spaCy/spacy/tests/regression/test_issue5501-6000.py
Adriane Boyd 36ecba224e
Set up GPU CI testing (#7293)
* Set up CI for tests with GPU agent

* Update tests for enabled GPU

* Fix steps filename

* Add parallel build jobs as a setting

* Fix test requirements

* Fix install test requirements condition

* Fix pipeline models test

* Reset current ops in prefer/require testing

* Fix more tests

* Remove separate test_models test

* Fix regression 5551

* fix StaticVectors for GPU use

* fix vocab tests

* Fix regression test 5082

* Move azure steps to .github and reenable default pool jobs

* Consolidate/rename azure steps

Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
2021-04-22 14:58:29 +02:00

93 lines
3.4 KiB
Python

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