mirror of
https://github.com/explosion/spaCy.git
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4e7259c6cf
* docbin init fix + documentation fix + unit tests * newline * try with zlib instead of gzip (python 2 incompatibilities)
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import spacy
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import pytest
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from spacy.lang.en import English
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from spacy.tokens import Doc, DocBin
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from spacy.compat import path2str
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from ..util import make_tempdir
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def test_serialize_empty_doc(en_vocab):
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doc = Doc(en_vocab)
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data = doc.to_bytes()
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doc2 = Doc(en_vocab)
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doc2.from_bytes(data)
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assert len(doc) == len(doc2)
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for token1, token2 in zip(doc, doc2):
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assert token1.text == token2.text
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def test_serialize_doc_roundtrip_bytes(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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doc_b = doc.to_bytes()
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new_doc = Doc(en_vocab).from_bytes(doc_b)
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assert new_doc.to_bytes() == doc_b
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def test_serialize_doc_roundtrip_disk(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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with make_tempdir() as d:
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file_path = d / "doc"
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doc.to_disk(file_path)
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doc_d = Doc(en_vocab).from_disk(file_path)
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assert doc.to_bytes() == doc_d.to_bytes()
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def test_serialize_doc_roundtrip_disk_str_path(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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with make_tempdir() as d:
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file_path = d / "doc"
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file_path = path2str(file_path)
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doc.to_disk(file_path)
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doc_d = Doc(en_vocab).from_disk(file_path)
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assert doc.to_bytes() == doc_d.to_bytes()
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def test_serialize_doc_exclude(en_vocab):
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doc = Doc(en_vocab, words=["hello", "world"])
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doc.user_data["foo"] = "bar"
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
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assert new_doc.user_data["foo"] == "bar"
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes(), exclude=["user_data"])
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assert not new_doc.user_data
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new_doc = Doc(en_vocab).from_bytes(doc.to_bytes(exclude=["user_data"]))
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assert not new_doc.user_data
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with pytest.raises(ValueError):
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doc.to_bytes(user_data=False)
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with pytest.raises(ValueError):
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Doc(en_vocab).from_bytes(doc.to_bytes(), tensor=False)
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def test_serialize_doc_bin():
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doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)
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texts = ["Some text", "Lots of texts...", "..."]
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nlp = English()
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for doc in nlp.pipe(texts):
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doc_bin.add(doc)
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bytes_data = doc_bin.to_bytes()
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# Deserialize later, e.g. in a new process
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nlp = spacy.blank("en")
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doc_bin = DocBin().from_bytes(bytes_data)
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docs = list(doc_bin.get_docs(nlp.vocab))
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