spaCy/spacy/tests/doc/test_pickle_doc.py

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from __future__ import unicode_literals
from ...language import Language
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from ...compat import pickle, unicode_
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def test_pickle_single_doc():
nlp = Language()
doc = nlp(u'pickle roundtrip')
data = pickle.dumps(doc, 1)
doc2 = pickle.loads(data)
assert doc2.text == 'pickle roundtrip'
def test_list_of_docs_pickles_efficiently():
nlp = Language()
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for i in range(10000):
_ = nlp.vocab[unicode_(i)]
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one_pickled = pickle.dumps(nlp(u'0'), -1)
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docs = list(nlp.pipe(unicode_(i) for i in range(100)))
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many_pickled = pickle.dumps(docs, -1)
assert len(many_pickled) < (len(one_pickled) * 2)
many_unpickled = pickle.loads(many_pickled)
assert many_unpickled[0].text == '0'
assert many_unpickled[-1].text == '99'
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assert len(many_unpickled) == 100
def test_user_data_from_disk():
nlp = Language()
doc = nlp(u'Hello')
doc.user_data[(0, 1)] = False
b = doc.to_bytes()
doc2 = doc.__class__(doc.vocab).from_bytes(b)
assert doc2.user_data[(0, 1)] == False
def test_user_data_unpickles():
nlp = Language()
doc = nlp(u'Hello')
doc.user_data[(0, 1)] = False
b = pickle.dumps(doc)
doc2 = pickle.loads(b)
assert doc2.user_data[(0, 1)] == False
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def test_hooks_unpickle():
def inner_func(d1, d2):
return 'hello!'
nlp = Language()
doc = nlp(u'Hello')
doc.user_hooks['similarity'] = inner_func
b = pickle.dumps(doc)
doc2 = pickle.loads(b)
assert doc2.similarity(None) == 'hello!'