spaCy/spacy/tests/regression/test_issue5230.py

112 lines
3.2 KiB
Python

# coding: utf8
import warnings
from unittest import TestCase
import pytest
import srsly
from numpy import zeros
from spacy.kb import KnowledgeBase
from spacy.vectors import Vectors
from spacy.language import Language
from spacy.pipeline import Pipe
from spacy.tests.util import make_tempdir
def nlp():
return Language()
def vectors():
data = zeros((3, 1), dtype="f")
keys = ["cat", "dog", "rat"]
return Vectors(data=data, keys=keys)
def custom_pipe():
# create dummy pipe partially implementing interface -- only want to test to_disk
class SerializableDummy(object):
def __init__(self, **cfg):
if cfg:
self.cfg = cfg
else:
self.cfg = None
super(SerializableDummy, self).__init__()
def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
def from_bytes(self, bytes_data, exclude):
return self
def to_disk(self, path, exclude=tuple(), **kwargs):
pass
def from_disk(self, path, exclude=tuple(), **kwargs):
return self
class MyPipe(Pipe):
def __init__(self, vocab, model=True, **cfg):
if cfg:
self.cfg = cfg
else:
self.cfg = None
self.model = SerializableDummy()
self.vocab = SerializableDummy()
return MyPipe(None)
def tagger():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("tagger"))
tagger = nlp.get_pipe("tagger")
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
tagger.begin_training(pipeline=nlp.pipeline)
return tagger
def entity_linker():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("entity_linker"))
entity_linker = nlp.get_pipe("entity_linker")
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
entity_linker.set_kb(kb)
entity_linker.begin_training(pipeline=nlp.pipeline)
return entity_linker
objects_to_test = (
[nlp(), vectors(), custom_pipe(), tagger(), entity_linker()],
["nlp", "vectors", "custom_pipe", "tagger", "entity_linker"],
)
def write_obj_and_catch_warnings(obj):
with make_tempdir() as d:
with warnings.catch_warnings(record=True) as warnings_list:
warnings.filterwarnings("always", category=ResourceWarning)
obj.to_disk(d)
return warnings_list
@pytest.mark.parametrize("obj", objects_to_test[0], ids=objects_to_test[1])
def test_to_disk_resource_warning(obj):
warnings_list = write_obj_and_catch_warnings(obj)
assert len(warnings_list) == 0
class TestToDiskResourceWarningUnittest(TestCase):
def test_resource_warning(self):
scenarios = zip(*objects_to_test)
for scenario in scenarios:
with self.subTest(msg=scenario[1]):
warnings_list = write_obj_and_catch_warnings(scenario[0])
self.assertEqual(len(warnings_list), 0)