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Clean up warnings in the test suite (#11331)
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@ -3,6 +3,7 @@ import weakref
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import numpy
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from numpy.testing import assert_array_equal
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import pytest
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import warnings
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from thinc.api import NumpyOps, get_current_ops
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from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
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@ -529,9 +530,9 @@ def test_doc_from_array_sent_starts(en_vocab):
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# no warning using default attrs
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attrs = doc._get_array_attrs()
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arr = doc.to_array(attrs)
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with pytest.warns(None) as record:
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with warnings.catch_warnings():
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warnings.simplefilter("error")
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new_doc.from_array(attrs, arr)
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assert len(record) == 0
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# only SENT_START uses SENT_START
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attrs = [SENT_START]
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arr = doc.to_array(attrs)
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@ -2,6 +2,9 @@ import pytest
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from spacy.tokens import Doc
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pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
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def test_ru_doc_lemmatization(ru_lemmatizer):
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words = ["мама", "мыла", "раму"]
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pos = ["NOUN", "VERB", "NOUN"]
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@ -1,6 +1,10 @@
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import pytest
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from spacy.tokens import Doc
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pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
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def test_uk_lemmatizer(uk_lemmatizer):
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"""Check that the default uk lemmatizer runs."""
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doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])
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@ -1,4 +1,5 @@
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import pytest
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import warnings
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import srsly
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from mock import Mock
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@ -344,13 +345,13 @@ def test_phrase_matcher_validation(en_vocab):
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matcher.add("TEST1", [doc1])
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with pytest.warns(UserWarning):
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matcher.add("TEST2", [doc2])
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with pytest.warns(None) as record:
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with warnings.catch_warnings():
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warnings.simplefilter("error")
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matcher.add("TEST3", [doc3])
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assert not record.list
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matcher = PhraseMatcher(en_vocab, attr="POS", validate=True)
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with pytest.warns(None) as record:
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with warnings.catch_warnings():
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warnings.simplefilter("error")
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matcher.add("TEST4", [doc2])
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assert not record.list
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def test_attr_validation(en_vocab):
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@ -1048,6 +1048,10 @@ def test_no_gold_ents(patterns):
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for eg in train_examples:
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eg.predicted = ruler(eg.predicted)
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# Entity ruler is no longer needed (initialization below wipes out the
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# patterns and causes warnings)
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nlp.remove_pipe("entity_ruler")
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def create_kb(vocab):
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# create artificial KB
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mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
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@ -337,3 +337,5 @@ def ensure_shape(vectors_loc):
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# store all the results in a list in memory
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lines2 = open_file(vectors_loc)
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yield from lines2
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lines2.close()
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lines.close()
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