mirror of https://github.com/explosion/spaCy.git
109 lines
3.5 KiB
Python
109 lines
3.5 KiB
Python
# coding: utf8
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import warnings
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import numpy
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import pytest
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import srsly
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from spacy.kb import KnowledgeBase
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from spacy.vectors import Vectors
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from spacy.language import Language
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from spacy.pipeline import Pipe
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from spacy.tests.util import make_tempdir
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def test_language_to_disk_resource_warning():
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nlp = Language()
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with make_tempdir() as d:
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with warnings.catch_warnings(record=True) as w:
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# catch only warnings raised in spacy.language since there may be others from other components or pipelines
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warnings.filterwarnings(
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"always", module="spacy.language", category=ResourceWarning
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)
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nlp.to_disk(d)
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assert len(w) == 0
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def test_vectors_to_disk_resource_warning():
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data = numpy.zeros((3, 300), dtype="f")
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keys = ["cat", "dog", "rat"]
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vectors = Vectors(data=data, keys=keys)
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with make_tempdir() as d:
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with warnings.catch_warnings(record=True) as w:
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warnings.filterwarnings("always", category=ResourceWarning)
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vectors.to_disk(d)
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assert len(w) == 0
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def test_custom_pipes_to_disk_resource_warning():
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# create dummy pipe partially implementing interface -- only want to test to_disk
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class SerializableDummy(object):
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def __init__(self, **cfg):
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if cfg:
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self.cfg = cfg
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else:
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self.cfg = None
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super(SerializableDummy, self).__init__()
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def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
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return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
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def from_bytes(self, bytes_data, exclude):
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return self
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def to_disk(self, path, exclude=tuple(), **kwargs):
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pass
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def from_disk(self, path, exclude=tuple(), **kwargs):
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return self
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class MyPipe(Pipe):
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def __init__(self, vocab, model=True, **cfg):
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if cfg:
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self.cfg = cfg
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else:
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self.cfg = None
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self.model = SerializableDummy()
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self.vocab = SerializableDummy()
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pipe = MyPipe(None)
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with make_tempdir() as d:
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with warnings.catch_warnings(record=True) as w:
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warnings.filterwarnings("always", category=ResourceWarning)
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pipe.to_disk(d)
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assert len(w) == 0
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def test_tagger_to_disk_resource_warning():
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nlp = Language()
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nlp.add_pipe(nlp.create_pipe("tagger"))
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tagger = nlp.get_pipe("tagger")
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# need to add model for two reasons:
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# 1. no model leads to error in serialization,
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# 2. the affected line is the one for model serialization
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tagger.begin_training(pipeline=nlp.pipeline)
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with make_tempdir() as d:
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with warnings.catch_warnings(record=True) as w:
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warnings.filterwarnings("always", category=ResourceWarning)
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tagger.to_disk(d)
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assert len(w) == 0
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def test_entity_linker_to_disk_resource_warning():
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nlp = Language()
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nlp.add_pipe(nlp.create_pipe("entity_linker"))
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entity_linker = nlp.get_pipe("entity_linker")
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# need to add model for two reasons:
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# 1. no model leads to error in serialization,
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# 2. the affected line is the one for model serialization
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kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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entity_linker.set_kb(kb)
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entity_linker.begin_training(pipeline=nlp.pipeline)
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with make_tempdir() as d:
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with warnings.catch_warnings(record=True) as w:
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warnings.filterwarnings("always", category=ResourceWarning)
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entity_linker.to_disk(d)
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assert len(w) == 0
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