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128 lines
3.6 KiB
Python
128 lines
3.6 KiB
Python
from __future__ import unicode_literals
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import pytest
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from spacy.tokens import Doc
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import spacy.en
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from spacy.serialize.packer import Packer
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def equal(doc1, doc2):
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# tokens
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assert [ t.orth for t in doc1 ] == [ t.orth for t in doc2 ]
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# tags
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assert [ t.pos for t in doc1 ] == [ t.pos for t in doc2 ]
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assert [ t.tag for t in doc1 ] == [ t.tag for t in doc2 ]
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# parse
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assert [ t.head.i for t in doc1 ] == [ t.head.i for t in doc2 ]
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assert [ t.dep for t in doc1 ] == [ t.dep for t in doc2 ]
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if doc1.is_parsed and doc2.is_parsed:
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assert [ s for s in doc1.sents ] == [ s for s in doc2.sents ]
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# entities
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assert [ t.ent_type for t in doc1 ] == [ t.ent_type for t in doc2 ]
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assert [ t.ent_iob for t in doc1 ] == [ t.ent_iob for t in doc2 ]
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assert [ ent for ent in doc1.ents ] == [ ent for ent in doc2.ents ]
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@pytest.mark.models
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def test_serialize_tokens(EN):
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doc1 = EN(u'This is a test sentence.',tag=False, parse=False, entity=False)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_tags(EN):
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doc1 = EN(u'This is a test sentence.',tag=True, parse=False, entity=False)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_parse(EN):
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doc1 = EN(u'This is a test sentence.',tag=False, parse=True, entity=False)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_ner(EN):
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doc1 = EN(u'This is a test sentence.', tag=False, parse=False, entity=True)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_tags_parse(EN):
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doc1 = EN(u'This is a test sentence.', tag=True, parse=True, entity=False)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_tags_ner(EN):
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doc1 = EN(u'This is a test sentence.', tag=True, parse=False, entity=True)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_ner_parse(EN):
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doc1 = EN(u'This is a test sentence.', tag=False, parse=True, entity=True)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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@pytest.mark.models
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def test_serialize_tokens_tags_parse_ner(EN):
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doc1 = EN(u'This is a test sentence.', tag=True, parse=True, entity=True)
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doc2 = Doc(EN.vocab).from_bytes(doc1.to_bytes())
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equal(doc1, doc2)
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def test_serialize_empty_doc():
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vocab = spacy.en.English.Defaults.create_vocab()
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doc = Doc(vocab)
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packer = Packer(vocab, {})
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b = packer.pack(doc)
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assert b == b''
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loaded = Doc(vocab).from_bytes(b)
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assert len(loaded) == 0
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def test_serialize_after_adding_entity():
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# Re issue #514
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vocab = spacy.en.English.Defaults.create_vocab()
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entity_recognizer = spacy.en.English.Defaults.create_entity()
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doc = Doc(vocab, words=u'This is a sentence about pasta .'.split())
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entity_recognizer.add_label('Food')
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entity_recognizer(doc)
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label_id = vocab.strings[u'Food']
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doc.ents = [(label_id, 5,6)]
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assert [(ent.label_, ent.text) for ent in doc.ents] == [(u'Food', u'pasta')]
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byte_string = doc.to_bytes()
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@pytest.mark.models
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def test_serialize_after_adding_entity(EN):
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EN.entity.add_label(u'Food')
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doc = EN(u'This is a sentence about pasta.')
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label_id = EN.vocab.strings[u'Food']
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doc.ents = [(label_id, 5,6)]
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byte_string = doc.to_bytes()
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doc2 = Doc(EN.vocab).from_bytes(byte_string)
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assert [(ent.label_, ent.text) for ent in doc2.ents] == [(u'Food', u'pasta')]
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