def test_pipe_function_component():
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name = "test_component"
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@Language.component(name)
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def component(doc: Doc) -> Doc:
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return doc
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assert name in registry.factories
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nlp = Language()
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with pytest.raises(ValueError):
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nlp.add_pipe(component)
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nlp.add_pipe(name)
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assert name in nlp.pipe_names
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assert nlp.pipe_factories[name] == name
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assert Language.get_factory_meta(name)
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assert nlp.get_pipe_meta(name)
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pipe = nlp.get_pipe(name)
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assert pipe == component
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pipe = nlp.create_pipe(name)
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assert pipe == component
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def test_pipe_class_component_init():
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name1 = "test_class_component1"
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name2 = "test_class_component2"
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@Language.factory(name1)
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class Component1:
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def __init__(self, nlp: Language, name: str):
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self.nlp = nlp
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def __call__(self, doc: Doc) -> Doc:
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return doc
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class Component2:
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def __init__(self, nlp: Language, name: str):
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self.nlp = nlp
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def __call__(self, doc: Doc) -> Doc:
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return doc
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@Language.factory(name2)
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def factory(nlp: Language, name=name2):
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return Component2(nlp, name)
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nlp = Language()
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for name, Component in [(name1, Component1), (name2, Component2)]:
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assert name in registry.factories
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with pytest.raises(ValueError):
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nlp.add_pipe(Component(nlp, name))
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nlp.add_pipe(name)
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assert name in nlp.pipe_names
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assert nlp.pipe_factories[name] == name
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assert Language.get_factory_meta(name)
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assert nlp.get_pipe_meta(name)
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pipe = nlp.get_pipe(name)
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assert isinstance(pipe, Component)
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assert isinstance(pipe.nlp, Language)
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pipe = nlp.create_pipe(name)
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assert isinstance(pipe, Component)
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assert isinstance(pipe.nlp, Language)
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def test_pipe_class_component_config():
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name = "test_class_component_config"
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@Language.factory(name)
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class Component:
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def __init__(
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self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
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):
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self.nlp = nlp
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self.value1 = value1
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self.value2 = value2
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self.is_base = True
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self.name = name
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def __call__(self, doc: Doc) -> Doc:
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return doc
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@English.factory(name)
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class ComponentEN:
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def __init__(
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self, nlp: Language, name: str, value1: StrictInt, value2: StrictStr
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):
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self.nlp = nlp
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self.value1 = value1
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self.value2 = value2
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self.is_base = False
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def __call__(self, doc: Doc) -> Doc:
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return doc
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nlp = Language()
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with pytest.raises(ConfigValidationError): # no config provided
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nlp.add_pipe(name)
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with pytest.raises(ConfigValidationError): # invalid config
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nlp.add_pipe(name, config={"value1": "10", "value2": "hello"})
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with pytest.warns(UserWarning):
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nlp.add_pipe(
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name, config={"value1": 10, "value2": "hello", "name": "wrong_name"}
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)
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pipe = nlp.get_pipe(name)
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assert isinstance(pipe.nlp, Language)
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assert pipe.value1 == 10
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assert pipe.value2 == "hello"
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assert pipe.is_base is True
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assert pipe.name == name
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nlp_en = English()
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with pytest.raises(ConfigValidationError): # invalid config
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nlp_en.add_pipe(name, config={"value1": "10", "value2": "hello"})
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nlp_en.add_pipe(name, config={"value1": 10, "value2": "hello"})
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pipe = nlp_en.get_pipe(name)
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assert isinstance(pipe.nlp, English)
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assert pipe.value1 == 10
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assert pipe.value2 == "hello"
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assert pipe.is_base is False
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def test_pipe_class_component_defaults():
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name = "test_class_component_defaults"
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@Language.factory(name)
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class Component:
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def __init__(
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self,
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nlp: Language,
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name: str,
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value1: StrictInt = StrictInt(10),
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value2: StrictStr = StrictStr("hello"),
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):
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self.nlp = nlp
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self.value1 = value1
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self.value2 = value2
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def __call__(self, doc: Doc) -> Doc:
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return doc
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nlp = Language()
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nlp.add_pipe(name)
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pipe = nlp.get_pipe(name)
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assert isinstance(pipe.nlp, Language)
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assert pipe.value1 == 10
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assert pipe.value2 == "hello"
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def test_pipe_class_component_model():
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name = "test_class_component_model"
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default_config = {
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"model": {
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"@architectures": "spacy.TextCatEnsemble.v2",
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"tok2vec": DEFAULT_TOK2VEC_MODEL,
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"linear_model": {
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"@architectures": "spacy.TextCatBOW.v3",
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"exclusive_classes": False,
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"ngram_size": 1,
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"no_output_layer": False,
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},
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},
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"value1": 10,
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}
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@Language.factory(name, default_config=default_config)
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class Component:
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def __init__(self, nlp: Language, model: Model, name: str, value1: StrictInt):
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self.nlp = nlp
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self.model = model
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self.value1 = value1
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self.name = name
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def __call__(self, doc: Doc) -> Doc:
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return doc
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nlp = Language()
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nlp.add_pipe(name)
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pipe = nlp.get_pipe(name)
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assert isinstance(pipe.nlp, Language)
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assert pipe.value1 == 10
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assert isinstance(pipe.model, Model)
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def test_pipe_class_component_model_custom():
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name = "test_class_component_model_custom"
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arch = f"{name}.arch"
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default_config = {"value1": 1, "model": {"@architectures": arch, "nO": 0, "nI": 0}}
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@Language.factory(name, default_config=default_config)
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class Component:
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def __init__(
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self,
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nlp: Language,
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model: Model,
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name: str,
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value1: StrictInt = StrictInt(10),
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):
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self.nlp = nlp
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self.model = model
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self.value1 = value1
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self.name = name
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def __call__(self, doc: Doc) -> Doc:
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return doc
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@registry.architectures(arch)
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def make_custom_arch(nO: StrictInt, nI: StrictInt):
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return Linear(nO, nI)
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nlp = Language()
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config = {"value1": 20, "model": {"@architectures": arch, "nO": 1, "nI": 2}}
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nlp.add_pipe(name, config=config)
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pipe = nlp.get_pipe(name)
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assert isinstance(pipe.nlp, Language)
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assert pipe.value1 == 20
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assert isinstance(pipe.model, Model)
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assert pipe.model.name == "linear"
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nlp = Language()
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with pytest.raises(ConfigValidationError):
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config = {"value1": "20", "model": {"@architectures": arch, "nO": 1, "nI": 2}}
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nlp.add_pipe(name, config=config)
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with pytest.raises(ConfigValidationError):
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config = {"value1": 20, "model": {"@architectures": arch, "nO": 1.0, "nI": 2.0}}
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nlp.add_pipe(name, config=config)
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def test_pipe_factories_wrong_formats():
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with pytest.raises(ValueError):
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# Decorator is not called
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@Language.component
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def component(foo: int, bar: str):
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...
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with pytest.raises(ValueError):
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# Decorator is not called
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@Language.factory
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def factory1(foo: int, bar: str):
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...
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with pytest.raises(ValueError):
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# Factory function is missing "nlp" and "name" arguments
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@Language.factory("test_pipe_factories_missing_args")
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def factory2(foo: int, bar: str):
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...
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def test_pipe_factory_meta_config_cleanup():
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"""Test that component-specific meta and config entries are represented
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correctly and cleaned up when pipes are removed, replaced or renamed."""
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nlp = Language()
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nlp.add_pipe("ner", name="ner_component")
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nlp.add_pipe("textcat")
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assert nlp.get_factory_meta("ner")
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assert nlp.get_pipe_meta("ner_component")
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assert nlp.get_pipe_config("ner_component")
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assert nlp.get_factory_meta("textcat")
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assert nlp.get_pipe_meta("textcat")
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assert nlp.get_pipe_config("textcat")
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nlp.rename_pipe("textcat", "tc")
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assert nlp.get_pipe_meta("tc")
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assert nlp.get_pipe_config("tc")
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with pytest.raises(ValueError):
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nlp.remove_pipe("ner")
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nlp.remove_pipe("ner_component")
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assert "ner_component" not in nlp._pipe_meta
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assert "ner_component" not in nlp._pipe_configs
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with pytest.raises(ValueError):
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nlp.replace_pipe("textcat", "parser")
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nlp.replace_pipe("tc", "parser")
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assert nlp.get_factory_meta("parser")
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assert nlp.get_pipe_meta("tc").factory == "parser"
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def test_pipe_factories_empty_dict_default():
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"""Test that default config values can be empty dicts and that no config
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validation error is raised."""
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# TODO: fix this
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name = "test_pipe_factories_empty_dict_default"
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@Language.factory(name, default_config={"foo": {}})
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def factory(nlp: Language, name: str, foo: dict):
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...
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nlp = Language()
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nlp.create_pipe(name)
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def test_pipe_factories_language_specific():
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"""Test that language sub-classes can have their own factories, with
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fallbacks to the base factories."""
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name1 = "specific_component1"
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name2 = "specific_component2"
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Language.component(name1, func=lambda: "base")
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English.component(name1, func=lambda: "en")
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German.component(name2, func=lambda: "de")
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assert Language.has_factory(name1)
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assert not Language.has_factory(name2)
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assert English.has_factory(name1)
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assert not English.has_factory(name2)
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assert German.has_factory(name1)
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assert German.has_factory(name2)
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nlp = Language()
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assert nlp.create_pipe(name1)() == "base"
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with pytest.raises(ValueError):
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nlp.create_pipe(name2)
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nlp_en = English()
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assert nlp_en.create_pipe(name1)() == "en"
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with pytest.raises(ValueError):
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nlp_en.create_pipe(name2)
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nlp_de = German()
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assert nlp_de.create_pipe(name1)() == "base"
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assert nlp_de.create_pipe(name2)() == "de"
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def test_language_factories_invalid():
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"""Test that assigning directly to Language.factories is now invalid and
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raises a custom error."""
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assert isinstance(Language.factories, SimpleFrozenDict)
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with pytest.raises(NotImplementedError):
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Language.factories["foo"] = "bar"
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nlp = Language()
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assert isinstance(nlp.factories, SimpleFrozenDict)
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assert len(nlp.factories)
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with pytest.raises(NotImplementedError):
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nlp.factories["foo"] = "bar"
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@pytest.mark.parametrize(
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"weights,override,expected",
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[
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([{"a": 1.0}, {"b": 1.0}, {"c": 1.0}], {}, {"a": 0.33, "b": 0.33, "c": 0.33}),
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([{"a": 1.0}, {"b": 50}, {"c": 100}], {}, {"a": 0.01, "b": 0.33, "c": 0.66}),
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(
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[{"a": 0.7, "b": 0.3}, {"c": 1.0}, {"d": 0.5, "e": 0.5}],
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{},
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{"a": 0.23, "b": 0.1, "c": 0.33, "d": 0.17, "e": 0.17},
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),
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(
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[{"a": 100, "b": 300}, {"c": 50, "d": 50}],
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{},
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{"a": 0.2, "b": 0.6, "c": 0.1, "d": 0.1},
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),
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([{"a": 0.5, "b": 0.5}, {"b": 1.0}], {}, {"a": 0.33, "b": 0.67}),
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([{"a": 0.5, "b": 0.0}], {}, {"a": 1.0, "b": 0.0}),
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([{"a": 0.5, "b": 0.5}, {"b": 1.0}], {"a": 0.0}, {"a": 0.0, "b": 1.0}),
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([{"a": 0.0, "b": 0.0}, {"c": 0.0}], {}, {"a": 0.0, "b": 0.0, "c": 0.0}),
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([{"a": 0.0, "b": 0.0}, {"c": 1.0}], {}, {"a": 0.0, "b": 0.0, "c": 1.0}),
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(
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[{"a": 0.0, "b": 0.0}, {"c": 0.0}],
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{"c": 0.2},
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{"a": 0.0, "b": 0.0, "c": 1.0},
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),
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(
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[{"a": 0.5, "b": 0.5, "c": 1.0, "d": 1.0}],
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{"a": 0.0, "b": 0.0},
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{"a": 0.0, "b": 0.0, "c": 0.5, "d": 0.5},
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),
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(
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[{"a": 0.5, "b": 0.5, "c": 1.0, "d": 1.0}],
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{"a": 0.0, "b": 0.0, "f": 0.0},
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{"a": 0.0, "b": 0.0, "c": 0.5, "d": 0.5, "f": 0.0},
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),
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],
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)
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def test_language_factories_combine_score_weights(weights, override, expected):
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result = combine_score_weights(weights, override)
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assert sum(result.values()) in (0.99, 1.0, 0.0)
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assert result == expected
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def test_language_factories_scores():
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name = "test_language_factories_scores"
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func = lambda nlp, name: lambda doc: doc
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weights1 = {"a1": 0.5, "a2": 0.5}
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weights2 = {"b1": 0.2, "b2": 0.7, "b3": 0.1}
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Language.factory(f"{name}1", default_score_weights=weights1, func=func)
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Language.factory(f"{name}2", default_score_weights=weights2, func=func)
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meta1 = Language.get_factory_meta(f"{name}1")
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assert meta1.default_score_weights == weights1
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meta2 = Language.get_factory_meta(f"{name}2")
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assert meta2.default_score_weights == weights2
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nlp = Language()
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nlp._config["training"]["score_weights"] = {}
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nlp.add_pipe(f"{name}1")
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nlp.add_pipe(f"{name}2")
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cfg = nlp.config["training"]
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expected_weights = {"a1": 0.25, "a2": 0.25, "b1": 0.1, "b2": 0.35, "b3": 0.05}
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assert cfg["score_weights"] == expected_weights
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# Test with custom defaults
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config = nlp.config.copy()
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config["training"]["score_weights"]["a1"] = 0.0
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config["training"]["score_weights"]["b3"] = 1.3
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nlp = English.from_config(config)
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score_weights = nlp.config["training"]["score_weights"]
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expected = {"a1": 0.0, "a2": 0.12, "b1": 0.05, "b2": 0.17, "b3": 0.65}
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assert score_weights == expected
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# Test with null values
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config = nlp.config.copy()
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config["training"]["score_weights"]["a1"] = None
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nlp = English.from_config(config)
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score_weights = nlp.config["training"]["score_weights"]
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expected = {"a1": None, "a2": 0.12, "b1": 0.05, "b2": 0.17, "b3": 0.66}
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assert score_weights == expected
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def test_pipe_factories_from_source():
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"""Test adding components from a source model."""
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source_nlp = English()
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source_nlp.add_pipe("tagger", name="my_tagger")
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nlp = English()
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with pytest.raises(ValueError):
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nlp.add_pipe("my_tagger", source="en_core_web_sm")
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nlp.add_pipe("my_tagger", source=source_nlp)
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assert "my_tagger" in nlp.pipe_names
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with pytest.raises(KeyError):
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nlp.add_pipe("custom", source=source_nlp)
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def test_pipe_factories_from_source_language_subclass():
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class CustomEnglishDefaults(English.Defaults):
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stop_words = set(["custom", "stop"])
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@registry.languages("custom_en")
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class CustomEnglish(English):
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lang = "custom_en"
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Defaults = CustomEnglishDefaults
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source_nlp = English()
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source_nlp.add_pipe("tagger")
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# custom subclass
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nlp = CustomEnglish()
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nlp.add_pipe("tagger", source=source_nlp)
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assert "tagger" in nlp.pipe_names
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# non-subclass
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nlp = German()
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nlp.add_pipe("tagger", source=source_nlp)
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assert "tagger" in nlp.pipe_names
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# mismatched vectors
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nlp = English()
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nlp.vocab.vectors.resize((1, 4))
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nlp.vocab.vectors.add("cat", vector=[1, 2, 3, 4])
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with pytest.warns(UserWarning):
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nlp.add_pipe("tagger", source=source_nlp)
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|
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def test_pipe_factories_from_source_custom():
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"""Test adding components from a source model with custom components."""
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name = "test_pipe_factories_from_source_custom"
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|
|
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@Language.factory(name, default_config={"arg": "hello"})
|
|
def test_factory(nlp, name, arg: str):
|
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return lambda doc: doc
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|
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger")
|
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source_nlp.add_pipe(name, config={"arg": "world"})
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nlp = English()
|
|
nlp.add_pipe(name, source=source_nlp)
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assert name in nlp.pipe_names
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assert nlp.get_pipe_meta(name).default_config["arg"] == "hello"
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config = nlp.config["components"][name]
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assert config["factory"] == name
|
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assert config["arg"] == "world"
|
|
|
|
|
|
def test_pipe_factories_from_source_config():
|
|
name = "test_pipe_factories_from_source_config"
|
|
|
|
@Language.factory(name, default_config={"arg": "hello"})
|
|
def test_factory(nlp, name, arg: str):
|
|
return lambda doc: doc
|
|
|
|
source_nlp = English()
|
|
source_nlp.add_pipe("tagger")
|
|
source_nlp.add_pipe(name, name="yolo", config={"arg": "world"})
|
|
dest_nlp_cfg = {"lang": "en", "pipeline": ["parser", "custom"]}
|
|
with make_tempdir() as tempdir:
|
|
source_nlp.to_disk(tempdir)
|
|
dest_components_cfg = {
|
|
"parser": {"factory": "parser"},
|
|
"custom": {"source": str(tempdir), "component": "yolo"},
|
|
}
|
|
dest_config = {"nlp": dest_nlp_cfg, "components": dest_components_cfg}
|
|
nlp = English.from_config(dest_config)
|
|
assert nlp.pipe_names == ["parser", "custom"]
|
|
assert nlp.pipe_factories == {"parser": "parser", "custom": name}
|
|
meta = nlp.get_pipe_meta("custom")
|
|
assert meta.factory == name
|
|
assert meta.default_config["arg"] == "hello"
|
|
config = nlp.config["components"]["custom"]
|
|
assert config["factory"] == name
|
|
assert config["arg"] == "world"
|
|
|
|
|
|
class PipeFactoriesIdempotent:
|
|
def __init__(self, nlp, name):
|
|
...
|
|
|
|
def __call__(self, doc):
|
|
...
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"i,func,func2",
|
|
[
|
|
(0, lambda nlp, name: lambda doc: doc, lambda doc: doc),
|
|
(1, PipeFactoriesIdempotent, PipeFactoriesIdempotent(None, None)),
|
|
],
|
|
)
|
|
def test_pipe_factories_decorator_idempotent(i, func, func2):
|
|
"""Check that decorator can be run multiple times if the function is the
|
|
same. This is especially relevant for live reloading because we don't
|
|
want spaCy to raise an error if a module registering components is reloaded.
|
|
"""
|
|
name = f"test_pipe_factories_decorator_idempotent_{i}"
|
|
for i in range(5):
|
|
Language.factory(name, func=func)
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
Language.factory(name, func=func)
|
|
# Make sure it also works for component decorator, which creates the
|
|
# factory function
|
|
name2 = f"{name}2"
|
|
for i in range(5):
|
|
Language.component(name2, func=func2)
|
|
nlp = Language()
|
|
nlp.add_pipe(name)
|
|
Language.component(name2, func=func2)
|
|
|
|
|
|
def test_pipe_factories_config_excludes_nlp():
|
|
"""Test that the extra values we temporarily add to component config
|
|
blocks/functions are removed and not copied around.
|
|
"""
|
|
name = "test_pipe_factories_config_excludes_nlp"
|
|
func = lambda nlp, name: lambda doc: doc
|
|
Language.factory(name, func=func)
|
|
config = {
|
|
"nlp": {"lang": "en", "pipeline": [name]},
|
|
"components": {name: {"factory": name}},
|
|
}
|
|
nlp = English.from_config(config)
|
|
assert nlp.pipe_names == [name]
|
|
pipe_cfg = nlp.get_pipe_config(name)
|
|
pipe_cfg == {"factory": name}
|
|
assert nlp._pipe_configs[name] == {"factory": name}
|