mirror of
https://github.com/explosion/spaCy.git
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136 lines
3.6 KiB
Python
136 lines
3.6 KiB
Python
from __future__ import unicode_literals
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import re
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import pytest
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import numpy
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from spacy.language import Language
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from spacy.en import English
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from spacy.vocab import Vocab
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from spacy.tokens.doc import Doc
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from spacy.tokenizer import Tokenizer
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from os import path
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import os
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from spacy import util
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from spacy.attrs import ORTH, SPACY, TAG, DEP, HEAD
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from spacy.serialize.packer import Packer
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from spacy.serialize.bits import BitArray
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@pytest.fixture
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def vocab():
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path = os.environ.get('SPACY_DATA')
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if path is None:
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path = util.match_best_version('en', None, util.get_data_path())
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else:
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path = util.match_best_version('en', None, path)
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vocab = English.Defaults.create_vocab()
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lex = vocab['dog']
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assert vocab[vocab.strings['dog']].orth_ == 'dog'
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lex = vocab['the']
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lex = vocab['quick']
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lex = vocab['jumped']
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return vocab
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@pytest.fixture
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def tokenizer(vocab):
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null_re = re.compile(r'!!!!!!!!!')
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tokenizer = Tokenizer(vocab, {}, null_re.search, null_re.search, null_re.finditer)
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return tokenizer
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def test_char_packer(vocab):
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packer = Packer(vocab, [])
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bits = BitArray()
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bits.seek(0)
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byte_str = bytearray(b'the dog jumped')
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packer.char_codec.encode(byte_str, bits)
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bits.seek(0)
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result = [b''] * len(byte_str)
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packer.char_codec.decode(bits, result)
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assert bytearray(result) == byte_str
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def test_packer_unannotated(tokenizer):
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packer = Packer(tokenizer.vocab, [])
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msg = tokenizer(u'the dog jumped')
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assert msg.string == 'the dog jumped'
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bits = packer.pack(msg)
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result = packer.unpack(bits)
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assert result.string == 'the dog jumped'
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@pytest.mark.models
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def test_packer_annotated(tokenizer):
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vocab = tokenizer.vocab
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nn = vocab.strings['NN']
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dt = vocab.strings['DT']
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vbd = vocab.strings['VBD']
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jj = vocab.strings['JJ']
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det = vocab.strings['det']
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nsubj = vocab.strings['nsubj']
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adj = vocab.strings['adj']
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root = vocab.strings['ROOT']
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attr_freqs = [
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(TAG, [(nn, 0.1), (dt, 0.2), (jj, 0.01), (vbd, 0.05)]),
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(DEP, {det: 0.2, nsubj: 0.1, adj: 0.05, root: 0.1}.items()),
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(HEAD, {0: 0.05, 1: 0.2, -1: 0.2, -2: 0.1, 2: 0.1}.items())
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]
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packer = Packer(vocab, attr_freqs)
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msg = tokenizer(u'the dog jumped')
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msg.from_array(
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[TAG, DEP, HEAD],
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numpy.array([
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[dt, det, 1],
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[nn, nsubj, 1],
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[vbd, root, 0]
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], dtype=numpy.int32))
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assert msg.string == 'the dog jumped'
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assert [t.tag_ for t in msg] == ['DT', 'NN', 'VBD']
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assert [t.dep_ for t in msg] == ['det', 'nsubj', 'ROOT']
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assert [(t.head.i - t.i) for t in msg] == [1, 1, 0]
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bits = packer.pack(msg)
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result = packer.unpack(bits)
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assert result.string == 'the dog jumped'
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assert [t.tag_ for t in result] == ['DT', 'NN', 'VBD']
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assert [t.dep_ for t in result] == ['det', 'nsubj', 'ROOT']
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assert [(t.head.i - t.i) for t in result] == [1, 1, 0]
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def test_packer_bad_chars(tokenizer):
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string = u'naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin'
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packer = Packer(tokenizer.vocab, [])
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doc = tokenizer(string)
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bits = packer.pack(doc)
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result = packer.unpack(bits)
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assert result.string == doc.string
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@pytest.mark.models
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def test_packer_bad_chars(EN):
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string = u'naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin'
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doc = EN(string)
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byte_string = doc.to_bytes()
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result = Doc(EN.vocab).from_bytes(byte_string)
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assert [t.tag_ for t in result] == [t.tag_ for t in doc]
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