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afc5714d32
* multi-label textcat component * formatting * fix comment * cleanup * fix from #6481 * random edit to push the tests * add explicit error when textcat is called with multi-label gold data * fix error nr * small fix
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
import pytest
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from thinc.api import Config, fix_random_seed
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from spacy.lang.en import English
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from spacy.pipeline.textcat import single_label_default_config, single_label_bow_config
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from spacy.pipeline.textcat import single_label_cnn_config
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from spacy.pipeline.textcat_multilabel import multi_label_default_config
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from spacy.pipeline.textcat_multilabel import multi_label_bow_config
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from spacy.pipeline.textcat_multilabel import multi_label_cnn_config
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from spacy.tokens import Span
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from spacy import displacy
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from spacy.pipeline import merge_entities
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from spacy.training import Example
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@pytest.mark.parametrize(
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"textcat_config",
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[
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single_label_default_config,
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single_label_bow_config,
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single_label_cnn_config,
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multi_label_default_config,
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multi_label_bow_config,
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multi_label_cnn_config,
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],
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)
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def test_issue5551(textcat_config):
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"""Test that after fixing the random seed, the results of the pipeline are truly identical"""
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component = "textcat"
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pipe_cfg = Config().from_str(textcat_config)
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results = []
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for i in range(3):
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fix_random_seed(0)
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nlp = English()
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text = "Once hot, form ping-pong-ball-sized balls of the mixture, each weighing roughly 25 g."
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annots = {"cats": {"Labe1": 1.0, "Label2": 0.0, "Label3": 0.0}}
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pipe = nlp.add_pipe(component, config=pipe_cfg, last=True)
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for label in set(annots["cats"]):
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pipe.add_label(label)
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# Train
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nlp.initialize()
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doc = nlp.make_doc(text)
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nlp.update([Example.from_dict(doc, annots)])
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# Store the result of each iteration
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result = pipe.model.predict([doc])
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results.append(list(result[0]))
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# All results should be the same because of the fixed seed
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assert len(results) == 3
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assert results[0] == results[1]
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assert results[0] == results[2]
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def test_issue5838():
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# Displacy's EntityRenderer break line
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# not working after last entity
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sample_text = "First line\nSecond line, with ent\nThird line\nFourth line\n"
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nlp = English()
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doc = nlp(sample_text)
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doc.ents = [Span(doc, 7, 8, label="test")]
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html = displacy.render(doc, style="ent")
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found = html.count("</br>")
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assert found == 4
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def test_issue5918():
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# Test edge case when merging entities.
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nlp = English()
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ruler = nlp.add_pipe("entity_ruler")
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patterns = [
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{"label": "ORG", "pattern": "Digicon Inc"},
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{"label": "ORG", "pattern": "Rotan Mosle Inc's"},
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{"label": "ORG", "pattern": "Rotan Mosle Technology Partners Ltd"},
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]
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ruler.add_patterns(patterns)
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text = """
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Digicon Inc said it has completed the previously-announced disposition
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of its computer systems division to an investment group led by
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Rotan Mosle Inc's Rotan Mosle Technology Partners Ltd affiliate.
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"""
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doc = nlp(text)
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assert len(doc.ents) == 3
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# make it so that the third span's head is within the entity (ent_iob=I)
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# bug #5918 would wrongly transfer that I to the full entity, resulting in 2 instead of 3 final ents.
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# TODO: test for logging here
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# with pytest.warns(UserWarning):
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# doc[29].head = doc[33]
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doc = merge_entities(doc)
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assert len(doc.ents) == 3
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