spaCy/tests/serialize/test_packer.py

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from __future__ import unicode_literals
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import re
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import pytest
import numpy
from spacy.vocab import Vocab
from spacy.tokens.doc import Doc
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from spacy.tokenizer import Tokenizer
from spacy.en import LOCAL_DATA_DIR
from os import path
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from spacy.attrs import ORTH, SPACY, TAG, DEP, HEAD
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from spacy.serialize.packer import Packer
from spacy.serialize.bits import BitArray
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def get_lex_props(string, prob=-22, is_oov=False):
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return {
'flags': 0,
'length': len(string),
'orth': string,
'lower': string,
'norm': string,
'shape': string,
'prefix': string[0],
'suffix': string[-3:],
'cluster': 0,
'prob': prob,
'sentiment': 0
}
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@pytest.fixture
def vocab():
vocab = Vocab(get_lex_props=get_lex_props)
vocab['dog'] = get_lex_props('dog', 0.001)
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assert vocab[vocab.strings['dog']].orth_ == 'dog'
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vocab['the'] = get_lex_props('the', 0.01)
vocab['quick'] = get_lex_props('quick', 0.005)
vocab['jumped'] = get_lex_props('jumped', 0.007)
return vocab
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@pytest.fixture
def tokenizer(vocab):
null_re = re.compile(r'!!!!!!!!!')
tokenizer = Tokenizer(vocab, {}, null_re, null_re, null_re)
return tokenizer
def test_char_packer(vocab):
packer = Packer(vocab, [])
bits = BitArray()
bits.seek(0)
byte_str = bytearray(b'the dog jumped')
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packer.char_codec.encode(byte_str, bits)
bits.seek(0)
result = [b''] * len(byte_str)
packer.char_codec.decode(bits, result)
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assert bytearray(result) == byte_str
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def test_packer_unannotated(tokenizer):
packer = Packer(tokenizer.vocab, [])
msg = tokenizer(u'the dog jumped')
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assert msg.string == 'the dog jumped'
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bits = packer.pack(msg)
result = packer.unpack(bits)
assert result.string == 'the dog jumped'
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def test_packer_annotated(tokenizer):
vocab = tokenizer.vocab
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nn = vocab.strings['NN']
dt = vocab.strings['DT']
vbd = vocab.strings['VBD']
jj = vocab.strings['JJ']
det = vocab.strings['det']
nsubj = vocab.strings['nsubj']
adj = vocab.strings['adj']
root = vocab.strings['ROOT']
attr_freqs = [
(TAG, [(nn, 0.1), (dt, 0.2), (jj, 0.01), (vbd, 0.05)]),
(DEP, {det: 0.2, nsubj: 0.1, adj: 0.05, root: 0.1}.items()),
(HEAD, {0: 0.05, 1: 0.2, -1: 0.2, -2: 0.1, 2: 0.1}.items())
]
packer = Packer(vocab, attr_freqs)
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msg = tokenizer(u'the dog jumped')
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msg.from_array(
[TAG, DEP, HEAD],
numpy.array([
[dt, det, 1],
[nn, nsubj, 1],
[vbd, root, 0]
], dtype=numpy.int32))
assert msg.string == 'the dog jumped'
assert [t.tag_ for t in msg] == ['DT', 'NN', 'VBD']
assert [t.dep_ for t in msg] == ['det', 'nsubj', 'ROOT']
assert [(t.head.i - t.i) for t in msg] == [1, 1, 0]
bits = packer.pack(msg)
result = packer.unpack(bits)
assert result.string == 'the dog jumped'
assert [t.tag_ for t in result] == ['DT', 'NN', 'VBD']
assert [t.dep_ for t in result] == ['det', 'nsubj', 'ROOT']
assert [(t.head.i - t.i) for t in result] == [1, 1, 0]
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def test_packer_bad_chars(tokenizer):
string = u'naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin'
packer = Packer(tokenizer.vocab, [])
doc = tokenizer(string)
bits = packer.pack(doc)
result = packer.unpack(bits)
assert result.string == doc.string
@pytest.mark.models
def test_packer_bad_chars(EN):
string = u'naja gut, is eher bl\xf6d und nicht mit reddit.com/digg.com vergleichbar; vielleicht auf dem weg dahin'
doc = EN(string)
byte_string = doc.to_bytes()
result = Doc(EN.vocab).from_bytes(byte_string)
assert [t.tag_ for t in result] == [t.tag_ for t in doc]