mirror of https://github.com/explosion/spaCy.git
154 lines
4.5 KiB
Python
154 lines
4.5 KiB
Python
import pytest
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from spacy.language import Language
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@pytest.fixture
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def nlp():
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return Language()
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def new_pipe(doc):
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return doc
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def test_add_pipe_no_name(nlp):
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nlp.add_pipe(new_pipe)
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assert "new_pipe" in nlp.pipe_names
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def test_add_pipe_duplicate_name(nlp):
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nlp.add_pipe(new_pipe, name="duplicate_name")
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with pytest.raises(ValueError):
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nlp.add_pipe(new_pipe, name="duplicate_name")
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@pytest.mark.parametrize("name", ["parser"])
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def test_add_pipe_first(nlp, name):
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nlp.add_pipe(new_pipe, name=name, first=True)
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assert nlp.pipeline[0][0] == name
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@pytest.mark.parametrize("name1,name2", [("parser", "lambda_pipe")])
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def test_add_pipe_last(nlp, name1, name2):
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nlp.add_pipe(lambda doc: doc, name=name2)
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nlp.add_pipe(new_pipe, name=name1, last=True)
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assert nlp.pipeline[0][0] != name1
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assert nlp.pipeline[-1][0] == name1
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def test_cant_add_pipe_first_and_last(nlp):
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with pytest.raises(ValueError):
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nlp.add_pipe(new_pipe, first=True, last=True)
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@pytest.mark.parametrize("name", ["my_component"])
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def test_get_pipe(nlp, name):
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with pytest.raises(KeyError):
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nlp.get_pipe(name)
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nlp.add_pipe(new_pipe, name=name)
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assert nlp.get_pipe(name) == new_pipe
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@pytest.mark.parametrize(
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"name,replacement,not_callable", [("my_component", lambda doc: doc, {})]
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)
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def test_replace_pipe(nlp, name, replacement, not_callable):
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, new_pipe)
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nlp.add_pipe(new_pipe, name=name)
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with pytest.raises(ValueError):
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nlp.replace_pipe(name, not_callable)
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nlp.replace_pipe(name, replacement)
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assert nlp.get_pipe(name) != new_pipe
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assert nlp.get_pipe(name) == replacement
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@pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")])
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def test_rename_pipe(nlp, old_name, new_name):
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with pytest.raises(ValueError):
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nlp.rename_pipe(old_name, new_name)
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nlp.add_pipe(new_pipe, name=old_name)
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nlp.rename_pipe(old_name, new_name)
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assert nlp.pipeline[0][0] == new_name
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@pytest.mark.parametrize("name", ["my_component"])
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def test_remove_pipe(nlp, name):
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with pytest.raises(ValueError):
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nlp.remove_pipe(name)
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nlp.add_pipe(new_pipe, name=name)
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assert len(nlp.pipeline) == 1
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removed_name, removed_component = nlp.remove_pipe(name)
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assert not len(nlp.pipeline)
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assert removed_name == name
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assert removed_component == new_pipe
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_method(nlp, name):
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nlp.add_pipe(new_pipe, name=name)
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assert nlp.has_pipe(name)
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disabled = nlp.disable_pipes(name)
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assert not nlp.has_pipe(name)
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disabled.restore()
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@pytest.mark.parametrize("name", ["my_component"])
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def test_disable_pipes_context(nlp, name):
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nlp.add_pipe(new_pipe, name=name)
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assert nlp.has_pipe(name)
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with nlp.disable_pipes(name):
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assert not nlp.has_pipe(name)
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assert nlp.has_pipe(name)
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def test_disable_pipes_list_arg(nlp):
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for name in ["c1", "c2", "c3"]:
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nlp.add_pipe(new_pipe, name=name)
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assert nlp.has_pipe(name)
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with nlp.disable_pipes(["c1", "c2"]):
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assert not nlp.has_pipe("c1")
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assert not nlp.has_pipe("c2")
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assert nlp.has_pipe("c3")
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@pytest.mark.parametrize("n_pipes", [100])
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def test_add_lots_of_pipes(nlp, n_pipes):
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for i in range(n_pipes):
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nlp.add_pipe(lambda doc: doc, name=f"pipe_{i}")
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assert len(nlp.pipe_names) == n_pipes
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@pytest.mark.parametrize("component", ["ner", {"hello": "world"}])
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def test_raise_for_invalid_components(nlp, component):
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with pytest.raises(ValueError):
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nlp.add_pipe(component)
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@pytest.mark.parametrize("component", ["ner", "tagger", "parser", "textcat"])
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def test_pipe_base_class_add_label(nlp, component):
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label = "TEST"
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pipe = nlp.create_pipe(component)
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pipe.add_label(label)
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if component == "tagger":
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# Tagger always has the default coarse-grained label scheme
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assert label in pipe.labels
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else:
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assert pipe.labels == (label,)
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def test_pipe_labels(nlp):
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input_labels = {
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"ner": ["PERSON", "ORG", "GPE"],
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"textcat": ["POSITIVE", "NEGATIVE"],
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}
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for name, labels in input_labels.items():
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pipe = nlp.create_pipe(name)
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for label in labels:
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pipe.add_label(label)
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assert len(pipe.labels) == len(labels)
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nlp.add_pipe(pipe)
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assert len(nlp.pipe_labels) == len(input_labels)
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for name, labels in nlp.pipe_labels.items():
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assert sorted(input_labels[name]) == sorted(labels)
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